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Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms

Sonya s. brady.

Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA

Linda Brubaker

Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA

Cynthia S. Fok

Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA

Sheila Gahagan

Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA

Cora E. Lewis

Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

Jessica Lewis

Yale School of Public Health, New Haven, CT, 06520, USA

Jerry L. Lowder

Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA

Jesse Nodora

Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA

Ann Stapleton

Department of Medicine, University of Washington, Seattle, WA, 98195, USA

Mary H. Palmer

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

This applied paper is intended to serve as a “how to” guide for public health researchers, practitioners, and policy makers who are interested in building conceptual models to convey their ideas to diverse audiences. Conceptual models can provide a visual representation of specific research questions. They also can show key components of programs, practices, and policies designed to promote health. Conceptual models may provide improved guidance for prevention and intervention efforts if they are based on frameworks that integrate social ecological and biological influences on health and incorporate health equity and social justice principles. To enhance understanding and utilization of this guide, we provide examples of conceptual models developed by the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium. PLUS is a transdisciplinary U.S. scientific network established by the National Institutes of Health in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Research Consortium is developing conceptual models to guide its prevention research agenda. Research findings may in turn influence future public health practices and policies. This guide can assist others in framing diverse public health and prevention science issues in innovative, potentially transformative ways.

Public health and prevention science students, researchers, practitioners, and policy makers all stand to benefit by becoming skilled in the development of conceptual models. Over 25 years ago, Jo Anne Earp and Susan Ennett (1991) described how a conceptual model could be used to depict the mechanisms by which a selected set of risk and protective factors may be associated with a health behavior or outcome of interest, as well as the conditions under which such associations are typically observed. This work demonstrated how conceptual models can be used to provide a visual representation of specific research questions and display the key components of prevention and intervention programs, practices, and policies designed to promote health. Since Earp and Ennett’s contribution, many publications that can be used to generate conceptual models have been introduced to the public health sphere. These writings describe frameworks that integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. By integrating diverse perspectives, those who design conceptual models can consider a wide range of factors that may influence health. A better understanding of what influences health can lead to the development of more effective health promotion programs, practices, and policies, as well as more efficient use of limited public health resources. Conceptual model development is an increasingly valued skill. For example, the National Institutes of Health have called for the inclusion of conceptual models when teams of researchers and practitioners respond to specific requests for proposals to conduct research on health promotion, including mental health (RFA-MH-18-705), bladder health (RFA-DK-19-015), and shared decision-making between patients and providers (PA-16-424; NIH, n.d. ).

This paper is intended to serve as a contemporary guide for building conceptual models. It is consistent with the mission of Health Promotion Practice to publish practical tools that advance the science and art of health promotion and disease prevention, particularly with respect to achieving health equity, addressing social determinants of health, and advancing evidence-based health promotion practice. To enhance understanding, examples of conceptual model development are provided from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established by the National Institute of Diabetes and Digestive and Kidney Diseases in 2015 to study bladder health and prevention of lower urinary tract symptoms (LUTS) in girls and women ( Harlow et al., 2018 ). LUTS encompass a variety of bothersome bladder symptoms, including urgency urinary incontinence (i.e., strong urge “to go” with urine loss before reaching a toilet), stress urinary incontinence (i.e., urine loss with physical activity or increases in abdominal pressure such as a cough or sneeze), bothersome frequent and/or urgent urination, nocturnal enuresis (i.e., bed-wetting), difficulty urinating, dribbling after urination, and bladder or urethral pain before, during, or after urination ( Abrams et al., 2010 ; Haylen et al., 2010). LUTS are common. For example, more than 200 million people worldwide and over 15% of women aged 40 years or older experience urinary incontinence, one of the most prevalent LUTS ( Minassian, Bazi, & Stewart, 2017 ; Norton & Brubaker, 2006 ).

While many multidisciplinary research networks focus on clinical treatment of LUTS, the PLUS Consortium stands alone in its focus on bladder health promotion and prevention of LUTS. Consistent with the World Health Organization’s (WHO) definition of health (WHO, 2006), the PLUS Consortium conceptualizes bladder health as “a complete state of physical, mental, and social well-being related to bladder function, and not merely the absence of LUTS,” with function that “permits daily activities, adapts to short term physical or environmental stressors, and allows optimal well-being (e.g., travel; exercise; social, occupational, or other activities)” ( Lukacz et al., 2018 ).

Conceptual models are different from other tools and concepts.

Table 1 highlights the distinction between conceptual models and closely related visual tools and concepts. The contrast between conceptual frameworks and conceptual models is of particular relevance to the present guide. A research-oriented conceptual framework encapsulates what is possible to study and is intentionally comprehensive; in contrast, a research-oriented conceptual model encapsulates what a team has prioritized and chosen to study and is intentionally focused in scope ( Earp & Ennett, 1991 ; Brady et al., 2018 ). Similarly, conceptual frameworks and models may depict the “universe” and selected focus, respectively, of public health practices and policies. The contrast between a theory and conceptual model is also of particular relevance to the present guide. While both theories and conceptual models describe associations among constructs in order to explain or predict outcomes, a theory is intentionally broad with respect to application. It can guide the development of one or more conceptual models to address a specific public health behavior or outcome. While a review of prominent theories is beyond the scope of this paper, several public health textbooks provide an overview of theories that may be used to guide etiologic research and health promotion programs, practices, and policies (e.g., DiClemente, Salazar, & Crosby, 2019 ; Edberg, 2015 ; Glanz, Rimer, & Viswanath, 2015 ; Simons-Morton, McLeroy, & Wedndel, 2012 ).

Distinctions between conceptual models and other visual tools and concepts used in public health and related disciplines.

Traditional and contemporary conceptualizations of public health can identify a broad range of factors that may function as determinants of health.

Traditional conceptual frameworks include social ecological and biopsychosocial models. Social ecological models , a foundation of public health approaches for more than 40 years ( McLeroy, Bibeau, Steckler, & Glanz, 1988 ; Sallis & Owen, 2015 ; Richard, Gauvin, & Raine, 2011 ), situate individuals within an ecosystem of risk and protective factors that extend outward from the intrapersonal level (e.g., biology, psychology) through the interpersonal (e.g., family, peers, partner), institutional (e.g., school, workplace, health clinic), community (e.g., cultural norms), and societal (e.g., policies, laws, economics) levels. These nested spheres of influence interact to produce individual and population health. Similarly, the biopsychosocial model posits that health is defined by a complex reciprocal interaction of biological, psychological, and social factors ( Engel, 1981 ). Given the focus of this paper, we note that both social ecological and biopsychosocial models are more consistent with the definition of a conceptual framework than a conceptual model (see Table 1 ).

Contemporary conceptualizations of public health enhance traditional frameworks by more explicitly integrating biology and social ecology, adopting life course perspectives, and incorporating health equity, social justice, and community engagement principles to guide research, practice, and policy. The Society-Behavior-Biology Nexus depicts nested spheres of influences both within and outside of an individual, who moves through life stages from infancy to old age ( Glass & McAtee, 2006 ). Systems of biological organization include multi-organ systems, cellular and molecular influences, and the genomic substrate. Levels of ecology include the micro (e.g., family, social networks), mezzo (e.g., schools, worksites, communities, healthcare systems), macro (e.g., states, nations), and global (e.g., geopolitics, environment). Biology and social ecology are integrated through the multi-level concept of embodiment (e.g., gene-environment interactions; impact of varying social-ecological resources on biology within and across populations) ( Glass & McAtee, 2006 ; Krieger, 2005 ). Social determinants are framed as societal constraints against and opportunities for health – risk regulators – which include material conditions; discriminatory practices, policies, and attitudes; neighborhood and community conditions; behavioral norms, rules, and expectations; conditions of work; and laws, policies, and regulations. Risk regulators can impact behavior or become embodied with respect to biological function ( Glass & McAtee, 2006 ; Krieger, 2005 ).

The WHO Conceptual Framework for Action on Social Determinants of Health describes how the structure of societies (i.e., governance, policies, values) determines population health ( Solar & Irwin, 2010 ). Social stratification by race, ethnicity, sex, gender, social class, and other factors leads to social hierarchies, which in turn shape social determinants of health. Distal structural determinants of health inequities (e.g., public policy, macroeconomics) are distinguished from more proximal social determinants of health (e.g., living and working conditions). The WHO framework asserts that societies produce health and disease, obligating policy makers to promote health equity and redress structural factors that produce under-resourced communities. Without such attention, health inequities evolve, often widening over time and across generations. The WHO framework can inform conceptual model development by encouraging the consideration of determinants at distal, structural levels (e.g., national policies).

Research teams have utilized contemporary conceptualizations of public health to promote health equity and social justice ( Warnecke et al., 2008 ; Balazs & Ray, 2014 ). For example, the National Institutes of Health (NIH) sponsored Centers for Population Health and Health Disparities developed a framework to show how distal factors (population-level policies and social conditions, institutional contexts) influence intermediate social context (e.g., collective efficacy, social capital), social relationships (e.g., networks, support, and influence), and physical context (e.g., building quality, neighborhood stability), which in turn influence factors that are more proximal to health (individual demographics and risk behaviors, biologic responses and pathways) ( Warnecke et al., 2008 ). The Energy and Resources Group at the University of California, Berkeley developed a framework to display mechanisms through which natural, built, and sociopolitical factors, along with state, county, and community actors, can create drinking water disparities ( Balazs & Ray, 2014 ). These frameworks highlight the key role of distal structural factors in both generating health inequities and remedying them.

Community partners can aid in developing conceptual models.

Increasingly, teams are incorporating community-engaged approaches in the development of research, practice, and policy (e.g., community members actively contributing to problem definition, agenda setting, implementation, and dissemination) ( Warnecke et al., 2008 ; O’Mara-Eves et al., 2013 ). Different resources exist to guide community engagement and enhance the likelihood of sustained, relevant action. For example, Lezine and Reed (2007) outlined different steps to build and apply political will in the development and implementation of public health policy; their approach integrates scientific evidence and community participation. Cacari-Stone and colleagues (2014) developed a conceptual model to show how community-based participatory research (CBPR), one approach to community engagement, can lead to policy change.

Three Steps of Conceptual Model Development.

The development of conceptual models can be divided into three basic steps: (1) identify resources for idea generation; (2) consider risk and protective factors; and (3) select factors for inclusion in the conceptual model. First, team members identify existing conceptual frameworks and models, theories, and key stakeholders (e.g., practitioners, policy makers, community members) that will serve as resources for idea generation. This step defines the “universe” of factors that can be studied in relation to specific health behaviors or outcomes of interest. Second, team members systematically consider risk and protective factors suggested by resources. This step highlights the importance of carefully selecting resources for idea generation; the risk and protective factors considered by a team will be constrained by its selected frameworks and models, theories, and stakeholders. Existing evidence linking risk and protective factors to the health behaviors or outcomes under study, as well as potential effect modifiers and confounders, can be identified through literature reviews. When data are insufficient, a team may wish to conduct key stakeholder interviews, focus groups, and other forms of hypothesis-generating data collection. The third step in the development of conceptual models is to narrow down considered risk and protective factors to those that will be included in the conceptual model. This can be achieved through a combination of theoretically-based, key stakeholder-based, and evidence-based rationales. Theories point to clusters of risk and protective factors that could be studied in relation to health behaviors or outcomes of interest, or targeted through prevention or intervention efforts. Key stakeholders can assess the relevance of different theories to a given public health context and suggest additional risk and protective factors that seem critical to the context. Findings from the extant literature can provide evidence in support of different links in the conceptual model.

If the intent of building a conceptual model is to develop an evidence-based program, practice, or policy, a team can conduct a literature review to answer the following “narrowing down” questions: (a) Is the risk or protective factor strongly linked to the health behavior or outcome of interest? (b) Have previous prevention or intervention programs, practices, or policies shown that the risk or protective factor is feasible to modify? (c) Was health improved as a result of modifying the risk or protective factor? Risk and protective factors can be retained in the conceptual model if they are strongly supported by evidence and judged highly relevant to context.

When the intent of building a conceptual model is to conduct research to better understand a health behavior or outcome, a team may choose to consult existing theories, key stakeholders, and the evidence-base for guidance in selecting risk and protective factors. To maximize potential public health impact, a team can answer the following “narrowing down” question: What potential risk and protective factors are judged to be highly likely to influence health behaviors or outcomes of interest? Ideally, the answers to public health research questions will expand the evidence base in a way that can directly inform programs, practices, and policies. Expansion of the evidence-base can be accomplished in a variety of potentially transformative ways, including the synthesis of ideas from more than one discipline and the application of paradigms from one discipline to another.

Regardless of the approach and rationale used to select risk and protective factors, the utility of the conceptual model may be enhanced by answering the final three sets of questions: (a) Have key “mechanistic factors” been considered and included in the model? What biological, psychological, and social processes might explain links between identified risk and protective factors and health behaviors or outcomes of interest? (b) Have key “upstream factors” been considered and included in the model? For example, are there societal and institutional policies and practices that serve as facilitators or barriers to health? (c) Have key “effect modifiers” been considered and included in the model? For example, are there factors that might make prevention or intervention programs, practices, or policies more or less effective among specific communities and populations?

Examples from the PLUS Research Consortium.

The PLUS Consortium is comprised of a transdisciplinary network of professionals, including community advocates, health care professionals, and scientists specializing in pediatrics, adolescent medicine, gerontology and geriatrics, nursing, midwifery, behavioral medicine, preventive medicine, psychiatry, neuroendocrinology, reproductive medicine, female pelvic medicine and reconstructive surgery, urology, infectious diseases, clinical and social epidemiology, prevention science, medical sociology, psychology, women’s studies, sexual and gender minority health, community-engaged research, community health promotion, scale development, research methods, and biostatistics. The PLUS Consortium has developed several conceptual models to guide research questions that will test whether specific risk and protective factors contribute to LUTS and bladder health.

Because the evidence-base for LUTS prevention is sparse, the traditional and contemporary conceptualizations of public health reviewed above, as well as expertise of PLUS investigators, were used as key resources to identify potential risk and protective factors for study (Step 1). Traditional and contemporary conceptualizations of public health encouraged consortium members to step outside of their disciplinary “comfort zones” to integrate social ecological and biological influences on health across the life course and consider the potential for health equity and social justice principles to guide the consortium’s prevention research agenda. While all of the conceptualizations reviewed above were considered, Glass and McAtee’s Society-Behavior-Biology Nexus was particularly influential because it visually represented different levels of social ecology and biology across the life course, as well as the process of embodiment. PLUS members served as an initial key stakeholder group that generated a conceptual framework and over 400 risk and protective factors prioritized for study in relation to bladder health and LUTS (Step 2) ( Brady et al., 2018 ). The conceptual models presented in this paper represent the work of subsets of consortium members who designed models to guide specific research questions (Step 3). Models were designed with the assistance of public health and prevention science team members who were familiar with social ecological frameworks and the development of conceptual models. Initial development of models occurred in real time during in-person and virtual (WebEx) meetings. This was often followed by revision of models via emailed chains of conversation. One person with experience in conceptual model development was responsible for integrating and communicating comments and mutual decisions, as well as revising the models.

Each conceptual model featured in this paper represents hypothesized associations between constructs; some links in each model are supported by existing evidence, while others are based on theoretical or biological plausibility. Figure 1 highlights institutional-level factors in relation to bladder health and LUTS, while Figure 2 highlights family- and community-level factors and Figure 3 highlights societal and commercial factors.

Work-related structural and social influences on musculoskeletal function and bladder health: Hypothesized mechanisms.

Explanation of Pathways: Four different work-related factors (shaded boxes) affect different aspects of musculoskeletal function, which in turn affect bladder health and LUTS. Workplace physical and psychological demands directly affect musculoskeletal function. Workplace ergonomics and travel/commute patterns indirectly affect musculoskeletal function through prolonged sitting or standing and posture (mediation pathways).

Trajectories of risk and resilience among individuals and communities exposed to ACEs and traumatic stressors: Hypothesized mechanisms.

Explanation of Pathways: Executive functioning difficulties and central nervous system dysregulation are shown in a single, partitioned box because these constructs are hypothesized to covary in their manifestation. Direct effects between two adjacent constructs are shown by solid lines (1a, 2a, 3a, 4, 5); effect modification by resources for resilience (shaded box) is shown by dashed lines (1b, 2b, 3b). ADHD: Attention-Deficit/Hyperactivity Disorder.

Societal and commercial influences on bladder health and LUTS: Hypothesized mechanisms involving fast food and soda.

Explanation of Pathways: This conceptual model highlights hypothesized mechanisms (mediators) that can explain associations between societal and commercial factors (shaded boxes) and bladder health and LUTS. This model can guide a set of statistical analyses that require the identification of predictor, mediating, and outcome variables. The model does not reflect the full complexity of associations that likely exist among constructs (e.g., bi-directional associations, feedback loops; see Systems Model entry in Table 1 ).

Figure 1 depicts a basic conceptual model showing how specific work-related structural and social factors may influence musculoskeletal function, which in turn may impact bladder health and LUTS development. Four key aspects of musculoskeletal dysfunction are overuse injury, strain, pain, and weakness (see center-right of Figure 1 ), which may be directly and indirectly influenced by work-related factors. The top, bottom, and left-most boxes depict work-related factors that are external to the individual and arguably imposed by society and institutions. Workplace physical and psychological demands are shown to directly impact musculoskeletal function. Workplace physical demands (e.g., repetitive heavy lifting) may result in musculoskeletal dysfunction, which in turn may lead to LUTS ( Park & Palmer, 2015 ). In addition, workplace psychological demands (e.g., job performance pressures, conflict with coworkers, inequitable expectations and evaluations of work) may be accompanied by stress, anxiety, and other forms of negative affect ( Larsman, Kadefors, & Sandsjö, 2013 ), which may lead to chronically increased pelvic floor muscle dysfunction and LUTS ( van der Velde, Laan, & Everaerd, 2001 ). Workplace ergonomics (e.g., improper chair or desk height) and travel/commute patterns (e.g., daily, long commutes and long airplane flights) may indirectly impact musculoskeletal dysfunction through prolonged sitting or standing and poor posture ( Barone Gibbs et al., 2018 ).

Additional research is needed to support hypothesized associations in Figure 1 , which are based in large part on the authors’ clinical and community-based observations. If different links are supported, corresponding workplace policies and practices can be promoted to ensure that physical demands are offset by varying the type and intensity of activity and providing breaks; psychological demands are fair, reasonable, and offset by supports; and workplace ergonomics are conducive to the health of all employees, regardless of status within the organization. In addition, local and state governments can support policies and practices that ensure adequate access to acceptable bathroom facilities along transportation routes and when possible, within public transportation conveyances.

Figure 2 shows an example of a more complex conceptual model. A trajectory of risk among individuals or communities exposed to adverse childhood experiences (ACEs) (e.g., abuse, neglect, household disruptions) (Felitti et al., 1998) and other traumatic stressors can be seen by following the solid lines from left to right. ACEs and traumatic stressors indirectly affect local dysregulation through two potential pathways: (I) development of executive functioning difficulties and central nervous system dysregulation (shown by 1a links) ( Nusslock & Miller, 2016 ; Smith et al., 2016 ), which in turn lead to local dysregulation (shown by link 4) ( Kanter et al., 2016 ); and (II) development of depression, anxiety, and ADHD symptoms (shown by 2a links), which in turn lead to executive functioning difficulties and central nervous system dysregulation (shown by link 3a) ( Nusslock & Miller, 2016 ), which then leads to local dysregulation (shown by link 4) ( Kanter et al., 2016 ; Yousefichaijan, Sharafkhah, Rafiei, & Salehi, 2016 ). Constructs that explain associations between stressful life circumstances and LUTS may collectively be thought of as a “chain of mediation,” in that they lie along a hypothesized causal, sequential pathway. Figure 2 also shows how a trajectory of risk/chain of mediation may be weakened or broken at different points along the pathway. The dashed lines of Figure 2 show modification of effects (“effect modification”) by resources for resilience (i.e., coping, social support). Effects of stressful life circumstances on LUTS are weakened in the presence of resources for resilience (shown by the dashed lines 1b, 2b, and 3b).

Although several of the links in Figure 2 are supported by evidence, additional research is needed. Figure 2 illustrates the importance of structural factors that stratify the citizens of a society into communities that are more or less likely to experience adverse childhood experiences and traumatic stressors, and have more or less opportunities to garner resources for resilience ( Glass & McAtee, 2006 ; Solar & Irwin, 2010 ; Warnecke et al., 2008 ). Policies attempting to ensure equitable allocation of resources, including but not limited to health care, are essential to preventing and weakening trajectories of risk that disproportionately impact under-resourced communities and families.

Figure 3 , our final example, highlights broader, societal and commercial influences on bladder health and LUTS, along with environmental, behavioral, and biological mechanisms specific to fast food and soda consumption. Consistent with the WHO Conceptual Framework for Action on Social Determinants of Health ( Solar & Irwin, 2010 ), Figure 3 begins with societal structures. Governance and policies shape the built environments of communities, in part through zoning of fast food restaurants, convenience stores, grocery stores, and farmers markets; these, in turn, impact the availability of fast food and soda in communities ( Sallis & Glanz, 2009 ). Additional policies can impact the affordability of fast food and soda relative to healthy products (e.g., taxation of sugar-sweetened beverages; subsidies for fresh produce) ( Franck, Grandi, & Eisenberg, 2013 ), as well as the advertising and marketing of fast food and beverages, especially towards children ( Harris et al., 2015 ). Low-income communities of color in the United States have historically received fewer resources as a result of inequitable policies; they have also been targeted by the fast food and soda industries ( Sallis & Glanz, 2009 ; Harris et al., 2015 ).

Availability, relative affordability, advertising, and marketing of fast food and soda within a community increase the likelihood that residents will consume “super-sized” food portions and soda, which contributes to obesity ( Sallis & Glanz, 2009 ; Harris et al., 2015 ). Obesity may directly impact LUTS by intra-abdominal pressure on the bladder ( Bavendam et al., 2016 ); it may also impact LUTS through diabetes-related mechanisms, including neurogenic bladder and urinary tract infections ( Bavendam et al., 2016 ; Podnar & Vodusek, 2015 ). Diet soda, which many individuals embrace as a means to reduce caloric intake and combat obesity, contains components that may increase urine volume (caffeine) and harm the health of the bladder lining (artificial sweeteners, carbonation/acidity) (Robinson, Hanna-Mitchell, Rantell, Thiagamoorthy, & Cardozo, 2015). A healthy bladder may be maintained or restored by healthy food and beverage choices; Figure 3 highlights constraints on healthy choices that are determined by upstream, societal factors.

Because the PLUS Research Consortium is just beginning its prevention research agenda, its current models are intended to guide etiologic research, as opposed to selection, implementation, and evaluation of health promotion and prevention strategies. Broader planning frameworks exist for this purpose, including PRECEDE-PROCEED and intervention mapping ( Bartholomew, Markham, Mullen, & Fernández, 2015 ; Bartholomew, Parcel, & Kok, 1998 ; Green & Kreuter, 2005 ), the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Strategic Prevention Framework (2017) , and the Center for Disease Control and Prevention’s (CDC) Framework for Program Evaluation in Public Health (1999) . These frameworks not only guide practitioners in assessing risk and protective factors at different levels of social ecology that may influence health, but also provide a structure for applying theories and conceptual models to the planning and evaluation of health promotion programs, practices, and policies. The PLUS Research Consortium will utilize existing planning frameworks when its work progresses to the point of designing, implementing, and evaluating bladder health promotion and LUTS prevention strategies through research.

Lessons Learned and Recommendations for Other Conceptual Model Development Teams.

After developing the conceptual models and supporting materials presented in this paper, authors reflected on lessons they had learned and what they would recommend to other teams.

Recommendation 1: Develop a shared language.

Students, researchers, practitioners, and policy makers interested in developing conceptual models may benefit from reviewing the terms in Table 1 , determining what is consistent with and distinct from their own discipline and training, and identifying additional tools and concepts that could aid in conceptual model development. Few of this paper’s authors were initially familiar with all of the visual tools and related concepts defined in Table 1 . Terms were added not only by authors, but also by other PLUS Consortium members (e.g., epidemiologists recommended the inclusion of “directed acyclic graph” and “systems model”). Teams who are developing conceptual models may develop a shared language through the process of reviewing, adding, and defining terms.

Recommendation 2: Establish a conceptual framework before developing a conceptual model.

Authors appreciated the distinction between conceptual frameworks and models, particularly with respect to how a framework could be a starting point to broaden one’s conceptualization of health beyond one’s own disciplinary training. Consortium members valued the integration of social ecological, behavioral, and biological perspectives of what influences health, as well as the opportunity to incorporate multiple levels of influence into a single conceptual model and corresponding set of research questions. Consortium members appreciated how the creation and refinement of conceptual models could then assist in clarifying specific research questions; identifying potential pathways through which different risk and protective factors may influence a health outcome; examining and challenging one’s own disciplinary assumptions; and articulating what is known or speculative with respect to the factors that influence health.

Recommendation 3: Seek to develop a diverse team and solicit input from others.

Authors appreciated how steps of conceptual model development included the consideration of how community partners and other key stakeholders can become involved in the process of development. By design, the PLUS Research Consortium includes community advocates, community-engaged researchers, and health care professionals and scientists representing a broad array of disciplines. Authors did not reach beyond the PLUS Consortium to develop the conceptual models featured in this paper, in part because the present paper was intended to describe the process of conceptual model development, rather than to present definitive models. Other conceptual model development teams may benefit from soliciting the input of individuals who are not well represented on their team, including community members, researchers, practitioners, and policy makers.

Recommendation 4: Anticipate and embrace the iterative, “trial and error” nature of conceptual model development.

Early in the process of developing conceptual models, authors developed a shared understanding that it was not necessary for all proposed links in a conceptual model to be informed by existing evidence. Theory, clinical observations, and the lived experience of community members are valid sources of information, as well. Authors also came to appreciate that it was not necessary to develop the “perfect” model during a first attempt to understand a health behavior or outcome, or to select the key components of an evidence-based program, practice, or policy. Indeed, attempting to achieve perfection may stifle creativity and innovation. The conceptual models presented in this paper were developed iteratively, both within the team of authors and consortium members who assisted in their development (see Acknowledgements ). Conceptual models should be evaluated through research, which may support or fail to support proposed links in a model. Conceptual models are meant to be refined, not only during their initial stage of development, but also in response to new information that is gleaned through subsequent research.

Summary and Conclusion.

Researchers, practitioners, and policy makers can use conceptual models to convey ideas to diverse audiences. We posit that conceptual models may have the greatest impact on public health if they integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. To illustrate this point, we have provided examples of conceptual model development from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established in the United States in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Consortium is developing conceptual models to guide its bladder health promotion and LUTS prevention research agenda. In concert with other researchers and community partners, the PLUS Consortium will be poised to inform future public health practices and policies. We hope our shared work will assist others in framing diverse public health matters in innovative, potentially transformative ways.

Acknowledgements

The authors acknowledge special contributions to featured conceptual models by the following PLUS Research Consortium members: Amanda Berry, Neill Epperson, Colleen Fitzgerald, Missy Lavender, Ariana Smith, and Beverly Williams. The authors also acknowledge the foundational work of Jo Anne Earp, Professor Emerita, and Susan T. Ennett, Professor, Department of Health Behavior, Gillings School of Public Health, University of North Carolina, Chapel Hill. Dr. Earp and Dr. Ennett’s pioneering “how to” guide for building conceptual models, published in 1991, inspired the present guide. In addition, the authors acknowledge Kenneth L. McLeroy, Professor Emeritus and retired Regents and Distinguished Professor, School of Public Health, Texas A&M University, for helpful discussion about manuscript content.

Participating PLUS research centers at the time of this writing are as follows:

Loyola University Chicago - 2160 S. 1 st Avenue, Maywood, Il 60153-3328

Linda Brubaker, MD, MS, Multi-PI; Elizabeth Mueller, MD, MSME, Multi-PI; Colleen M. Fitzgerald, MD, MS, Investigator; Cecilia T. Hardacker, RN, MSN, Investigator; Jeni Hebert-Beirne, PhD, MPH, Investigator; Missy Lavender, MBA, Investigator; David A. Shoham, PhD, Investigator

University of Alabama at Birmingham - 1720 2nd Ave South, Birmingham, AL 35294

Kathryn Burgio, PhD, PI; Cora E. Lewis, MD, MSPH, Investigator; Alayne Markland, DO, MSc, Investigator; Gerald McGwin, PhD, Investigator; Beverly Williams, PhD, Investigator

University of California San Diego - 9500 Gilman Drive, La Jolla, CA 92093-0021

Emily S. Lukacz, MD, PI; Sheila Gahagan, MD, MPH, Investigator; D. Yvette LaCoursiere, MD, MPH, Investigator; Jesse N. Nodora, DrPH, Investigator

University of Michigan - 500 S. State Street, Ann Arbor, MI 48109

Janis M. Miller, PhD, MSN, PI; Lawrence Chin-I An, MD, Investigator; Lisa Kane Low, PhD, MS, CNM, Investigator

University of Pennsylvania – Urology, 3rd FL West, Perelman Bldg, 34th & Spruce St, Philadelphia, PA 19104

Diane Kaschak Newman, DNP, ANP-BC, FAAN PI; Amanda Berry, PhD, CRNP, Investigator; C. Neill Epperson, MD, Investigator; Kathryn H. Schmitz, PhD, MPH, FACSM, FTOS, Investigator; Ariana L. Smith, MD, Investigator; Ann Stapleton, MD, FIDSA, FACP, Investigator; Jean Wyman, PhD, RN, FAAN, Investigator

Washington University in St. Louis - One Brookings Drive, St. Louis, MO 63130

Siobhan Sutcliffe, PhD, PI; Colleen McNicholas, DO, MSc, Investigator; Aimee James, PhD, MPH, Investigator; Jerry Lowder, MD, MSc, Investigator;

Yale University - PO Box 208058 New Haven, CT 06520-8058

Leslie Rickey, MD, PI; Deepa Camenga, MD, MHS, Investigator; Shayna D. Cunningham, PhD, Investigator; Toby Chai, MD, Investigator; Jessica B. Lewis, PhD, MFT, Investigator

Steering Committee Chair: Mary H. Palmer, PhD, RN: University of North Carolina

NIH Program Office: National Institute of Diabetes and Digestive and Kidney Diseases, Division of Kidney, Urologic, and Hematologic Diseases, Bethesda, MD

NIH Project Scientist: Tamara Bavendam MD, MS; Project Officer: Ziya Kirkali, MD; Scientific Advisors: Chris Mullins, PhD and Jenna Norton, MPH; Scientific and Data Coordinating Center (SDCC): University of Minnesota - 3 Morrill Hall, 100 Church St. S.E., Minneapolis MN 55455

Bernard Harlow, PhD, Multi-PI; Kyle Rudser, PhD, Multi-PI; Sonya S. Brady, PhD, Investigator; John Connett, PhD, Investigator; Haitao Chu, MD, PhD, Investigator; Cynthia Fok, MD, MPH, Investigator; Todd Rockwood, PhD, Investigator; Melissa Constantine, PhD, MPAff, Investigator

This work of the Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium was supported by the National Institutes of Health (NIH) through cooperative agreements (grant numbers U01DK106786, U01DK106853, U01DK106858, U01DK106898, U01DK106893, U01DK106827, U01DK106908, U01DK106892). Additional support was provided by the National Institute on Aging, NIH Office of Research on Women’s Health, and NIH Office of Behavioral and Social Sciences Research. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

Contributor Information

Sonya S. Brady, Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA.

Linda Brubaker, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA.

Cynthia S. Fok, Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA.

Sheila Gahagan, Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.

Cora E. Lewis, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

Jessica Lewis, Yale School of Public Health, New Haven, CT, 06520, USA.

Jerry L. Lowder, Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA.

Jesse Nodora, Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA.

Ann Stapleton, Department of Medicine, University of Washington, Seattle, WA, 98195, USA.

Mary H. Palmer, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
• The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms

Collaborators.

• Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium : Elizabeth Mueller ,  Colleen M Fitzgerald ,  Cecilia T Hardacker ,  Jeni Hebert-Beirne ,  Missy Lavender ,  David A Shoham ,  Kathryn Burgio ,  Alayne Markland ,  Gerald McGwin ,  Beverly Williams ,  Emily S Lukacz ,  D Yvette LaCoursiere ,  Jesse N Nodora ,  Janis M Miller ,  Lawrence Chin-I An ,  Lisa Kane Low ,  Diane Kaschak Newman ,  Amanda Berry ,  C Neill Epperson ,  Kathryn H Schmitz ,  Ariana L Smith ,  Jean Wyman ,  Siobhan Sutcliffe ,  Colleen McNicholas ,  Aimee James ,  Jerry Lowder ,  Leslie Rickey ,  Deepa Camenga ,  Shayna D Cunningham ,  Toby Chai ,  Jessica B Lewis ,  Bernard Harlow ,  Kyle Rudser ,  John Connett ,  Haitao Chu ,  Cynthia Fok ,  Todd Rockwood ,  Melissa Constantine

Affiliations

• 1 University of Minnesota, Minneapolis, MN, USA.
• 2 University of California San Diego, La Jolla, CA, USA.
• 3 University of Alabama at Birmingham, Birmingham, AL, USA.
• 4 Yale School of Public Health, New Haven, CT, USA.
• 5 Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
• 6 University of Washington, Seattle, WA, USA.
• 7 University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
• PMID: 31910039
• PMCID: PMC7869957
• DOI: 10.1177/1524839919890869

This applied paper is intended to serve as a "how to" guide for public health researchers, practitioners, and policy makers who are interested in building conceptual models to convey their ideas to diverse audiences. Conceptual models can provide a visual representation of specific research questions. They also can show key components of programs, practices, and policies designed to promote health. Conceptual models may provide improved guidance for prevention and intervention efforts if they are based on frameworks that integrate social ecological and biological influences on health and incorporate health equity and social justice principles. To enhance understanding and utilization of this guide, we provide examples of conceptual models developed by the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium. PLUS is a transdisciplinary U.S. scientific network established by the National Institutes of Health in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Research Consortium is developing conceptual models to guide its prevention research agenda. Research findings may in turn influence future public health practices and policies. This guide can assist others in framing diverse public health and prevention science issues in innovative, potentially transformative ways.

Keywords: bladder health; conceptual framework; conceptual model; lower urinary tract symptoms; social ecology; theory.

Publication types

• Research Support, N.I.H., Extramural
• Health Equity*
• Health Policy
• Health Promotion
• Health Services Research / trends*
• Lower Urinary Tract Symptoms / prevention & control*
• Public Health*
• Social Justice
• Urinary Bladder

Grants and funding

• U01 DK106858/DK/NIDDK NIH HHS/United States
• U01 DK106786/DK/NIDDK NIH HHS/United States
• U01 DK106892/DK/NIDDK NIH HHS/United States
• U01 DK106853/DK/NIDDK NIH HHS/United States
• U01 DK106893/DK/NIDDK NIH HHS/United States
• U01 DK106827/DK/NIDDK NIH HHS/United States
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Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

• Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
• Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
• Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
• Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
• Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
• Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
• Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
• Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
• Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
• Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
• Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

• In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
• In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
• In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
• In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
• In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

• Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
• Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
• Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
• Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
• Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

• Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
• Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
• Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
• Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

• To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
• To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
• To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
• To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
• To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

• Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
• Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
• Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
• Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
• Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
• Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

• Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
• Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
• Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
• Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
• Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
• Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

• Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
• Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
• Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
• Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
• Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
• Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Conceptual Model and Hypotheses

• First Online: 01 January 2014

Cite this chapter

• Sik Sumaedi 7 ,
• I Gede Mahatma Yuda Bakti 7 ,
• Nidya Judhi Astrini 7 ,
• Tri Rakhmawati 7 ,
• Tri Widianti 7 &
• Medi Yarmen 7  

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Sik Sumaedi, I Gede Mahatma Yuda Bakti, Nidya Judhi Astrini, Tri Rakhmawati, Tri Widianti & Medi Yarmen

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Sumaedi, S., Bakti, I.G.M.Y., Astrini, N.J., Rakhmawati, T., Widianti, T., Yarmen, M. (2014). Conceptual Model and Hypotheses. In: Public Transport Passengers’ Behavioural Intentions. SpringerBriefs in Business. Springer, Singapore. https://doi.org/10.1007/978-981-4585-24-8_3

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