what are the types of data analysis in research

Data & Finance for Work & Life

Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

• It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
• As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
• It can be broken down into mathematical and AI analysis.
• Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
• Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
• Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

• It accounts for less than 30% of all data analysis and is common in social sciences .
• It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
• Because of this, some argue that it’s ultimately a quantitative type.
• Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
• Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
• Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

• Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
• Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
• Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
• Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

• Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
• Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
• Nature of Data: numeric.
• Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

• Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
• Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
• Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
• Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

• Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
• Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
• Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
• Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

• Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
• Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
• Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
• Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

• Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
• Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
• Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
• Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

• Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
• Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
• Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
• Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
• Here’s an example set:

Classification Method

• Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
• Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
• Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
• Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

• Description: the forecasting method uses time past series data to forecast the future.
• Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
• Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
• Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

• Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
• Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
• Nature of Data: the nature of optimizable data is a data set of at least two points.
• Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

• Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
• Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
• Nature of Data: data useful for content analysis is textual data.
• Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

• Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
• Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
• Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
• Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

• Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
• Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
• Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
• Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

• Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
• Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
• Nature of Data: the nature of data useful for framework analysis is textual.
• Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

• Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
• Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
• Nature of Data: the nature of data useful in the grounded theory method is textual.
• Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

• Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
• Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
• Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
• Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

• Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
• Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
• Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
• Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

• Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
• Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
• Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
• Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

• Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
• Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
• Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
• Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

• Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
• Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
• Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
• Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

• Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
• Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
• Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
• Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

• Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
• Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
• Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
• Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

• Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
• Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
• Nature of Data: the nature of data useful for moving averages is time series data .
• Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

• Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
• Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
• Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
• Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

• Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
• Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
• Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
• Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

• Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
• Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
• Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
• Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
• Video example :

Fuzzy Logic Technique

• Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
• Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
• Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
• Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

• Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
• Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
• Nature of Data: the nature of data useful in text analysis is words.
• Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

• Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
• Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
• Nature of Data: the nature of data useful for coding is long text documents.
• Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

• Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
• Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
• Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
• Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

• Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
• Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
• Nature of Data: the nature of data useful for word frequency is long, informative documents.
• Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

• Quantitative
• Qualitative
• Mathematical
• Machine Learning and AI
• Descriptive
• Prescriptive
• Classification
• Forecasting
• Optimization
• Grounded theory
• Artificial Neural Networks
• Decision Trees
• Evolutionary Programming
• Fuzzy Logic
• Text analysis
• Idea Pattern Analysis
• Word Frequency Analysis
• Nïave Bayes
• Exponential smoothing
• Moving average
• Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

• Content (qualitative)
• Narrative (qualitative)
• Discourse (qualitative)
• Framework (qualitative)
• Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

About the author.

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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8 Types of Data Analysis

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Built In’s expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industry’s definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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• Last Updated: May 3, 2024 9:38 AM
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What is Data Analysis? (Types, Methods, and Tools)

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
• JSON Analytics + Real-Time Insights
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Product Blog

• Posted in: Analytics , Application Design , Best Practices and Tutorials
• Tagged in: data analytics , data visualization , time series

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The 4 Types of Data Analysis [Ultimate Guide]

The most successful businesses and organizations are those that constantly learn and adapt.

No matter what industry you’re operating in, it’s essential to understand what has happened in the past, what’s going on now, and to anticipate what might happen in the future. So how do companies do that?

The answer lies in data analytics . Most companies are collecting data all the time—but, in its raw form, this data doesn’t really mean anything. It’s what you do with the data that counts. Data analytics is the process of analyzing raw data in order to draw out patterns, trends, and insights that can tell you something meaningful about a particular area of the business. These insights are then used to make smart, data-driven decisions.

The kinds of insights you get from your data depends on the type of analysis you perform. In data analytics and data science, there are four main types of data analysis: Descriptive , diagnostic , predictive , and prescriptive .

In this post, we’ll explain each of the four and consider why they’re useful. If you’re interested in a particular type of analysis, jump straight to the relevant section using the clickable menu below.

• Types of data analysis: Descriptive
• Types of data analysis: Diagnostic
• Types of data analysis: Predictive
• Types of data analysis: Prescriptive
• Key takeaways and further reading

So, what are the four main types of data analysis? Let’s find out.

1. Types of data analysis: Descriptive (What happened?)

Descriptive analytics looks at what has happened in the past.

As the name suggests, the purpose of descriptive analytics is to simply describe what has happened; it doesn’t try to explain why this might have happened or to establish cause-and-effect relationships. The aim is solely to provide an easily digestible snapshot.

Google Analytics is a good example of descriptive analytics in action; it provides a simple overview of what’s been going on with your website, showing you how many people visited in a given time period, for example, or where your visitors came from. Similarly, tools like HubSpot will show you how many people opened a particular email or engaged with a certain campaign.

There are two main techniques used in descriptive analytics: Data aggregation and data mining.

Data aggregation

Data aggregation is the process of gathering data and presenting it in a summarized format.

Let’s imagine an ecommerce company collects all kinds of data relating to their customers and people who visit their website. The aggregate data, or summarized data, would provide an overview of this wider dataset—such as the average customer age, for example, or the average number of purchases made.

Data mining

Data mining is the analysis part . This is when the analyst explores the data in order to uncover any patterns or trends. The outcome of descriptive analysis is a visual representation of the data—as a bar graph, for example, or a pie chart.

So: Descriptive analytics condenses large volumes of data into a clear, simple overview of what has happened. This is often the starting point for more in-depth analysis, as we’ll now explore.

2. Types of data analysis: Diagnostic (Why did it happen?)

Diagnostic analytics seeks to delve deeper in order to understand why something happened. The main purpose of diagnostic analytics is to identify and respond to anomalies within your data . For example: If your descriptive analysis shows that there was a 20% drop in sales for the month of March, you’ll want to find out why. The next logical step is to perform a diagnostic analysis.

In order to get to the root cause, the analyst will start by identifying any additional data sources that might offer further insight into why the drop in sales occurred. They might drill down to find that, despite a healthy volume of website visitors and a good number of “add to cart” actions, very few customers proceeded to actually check out and make a purchase.

Upon further inspection, it comes to light that the majority of customers abandoned ship at the point of filling out their delivery address. Now we’re getting somewhere! It’s starting to look like there was a problem with the address form; perhaps it wasn’t loading properly on mobile, or was simply too long and frustrating. With a little bit of digging, you’re closer to finding an explanation for your data anomaly.

Diagnostic analytics isn’t just about fixing problems, though; you can also use it to see what’s driving positive results. Perhaps the data tells you that website traffic was through the roof in October—a whopping 60% increase compared to the previous month! When you drill down, it seems that this spike in traffic corresponds to a celebrity mentioning one of your skincare products in their Instagram story.

This opens your eyes to the power of influencer marketing , giving you something to think about for your future marketing strategy.

When running diagnostic analytics, there are a number of different techniques that you might employ, such as probability theory, regression analysis, filtering, and time-series analysis. You can learn more about each of these techniques in our introduction to data analytics .

So: While descriptive analytics looks at what happened, diagnostic analytics explores why it happened.

3. Types of data analysis: Predictive (What is likely to happen in the future?)

Predictive analytics seeks to predict what is likely to happen in the future. Based on past patterns and trends, data analysts can devise predictive models which estimate the likelihood of a future event or outcome. This is especially useful as it enables businesses to plan ahead.

Predictive models use the relationship between a set of variables to make predictions; for example, you might use the correlation between seasonality and sales figures to predict when sales are likely to drop. If your predictive model tells you that sales are likely to go down in summer, you might use this information to come up with a summer-related promotional campaign, or to decrease expenditure elsewhere to make up for the seasonal dip.

Perhaps you own a restaurant and want to predict how many takeaway orders you’re likely to get on a typical Saturday night. Based on what your predictive model tells you, you might decide to get an extra delivery driver on hand.

In addition to forecasting, predictive analytics is also used for classification. A commonly used classification algorithm is logistic regression, which is used to predict a binary outcome based on a set of independent variables. For example: A credit card company might use a predictive model, and specifically logistic regression, to predict whether or not a given customer will default on their payments—in other words, to classify them in one of two categories: “will default” or “will not default”.

Based on these predictions of what category the customer will fall into, the company can quickly assess who might be a good candidate for a credit card. You can learn more about logistic regression and other types of regression analysis .

Machine learning (ML)

Machine learning is a branch of predictive analytics. Just as humans use predictive analytics to devise models and forecast future outcomes, machine learning models are designed to recognize patterns in the data and automatically evolve in order to make accurate predictions. If you’re interested in learning more, there are some useful guides to the similarities and differences between (human-led) predictive analytics and machine learning .

Learn more in our full guide to machine learning .

As you can see, predictive analytics is used to forecast all sorts of future outcomes, and while it can never be one hundred percent accurate, it does eliminate much of the guesswork. This is crucial when it comes to making business decisions and determining the most appropriate course of action.

So: Predictive analytics builds on what happened in the past and why to predict what is likely to happen in the future.

4. Types of data analysis: Prescriptive (What’s the best course of action?)

Prescriptive analytics looks at what has happened, why it happened, and what might happen in order to determine what should be done next.

In other words, prescriptive analytics shows you how you can best take advantage of the future outcomes that have been predicted. What steps can you take to avoid a future problem? What can you do to capitalize on an emerging trend?

Prescriptive analytics is, without doubt, the most complex type of analysis, involving algorithms, machine learning, statistical methods, and computational modeling procedures. Essentially, a prescriptive model considers all the possible decision patterns or pathways a company might take, and their likely outcomes.

This enables you to see how each combination of conditions and decisions might impact the future, and allows you to measure the impact a certain decision might have. Based on all the possible scenarios and potential outcomes, the company can decide what is the best “route” or action to take.

An oft-cited example of prescriptive analytics in action is maps and traffic apps. When figuring out the best way to get you from A to B, Google Maps will consider all the possible modes of transport (e.g. bus, walking, or driving), the current traffic conditions and possible roadworks in order to calculate the best route. In much the same way, prescriptive models are used to calculate all the possible “routes” a company might take to reach their goals in order to determine the best possible option.

Knowing what actions to take for the best chances of success is a major advantage for any type of organization, so it’s no wonder that prescriptive analytics has a huge role to play in business.

So: Prescriptive analytics looks at what has happened, why it happened, and what might happen in order to determine the best course of action for the future.

5. Key takeaways and further reading

In some ways, data analytics is a bit like a treasure hunt; based on clues and insights from the past, you can work out what your next move should be.

With the right type of analysis, all kinds of businesses and organizations can use their data to make smarter decisions, invest more wisely, improve internal processes, and ultimately increase their chances of success. To summarize, there are four main types of data analysis to be aware of:

• Descriptive analytics: What happened?
• Diagnostic analytics: Why did it happen?
• Predictive analytics: What is likely to happen in the future?
• Prescriptive analytics: What is the best course of action to take?

Now you’re familiar with the different types of data analysis, you can start to explore specific analysis techniques, such as time series analysis, cohort analysis, and regression—to name just a few! We explore some of the most useful data analysis techniques in this guide .

If you’re not already familiar, it’s also worth learning about the different levels of measurement (nominal, ordinal, interval, and ratio) for data .

Ready for a hands-on introduction to the field? Give this free, five-day data analytics short course a go! And, if you’d like to learn more, check out some of these excellent free courses for beginners . Then, to see what it takes to start a career in the field, check out the following:

• How to become a data analyst: Your five-step plan
• What are the key skills every data analyst needs?
• What’s it actually like to work as a data analyst?

Types of Data Analysis

Analysis of data is a vital part of running a successful business. When data is used effectively, it leads to better understanding of a business’s previous performance and better decision-making for its future activities. There are many ways that data can be utilized, at all levels of a company’s operations.

There are four types of data analysis that are in use across all industries. While we separate these into categories, they are all linked together and build upon each other. As you begin moving from the simplest type of analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases.

Four Types of Data Analysis

The four types of data analysis are:

Descriptive Analysis

Diagnostic analysis, predictive analysis, prescriptive analysis.

Below, we will introduce each type and give examples of how they are utilized in business.

The first type of data analysis is descriptive analysis. It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.

The biggest use of descriptive analysis in business is to track Key Performance Indicators (KPIs). KPIs describe how a business is performing based on chosen benchmarks.

Business applications of descriptive analysis include:

• KPI dashboards
• Monthly revenue reports
• Sales leads overview

After asking the main question of “what happened”, the next step is to dive deeper and ask why did it happen? This is where diagnostic analysis comes in.

Diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.

A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, it is possible you have already collected certain data pertaining to the issue. By already having the data at your disposal, it ends having to repeat work and makes all problems interconnected.

Business applications of diagnostic analysis include:

• A freight company investigating the cause of slow shipments in a certain region
• A SaaS company drilling down to determine which marketing activities increased trials

Predictive analysis attempts to answer the question “what is likely to happen”. This type of analytics utilizes previous data to make predictions about future outcomes.

This type of analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data.

While descriptive and diagnostic analysis are common practices in business, predictive analysis is where many organizations begin show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or not prepared to educate current teams.

Business applications of predictive analysis include:

• Risk Assessment
• Sales Forecasting
• Using customer segmentation to determine which leads have the best chance of converting
• Predictive analytics in customer success teams

The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.

Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence.

Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making. For other organizations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.

As we have shown, each of these types of data analysis are connected and rely on each other to a certain degree. They each serve a different purpose and provide varying insights. Moving from descriptive analysis towards predictive and prescriptive analysis requires much more technical ability, but also unlocks more insight for your organization.

• Journal of Accountancy – The next frontier in data analytics
• ScienceSoft – 4 Types of Data Analytics to Improve Decision-Making
• Ingram Micro – Four Types of Big Data Analytics and Examples of Their Use

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Types of data analysis

The means by which you analyse your data are largely determined by the nature of your research question , the approach and paradigm within which your research operates, the methods used, and consequently the type of data elicited. In turn, the language and terms you use in both conducting and reporting your data analysis should reflect these.

The list below includes some of the more commonly used means of qualitative data analysis in educational research – although this is by no means exhaustive. It is also important to point out that each of the terms given below generally encompass a range of possible methods or options and there can be overlap between them. In all cases, further reading is essential to ensure that the process of data analysis is valid, transparent and appropriately systematic, and we have provided below (as well as in our further resources and tools and resources for qualitative data analysis sections) some recommendations for this.

If your research is likely to involve quantitative analysis, we recommend the books listed below.

Types of qualitative data analysis

• Thematic analysis
• Coding and/or content analysis
• Concept map analysis
• Discourse or narrative analysis
• Grouded theory
• Phenomenological analysis or interpretative phenomenological analysis (IPA)

Further reading and resources

As a starting point for most of these, we would recommend the relevant chapter from Part 5 of Cohen, Manion and Morrison (2018), Research Methods in Education. You may also find the following helpful:

For qualitative approaches

Savin-Baden, M. & Howell Major, C. (2013) Data analysis. In Qualitative Research: The essential guide to theory and practice . (Abingdon, Routledge, pp. 434-450).

For quantitative approaches

Bors, D. (2018) Data analysis for the social sciences (Sage, London).

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8 Types of Data Analysis

If you’re interested in pursuing a career in data science, gaining a deeper understanding of how to distill data down using different methods is key; data analysis is fundamental to just about any business and industry. Companies rely on the data they collect to inform future business decisions, so understanding different types of data analysis and how they work in tandem is a fundamental skill set in any data professional’s toolbox.

In this article, we will discuss eight key types of data analysis that are prevalent across the data science industry. If you’re pursuing work in an industry where data is critical to the company’s success, or want to enter a new field in competitive arenas like government, healthcare, or media, having a working knowledge of these core data analysis techniques will help give you an edge.

Are you currently working as a data professional, but want to advance your career by acquiring new skills within data analysis? Do you want to branch out into a different type of data analysis altogether, like working with data that uncovers social or consumer needs? Understanding how to use the below eight types of data analysis will give you an advantage within the field.

Organizations across a variety of industries rely on data analysts in order to identify trends and patterns, uncover insights, and drive business decisions; and the individuals responsible for collecting, sorting, and analyzing that data will continue to be in-demand as more data is created each day. From Netflix’s recommendation system driven by user data, to data-enabled technologies that help restaurants manage their inventory, to medical systems used to track public health issues, there is no shortage of ways aspiring data analysts can apply their knowledge in today’s workforce.

Having a strong understanding of data analysis techniques doesn’t necessarily mean enrolling in a more formal higher education program. Enrolling in a data science boot camp can get you up to speed on the methods of data analysis used by professionals to solve a variety of real-world business problems in just 24 weeks. Northwestern Data Science and Visualization Boot Camp gives individuals a deeper understanding of how different types of data analysis are used by professionals in the industry.

8 Different Types of Data Analysis

Working as a data analyst professional means analyzing data through a pipeline and working with different types of data analysis. While the types of data analysis methods are different, they all relate to one another and work together to allow data professionals to have a robust understanding of how data impacts a business’s bottom line.

Descriptive analysis serves as the foundation of data insight and informs what happened within the data. Diagnostic analysis looks at why the data happened, while predictive analysis questions what will happen. Prescriptive analysis measures what action should be taken. Exploratory analysis is a data mining approach that determines characteristics of data sets, and inferential analysis draws conclusions about data trends. Causal analysis measures cause and effect, and mechanistic analysis distills changes in variables that cause changes to other variables.

We explore each data analysis type more in the sections below.

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Descriptive Analysis

Descriptive data analysis is often referenced as the foundation of all data analysis; it is the most common type of data analysis used by organizations. Descriptive analysis focuses on analyzing past data to describe what happened in certain events and situations.

Looking at historical performance in this way gives companies the opportunity to evaluate what happened in the past, and use this data to inform future applications. Descriptive analysis is often used in presentations to describe business outcomes to important stakeholders. Key performance indicators (KPIs) are a common example of a descriptive analysis metric. Return on investment (ROI) is another metric used in descriptive analysis.

Descriptive analysis usually involves the use of data dashboards. Google Analytics is one commonly used tool for conducting descriptive analysis. When creating a presentation based on descriptive analysis in research, data professionals seek to tell a story using metrics. For example, identifying deviations, calculating frequencies and percentages, and showcasing them in a cohesive way that helps companies understand how certain areas of their business are performing. Depending on the industry you’re working in, performing a descriptive analysis can mean working with percentages, revenue, page views, and even first- or third-party data.

Diagnostic Analysis

If descriptive analysis tells the story of what happened, diagnostic analysis takes things a step further and seeks to answer why it happened. Techniques used in diagnostic analysis utilize the findings in descriptive analysis to dig deeper into the metrics and pinpoint what caused the data to present the way that it did.

Diagnostic analytics zeroes in on anomalies within descriptive analysis and seeks to make sense of unexpected changes or shifts in a metric that were outliers from the rest of the data collected within a set. During diagnostic analysis, all collected relevant data helps make sense of what occurred to cause this shift. From there, data professionals use statistical techniques to identify trends that can explain the anomaly.

Diagnostic analysis is important to businesses because it enables professionals to make sense and gain an understanding of what happened within a data set, providing answers and a framework for what to do in the future. Diagnostic analysis is looked at as problem-solving analysis. However, diagnostic analysis cannot be performed unless robust descriptive analysis has been conducted. Diagnostic analysis can only be successful and helpful to professionals if descriptive analysis has been done effectively, which is important to keep in mind when working as a data professional.

Predictive Analysis

Descriptive analysis showed us what happened, and diagnostic analysis gave us a better understanding of why it happened. Now, it’s time to take this knowledge and leverage it to determine what may happen in the future. That’s where predictive analysis comes in.

Using descriptive analysis and diagnostic analysis as a framework, predictive analysis answers pertinent questions for businesses about what may happen in the future. Predictive analysis models you may work with to gain an understanding of this are decision trees. This support tool creates a tree-like model to showcase decisions alongside potential outcomes; neural networks, computing systems that mimic user behavior of the data being analyzed; and regression analysis, a predictive modeling tool that estimates relationships between variables.

While not always the case, predictive analysis is often used when a business is experiencing difficulty and needs to be able to look into potential outcomes with relative certainty. Types of predictive analysis models that help businesses overcome possible issues are sales forecasting and risk assessment. Predictive analysis can also be used to help customer success teams inform their approach.

Though predictive analysis isn’t always a sure bet, the success rate of predicting outcomes across different business sectors is most accurate when a robust set of data is being used to inform patterns and predictions.

Prescriptive Analysis

Predictive analysis shows what will happen, then prescriptive analytics tells the story of how it will happen. For this reason, prescriptive analysis is considered one of the most powerful data analytics models. Prescriptive analysis relies heavily on the information found in the predictive analysis phase. Leveraging the predicted outcomes, prescriptive analysis builds an actionable game plan that businesses can utilize and use to their benefit once they understand what will happen.

Conducting prescriptive analysis relies on machine learning strategies. These strategies pinpoint repetitions in data sets, which calculates different outcomes. Prescriptive analysis can inform data strategies for many facets of business, including HR, the customer experience, finance, marketing, and logistical analytics.

Prescriptive analysis takes every possible outcome, then formulates a strategy to follow that’s predicted to be the most successful path forward. When presenting a prescriptive model, the best path might not always be clear to the data professional. The course of action that’s most desirable largely depends on the company’s overall goals and what the most important outcome is above all else.

Google Maps is one example of prescriptive analysis at work. This technology will calculate all of the modes of transportation available to get you to point A to point B. Though the fastest route is often the most popular choice, users who would rather take a route with fewer turns or without tolls have other options to choose from — the same way businesses using prescriptive analysis can choose what path makes the most sense for the company objective.

Exploratory Analysis

Exploratory analysis — sometimes referred to as EDA — looks at data sets to better understand how to get the answers needed from the available data. The process for exploratory analysis typically involves investigating characteristics within data sets to see what else they reveal.

Exploratory analysis offers an opportunity to look at the data and ensure accuracy, distill patterns, zero in on interesting relationships within data sets, and detect outliers. Exploratory analysis is often thought of as a more sophisticated analysis, as it’s more malleable and can help inform the questions being asked by company stakeholders, rather than the other way around.

Exploratory analysis can happen early on in the data analysis pipeline, or it can be used to dig deeper into already established data sets to evaluate the accuracy of — and answer questions about — variables and deviations.

Data science tools used to conduct exploratory analysis include Python, a programming language that can be used to help detect missing values within data sets, and R, another programming language leveraged by statisticians to conduct data analysis and uncover important EDA statistics.

Inferential Analysis

Inferential analysis takes the work done in descriptive analysis and draws conclusions from it. Work done during inferential analysis often seeks to identify relationships between different data points, drawing conclusions on a certain data set or population segment based on the data collected in the descriptive analysis phase.

Inferential analysis is helpful for businesses looking to test out a certain theory or hypothesis. For example, A/B testing can be created based on the work done through inferential analysis. This kind of data mining can be leveraged as a starting point based on the inferences made to either prove or disprove these theories.

When conducting inferential analysis, tools used include a linear regression analysis — an X and Y scale used to measure the relationship between two variables. Correlation analysis may also be conducted, which puts the extent to which two variables are connected to one another to the test using a scaled approach.

Causal Analysis

Causal analysis focuses on analyzing the cause and effect of related variables within data sets. These relationships can either be discovered through previous data analysis, or they can be relationships that could potentially be either beneficial or harmful to a company’s desired outcome. Causal analysis can often be conducted during the course of an experiment, where the data professional works with outcomes from a certain theory being tested.

For example, causal analysis can be conducted in drug studies to measure the efficacy of a drug on a person’s condition, or to determine if a type of ad campaign is effective in influencing a person’s purchase decision.

Causal analysis can be used to disprove causation within related sets of data as well. Just because data sets are correlated with one another does not necessarily mean that one caused the other, or vice versa. In instances where businesses need to better understand whether or not cause and effect exist within correlated data, causal analysis is a helpful tool.

Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in data that occur from exact changes in other data. Mechanistic analysis is used in scenarios where the margin for error needs to be at or close to zero. It’s a very precise way of evaluating data and understanding the relationship between how data from one set impacts the other.

Mechanistic analysis is a popular form of analysis used in the medical industry for this reason. When testing the efficacy of a medical device, mechanistic analysis can be used to measure the machine’s output along with the effect it has on the patient it’s being used on. This analysis method has helped inform new treatment methods within the medical field and led to scientific advancements.

If you’re interested in pursuing a career in science or health care, understanding mechanistic analysis and how to conduct it effectively will give you an edge.

Learn Data Analysis Today

Now that you understand 8 common types of data analysis, it’s time to start putting these techniques into practice.

What’s the most effective way to learn data analysis, you may ask? Taking a data science boot camp can help you gain a deeper understanding of data analysis techniques and allow you to learn how these important data analysis methods are put into action across a wide variety of industries. Check out Northwestern’s Data Science and Visualization Boot Camp to take a closer look into our curriculum, what you’ll learn, and how a data science boot camp can easily complement your current work or study schedule.

Types of Data Analysis FAQs

Data analysis helps business leaders make informed decisions, helps companies reduce costs, and allows businesses to target potential customers more strategically. Here are a few more types of data analysis FAQs you may be curious to know more about.

What are the essential steps to data analysis?

Data analysis happens in five key stages. Data is identified using measurement tools, then collected within these platforms. Data is cleaned and prepped for analysis, removing errors and distilling it into an easy-to-understand format. Data is then analyzed using tools and techniques and finally interpreted by a data professional.

Why is data analysis used in research?

Data analysis is used by researchers to make their jobs easier, providing them with key data patterns, statistics, and outcomes that they can use to inform their research strategy. With so much data available to researchers across different industries today, working with a data professional who can sift through data sets and provide researchers with varied forms of analysis can be critical to success in these fields.

Advanced analytics vs predictive analytics: What's the difference?

Advanced analytics is the umbrella term for analysis that uses automation and artificial intelligence (AI) to delve into data sets and generate deeper insights. Predictive analytics falls under the category of advanced analytics as it uses machine learning to identify patterns and make predictions regarding outcomes.

Data analysis is one of the most important disciplines that exists in the working economy today. Having professionals who are able to analyze and distill data, then generate meaningful insights and actionable patterns from these data sets, are critical to the success of businesses across just about every industry.

If you’re interested in pursuing a career in data analytics, enrolling in data science boot camp is an effective way to set yourself apart from others, gain a deeper understanding of the different types of data analysis, and get hands-on practice using real-world scenarios.

Data Analysis Techniques in Research – Methods, Tools & Examples

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Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Data Analysis: Definition, Types and Examples

Nowadays, data is collected at various stages of processes and transactions, which has the potential to improve the way we work significantly. However, to fully realize the value of data analysis, this data must be analyzed to gain valuable insights into improving products and services.

Data analysis consists aspect of making informed decisions in various industries. With the advancement of technology, it has become a dynamic and exciting field But what is it in simple words?

What is Data Analysis?

Data analysis is the science of examining data to conclude the information to make decisions or expand knowledge on various subjects. It consists of subjecting data to operations. This process happens to obtain precise conclusions to help us achieve our goals, such as operations that cannot be previously defined since data collection may reveal specific difficulties.

“A lot of this [data analysis] will help humans work smarter and faster because we have data on everything that happens.” –Daniel Burrus, business consultant and speaker on business and innovation issues.

Why is data analytics important?

Data analytics help businesses understand the target market faster, increase sales, reduce costs, increase revenue, and allow for better problem-solving. Data analysis is important for several reasons, as it plays a critical role in various aspects of modern businesses and organizations. Here are some key reasons why data analysis important is crucial:

Informed decision-making

Data analytics helps businesses make more informed and data-driven decisions. By analyzing data, organizations can gain insights into customer behavior, market trends, and operational performance, enabling them to make better choices that are supported by evidence rather than relying on intuition alone.

Identifying opportunities and challenges

Data analytics allows businesses to identify new opportunities for growth, product development, or market expansion. It also helps identify potential challenges and risks, allowing organizations to address them proactively.

Improving efficiency and productivity

Organizations can identify inefficiencies and bottlenecks by analyzing processes and performance data, leading to process optimization and improved productivity. This, in turn, can result in cost savings and better resource allocation.

Customer understanding and personalization

Data analytics enables businesses to understand their customers better, including their preferences, buying behaviors, and pain points. With this understanding, organizations can offer personalized products and services, enhancing customer satisfaction and loyalty.

Competitive advantage

Organizations that leverage data analytics effectively gain a competitive edge in today’s data-driven world. By analyzing data, businesses can identify unique insights and trends that better understand the market and their competitors, helping them stay ahead of the competition.

Performance tracking and evaluation

Data analytics allows organizations to track and measure their performance against key performance indicators (KPIs) and goals. This helps in evaluating the success of various strategies and initiatives, enabling continuous improvement.

Predictive analytics

Data analytics can be used for predictive modeling, helping organizations forecast future trends and outcomes. This is valuable for financial planning, demand forecasting, risk management, and proactive decision-making.

Data-driven innovation

Data analytics can fuel innovation by providing insights that lead to the development of new products, services, or business models. Innovations based on data analysis can lead to groundbreaking advancements and disruption in various industries.

Fraud detection and security

Data analytics can be used to detect anomalies and patterns indicative of fraudulent activities. It plays a crucial role in enhancing security and protecting businesses from financial losses and reputational risk .

Regulatory compliance

In many industries, regulations, and laws are mandatory. Data analytics can help organizations ensure that they meet these compliance requirements by tracking and auditing relevant data.

Types of data analysis

There are several types of data analysis, each with a specific purpose and method. Let’s talk about some significant types:

Descriptive Analysis

Descriptive analysis is used to summarize and describe the main features of a dataset. It involves calculating measures of central tendency and dispersion to describe the data. The descriptive analysis provides a comprehensive overview of the data and insights into its properties and structure.

LEARN ABOUT: Descriptive Analysis

Inferential Analysis

The inferential analysis is used statistical analysis plan and testing to make inferences about the population parameters, such as the mean or proportion. This unit of analysis involves using models and hypothesis testing to make predictions and draw conclusions about the population.

LEARN ABOUT:   Statistical Analysis Methods

Predictive Analysis

Predictive analysis is used to predict future events or outcomes based on historical data and other relevant information. It involves using statistical models and machine learning algorithms to identify patterns in the data and make predictions about future outcomes.

Prescriptive Analysis

Prescriptive analysis is a decision-making analysis that uses mathematical modeling, optimization algorithms, and other data-driven techniques to identify the action for a given problem or situation. It combines mathematical models, data, and business constraints to find the best move or decision.

Text Analysis

Text analysis is a process of extracting meaningful information from unstructured text data. It involves a variety of techniques, including natural language processing (NLP), text mining, sentiment analysis, and topic modeling, to uncover insights and patterns in text data.

Diagnostic Analysis

The diagnostic analysis seeks to identify the root causes of specific events or outcomes. It is often used in troubleshooting problems or investigating anomalies in data.

LEARN ABOUT: Data Analytics Projects

Uses of data analysis

It is used in many industries regardless of the branch. It gives us the basis for making decisions or confirming a hypothesis.

A researcher or data analyst mainly performs data analysis to predict consumer behavior and help companies place their products and services in the market accordingly. For instance, sales data analysis can help you identify the product range not-so-popular in a specific demographic group. It can give you insights into tweaking your current marketing campaign to better connect with the target audience and address their needs. 

Human Resources

Organizations can use data analysis tools to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data to find out the best resources whose skill set matches the organizational goals.

Universities and academic institutions can perform the analysis to measure student performance and gather insights on how certain behaviors can further improve education.

Techniques for data analysis

It is essential to analyze raw data to understand it. We must resort to various data analysis techniques that depend on the type of information collected, so it is crucial to define the method before implementing it.

• Qualitative data: Researchers collect qualitative data from the underlying emotions, body language, and expressions. Its foundation is the data interpretation of verbal responses. The most common ways of obtaining this information are through open-ended interviews, focus groups, and observation groups, where researchers generally analyze patterns in observations throughout the data collection phase.
• Quantitative data: Quantitative data presents itself in numerical form. It focuses on tangible results.

Data analysis focuses on reaching a conclusion based solely on the researcher’s current knowledge. How you collect your data should relate to how you plan to analyze and use it. You also need to collect accurate and trustworthy information. 

Many data collection techniques exist, but experts’ most commonly used method is online surveys. It offers significant benefits, such as reducing time and money compared to traditional data collection methods .

Data analysis and data analytics are two interconnected but distinct processes in data science. Data analysis involves examining raw data using various techniques to uncover patterns, correlations, and insights. It’s about understanding historical data to make informed conclusions. On the other hand, data analytics goes a step further by utilizing those insights to predict future trends, prescribe actions, and guide decision-making.

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Data Analysis Methods

The term data analysis technique has often been used interchangeably by professional researchers. Frequently people also throw out the previous analysis type. We’re hoping for this to be an important distinction between how and when data analyses are done. 

However, there are many different techniques that allow for data analysis. Here are some of the main common methods used for data analysis:

Descriptive Statistics

Descriptive statistics involves summarizing and describing the main features of a dataset, such as mean, median, mode, standard deviation, range, and percentiles. It provides a basic understanding of the data’s distribution and characteristics.

Inferential Statistics

Inferential statistics are used to make inferences and draw conclusions about a larger population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.

Data Visualization

Data visualization is the graphical representation of data to help analysts and stakeholders understand patterns, trends, and insights. Common visualization techniques include bar charts, line graphs, scatter plots, heat maps, and pie charts.

Exploratory Data Analysis (EDA)

EDA involves analyzing and visualizing data to discover patterns, relationships, and potential outliers. It helps in gaining insights into the data before formal statistical testing.

Predictive Modeling

Predictive modeling uses algorithms and statistical techniques to build models that can make predictions about future outcomes based on historical data. Machine learning algorithms, such as decision trees, logistic regression, and neural networks, are commonly used for predictive modeling.

Time Series Analysis

Time series analysis is used to analyze data collected over time, such as stock prices, temperature readings, or sales data. It involves identifying trends and seasonality and forecasting future values.

Cluster Analysis

Cluster analysis is used to group similar data points together based on certain features or characteristics. It helps in identifying patterns and segmenting data into meaningful clusters.

Factor Analysis and Principal Component Analysis (PCA)

These techniques are used to reduce the dimensionality of data and identify underlying factors or components that explain the variance in the data.

Text Mining and Natural Language Processing (NLP)

Text mining and NLP techniques are used to analyze and extract information from unstructured text data, such as social media posts, customer reviews, or survey responses.

Qualitative Data Analysis

Qualitative data analysis involves interpreting non-numeric data, such as text, images, audio, or video. Techniques like content analysis, thematic analysis, and grounded theory are used to analyze qualitative data.

Quantitative Data Analysis

Quantitative analysis focuses on analyzing numerical data to discover relationships, trends, and patterns. This analysis often involves statistical methods.

Data Mining

Data mining involves discovering patterns, relationships, or insights from large datasets using various algorithms and techniques.

Regression Analysis

Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It helps understand how changes in one variable impact the other(s).

Step-by-step guide data analysis

With these five steps in your data analysis process, you will make better decisions for your business because data that has been well collected and analyzed support your choices.

LEARN ABOUT: Data Mining Techniques

Step 1: Define your questions

Start by selecting the right questions. Questions should be measurable, clear, and concise. Design your questions to qualify or disqualify possible solutions to your specific problem.

Step 2: Establish measurement priorities

This step divides into two sub-steps:

• Decide what to measure: Analyze what kind of data you need.
• Decide how to measure it: Thinking about how to measure your data is just as important, especially before the data collection phase, because your measurement process supports or discredits your thematic analysis later on.

Step 3: Collect data

With the question clearly defined and your measurement priorities established, now it’s time to collect your data. As you manage and organize your data, remember to keep these essential points in mind:

• Before collecting new data, determine what information you could gather from existing databases or sources.
• Determine a storage and file naming system to help all team members collaborate in advance. This process saves time and prevents team members from collecting the same information twice.
• If you need to collect data through surveys, observation, or interviews, develop a questionnaire in advance to ensure consistency and save time.
• Keep the collected data organized with a log of collection dates, and add any source notes as you go along.

Step 4: Analyze the data

Once you’ve collected the correct data to answer your Step 1 question, it’s time to conduct a deeper statistical analysis . Find relationships, identify trends, and sort and filter your data according to variables. You will find the exact data you need as you analyze the data.

Step 5: Interpret the results

After analyzing the data and possibly conducting further research, it is finally time to interpret the results. Ask yourself these key questions:

• Does the data answer your original question? How?
• Does the data help you defend any objections? How?
• Are there any limitations to the conclusions, any angles you haven’t considered?

If the interpretation of data holds up under these questions and considerations, you have reached a productive conclusion. The only remaining step is to use the process results to decide how you will act.

Join us as we look into the most frequently used question types and how to analyze your findings effectively.

Make the right decisions by analyzing data the right way!

Data analysis advantages

Many industries use data to draw conclusions and decide on actions to implement. It is worth mentioning that science also uses to test or discard existing theories or models.

There’s more than one advantage to data analysis done right. Here are some examples:

• Make faster and more informed business decisions backed by facts.
• Identify performance issues that require action.
• Gain a deeper understanding of customer requirements, which creates better business relationships.
• Increase awareness of risks to implement preventive measures.
• Visualize different dimensions of the data.
• Gain competitive advantage.
• A better understanding of the financial performance of the business.
• Identify ways to reduce costs and thus increase profits.

These questions are examples of different types of data analysis. You can include them in your post-event surveys aimed at your customers:

• Questions start with: Why? How? 

Example of qualitative data research analysis: Panels where a discussion is held, and consumers are interviewed about what they like or dislike about the place.

• Data is collected by asking questions like: How many? Who? How often? Where?

Example of quantitative research analysis: Surveys focused on measuring sales, trends, reports, or perceptions.

Data analysis with QuestionPro

Data analysis is crucial in aiding organizations and individuals in making informed decisions by comprehensively understanding the data. If you’re in need of various data analysis techniques solutions, consider using QuestionPro. Our software allows you to collect data easily, create real-time reports, and analyze data. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

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Home » Research Data – Types Methods and Examples

Research Data – Types Methods and Examples

Table of Contents

Research Data

Research data refers to any information or evidence gathered through systematic investigation or experimentation to support or refute a hypothesis or answer a research question.

It includes both primary and secondary data, and can be in various formats such as numerical, textual, audiovisual, or visual. Research data plays a critical role in scientific inquiry and is often subject to rigorous analysis, interpretation, and dissemination to advance knowledge and inform decision-making.

Types of Research Data

There are generally four types of research data:

Quantitative Data

This type of data involves the collection and analysis of numerical data. It is often gathered through surveys, experiments, or other types of structured data collection methods. Quantitative data can be analyzed using statistical techniques to identify patterns or relationships in the data.

Qualitative Data

This type of data is non-numerical and often involves the collection and analysis of words, images, or sounds. It is often gathered through methods such as interviews, focus groups, or observation. Qualitative data can be analyzed using techniques such as content analysis, thematic analysis, or discourse analysis.

Primary Data

This type of data is collected by the researcher directly from the source. It can include data gathered through surveys, experiments, interviews, or observation. Primary data is often used to answer specific research questions or to test hypotheses.

Secondary Data

This type of data is collected by someone other than the researcher. It can include data from sources such as government reports, academic journals, or industry publications. Secondary data is often used to supplement or support primary data or to provide context for a research project.

Research Data Formates

There are several formats in which research data can be collected and stored. Some common formats include:

• Text : This format includes any type of written data, such as interview transcripts, survey responses, or open-ended questionnaire answers.
• Numeric : This format includes any data that can be expressed as numerical values, such as measurements or counts.
• Audio : This format includes any recorded data in an audio form, such as interviews or focus group discussions.
• Video : This format includes any recorded data in a video form, such as observations of behavior or experimental procedures.
• Images : This format includes any visual data, such as photographs, drawings, or scans of documents.
• Mixed media: This format includes any combination of the above formats, such as a survey response that includes both text and numeric data, or an observation study that includes both video and audio recordings.
• Sensor Data: This format includes data collected from various sensors or devices, such as GPS, accelerometers, or heart rate monitors.
• Social Media Data: This format includes data collected from social media platforms, such as tweets, posts, or comments.
• Geographic Information System (GIS) Data: This format includes data with a spatial component, such as maps or satellite imagery.
• Machine-Readable Data : This format includes data that can be read and processed by machines, such as data in XML or JSON format.
• Metadata: This format includes data that describes other data, such as information about the source, format, or content of a dataset.

Data Collection Methods

Some common research data collection methods include:

• Surveys : Surveys involve asking participants to answer a series of questions about a particular topic. Surveys can be conducted online, over the phone, or in person.
• Interviews : Interviews involve asking participants a series of open-ended questions in order to gather detailed information about their experiences or perspectives. Interviews can be conducted in person, over the phone, or via video conferencing.
• Focus groups: Focus groups involve bringing together a small group of participants to discuss a particular topic or issue in depth. The group is typically led by a moderator who asks questions and encourages discussion among the participants.
• Observations : Observations involve watching and recording behaviors or events as they naturally occur. Observations can be conducted in person or through the use of video or audio recordings.
• Experiments : Experiments involve manipulating one or more variables in order to measure the effect on an outcome of interest. Experiments can be conducted in a laboratory or in the field.
• Case studies: Case studies involve conducting an in-depth analysis of a particular individual, group, or organization. Case studies typically involve gathering data from multiple sources, including interviews, observations, and document analysis.
• Secondary data analysis: Secondary data analysis involves analyzing existing data that was collected for another purpose. Examples of secondary data sources include government records, academic research studies, and market research reports.

Analysis Methods

Some common research data analysis methods include:

• Descriptive statistics: Descriptive statistics involve summarizing and describing the main features of a dataset, such as the mean, median, and standard deviation. Descriptive statistics are often used to provide an initial overview of the data.
• Inferential statistics: Inferential statistics involve using statistical techniques to draw conclusions about a population based on a sample of data. Inferential statistics are often used to test hypotheses and determine the statistical significance of relationships between variables.
• Content analysis : Content analysis involves analyzing the content of text, audio, or video data to identify patterns, themes, or other meaningful features. Content analysis is often used in qualitative research to analyze open-ended survey responses, interviews, or other types of text data.
• Discourse analysis: Discourse analysis involves analyzing the language used in text, audio, or video data to understand how meaning is constructed and communicated. Discourse analysis is often used in qualitative research to analyze interviews, focus group discussions, or other types of text data.
• Grounded theory : Grounded theory involves developing a theory or model based on an analysis of qualitative data. Grounded theory is often used in exploratory research to generate new insights and hypotheses.
• Network analysis: Network analysis involves analyzing the relationships between entities, such as individuals or organizations, in a network. Network analysis is often used in social network analysis to understand the structure and dynamics of social networks.
• Structural equation modeling: Structural equation modeling involves using statistical techniques to test complex models that include multiple variables and relationships. Structural equation modeling is often used in social science research to test theories about the relationships between variables.

Purpose of Research Data

Research data serves several important purposes, including:

• Supporting scientific discoveries : Research data provides the basis for scientific discoveries and innovations. Researchers use data to test hypotheses, develop new theories, and advance scientific knowledge in their field.
• Validating research findings: Research data provides the evidence necessary to validate research findings. By analyzing and interpreting data, researchers can determine the statistical significance of relationships between variables and draw conclusions about the research question.
• Informing policy decisions: Research data can be used to inform policy decisions by providing evidence about the effectiveness of different policies or interventions. Policymakers can use data to make informed decisions about how to allocate resources and address social or economic challenges.
• Promoting transparency and accountability: Research data promotes transparency and accountability by allowing other researchers to verify and replicate research findings. Data sharing also promotes transparency by allowing others to examine the methods used to collect and analyze data.
• Supporting education and training: Research data can be used to support education and training by providing examples of research methods, data analysis techniques, and research findings. Students and researchers can use data to learn new research skills and to develop their own research projects.

Applications of Research Data

Research data has numerous applications across various fields, including social sciences, natural sciences, engineering, and health sciences. The applications of research data can be broadly classified into the following categories:

• Academic research: Research data is widely used in academic research to test hypotheses, develop new theories, and advance scientific knowledge. Researchers use data to explore complex relationships between variables, identify patterns, and make predictions.
• Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement. Data analysis techniques such as market research, customer analytics, and financial analysis are widely used to gain insights and inform strategic decision-making.
• Healthcare: Research data is used in healthcare to improve patient outcomes, develop new treatments, and identify health risks. Researchers use data to analyze health trends, track disease outbreaks, and develop evidence-based treatment protocols.
• Education : Research data is used in education to improve teaching and learning outcomes. Data analysis techniques such as assessments, surveys, and evaluations are used to measure student progress, evaluate program effectiveness, and inform policy decisions.
• Government and public policy: Research data is used in government and public policy to inform decision-making and policy development. Data analysis techniques such as demographic analysis, cost-benefit analysis, and impact evaluation are widely used to evaluate policy effectiveness, identify social or economic challenges, and develop evidence-based policy solutions.
• Environmental management: Research data is used in environmental management to monitor environmental conditions, track changes, and identify emerging threats. Data analysis techniques such as spatial analysis, remote sensing, and modeling are used to map environmental features, monitor ecosystem health, and inform policy decisions.

Advantages of Research Data

Research data has numerous advantages, including:

• Empirical evidence: Research data provides empirical evidence that can be used to support or refute theories, test hypotheses, and inform decision-making. This evidence-based approach helps to ensure that decisions are based on objective, measurable data rather than subjective opinions or assumptions.
• Accuracy and reliability : Research data is typically collected using rigorous scientific methods and protocols, which helps to ensure its accuracy and reliability. Data can be validated and verified using statistical methods, which further enhances its credibility.
• Replicability: Research data can be replicated and validated by other researchers, which helps to promote transparency and accountability in research. By making data available for others to analyze and interpret, researchers can ensure that their findings are robust and reliable.
• Insights and discoveries : Research data can provide insights into complex relationships between variables, identify patterns and trends, and reveal new discoveries. These insights can lead to the development of new theories, treatments, and interventions that can improve outcomes in various fields.
• Informed decision-making: Research data can inform decision-making in a range of fields, including healthcare, business, education, and public policy. Data analysis techniques can be used to identify trends, evaluate the effectiveness of interventions, and inform policy decisions.
• Efficiency and cost-effectiveness: Research data can help to improve efficiency and cost-effectiveness by identifying areas where resources can be directed most effectively. By using data to identify the most promising approaches or interventions, researchers can optimize the use of resources and improve outcomes.

Limitations of Research Data

Research data has several limitations that researchers should be aware of, including:

• Bias and subjectivity: Research data can be influenced by biases and subjectivity, which can affect the accuracy and reliability of the data. Researchers must take steps to minimize bias and subjectivity in data collection and analysis.
• Incomplete data : Research data can be incomplete or missing, which can affect the validity of the findings. Researchers must ensure that data is complete and representative to ensure that their findings are reliable.
• Limited scope: Research data may be limited in scope, which can limit the generalizability of the findings. Researchers must carefully consider the scope of their research and ensure that their findings are applicable to the broader population.
• Data quality: Research data can be affected by issues such as measurement error, data entry errors, and missing data, which can affect the quality of the data. Researchers must ensure that data is collected and analyzed using rigorous methods to minimize these issues.
• Ethical concerns: Research data can raise ethical concerns, particularly when it involves human subjects. Researchers must ensure that their research complies with ethical standards and protects the rights and privacy of human subjects.
• Data security: Research data must be protected to prevent unauthorized access or use. Researchers must ensure that data is stored and transmitted securely to protect the confidentiality and integrity of the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

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How to analyze survey data: Survey data analysis best practices

Survey data analysis involves reviewing raw materials and transforming them into key insights. Learn how to analyze survey data and best practices here.

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The results are in. You’ve written the questions, found the right people to ask, and got your answers back—now what?

Perfect surveys sent to insightful respondents can become entirely useless if the results aren't coherently and comprehensively analyzed..

So, don’t run and hide.

We know the phrase “survey data analysis” sounds technical and exclusive, but it’s not as scary as it seems. By following a few simple guidelines on how to analyze survey data, you can draw insights from it yourself.

What is survey data analysis?

Survey data analysis is the process of turning survey responses into compelling insights. This includes taking all of your collected data and transforming it into something actionable. Whether it’s open-ended essays, multiple-choice answers, or other questions to ask , you can take this information and uncover patterns and helpful insights. 

Some survey data analysis methods include sorting data into categories and using statistical tactics to identify trends and patterns. The goal is to take these raw data responses and turn them into a clear story that answers your research questions to help you make informed decisions.

Getting started with survey data analysis

Before you get started crunching the numbers and performing a survey data analysis, there are a few pieces of information you need to gather.

First, you need to know the number of total respondents. This number will indicate how large your sample is and how much you can rely on your results. It’s always a good idea to gather people’s opinions, but if 5,000 people attended a concert and only five people answered your survey, you can’t really treat those five answers as representative of the whole group.

Secondly, you need to calculate your survey response rate . This is a straightforward percentage—calculate it by dividing the number of responses you received in total by the number of people you asked to fill out the survey. The higher your response rate and the higher your total number of respondents, the more you can trust your survey data to be representative of the sample as a whole.

How to analyze survey data

The first step when analyzing survey data is to turn your individualized responses into aggregated numbers. This sounds complicated, but really, it just means you need to do some counting.

For every question in your survey, you need to know the total number of people who answered with each response. Take a look at this example question:

By aggregating your responses, you're simply counting how many people answered a, b, c, and d, respectively. If 100 people took your survey, the aggregated results would look something like this:

In the last six months: 30

Six months to a year ago: 40

One to two years ago: 20

Over two years ago: 10

Now, if your survey was conducted through a survey host, your online survey results should be aggregated automatically, so there’ll be no need to add the numbers.

Qualitative vs. quantitative data

Once you have all of your aggregated answers, it’s time to start making some sense of the survey data.

Our brains can make sense of percentages much more quickly and easily than whole numbers. It's also far easier to compare different percentages rather than whole numbers.

Say you wrote a survey asking 5-year-olds for their favorite colors. Just saying that 67 children chose red as their favorite color means very little. However, saying that 23% of the children chose red as their favorite color, compared to 50% who chose blue, gives you a much clearer indication of the relative popularity of one color.

If you’ve asked people to write feedback or long-form answers, leave these until the end.

You don’t want the qualitative data to bias your quantitative analysis. Focus on the numbers first, and hopefully, once you have a clear idea of what the sentiment is, the qualitative answers will be able to help you understand why that might be the case.

How to cross-tabulate survey data

Cross-tabulating your data is where you can really begin to draw insights from your survey results instead of just statistics. It can help you add context to your numbers and explore how different groups of people behave or how different factors might affect a single outcome.

When you plan your survey, you'll have considered the different comparisons you'd like to make. For example, maybe you’d like to know if older people are more likely to enjoy eating olives.

Your question might be something like this:

Now, in the first round of your data analysis, you might have already divided the respondents into two groups to work out the split between people who like and don't like eating olives.

So let’s say the results of this olive question were:

Like olives: 542 people (46%)

Dislike olives: 630 people (54%)

To cross-tabulate your data, you’ll need to map another variable onto this one.

We’re interested in whether tastes change with age, so let’s use that age as our second variable and ask:

With these results, you can plug them into a Google Sheet and start to see if there are any correlations:

Imagine you have a client who is looking at marketing their olive brand directly to people under 35. You could ask these two questions and look at the split between olive lovers and haters just within this subgroup and see how it compares to the overall average splits.

Benchmarking survey data

Data means very little to us without context and meaning. Turning your numbers into percentages makes comparisons easier, but although proportionally, we can recognize exactly what 75% means, how can we know if that is good?

The answer is benchmarks.

Setting benchmarks is key to making sense of the data and working out what those percentages really mean.

Some of the most common benchmarking techniques involve comparisons between this survey’s results and the data from the last time the survey was collected. To do this effectively, you need to make sure that you are comparing the results of the same question from each survey .

Setting a benchmark using last year’s data is easy. You simply take the percentage splits of responses to a certain question and treat these as your starting point. Then, you can easily see if this month’s data is above or below that benchmark.

Year-over-year or month-over-month comparisons are an excellent way of tracking progress. They allow you to see whether trends are emerging or how much responses have changed in a given period. This is known as longitudinal analysis.

If this is your first time collecting data, no worries, you can still set some benchmarks. Instead of comparing your results to last month's or last year’s data, you can calculate the overall total split between responses for each question and treat this as your benchmark or baseline.

Once you begin to cross-tabulate and break your respondents down into further categories, you can compare their results to your benchmark to place their statistics in context. 

If a value is higher than the average , we can say that this category is over-indexing , and if the value is lower , we can say that the category under-indexes . This gives some context to the statistics and starts letting you draw out some real insights from your survey data.

Why you need to analyze survey data

Quantitative data is extremely valuable when interpreting survey results. However, the numbers themselves are unlikely to provide a concrete answer as to why something happened or why people hold a certain opinion.

Understanding why respondents answered in the way that they did is when you can really start to address problems and make changes. This is where the real insight is born.

Sometimes, the “why” will be answered with direct questions in the survey and sometimes with multiple-choice boxes. Other times, it will be up to you as the survey analyst to determine causation, if possible. And this is where we need to be careful.

It's easy to become sucked into a trap when analyzing survey data and start to see patterns everywhere. This isn't necessarily a bad thing, as identifying a correlation between two variables is a key part of interpreting survey results. However, the danger is that we often make assumptions instead.

Assumptions about the data can be hopes or expectations, conscious or subconscious. However, realizing when we are making assumptions can help us avoid any problems further down the line and prevent us from wasting time.

Ultimately, no one wants to find out their assumptions were false after the survey analysis is complete. Similarly, you wouldn’t want a critical assumption to be false and never even realized.

Survey data analysis examples

Correlation occurs when two different variables move at the same time.

A classic example is the sale of seasonal products. During the summer, swimming pool and barbecue sales rise. When plotted on a graph, the two variables move in the same direction at the same time. However, there's no direct connection between these two variables. People buying barbecues isn't the reason the sales of swimming pools increase.

Causation, on the other hand, occurs when one factor directly causes a change in another factor.

For example, in the case of seasonal products, the weather is a key factor. As the temperature rises in the summer, so do barbecue sales. Barbecue sales here are a variable that's dependent on the weather, and there's a key link between them.

When interpreting survey results, it's easy to mistake correlation for causation. Just because two variables move at the same time, it doesn't mean that one is directly influencing the other.

This is where qualitative data comes in. If you’ve asked your respondents to fill in longer-form answers to explain why they chose a certain response, analyzing these answers can give you the insight you need to work out why.

How to report back on your survey data

When sharing your survey data analysis, remember that the story is what makes it interesting, not the numbers.

The percentages you've calculated are vital evidence for your argument, but your analysis needs a narrative to have a real impact on people's thinking.

If you can, always provide context with your statistics, either comparing them to the same survey from last year or comparing groups of people in the same year’s data. Benchmark your numbers so that your audience is immediately aware of whether what they are seeing is positive or negative.

If you are unable to provide recommended actions based on your survey data analysis, at least signpost the key areas that need attention so the relevant parties can begin to tackle the problem if necessary.

When you visualize your data, remember that while long reports can be fascinating, most people won’t read them. Whoever you are presenting to is unlikely to want to listen or read as you walk them through your survey analysis methods step-by-step, so don’t feel like you have to include every single calculation you made in your report.

Put yourself in your audience’s shoes and determine their interests and priorities. Only give them the information if it is relevant to them, they will understand it, and there's something they can do with this new information.

About the author

We're Typeform - a team on a mission to transform data collection by bringing you refreshingly different forms.

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Data Topics

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Types of Data Visualization and Their Uses

In today’s data-first business environment, the ability to convey complex information in an understandable and visually appealing manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as […]

In today’s data-first business environment, the ability to convey complex information in an understandable and  visually appealing  manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as 13 milliseconds.

In essence, data visualization is indispensable for distilling complex information into digestible formats that support both  quick comprehension  and informed decision-making. Its role in analysis and reporting underscores its value as a critical tool in any data-centric activity. 

Types of Data Visualization: Charts, Graphs, Infographics, and Dashboards

The diverse landscape of data visualization begins with simple charts and graphs but moves beyond infographics and animated dashboards.  Charts , in their various forms – be it bar charts for comparing quantities across categories or line charts depicting trends over time – serve as efficient tools for data representation. Graphs extend this utility further: Scatter plots reveal correlations between variables, while pie graphs offer a visual slice of proportional relationships within a dataset. 

Venturing beyond these traditional forms,  infographics  emerge as powerful storytelling tools, combining graphical elements with narrative to enlighten audiences on complex subjects. Unlike standard charts or graphs that focus on numerical data representation, infographics can incorporate timelines, flowcharts, and comparative images to weave a more comprehensive story around the data. 

A dashboard, when  effectively designed , serves as an instrument for synthesizing complex data into accessible and actionable insights. Dashboards very often encapsulate a wide array of information, from real-time data streams to historical trends, and present it through an amalgamation of charts, graphs, and indicators. 

A dashboard’s efficacy lies in its ability to tailor the visual narrative to the specific needs and objectives of its audience. By  selectively  filtering and highlighting critical data points, dashboards facilitate a focused analysis that aligns with organizational goals or individual projects. 

The best type of data visualization to use depends on the data at hand and the purpose of its presentation. Whether aiming to highlight trends, compare values, or elucidate complex relationships, selecting the appropriate visual form is crucial for effectively communicating insights buried within datasets. Through thoughtful design and strategic selection among these varied types of visualizations, one can illuminate patterns and narratives hidden within numbers – transforming raw data into meaningful knowledge.   

Other Types of Data Visualization: Maps and Geospatial Visualization  

Utilizing maps and geospatial visualization serves as a powerful method for uncovering and displaying insightful patterns hidden within complex datasets. At the intersection of geography and data analysis, this technique transforms numerical and categorical data into visual formats that are easily interpretable, such as heat maps, choropleths, or symbolic representations on geographical layouts. This approach enables viewers  to quickly grasp spatial relationships, distributions, trends, and anomalies that might be overlooked in traditional tabular data presentations. 

For instance, in public health,  geospatial visualizations  can highlight regions with high incidences of certain diseases, guiding targeted interventions. In environmental studies, they can illustrate changes in land use or the impact of climate change across different areas over time. By embedding data within its geographical context, these visualizations foster a deeper understanding of how location influences the phenomena being studied. 

Furthermore, the advent of interactive web-based mapping tools has enhanced the accessibility and utility of geospatial visualizations. Users can now engage with the data more directly – zooming in on areas of interest, filtering layers to refine their focus, or even contributing their own data points – making these visualizations an indispensable tool for researchers and decision-makers alike who are looking to extract meaningful patterns from spatially oriented datasets. 

Additionally,  scatter plots  excel in revealing correlations between two variables. By plotting data points on a two-dimensional graph, they allow analysts to discern potential relationships or trends that might not be evident from raw data alone. This makes scatter plots a staple in statistical analysis and scientific research where establishing cause-and-effect relationships is crucial. 

Bubble charts take the concept of scatter plots further by introducing a third dimension – typically represented by the size of the bubbles – thereby enabling an even more layered understanding of data relationships. Whether it’s comparing economic indicators across countries or visualizing population demographics, bubble charts provide a dynamic means to encapsulate complex interrelations within datasets, making them an indispensable tool for advanced data visualization. 

Innovative Data Visualization Techniques: Word Clouds and Network Diagrams 

Some innovative techniques have emerged in the realm of data visualization that not only simplify complex datasets but also enhance engagement and understanding. Among these, word clouds and network diagrams stand out for their  unique approaches  to presenting information. 

Word clouds represent textual data with size variations to emphasize the frequency or importance of words within a dataset. This technique transforms qualitative data into a visually appealing format, making it easier to identify dominant themes or sentiments in large text segments.

Network diagrams introduce an entirely different dimension by illustrating relationships between entities. Through nodes and connecting lines, they depict how individual components interact within a system – be it social networks, organizational structures, or technological infrastructures. This visualization method excels in uncovering patterns of connectivity and influence that might remain hidden in traditional charts or tables. 

Purpose and Uses of Each Type of Data Visualization 

The various types of data visualization – from bar graphs and line charts to heat maps and scatter plots – cater to different analytical needs and objectives. Each type is meticulously designed to highlight specific aspects of the data, making it imperative to understand their unique applications and strengths. This foundational knowledge empowers users to select the most effective visualization technique for their specific dataset and analysis goals.

Line Charts: Tracking Changes Over Time  Line charts are quintessential in the realm of data visualization for their simplicity and effectiveness in showcasing trends and changes over time. By connecting individual data points with straight lines, they offer a clear depiction of how values rise and fall across a chronological axis. This makes line charts particularly useful for tracking the evolution of quantities – be it the fluctuating stock prices in financial markets, the ebb and flow of temperatures across seasons, or the gradual growth of a company’s revenue over successive quarters. The visual narrative that line charts provide helps analysts, researchers, and casual observers alike to discern patterns within the data, such as cycles or anomalies.    

Bar Charts and Histograms: Comparing Categories and   Distributions  Bar charts  are highly suitable for representing comparative data. By plotting each category of comparison with a bar whose height or length reflects its value, bar charts make it easy to visualize relative values at a glance.

Histograms  show the distribution of groups of data in a dataset. This is particularly useful for understanding the shape of data distributions – whether they are skewed, normal, or have any outliers. Histograms provide insight into the underlying structure of data, revealing patterns that might not be apparent.  

Pie Charts: Visualizing Proportional Data   Pie charts  serve as a compelling visualization tool for representing proportional data, offering a clear snapshot of how different parts contribute to a whole. By dividing a circle into slices whose sizes are proportional to their quantity, pie charts provide an immediate visual comparison among various categories. This makes them especially useful in illustrating market shares, budget allocations, or the distribution of population segments.

The simplicity of pie charts allows for quick interpretation, making it easier for viewers to grasp complex data at a glance. However, when dealing with numerous categories or when precise comparisons are necessary, the effectiveness of pie charts may diminish. Despite this limitation, their ability to succinctly convey the relative significance of parts within a whole ensures their enduring popularity in data visualization across diverse fields. 

Scatter Plots: Identifying Relationship and Correlations Between Variables Scatter plots  are primarily used for spotting relationships and correlations between variables. These plots show data points related to one variable on one axis and a different variable on another axis. This visual arrangement allows viewers to determine patterns or trends that might indicate a correlation or relationship between the variables in question. 

For instance, if an increase in one variable consistently causes an increase (or decrease) in the other, this suggests a potential correlation. Scatter plots are particularly valuable for preliminary analyses where researchers seek to identify variables that warrant further investigation. Their straightforward yet powerful nature makes them indispensable for exploring complex datasets, providing clear insights into the dynamics between different factors at play. 

Heat Maps: Representing Complex Data Matrices through Color Gradients Heat maps  serve as a powerful tool in representing complex data matrices, using color gradients to convey information that might otherwise be challenging to digest. At their core, heat maps transform numerical values into a visual spectrum of colors, enabling viewers to quickly grasp patterns, outliers, and trends within the data. This method becomes more effective when the complex relationships between multiple variables need to be reviewed.  

For instance, in fields like genomics or meteorology, heat maps can illustrate gene expression levels or temperature fluctuations across different regions and times. By assigning warmer colors to higher values and cooler colors to lower ones, heat maps facilitate an intuitive understanding of data distribution and concentration areas, making them indispensable for exploratory data analysis and decision-making processes.

Dashboards and Infographics: Integrating Multiple Data Visualizations  Dashboards and infographics represent a synergistic approach in data visualization, blending various graphical elements to offer a holistic view of complex datasets.  Dashboards,  with their capacity to integrate multiple data visualizations such as charts, graphs, and maps onto a single interface, are instrumental in monitoring real-time data and tracking performance metrics across different parameters. They serve as an essential tool for decision-makers who require a comprehensive overview to identify trends and anomalies swiftly. 

Infographics, on the other hand, transform intricate data sets into engaging, easily digestible visual stories. By illustrating strong narratives with striking visuals and solid statistics, infographics make complex information easily digestible to any type of audience. 

Together, dashboards and infographics convey multifaceted data insights in an integrated manner – facilitating informed decisions through comprehensive yet clear snapshots of data landscapes.     

The independent source for health policy research, polling, and news.

Do States with Easier Access to Guns have More Suicide Deaths by Firearm?

Heather Saunders Published: Jul 18, 2022

Nearly half a million lives (480,622) were lost to suicide from 2010 to 2020. During the same period, the suicide death rate increased by 12%, and as of 2009, the number of suicides outnumbered those caused by motor vehicle accidents. Suicides are most prevalent among people who live in rural areas, males, American Indian or Alaska Natives, and White people, but they are rising fastest in some people of color, younger individuals, and people who live in rural areas. On July 16, 2022, the federally mandated crisis number, 988, will be available to all landline and cell phone users, providing a single three-digit number to access a network of over 200 local and state-funded crisis centers. While the overall number of suicide deaths decreased slightly from 47,511 to 45,979 between 2019 to 2020, the suicides involving firearms increased over the same period (from 23,941 to 24,292). The recent mass shootings in Uvalde and Buffalo have catalyzed discussion around mental health and gun policy. In the same week that the federal Bipartisan Safer Communities Act was signed strengthening background checks for young adults, adding incentives for red flag laws, and reducing access to guns for individuals with a domestic violence history, the Supreme Court struck down New York’s “proper cause” requirement for concealed carry allowances. In this issue brief, we use the Center for Disease Control and Prevention (CDC) Wonder database and the State Firearm Law Database to examine the association between suicide deaths by firearm and the number of state-level firearm law provisions.

Suicides account for over half of all firearm deaths (54%), and over half of all suicides involve a firearm (53%). Though mass shootings are more widely covered, data reveal that suicides are a more common cause of firearm-related deaths than homicide. In 2020, a little more than half (54%) of all firearm-related deaths were suicides, 43% were homicides, and 2% were accidental discharges or undetermined causes. This represents a slight decrease from 2018 and 2019, where suicides by firearms accounted for over 60% of all firearm deaths in that period. Looking at suicides, we find that guns were involved in 53% of suicides in 2020, representing the majority of all suicides.

Variation in state-level suicide rates is largely driven by rates of suicide by firearm. Suicides involving firearms vary from the lowest rate of 1.8 per 100,000 in New Jersey and Massachusetts to a high of 20.9 per 100,000 in Wyoming, representing an absolute difference of 19.1. In contrast, the rate of suicide by other means is more stable across states, ranging from a low of 4.6 in Mississippi to a high of 11.4 in South Dakota, representing an absolute difference of 6.8.

There is a wide range of firearm law provisions across states, with Idaho having the fewest at just one and California having the most at 111. Because there is no comprehensive national firearm registry and very few state registries, it is difficult to track gun ownership in the US, so estimates of gun ownership rely on survey data or measures closely related to gun ownership–such as the number of firearm laws. The State Firearm Law Database is a catalog of the presence or absence of 134 firearm law provisions across all 50 states; this analysis uses firearm laws present in 2019. Even though state laws vary widely in detail and number, there are some common themes across states. Many states restrict firearm access to those considered high-risk, including people with felony convictions (37 states), domestic violence misdemeanors (31 states), or those deemed by the court to be a danger (28 states). A number of states regulate concealed carry permits–for example, 37 require background checks for applicants and 28 require authorities to revoke concealed carry permits under certain conditions, though some concealed carry laws may be subject to change given the recent Supreme Court decision.  Other major categories of gun laws include dealer regulations, ammunition regulations and child access prevention, among others. In 2019, the average number of firearm law provisions per state was 29 and ranged from one provision in Idaho to 111 in California ( Appendix Table 1).

More than twice as many suicides by firearm occur in states with the fewest gun laws, relative to states with the most laws. We grouped states into three categories according to the number of firearm law provisions. States with the lowest number of gun law provisions (17 states) had an average of six provisions and were placed in the “least” category; states with a moderate number of laws (16 states) had an average of 19 provisions and were placed in the “moderate” category; and states with the most firearm laws (17 states) had an average of 61 provisions and were placed in the “most” firearm provisions category. Using CDC WONDER underlying cause of death data, we calculated the age-adjusted rate of suicide by firearm for each category of states. We find that suicide by firearm is highest in states with the fewest gun laws (10.8 per 100,000), lower in states with moderate gun laws (8.4 per 100,000), and the lowest in states with the most gun laws (4.9 per 100,000) (Figure 3). The analysis is not designed to necessarily demonstrate a causal relationship between gun laws and suicides by firearm, and it is possible that there are other factors that explain the relationship.

Firearms are the most lethal method of suicide attempts, and about half of suicide attempts take place within 10 minutes of the current suicide thought, so having access to firearms is a suicide risk factor. The availability of firearms has been linked to suicides in a number of peer-reviewed studies . In one such study , researchers examined the association between firearm availability and suicide while also accounting for the potential confounding influence of state-level suicidal behaviors (as measured by suicide attempts). Researchers found that higher rates of gun ownership were associated with increased suicide by firearm deaths, but not with other types of suicide. Taking a look at suicide deaths starting from the date of a handgun purchase and comparing them to people who did not purchase handguns, another study found that people who purchased handguns were more likely to die from suicide by firearm than those who did not–with men 8 times more likely and women 35 times more likely compared to non-owners.

Non-firearm suicides rates are relatively stable across states suggesting that other types of suicides are not more likely in areas where guns are harder to access. To examine whether non-firearm suicides are higher in states where guns are more difficult to access, we used the state-level firearm law provision groups described above and calculated the age-adjusted rate for each group (states with the least, moderate, and the most firearm law provisions). The results of this analysis provide insight into whether there are other factors that may be contributing to the relationship between gun laws and firearm suicides, such as whether people in states that lack easy access to firearms have higher suicide rates by other means. The rate of non-firearm suicides is relatively stable across all groups, ranging from a low rate of 6.5 in states with the most firearm laws to a high of 6.9 in states with the lowest number of firearm laws. The absolute difference of 0.4 is statistically significant, but small. Non-firearm suicides remain relatively stable across groups, suggesting that other types of suicides are not more likely in areas where guns are harder to get (Figure 3). Though we do not observe an increase of suicide death by other means in states with less access to guns, there may still be differences across states that could explain these findings.

If the suicide rate by firearm in all states was similar to the rate in the states with the most gun laws, approximately 6,800 lives may have been saved in 2020, a reduction of about 15% of all suicide-related deaths. Applying the crude rate of 5.3 per 100,000 to the total population in 2020, we estimate that nearly 6,800 suicide deaths may have been averted if rates of suicide by firearm were similar to states with the most gun control laws.

Recent federal legislation strengthens some gun control measures, but it may take several years to impact firearm mortality. In the recently passed federal legislation, the Bipartisan Safer Communities Act , there is an emphasis on strengthening some measures of gun control including background checks for young adults and reducing gun access for those who have a history of domestic violence, among other provisions. Also included in the legislation are additional funds for mental health services in schools and for child and family mental health services. Despite federal movement toward strengthening gun control, a recent Supreme Court decision struck down state legislation that placed additional restrictions on concealed carry permits. It is not known how the Supreme Court’s decision will impact the frequency of concealed carry firearms and the rate of firearm mortality. More firearm regulations are associated with fewer homicides and suicides , but the newly passed federal gun laws may take several years to reduce firearm mortality .

If you or someone you know is considering suicide, contact the National Suicide Prevention Lifeline at the new three-digit dialing code 988 or 1-800-273-8255 (En Español: 1-888-628-9454; Deaf and Hard of Hearing: 1-800-799-4889).

This work was supported in part by Well Being Trust. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

• Mental Health
• Gun Violence
• State Level

Also of Interest

• The Impact of Gun Violence on Children and Adolescents
• Child and Teen Firearm Mortality in the U.S. and Peer Countries
• A Look at the Latest Suicide Data and Change Over the Last Decade