how to do quantitative research analysis

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Thank you. I will try my best to write my results.

Awesome content 👏🏾

this was great explaination

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

The Steps of Quantitative Research

Table of Contents

Last Updated on June 3, 2023 by Karl Thompson

Quantitative research is a strategy which involves the collection of numerical data, a deductive view of the relationship between theory and research, a preference for a natural science approach (and for positivism in particular), and an objectivist conception of social reality.

It is important to note that quantitative research thus means more than the quantification of aspects of social life, it also has a distinctive epistemological and ontological position which distinguishes it from more qualitative research.

11 stages of quantitative research

The fact that quantitative research  starts off with theory signifies the broadly deductive approach to the relationship between theory and research in this tradition. The sociological theory most closely associated with this approach is Functionalism , which is a development of the positivist origins of sociology.

Hypothesis 

Some quantitative research derives a hypothesis from the theoretical starting point to test. A hypothesis is a specific testable statement about how one or more independent variable will impact a dependent variable.

However, a great deal of quantitative research does not entail the specification of a hypothesis, and instead theory acts loosely as a set of concerns in relation to which social researcher collects data. The specification of hypotheses to be tested is particularly likely to be found in experimental research but is often found as well in survey research, which is usually based on cross-sectional design.

Research design  

The third step in quantitative research entails the selection of a research design which has implications for a variety of issues, such as the external validity of findings and researchers’ ability to impute causality to their findings.

Operationalising concepts

Operationalising concepts is a process where the researcher devises measures of the concepts which she wishes to investigate. This typically involves breaking down abstract sociological concepts into more specific measures which can be easily understood by respondents.

For example, ‘social class’ can be operationalised into ‘occupation’ and ‘strength of religious belief’ can be measured by using a range of questions about ‘ideas about God’ and ‘attendance at religious services’.

Selection of a research site

Selection of respondents.

Step six involves ‘choosing a sample of participants’ to take part in the study – which can involve any number of sampling techniques, depending on the hypothesis, and practical and ethical factors. If the hypothesis requires comparison between two different groups (men and women for example), then the sample should reflect this.

Data collection

In cross-sectional research using surveys, this will involve interviewing the sample members by structured-interview or using a pre-coded questionnaire. For observational research this will involve watching the setting and behaviour of people and then assigning categories to each element of behaviour.

Processing data

Data analysis.

In step nine, analysing data, the researcher uses a number of statistical techniques to look for significant correlations between variables, to see if one variable has a significant effect on another variable.

Findings and conclusions  

On the basis of the analysis of the data, the researcher must interpret the results of the analysis. It is at this stage that the findings will emerge : if there is a hypothesis, is it supported? What are the implications of the findings for the theoretical ideas that formed the background of the research?

Publishing Results  

Video summary…..

The presence of an element of both deductivism (step two) and inductivism is indicative of the positivist foundations of quantitative research.

Signposting and Related Posts

Quantitative research is one the major approaches to research methods alongside Qualitative research.

This post is probably quite advanced for most students of A-level sociology and so best treated as extension work for 16-19 year olds, the material here is really moving up towards undergraduate degree level.

Sources/ Find out More about Quantitative Research

Share this:, leave a reply cancel reply.

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Discover more from ReviseSociology

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

Create a Free Account

Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

MORE LIKE THIS

Net Trust Score: Tool for Measuring Trust in Organization

Sep 2, 2024

Why You Should Attend XDAY 2024

Aug 30, 2024

Alchemer vs Qualtrics: Find out which one you should choose

Target Population: What It Is + Strategies for Targeting

Aug 29, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

What is Quantitative Analysis?

Quantitative analysis techniques, applications of quantitative analysis in the business sector, the bottom line, related readings, quantitative analysis.

The collection and evaluation of measurable and verifiable financial data to understand the behavior and performance of a business

Quantitative analysis is the process of collecting and evaluating measurable and verifiable data such as revenues, market share, and wages in order to understand the behavior and performance of a business. In the past, business owners and company directors relied heavily on their experience and instinct when making decisions. However, with data technology, quantitative analysis is now considered a better approach to making informed decisions.

A quantitative analyst’s main task is to present a given hypothetical situation in terms of numerical values. Quantitative analysis helps in evaluating performance, assessing financial instruments, and making predictions. It encompasses three main techniques of measuring data: regression analysis , linear programming, and data mining.

1. Regression Analysis

Regression analysis is a common technique that is not only employed by business owners but also by statisticians and economists. It involves using statistical equations to predict or estimate the impact of one variable on another. For instance, regression analysis can determine how interest rates affect consumers’ behavior regarding asset investment. One other core application of regression analysis is establishing the effect of education and work experience on employees’ annual earnings.

In the business sector, owners can use regression analysis to determine the impact of advertising expenses on business profits. Using this approach, a business owner can establish a positive or negative correlation between two variables.

2. Linear Programming

Most companies occasionally encounter a shortage of resources such as facility space, production machinery , and labor. In such situations, company managers must find ways to allocate resources effectively. Linear programming is a quantitative method that determines how to achieve such an optimal solution. It is also used to determine how a company can make optimal profits and reduce its operating costs, subject to a given set of constraints, such as labor.

3. Data Mining

Data mining is a combination of computer programming skills and statistical methods. The popularity of data mining continues to grow in parallel with the increase in the quantity and size of available data sets. Data mining techniques are used to evaluate very large sets of data to find patterns or correlations concealed within them.

Business owners are often forced to make decisions under conditions of uncertainty. Luckily, quantitative techniques enable them to make the best estimates and thus minimize the risks associated with a particular decision. Ideally, quantitative models provide company owners with a better understanding of information to enable them to make the best possible decisions.

Project Management

One area where quantitative analysis is considered an indispensable tool is in project management . As mentioned earlier, quantitative methods are used to find the best ways of allocating resources, especially if these resources are scarce. Projects are then scheduled based on the availability of certain resources.

Production Planning

Quantitative analysis also helps individuals to make informed product-planning decisions. Let’s say a company finds it challenging to estimate the size and location of a new production facility. Quantitative analysis can be employed to assess different proposals for costs, timing, and location. With effective product planning and scheduling, companies will be more able to meet their customers’ needs while maximizing their profits.

Every business needs a proper marketing strategy. However, setting a budget for the marketing department can be tricky, especially if its objectives are not set. With the right quantitative method, marketers can easily set the required budget and allocate media purchases. The decisions can be based on data obtained from marketing campaigns.

The accounting department of a business also relies heavily on quantitative analysis. Accounting personnel uses different quantitative data and methods, such as the discounted cash flow model , to estimate the value of an investment. Products can also be evaluated based on the costs of producing them and the profits they generate.

Purchase and Inventory

One of the greatest challenges that businesses face is being able to predict the demand for a product or service. However, with quantitative techniques, companies can be guided on just how many materials they need to purchase, the level of inventory to maintain, and the costs they’re likely to incur when shipping and storing finished goods.

Quantitative analysis is the use of mathematical and statistical techniques to assess the performance of a business. Before the advent of quantitative analysis, many company directors based their decisions on experience and gut. Business owners can now use quantitative methods to predict trends, determine the allocation of resources, and manage projects.

Quantitative techniques are also used to evaluate investments. In such a way, organizations can determine the best assets to invest in and the best time to do so. Some of the quantitative analysis methods include regression analysis, linear programming, and data mining.

Thank you for reading CFI’s guide to quantitative analysis. To keep learning and advancing your career, the following CFI resources will be helpful:

• Correlation
• Consumer Surplus Formula
• Quantitative Trading
• Econometrics
• See all data science resources
• Share this article

Create a free account to unlock this Template

Access and download collection of free Templates to help power your productivity and performance.

Already have an account? Log in

Supercharge your skills with Premium Templates

Take your learning and productivity to the next level with our Premium Templates.

Upgrading to a paid membership gives you access to our extensive collection of plug-and-play Templates designed to power your performance—as well as CFI's full course catalog and accredited Certification Programs.

Already have a Self-Study or Full-Immersion membership? Log in

Access Exclusive Templates

Gain unlimited access to more than 250 productivity Templates, CFI's full course catalog and accredited Certification Programs, hundreds of resources, expert reviews and support, the chance to work with real-world finance and research tools, and more.

Already have a Full-Immersion membership? Log in

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/

Is this article helpful?

Raimo Streefkerk

Other students also liked, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, mixed methods research | definition, guide & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Lean Six Sigma Training Certification

• Facebook Instagram Twitter LinkedIn YouTube
• (877) 497-4462

Quantitative Data Analysis Methods. Applications, Methods, and Case Studies

August 29th, 2024

The ability to properly analyze and understand numbers has become very valuable, especially in today’s time.

Analyzing numerical data systematically involves thoughtfully collecting, organizing, and studying data to discover patterns, trends, and connections that can guide important choices.  

Key Highlights

• Analyzing data numerically involves gathering info, organizing it neatly, and examining the numbers to gain insights and make choices informed by data.
• It involves various methods like descriptive statistics, predictive modeling, machine learning, and other statistical techniques. These help make sense of everything.
• For businesses, researchers, and organizations, it’s important to analyze numbers to spot patterns, relationships, and how things change over time within their info.
• Doing analyses allows for data-driven decision-making, projecting outcomes, assessing risks intelligently, and refining strategies and workflows. Finding meaning in the metrics helps optimize processes.

What is Quantitative Data Analysis?

Analyzing numbers is useful for learning from information. It applies stats methods and computational processes to study and make sense of data so you can spot patterns, connections, and how things change over time – giving insight to guide decisions.

At the core, quantitative analysis builds on math and stats fundamentals to turn raw figures into meaningful knowledge.

The process usually starts with gathering related numbers and organizing them neatly. Then analysts use different statistical techniques like descriptive stats, predictive modeling, and more to pull out valuable lessons.

Descriptive stats provide a summary of the key details, like averages and how spread out the numbers are. This helps analysts understand the basics and find any weird outliers.

Inferential stats allow analysts to predict broader trends based on a sample. Things like hypothesis testing , regression analysis, and correlation investigations help identify significant relationships.

Machine learning and predictive modeling have also enhanced working with numbers. These sophisticated methods let analysts create models that can forecast outcomes, recognize patterns across huge datasets, and uncover hidden insights beyond basic stats alone.

Leveraging data-based evidence supports more informed management of resources.

Data Collection and Preparation

The first step in any quantitative data analysis is collecting the relevant data. This involves determining what data is needed to answer the research question or business objective.

Data can come from a variety of sources such as surveys, experiments, observational studies, transactions, sensors, and more. 

Once the data is obtained, it typically needs to go through a data preprocessing or data cleaning phase.

Real-world data is often messy, containing missing values, errors, inconsistencies, and outliers that can negatively impact the analysis if not handled properly. Common data cleaning tasks include:

• Handling missing data through imputation or case deletion
• Identifying and treating outliers 
• Transforming variables (e.g. log transformations)
• Encoding categorical variables
• Removing duplicate observations

The goal of data cleaning is to ensure that quantitative data analysis techniques can be applied accurately to high-quality data. Proper data collection and preparation lays the foundation for reliable results.

In addition to cleaning, the data may need to be structured or formatted in a way that statistical software and data analysis tools can read it properly.

For large datasets, data management principles like establishing data pipelines become important.

Descriptive Statistics of Quantitative Data Analysis

Descriptive statistics is a crucial aspect of quantitative data analysis that involves summarizing and describing the main characteristics of a dataset.

This branch of statistics aims to provide a clear and concise representation of the data, making it easier to understand and interpret.

Descriptive statistics are typically the first step in analyzing data, as they provide a foundation for further statistical analyses and help identify patterns, trends, and potential outliers.

The most common descriptive statistics measures include:

• Mean : The arithmetic average of the data points.
• Median : The middle value in a sorted dataset.
• Mode : The value that occurs most frequently in the dataset.
• Range : The difference between the highest and lowest values in the dataset.
• Variance : The average of the squared deviations from the mean.
• Standard Deviation : The square root of the variance, providing a measure of the spread of data around the mean.
• Histograms : Visual representations of the distribution of data using bars.
• Box Plots : Graphical displays that depict the distribution’s median, quartiles, and outliers.
• Scatter Plots : Displays the relationship between two quantitative variables.

Descriptive statistics play a vital role in data exploration and understanding the initial characteristics of a dataset.

They provide a summary of the data, allowing researchers and analysts to identify patterns, detect potential outliers, and make informed decisions about further analyses.

However, it’s important to note that descriptive statistics alone do not provide insights into the underlying relationships or causal mechanisms within the data.

To draw meaningful conclusions and make inferences about the population, inferential statistics and advanced analytical techniques are required.

Inferential Statistics

While descriptive statistics provide a summary of data, inferential statistics allow you to make inferences and draw conclusions from that data.

Inferential statistics involve taking findings from a sample and generalizing them to a larger population. This is crucial when it is impractical or impossible to study an entire population.

The core of inferential statistics revolves around hypothesis testing . A hypothesis is a statement about a population parameter that needs to be evaluated based on sample data.

The process involves formulating a null and alternative hypothesis, calculating an appropriate test statistic, determining the p-value, and making a decision whether to reject or fail to reject the null hypothesis.

Some common inferential techniques include:

T-tests – Used to determine if the mean of a population differs significantly from a hypothesized value or if the means of two populations differ significantly.

ANOVA ( Analysis of Variance ) – Used to determine if the means of three or more groups are different.  

Regression analysis – Used to model the relationship between a dependent variable and one or more independent variables. This allows you to understand drivers and make predictions.

Correlation analysis – Used to measure the strength and direction of the relationship between two variables.

Inferential statistics are critical for quantitative research, allowing you to test hypotheses, establish causality, and make data-driven decisions with confidence in the findings.

However, the validity depends on meeting the assumptions of the statistical tests and having a properly designed study with adequate sample sizes.

The interpretation of inferential statistics requires care. P-values indicate the probability of obtaining the observed data assuming the null hypothesis is true – they do not confirm or deny the hypothesis directly. Effect sizes are also crucial for assessing the practical significance beyond just statistical significance.

Predictive Modeling and Machine Learning

Quantitative data analysis goes beyond just describing and making inferences about data – it can also be used to build predictive models that forecast future events or behaviors.

Predictive modeling uses statistical techniques to analyze current and historical data to predict unknown future values. 

Some of the key techniques used in predictive modeling include regression analysis , decision trees , neural networks, and other machine learning algorithms.

Regression analysis is used to understand the relationship between a dependent variable and one or more independent variables.

It allows you to model that relationship and make predictions. More advanced techniques like decision trees and neural networks can capture highly complex, non-linear relationships in data.

Machine learning has become an integral part of quantitative data analysis and predictive modeling. Machine learning algorithms can automatically learn and improve from experience without being explicitly programmed.

They can identify hidden insights and patterns in large, complex datasets that would be extremely difficult or impossible for humans to find manually.

Some popular machine learning techniques used for predictive modeling include:

• Supervised learning (decision trees, random forests, support vector machines)
• Unsupervised learning ( k-means clustering , hierarchical clustering) 
• Neural networks and deep learning
• Ensemble methods (boosting, bagging)

Predictive models have a wide range of applications across industries, from forecasting product demand and sales to identifying risk of customer churn to detecting fraud.

With the rise of big data , machine learning is becoming increasingly important for building accurate predictive models from large, varied data sources.

Quantitative Data Analysis Tools and Software

To effectively perform quantitative data analysis, having the right tools and software is essential. There are numerous options available, ranging from open-source solutions to commercial platforms.

The choice depends on factors such as the size and complexity of the data, the specific analysis techniques required, and the budget.

Statistical Software Packages

• R : A powerful open-source programming language and software environment for statistical computing and graphics. It offers a vast collection of packages for various data analysis tasks.
• Python : Another popular open-source programming language with excellent data analysis capabilities through libraries like NumPy, Pandas, Matplotlib, and sci-kit-learn.
• SPSS : A commercial software package widely used in academic and research settings for statistical analysis, data management, and data documentation.
• SAS : A comprehensive software suite for advanced analytics, business intelligence, data management, and predictive analytics.
• STATA : A general-purpose statistical software package commonly used in research, especially in the fields of economics, sociology, and political science.

Spreadsheet Applications

• Microsoft Excel : A widely used spreadsheet application that offers built-in statistical functions and data visualization tools, making it suitable for basic data analysis tasks.
• Google Sheets : A free, web-based alternative to Excel, offering similar functionality and collaboration features.

Data Visualization Tools

• Tableau : A powerful data visualization tool that allows users to create interactive dashboards and reports, enabling effective communication of quantitative data.
• Power BI : Microsoft’s business intelligence platform that combines data visualization capabilities with data preparation and data modeling features.
• Plotly : A high-level, declarative charting library that can be used with Python, R, and other programming languages to create interactive, publication-quality graphs.

Business Intelligence (BI) and Analytics Platforms

• Microsoft Power BI : A cloud-based business analytics service that provides data visualization, data preparation, and data discovery capabilities.
• Tableau Server/Online : A platform that enables sharing and collaboration around data visualizations and dashboards created with Tableau Desktop.
• Qlik Sense : A data analytics platform that combines data integration, data visualization, and guided analytics capabilities.

Cloud-based Data Analysis Platforms

• Amazon Web Services (AWS) Analytics Services : A suite of cloud-based services for data analysis, including Amazon Athena, Amazon EMR, and Amazon Redshift.
• Google Cloud Platform (GCP) Data Analytics : GCP offers various data analytics tools and services, such as BigQuery, Dataflow, and Dataprep.
• Microsoft Azure Analytics Services : Azure provides a range of analytics services, including Azure Synapse Analytics, Azure Data Explorer, and Azure Machine Learning.

Applications of Quantitative Data Analysis

Quantitative data analysis techniques find widespread applications across numerous domains and industries. Here are some notable examples:

Business Analytics

Businesses rely heavily on quantitative methods to gain insights from customer data, sales figures, market trends, and operational metrics.

Techniques like regression analysis help model customer behavior, while clustering algorithms enable customer segmentation. Forecasting models allow businesses to predict future demand, inventory needs, and revenue projections.

Healthcare and Biomedical Research with Quantitative Data Analysis

Analysis of clinical trial data, disease prevalence statistics, and patient outcomes employs quantitative methods extensively.

Hypothesis testing determines the efficacy of new drugs or treatments. Survival analysis models patient longevity. Data mining techniques identify risk factors and detect anomalies in healthcare data.

Marketing and Consumer Research

Marketing teams use quantitative data from surveys, A/B tests, and online behavior tracking to optimize campaigns. Regression models predict customer churn or likelihood to purchase.

Sentiment analysis derives insights from social media data and product reviews. Conjoint analysis determines which product features impact consumer preferences.

Finance and Risk Management with Quantitative Data Analysis

Quantitative finance relies on statistical models for portfolio optimization, derivative pricing, risk quantification, and trading strategy formulation. Value at Risk (VaR) models assess potential losses. Monte Carlo simulations evaluate the risk of complex financial instruments.

Social and Opinion Research

From political polls to consumer surveys, quantitative data analysis techniques like weighting, sampling, and survey data adjustment are critical. Researchers employ methods like factor analysis, cluster analysis, and structural equation modeling .

Case Studies

Case study 1: netflix’s data-driven recommendations.

Netflix extensively uses quantitative data analysis, particularly machine learning, to drive its recommendation engine.

By mining user behavior data and combining it with metadata about movies and shows, they build predictive models to accurately forecast what a user would enjoy watching next.

Case Study 2: Moneyball – Analytics in Sports

The adoption of sabermetrics and analytics by baseball teams like the Oakland Athletics, as depicted in the movie Moneyball, revolutionized player scouting and strategy.

By quantifying player performance through new statistical metrics, teams could identify undervalued talent and gain a competitive edge.

Quantitative data analysis is a powerful toolset that allows organizations to derive valuable insights from their data to make informed decisions.

By applying the various techniques and methods discussed, such as descriptive statistics, inferential statistics , predictive modeling , and machine learning, businesses can gain a competitive edge by uncovering patterns, trends, and relationships hidden within their data.

However, it’s important to note that quantitative data analysis is not a one-time exercise. As businesses continue to generate and collect more data, the analysis process should be an ongoing, iterative cycle.

If you’re looking to further enhance their quantitative data analysis capabilities, there are several potential next steps to consider:

• Continuous learning and skill development : The field of data analysis is constantly evolving, with new statistical methods, modeling techniques, and software tools emerging regularly. Investing in ongoing training and education can help analysts stay up-to-date with the latest advancements and best practices.
• Investing in specialized tools and infrastructure : As data volumes continue to grow, organizations may need to invest in more powerful data analysis tools, such as big data platforms, cloud-based solutions, or specialized software packages tailored to their specific industry or use case.
• Collaboration and knowledge sharing : Fostering a culture of collaboration and knowledge sharing within the organization can help analysts learn from each other’s experiences, share best practices, and collectively improve the organization’s analytical capabilities.
• Integrating qualitative data : While this article has focused primarily on quantitative data analysis, incorporating qualitative data sources, such as customer feedback, social media data, or expert opinions, can provide additional context and enrich the analysis process.
• Ethical considerations and data governance : As data analysis becomes more prevalent, it’s crucial to address ethical concerns related to data privacy, bias, and responsible use of analytics.

Implementing robust data governance policies and adhering to ethical guidelines can help organizations maintain trust and accountability.

SixSigma.us offers both Live Virtual classes as well as Online Self-Paced training. Most option includes access to the same great Master Black Belt instructors that teach our World Class in-person sessions. Sign-up today!

Virtual Classroom Training Programs Self-Paced Online Training Programs

SixSigma.us Accreditation & Affiliations

Monthly Management Tips

• Be the first one to receive the latest updates and information from 6Sigma
• Get curated resources from industry-experts
• Gain an edge with complete guides and other exclusive materials
• Become a part of one of the largest Six Sigma community
• Unlock your path to become a Six Sigma professional

" * " indicates required fields

• Search Search Please fill out this field.

What Is Quantitative Analysis?

• Quantitative vs. Qualitative

Risk Reduction

• Pros and Cons

The Bottom Line

• Quantitative Analysis

Quantitative Analysis: A Simple Overview

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT).

Quantitative analysis (also known as quant analysis or QA) in finance is an approach that emphasizes mathematical and statistical analysis to help determine the value of a financial asset, such as a stock or option. Quantitative trading analysts (also known as " quants ") use a variety of data to develop trading algorithms and computer models, including historical investment and stock market data.

The information generated by these computer models helps investors analyze investment opportunities and develop what they believe will be a successful trading strategy . Typically, this trading strategy will include very specific information about entry and exit points , the expected risk of the trade, and the expected return.

The ultimate goal of financial quantitative analysis is to use quantifiable statistics and metrics to assist investors in making profitable investment decisions. In this article, we review the history of quantitative investing, compare it to qualitative analysis , and provide an example of a quant-based strategy in action.

Key Takeaways

• Quantitative analysis emerged from the rise of the computer era, which made it easier than ever before to analyze huge amounts of data in short amounts of time.
• Quantitative trading analysts (quants) identify trading patterns, build models to assess those patterns, and make predictions about the price and direction of securities.
• Once the models are built and the information is gathered, quants use the data to set up automated trades of securities.
• Quantitative analysis is different from qualitative analysis, which looks at non-statistical aspects of a company to make predictions.
• Quantitative analysis can be used to mitigate risk by identifying which investments provide the best level of return relative to an investor's preferred level of risk.

Origins of Quant Investing

Nobel Prize-winning economist Harry Markowitz is generally credited with beginning the quantitative investment movement when he published “Portfolio Selection” in the Journal of Finance in March 1952.   Markowitz introduced modern portfolio theory (MPT), which showed investors how to construct a diversified portfolio of assets capable of maximizing returns for various risk levels. Markowitz used math to quantify diversification and is cited as an early adopter of the concept that mathematical models could be applied to investing.

Robert Merton, a pioneer in modern financial theory, won a Nobel Prize for his research into mathematical methods for pricing derivatives .   The work of Markowitz and Merton laid the foundation for the quantitative (quant) approach to investing.

Quantitative vs. Qualitative Analysis

Unlike traditional qualitative investment analysts , quants don’t visit companies, meet the management teams, or research the products the firms sell to identify a competitive edge. They often don’t know or care about the qualitative aspects of the companies they invest in or the products or services these companies provide. Instead, they rely purely on math to make investment decisions.

Quants—who frequently have a scientific background and a degree in statistics or math—will use their knowledge of computers and programming languages to build customized trading systems that automate the trading process. The inputs to their programs might range from key financial ratios (such as the price-to-earnings ratio ) to more complex calculations, such as discounted cash flow (DCF) valuations.

Hedge fund managers embraced the methodology. Advances in computing technology further advanced the field, allowing complex algorithms could be calculated in the blink of an eye and creating automated trading strategies. The field flourished during the dotcom boom and bust .

Quant strategies stumbled in the Great Recession as they failed to account for the impact mortgage-backed securities had on the market and economy as a whole. However, quant strategies remain in use today and have gained notable attention for their role in high-frequency trading (HFT), which relies on math to make trading decisions.

Quantitative investing is also widely practiced both as a stand-alone discipline and in conjunction with traditional qualitative analysis for both return enhancement and risk mitigation.

Quantitative analysts don't look at who manages a company, what its balance sheet looks like, what products it makes, or any other qualitative factor. They focus entirely on the numbers and choose the investment that, mathematically speaking, offers the best return for the lowest level of risk.

Data Used in Quantitative Analysis

The rise of the computer era made it possible to crunch enormous volumes of data in extraordinarily short periods of time. This has led to increasingly complex quantitative trading strategies, as traders seek to identify consistent patterns, model those patterns, and use them to predict price movements in securities.

Quants implement their strategies using publicly available data. The identification of patterns enables them to set up automatic triggers to buy or sell securities.

For example, a trading strategy based on trading volume patterns may have identified a correlation between trading volume and prices. So if the trading volume on a particular stock rises when the stock’s price hits $25 per share and drops when the price hits $30, a quant might set up an automatic buy at $25.50 and an automatic sell at $29.50.

Similar strategies can be based on earnings, earnings forecasts , earnings surprises, and a host of other factors. In each case, pure quant traders don’t care about the company’s sales prospects, management team, product quality, or any other aspect of its business. They are placing their orders to buy and sell based strictly on the numbers accounted for in the patterns they have identified.

Quantitative analysis can be used to identify patterns that may lend themselves to profitable security trades, but that isn’t its only value. While making money is a goal every investor can understand, quantitative analysis can also be used to reduce risk.

The pursuit of so-called “risk-adjusted returns” involves comparing risk measures such as alpha, beta, r-squared, standard deviation, and the Sharpe ratio to identify the investment that will deliver the highest level of return for the given level of risk. The idea is that investors should take no more risk than is necessary to achieve their targeted level of return.

So if the data reveals that two investments are likely to generate similar returns, but that one will be significantly more volatile in terms of up and down price swings, quants (and common sense) would recommend the less risky investment.

Risk-parity portfolios are an example of quant-based strategies in action. The basic concept involves making asset allocation decisions based on market volatility . When volatility declines, the level of risk-taking in the portfolio goes up. When volatility increases, the level of risk-taking in the portfolio goes down .

Example of Quantitative Analysis

To make the example a little more realistic, consider a portfolio that divides its assets between cash and an S&P 500 index fund . Using the Chicago Board Options Exchange Volatility Index ( VIX ) as a proxy for stock market volatility, when volatility rises, our hypothetical portfolio would shift its assets toward cash.

When volatility declines, our portfolio would shift assets to the S&P 500 index fund. Models can be significantly more complex than the one we reference here, perhaps including stocks, bonds, commodities, currencies, and other investments, but the concept remains the same.

Pros and Cons of Quant Trading

Like any trading strategy, quantitative analysis offers both advantages and disadvantages.

• Unemotional : In quant trading, the patterns and numbers are all that matter. It is an effective buy-sell discipline, as it can be executed consistently, unhindered by the emotion that is often associated with financial decisions.
• Cost-effective : Firms that rely on quant strategies don't need to hire large teams of analysts and portfolio managers or travel to assess potential investments. They use computers to analyze the data and execute the trades.

Disadvantages

• Vulnerable to manipulated data : Quant analysis involves culling through vast amounts of data. Choosing the right data is by no means a guarantee, just as trading patterns that appear to suggest certain outcomes may work perfectly until they don’t. Even when a pattern appears to work, validating the patterns can be a challenge.
• Qualitative factors matter : Inflection points , such as the stock market downturn of 2008-09, can be tough on these strategies, as patterns can change suddenly. Humans can see a scandal or management change as it is developing, while a purely mathematical approach cannot necessarily do so.
• Widely used : A strategy becomes less effective as an increasing number of investors attempt to employ it. Patterns that work will become less effective as more and more investors try to profit from them.

What Is Quant Finance?

Quant finance, short for quantitative finance, is using large datasets and mathematical models to analyze patterns in financial markets. It is used by traders to make predictions about how markets will behave, then buy or sell securities based on those predictions.

What Is a Quant?

Quants or quant traders are traders who use quantitative analysis to analyze financial markets and make trading decisions.

What Is the Difference Between Quantitative Analysis and Qualitative Analysis?

Quantitative analysis uses statistical models to make predictions or reach conclusions based solely on things that can be measured. Qualitative analysis makes predictions using subjective, non-numerical data, such as opinions, attitudes, or experiences.

Many investment strategies use a blend of both quantitative and qualitative strategies. They use quant strategies to identify potential investments and then use qualitative analysis to take their research efforts to the next level in identifying the final investment.

They may also use qualitative insight to select investments and quant data for risk management . While both quantitative and qualitative investment strategies have their proponents and their critics, the strategies do not need to be mutually exclusive.

Cowles Foundation for Research in Economics at Yale University. " Portfolio Selection, Efficient Diversification of Investments ."

CFA Institute Research Foundation. " Robert C. Merton and the Science of Finance ," Page 1.

• Terms of Service
• Editorial Policy
• Privacy Policy

Home > Blog >

Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video. 

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

• It provides a deep, nuanced understanding of complex phenomena.
• It captures the 'why' behind behaviors and opinions.
• It allows for unexpected discoveries and new research directions.
• It puts people's experiences and perspectives at the forefront.
• It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

• Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
• Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
• Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
• Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
• Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

• Data reduction: Simplifying and focusing the raw data through coding and categorization.
• Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
• Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis. 

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

• Interviews : One-on-one conversations to gather in-depth information.
• Focus Groups : Group discussions to explore collective opinions and experiences.
• Observations : Watching and recording behaviors in natural settings.
• Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria. 

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor. 

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences. 

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions. 

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context. 

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis. 

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult. 

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge. 

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

G2 rating: 4.6/5

Pricing: Starts at $30 monthly.

Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job. 

Here’s how Looppanel’s features can help with qualitative data analysis:

• Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
• AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
• Automatic Tagging: Easily tag and organize your data with free AI tools.
• Insight Generation: Create shareable insights that fit right into your other tools.
• Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
• Smart Summary: Ask the AI a question on your research, and it will give you an answer, using extracts from your data as citations.

Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

• Document analysis : It can easily extract and analyze text from various file formats.
• Summarization : GPT can condense lengthy documents into concise summaries.
• Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
• Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

• Use it to summarize long documents or transcripts
• Ask it to identify key themes in your data
• Use it for basic sentiment analysis
• Have it generate potential codes based on your research questions
• Use it to brainstorm interpretations of your findings

G2 rating: 4.7/5 Pricing: Custom

Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

• Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
• AI-Powered Coding: Uses AI to suggest codes and summarize documents.
• Collaboration Tools: Ideal for teams working on complex research projects.
• Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

• Data Import and Organization: Easily manage different data types, including text, audio, and video.
• AI-Powered Coding: Speeds up the coding process with machine learning.
• Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
• Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps. 

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect? 

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions. 

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can. 

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

Follow us on

Get the best resources for ux research, in your inbox, related articles.

Resources & Guides

February 15, 2024

How to use AI for Qualitative Data Analysis

August 15, 2024

Transcription in Qualitative Research: A Comprehensive Guide for UX Researchers

May 22, 2024

Triangulation in Qualitative Research: A Comprehensive Guide [2024]

Looppanel automatically records your calls, transcribes them, and centralizes all your research data in one place

Pardon Our Interruption

As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:

• You've disabled JavaScript in your web browser.
• You're a power user moving through this website with super-human speed.
• You've disabled cookies in your web browser.
• A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article .

To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.

People-environment relations following COVID-19 pandemic lifestyle restrictions: a multinational, explorative analysis of intended biophilic design changes

• Open access
• Published: 02 September 2024
• Volume 5 , article number  229 , ( 2024 )

Cite this article

You have full access to this open access article

• Kalterina Shulla 1 ,
• Bernd-Friedrich Voigt 2 ,
• Salim Lardjane 3 ,
• Kerstin Fischer 4 ,
• Piotr Kędzierski 5 ,
• Giuseppe Scandone 6 &
• Thomas Süße 7  

The study analyzes the consequences of the COVID-19 pandemic restrictions for the human–environment relations through the lenses of biophilic design. The mixed-method quantitative and qualitative explanatory research combines contextual and personal variables, such as, among others, country, age group, gender, overcrowding, time spent outside, access to nature/food and the exposure to biophilic elements, during and after the lockdown. The results indicate that psychological pressure on individuals caused by pandemic restrictions imposed early 2020, triggered changes in human-environmental relation. More precisely, our comparative analysis of six European countries (Italy, Germany, Poland, Spain, Denmark and Sweden) indicates that people-environment relations do not depend on the objective severity of country-wise restrictions, but rather on the individual perceptions of these restrictions. The results complement the lack of the research for the role of biophilic design in understanding and enhancing human–environment relations during the COVID-19 pandemic restrictions and thereafter.

Avoid common mistakes on your manuscript.

1 Introduction

The power of nature for physical and mental well-being and the healing role in stressful conditions is undeniable, as human aesthetic, intellectual, cognitive and spiritual cravings are fulfilled [ 1 ]. Exposure to real or simulated natural views can quickly trigger restorative activity in the brain and reduce stress levels [ 2 ]. Contemplating a nature-integrated urban environment can enhance positive emotions [ 3 , 4 ] as deprivation from it can worsen negative states [ 5 ]. In early 2020, the COVID-19 pandemic imposed worldwide restrictions that were unique because of their variety and differing severity which ultimately resulted in graduated states of psychological pressure on individuals [ 6 , 7 ]. However, when people are forced to cope with crises in unusual circumstances, changes are triggered in their patterns of living and working toward a more sustainable and resilient lifestyle [ 8 ]. “Worry” can divert life priorities, but a strong self-concept of nature can serve as a buffer for a moderate impact on environmental values [ 9 ], enabling “salutogenic” experiences out of stress and psychological states even in extreme environments [ 10 ]. The restrictions of the COVID-19 global pandemic highlighted an attention towards Biophilic Design, as it embraces elements of direct and indirect experience of nature.

As personal growth is attributed to societal dynamics and changes in norms [ 11 ], the way people use and perceive the environment can be encouraged or limited by such norms and cultural contexts [ 12 ]. Ironically, despite its far-reaching negative effects, the pandemic also created a new environment for self-reflection and changes in personal perceptions and actions toward its natural surroundings [ 13 ] and the individuals’ lifestyle choices, such as choice of food, housing, mobility, etc.; factors which are mainly not beyond individual control [ 14 ]. While the ecological footprint and environmental impact of the crisis have been widely considered in research of sustainable design practices [ 15 ], biophilic design (BD) encompasses the mutual benefits of connecting with nature to both humans (physiological and psychological benefits) and the environment [ 16 ]. Therefore, this study aims to understand the effects of the COVID-19 pandemic on changes of people-environment relations considering objective as well as perceived severity of lifestyle restrictions. Beyond this, the research aims to depict a pattern of predictive variables regarding the likelihood of integrating BD in future life. We do so in combining a country-comparative research approach, explorative quantitative and qualitative analysis of intended BD changes.

2 Conceptual background

The term ‘biophilia’ (love of life), composed of the ancient Greek words for “life” (bio) and “love” (philia), describes harmonious relationships between humans and the biosphere [ 17 ]. The term was first used by Erich Fromm in “The Heart of Man” (1964) and later by Edward Wilson in “Biophilia” [ 18 ]. The individual’s physiological and psychological response to nature enables the effects of BD elements such as the direct experience of nature in the built environment (natural light, air, plants, animals, water, landscapes), the indirect experience of nature (contact with the representation or image of nature, natural materials, etc.) and the experience of space and place (spatial features of prospect refuge, etc.) [ 19 , 20 ].

The human response to design stimuli allows BD elements to improve quality and sustainability by enhancing health and well-being, productivity, biodiversity, circularity, and resilience [ 21 ]. The green building movement in the early 1990s enforced the link between improved environmental quality and worker productivity [ 22 ] through the use of BD to connect with the indoor environment. Additional benefits include addressing workplace stress, student performance, patient recovery, and community cohesiveness [ 23 ] and improving well-being in prisons [ 24 ]. The BD elements in the landscape (even those not perceived as such) enable the incorporation of diverse strategies into the built environment [ 25 ]. In urban settings, these elements, the new “Hanging Gardens of Babylon”, are indicators of sustainability and resilience. When there is freedom to choose for home or workplace environment relations, the choice is often dominated by a viewpoint with a generous prospect, elevated position, open, savanna-like terrain, proximity to a body of water, etc. [ 26 ].

BD research is mainly related to two theoretical concepts from environmental psychology: The first is Stress Reduction Theory (SRT) [ 27 , 28 ] which explains the extent to which contemplating nature can trigger restorative activity in the brain, which in turn is responsible for reduced stress levels and positive emotions. The second explanatory concept comes from Attention Restoration Theory (ART) [ 29 ], which states that a lack of concentration as well as mental fatigue, which can be attributed to a prolonged direct attention span, can be positively influenced by a visual or physical stay in nature and the increase in concentration can be achieved through restorative processes with less energy-draining attention [ 30 ]. When a person is facing an unpleasant and stressful change in its person-environment relation because of a perceived external behavioral control, patterns of BD (see Annex 2) and biophilia values [ 31 ] can trigger individual restorative responses [ 10 ]. These responses might ultimately result in an adjustment of the environmental surroundings, or at least enhance a person`s motivation to do so by effecting the likelihood of using BD. Mindsponge Theory [ 32 ] conceptualizes this relation of perceived behavioral control in nature and models it with intentions towards behavior change [ 33 ]. A crucial part of this systemic conceptualization of the person-environment relation is the element of “perception of external information by the sensory systems, such as visual or auditory information” [ 34 ].

Literature shows that pandemic restrictions have divergent but socioeconomically moderate psychological effects (either positively or negatively related to states of stress) and that enforced restrictions can be perceived differently (i.e., at the individual level). In addition, deprivation from one of the domains can have such great importance that it can dominate the totality of the measures and, as a consequence, can result in a perceived stronger severity of the measures despite the moderate or weak objective status of the country [ 35 ]. The severe restrictions imposed in Europe and all over the world (although differing across countries), especially during the first wave of the pandemic (March–June 2020), limited life choices [ 36 , 37 ]. These restrictions were accompanied by psychological distress and a decrease in psychological well-being in the general public [ 38 ], among others, due to limited access to physical activity, lack of blue/green landscapes, views of nature from home [ 39 ] and remote interactions, which caused loneliness, especially for women and younger adults [ 40 ]. During this period in Italy, the lack of adequate space, terraces and gardens resulted in increased stress and aggressiveness [ 41 ], where the correlation with the “home satisfaction” factor in those conditions was related to spatial features of adequacy, flexibility, and crowding [ 42 ]. As human risk perception can lead to immediate action, in France, hours and days before the lockdown, people moved from their homes to other places, closer to family, or with better living conditions in terms of size, crowding, landscape, etc. [ 43 ]. Additional challenges in the living environment were also due to the necessity of adapting to working from home [ 44 , 45 ]. During the first wave, for instance, more than 60% of the workers in Germany were obligated to work from home, confronting the lack of a separable home-office working space and triggering a large-scale invasion of work into the private sphere [ 46 ].

The related post pandemic research has analyzed the role of biophilic features for recovery from COVID-19. Afacan (2021) explores the role of biophilic design in enhancing psychological resilience during the pandemic, related to recovery tension mood, depression and anger [ 47 ]. Furthermore, integrating natural elements into both residential and public spaces, especially in times of crisis, can significantly improve mental and physical health and foster a sense of community and connection [ 48 ]. BD principles are vital for enhancing post-pandemic living spaces, through maximizing natural light and ventilation, incorporating plants and green spaces, using natural materials, and designing flexible, multi-functional spaces. These approaches not only create aesthetically pleasing environments but also support well-being and sustainability, making living spaces more adaptable and resilient to future crises [ 49 ]. Incorporating natural elements into architectural design not only create aesthetically pleasing environments but also encourage deeper connections with nature, leading to healthier, more resilient living spaces, and better mental and physical health [ 50 ]. Furthermore, investigations on the relevance of various influential factors for the efficiency and effectiveness of working from home, for physical and mental well-being have been conducted [ 51 , 52 ]. There is a need for coordinated cross-disciplinary research to address COVID-19's mental health impacts and understanding the pandemic's psychological effects during and after the pandemic [ 53 ]. However, the role of BD as an indicator of enhancing connection between nature and humans triggered by lived experiences during the pandemic is under-researched. This study aims to fill this gap, by analyzing people-environment relations following COVID-19 pandemic lifestyle restrictions, through a multinational, explorative analysis of intended BD changes.

3 Systematization of restrictive measures during the pandemic in Italy, Germany, Poland, Spain, Denmark and Sweden

The restrictive measures taken during the COVID-19 pandemic for most of the countries, consists on establishing lockdowns, declaring state of emergency, ban on outside activities, border and travel/international flights, and events. The Oxford COVID-19 Government Response Tracker (OxCGRT) defines the stringency of the measures in eight domains: school and workplace closings, canceling public events, restrictions on gathering size, close public transport, stay-at-home requirements, restrictions on internal movement and international travel [ 54 , 55 ]. These restrictions were considered as basis for defining the comparative groups, contrasting case/country selections: (1) countries that experienced strong/moderate restrictions (Italy, Spain, Germany and Poland) and ‘(2) countries with relatively weak restrictions (Denmark and Sweden). Countries are used as proxies, not considering internal differences (i.e., Italy, “in November 2020, was divided into three zones (red, orange, and yellow)) depending on the severity of the outbreak, with different restrictions applied in each zone.

The six selected countries were affected differently by the pandemic, as reflected by the varying severity of the measures taken. During the first wave of the pandemic, the state of emergency was declared in Italy, Spain, and Denmark. The lockdown was implemented in Italy, Spain and Poland, partially in Germany, while Denmark and Sweden had no national lockdowns (see Table  1 , below) for an overview of the restrictions considering the above domains, plus the lockdown status and the state of the emergency in the six countries). Sections  3.1 . and 3.2 . display detailed illustrations of the restrictive measures in the groups.

3.1 Countries with strong/moderate restrictions, Italy and Spain

Italy, one of the first countries in Europe to be heavily impacted by the COVID-19 pandemic, took a series of strict measures to curb the spread of the virus, with a significant impact on citizens, among the positioned in the group of countries with more self-protective measures [ 56 ]. The Italian government declared a state of emergency on January 31st, 2020 (which lasted until 1st of April 2022), and a nationwide lockdown on March 9th, 2020, closing all nonessential businesses and allowing people to leave their homes for essential reasons, such as buying groceries or going to the doctor [ 57 ]. Face masks were mandatory in all public spaces, and social distancing was enforced. In the summer of 2020, restrictions were gradually lifted, and people were allowed to travel within the country for tourism purposes. However, new restrictions were imposed in the fall due to a rise in cases; nevertheless, these restrictions were less severe, although social distancing was still enforced. Vaccination campaigns were underway, and people who were fully vaccinated had more freedom, such as attending events and travelling abroad. The pandemic resulted in an increase in remote work, with many companies allowing their employees to work from home [ 58 ]. This consequently led people to move out of cities toward smaller towns and villages with more affordable housing and space. The pandemic has accelerated the trend of suburbanization in Italy, with more people looking for larger homes with outdoor spaces [ 59 ]. According to the Digital Innovation Observatories of the School of Management of the Politecnico di Milano, in 2022, there were approximately 3.6 million remote workers, almost 500 thousand fewer than in 2021, with a decrease in particular in the Public Administration (PA) and Small Medium Enterprises (SMEs); however, there is slight but constant growth in large companies, which, with 1.84 million workers, accounted for approximately half of the total smart workers. Despite this, there is increased awareness and action in organizations to create workspace environments that motivate and give meaning to work in the office. Approximately 52% of large companies, 30% of SMEs and 25% of PAs have already carried out interventions to modify the environment or are doing so in recent months. In the future, these initiatives are planned or under evaluation for 26% of large companies, 21% of public administrations and 14% of SMEs [ 60 ]. Furthermore, during the pandemic parks and public gardens have become more important as places for people to exercise and relax while maintaining social distance. A study conducted in Italy during the first COVID-19 pandemic wave (April–May 2020) highlighted the fact that restrictions influenced citizens’ perceptions of urban green spaces, with a consequent increase in general interest in parks and public garden [ 61 ].

Spain was one of the European countries with the highest incidence during the first wave [ 62 ], and in the global context, Spain experienced one of the worst situations [ 63 ]. The government imposed a nationwide lockdown by mid-March 2020, which included the prohibition of nonessential transit and blanket recommendations for WFH. Easing measures started later in May through several phases. Having high heterogeneity across the territory, the lifting of limitations would progress through the phases as rearranged during the process, making the progressive lifting of the restrictions challenging due to the highly decentralized system [ 64 ]. First, outside exercise was allowed, but borders remained closed, and no travel between different territorial units was permitted. Face masks were highly recommended both on public transport and outside. Afterward, shops, food markets and restaurants reopened with social distancing and reduced capacity, while public transit reopened with full service but reduced passenger numbers. The country entered a ‘new normality’ in late June, where travel between provinces was also allowed again. These restrictions were difficult to implement because of the conflicted political environment, which resulted in some territorial administrations taking preliminary measures at the subnational level in an uncoordinated manner [ 65 ]. Apart from the economic impact, the lockdown measures also had a significant social impact in Spain. Official data indicate an increase in gender-based violence (a 48% increase in calls for gender violence helplines during the first weeks of April 2020 compared to before the lockdown) [ 66 ]. Regarding WFH in Spain, nearly 83% of professionals were not granted the opportunity to WFH in 2020, whereas only 9.8% were telecommuting. In 2021, however, there was an increase in home office use to 25.2%. During the first month of the pandemic, there was a 38% reduction in physical activity [ 67 ].

Germany has taken a medium amount of protective measures during the pandemic [ 56 ]. The first contact restrictions were already announced in March 2020, followed by restrictions on travel and the closing of small shops and schools. In April, the obligation to remain in quarantine for 2 weeks when returning from another country and the recommendation to wear a face mask were lifted. Soon after, face masks were required for public transportation. In May 2020, schools and small shops opened slowly again. Contact restrictions depended on the number of cases in the district. In October, there were increasing lockdowns and contact restrictions introduced across the country, which lasted until January 2021. In August 2021, shops, restaurants, etc., are increasingly being opened for people who have gone through an infection, have been vaccinated twice or have a recent negative test. In December, these opportunities held wide only for people who were fully vaccinated or had recovered. The pandemic resulted in intensified suburbanization in Germany during 2020, although this process has steadily increased due to internal migration because of the lowest rates since the mid-1990s [ 68 ]. The residential green spaces attached to residential buildings, mostly designed with “semi-public access”, were appreciated by residents as creating refugia in challenging times and were more actively used than they were in prepandemic times, especially when sitting in parks was not allowed [ 69 ]. During the pandemic in Hamburg, a larger number of visitors were recorded in protected nature areas and local nature reserves, to an extent causing considerable problems for the wildlife there. For instance, in the nature reserve Duvenstedter Brook, many people nature parks more as urban recreational areas, chasing deer or playing badminton on protected stretches of heath.

Poland experienced a relatively mild first phase of the pandemic compared to other European countries, where the first case was identified a month later than in Germany and France. The government declared lockdown and enforced self-isolation measures (24 March 2020) and even applied measures that were not yet recommended by international institutions; for example, the first EU country to shut down its external borders, including those with other EU Member States. Factors slowing the progression of the initial phases of the pandemic include the relatively younger population compared to the most affected European countries, the larger population living in rural areas and the low rate of mobility domestically and internationally among the Polish people [ 70 ]. The impact of COVID-19 also resulted in changes in real estate and suburbanization in Poland, driving a wave of people to buy property (houses with land plots) to escape the dread of living in apartment buildings, either occasionally or permanently [ 71 ]. In addition, the pandemic fostered many measures by the Polish government related to the economy, taxes, employment and extraordinary changes in court proceedings and the system of justice.

3.2 Countries with weak restrictions, Denmark and Sweden

In Denmark, the anti-COVID-19 measures taken were comparatively brief [ 72 ]. Denmark was indeed in the group of countries in which there were fewer self-protective measures [ 56 ]. Starting in the middle of March 2020, schools, public institutions, hairdressers, restaurants, shopping malls, etc., were closed down, but on April 20th, 2020, these were opened again, with fitness centers and swimming pools being the last ones to open again on June 1st. Over the course of the summer, face masks were first recommended and then needed, first for public transport and later for restaurants, shops and public institutions. In northern Jutland, seven municipalities were completely isolated from their environment due to an outbreak of a new variant on some mink farms. Apart from those restrictions, no mobility restrictions were imposed within the country. This changed shortly before Christmas 2020, when schools, restaurants, public institutions, theatres, etc., were closed for almost two months; starting on February 28th, the country started opening everything again. On May 21st, almost all restrictions were lifted. Furthermore, many Danes have their own houses, gardens and/or summer houses. In 2020, 2.7 million Danes lived in detached houses, whereas 1.6 million lived in multiunit houses where they did not own themselves; this relationship has not changed between 2021 and 2022 [ 73 ]. Given the short period in which public life was restricted and given access to nature for a large proportion of the population, it can be expected that the impact of COVID-19 measures on the Danish population’s attitude towards the environment is not very pronounced.

Sweden chose a different strategy during the pandemic, mainly based on voluntary measures and citizen behaviors and recommendations rather than restrictions, and a complete lockdown was never implemented (Sweden country snapshot: public health agencies and services in the response to COVID-19, according to World Health Organization WHO. No state of emergency was declared because the Swedish Constitution does not provide for a state of emergency during a public health crisis. This less rigid approach focused more on mitigation measures for slowing, but not stopping, the pandemic and relied on existing high levels of institutional and interpersonal trust. The affected geographical regions or households were not under enforced quarantine, and facemasks were not recommended outside health care [ 74 ]. Recommendations consisted mainly of “staying at home even with the slightest symptom of an infection, physical distancing, enhanced hygiene measures, avoiding public transportation, and working from home if possible” [ 75 ]. Physical distancing was recommended in public spaces but mandatory in bars, restaurants and events. A maximum of 50 people was allowed to gather. In some opinions, this was considered to have caused less serious consequences than did the severe policies used in most countries [ 76 ]. WFH, which accounts for approximately 40% of the total workforce in Sweden and is independent of previous work experience, influences the establishment of new habits [ 77 ]. Studies suggest that workload, performance and well-being decreased during the pandemic [ 78 ].

Assuming positive effects on well-being through a stronger connection between individuals and the natural environment while also considering the unusual circumstances of the world pandemic, this study addresses individuals’ likelihood of using BD after the pandemic. More precisely, the research interest stretches out to identify contextual variables (country, overcrowding, time spent outside, and access to nature/food,) and personal variables (age group and sex) influencing the likelihood of using BD by focusing on the following:

Individuals’ exposure (during and after the lockdown) to several BD elements intentionally or unintentionally, including indoors (color, water, air, sunlight, plants, animals, natural materials, views and vistas, façade greening, geology and landscape, habitats and ecosystems) and outdoors (location, green neighborhood, wide prospect, proximity to natural resorts, etc.), as reported through a questionnaire to test whether the severity of the lockdown restriction of the COVID-19 pandemic fostered stronger people-environment relations, as valued by the likelihood of using BD elements.

The role of the context of (strong/weak) restrictions in several European countries (Italy, Germany, Poland, Spain, Denmark and Sweden) to test whether people-environment relations differ according to the objective and/or perceived severity of the measures in a country context.

The study design was exploratory, mixed-method, cross-sectoral and comparative. The data were collected through a survey directed to European countries via an online Google form conducted from 30 January to 28 February 2023 (see Annex 1). Following a random, uncontrolled sampling strategy, the survey was shared with learning networks such as the Bosch Alumni Network (an international network across 140 countries currently hosting more than 8000 members), the network of European RCEs (Regional Centers of Expertise on Education for Sustainable Development), and the COST (European Cooperation in Science and Technology) action networks of Indoor Air Pollution and Circular city, as well as with practitioners of several universities in Europe. The questions were closed and open (with the purpose of revealing the unexpected elements of change) and organized into four sections: (1) background questions containing variables such as age (different generations have different attitudes and approaches to restrictions), gender and countries (which are used as proxies); (2) questions about exposure to BD elements (color, water, air, sunlight, plants, animals, views and vistas, geology and landscape, habitats and ecosystems) indoors and outdoors, before and after the lockdown; based on the indoor and outdoor elements of the BD [ 80 , 81 , 82 ], from the framework of 14 Biophilic Patterns [ 31 ]; (3) questions about flexibility and adaptation of the living environment after the lockdown concerning the elements of BD; and (4) additional information on any major changes incentivized by the lockdown restrictions concerning lifestyle and wellbeing.

The data were processed through mixed methods, namely, descriptive statistics, statistical model building and testing and a thematic analysis [ 83 ] of the qualitative data using affinity diagramming (Lucero) [ 84 ]. Table 2 provides an overview of the methodological approach.

To facilitate quantitative correspondence analyses [ 88 , 89 ], numerical variables were recorded in three modalities (less, same, more) or (low, intermediate, high). Data visualizations were derived as two-dimensional planes using the FactoMiner package [ 85 ] of R statistical software [ 90 ]. Finally, a logistic model of the included variables was estimated to explain the likelihood of including BD using R statistical software.

The qualitative data were analyzed using affinity diagrams [ 84 ] to identify the categories that emerged. Affinity diagramming is a variant of thematic analysis [ 83 ]. In this process, all the comments were printed in different colors depending on the country and were clustered and labeled in several iterative steps so that the categories emerged bottom up in several different steps: (a) initial familiarization with the data, (b) creating initial codes, (c) collating codes with supporting quotes, (d) grouping codes into themes, (e) reviewing and revising themes, and (f) writing up the narrative based on the categories emerging. This thematic analysis process was carried out using the affinity mapping technique by creating large visual representations of the data points (chart making and ‘walking the wall’). Affinity diagrams allow identifying patterns in participants’ answers, illustrating what consequences of the restrictions on their lifestyles they were foregrounding themselves. For the quantification of the comments, the instances in each category per country were counted and divided by the total number of comments for each country, in line with the recommendations provided by Lucero [ 84 ]. For instance, seven Swedish participants made a comment that reported a change toward a healthier lifestyle, which corresponds to 16.7% of the total number of comments (42) made by the Swedish participants.

5.1 General descriptive analyses

The 403 participants in the survey were mainly from European Union countries and the United Kingdom (89%), such as Italy (17%), Germany (16%), Sweden (14%), Poland (11%), Denmark (9%), Spain (9%), the UK (3%), France (2%), and other EU countries (8% Czech Republic, Belgium, Greece, Romania, Bulgaria, The Netherlands, Portugal and Lithuania). The rest were from EU neighboring countries (9%, Albania, Serbia, Bosnia-Herzegovina, Belarus, Moldova and Turkey) and from other countries in the world (2% United States of America, Canada, Cameroon, Jordan, Kenya and Saudi Arabia). Ninety percent of the respondents had a level of education as a graduate/postgraduate from different fields. The majority of respondents belong to the 90-ties and 80-ties (37% and 27%, respectively). The rest were born on 70-ties (16.5%), 60-ties (9.5%), and 2000s (6%) in the 50-ties (2%). A total of 58% of the respondents were female, 41% were male, and 1% other.

The perceived severity of the restrictions from the respondents corresponds with the objective severity of restriction in Italy and Spain (strong) and in Germany and Poland (moderate) for Sweden (weak); for the Danish participants, the restrictive measures are perceived as moderate and strong by the majority of the respondents in contrast with the countries’ weak objective status. Table 3 displays objective restrictions (based on the criteria followed by the Oxford Covid-19 government response tracker; as also displayed in Table  1 ) and subjective restrictions as perceived by the respondents of the six countries. In total, 403 participants in this survey perceived the restrictive measures taken in their countries as strong and moderate.

The descriptive statistics revealed that the most influential variables were (1) overcrowding/limited space discomfort (58%), (2) difficulties to work (50%), and 3) difficulties accessing green spaces (33%). Although no significant limits were reported for the choice of food (only 10%), the respondents reported changes in their nutritional status after the pandemic related to: the use of regional products (52.7%), switching to organic products (48.7%) and growing their own vegetation through urban gardening or farming (43.3%). Approximately 80% of the respondents considered visual and nonvisual connections with nature after the pandemic to be very important.

A comparison of the “time spent outside in nature”, “during” and “after” the pandemic with that “before” the pandemic revealed that “during”, for 43% of the respondents is “less”, and “after” for, 60% of the participants is “more”. One-quarter of the participants had a steady attitude “same” for “before” and “after” the pandemic.

Figure  1 shows the “Likelihood of including Biophilic design” in relation to the “Time spent outside”, indicating that this is more likely for respondents who have spent “more” or “less” time outside. The graph was generated using the data from the six selected countries: Denmark, Germany, Italy, Poland, Spain, and Sweden.

Likelihood of including biophilic design in relation to “time spent outside” during the pandemic

Figure  2 shows the exposure to BD in the living environment before and after the pandemic, specifically to the following elements: balcony/terrace; private garden/common garden; green roof/façade; views and vistas from home, green or blue; plants/vegetation growing in home gardens/roofs/vases; glass surfaces, sunlight illumination (dynamic & diffuse light); orientation, ventilation, thermal and airflow variability; natural materials (natural wood grains; leather; stone, fossil textures; bamboo, rattan, dried grasses, cork, organic palette. There were no major changes in the specific elements of BD in indoor living environments despite slight increases in the amount of vegetation growing in home gardens/roofs/vases and in vistas from home and glass surfaces, sunlight and illumination. Nevertheless, the majority of respondents reported that they would like to include these BD elements in the future.

Exposure to BD elements before and after the pandemic: balcony/terrace; private garden/common garden; green roof/façade; views and vistas from home, green or blue; plants/vegetation growing in home gardens/roofs/vases; glass surfaces, sunlight illumination (dynamic & diffuse light); orientation, ventilation, thermal and airflow variability; natural materials (natural wood grains; leather; stone, fossil textures; bamboo, rattan, dried grasses, cork, organic palette. Axes x- represents the BD elements. Axes y-represent the % of survey participants

Furthermore, the likelihood of BD outdoors when changing habitation is considered important, especially linked to proximity to urban gardens/green areas (53.1%), proximity to a water body (sea, lake, river, etc.) 45.1%, proximity to rural areas/suburbs (natural terrain with trees and vegetation) 42.7%, proximity to city centers and services (39.1%), proximity to relatives or family (36.7%), and elevated position (i.e., looking downhill or a viewpoint with a wide prospect) by 22.7% of respondents. Other changes are related to working habits, where 65% of respondents preferred flexible virtual/office presence time, 44% fewer working hours, 21% preferred to switch to a full-time home office and only 8% preferred full-time presence. One-quarter of the respondents had changed jobs/occupations after the lockdown. As a result, 63% of participants reported having adopted their home to create space for home office and 39% for recreational activities.

Only a small percentage (11%) of respondents reported having created an indoor individual space for Prospect (an unimpeded view over a distance for surveillance and planning), Refuge (withdrawal from environmental conditions or the main flow of activity, in which the individual is protected from behind and overhead), Mystery (partially obscured views or other sensory devices that entice the individual to travel deeper into the environment) and Risk (an identifiable threat coupled with a reliable safeguard). Furthermore, these elements are considered very likely to be included in the future by the majority of the respondents. Fifty-three percent of the respondents reported other changes, especially related to activities in nature, meditation and self-reflection, work, nutrition towards a more vegetal diet and taking a pet.

5.2 Statistical analysis

A first logistic model was estimated using all the available data (all countries) (see Table  3 ) to evaluate the effect of variables on the high vs. low/moderate Likelihood of including BD elements in the environment”. Variables that have significant descriptive power can be described by three cluster types: age and sex; variables pertaining to the severity of the experience lived during lockdown (overcrowding, limited choice of food, and time spent outside during lockdown); and variables reflecting the need for interaction (pet). The most significant variable is undoubtedly the degree of “overcrowding” experienced during lockdown, which may, as the time spent outside during lockdown, act as a proxy for the experienced severity of the restrictions (as distinguished from the perceived severity and from the objective severity of the restrictions-) see Table  4 ).

Based on these overall results, we conducted narrower correspondence analyses to focus the dependence of the “likelihood” on the above identified variables with respect to the subsample of the six selected cluster countries. Figures 3 , 4 and 5 show the results for the variables “Age”, “Time spent outside during the pandemic” and “Overcrowding”.

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: age

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: time spent outside during the pandemic

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: overcrowding

Figure  3 indicates a moderate “Likelihood of including BD in the environment” for males, while females and individuals who chose the “other” option have more extreme points of view (either low or high likelihood). Figure  4 indicates that the “Likelihood of including BD elements in the environment” is high for respondents who went outside “less” during the pandemic but also for those who went outside “more”. The other dependencies are as follows: the more severe the effect of the lockdown is, the higher the “Likelihood of including BD elements in the environment” as shown in Fig.  5 , for the dependence on overcrowding. An analysis of the variable “Choice of food” did not reveal relevant additional information.

Based on these insights, we ran further analyses on the subsample of 6 countries, aggregated by country. We included objective variables, such as the severity of the restrictions and country. However, neither variable showed a significant influence. After recursively discarding the nonsignificant factors, the final logistic model obtained is displayed in Table  5 .

Two factors appear to correlate the most with the” Likelihood of including BD in the environment” (which was also found by the descriptive analysis):”Time spent outside during lockdown” and the “Will to change the environment”. In particular, it was found that (1) having spent “less” time outside or “more” time outside during lockdown was positively correlated with the likelihood of including BD in the environment, and (2) the higher the willingness to change the environment after experiencing COVID-19 lockdown restrictions was, the higher the likelihood of including BD in the environment. Those who spent “less” time outside during lockdown had approximately two times greater chances of including BD in their environment in the future than those who spent the “same” amount of time outside. Those who spent “more” time outside during lockdown had approximately four times greater chances of including BD in their environment in the future (than those who spent the “same” amount of time outside). Those for whom the “Will to change the environment” is “high” have approximately seven times more chances to include BD in their environment in the future (than those for which the “Will to change the environment is “low”). Those for whom the “Will to change the environment” is “moderate” have approximately the same chances of including BD in their environment in the future (as those for whom the “Will to change the environment” is “low”) (Figs. 6 , 7 and 8 ).

Causal structure diagram explaining the likelihood of including biophilic design in person-environment relation

Lifestyle changes as reported by the survey participants by country. Axes y represent the % of survey participants and Axes x represent the reporting lifestyle changes

The relative role of lifestyle changes as reported by the participants

The strongest combination of factors appears to be: “Will to change the environment”- “high” and “Time spent outside during lockdown”- “more”. For this combination, the probability of including BD in the environment in the future is estimated to be 91%. The weakest combination of factors appears to be: “Will to change the environment”- “low”, and”Time spent outside during lockdown”- “same”. For this combination, the probability of including BD in the environment in the future is estimated to be 24%. Finally, these two variables may act as proxies for the severity of the restrictions experienced (as a subjective indicator of severity rather than objective severity by lockdown measures, as indicated through indices of the Oxford COVID-19 government response tracker).

We conducted further analysis to explain the motivation to include BD elements in the future environment by running a Bayesian causal analysis [ 88 , 89 ] using the R package bnlearn [ 90 ]. The dataset included the six cluster countries. A first Bayesian network representation of the joint distribution of all variables coded at two levels (high vs. low or moderate, more vs. less or same) was obtained and gradually modified to account for causal dependencies while not degrading the fitness measure retained (BIC criterion). For the final model, two main causal factors explain the” Likelihood of including BD elements in the future environment”: “Importance given to BD” and” “Importance of spending time outside”. Moreover, following the causal path, the lockdown had a causal impact on these two factors, mainly through the experience of “Overcrowding during lockdown”, which may be considered a good proxy of the perceived severity of restrictions. The obtained causal structure can be summarized by the following diagram:

As indicated, individuals who experienced strong overcrowding during lockdown are more likely to attach high importance to BD elements in their environment and hence are more likely to change their environment through BD elements and to value spending time outside. Interestingly, people who are less willing to change their environment, give less importance to BD in general and are less likely to have experienced overcrowding during lockdown. Furthermore, people who experienced more overcrowding during lockdown may have had fewer opportunities to go outside during lockdown and may have had problems accessing food, and being “stuck at home”, which explains the relevance of these variables in our logistic regressions. We confirmed this insight by running the following contingency Tables 6 and 7 :

5.3 Qualitative analyses

Altogether and across countries, 32 comments concerned a healthier lifestyle, which amounted to 16.4% of the total number of comments (195). The qualitative analysis of a total of 210 qualitative comments from the Danish, German, Italian, Polish, Spanish and Swedish participants yielded 12 different thematic categories, while participants were free to write as much as they wanted; most participants made exactly one point; only three comments were assigned to two different categories. First, there is one group of 20 comments that seem to refer to the time during the pandemic, not afterwards. In the following, we disregard these comments, leaving us with 193 valid data points. In addition, 44 comments (mostly by the Swedish participants) suggested that there were no changes in their lifestyle due to the pandemic.

The next largest category comprises comments in which participants describe how they perform more outdoor activities. For instance, participants reported on longer walks, spending time in the garden and enjoying fresh air; for example, participants wrote “ I spend more time in my city’s park ”, that they “ try to integrate exercise and fresh air ” or that they “ take time to enjoy nature .” Relatedly, eighteen participants reported having resumed a healthier lifestyle, eating less meat, performing more physical activity and cooking more at home. One participant writes, “ The COVID-19 pandemic and lockdown were starting points for changing what I don’t like in my life. The years after the lockdown were full of news in my life and radically changed myself. ” An additional ten participants reported doing more sports, for instance, “ starting fitness at home ”, “ doing more sports ” or doing “ more physical activities outdoors ”. In addition, eleven participants reported more attention given to their mental health. Many have taken up regular meditation exercises, while others report more awareness and appreciation of the small things of life; for instance, some simply take “ more time for meditation ”, another wrote, “ The overwhelming anxiety forced me to become more meditative ” and again, others state that they prioritize time for themselves now and value time alone, outside or time for recreational purposes more.

Like those with greater appreciation of life and nature, ten participants also reported greater appreciation of social connections; one participant reported moving into the city because the countryside was lonely. More generally, participants described spending more time with friends and family; many stated that they go out more often and “ spend more time with family ” organize “ weekly potlucks with my friends to create a sense of community ” or “ think about friendship in a new way ”.

Eighteen participants also reported changes in their homes; several had obtained a pet or more plants, i.e., “ took a pet and changed home ”, but some also described creating a separate space for a home office; for instance, “ I created a home office with a good amount of daylight and a view outside ”. Some even bought a house or moved into the countryside—one even changed country. Eight participants reported on improvements to their homes: “ cleaning my room more often and having less things there, making it a productive space ” or “ general home and garden improvements ”. Furthermore, participants reported that they improved the efficiency of their work or achieved a better work-life balance, i.e., by means of “ smart working ” or the use of “ new technologies that allow me to rest better ”. Eight participants had found a new job or changed careers. Three reported new skills that they acquired during the pandemic. Finally, three participants state that they are still careful and attend to social distancing; for instance, they say that they are still “ careful ”, “ keep distance from people ” and “ pay more attention to hygiene in public spaces ”. A family in Poland sold their apartment to buy a house in the suburbs, which they had never considered before the pandemic, to obtain more living space and better air quality, with a sizable plot of land, a vegetable garden, and a small orchard.

Participants’ qualitative data thus align with the quantitative survey data, suggesting that the pandemic indeed had an effect on lifestyle choices such that they became more attentive to nature, value nature and outdoor activities in the sun and fresh air. For some, these new preferences have led to changes in habitation. Regarding the differences among the six countries under consideration, the Polish participants reported more career changes than did the participants from other countries and less than the effect of the pandemic on their outdoor activities and attention to mental health and physical activity; however, they seem to have used the pandemic widely to change their eating habits. Among the Swedish, Polish and Danish participants, many reported no changes; especially among the Swedish participants, 38.1% stated that there were no changes at all. These results are in line with the severity of the restrictions imposed in these countries during the pandemic. Furthermore, extended social distancing effects were mentioned only by German and Swedish participants. In all other respects, participants from all six countries reported similar changes in lifestyle; this is interesting in its own right since across countries, people seem to have used the pandemic to rethink their lifestyles, where many of the changes reported are towards a more biophilic and sustainable lifestyle. However, it needs to be stated that these conclusions do only apply for those participants who responded to the qualitative part of the questionnaire.

The qualitative analysis added further insights to the interpretation of our quantitative analysis because it indicated that whether people changed their lifestyles was related to the severity of the country lockdown measures, but how and what changed depended to some extent on the respondents’ personal circumstances. There are also other changes that are not related to BD concepts, such as new jobs, increased efficiency, and new education.

6 Conclusions

The study background reinforces the idea that the COVID-19 pandemic restrictions and lockdowns were accompanied by general discomfort due to alterations in livelihood, work, and activities in nature and in-person social interaction. Based on the findings of this study, it can be argued that the severity of restrictive measures (strong/moderate/weak) imposed by countries during the global COVID-19 pandemic influenced the likelihood of including BD for changes in people-environment relationships. However, this effect occurs mainly when restrictions are individually perceived or experienced as severe due to personal circumstances. Our findings suggest that pandemic restrictions triggered a motivation to include BD and by this to change the person-environment relation. In this regard, individual-level perceived severity of restrictions appears to be a stronger proxy of the intended environmental behavior change than country-level objective indictors. These findings underline the relevance of the element of individual environment perception within the systemic reasoning of mindsponge theory [ 22 ]. It can also be concluded, that, in times of perceived crisis of the person-environment relation, the restorative effects of BD proposed by SRT [] and ART [] may serve as an explanation of an individual`s intention to change the lifestyle towards more natural surroundings. The mixed method analyses conducted at different sample levels revealed that, despite drastic social distancing measures, the experienced discomfort created by “Overcrowding” was identified as the most influential variable in relation to the “Time spent outside nature”, independent of other variables such as country, gender, and age. Likewise, the comparative data evaluation between Italy, Spain, Germany, Poland, Sweden and Denmark does not reinforce the assumption that people-environment relations differ according to the severity of the measures in a country context but rather according to individual responses to crises.

Experienced restrictions (mainly defined by “Overcrowding” and “Time spent outside in nature”) influence the likelihood of including BD elements in the future. Individuals who experienced changes in their individual person-environment relations through pandemic restrictions by having “more” or “less” access to nature were more likely to change their behavior, as indicated by the use of BD elements, both indoors and outdoors, compared to individuals who experienced “same” (unaffected) nature access. However, “Overcrowding” here is also subjective to individual perceptions (unrelated, for example, to the official definition for person/sqm2) and may have created more discomfort for individuals who were less likely to have had the opportunity to go outside during lockdown and may have had problems accessing food, being “stuck at home” or “difficulties to work”, as also explained by the role of these variables in the logistic regressions.

The COVID-19 pandemic appears to have influenced the trend toward relocation in proximity to urban gardens/green areas, water (sea, lake, river, etc.) or to rural areas/suburbs and not so much in specific elements indoors (as supported by the suburbanization statistics in the 6 countries). Often, the desired changes contradict with possibilities, as the results indicate an appreciation of natural elements but not always being possible to change, linked with “whenever is freedom to choose”.

To conclude, there is a post pandemic tendency toward greater connection with nature and healthier habits regarding nutrition and lifestyle within the freedom of individual choice. The likelihood of changing the environment through BD elements is related to experienced and perceived changes during the pandemic. Individuals who were experiencing “Overcrowding” are more likely to place high importance on BD elements in their environment now and hence are more willing to change their person-environment relationships. Given these preconditions, respondents revealed higher likelihood of including BD elements in the future, while a relatively unchanged routine during the pandemic did not result in post pandemic changes or increased attention to BD elements, both indoors and outdoors.

7 Limitations and implications for theory and practice

This is a cross-sectoral and not longitudinal survey-based data collection study due to the impossibility of starting a survey during the pandemic. Thus, we referred to people recalling, considering the survey time in early 2023, when the pandemic had not yet officially ended. For the purpose of this study, the survey questions that relate to the perceived severity of the restrictions are limited to discomfort by overcrowding, difficulties working, access to green spaces, exposure to BD elements and choice of food, excluding other elements, for example, travelling (local and international), and access to social events and contacts.

Regarding the lifestyle changes reported by the participants, it must be considered that all those who did not complete the open question in the survey may not have actually changed anything (the reason for not providing information on lifestyle changes is not known). Furthermore, when reporting on their lifestyle changes, the participants had recently been primed to consider biophilic lifestyle changes since they were asked about those changes in the questions before. The order in which the questions were asked may thus have introduced a certain bias regarding the reporting of biophilic lifestyle changes, meaning that these changes may have occurred, but people may have been less inclined to report on other kinds of changes.

Our study relies on data of a specific demographic group, particularly educational background, which may not be representative of the general population. The survey was distributed among participants from particular organizations or institutions, and 90% of the respondents had a higher level of education which may not mirror a generalization for the broader population. Therefore, our evaluations must be interpreted in light of the preferences of this specific demographic group. Furthermore, our conclusions consider the data of only six countries. While the country selection was specifically motivated by a classification of objective severity of measures, we understand, that a different or extended set of countries may have resulted in different results and interpretations. Future research can extend the range and heterogeneity of the data based on country and further demographic variables to enhance generalizability of conclusions. At the same time, this may serve as an evaluation of our conclusion, that it is the perceived severity (at most over-crowding) rather than the objective severity of restrictions that has the stronger impact on the likelihood of including BD in future life. To this regard, a shift of level of analysis towards regional or local surroundings may provide further insights on the relevance of (perceived) overcrowding for the likelihood of BD in person-environment relations. This will ultimately help extending the scope of this research approach beyond effects related to COVID-19 pandemic.

The pandemic highlighted the value of nature in cities and the living environment for the health and well-being of citizens and it uncovered the unhealthy aspects of current urbanization and living-working styles. Our qualitative data underline the general finding that the pandemic has increased the trend of suburbanization, stressing that accessible urban nature is a key component of creating sustainable urban communities and human health and well-being. However, we need to point out, that the majority of respondents in our study wishes/plans for changes rather than does. As such, our results emphasize that proper societal structures and long-term measures are important for enabling larger-scale changes in people-environment relations. Future longitudinal studies will have to find out if such measures will be effective.

Data availability

The datasets generated during and/or analyses during the current study are available from the corresponding author upon reasonable request.

Kellert SR. Introduction. In: Kellert SR, Wilson EO, editors. The biophilia hypothesis. Washington, DC: Island Press; 1993.

Google Scholar  

Ulrich R, Simons R, Losito B, Fiorito E, Miles M, Zelson M. Stress Recovery During Exposure to Natural and Urban Environments. J Environ Psychol. 1991;11:201–30. https://doi.org/10.1016/S0272-4944(05)80184-7 .

Article   Google Scholar  

Li Q, Otsuka T, Kobayashi M, Wakayama Y, Inagaki H, Katsumata M, Kagawa T. Acute effects of walking in forest environments on cardiovascular and metabolic parameters. Eur J Appl Physiol. 2011;111:2845–53. https://doi.org/10.1007/s00421-011-1918-z .

Article   CAS   Google Scholar  

Berto R. The role of nature in coping with psycho-physiological stress: a literature review on restorativeness. Behav Sci. 2014;4(4):394–409. https://doi.org/10.3390/bs4040394 .

Downton P, Jones D, Zeunert J, Roös P. Biophilic design applications: putting theory and patterns into built environment practice. Knowledge E. 2017. https://doi.org/10.18502/keg.v2i2.596 .

Lee KO, Mai KM, Park S. Green space accessibility helps buffer declined mental health during the COVID-19 pandemic: evidence from big data in the United Kingdom. Nat Mental Health. 2023;1:124–34. https://doi.org/10.1038/s44220-023-00018-y .

Wang J, Fan Y, Palacios J, et al. Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nat Hum Behav. 2022;6:349–58. https://doi.org/10.1038/s41562-022-01312-y .

European Environmental Agency, EEA. (2022). Strand, R., Kovacic, Z., Funtowicz, S. (European Centre for Governance in Complexity) Benini, L., Jesus, A. (EEA) COVID-19: lessons for sustainability?—European Environment Agency (europa.eu).

Sneddon J, Daniel E, Fischer R, et al. The impact of the COVID-19 pandemic on environmental values. Sustain Sci. 2022;17:2155–63. https://doi.org/10.1007/s11625-022-01151-w .

Nicolas M, Martinent G, Palinkas L, Suedfeld P. Dynamics of stress and recovery and relationships with perceived environmental mastery in extreme environments. J Environ Psychol. 2022;83:101853. https://doi.org/10.1016/j.jenvp.2022.101853 .

Yabe T, Bueno BGB, Dong X, Pentland A, Moro E. Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters. Nat Commun. 2023;14(1):2310. https://doi.org/10.1038/s41467-023-37913-y .

Rainisio N, Inghilleri P. Culture, environmental psychology, and well-being: An emergent theoretical framework. In: Rainisio N, editor. Well-being and cultures: Perspectives from positive psychology. Dordrecht: Springer, Netherlands; 2012. p. 103–16. https://doi.org/10.1007/978-94-007-4611-4_7 .

Chapter   Google Scholar  

Kalantidou E. Not going back to normal: designing psychologies toward environmental and social resilience. Hu Arenas. 2023;6:131–46. https://doi.org/10.1007/s42087-021-00198-y .

Akenji L. Consumer scapegoatism and limits to green consumerism. J Clean Prod. 2014;63:13–23. https://doi.org/10.1016/j.jclepro.2013.05.022 .

Mihelcic JR, Zimmerman JB. Environmental engineering: fundamentals, sustainability, design. Hoboken: John Wiley & Sons; 2014.

Wijesooriya N, Brambilla A. Bridging biophilic design and environmentally sustainable design: a critical review. J Cleaner Prod. 2021;283: 124591. https://doi.org/10.1016/j.jclepro.2020.124591 .

Barbiero G, Berto R. Biophilia as evolutionary adaptation: an onto-and phylogenetic framework for biophilic design. Front Psychol. 2021;12: 700709. https://doi.org/10.3389/fpsyg.2021.700709 .

Wilson EO. Biophilia. Cambridge: Harvard University Press; 1984.

Book   Google Scholar  

Kellert SR, Wilson EO. The biophilia hypothesis. Washington, DC: Island Press; 1993.

Kellert SR, Calabrese EF. 2015. The practice of biophilic design. http://www.biophilic-design.com

Zhong W, Schröder T, Bekkering J. Biophilic design in architecture and its contributions to health, well-being, and sustainability: a critical review. Front Architect Res. 2022;11(1):114–41. https://doi.org/10.1016/j.foar.2021.07.006 .

Romm J, Browning WD. Greening the building and the bottom line. Terrapin Bright Green; 1994. https://terrapinbrightgreen.com

Patterns of Biophilic Design (terrapinbrightgreen.com)

Söderlund J, Newman P. Improving Mental Health in Prisons Through Biophilic Design. The Prison Journal. 2017;97(6):750–72. https://doi.org/10.1177/0032885517734516 .

Hady SIMA. Activating biophilic design patterns as a sustainable landscape approach. J Eng Appl Sci. 2021;68:46. https://doi.org/10.1186/s44147-021-00031-x .

Roös P, Downton PJ, D. Zeunert, J. (2016). Biophilia in urban design—patterns and principles for smart Australian cities.

Ulrich RS, Simons RF, Losito BD, Fiorito E, Miles MA, Zelson M. Stress recovery during exposure to natural and urban environments. J Environ Psychol. 1991;11(3):201–30. https://doi.org/10.1016/S0272-4944(05)80184-7 .

Gaekwad JS, Moslehian AS, Roös PB. A meta-analysis of physiological stress responses to natural environments: biophilia and stress recovery theory perspectives. J Environ Psychol. 2023. https://doi.org/10.1016/j.jenvp.2023.102085 .

Kaplan S. The restorative benefits of nature: toward an integrative framework. J Environ Psychol. 1995;15:169–82. https://doi.org/10.1016/0272-4944(95)90001-2 .

Stevenson MP, Schilhab T, Bentsen P. Attention restoration theory II: a systematic review to clarify attention processes affected by exposure to natural environments. J Toxicol Environ Health Part B. 2018;21(4):227–68. https://doi.org/10.1080/10937404.2016.1196155 .

Browning WD, Ryan CO, Clancy JO. 14 patterns of biophilic design. New York: Terrapin Bright Green, LLC; 2014. https://terrapinbrightgreen.com

Vuong Q-H. Mindsponge theory. De Gruyter. 2023. https://doi.org/10.2478/9788367405157-002 .

Nguyen M-H, La V-P, Le T-T, Vuong Q-H. Introduction to Bayesian mindsponge framework analytics: an innovative method for social and psychological research. MethodsX. 2022;9: 101808. https://doi.org/10.1016/j.mex.2022.101808 .

Montemayor C, Haladjian HH. Perception and cognition are largely independent, but still affect each other in systematic ways: arguments from evolution and the consciousness-attention dissociation. Front Psychol. 2017;8:40. https://doi.org/10.3389/fpsyg.2017.00040 .

Ren X. Pandemic and lockdown: a territorial approach to COVID-19 in China, Italy and the United States. Eur Geogr Econ. 2020;61(4–5):423–34. https://doi.org/10.1080/15387216.2020.1762103 .

Mækelæ M, Reggev N, Dutra N, Tamayo R, et al. Perceived efficacy of COVID-19 restrictions, reactions and their impact on mental health during the early phase of the outbreak in six countries. Royal Soc Open Sci. 2020;7: 200644. https://doi.org/10.1098/rsos.200644 .

Fors Connolly F, Olofsson J, Malmberg G, Stattin M. SHARE Working Paper Series 62–2021: adjustment of daily activities to restrictions and reported spread of the COVID-19 pandemic across Europe. 2021; https://doi.org/10.17617/2.3292885

Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav Immun. 2020;89:531–42. https://doi.org/10.1016/j.bbi.2020.05.048 .

Cumbrera MG, Foley R, Correa-Fernández J, González-Marín A, Braçe O, Hewlett D. The importance for wellbeing of having views of nature from and in the home during the COVID-19 pandemic. Results from the GreenCOVID study. J Environ Psychol. 2022;83:101864. https://doi.org/10.1016/j.jenvp.2022.101864 .

Caro JC, Clark AE, D’Ambrosio C, Vögele C. The impact of COVID-19 lockdown stringency on loneliness in five European countries. Soc Sci Med. 2022;314:15492. https://doi.org/10.1016/j.socscimed.2022.115492 .

D’Alessandro D, Gola M, Appolloni L, Dettori M, Fara GM, Rebecchi A, Settimo G, Capolongo S. COVID-19 and living space challenge Well-being and public health recommendations for a healthy, safe, and sustainable housing. Acta Biomed. 2020. https://doi.org/10.23750/abm.v91i9-S.10115 .

Fornara F, Mosca O, Bosco A, et al. Space at home and psychological distress during the Covid-19 lockdown in Italy. J Environ Psychol. 2022;79(2022):101747. https://doi.org/10.1016/j.jenvp.2021.101747 .

Atkinson-Clement C, Pigalle E. What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown. Hum Soc Sci Commun. 2021;8:81. https://doi.org/10.1057/s41599-021-00749-2 .

Bertoni M, Cavapozzi D, Pasini G, Pavese C. Remote working and mental health during the first wave of the COVID-19 pandemic. SSRN Electron J. 2022. https://doi.org/10.2139/ssrn.4111999 .

Awada M, Lucas G, Becerik-Gerber B, Roll S. Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience. Work. 2021. https://doi.org/10.3233/WOR-210301 .

Shulla K, Voigt BF, Cibian S, et al. Effects of COVID-19 on the sustainable development goals (SDGs). Dis Sustain. 2021;2:15. https://doi.org/10.1007/s43621-021-00026-x .

Afacan Y. Impacts of biophilic design on the development of gerotranscendence and the profile of mood states during the COVID-19 pandemic. Ageing Soc. 2021;43:1–25. https://doi.org/10.1017/S0144686X21001860 .

Gür M, Kaprol T. The participation of biophilic design in the design of the post-pandemic living space. Hershey: IGI Global; 2022. https://doi.org/10.4018/978-1-7998-6725-8.ch004 .

Biophilic Design: The future of greener living spaces—Econyl

Role of Biophilic Design in Sustainable Architecture (archiplexgroup.com)

Bolisani E, Scarso E, Ipsen C, Kirchner K, Hansen J. Working from home during COVID-19 pandemic: lessons learned and issues. Manag Market. 2020;15(1):458–76. https://doi.org/10.2478/mmcks-2020-0027 .

Deepa V, Baber H, Shukla B, Sujatha R, Khan D. Does lack of social interaction act as a barrier to effectiveness in work from home? COVID-19 and gender. J Organ Effective. 2023;10(1):94–111. https://doi.org/10.1108/JOEPP-11-2021-0311 .

Holmes EA, O’Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, Bullmore E. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry. 2020;7(6):547–60. https://doi.org/10.1016/S2215-0366(20)30168-1 .

Hale T, Angrist N, Goldszmidt R, et al. A global panel database of pandemic policies (Oxford COVID-19 government response tracker). Nat Hum Behav. 2021;5:529–38. https://doi.org/10.1038/s41562-021-01079-8 .

Hale T. 2023. Working paper version 15.0.docx (ox.ac.uk)

SHARE-ERIC 2021. Results of the 1st SHARE Corona Survey; Project SHARE-COVID19 (Project Number 101015924, Report No. 1, March 2021). Munich: SHARE-ERIC. https://doi.org/10.17617/2.3356927

Protezionecivile. Coronavirus: state of emergency ends on March 31. https://www.protezionecivile.gov.it/en/notizia/coronavirus-state-emergency-ends-march-31/

Ipsen C, van Veldhoven M, Kirchner K, Hansen JP. Six key advantages and disadvantages of working from home in Europe during COVID-19. Int J Environ Res Public Health. 2021;18(4):1826. https://doi.org/10.3390/ijerph18041826 .

Kercuku A, Curci F, Lanzani A, Zanfi F. Italia di mezzo: The emerging marginality of intermediate territories between metropolises and inner areas. REGION. 2023;10:89–112. https://doi.org/10.18335/region.v10i1.397 .

Osservatori. Smart working in Italia: Numeri e trend. https://www.osservatori.net/it/ricerche/comunicati-stampa/smart-working-italia-numeri-trend

Larcher F, Pomatto E, Battisti L, Gullino P, Devecchi M. Perceptions of urban green areas during the social distancing period for COVID-19 containment in Italy. Horticulturae. 2021. https://doi.org/10.3390/horticulturae7030055 .

López MG, Chiner-Oms Á, García de Viedma D, et al. The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant. Nat Genet. 2021;53:1405–14. https://doi.org/10.1038/s41588-021-00936-6 .

Guirao A. The Covid-19 outbreak in Spain. A simple dynamics model, some lessons, and a theoretical framework for control response. Infect Dis Model. 2020;5:652–69. https://doi.org/10.1016/j.idm.2020.08.010 .

Monge S, Latasa Zamalloa P, Sierra Moros MJ, et al. Lifting COVID-19 mitigation measures in Spain (may–june 2020). Enferm Infecc Microbiol Clin. 2023;41(1):11–7. https://doi.org/10.1016/j.eimce.2021.05.019 .

Viguria AU, Casamitjana N. Early interventions and impact of COVID-19 in Spain. Int J Environ Res Public Health. 2021. https://doi.org/10.3390/ijerph18084026 .

Instituto de la Mujer. Impacto de género del COVID-19. 2020. https://www.inmujeres.gob.es/

FitBit the impact of coronavirus on global activity. https://blog.fitbit.com/covid-19-global-activity . Accessed 22 Mar 2020.

Stawarz N, Rosenbaum-Feldbrügge M, Sander N, Sulak H, Knobloch V. The impact of the COVID-19 pandemic on internal migration in Germany: a descriptive analysis. Popul Space Place. 2022. https://doi.org/10.1002/psp.2566 .

Säumel I, Sanft SJ. Crisis mediated new discoveries, claims and encounters: changing use and perception of residential greenery in multistory housing in Berlin, Germany. Urban For Urban Green. 2022. https://doi.org/10.1016/j.ufug.2022.127622 .

Gruszczynski L, Zatoński M, Mckee M. Do regulations matter in fighting the COVID-19 pandemic? Lessons from Poland. European Journal of Risk Regulation. 2021;12(4):739–57. https://doi.org/10.1017/err.2021.53 .

The New York Times. House hunting in Poland: why more buyers are looking there. https://www.nytimes.com/2020/08/26/realestate/house-hunting-poland.html

Statens Serum Institut. COVID-19 ramte verden og Danmark: Se tidslinjen her. https://www.ssi.dk/aktuelt/nyheder/2022/da-covid-19-ramte-verden-og-danmark-se-tidslinjen-her

Statistics Denmark. Housing conditions. https://www.dst.dk/en/Statistik/emner/borgere/boligforhold

Ludvigsson JF. The first eight months of Sweden’s COVID-19 strategy and the key actions and actors that were involved. Acta Paediatr. 2020;109(12):2459–71. https://doi.org/10.1111/apa.15582 .

Carlson J, Tegnell A. Swedish response to COVID-19. China CDC Wkly. 2020. https://doi.org/10.46234/ccdcw2020.215 .

Björkman A, Gisslén M, Gullberg M, Ludvigsson J. The Swedish COVID-19 approach: a scientific dialogue on mitigation policies. Front Public Health. 2023. https://doi.org/10.3389/fpubh.2023.1206732 .

Vilhelmson, et al. Sustained work from home post-pandemic? A Swedish case. Findings. 2023. https://doi.org/10.32866/001c.74470 .

Hallman DM, Januario LB, Mathiassen SE, et al. Working from home during the COVID-19 outbreak in Sweden: effects on 24-h time-use in office workers. BMC Public Health. 2021;21:528. https://doi.org/10.1186/s12889-021-10582-6 .

Politico. Europe’s coronavirus lockdown measures compared. https://www.politico.eu/article/europes-coronavirus-lockdown-measures-compared/

Kellert SR. Dimensions, elements, and attributes of biophilic design. Biophilic Design. 2008;2015:3–19.

Beatley T. Biophilic cities: integrating nature into urban design and planning. Washington, DC: Island Press; 2010.

Beatley T. Handbook of biophilic design. Washington, DC: Island Press; 2016.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Lucero A. Using affinity diagrams to evaluate interactive prototypes. In: Abascal J, Barbosa S, Fetter M, Gross T, Palanque P, Winckler M, editors. Human-computer interaction—INTERACT 2015. INTERACT 2015. Lecture notes in computer science, vol. 9297. Cham: Springer; 2015. https://doi.org/10.1007/978-3-319-22668-2_19 .

Lê S, Josse J, Husson F. FactoMineR: an r package for multivariate analysis. J Stat Softw. 2008;25(1):1–18. https://doi.org/10.18637/jss.v025.i015 .

Venables WN, Ripley BD. Modern applied statistics with S. New York: Springer; 2002.

Selvin S. Statistical analysis of epidemiologic data (monographs in epidemiology and biostatistics, V. 35). 3rd ed. Oxford: Oxford University Press; 2004.

Greenacre M. Theory and application of correspondence analysis. London: Academic Press; 1983.

Greenacre M. Correspondence Analysis in Practice. 2nd ed. Boca Raton: Chapman and Hall/CRC; 2007.

R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2023.

Download references

Acknowledgements

The study is the third part of the project “Publication series: Sustainability in post pandemic society”, funded by the International Alumni Centre Berlin (iac), a center of excellence funded by the Robert Bosch Stiftung for impact-oriented alumni work and networks in philanthropy.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

University of Bonn, Bonn, Germany

Kalterina Shulla

South Westphalia University of Applied Sciences, Meschede, Germany

Bernd-Friedrich Voigt

LMBA UMR CNRS 6205, University of South Brittany, Lorient, France

Salim Lardjane

University of Southern Denmark, Odense, Denmark

Kerstin Fischer

Otwock, Poland

Piotr Kędzierski

QG Enviro, Lecce, Italy

Giuseppe Scandone

University of Applied Sciences and Arts, Bielefeld, Germany

Thomas Süße

You can also search for this author in PubMed   Google Scholar

Contributions

KSH and BFV contributed to the conceptualization and design, writing and revision and methodological framework, the SL contributed to the statistical analyses, the KF contributed to the qualitative analyses and country background, and the PL, GS and TS contributed to corresponding countries’ background and introduction. All the authors contributed to the data collection.

Corresponding authors

Correspondence to Kalterina Shulla or Bernd-Friedrich Voigt .

Ethics declarations

Ethics approval and consent to participate.

Ethics approval and consent to participate were obtained from all the participants.

Consent for publication

The research does not involve human research participants. The survey was anonymous.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

1.1 Survey: changes in people-environment relations in a post-pandemic society

2.1 Three categories of the 14 biophilic patterns according to Browning, Ryan and Clancy [ 67 ]

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Shulla, K., Voigt, BF., Lardjane, S. et al. People-environment relations following COVID-19 pandemic lifestyle restrictions: a multinational, explorative analysis of intended biophilic design changes. Discov Sustain 5 , 229 (2024). https://doi.org/10.1007/s43621-024-00423-y

Download citation

Received : 16 May 2024

Accepted : 16 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s43621-024-00423-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Biophilic design
• Environmental psychology
• People-environment relation
• COVID-19 pandemic

Advertisement

• Find a journal
• Publish with us
• Track your research