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Methodological Framework – Types, Examples and Guide

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Methodological Framework

Definition:

Methodological framework is a set of procedures, methods, and tools that guide the research process in a systematic and structured manner. It provides a structure for conducting research, collecting and analyzing data, and drawing conclusions. The framework outlines the steps to be taken in a research project, including the research question, hypothesis, data collection methods, data analysis techniques, and the interpretation of the results.

Types of Methodological Framework

There are different types of methodological frameworks that researchers can use depending on the nature of their research question, the type of data they want to collect, and the research methodology they want to employ. Some common types of methodological frameworks include:

Quantitative Research Framework

This type of framework uses numerical data and statistical analysis to test hypotheses and draw conclusions. It involves the collection of structured data through surveys, experiments, or other quantitative methods.

Qualitative Research Framework

This framework is used to explore complex social phenomena and involves the collection of non-numerical data through methods such as interviews, observation, and document analysis. Qualitative research typically involves the use of open-ended questions and in-depth analysis of data.

Mixed Methods Research Framework

This framework combines quantitative and qualitative research methods to address research questions from multiple angles. It involves collecting both numerical and non-numerical data and using both statistical analysis and interpretive techniques to analyze the data.

Action Research Framework

This framework involves the collaboration between researchers and participants to identify and address practical problems in real-world settings. It involves a cyclical process of planning, action, reflection, and evaluation to improve a specific situation or practice.

Case Study Research Framework

This framework involves the in-depth investigation of a specific case or phenomenon, often using qualitative methods. It aims to understand the complexity of the case and draw generalizations from the findings.

How to Develop a Methodological Framework

Developing a methodological framework involves a series of steps that help to guide the research process in a systematic and structured manner. Here are the general steps involved in developing a methodological framework:

• Define the research problem: The first step is to clearly define the research problem or question. This involves identifying the purpose of the research, the research objectives, and the scope of the study.
• Select an appropriate research methodology: The research methodology selected should align with the research problem and research question. Common research methodologies include quantitative, qualitative, mixed-methods, case study, or action research.
• Develop the research design: Once the research methodology is selected, the research design should be developed. This involves identifying the data collection methods, sampling strategy, and data analysis techniques.
• Identify and justify the data collection methods: The data collection methods should be chosen based on the research methodology and research design. For example, if the research methodology is qualitative, data collection methods such as interviews, observation, or document analysis may be used.
• Identify and justify the data analysis techniques: The data analysis techniques should also be chosen based on the research methodology and research design. For quantitative research, this may include statistical analysis techniques, while for qualitative research, this may include interpretive techniques such as thematic analysis.
• Consider ethical considerations: Ethical considerations should be taken into account throughout the research process. This includes obtaining informed consent, ensuring confidentiality and privacy, and protecting the rights of participants.
• Identify potential limitations: It is important to identify potential limitations or biases that may affect the research findings. This includes discussing potential sources of error or bias in the research design, data collection methods, or data analysis techniques.
• Consider the significance and implications of the research: The significance and implications of the research findings should be considered, including their potential contributions to theory, practice, or policy.
• Refine the framework: The methodological framework should be refined based on feedback from peers, experts, or other stakeholders. This involves identifying any areas for improvement in the research design, data collection methods, or data analysis techniques.

Applications of Methodological Framework

Here are some examples of how a methodological framework can be applied in various fields:

• Social sciences: In social sciences, a methodological framework can be used to conduct research on various topics, such as psychology, sociology, and anthropology. For example, a researcher may use a qualitative research methodology to investigate the experiences and perceptions of individuals living in poverty.
• Natural sciences: In natural sciences, a methodological framework can be used to conduct research on various topics, such as biology, chemistry, and physics. For example, a researcher may use a quantitative research methodology to investigate the effects of different fertilizers on crop yield.
• Engineering : In engineering, a methodological framework can be used to design and test new technologies or systems. For example, a researcher may use a mixed-methods research methodology to investigate the usability and effectiveness of a new software application.
• Business : In business, a methodological framework can be used to conduct research on various topics, such as marketing, management, and finance. For example, a researcher may use a quantitative research methodology to investigate the relationship between customer satisfaction and customer loyalty.

When to use Methodological Framework

Here are some specific situations when a methodological framework can be particularly useful:

• When conducting original research: If you are conducting original research, a methodological framework can help ensure that your study is designed in a structured and systematic manner, which increases the reliability and validity of the findings.
• When conducting a literature review: A methodological framework can be used when conducting a literature review to ensure that the review is conducted in a structured and systematic manner. This helps to identify relevant studies and synthesize the findings from multiple studies.
• When replicating previous research: If you are replicating previous research, a methodological framework can help ensure that the replication is conducted in a rigorous and systematic manner. This helps to ensure that the findings are consistent with the original study.
• When developing a research proposal : A methodological framework can be used when developing a research proposal to ensure that the proposal is designed in a structured and systematic manner. This helps to convince reviewers that the study is well-designed and likely to produce valid and reliable findings.
• When teaching research methods: A methodological framework can be used when teaching research methods to provide students with a structured approach to designing and conducting research. This helps to ensure that students understand the research process and are able to conduct research in a rigorous and systematic manner.

Examples of Methodological Framework

Here are some real-time examples of how methodological frameworks are used in various fields:

• In healthcare research, a mixed-methods research framework can be used to evaluate the effectiveness of a new treatment approach. The quantitative component may involve measuring the changes in patient outcomes, while the qualitative component may involve interviewing patients and healthcare providers to understand their perspectives on the treatment.
• In engineering, a design science research framework can be used to develop and test a new software application. The researchers may identify a problem with existing software, develop a new solution, and test it in a real-world setting.
• In business, a case study research framework can be used to understand the impact of a new marketing strategy on a particular company. The researcher may analyze data from the company’s financial statements, conduct interviews with key stakeholders, and observe the implementation of the strategy in order to understand its effectiveness.
• In education, an action research framework can be used to improve teaching practices. A teacher may identify a problem in their classroom, develop a plan to address the problem, implement the plan, and reflect on the results in order to improve their teaching practices.
• In social science research, a grounded theory framework can be used to develop a theory from qualitative data. A researcher may collect data from interviews or observations and use that data to develop a theory about a particular phenomenon.

Purpose of Methodological Framework

The purpose of a methodological framework is to provide a structured and systematic approach to designing, conducting, and analyzing research. The framework serves as a guide for researchers to follow, ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable, valid, and generalizable. Some key purposes of a methodological framework are:

• To provide a clear and concise description of the research process: The framework outlines the steps involved in conducting the research, including the research question, data collection methods, data analysis, and interpretation of results.
• To ensure that the research is conducted in a systematic and rigorous manner : The framework provides a structured approach to the research, helping to ensure that the research is conducted in a way that minimizes bias and maximizes the accuracy and reliability of the results.
• To improve the quality of the research: The framework helps to ensure that the research is of high quality and meets the standards of the field. This can help to increase the impact and relevance of the research.
• To increase transparency and replicability: The framework provides a clear and transparent description of the research process, making it easier for others to understand and replicate the research.
• To facilitate communication and collaboration: The framework provides a common language and structure for researchers to communicate their research findings and collaborate with others in the field.

Characteristics of Methodological Framework

Here are some common characteristics of a methodological framework:

• Systematic : A methodological framework is a systematic approach to research that provides a clear and structured guide for researchers to follow. It outlines the steps involved in conducting research, from developing a research question to analyzing and interpreting data.
• Transparent : A methodological framework promotes transparency in research by providing a clear and concise description of the research process. This helps to ensure that others can understand and replicate the research.
• Flexible : A methodological framework should be flexible enough to accommodate different research designs and methodologies. It should allow for modifications based on the specific research question, data collection methods, and analysis techniques.
• Contextual : A methodological framework should take into account the contextual factors that may impact the research. This includes the cultural, social, and historical context of the research, as well as the research setting and the characteristics of the participants.
• Rigorous : A methodological framework promotes rigor in research by ensuring that the research is conducted in a systematic and unbiased manner. It includes strategies for minimizing bias and ensuring the validity and reliability of the findings.
• Theory-driven: A methodological framework should be grounded in theoretical concepts and principles that guide the research. This helps to ensure that the research is relevant and meaningful, and that the findings can be applied to broader theoretical frameworks.

Advantages of Methodological Framework

There are several advantages to using a methodological framework in research:

• Structured approach: A methodological framework provides a clear and structured approach to conducting research, which helps to ensure that the research is conducted in a systematic and rigorous manner.
• Increased efficiency: A methodological framework can increase the efficiency of the research process by providing a clear roadmap for researchers to follow, reducing the time and resources required to conduct the research.
• Reproducibility: A methodological framework promotes reproducibility by providing a clear and transparent description of the research process, making it easier for others to replicate the research.
• Improved quality : A methodological framework can improve the quality of research by ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable and valid.
• Standardization : A methodological framework promotes standardization in research, helping to ensure that the research meets the standards of the field and is comparable to other research studies.
• Better communication : A methodological framework provides a common language and structure for researchers to communicate their research findings, facilitating communication and collaboration among researchers.
• Theory development: A methodological framework can contribute to the development of theory by providing a structured approach to data collection and analysis that is grounded in theoretical concepts and principles.

Limitations of Methodological Framework

While there are many advantages to using a methodological framework in research, there are also some limitations to be aware of:

• Flexibility : While a methodological framework can provide a structured approach to research, it may also limit flexibility in the research process. Researchers may feel constrained by the framework and unable to deviate from the prescribed steps, which may limit their ability to adapt to unexpected findings or changes in the research context.
• Applicability : Methodological frameworks may not be equally applicable to all research questions and contexts. Some frameworks may be more suitable for certain types of research than others, and researchers may need to modify or adapt the framework to fit their specific research question and context.
• Complexity : Some methodological frameworks can be complex and difficult to understand, particularly for novice researchers. This may limit their usefulness in certain contexts or for certain types of research.
• Time and resource constraints : Using a methodological framework may require additional time and resources to fully implement, which may not be feasible for all researchers or research projects.
• Overemphasis on methodology: While a methodological framework can provide a structured approach to research methodology, it may overemphasize the importance of methodology over other aspects of research, such as theoretical frameworks or ethical considerations.

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Muhammad Hassan

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• Correspondence
• Open access
• Published: 18 September 2013

Using the framework method for the analysis of qualitative data in multi-disciplinary health research

• Nicola K Gale 1 ,
• Gemma Heath 2 ,
• Elaine Cameron 3 ,
• Sabina Rashid 4 &
• Sabi Redwood 2  

BMC Medical Research Methodology volume  13 , Article number:  117 ( 2013 ) Cite this article

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The Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations.

The article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study.

Used effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research.

The Framework Method for the management and analysis of qualitative data has been used since the 1980s [ 1 ]. The method originated in large-scale social policy research but is becoming an increasingly popular approach in medical and health research; however, there is some confusion about its potential application and limitations. In this article we discuss when it is appropriate to use the Framework Method and how it compares to other qualitative analysis methods. In particular, we explore how it can be used in multi-disciplinary health research teams. Multi-disciplinary and mixed methods studies are becoming increasingly commonplace in applied health research. As well as disciplines familiar with qualitative research, such as nursing, psychology and sociology, teams often include epidemiologists, health economists, management scientists and others. Furthermore, applied health research often has clinical representation and, increasingly, patient and public involvement [ 2 ]. We argue that while leadership is undoubtedly required from an experienced qualitative methodologist, non-specialists from the wider team can and should be involved in the analysis process. We then present a step-by-step guide to the application of the Framework Method, illustrated using a worked example (See Additional File 1 ) from a published study [ 3 ] to illustrate the main stages of the process. Technical terms are included in the glossary (below). Finally, we discuss the strengths and limitations of the approach.

Glossary of key terms used in the Framework Method

Analytical framework: A set of codes organised into categories that have been jointly developed by researchers involved in analysis that can be used to manage and organise the data. The framework creates a new structure for the data (rather than the full original accounts given by participants) that is helpful to summarize/reduce the data in a way that can support answering the research questions.

Analytic memo: A written investigation of a particular concept, theme or problem, reflecting on emerging issues in the data that captures the analytic process (see Additional file 1 , Section 7).

Categories: During the analysis process, codes are grouped into clusters around similar and interrelated ideas or concepts. Categories and codes are usually arranged in a tree diagram structure in the analytical framework. While categories are closely and explicitly linked to the raw data, developing categories is a way to start the process of abstraction of the data (i.e. towards the general rather than the specific or anecdotal).

Charting: Entering summarized data into the Framework Method matrix (see Additional File 1 , Section 6).

Code: A descriptive or conceptual label that is assigned to excerpts of raw data in a process called ‘coding’ (see Additional File 1 , Section 3).

Data: Qualitative data usually needs to be in textual form before analysis. These texts can either be elicited texts (written specifically for the research, such as food diaries), or extant texts (pre-existing texts, such as meeting minutes, policy documents or weblogs), or can be produced by transcribing interview or focus group data, or creating ‘field’ notes while conducting participant-observation or observing objects or social situations.

Indexing: The systematic application of codes from the agreed analytical framework to the whole dataset (see Additional File 1 , Section 5).

Matrix: A spreadsheet contains numerous cells into which summarized data are entered by codes (columns) and cases (rows) (see Additional File 1 , Section 6).

Themes: Interpretive concepts or propositions that describe or explain aspects of the data, which are the final output of the analysis of the whole dataset. Themes are articulated and developed by interrogating data categories through comparison between and within cases. Usually a number of categories would fall under each theme or sub-theme [ 3 ].

Transcript: A written verbatim (word-for-word) account of a verbal interaction, such as an interview or conversation.

The Framework Method sits within a broad family of analysis methods often termed thematic analysis or qualitative content analysis. These approaches identify commonalities and differences in qualitative data, before focusing on relationships between different parts of the data, thereby seeking to draw descriptive and/or explanatory conclusions clustered around themes. The Framework Method was developed by researchers, Jane Ritchie and Liz Spencer, from the Qualitative Research Unit at the National Centre for Social Research in the United Kingdom in the late 1980s for use in large-scale policy research [ 1 ]. It is now used widely in other areas, including health research [ 3 – 12 ]. Its defining feature is the matrix output: rows (cases), columns (codes) and ‘cells’ of summarised data, providing a structure into which the researcher can systematically reduce the data, in order to analyse it by case and by code [ 1 ]. Most often a ‘case’ is an individual interviewee, but this can be adapted to other units of analysis, such as predefined groups or organisations. While in-depth analyses of key themes can take place across the whole data set, the views of each research participant remain connected to other aspects of their account within the matrix so that the context of the individual’s views is not lost. Comparing and contrasting data is vital to qualitative analysis and the ability to compare with ease data across cases as well as within individual cases is built into the structure and process of the Framework Method.

The Framework Method provides clear steps to follow and produces highly structured outputs of summarised data. It is therefore useful where multiple researchers are working on a project, particularly in multi-disciplinary research teams were not all members have experience of qualitative data analysis, and for managing large data sets where obtaining a holistic, descriptive overview of the entire data set is desirable. However, caution is recommended before selecting the method as it is not a suitable tool for analysing all types of qualitative data or for answering all qualitative research questions, nor is it an ‘easy’ version of qualitative research for quantitative researchers. Importantly, the Framework Method cannot accommodate highly heterogeneous data, i.e. data must cover similar topics or key issues so that it is possible to categorize it. Individual interviewees may, of course, have very different views or experiences in relation to each topic, which can then be compared and contrasted. The Framework Method is most commonly used for the thematic analysis of semi-structured interview transcripts, which is what we focus on in this article, although it could, in principle, be adapted for other types of textual data [ 13 ], including documents, such as meeting minutes or diaries [ 12 ], or field notes from observations [ 10 ].

For quantitative researchers working with qualitative colleagues or when exploring qualitative research for the first time, the nature of the Framework Method is seductive because its methodical processes and ‘spreadsheet’ approach seem more closely aligned to the quantitative paradigm [ 14 ]. Although the Framework Method is a highly systematic method of categorizing and organizing what may seem like unwieldy qualitative data, it is not a panacea for problematic issues commonly associated with qualitative data analysis such as how to make analytic choices and make interpretive strategies visible and auditable. Qualitative research skills are required to appropriately interpret the matrix, and facilitate the generation of descriptions, categories, explanations and typologies. Moreover, reflexivity, rigour and quality are issues that are requisite in the Framework Method just as they are in other qualitative methods. It is therefore essential that studies using the Framework Method for analysis are overseen by an experienced qualitative researcher, though this does not preclude those new to qualitative research from contributing to the analysis as part of a wider research team.

There are a number of approaches to qualitative data analysis, including those that pay close attention to language and how it is being used in social interaction such as discourse analysis [ 15 ] and ethnomethodology [ 16 ]; those that are concerned with experience, meaning and language such as phenomenology [ 17 , 18 ] and narrative methods [ 19 ]; and those that seek to develop theory derived from data through a set of procedures and interconnected stages such as Grounded Theory [ 20 , 21 ]. Many of these approaches are associated with specific disciplines and are underpinned by philosophical ideas which shape the process of analysis [ 22 ]. The Framework Method, however, is not aligned with a particular epistemological, philosophical, or theoretical approach. Rather it is a flexible tool that can be adapted for use with many qualitative approaches that aim to generate themes.

The development of themes is a common feature of qualitative data analysis, involving the systematic search for patterns to generate full descriptions capable of shedding light on the phenomenon under investigation. In particular, many qualitative approaches use the ‘constant comparative method’ , developed as part of Grounded Theory, which involves making systematic comparisons across cases to refine each theme [ 21 , 23 ]. Unlike Grounded Theory, the Framework Method is not necessarily concerned with generating social theory, but can greatly facilitate constant comparative techniques through the review of data across the matrix.

Perhaps because the Framework Method is so obviously systematic, it has often, as other commentators have noted, been conflated with a deductive approach to qualitative analysis [ 13 , 14 ]. However, the tool itself has no allegiance to either inductive or deductive thematic analysis; where the research sits along this inductive-deductive continuum depends on the research question. A question such as, ‘Can patients give an accurate biomedical account of the onset of their cardiovascular disease?’ is essentially a yes/no question (although it may be nuanced by the extent of their account or by appropriate use of terminology) and so requires a deductive approach to both data collection and analysis (e.g. structured or semi-structured interviews and directed qualitative content analysis [ 24 ]). Similarly, a deductive approach may be taken if basing analysis on a pre-existing theory, such as behaviour change theories, for example in the case of a research question such as ‘How does the Theory of Planned Behaviour help explain GP prescribing?’ [ 11 ]. However, a research question such as, ‘How do people construct accounts of the onset of their cardiovascular disease?’ would require a more inductive approach that allows for the unexpected, and permits more socially-located responses [ 25 ] from interviewees that may include matters of cultural beliefs, habits of food preparation, concepts of ‘fate’, or links to other important events in their lives, such as grief, which cannot be predicted by the researcher in advance (e.g. an interviewee-led open ended interview and grounded theory [ 20 ]). In all these cases, it may be appropriate to use the Framework Method to manage the data. The difference would become apparent in how themes are selected: in the deductive approach, themes and codes are pre-selected based on previous literature, previous theories or the specifics of the research question; whereas in the inductive approach, themes are generated from the data though open (unrestricted) coding, followed by refinement of themes. In many cases, a combined approach is appropriate when the project has some specific issues to explore, but also aims to leave space to discover other unexpected aspects of the participants’ experience or the way they assign meaning to phenomena. In sum, the Framework Method can be adapted for use with deductive, inductive, or combined types of qualitative analysis. However, there are some research questions where analysing data by case and theme is not appropriate and so the Framework Method should be avoided. For instance, depending on the research question, life history data might be better analysed using narrative analysis [ 19 ]; recorded consultations between patients and their healthcare practitioners using conversation analysis [ 26 ]; and documentary data, such as resources for pregnant women, using discourse analysis [ 27 ].

It is not within the scope of this paper to consider study design or data collection in any depth, but before moving on to describe the Framework Method analysis process, it is worth taking a step back to consider briefly what needs to happen before analysis begins. The selection of analysis method should have been considered at the proposal stage of the research and should fit with the research questions and overall aims of the study. Many qualitative studies, particularly ones using inductive analysis, are emergent in nature; this can be a challenge and the researchers can only provide an “imaginative rehearsal” of what is to come [ 28 ]. In mixed methods studies, the role of the qualitative component within the wider goals of the project must also be considered. In the data collection stage, resources must be allocated for properly trained researchers to conduct the qualitative interviewing because it is a highly skilled activity. In some cases, a research team may decide that they would like to use lay people, patients or peers to do the interviews [ 29 – 32 ] and in this case they must be properly trained and mentored which requires time and resources. At this early stage it is also useful to consider whether the team will use Computer Assisted Qualitative Data Analysis Software (CAQDAS), which can assist with data management and analysis.

As any form of qualitative or quantitative analysis is not a purely technical process, but influenced by the characteristics of the researchers and their disciplinary paradigms, critical reflection throughout the research process is paramount, including in the design of the study, the construction or collection of data, and the analysis. All members of the team should keep a research diary, where they record reflexive notes, impressions of the data and thoughts about analysis throughout the process. Experienced qualitative researchers become more skilled at sifting through data and analysing it in a rigorous and reflexive way. They cannot be too attached to certainty, but must remain flexible and adaptive throughout the research in order to generate rich and nuanced findings that embrace and explain the complexity of real social life and can be applied to complex social issues. It is important to remember when using the Framework Method that, unlike quantitative research where data collection and data analysis are strictly sequential and mutually exclusive stages of the research process, in qualitative analysis there is, to a greater or lesser extent depending on the project, ongoing interplay between data collection, analysis, and theory development. For example, new ideas or insights from participants may suggest potentially fruitful lines of enquiry, or close analysis might reveal subtle inconsistencies in an account which require further exploration.

Procedure for analysis

Stage 1: transcription.

A good quality audio recording and, ideally, a verbatim (word for word) transcription of the interview is needed. For Framework Method analysis, it is not necessarily important to include the conventions of dialogue transcriptions which can be difficult to read (e.g. pauses or two people talking simultaneously), because the content is what is of primary interest. Transcripts should have large margins and adequate line spacing for later coding and making notes. The process of transcription is a good opportunity to become immersed in the data and is to be strongly encouraged for new researchers. However, in some projects, the decision may be made that it is a better use of resources to outsource this task to a professional transcriber.

Stage 2: Familiarisation with the interview

Becoming familiar with the whole interview using the audio recording and/or transcript and any contextual or reflective notes that were recorded by the interviewer is a vital stage in interpretation. It can also be helpful to re-listen to all or parts of the audio recording. In multi-disciplinary or large research projects, those involved in analysing the data may be different from those who conducted or transcribed the interviews, which makes this stage particularly important. One margin can be used to record any analytical notes, thoughts or impressions.

Stage 3: Coding

After familiarization, the researcher carefully reads the transcript line by line, applying a paraphrase or label (a ‘code’) that describes what they have interpreted in the passage as important. In more inductive studies, at this stage ‘open coding’ takes place, i.e. coding anything that might be relevant from as many different perspectives as possible. Codes could refer to substantive things (e.g. particular behaviours, incidents or structures), values (e.g. those that inform or underpin certain statements, such as a belief in evidence-based medicine or in patient choice), emotions (e.g. sorrow, frustration, love) and more impressionistic/methodological elements (e.g. interviewee found something difficult to explain, interviewee became emotional, interviewer felt uncomfortable) [ 33 ]. In purely deductive studies, the codes may have been pre-defined (e.g. by an existing theory, or specific areas of interest to the project) so this stage may not be strictly necessary and you could just move straight onto indexing, although it is generally helpful even if you are taking a broadly deductive approach to do some open coding on at least a few of the transcripts to ensure important aspects of the data are not missed. Coding aims to classify all of the data so that it can be compared systematically with other parts of the data set. At least two researchers (or at least one from each discipline or speciality in a multi-disciplinary research team) should independently code the first few transcripts, if feasible. Patients, public involvement representatives or clinicians can also be productively involved at this stage, because they can offer alternative viewpoints thus ensuring that one particular perspective does not dominate. It is vital in inductive coding to look out for the unexpected and not to just code in a literal, descriptive way so the involvement of people from different perspectives can aid greatly in this. As well as getting a holistic impression of what was said, coding line-by-line can often alert the researcher to consider that which may ordinarily remain invisible because it is not clearly expressed or does not ‘fit’ with the rest of the account. In this way the developing analysis is challenged; to reconcile and explain anomalies in the data can make the analysis stronger. Coding can also be done digitally using CAQDAS, which is a useful way to keep track automatically of new codes. However, some researchers prefer to do the early stages of coding with a paper and pen, and only start to use CAQDAS once they reach Stage 5 (see below).

Stage 4: Developing a working analytical framework

After coding the first few transcripts, all researchers involved should meet to compare the labels they have applied and agree on a set of codes to apply to all subsequent transcripts. Codes can be grouped together into categories (using a tree diagram if helpful), which are then clearly defined. This forms a working analytical framework. It is likely that several iterations of the analytical framework will be required before no additional codes emerge. It is always worth having an ‘other’ code under each category to avoid ignoring data that does not fit; the analytical framework is never ‘final’ until the last transcript has been coded.

Stage 5: Applying the analytical framework

The working analytical framework is then applied by indexing subsequent transcripts using the existing categories and codes. Each code is usually assigned a number or abbreviation for easy identification (and so the full names of the codes do not have to be written out each time) and written directly onto the transcripts. Computer Assisted Qualitative Data Analysis Software (CAQDAS) is particularly useful at this stage because it can speed up the process and ensures that, at later stages, data is easily retrievable. It is worth noting that unlike software for statistical analyses, which actually carries out the calculations with the correct instruction, putting the data into a qualitative analysis software package does not analyse the data; it is simply an effective way of storing and organising the data so that they are accessible for the analysis process.

Stage 6: Charting data into the framework matrix

Qualitative data are voluminous (an hour of interview can generate 15–30 pages of text) and being able to manage and summarize (reduce) data is a vital aspect of the analysis process. A spreadsheet is used to generate a matrix and the data are ‘charted’ into the matrix. Charting involves summarizing the data by category from each transcript. Good charting requires an ability to strike a balance between reducing the data on the one hand and retaining the original meanings and ‘feel’ of the interviewees’ words on the other. The chart should include references to interesting or illustrative quotations. These can be tagged automatically if you are using CAQDAS to manage your data (N-Vivo version 9 onwards has the capability to generate framework matrices), or otherwise a capital ‘Q’, an (anonymized) transcript number, page and line reference will suffice. It is helpful in multi-disciplinary teams to compare and contrast styles of summarizing in the early stages of the analysis process to ensure consistency within the team. Any abbreviations used should be agreed by the team. Once members of the team are familiar with the analytical framework and well practised at coding and charting, on average, it will take about half a day per hour-long transcript to reach this stage. In the early stages, it takes much longer.

Stage 7: Interpreting the data

It is useful throughout the research to have a separate note book or computer file to note down impressions, ideas and early interpretations of the data. It may be worth breaking off at any stage to explore an interesting idea, concept or potential theme by writing an analytic memo [ 20 , 21 ] to then discuss with other members of the research team, including lay and clinical members. Gradually, characteristics of and differences between the data are identified, perhaps generating typologies, interrogating theoretical concepts (either prior concepts or ones emerging from the data) or mapping connections between categories to explore relationships and/or causality. If the data are rich enough, the findings generated through this process can go beyond description of particular cases to explanation of, for example, reasons for the emergence of a phenomena, predicting how an organisation or other social actor is likely to instigate or respond to a situation, or identifying areas that are not functioning well within an organisation or system. It is worth noting that this stage often takes longer than anticipated and that any project plan should ensure that sufficient time is allocated to meetings and individual researcher time to conduct interpretation and writing up of findings (see Additional file 1 , Section 7).

The Framework Method has been developed and used successfully in research for over 25 years, and has recently become a popular analysis method in qualitative health research. The issue of how to assess quality in qualitative research has been highly debated [ 20 , 34 – 40 ], but ensuring rigour and transparency in analysis is a vital component. There are, of course, many ways to do this but in the Framework Method the following are helpful:

Summarizing the data during charting, as well as being a practical way to reduce the data, means that all members of a multi-disciplinary team, including lay, clinical and (quantitative) academic members can engage with the data and offer their perspectives during the analysis process without necessarily needing to read all the transcripts or be involved in the more technical parts of analysis.

Charting also ensures that researchers pay close attention to describing the data using each participant’s own subjective frames and expressions in the first instance, before moving onto interpretation.

The summarized data is kept within the wider context of each case, thereby encouraging thick description that pays attention to complex layers of meaning and understanding [ 38 ].

The matrix structure is visually straightforward and can facilitate recognition of patterns in the data by any member of the research team, including through drawing attention to contradictory data, deviant cases or empty cells.

The systematic procedure (described in this article) makes it easy to follow, even for multi-disciplinary teams and/or with large data sets.

It is flexible enough that non-interview data (such as field notes taken during the interview or reflexive considerations) can be included in the matrix.

It is not aligned with a particular epistemological viewpoint or theoretical approach and therefore can be adapted for use in inductive or deductive analysis or a combination of the two (e.g. using pre-existing theoretical constructs deductively, then revising the theory with inductive aspects; or using an inductive approach to identify themes in the data, before returning to the literature and using theories deductively to help further explain certain themes).

It is easy to identify relevant data extracts to illustrate themes and to check whether there is sufficient evidence for a proposed theme.

Finally, there is a clear audit trail from original raw data to final themes, including the illustrative quotes.

There are also a number of potential pitfalls to this approach:

The systematic approach and matrix format, as we noted in the background, is intuitively appealing to those trained quantitatively but the ‘spreadsheet’ look perhaps further increases the temptation for those without an in-depth understanding of qualitative research to attempt to quantify qualitative data (e.g. “13 out of 20 participants said X). This kind of statement is clearly meaningless because the sampling in qualitative research is not designed to be representative of a wider population, but purposive to capture diversity around a phenomenon [ 41 ].

Like all qualitative analysis methods, the Framework Method is time consuming and resource-intensive. When involving multiple stakeholders and disciplines in the analysis and interpretation of the data, as is good practice in applied health research, the time needed is extended. This time needs to be factored into the project proposal at the pre-funding stage.

There is a high training component to successfully using the method in a new multi-disciplinary team. Depending on their role in the analysis, members of the research team may have to learn how to code, index, and chart data, to think reflexively about how their identities and experience affect the analysis process, and/or they may have to learn about the methods of generalisation (i.e. analytic generalisation and transferability, rather than statistical generalisation [ 41 ]) to help to interpret legitimately the meaning and significance of the data.

While the Framework Method is amenable to the participation of non-experts in data analysis, it is critical to the successful use of the method that an experienced qualitative researcher leads the project (even if the overall lead for a large mixed methods study is a different person). The qualitative lead would ideally be joined by other researchers with at least some prior training in or experience of qualitative analysis. The responsibilities of the lead qualitative researcher are: to contribute to study design, project timelines and resource planning; to mentor junior qualitative researchers; to train clinical, lay and other (non-qualitative) academics to contribute as appropriate to the analysis process; to facilitate analysis meetings in a way that encourages critical and reflexive engagement with the data and other team members; and finally to lead the write-up of the study.

We have argued that Framework Method studies can be conducted by multi-disciplinary research teams that include, for example, healthcare professionals, psychologists, sociologists, economists, and lay people/service users. The inclusion of so many different perspectives means that decision-making in the analysis process can be very time consuming and resource-intensive. It may require extensive, reflexive and critical dialogue about how the ideas expressed by interviewees and identified in the transcript are related to pre-existing concepts and theories from each discipline, and to the real ‘problems’ in the health system that the project is addressing. This kind of team effort is, however, an excellent forum for driving forward interdisciplinary collaboration, as well as clinical and lay involvement in research, to ensure that ‘the whole is greater than the sum of the parts’, by enhancing the credibility and relevance of the findings.

The Framework Method is appropriate for thematic analysis of textual data, particularly interview transcripts, where it is important to be able to compare and contrast data by themes across many cases, while also situating each perspective in context by retaining the connection to other aspects of each individual’s account. Experienced qualitative researchers should lead and facilitate all aspects of the analysis, although the Framework Method’s systematic approach makes it suitable for involving all members of a multi-disciplinary team. An open, critical and reflexive approach from all team members is essential for rigorous qualitative analysis.

Acceptance of the complexity of real life health systems and the existence of multiple perspectives on health issues is necessary to produce high quality qualitative research. If done well, qualitative studies can shed explanatory and predictive light on important phenomena, relate constructively to quantitative parts of a larger study, and contribute to the improvement of health services and development of health policy. The Framework Method, when selected and implemented appropriately, can be a suitable tool for achieving these aims through producing credible and relevant findings.

The Framework Method is an excellent tool for supporting thematic (qualitative content) analysis because it provides a systematic model for managing and mapping the data.

The Framework Method is most suitable for analysis of interview data, where it is desirable to generate themes by making comparisons within and between cases.

The management of large data sets is facilitated by the Framework Method as its matrix form provides an intuitively structured overview of summarised data.

The clear, step-by-step process of the Framework Method makes it is suitable for interdisciplinary and collaborative projects.

The use of the method should be led and facilitated by an experienced qualitative researcher.

Ritchie J, Lewis J: Qualitative research practice: a guide for social science students and researchers. 2003, London: Sage

Google Scholar  

Ives J, Damery S, Redwod S: PPI, paradoxes and Plato: who's sailing the ship?. J Med Ethics. 2013, 39 (3): 181-185. 10.1136/medethics-2011-100150.

Article   Google Scholar  

Heath G, Cameron E, Cummins C, Greenfield S, Pattison H, Kelly D, Redwood S: Paediatric ‘care closer to home’: stake-holder views and barriers to implementation. Health Place. 2012, 18 (5): 1068-1073. 10.1016/j.healthplace.2012.05.003.

Elkington H, White P, Addington-Hall J, Higgs R, Petternari C: The last year of life of COPD: a qualitative study of symptoms and services. Respir Med. 2004, 98 (5): 439-445. 10.1016/j.rmed.2003.11.006.

Murtagh J, Dixey R, Rudolf M: A qualitative investigation into the levers and barriers to weight loss in children: opinions of obese children. Archives Dis Child. 2006, 91 (11): 920-923. 10.1136/adc.2005.085712.

Barnard M, Webster S, O’Connor W, Jones A, Donmall M: The drug treatment outcomes research study (DTORS): qualitative study. 2009, London: Home Office

Ayatollahi H, Bath PA, Goodacre S: Factors influencing the use of IT in the emergency department: a qualitative study. Health Inform J. 2010, 16 (3): 189-200. 10.1177/1460458210377480.

Sheard L, Prout H, Dowding D, Noble S, Watt I, Maraveyas A, Johnson M: Barriers to the diagnosis and treatment of venous thromboembolism in advanced cancer patients: a qualitative study. Palliative Med. 2012, 27 (2): 339-348.

Ellis J, Wagland R, Tishelman C, Williams ML, Bailey CD, Haines J, Caress A, Lorigan P, Smith JA, Booton R, et al: Considerations in developing and delivering a nonpharmacological intervention for symptom management in lung cancer: the views of patients and informal caregivers. J Pain Symptom Manag (0). 2012, 44 (6): 831-842. 10.1016/j.jpainsymman.2011.12.274.

Gale N, Sultan H: Telehealth as ‘peace of mind’: embodiment, emotions and the home as the primary health space for people with chronic obstructive pulmonary disorder. Health place. 2013, 21: 140-147.

Rashidian A, Eccles MP, Russell I: Falling on stony ground? A qualitative study of implementation of clinical guidelines’ prescribing recommendations in primary care. Health policy. 2008, 85 (2): 148-161. 10.1016/j.healthpol.2007.07.011.

Jones RK: The unsolicited diary as a qualitative research tool for advanced research capacity in the field of health and illness. Qualitative Health Res. 2000, 10 (4): 555-567. 10.1177/104973200129118543.

Pope C, Ziebland S, Mays N: Analysing qualitative data. British Med J. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Pope C, Mays N: Critical reflections on the rise of qualitative research. British Med J. 2009, 339: 737-739.

Fairclough N: Critical discourse analysis: the critical study of language. 2010, London: Longman

Garfinkel H: Ethnomethodology’s program. Soc Psychol Quarter. 1996, 59 (1): 5-21. 10.2307/2787116.

Merleau-Ponty M: The phenomenology of perception. 1962, London: Routledge and Kegan Paul

Svenaeus F: The phenomenology of health and illness. Handbook of phenomenology and medicine. 2001, Netherlands: Springer, 87-108.

Reissmann CK: Narrative methods for the human sciences. 2008, London: Sage

Charmaz K: Constructing grounded theory: a practical guide through qualitative analysis. 2006, London: Sage

Glaser A, Strauss AL: The discovery of grounded theory. 1967, Chicago: Aldine

Crotty M: The foundations of social research: meaning and perspective in the research process. 1998, London: Sage

Boeije H: A purposeful approach to the constant comparative method in the analysis of qualitative interviews. Qual Quant. 2002, 36 (4): 391-409. 10.1023/A:1020909529486.

Hsieh H-F, Shannon SE: Three approaches to qualitative content analysis. Qual Health Res. 2005, 15 (9): 1277-1288. 10.1177/1049732305276687.

Redwood S, Gale NK, Greenfield S: ‘You give us rangoli, we give you talk’: using an art-based activity to elicit data from a seldom heard group. BMC Med Res Methodol. 2012, 12 (1): 7-10.1186/1471-2288-12-7.

Mishler EG: The struggle between the voice of medicine and the voice of the lifeworld. The sociology of health and illness: critical perspectives. Edited by: Conrad P, Kern R. 1990, New York: St Martins Press, Third

Hodges BD, Kuper A, Reeves S: Discourse analysis. British Med J. 2008, 337: 570-572. 10.1136/bmj.39370.701782.DE.

Sandelowski M, Barroso J: Writing the proposal for a qualitative research methodology project. Qual Health Res. 2003, 13 (6): 781-820. 10.1177/1049732303013006003.

Ellins J: It’s better together: involving older people in research. HSMC Newsletter Focus Serv Users Publ. 2010, 16 (1): 4-

Phillimore J, Goodson L, Hennessy D, Ergun E: Empowering Birmingham’s migrant and refugee community organisations: making a difference. 2009, York: Joseph Rowntree Foundation

Leamy M, Clough R: How older people became researchers. 2006, York: Joseph Rowntree Foundation

Glasby J, Miller R, Ellins J, Durose J, Davidson D, McIver S, Littlechild R, Tanner D, Snelling I, Spence K: Final report NIHR service delivery and organisation programme. Understanding and improving transitions of older people: a user and carer centred approach. 2012, London: The Stationery Office

Saldaña J: The coding manual for qualitative researchers. 2009, London: Sage

Lincoln YS: Emerging criteria for quality in qualitative and interpretive research. Qual Inquiry. 1995, 1 (3): 275-289. 10.1177/107780049500100301.

Mays N, Pope C: Qualitative research in health care: assessing quality in qualitative research. BMJ British Med J. 2000, 320 (7226): 50-10.1136/bmj.320.7226.50.

Seale C: Quality in qualitative research. Qual Inquiry. 1999, 5 (4): 465-478. 10.1177/107780049900500402.

Dingwall R, Murphy E, Watson P, Greatbatch D, Parker S: Catching goldfish: quality in qualitative research. J Health serv Res Policy. 1998, 3 (3): 167-172.

Popay J, Rogers A, Williams G: Rationale and standards for the systematic review of qualitative literature in health services research. Qual Health Res. 1998, 8 (3): 341-351. 10.1177/104973239800800305.

Morse JM, Barrett M, Mayan M, Olson K, Spiers J: Verification strategies for establishing reliability and validity in qualitative research. Int J Qual Methods. 2008, 1 (2): 13-22.

Smith JA: Reflecting on the development of interpretative phenomenological analysis and its contribution to qualitative research in psychology. Qual Res Psychol. 2004, 1 (1): 39-54.

Polit DF, Beck CT: Generalization in quantitative and qualitative research: Myths and strategies. Int J Nurs Studies. 2010, 47 (11): 1451-1458. 10.1016/j.ijnurstu.2010.06.004.

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All authors were funded by the National Institute for Health Research (NIHR) through the Collaborations for Leadership in Applied Health Research and Care for Birmingham and Black Country (CLAHRC-BBC) programme. The views in this publication expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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All authors were involved in the development of the concept of the article and drafting the article. NG wrote the first draft of the article, GH and EC prepared the text and figures related to the illustrative example, SRa did the literature search to identify if there were any similar articles currently available and contributed to drafting of the article, and SRe contributed to drafting of the article and the illustrative example. All authors read and approved the final manuscript.

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Gale, N.K., Heath, G., Cameron, E. et al. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 13 , 117 (2013). https://doi.org/10.1186/1471-2288-13-117

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Wang, X. (2022). Research Framework and Research Methodology. In: Destigmatisation of People Living with HIV/AIDS in China. A Sociological View of AIDS. Springer, Singapore. https://doi.org/10.1007/978-981-16-8534-7_3

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Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

• It is logical and coherent
• It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
• It has a domain where it applies
• It has clearly described relationships among variables
• It describes, explains, and makes specific predictions
• It comprises of concepts, themes, principles, and constructs
• It must have been based on empirical data [i.e., it is not a guess]
• It must have made claims that are subject to testing, been tested and verified
• It must be clear and concise
• Its assertions or predictions must be different and better than those in existing theories
• Its predictions must be general enough to be applicable to and understood within multiple contexts
• Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
• The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
• Its concepts and principles explain what is going on and why
• Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
• The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
• Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
• The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

• Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
• The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
• Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

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.

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Research design: the methodology for interdisciplinary research framework

1 Biometris, Wageningen University and Research, PO Box 16, 6700 AA Wageningen, The Netherlands

Jarl K. Kampen

2 Statua, Dept. of Epidemiology and Medical Statistics, Antwerp University, Venusstraat 35, 2000 Antwerp, Belgium

Many of today’s global scientific challenges require the joint involvement of researchers from different disciplinary backgrounds (social sciences, environmental sciences, climatology, medicine, etc.). Such interdisciplinary research teams face many challenges resulting from differences in training and scientific culture. Interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences. For that purpose this paper presents the Methodology for Interdisciplinary Research (MIR) framework. The MIR framework was developed to help cross disciplinary borders, especially those between the natural sciences and the social sciences. The framework has been specifically constructed to facilitate the design of interdisciplinary scientific research, and can be applied in an educational program, as a reference for monitoring the phases of interdisciplinary research, and as a tool to design such research in a process approach. It is suitable for research projects of different sizes and levels of complexity, and it allows for a range of methods’ combinations (case study, mixed methods, etc.). The different phases of designing interdisciplinary research in the MIR framework are described and illustrated by real-life applications in teaching and research. We further discuss the framework’s utility in research design in landscape architecture, mixed methods research, and provide an outlook to the framework’s potential in inclusive interdisciplinary research, and last but not least, research integrity.

Introduction

Current challenges, e.g., energy, water, food security, one world health and urbanization, involve the interaction between humans and their environment. A (mono)disciplinary approach, be it a psychological, economical or technical one, is too limited to capture any one of these challenges. The study of the interaction between humans and their environment requires knowledge, ideas and research methodology from different disciplines (e.g., ecology or chemistry in the natural sciences, psychology or economy in the social sciences). So collaboration between natural and social sciences is called for (Walsh et al. 1975 ).

Over the past decades, different forms of collaboration have been distinguished although the terminology used is diverse and ambiguous. For the present paper, the term interdisciplinary research is used for (Aboelela et al. 2007 , p. 341):

any study or group of studies undertaken by scholars from two or more distinct scientific disciplines. The research is based upon a conceptual model that links or integrates theoretical frameworks from those disciplines, uses study design and methodology that is not limited to any one field, and requires the use of perspectives and skills of the involved disciplines throughout multiple phases of the research process.

Scientific disciplines (e.g., ecology, chemistry, biology, psychology, sociology, economy, philosophy, linguistics, etc.) are categorized into distinct scientific cultures: the natural sciences, the social sciences and the humanities (Kagan 2009 ). Interdisciplinary research may involve different disciplines within a single scientific culture, and it can also cross cultural boundaries as in the study of humans and their environment.

A systematic review of the literature on natural-social science collaboration (Fischer et al. 2011 ) confirmed the general impression of this collaboration to be a challenge. The nearly 100 papers in their analytic set mentioned more instances of barriers than of opportunities (72 and 46, respectively). Four critical factors for success or failure in natural-social science collaboration were identified: the paradigms or epistemologies in the current (mono-disciplinary) sciences, the skills and competences of the scientists involved, the institutional context of the research, and the organization of collaborations (Fischer et al. 2011 ). The so-called “paradigm war” between neopositivist versus constructivists within the social and behavioral sciences (Onwuegbuzie and Leech 2005 ) may complicate pragmatic collaboration further.

It has been argued that interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences (Frischknecht 2000 ) and accordingly, some interdisciplinary programs have been developed since (Baker and Little 2006 ; Spelt et al. 2009 ). The overall effect of interdisciplinary programs can be expected to be small as most programs are mono-disciplinary and based on a single paradigm (positivist-constructivist, qualitative-quantitative; see e.g., Onwuegbuzie and Leech 2005 ). We saw in our methodology teaching, consultancy and research practices working with heterogeneous groups of students and staff, that most had received mono-disciplinary training with a minority that had received multidisciplinary training, with few exceptions within the same paradigm. During our teaching and consultancy for heterogeneous groups of students and staff aimed at designing interdisciplinary research, we built the framework for methodology in interdisciplinary research (MIR). With the MIR framework, we aspire to contribute to the critical factors skills and competences (Fischer et al. 2011 ) for social and natural sciences collaboration. Note that the scale of interdisciplinary research projects we have in mind may vary from comparably modest ones (e.g., finding a link between noise reducing asphalt and quality of life; Vuye et al. 2016 ) to very large projects (finding a link between anthropogenic greenhouse gas emissions, climate change, and food security; IPCC 2015 ).

In the following section of this paper we describe the MIR framework and elaborate on its components. The third section gives two examples of the application of the MIR framework. The paper concludes with a discussion of the MIR framework in the broader contexts of mixed methods research, inclusive research, and other promising strains of research.

The methodology in interdisciplinary research framework

Research as a process in the methodology in interdisciplinary research framework.

The Methodology for Interdisciplinary Research (MIR) framework was built on the process approach (Kumar 1999 ), because in the process approach, the research question or hypothesis is leading for all decisions in the various stages of research. That means that it helps the MIR framework to put the common goal of the researchers at the center, instead of the diversity of their respective backgrounds. The MIR framework also introduces an agenda: the research team needs to carefully think through different parts of the design of their study before starting its execution (Fig.  1 ). First, the team discusses the conceptual design of their study which contains the ‘why’ and ‘what’ of the research. Second, the team discusses the technical design of the study which contains the ‘how’ of the research. Only after the team agrees that the complete research design is sufficiently crystalized, the execution of the work (including fieldwork) starts.

The Methodology of Interdisciplinary Research framework

Whereas the conceptual and technical designs are by definition interdisciplinary team work, the respective team members may do their (mono)disciplinary parts of fieldwork and data analysis on a modular basis (see Bruns et al. 2017 : p. 21). Finally, when all evidence is collected, an interdisciplinary synthesis of analyses follows which conclusions are input for the final report. This implies that the MIR framework allows for a range of scales of research projects, e.g., a mixed methods project and its smaller qualitative and quantitative modules, or a multi-national sustainability project and its national sociological, economic and ecological modules.

The conceptual design

Interdisciplinary research design starts with the “conceptual design” which addresses the ‘why’ and ‘what’ of a research project at a conceptual level to ascertain the common goals pivotal to interdisciplinary collaboration (Fischer et al. 2011 ). The conceptual design includes mostly activities such as thinking, exchanging interdisciplinary knowledge, reading and discussing. The product of the conceptual design is called the “conceptual frame work” which comprises of the research objective (what is to be achieved by the research), the theory or theories that are central in the research project, the research questions (what knowledge is to be produced), and the (partial) operationalization of constructs and concepts that will be measured or recorded during execution. While the members of the interdisciplinary team and the commissioner of the research must reach a consensus about the research objective, the ‘why’, the focus in research design must be the production of the knowledge required to achieve that objective the ‘what’.

With respect to the ‘why’ of a research project, an interdisciplinary team typically starts with a general aim as requested by the commissioner or funding agency, and a set of theories to formulate a research objective. This role of theory is not always obvious to students from the natural sciences, who tend to think in terms of ‘models’ with directly observable variables. On the other hand, students from the social sciences tend to think in theories with little attention to observable variables. In the MIR framework, models as simplified descriptions or explanations of what is studied in the natural sciences play the same role in informing research design, raising research questions, and informing how a concept is understood, as do theories in social science.

Research questions concern concepts, i.e. general notions or ideas based on theory or common sense that are multifaceted and not directly visible or measurable. For example, neither food security (with its many different facets) nor a person’s attitude towards food storage may be directly observed. The operationalization of concepts, the transformation of concepts into observable indicators, in interdisciplinary research requires multiple steps, each informed by theory. For instance, in line with particular theoretical frameworks, sustainability and food security may be seen as the composite of a social, an economic and an ecological dimension (e.g., Godfray et al. 2010 ).

As the concept of interest is multi-disciplinary and multi-dimensional, the interdisciplinary team will need to read, discuss and decide on how these dimensions and their indicators are weighted to measure the composite interdisciplinary concept to get the required interdisciplinary measurements. The resulting measure or measures for the interdisciplinary concept may be of the nominal, ordinal, interval and ratio level, or a combination thereof. This operationalization procedure is known as the port-folio approach to widely defined measurements (Tobi 2014 ). Only after the research team has finalized the operationalization of the concepts under study, the research questions and hypotheses can be made operational. For example, a module with descriptive research questions may now be turned into an operational one like, what are the means and variances of X1, X2, and X3 in a given population? A causal research question may take on the form, is X (a composite of X1, X2 and X3) a plausible cause for the presence or absence of Y? A typical qualitative module could study, how do people talk about X1, X2 and X3 in their everyday lives?

The technical design

Members of an interdisciplinary team usually have had different training with respect to research methods, which makes discussing and deciding on the technical design more challenging but also potentially more creative than in a mono-disciplinary team. The technical design addresses the issues ‘how, where and when will research units be studied’ (study design), ‘how will measurement proceed’ (instrument selection or design), ‘how and how many research units will be recruited’ (sampling plan), and ‘how will collected data be analyzed and synthesized’ (analysis plan). The MIR framework provides the team a set of topics and their relationships to one another and to generally accepted quality criteria (see Fig.  1 ), which helps in designing this part of the project.

Interdisciplinary teams need be pragmatic as the research questions agreed on are leading in decisions on the data collection set-up (e.g., a cross-sectional study of inhabitants of a region, a laboratory experiment, a cohort study, a case control study, etc.), the so-called “study design” (e.g., Kumar 2014 ; De Vaus 2001 ; Adler and Clark 2011 ; Tobi and van den Brink 2017 ) instead of traditional ‘pet’ approaches. Typical study designs for descriptive research questions and research questions on associations are the cross-sectional study design. Longitudinal study designs are required to investigate development over time and cause-effect relationships ideally are studied in experiments (e.g., Kumar 2014 ; Shipley 2016 ). Phenomenological questions concern a phenomenon about which little is known and which has to be studied in the environment where it takes place, which calls for a case study design (e.g., Adler and Clark 2011 : p. 178). For each module, the study design is to be further explicated by the number of data collection waves, the level of control by the researcher and its reference period (e.g., Kumar 2014 ) to ensure the teams common understanding.

Then, decisions about the way data is to be collected, e.g., by means of certified instruments, observation, interviews, questionnaires, queries on existing data bases, or a combination of these are to be made. It is especially important to discuss the role of the observer (researcher) as this is often a source of misunderstanding in interdisciplinary teams. In the sciences, the observer is usually considered a neutral outsider when reading a standardized measurement instrument (e.g., a pyranometer to measure incoming solar radiation). In contrast, in the social sciences, the observer may be (part of) the measurement instrument, for example in participant observation or when doing in-depth interviews. After all, in participant observation the researcher observes from a member’s perspective and influences what is observed owing to the researcher’s participation (Flick 2006 : p. 220). Similarly in interviews, by which we mean “a conversation that has a structure and a purpose determined by the one party—the interviewer” (Kvale 2007 : p. 7), the interviewer and the interviewee are part of the measurement instrument (Kvale and Brinkmann 2009 : p. 2). In on-line and mail questionnaires the interviewer is eliminated as part of the instrument by standardizing the questions and answer options. Queries on existing data bases refer to the use of secondary data or secondary analysis. Different disciplines tend to use different bibliographic data bases (e.g., CAB Abstracts, ABI/INFORM or ERIC) and different data repositories (e.g., the European Social Survey at europeansocialsurvey.org or the International Council for Science data repository hosted by www.pangaea.de ).

Depending on whether or not the available, existing, measurement instruments tally with the interdisciplinary operationalisations from the conceptual design, the research team may or may not need to design instruments. Note that in some cases the social scientists’ instinct may be to rely on a questionnaire whereas the collaboration with another discipline may result in more objective possibilities (e.g., compare asking people about what they do with surplus medication, versus measuring chemical components from their input into the sewer system). Instrument design may take on different forms, such as the design of a device (e.g., pyranometer), a questionnaire (Dillman 2007 ) or a part thereof (e.g., a scale see DeVellis 2012 ; Danner et al. 2016 ), an interview guide with topics or questions for the interviewees, or a data extraction form in the context of secondary analysis and literature review (e.g., the Cochrane Collaboration aiming at health and medical sciences or the Campbell Collaboration aiming at evidence based policies).

Researchers from different disciplines are inclined to think of different research objects (e.g., animals, humans or plots), which is where the (specific) research questions come in as these identify the (possibly different) research objects unambiguously. In general, research questions that aim at making an inventory, whether it is an inventory of biodiversity or of lodging, call for a random sampling design. Both in the biodiversity and lodging example, one may opt for random sampling of geographic areas by means of a list of coordinates. Studies that aim to explain a particular phenomenon in a particular context would call for a purposive sampling design (non-random selection). Because studies of biodiversity and housing obey the same laws in terms of appropriate sampling design for similar research questions, individual students and researchers are sensitized to commonalities of their respective (mono)disciplines. For example, a research team interested in the effects of landslides on a socio-ecological system may select for their study one village that suffered from landslides and one village that did not suffer from landslides that have other characteristics in common (e.g., kind of soil, land use, land property legislation, family structure, income distribution, et cetera).

The data analysis plan describes how data will be analysed, for each of the separate modules and for the project at large. In the context of a multi-disciplinary quantitative research project, the data analysis plan will list the intended uni-, bi- and multivariate analyses such as measures for distributions (e.g., means and variances), measures for association (e.g., Pearson Chi square or Kendall Tau) and data reduction and modelling techniques (e.g., factor analysis and multiple linear regression or structural equation modelling) for each of the research modules using the data collected. When applicable, it will describe interim analyses and follow-up rules. In addition to the plans at modular level, the data analysis plan must describe how the input from the separate modules, i.e. different analyses, will be synthesized to answer the overall research question. In case of mixed methods research, the particular type of mixed methods design chosen describes how, when, and to what extent the team will synthesize the results from the different modules.

Unfortunately, in our experience, when some of the research modules rely on a qualitative approach, teams tend to refrain from designing a data analysis plan before starting the field work. While absence of a data analysis plan may be regarded acceptable in fields that rely exclusively on qualitative research (e.g., ethnography), failure to communicate how data will be analysed and what potential evidence will be produced posits a deathblow to interdisciplinarity. For many researchers not familiar with qualitative research, the black box presented as “qualitative data analysis” is a big hurdle, and a transparent and systematic plan is a sine qua non for any scientific collaboration. The absence of a data analysis plan for all modules results in an absence of synthesis of perspectives and skills of the disciplines involved, and in separate (disciplinary) research papers or separate chapters in the research report without an answer to the overall research question. So, although researchers may find it hard to write the data analysis plan for qualitative data, it is pivotal in interdisciplinary research teams.

Similar to the quantitative data analysis plan, the qualitative data analysis plan presents the description of how the researcher will get acquainted with the data collected (e.g., by constructing a narrative summary per interviewee or a paired-comparison of essays). Additionally, the rules to decide on data saturation need be presented. Finally, the types of qualitative analyses are to be described in the data analysis plan. Because there is little or no standardized terminology in qualitative data analysis, it is important to include a precise description as well as references to the works that describe the method intended (e.g., domain analysis as described by Spradley 1979 ; or grounded theory by means of constant-comparison as described by Boeije 2009 ).

Integration

To benefit optimally from the research being interdisciplinary the modules need to be brought together in the integration stage. The modules may be mono- or interdisciplinary and may rely on quantitative, qualitative or mixed methods approaches. So the MIR framework fits the view that distinguishes three multimethods approaches (quali–quali, quanti–quanti, and quali–quant).

Although the MIR framework has not been designed with the intention to promote mixed methods research, it is suitable for the design of mixed methods research as the kind of research that calls for both quantitative and qualitative components (Creswell and Piano Clark 2011 ). Indeed, just like the pioneers in mixed methods research (Creswell and Piano Clark 2011 : p. 2), the MIR framework deconstructs the package deals of paradigm and data to be collected. The synthesis of the different mono or interdisciplinary modules may benefit from research done on “the unique challenges and possibilities of integration of qualitative and quantitative approaches” (Fetters and Molina-Azorin 2017 : p. 5). We distinguish (sub) sets of modules being designed as convergent, sequential or embedded (adapted from mixed methods design e.g., Creswell and Piano Clark 2011 : pp. 69–70). Convergent modules, whether mono or interdisciplinary, may be done parallel and are integrated after completion. Sequential modules are done after one another and the first modules inform the latter ones (this includes transformative and multiphase mixed methods design). Embedded modules are intertwined. Here, modules depend on one another for data collection and analysis, and synthesis may be planned both during and after completion of the embedded modules.

Scientific quality and ethical considerations in the design of interdisciplinary research

A minimum set of jargon related to the assessment of scientific quality of research (e.g., triangulation, validity, reliability, saturation, etc.) can be found scattered in Fig.  1 . Some terms are reserved by particular paradigms, others may be seen in several paradigms with more or less subtle differences in meaning. In the latter case, it is important that team members are prepared to explain and share ownership of the term and respect the different meanings. By paying explicit attention to the quality concepts, researchers from different disciplines learn to appreciate each other’s concerns for good quality research and recognize commonalities. For example, the team may discuss measurement validity of both a standardized quantitative instrument and that of an interview and discover that the calibration of the machine serves a similar purpose as the confirmation of the guarantee of anonymity at the start of an interview.

Throughout the process of research design, ethics require explicit discussion among all stakeholders in the project. Ethical issues run through all components in the MIR framework in Fig.  1 . Where social and medical scientists may be more sensitive to ethical issues related to humans (e.g., the 1979 Belmont Report criteria of beneficence, justice, and respect), others may be more sensitive to issues related to animal welfare, ecology, legislation, the funding agency (e.g., implications for policy), data and information sharing (e.g., open access publishing), sloppy research practices, or long term consequences of the research. This is why ethics are an issue for the entire interdisciplinary team and cannot be discussed on project module level only.

The MIR framework in practice: two examples

Teaching research methodology to heterogeneous groups of students, institutional context and background of the mir framework.

Wageningen University and Research (WUR) advocates in its teaching and research an interdisciplinary approach to the study of global issues related to the motto “To explore the potential of nature to improve the quality of life.” Wageningen University’s student population is multidisciplinary and international (e.g., Tobi and Kampen 2013 ). Traditionally, this challenge of diversity in one classroom is met by covering a width of methodological topics and examples from different disciplines. However, when students of various programmes received methodological education in mixed classes, students of some disciplines would regard with disinterest or even disdain methods and techniques of the other disciplines. Different disciplines, especially from the qualitative respectively quantitative tradition in the social sciences (Onwuegbuzie and Leech 2005 : p. 273), claim certain study designs, methods of data collection and analysis as their territory, a claim reflected in many textbooks. We found that students from a qualitative tradition would not be interested, and would not even study, content like the design of experiments and quantitative data collection; and students from a quantitative tradition would ignore case study design and qualitative data collection. These students assumed they didn’t need any knowledge about ‘the other tradition’ for their future careers, despite the call for interdisciplinarity.

To enhance interdisciplinarity, WUR provides an MSc course mandatory for most students, in which multi-disciplinary teams do research for a commissioner. Students reported difficulties similar to the ones found in the literature: miscommunication due to talking different scientific languages and feelings of distrust and disrespect due to prejudice. This suggested that research methodology courses ought help prepare for interdisciplinary collaboration by introducing a single methodological framework that 1) creates sensitivity to the pros and challenges of interdisciplinary research by means of a common vocabulary and fosters respect for other disciplines, 2) starts from the research questions as pivotal in decision making on research methods instead of tradition or ontology, and 3) allows available methodologies and methods to be potentially applicable to any scientific research problem.

Teaching with MIR—the conceptual framework

As a first step, we replaced textbooks by ones refusing the idea that any scientific tradition has exclusive ownership of any methodological approach or method. The MIR framework further guides our methodology teaching in two ways. First, it presents a logical sequence of topics (first conceptual design, then technical design; first research question(s) or hypotheses, then study design; etc.). Second, it allows for a conceptual separation of topics (e.g., study design from instrument design). Educational programmes at Wageningen University and Research consistently stress the vital importance of good research design. In fact, 50% of the mark in most BSc and MSc courses in research methodology is based on the assessment of a research proposal that students design in small (2-4 students) and heterogeneous (discipline, gender and nationality) groups. The research proposal must describe a project which can be executed in practice, and which limitations (measurement, internal, and external validity) are carefully discussed.

Groups start by selecting a general research topic. They discuss together previously attained courses from a range of programs to identify personal and group interests, with the aim to reach an initial research objective and a general research question as input for the conceptual design. Often, their initial research objective and research question are too broad to be researchable (e.g., Kumar 2014 : p. 64; Adler and Clark 2011 : p. 71). In plenary sessions, the (basics of) critical assessment of empirical research papers is taught with special attention to the ‘what’ and ‘why’ section of research papers. During tutorials students generate research questions until the group agrees on a research objective, with one general research question that consists of a small set of specific research questions. Each of the specific research questions may stem from a different discipline, whereas answering the general research question requires integrating the answers to all specific research questions.

The group then identifies the key concepts in their research questions, while exchanging thoughts on possible attributes based on what they have learnt from previous courses (theories) and literature. When doing so they may judge the research question as too broad, in which case they will turn to the question strategies toolbox again. Once they agree on the formulation of the research questions and the choice of concepts, tasks are divided. In general, each student turns to the literature he/she is most familiar with or interested in, for the operationalization of the concept into measurable attributes and writes a paragraph or two about it. In the next meeting, the groups read and discuss the input and decide on the set-up and division of tasks with respect to the technical design.

Teaching with MIR—the technical framework

The technical part of research design distinguishes between study design, instrument design, sampling design, and the data analysis plan. In class, we first present students with a range of study designs (cross sectional, experimental, etc.). Student groups select an appropriate study design by comparing the demands made by the research questions with criteria for internal validity. When a (specific) research question calls for a study design that is not seen as practically feasible or ethically possible, they will rephrase the research question until the demands of the research question tally with the characteristics of at least one ethical, feasible and internally valid study design.

While following plenary sessions during which different random and non-random sampling or selection strategies are taught, groups start working on their sampling design. The groups make two decisions informed by their research question: the population(s) of research units, and the requirements of the sampling strategy for each population. Like many other aspects in research design, this can be an iterative process. For example, suppose the research question mentioned “local policy makers,” which is too vague for a sampling design. Then the decision may be to limit the study to “policy makers at the municipality level in the Netherlands” and adapt the general and the specific research questions accordingly. Next, the group identifies whether a sample design needs to focus on diversity (e.g., when the objective is to make an inventory of possible local policies), representativeness (e.g., when the objective is to estimate prevalence of types of local policies), or people with particular information (e.g., when the objective is to study people having experience with a given local policy). When a sample has to representative, the students must produce an assessment of external validity, whereas when the aim is to map diversity the students must discuss possible ways of source triangulation. Finally, in conjunction with the data analysis plan, students decide on the sample size and/or the saturation criteria.

When the group has agreed on their population(s) and the strategy for recruiting research units, the next step is to finalize the technical aspects of operationalisation i.e. addressing the issue of exactly how information will be extracted from the research units. Depending on what is practically feasible qua measurement, the choice of a data collection instrument may be a standardised (e.g., a spectrograph, a questionnaire) or less standardised (e.g., semi-structured interviews, visual inspection) one. The students have to discuss the possibilities of method triangulation, and explain the possible weaknesses of their data collection plan in terms of measurement validity and reliability.

Recent developments

Presently little attention is payed to the data analysis plan, procedures for synthesis and reporting because the programmes differ on their offer in data analysis courses, and because execution of the research is not part of the BSc and MSc methodology courses. Recently, we have designed one course for an interdisciplinary BSc program in which the research question is put central in learning and deciding on statistics and qualitative data analysis. Nonetheless, during the past years the number of methodology courses for graduate students that supported the MIR framework have been expanded, e.g., a course “From Topic to Proposal”; separate training modules on questionnaire construction, interviewing, and observation; and optional courses on quantitative and qualitative data analysis. These courses are open to (and attended by) PhD students regardless of their program. In Flanders (Belgium), the Flemish Training Network for Statistics and Methodology (FLAMES) has for the last four years successfully applied the approach outlined in Fig.  1 in its courses for research design and data collection methods. The division of the research process in terms of a conceptual design, technical design, operationalisation, analysis plan, and sampling plan, has proved to be appealing for students of disciplines ranging from linguistics to bioengineering.

Researching with MIR: noise reducing asphalt layers and quality of life

Research objective and research question.

This example of the application of the MIR framework comes from a study about the effects of “noise reducing asphalt layers” on the quality of life (Vuye et al. 2016 ), a project commissioned by the City of Antwerp in 2015 and executed by a multidisciplinary research team of Antwerp University (Belgium). The principal researcher was an engineer from the Faculty of Applied Engineering (dept. Construction), supported by two researchers from the Faculty of Medicine and Health Sciences (dept. of Epidemiology and Social Statistics), one with a background in qualitative and one with a background in quantitative research methods. A number of meetings were held where the research team and the commissioners discussed the research objective (the ‘what’ and ‘why’).The research objective was in part dictated by the European Noise Directive 2002/49/EC, which forces all EU member states to draft noise action plans, and the challenge in this study was to produce evidence of a link between the acoustic and mechanical properties of different types of asphalt, and the quality of life of people living in the vicinity of the treated roads. While there was literature available about the effects of road surface on sound, and other studies had studied the link between noise and health, no study was found that produced evidence simultaneously about noise levels of roads and quality of life. The team therefore decided to test the hypothesis that traffic noise reduction has a beneficial effect on the quality of life of people into the central research. The general research question was, “to what extent does the placing of noise reducing asphalt layers increase the quality of life of the residents?”

Study design

In order to test the effect of types of asphalt, initially a pretest–posttest experiment was designed, which was expanded by several added experimental (change of road surface) and control (no change of road surface) groups. The research team gradually became aware that quality of life may not be instantly affected by lower noise levels, and that a time lag is involved. A second posttest aimed to follow up on this effect although it could only be implemented in a selection of experimental sites.

Instrument selection and design

Sound pressure levels were measured by an ISO-standardized procedure called the Statistical Pass-By (SPB) method. A detailed description of the method is in Vuye et al. ( 2016 ). No such objective procedure is available for measuring quality of life, which can only be assessed by self-reports of the residents. Some time was needed for the research team to accept that measuring a multidimensional concept like quality of life is more complicated than just having people rate their “quality of life” on a 10 point scale. For instance, questions had to be phrased in a way that gave not away the purpose of the research (Hawthorne effect), leading to the inclusion of questions about more nuisances than traffic noise alone. This led to the design of a self-administered questionnaire, with questions of Flanders Survey on Living Environment (Departement Leefmilieu, Natuur & Energie 2013 ) appended by new questions. Among other things, the questionnaire probed for experienced nuisance by sound, quality of sleep, effort to concentrate, effort to have a conversation inside or outside the home, physical complaints such as headaches, etc.

Sampling design

The selected sites needed to accommodate both types of measurements: that of noise from traffic and quality of life of residents. This was a complicating factor that required several rounds of deliberation. While countrywide only certain roads were available for changing the road surface, these roads had to be mutually comparable in terms of the composition of the population, type of residential area (e.g., reports from the top floor of a tall apartment building cannot be compared to those at ground level), average volume of traffic, vicinity of hospitals, railroads and airports, etc. At the level of roads therefore, targeted sampling was applied, whereas at the level of residents the aim was to realize a census of all households within a given perimeter from the treated road surfaces. Considerations about the reliability of applied instruments were guiding decisions with respect to sampling. While the measurements of the SPB method were sufficiently reliable to allow for relatively few measurements, the questionnaire suffered from considerable nonresponse which hampered statistical power. It was therefore decided to increase the power of the study by adding control groups in areas where the road surface was not replaced. This way, detecting an effect of the intervention did not solely depend on the turnout of the pre and the post-test.

Data analysis plan

The statistical analysis had to account for the fact that data were collected at two different levels: the level of the residents filling out the questionnaires, and the level of the roads which surface was changed. Because survey participation was confidential, results of the pre- and posttest could only be compared at aggregate (street) level. The analysis had to control for confounding variables (e.g., sample composition, variety in traffic volume, etc.), experimental factors (varieties in experimental conditions, and controls), and non-normal dependent variables. The statistical model appropriate for analysis of such data is a Generalised Linear Mixed Model.

Data were collected during the course of 2015, 2016 and 2017 and are awaiting final analysis in Spring 2017. Intermediate analyses resulted in several MSc theses, conference presentations, and working papers that reported on parts of the research.

In this paper we presented the Methodology in Interdisciplinary Research framework that we developed over the past decade building on our experience as lecturers, consultants and researchers. The MIR framework recognizes research methodology and methods as important content in the critical factor skills and competences. It approaches research and collaboration as a process that needs to be designed with the sole purpose to answer the general research question. For the conceptual design the team members have to discuss and agree on the objective of their communal efforts without squeezing it into one single discipline and, thus, ignoring complexity. The specific research questions, when formulated, contribute to (self) respect in collaboration as they represent and stand witness of the need for interdisciplinarity. In the technical design, different parts were distinguished to stimulate researchers to think and design research out of their respective disciplinary boxes and consider, for example, an experimental design with qualitative data collection, or a case study design based on quantitative information.

In our teaching and consultancy, we first developed a MIR framework for social sciences, economics, health and environmental sciences interdisciplinarity. It was challenged to include research in the design discipline of landscape architecture. What characterizes research in landscape architecture and other design principles, is that the design product as well as the design process may be the object of study. Lenzholder et al. ( 2017 ) therefore distinguish three kinds of research in landscape architecture. The first kind, “Research into design” studies the design product post hoc and the MIR framework suits the interdisciplinary study of such a product. In contrast, “Research for design” generates knowledge that feeds into the noun and the verb ‘design’, which means it precedes the design(ing). The third kind, Research through Design(ing) employs designing as a research method. At first, just like Deming and Swaffield ( 2011 ), we were a bit skeptical about “designing” as a research method. Lenzholder et al. ( 2017 ) pose that the meaning of research through design has evolved through a (neo)positivist, constructivist and transformative paradigm to include a pragmatic stance that resembles the pragmatic stance assumed in the MIR framework. We learned that, because landscape architecture is such an interdisciplinary field, the process approach and the distinction between a conceptual and technical research design was considered very helpful and embraced by researchers in landscape architecture (Tobi and van den Brink 2017 ).

Mixed methods research (MMR) has been considered to study topics as diverse as education (e.g., Powell et al. 2008 ), environmental management (e.g., Molina-Azorin and Lopez-Gamero 2016 ), health psychology (e.g., Bishop 2015 ) and information systems (e.g., Venkatesh et al. 2013 ). Nonetheless, the MIR framework is the first to put MMR in the context of integrating disciplines beyond social inquiry (Greene 2008 ). The splitting of the research into modules stimulates the identification and recognition of the contribution of both distinct and collaborating disciplines irrespective of whether they contribute qualitative and/or quantitative research in the interdisciplinary research design. As mentioned in Sect.  2.4 the integration of the different research modules in one interdisciplinary project design may follow one of the mixed methods designs. For example, we witnessed at several occasions the integration of social and health sciences in interdisciplinary teams opting for sequential modules in a sequential exploratory mixed methods fashion (e.g., Adamson 2005 : 234). In sustainability science research, we have seen the design of concurrent modules for a concurrent nested mixed methods strategy (ibid) in research integrating the social and natural sciences and economics.

The limitations of the MIR framework are those of any kind of collaboration: it cannot work wonders in the absence of awareness of the necessity and it requires the willingness to work, learn, and research together. We developed MIR framework in and alongside our own teaching, consultancy and research, it has not been formally evaluated and compared in an experiment with teaching, consultancy and research with, for example, the regulative cycle for problem solving (van Strien 1986 ), or the wheel of science from Babbie ( 2013 ). In fact, although we wrote “developed” in the previous sentence, we are fully aware of the need to further develop and refine the framework as is.

The importance of the MIR framework lies in the complex, multifaceted nature of issues like sustainability, food security and one world health. For progress in the study of these pressing issues the understanding, construction and quality of interdisciplinary portfolio measurements (Tobi 2014 ) are pivotal and require further study as well as procedures facilitating the integration across different disciplines.

Another important strain of further research relates to the continuum of Responsible Conduct of Research (RCR), Questionable Research Practices (QRP), and deliberate misconduct (Steneck 2006 ). QRP includes failing to report all of a study’s conditions, stopping collecting data earlier than planned because one found the result one had been looking for, etc. (e.g., John et al. 2012 ; Simmons et al. 2011 ; Kampen and Tamás 2014 ). A meta-analysis on selfreports obtained through surveys revealed that about 2% of researchers had admitted to research misconduct at least once, whereas up to 33% admitted to QRPs (Fanelli 2009 ). While the frequency of QRPs may easily eclipse that of deliberate fraud (John et al. 2012 ) these practices have received less attention than deliberate misconduct. Claimed research findings may often be accurate measures of the prevailing biases and methodological rigor in a research field (Fanelli and Ioannidis 2013 ; Fanelli 2010 ). If research misconduct and QRP are to be understood then the disciplinary context must be grasped as a locus of both legitimate and illegitimate activity (Fox 1990 ). It would be valuable to investigate how working in interdisciplinary teams and, consequently, exposure to other standards of QRP and RCR influence research integrity as the appropriate research behavior from the perspective of different professional standards (Steneck 2006 : p. 56). These differences in scientific cultures concern criteria for quality in design and execution of research, reporting (e.g., criteria for authorship of a paper, preferred publication outlets, citation practices, etc.), archiving and sharing of data, and so on.

Other strains of research include interdisciplinary collaboration and negotiation, where we expect contributions from the “science of team science” (Falk-Krzesinski et al. 2010 ); and compatibility of the MIR framework with new research paradigms such as “inclusive research” (a mode of research involving people with intellectual disabilities as more than just objects of research; e.g., Walmsley and Johnson 2003 ). Because of the complexity and novelty of inclusive health research a consensus statement was developed on how to conduct health research inclusively (Frankena et al., under review). The eight attributes of inclusive health research identified may also be taken as guiding attributes in the design of inclusive research according to the MIR framework. For starters, there is the possibility of inclusiveness in the conceptual framework, particularly in determining research objectives, and in discussing possible theoretical frameworks with team members with an intellectual disability which Frankena et al. labelled the “Designing the study” attribute. There are also opportunities for inclusiveness in the technical design, and in execution. For example, the inclusiveness attribute “generating data” overlaps with the operationalization and measurement instrument design/selection and the attribute “analyzing data” aligns with the data analysis plan in the technical design.

On a final note, we hope to have aroused the reader’s interest in, and to have demonstrated the need for, a methodology for interdisciplinary research design. We further hope that the MIR framework proposed and explained in this article helps those involved in designing an interdisciplinary research project to get a clearer view of the various processes that must be secured during the project’s design and execution. And we look forward to further collaboration with scientists from all cultures to contribute to improving the MIR framework and make interdisciplinary collaborations successful.

Acknowledgements

The MIR framework is the result of many discussions with students, researchers and colleagues, with special thanks to Peter Tamás, Jennifer Barrett, Loes Maas, Giel Dik, Ruud Zaalberg, Jurian Meijering, Vanessa Torres van Grinsven, Matthijs Brink, Gerda Casimir, and, last but not least, Jenneken Naaldenberg.

• Aboelela SW, Larson E, Bakken S, Carrasquillo O, Formicola A, Glied SA, Gebbie KM. Defining interdisciplinary research: conclusions from a critical review of the literature. Health Serv. Res. 2007; 42 (1):329–346. doi: 10.1111/j.1475-6773.2006.00621.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Adamson J. Combined qualitative and quantitative designs. In: Bowling A, Ebrahim S, editors. Handbook of Health Research Methods: Investigation, Measurement and Analysis. Maidenhead: Open University Press; 2005. pp. 230–245. [ Google Scholar ]
• Adler ES, Clark R. An Invitation to Social Research: How it’s Done. 4. London: Sage; 2011. [ Google Scholar ]
• Babbie ER. The Practice of Social Research. 13. Belmont Ca: Wadsworth Cengage Learning; 2013. [ Google Scholar ]
• Baker GH, Little RG. Enhancing homeland security: development of a course on critical infrastructure systems. J. Homel. Secur. Emerg. Manag. 2006 [ Google Scholar ]
• Bishop FL. Using mixed methods research designs in health psychology: an illustrated discussion from a pragmatist perspective. Br. J. Health. Psychol. 2015; 20 (1):5–20. doi: 10.1111/bjhp.12122. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boeije HR. Analysis in Qualitative Research. London: Sage; 2009. [ Google Scholar ]
• Bruns D, van den Brink A, Tobi H, Bell S. Advancing landscape architecture research. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods And Methodology. New York: Routledge; 2017. pp. 11–23. [ Google Scholar ]
• Creswell JW, Piano Clark VL. Designing and Conducting Mixed Methods Research. 2. Los Angeles: Sage; 2011. [ Google Scholar ]
• Danner D, Blasius J, Breyer B, Eifler S, Menold N, Paulhus DL, Ziegler M. Current challenges, new developments, and future directions in scale construction. Eur. J. Psychol. Assess. 2016; 32 (3):175–180. doi: 10.1027/1015-5759/a000375. [ CrossRef ] [ Google Scholar ]
• Deming ME, Swaffield S. Landscape Architecture Research. Hoboken: Wiley; 2011. [ Google Scholar ]
• Departement Leefmilieu, Natuur en Energie: Uitvoeren van een uitgebreide schriftelijke enquête en een beperkte CAWI-enquête ter bepaling van het percentage gehinderden door geur, geluid en licht in Vlaanderen–SLO-3. Leuven: Market Analysis & Synthesis. www.lne.be/sites/default/files/atoms/files/lne-slo-3-eindrapport.pdf (2013). Accessed 8 March 2017
• De Vaus D. Research Design in Social Research. London: Sage; 2001. [ Google Scholar ]
• DeVellis RF. Scale Development: Theory and Applications. 3. Los Angeles: Sage; 2012. [ Google Scholar ]
• Dillman DA. Mail and Internet Surveys. 2. Hobroken: Wiley; 2007. [ Google Scholar ]
• Falk-Krzesinski HJ, Borner K, Contractor N, Fiore SM, Hall KL, Keyton J, Uzzi B, et al. Advancing the science of team science. CTS Clin. Transl. Sci. 2010; 3 (5):263–266. doi: 10.1111/j.1752-8062.2010.00223.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fanelli D. How many scientists fabricate and falsify research? A systematic review and metaanalysis of survey data. PLoS ONE. 2009 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fanelli D. Positive results increase down the hierarchy of the sciences. PLoS ONE. 2010 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fanelli D, Ioannidis JPA. US studies may overestimate effect sizes in softer research. Proc. Natl. Acad. Sci. USA. 2013; 110 (37):15031–15036. doi: 10.1073/pnas.1302997110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fetters MD, Molina-Azorin JF. The journal of mixed methods research starts a new decade: principles for bringing in the new and divesting of the old language of the field. J. Mixed Methods Res. 2017; 11 (1):3–10. doi: 10.1177/1558689816682092. [ CrossRef ] [ Google Scholar ]
• Fischer ARH, Tobi H, Ronteltap A. When natural met social: a review of collaboration between the natural and social sciences. Interdiscip. Sci. Rev. 2011; 36 (4):341–358. doi: 10.1179/030801811X13160755918688. [ CrossRef ] [ Google Scholar ]
• Flick U. An Introduction to Qualitative Research. 3. London: Sage; 2006. [ Google Scholar ]
• Fox MF. Fraud, ethics, and the disciplinary contexts of science and scholarship. Am. Sociol. 1990; 21 (1):67–71. doi: 10.1007/BF02691783. [ CrossRef ] [ Google Scholar ]
• Frischknecht PM. Environmental science education at the Swiss Federal Institute of Technology (ETH) Water Sci. Technol. 2000; 41 (2):31–36. [ PubMed ] [ Google Scholar ]
• Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C. Food security: the challenge of feeding 9 billion people. Science. 2010; 327 (5967):812–818. doi: 10.1126/science.1185383. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Greene JC. Is mixed methods social inquiry a distinctive methodology? J. Mixed Methods Res. 2008; 2 (1):7–22. doi: 10.1177/1558689807309969. [ CrossRef ] [ Google Scholar ]
• IPCC.: Climate Change 2014 Synthesis Report. Geneva: Intergovernmental Panel on Climate Change. www.ipcc.ch/pdf/assessment-report/ar5/syr/SYR_AR5_FINAL_full_wcover.pdf (2015) Accessed 8 March 2017
• John LK, Loewenstein G, Prelec D. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 2012; 23 (5):524–532. doi: 10.1177/0956797611430953. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kagan J. The Three Cultures: Natural Sciences, Social Sciences and the Humanities in the 21st Century. Cambridge: Cambridge University Press; 2009. [ Google Scholar ]
• Kampen JK, Tamás P. Should I take this seriously? A simple checklist for calling bullshit on policy supporting research. Qual. Quant. 2014; 48 :1213–1223. doi: 10.1007/s11135-013-9830-8. [ CrossRef ] [ Google Scholar ]
• Kumar R. Research Methodology: A Step-by-Step Guide for Beginners. 1. Los Angeles: Sage; 1999. [ Google Scholar ]
• Kumar R. Research Methodology: A Step-by-Step Guide for Beginners. 4. Los Angeles: Sage; 2014. [ Google Scholar ]
• Kvale S. Doing Interviews. London: Sage; 2007. [ Google Scholar ]
• Kvale S, Brinkmann S. Interviews: Learning the Craft of Qualitative Interviews. 2. London: Sage; 2009. [ Google Scholar ]
• Lenzholder S, Duchhart I, van den Brink A. The relationship between research design. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods and Methodology. New York: Routledge; 2017. pp. 54–64. [ Google Scholar ]
• Molina-Azorin JF, Lopez-Gamero MD. Mixed methods studies in environmental management research: prevalence, purposes and designs. Bus. Strateg. Environ. 2016; 25 (2):134–148. doi: 10.1002/bse.1862. [ CrossRef ] [ Google Scholar ]
• Onwuegbuzie AJ, Leech NL. Taking the “Q” out of research: teaching research methodology courses without the divide between quantitative and qualitative paradigms. Qual. Quant. 2005; 39 (3):267–296. doi: 10.1007/s11135-004-1670-0. [ CrossRef ] [ Google Scholar ]
• Powell H, Mihalas S, Onwuegbuzie AJ, Suldo S, Daley CE. Mixed methods research in school psychology: a mixed methods investigation of trends in the literature. Psychol. Sch. 2008; 45 (4):291–309. doi: 10.1002/pits.20296. [ CrossRef ] [ Google Scholar ]
• Shipley B. Cause and Correlation in Biology. 2. Cambridge: Cambridge University Press; 2016. [ Google Scholar ]
• Simmons JP, Nelson LD, Simonsohn U. False positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 2011; 22 :1359–1366. doi: 10.1177/0956797611417632. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spelt EJH, Biemans HJA, Tobi H, Luning PA, Mulder M. Teaching and learning in interdisciplinary higher education: a systematic review. Educ. Psychol. Rev. 2009; 21 (4):365–378. doi: 10.1007/s10648-009-9113-z. [ CrossRef ] [ Google Scholar ]
• Spradley JP. The Ethnographic Interview. New York: Holt, Rinehart and Winston; 1979. [ Google Scholar ]
• Steneck NH. Fostering integrity in research: definitions, current knowledge, and future directions. Sci. Eng. Eth. 2006; 12 (1):53–74. doi: 10.1007/s11948-006-0006-y. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tobi H. Measurement in interdisciplinary research: the contributions of widely-defined measurement and portfolio representations. Measurement. 2014; 48 :228–231. doi: 10.1016/j.measurement.2013.11.013. [ CrossRef ] [ Google Scholar ]
• Tobi H, Kampen JK. Survey error in an international context: an empirical assessment of crosscultural differences regarding scale effects. Qual. Quant. 2013; 47 (1):553–559. doi: 10.1007/s11135-011-9476-3. [ CrossRef ] [ Google Scholar ]
• Tobi H, van den Brink A. A process approach to research in landscape architecture. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods and Methodology. New York: Routledge; 2017. pp. 24–34. [ Google Scholar ]
• van Strien PJ. Praktijk als wetenschap: Methodologie van het sociaal-wetenschappelijk handelen [Practice as science. Methodology of social scientific acting.] Assen: Van Gorcum; 1986. [ Google Scholar ]
• Venkatesh V, Brown SA, Bala H. Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Q. 2013; 37 (1):21–54. doi: 10.25300/MISQ/2013/37.1.02. [ CrossRef ] [ Google Scholar ]
• Vuye C, Bergiers A, Vanhooreweder B. The acoustical durability of thin noise reducing asphalt layers. Coatings. 2016 [ Google Scholar ]
• Walmsley J, Johnson K. Inclusive Research with People with Learning Disabilities: Past, Present and Futures. London: Jessica Kingsley; 2003. [ Google Scholar ]
• Walsh WB, Smith GL, London M. Developing an interface between engineering and social sciences- interdisciplinary team-approach to solving societal problems. Am. Psychol. 1975; 30 (11):1067–1071. doi: 10.1037/0003-066X.30.11.1067. [ CrossRef ] [ Google Scholar ]

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Review article, integrative framework of multiple processes to explain plant productivity–richness relationships.

• 1 Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Regions, Ministry of Education, Chang`an University, Xi`an, China
• 2 AGMUS Institute of Mathematics, Caribbean Computing Center for Excellence, San Juan, PR, United States
• 3 Department of Biological, Geological and Environmental Science, University of Bologna, Bologna, Italy

Plant diversity and productivity, two crucial properties that sustain ecosystem structures, functions, and services, are intrinsically linked to numerous ecological fields, making productivity–richness relationships (PRR) a central ecological concern. Despite extensive research from the Darwinian era to the 21st century, the various shapes of PRR and their underlying theories have sparked ongoing debates. While several processes, theories, and integrative models have been proposed to explain PRR, a comprehensive understanding of the types of PRR, the effects of these processes on plant productivity and richness, and the relationships between PRR shapes remains elusive. This paper proposes a new integrative framework that focuses on these aspects, aiming to elucidate the diverse shapes of PRR and their interconnections. We review recent integrative methods that explain the roles of processes and the varying shapes in PRR to support this new framework. The paper traces the distinct phases in PRR research, including the discovery of PRR shapes, tests of influencing processes, and integrative research. We discuss the application of the Structural Equation Model (SEM), Statistical Dynamical Model (SDM), and Differential Dynamical Model (DDM) in integrative research. This integrative framework can guide theoretical and applied ecologists in identifying, deriving, explaining, and predicting the interconnected but distinct shapes of PRR. The humped, asymptotic, positive, negative, and irregular shapes of PRR are interconnected, with one shape potentially transforming into another. The balance between the positive and negative effects of different processes determines the different shapes of PRR, ultimately leading to a globally positive effect of plant diversity on plant productivity and other ecosystem functions.

Introduction

Plant diversity and productivity are fundamental for the structure and functioning of ecosystems, including the composition, proportion, interrelation of organisms in the food chain and a variety of ecosystem functions ( Humborg et al., 1997 ; Grace et al., 2016 ; Laforest-Lapointe et al., 2017 ). Ecosystems with diverse plant species are essential for achieving sustainable primary productivity and stability, although there are a few counter-examples ( Bezemer & van der Putten, 2007 ). Additionally, diverse ecosystems can provide valuable ecosystem services, such as carbon sequestration, oxygen release, wood production, water resource regeneration, and soil erosion control ( Sugden, 2018 ; Leclère et al., 2020 ). Consequently, plant productivity and richness relationships (PRR) have become a core issue for ecologists worldwide ( Tilman et al., 2001 ; Chen et al., 2018 ).

Ecologists have observed various shapes of PRR across different continents and ecosystems, including forests, grasslands, lakes, and seas ( Mittelbach et al., 2003 ; Whittaker and Heegaard, 2003 ; Adler et al., 2011 ; Pierce, 2014 ). However, information on the occurrence of these PRR shapes is scattered and irregular, leading to confusion among ecologists ( Gillman and Wright, 2006 ; Whittaker and Heegaard, 2003 ; Pierce, 2014 ). Numerous processes and theories have been proposed to explain the shapes of PRR ( Abrams, 1995 ; Willig, 2011 ). For instance, intra- and inter-specific competition effects have been proposed to explain PRR, clarifying specific sections or shapes of PRR ( Stevens and Carson, 1999 ; Michalet et al., 2006 ). The dynamic equilibrium hypothesis has been applied to explain the growth and decline of populations in humped-shaped PRR ( Huston, 1979 ; Chiarucci et al., 2006 ). Species-pool effect, environmental heterogeneity, and negative density dependence are often considered to regulate species richness, while selection effects, complementary effects, and inter-specific facilitation influence plant productivity in PRR ( Zobel et al., 1998 ; Hector et al., 1999 ; Loreau et al., 2001 ; Grossman et al., 2017 ). Due to the diversity of PRR shapes and corresponding explanations, the general pattern of PRR and its underlying mechanisms have been the subject of debate since the 1950s ( Abrams, 1995 ; Schmid, 2002 ; Adler et al., 2011 ; Duffy et al., 2017 ). However, ecologists have not clearly classified the types of PRR, despite identifying many different shapes and proposing various explanations. Furthermore, the positive and negative effects of each process on plant productivity and richness, as well as the relationships between different shapes of PRR, have rarely been analyzed.

Ecologists have also employed mathematical models to integrate the effects of different processes, aiming for a comprehensive explanation of PRR ( Tilman et al., 1997 ; Loreau, 1998 ; Grace et al., 2014 , Grace et al., 2016 ; Liang et al., 2016b ; Wang et al., 2019 ). For example, competition models quantify the impact of inter-specific competition influenced by abiotic factors on plant productivity and species richness in PRR ( Huston, 1979 ; Tilman et al., 1997 ). Mechanistic models, which consider selection effects, complementary effects, resource availability, and species’ functional traits, have been established to reveal the effects of species richness on plant productivity in competition for limiting soil nutrients ( Loreau, 1998 ). Structural equation models, as a form of stochastic process analysis, have been widely used to quantify the roles of different processes in regulating plant diversity, productivity, biomass, and soil organic carbon in PRR ( Grace et al., 2016 ; Chen et al., 2018 ). However, these integrative methods have been applied independently and have not incorporated actual values of each process contributing to plant species richness and productivity based on sampling analysis, which would enhance the understanding of PRR shapes and their relationships.

In this review, we propose a new integrative framework to explain PRR based on multiple processes or theories and previous integrative studies. The framework incorporates processes or theories proposed by ecologists after extensive research, as well as integrative models and results of PRR. Additionally, we conduct a comprehensive review of the positive and negative effects of processes on PRR, as well as relevant theories. We also examine recent integration analyses that utilize structural equation models to quantify the roles of different processes in shaping PRR, and integration analyses that employ dynamical models to provide insights into the mechanisms underlying PRR shapes. These reviews serve as valuable support for the proposed new integrative framework. Our aim is to promote further research on PRR in the field of biodiversity and ecosystem functions.

Integration framework of multiple processes or theories

The integrative framework comprises three sections:

(1) Definition of the two types of PRR, and multiple processes influencing plant richness and productivity in PRR at the top layer ( Figure 1 ). The two types of PRR encompass: (i) the plant productivity-species richness relationship (PSRR), where plant productivity serves as an independent variable and species richness as a dependent variable, describing the patterns of diversity influenced by productivity and other changing processes; (ii) the species richness-plant productivity relationship (SRPR), which represents the converse relationship to PSRR. In SRPR, species richness acts as the independent variable and plant productivity as the dependent variable, elucidating the effects of plant diversity on productivity and its role in regulating ecosystem functioning, stability, and services ( Wang, 2017 ; Wang et al., 2019 ; Figure 1 ). PSRR and SRPR are closely linked to key processes in ecology. However, the classification and definition of these two types of PRR have been vague in previous studies, contributing to the ongoing debate on the shapes of PRR and the underlying mechanisms ( Mittelbach et al., 2003 ; Cardinale et al., 2007 ; Whittaker, 2010 ; Grace et al., 2014 ). In the framework, processes or theories are also classified into two types, affecting or explaining PSRR and SRPR, respectively. However, some processes, such as disturbance, can influence both PSRR and SRPR ( Grace et al., 2016 ). Generally, processes affecting PSRR or SRPR can have either positive or negative effects on species richness, plant productivity, and subsequently on PRR. Some processes may even have both positive and negative effects ( Wang, 2017 ; Wang et al., 2019 ). However, the explicit definition of the positive or negative effects, or the dual effects of these processes, has been rare. Some processes have not received sufficient attention, and we provide a generalization of them in Box 1 .

Figure 1 Processes acting on productivity-richness relationship (PRR). Top layer: The green arrow represents productivity as an independent variable that influences species richness and related patterns in conjunction with other processes. The blue arrow indicates species richness as an independent variable that affects productivity and related ecosystem properties in conjunction with other processes. The red dashed box encompasses various processes that directly or indirectly impact productivity or species richness, consequently altering PRR. NDD, Negative Density Dependence; PSRR, Productivity-Richness Relationship with productivity as the independent variable and species richness as the dependent variable; SRPR, Species Richness-Productivity Relationship with species richness as the independent variable and productivity as the dependent variable; IICE, Intra- and Inter-specific Competition Effects. Middle layer: The first and fourth equations represent the rates of change in species richness (S, a dependent variable) with plant productivity (P, an independent variable), respectively. These equations integrate different processes (i.e., variables, a1-an, x1-xn) to derive the shapes of PSRR. The second and third equations reflect the rates of change in plant productivity (a dependent variable) with species richness (an independent variable) and integrate diverse processes to derive the shapes of SRPR. Bottom layer: The results depict the diverse shapes of PRR derived from integrative analysis and dynamic models: (A) Humped; (B) Positive; (C) Asymptotic; (D) Negative; (E) Irregular. These shapes are interconnected, and one shape can transition into another shape with changes in the overall positive and negative effects of processes. A and C represent the dominant shapes of PSRR and SRPR, respectively, in the absence of exclusion of other shapes ( Mittelbach et al., 2003 ; Fraser et al., 2015 ; Liang et al., 2016b ). The purple arrows represent that the different forms (A,B,C,D and E) of PRR can be transformed each other.

Box 1. Integrated ecological processes and theories in the framework.

(1) Intrinsic rate of increase in species richness with productivity (IRISR) . IRISR is a positive process to directly increase species richness with increasing plant productivity because high productivity can increase metabolic rate, mutation rate of genes and rapid speciation, resulting in higher species richness in communities ( Allen et al., 2002 ; Stegen et al., 2009 ). The process has not been explicitly defined before but it exists with a high possibility at a scale of evolutionary time. (+species richness/+/-productivity)*.

(2) Intra- and inter-specific competition effects (IICE) . IICE is an effect of competition among individuals of same and different species on species richness and productivity, which include competition stress, competitive exclusion and assemblage-level thinning to decrease species richness and productivity or increase productivity ( Goldberg & Miller, 1990 ; Huston & DeAngelis, 1994 ). (-species richness/+/-productivity)

(3) Dynamic equilibrium hypothesis . The hypothesis proposes that poor competitors are excluded rapidly in highly productive habitats with rare disturbance, leading to low diversity; a strong disturbance also results in the disappearance of inadaptable species, leading to low species richness; with moderate disturbances, diversity remains relatively high in the habitats of any productivity to form the peak of the humped shape of PSRR ( Huston, 1979 ; Michalet et al., 2006 ). (+/-species richness/+/-productivity).

(4) Resource ratio theory. Resource ratio theory argues that as the availability of any one resource R 1 increases, another resource R 2 is likely to become limiting; because different species are superior competitors for different resources, a balanced resource supply between R 1 and R 2 can help maintain species coexistence ( Tilman, 1982 ; Cardinale et al., 2009 ). (+species richness).

(5) Species-pool effect . Species pools are a set of plant species with each species of a community, local, or regional flora being a member of any community, local, or regional species pool, with different degrees of probability; species-pool effect is a contribution of species from a species pool to species richness in the community on a certain scale ( Zobel et al., 1998 ; Foster et al., 2004 ). (+species richness).

(6) Disturbances . Disturbances are some processes such as grazing, fire, severe windstorms, wave damage, land cover alterations, habitat fragmentation, and forest destruction, which often alters plant productivity and species richness, primarily via a negative or positive effect ( Hughes et al., 2007 ; Wu et al., 2019 ). (-/+species richness and productivity)

(7) Environmental heterogeneity . Environmental heterogeneity is locally diverse configurations in resource types with different availability levels along with more complex configurations in abiotic and biotic resources and more heterogeneities but environmental heterogeneity is the configurations of diverse habitats, i.e., habitat heterogeneity, on a landscape scale ( Amarasekare, 2003 ; Lasky et al., 2014 ). (+species richness).

(8) Density effects . Density effects are an ecological process resulting in species richness with increasing number of plant individuals in a plant community; plant density increases with increasing species richness also leads to high and low biomass production at low and high inter-specific and intra-specific competition levels, respectively ( Marquard et al., 2009 ). (+species richness/+/-productivity).

(9) Negative density dependence ( NDD ) . NDD is a process by which population growth rates decline at high densities as a result of natural enemies (e.g., predators, pathogens, or herbivores) and/or competition for space and resources to lead to the coexistence of species ( Yenni et al., 2012 ; LaManna et al., 2017a , LaManna et al., 2017b ). (+ species richness).

(10) Selective and complementary effects . Selection effect is the standard positive covariance effect, as a diverse community stochastically contains highly productive species ( Balvanera et al., 2006 ; Loreau et al., 2001 ); complementary effect refers to an effect caused by species`differentiation in resource use and/or inter-specific facilitation at higher levels of species richness ( Balvanera et al., 2006 ; Cardinale et al., 2007 ). (+productivity).

(11) Resource availability . Resource availability is relatively higher quantities of limited resources which ensures that weaker competitors are able to capture the limited resources for the maintenance of a population leading to the diversity and productivity of coexisting species ( Tilman, 1982 ; Cardinale et al., 2009 ). (+ productivity/+species richness).

* “+” or “-” represents positive or negative effect on species richness or productivity.

(2) Integrative models in the middle layer. The integrative models encompass the structural equation model, statistical dynamical model, and differential dynamical model within the framework. The structural equation model is a statistical method used to analyze the relationship between variables based on their covariance matrix. It enables the estimation, testing, and quantification of causality ( Grace et al., 2016 ; Chen et al., 2018 ). The structural equation model has been applied to various practical scenarios, including multi-dependent variable analysis, latent variable analysis, and intermediate variable analysis. It can be viewed as a combination of path analysis and confirmatory factor analysis. The positive or negative effects (i.e., contributions) of multiple processes on plant species richness and productivity in PRR are quantified integrally using the structural equation model. The quantified effects (standardized) are then assigned as coefficients of the integrated processes in the dynamical model to derive the shapes of PRR. This application of the structural equation model to the framework avoids the subjective assignment of coefficients for the process variables and enhances the practicality of the differential equation in the framework.

The statistical dynamical model is a type of dynamic model that describes the occurrence of random processes. It is often employed in meta-analysis and sampling analysis to identify the shapes of PRR ( Mittelbach et al., 2003 ; Liang et al., 2016b ). In the framework, statistical dynamical models can determine the occurrence ratios of different-shaped PRR. The differential dynamical model is a type of dynamic model used to describe the continuous change of dependent and independent variables regulated by multiple processes. Ecologists commonly establish such models to derive the shapes of PRR based on assumed parameter values of processes. These models further reveal how the shapes of PRR occur under the regulation of these processes and how they are linked with each other, i.e., underlying mechanisms ( Loreau, 1998 ; Wang, 2017 ; Wang et al., 2019 ). In the framework, the actual parameter values of processes from the analysis of structural equation models may be introduced to differential dynamical models for deriving the shapes of PRR which have been identified by statistical dynamic models. Therefore, the three types of models are related to each other.

(3) Integrative results in the bottom layer. As shown in Figure 1 , the integrative framework allows for the derivation of five typical shapes of the PRR by applying the three types of models discussed earlier. This approach differs from previous methods that relied on assumed coefficients to determine the shapes of PRR ( Loreau, 1998 ; Liang et al., 2016b ; Wang, 2017 ; Wang et al., 2019 ). When the positive effects of integrated processes dominate, the PRR shapes exhibit an upward trend. Conversely, when the negative effects of integrated processes dominate, the PRR shapes show a downward trend. When the positive and negative effects of integrated processes are approximately equal, the PRR shapes display a horizontal or fluctuating pattern. Finally, when the positive and negative effects of integrated processes successively dominate, the PRR shapes exhibit a humped pattern. This integrative framework effectively resolves the long-standing debate surrounding the shapes of PRR and their underlying mechanisms ( Schmid, 2002 ; Adler et al., 2011 ; Duffy et al., 2017 ).

The integrative framework provides an explanation for the occurrence of different shapes in the productivity-richness relationship observed in the real world, considering the effects of multiple variables. It can specifically demonstrate which processes are strong or weak, and whether they have a positive or negative effect, thereby determining the shapes of the PSRR and SRPR. In contrast, a meta-analysis or statistical dynamical approaches such as P=α(X)S B cannot achieve this level of understanding. While statistical dynamical models can be used to simply identify the shapes of SRPR ( Liang et al., 2016b ), the integrative framework allows for tracking the dynamics of the interactions among different processes that influence the shapes of PSRR and SRPR. For example, it can capture the dynamics of species-pool effects and inter-specific competition by utilizing differential equations, which offer greater flexibility in dealing with variable dynamics compared to statistical dynamical methods. Ecologists can identify the inflection points at which the shapes of PSRR and SRPR change from one pattern to another, and determine the corresponding processes or integrative processes responsible for these changes ( Wang et al., 2019 ). Consequently, the integrative framework provides a clearer understanding of the underlying mechanisms driving PSRR and SRPR, resolving key debates regarding the drivers of hump-shaped patterns and other patterns. By combining the strengths of structural equation models, statistical dynamical models, and differential dynamical models while avoiding their shortcomings, this framework presents a novel technology roadmap for deriving the shapes of PSRR and SRPR.

The integrative framework has broad applications in the study of diversity patterns, ecosystem functions and services, underlying mechanisms, and ecosystem management. Ecologists can start by conducting field vegetation investigations to collect data on productivity, species richness, and the processes influencing productivity and species richness in a particular research region, either through new data collection or using existing datasets. The interaction relationships among productivity, species richness, and influencing processes can then be analyzed using structural equation modeling, providing factor loadings and determinant coefficients through analysis. Subsequently, the field data can be used to identify the shapes of PSRR and SRPR using statistical dynamical models under specific conditions, thereby determining the shapes of PRR. The differential equation set for PSRR and SRPR can be established by utilizing the factor loadings as coefficients for the variables of productivity, species richness, and processes. Mathematical methods such as Fortran or Python can be employed to solve the equations and obtain solutions for each variable, including productivity, species richness, and processes. The dynamics of these variables can be modeled with changes in other variables such as disturbance and resource availability, and compared with the shapes identified by statistical dynamical models. The differential equations can be further refined to predict PSRR and SRPR for management purposes in similar regions. These methods are also applicable to purely theoretical research.

The following review includes two sections that utilize structural equation models and dynamical models (both statistical and differential) to analyze the integration of processes in PRR and explain the formation of PRR shapes. These sections serve to recapitulate the contributions of previous integration research on PRR while highlighting certain research limitations. These limitations align with the issues that the integration framework proposed in this review aims to address. As a result, these two sections provide valuable support for the proposed new integrative framework.

Integration analysis with structural equation models to quantify the roles of processes in PRR

Previous studies have recognized that individual processes or theories can only explain specific sections or dominant shapes of PRR, although they have contributed to the understanding of PRR ( Axmanová et al., 2012 ; Pierce, 2014 ). As the dominant shapes of PRR have been challenged by diverse patterns, some researchers have argued that PRR is variable, complex, and scale-dependent, influenced by numerous abiotic and biotic processes ( Grace et al., 2007 ; Willig, 2011 ). Consequently, ecologists have shifted their focus towards incorporating more processes to explain the shapes of PRR, utilizing structural equation models to integrate different processes within the bivariate relationship of plant richness and productivity ( Grace et al., 2014 , Grace et al., 2016 ). The structural equation model approach allows for the calculation of the role values of each process affecting species richness and productivity based on field investigations and meta-analyses of previous studies.

In one specific integration, Grace et al. (2014) established a causal network for the humped shape of PSRR, assuming the hump as the basic shape. Using a structural equation model, the corresponding processes influencing plant richness and productivity in the humped shapes were quantified. Surprisingly, this analysis did not support the assumed humped shape of PSRR but instead revealed alternative shapes and influencing processes. This study demonstrates how causal networks can be established through hypotheses and explicit tests to explain PSRR as an abstracting system, providing powerful predictions beyond bivariate analysis. Building upon this concept, further structural equation modeling was employed to integrate competing theories into a multi-process hypothesis and evaluate it using global data from 1,126 plots in grass-dominated sites ( Grace et al., 2016 ). The variables measured included plant species richness, productivity, total biomass, and various drivers such as soil fertility, climate, heterogeneity, soil suitability, and shading. In contrast to a bivariate species richness-productivity model, this modeling approach explained 61% of the variation in richness at the site and plot levels, quantifying the roles of different processes in regulating PSRR and SRPR ( Figure 2 ).

Figure 2 Roles of multiple processes in PRR quantified by a structural equation model. This figure illustrates the roles of multiple processes in PRR as quantified by a structural equation model. Solid arrows indicate positive effects, while dashed arrows represent negative effects. The digits alongside the lines indicate the magnitude of these effects. The lowercase letters represent the different plots for the data of collection. NS, no significance. Adapted from Grace et al. (2016) .

In another integration, field observations from 6,098 forest, shrubland, and grassland sites across China were collected to integrally quantify the first-type effects of climate, soils, and human impacts on soil organic carbon (SOC) storage, as well as the second-type effects mediated by species richness, above-ground net primary productivity (ANPP), and below-ground biomass (BB), using a structural equation model ( Chen et al., 2018 ). The analysis revealed a positive SRPR and a positive biomass-SOC relationship. Favorable climates (high temperature and precipitation) consistently had a negative effect on SOC storage but a positive effect on species richness, ANPP, and BB. The positive relationships between species richness and ANPP/BB offset the negative effect of favorable climate on SOC storage. Maintaining high levels of diversity can enhance soil carbon sequestration ( Chen et al., 2018 ). These results are supported by other local studies conducted in China and Canada ( Chen et al., 2018 ; Huang et al., 2018 ; Chen et al., 2020 ).

The aforementioned studies by Grace et al. (2014 , 2016) primarily focused on PRR influenced by abiotic processes, while the study by Chen et al. (2018) attempted to reveal the underlying mechanisms linking SOC storage with PRR. The results indicated that species richness had positive effects on productivity, biomass, and subsequently SOC storage, highlighting the regulation of PRR by diverse processes. Structural equation modeling represents a significant advancement in the analysis of PRR beyond two-dimensional variables of plant productivity and diversity. However, the data on species richness, productivity, and abiotic and biotic processes used in structural equation models are often collected simultaneously. Abiotic and biotic processes continuously vary and exhibit hysteresis in the regulation of PRR. In other words, the sampled abiotic and biotic processes, such as soil fertility, when plant richness and productivity are measured, will primarily affect plant richness and productivity in the future. Additionally, a single application of a structural equation model cannot identify the shapes of PRR. Therefore, it is necessary to consider dynamic processes when establishing a model network to assess the effects of processes on PRR. Nevertheless, the role values of different processes in regulating plant richness and productivity, quantified by structural equation models, can be used as coefficients for independent and dependent variables in dynamic models. The application of a structural equation model alone cannot derive or model the shapes of PRR or reveal underlying mechanisms. Instead, it encourages us to leverage its advantages in combination with other methods within the integrative framework.

Integration analysis with dynamical models to explain the shapes of PRR

In order to predict the variation of species richness in PRR and elucidate the underlying mechanisms, ecologists have previously developed integrative models such as the CSR strategy, non-equilibrium interaction model, multispecies patch-occupancy model, resource-ratio model, and modified neutral model ( Grime, 1974 ; Huston, 1979 ; Hastings, 1980 ; Tilman, 1982 ; Kadmon and Benjamini, 2001 ). These models, with their respective differences, aimed to understand the mechanisms of plant diversity and could be integrated to explain the humped shape of PRR, which was widely accepted by many ecologists at that time ( Figure 3A ). To explain the shapes of SRPR, integrative models were developed to characterize inter-specific competitive interactions among randomly chosen species and a spatially structured ecosystem competing for a limiting soil nutrient. These models were based on complementary effects, inter-specific facilitation, and selection effects, which provided an explanation for why species richness had positive effects on productivity ( Tilman et al., 1997 ; Loreau, 1998 ; Loreau et al., 2001 ; Figure 3B ). These theoretical approaches represented early integration analyses with dynamical models and significantly contributed to the understanding of the underlying mechanisms of SRPR.

Figure 3 Two dominant shapes of PSRR and SRPR in early integrative studies. (A) Humped shape of PSRR: The figure illustrates the humped shape of PSRR. In this shape, the plant community exists in a non-equilibrium state with multi-species patch occupancy along a gradient of resource availability and environmental severity. On the high environmental severity side, strong environmental stress or disturbance selects for stress-tolerant species adapted to such conditions, resulting in low species richness. Conversely, on the high resource availability side, strong competitive species dominate the competition for limiting resources, such as light, excluding other species that freely immigrate but are not adapted to such competitive habitats, leading to low species richness. Intermediate levels of stress or disturbance between the two sides favor both neutral and stress-tolerant species, and strong competitive species can also thrive with neutral species, allowing for the coexistence of multiple species and maintaining high richness. (B) Relationship among species richness, productivity, and resource-use intensity in SRPR: The figure depicts the relationship among species richness, productivity, and resource-use intensity in SRPR. An ecosystem with high species richness exhibits complementarity in resource use, leading to increased resource absorption by plants and higher productivity. At the same time, inter-specific competition is intense in the ecosystem. Additionally, as species richness increases, more productive and reciprocal species occur in the ecosystem, resulting in high productivity. This phenomenon is attributed to the selection effect and inter-specific facilitation, where more productive species are favored and occur in greater numbers as species richness increases.

However, these early integrative models were primarily designed to integrate the important processes suggested (or excluded) by researchers to explain (or support) the widely accepted shapes of PRR. While these studies made efforts to reveal the mechanisms of PRR, the focused integrative methods weakened the universality of the results regarding the diverse shapes of PRR. Recent integrative analyses using dynamical models have taken a different approach. On one hand, they have moved away from focused studies that only consider a few processes related to the dominant shapes of PRR, such as the effects of environmental heterogeneity, resource availability, plant density, trait variability, etc., to clarify the underlying mechanisms ( Hodapp et al., 2016 ; Wang, 2017 ; Hodapp et al., 2018 ; Wang et al., 2019 ). On the other hand, unlike early integration, these analyses have attempted to incorporate as many processes as possible that have been identified by ecologists as factors influencing plant richness and productivity ( Box 1 ). These integrative analyses focus on two types of methods: using statistical dynamic models to test the shapes of PRR observed in literature and field studies, and using differential dynamic models to integrate multiple processes in order to derive the shapes of PRR and analyze the underlying mechanisms.

Statistical dynamic model

To address the limitations of early integrative studies that focused only on dominant shapes of PRR, ecologists have employed statistical dynamic models. These models combine statistical and dynamic methods, originating from weather forecasting models, to test the occurrence ratios of different shapes of PRR in previous species-assembly experiments and field investigations ( Cardinale et al., 2007 ; Adler et al., 2011 ). One commonly used statistical dynamic model is meta-analysis, which analyzes study cases to determine the shapes of PRR as a function of various dynamic factors such as scales, investigation methods, plant taxa, grains, and regions ( Mittelbach et al., 2003 ; Gillman and Wright, 2006 ; Cardinale et al., 2007 ; Whittaker, 2010 ). Meta-analyses have indicated that, while there is still debate regarding the shapes of PRR, the humped shape is dominant for PSRR in all collected cases, with a relatively lower probability of occurrence for other shapes such as negative, U-shaped, and unrelated forms ( Mittelbach et al., 2003 ; Adler et al., 2011 ; Fraser et al., 2015 ; Figure 4A ). For SRPR, a positive or asymptotic shape is dominant compared to other shapes ( Cardinale et al., 2007 ; Duffy et al., 2017 ). It is evident that scales, investigation methods, and plant taxa influence these statistical results. However, meta-analysis fails to capture the changes in PRR and the relationships between different shapes of PPR, as it provides static results without considering the impact of plant productivity, diversity, or other processes affecting PRR. Nevertheless, statistical models are valuable tools for identifying and validating the shapes of PRR in previous study cases within the framework ( Figure 1 ).

Figure 4 Shapes of PSRR and SRPR based on multiple references cited in the text. (A) Statistical results of the shapes of PSRR observed in study cases at various scales, including local, landscape, regional, and continental to global scales. The shapes are represented by the following abbreviations: H (humped), P (positive), Ne (negative), U (U-shaped), and No (unrelated). (B) Sampling results of the shapes of SRPR based on the coefficient B representing the effect of tree diversity on forest productivity. Left: B values ranging from 0 to 1 correspond to positive and asymptotic shapes, while B ≤ 0 corresponds to level and negative shapes. Right: Dominance of different shapes based on the distribution of the sampling data. Tree diversity is represented by S , and productivity is represented by P . Adapted from Liang et al. (2016b) .

Another statistical dynamic model is the use of simple regression with empirical equations or direct regression analysis to demonstrate the different shapes of PRR based on field sampling results ( Axmanová et al., 2012 ; Steudel et al., 2012 ; Huang et al., 2018 ). In such models, the coefficient of species richness (independent variable) is utilized to determine the shapes of SRPR corresponding to the sampling results ( Figure 4B ). For instance, an empirical dynamical model P=α·f(X)·S B (P, productivity; X, environmental factors such as soil and climate; S, species richness; α, coefficient; B, the effects of species richness on productivity) was employed to quantify the dependence of productivity on species richness and measure the marginal productivity, which represents the change in productivity resulting from a one-unit decline in species richness, while accounting for climatic, soil, and plot-specific covariates ( Liang et al., 2016b ). When B > 1, the shape of SRPR is concave-down; when B = 1, the shape is positive; when 1 > B > 0, the shape is asymptotic; when B = 0, the shape is parallel (no effect); when B < 0, the shape is negative. Direct sampling data from various sources indicated that the average θ was 0.26, suggesting a predominantly positively asymptotic shape. Other forms occupied only a small percentage. A sampling study across the Amazon Basin, involving 90 one-hectare plots, also demonstrated the dominant positively asymptotic effect of taxonomic and evolutionary diversity on productivity, which was separated from environmental factors using generalized least-squares modeling ( De Souza et al., 2019 ). These field sampling results were consistent with meta-analyses of other ecologists’ studies, although meta-analysis represents a secondary form of sampling ( Hooper et al., 2005 ; Grace et al., 2007 ; Forrester & Bauhus, 2016 ; Duffy et al., 2017 ).

The statistical dynamical models based on field sampling are effective and straightforward approaches for identifying the shapes of PRR. Additionally, by utilizing a coefficient known as marginal productivity—the change in productivity resulting from a one-unit decline in species richness—the relationship between different shapes of PRR can be defined in a simple manner. However, these models have limited flexibility in considering variables other than productivity and species richness (represented by variable X). This limitation hinders the ability to reveal the interactions among these processes since X is often quantified using linear methods rather than non-linear ones ( Liang et al., 2016b ). In reality, the non-linear interactions of other processes significantly impact PRR, as demonstrated by earlier studies examining interactions among disturbance, competition, stress, resource availability, and more ( Grime, 1974 ; Huston, 1979 ; Hastings, 1980 ; Tilman, 1982 ). Unfortunately, the statistical dynamical models fail to adequately quantify these non-linear interactions of other processes, leading to increased errors in explaining the shapes of PRR.

Differential dynamical model

Some ecologists argue that PRR is governed by diverse and complex processes, and to clarify the shapes of PRR, it is necessary to assess the different effects of these processes on plant richness and productivity and simulate their interactions ( Willig, 2011 ; Grace et al., 2014 ; Wang, 2017 ). In line with this perspective, a set of differential equations, known as the PSRR model, was established based on the positive and/or negative effects of 21 widely accepted processes on plant productivity and species richness, as identified in the relevant literature ( Wang et al., 2019 ). These equations integrate the effects of these processes into a comprehensive measure of plant productivity, allowing for the derivation of the shapes of PSRR. Each process is assigned a different parameter value to represent its strength, and these parameter values can be adjusted to regulate the strengths of the processes. Plant richness is explicitly defined as a dependent variable, while plant productivity serves as an independent variable in the equations, quantifying the effects of plant productivity on species richness. Subsequently, the PSRR model is transformed into the SRPR model, which represents the feedback relationships to PSRR. In the SRPR model, plant productivity is determined as a dependent variable, and species richness as an independent variable. Using the PSRR model, the five typical shapes of PSRR, the dynamics of IICE ( Box 1 ), and the effects of the species pool on these shapes with increasing productivity were derived and verified using field data ( Wang et al., 2019 ; Figure 5 ). It was observed that the shapes of PSRR can change from one shape to another by altering the parameter values representing the strengths of the processes. Since the same set of parameters is used in the SRPR model, the diverse shapes of SRPR can also be derived. These derivations indicate that different strengths of processes acting on species richness and productivity give rise to different shapes of PSRR and SRPR. Specifically, when the integrated processes show a dominant positive effect, the shape of PSRR or SRPR is linear or asymptotic; when the integrated processes show a dominant negative effect, the shape of PSRR or SRPR is negative; and when the integrated processes successively show a dominant positive and negative effect, the shape of PSRR or SRPR is humped. These integrative methods can explain the documented PSRR and SRPR patterns observed in empirical studies conducted over several decades on various terrestrial, freshwater, and marine taxa from different regions of the world ( Mittelbach et al., 2003 ; Gillman & Wright, 2006 ; Whittaker, 2010 ; Grace et al., 2014 ; Liang et al., 2016b ; Fichtner et al., 2017 ). Furthermore, these results reveal the connections between the different shapes of PSRR and SRPR and the underlying processes ( Wang, 2017 ; Wang et al., 2019 ).

Figure 5 Typical Shapes of PSRR. (A1–E1) These curves represent the humped, asymptotic, positive, negative, and irregular shapes, respectively, derived by the PSRR model, which incorporates almost all processes affecting species richness. Adapted from Wang et al. (2019) . (A2–E2) These curves illustrate the dynamics of intra- and inter-specific competition effects (b) and the potential species-pool effect (Sp), which directly influence the shapes of PSRR. (A3–E3) These curves depict the observed species richness along a productivity gradient at a local plot across Germany, Czech Republic, Russia, USA, and Australia, respectively. The regression curves represent the results fitted based on these observed species richness and productivity. The fitted curves correspond to the outcomes obtained by fitting the observed data with the PSRR model. Notably, there was no significant difference between the fitted and observed species richness.

While the differential dynamical model offers a flexible solution for revealing the dynamical interactions of different processes affecting PRR and can elucidate the mechanisms underlying PRR, it is challenging to determine the coefficients of the numerous variables in the model. Moreover, the shapes of PRR derived or modeled using this non-linear differential model are generally diverse and require validation using field sampling data. Therefore, the structural equation model and statistical dynamical model can complement the limitations of the differential dynamical model within an integrative framework.

The recent integrative studies using statistical and differential dynamic models ( Cardinale et al., 2007 ; Liang et al., 2016a , b ; Wang et al., 2019 ) have improved the limited universality of results obtained by earlier studies that primarily integrated only a few processes to explain the accepted dominant shapes of PRR. The differential dynamical model provides insights into why and how the diverse PRR patterns discovered by statistical dynamical models based on meta-analysis and field sampling occur in the real world. Based on the differential dynamic model, it has been found that: (i) ecological processes that have a positive or negative effect on plant richness and productivity in PSRR and SRPR can vary temporally or spatially; (ii) processes that have a strongly positive effect at one productivity or richness level may have a weakly positive or negative effect at another level; and (iii) the integration of all positive and/or negative effects of processes, species richness, and plant productivity into a total effect (which continually changes but may be positive or negative) fundamentally determines the shapes of PSRR and SRPR ( Wang et al., 2019 ; Leclère et al., 2020 ). However, these integrative methods still require further improvement. Theoretically, integrative methods are based on the analysis of processes affecting plant richness and productivity to establish dynamical models of PRR ( Tilman et al., 1997 ; Loreau, 1998 ; Wang et al., 2019 ). The parameter values representing the effects of processes on PRR in dynamical models are often assumed and subjectively determined, although many derived PRR shapes have been validated by field data. Such an approach can influence the reliability of the derived PRR shapes. Therefore, within the framework of explaining PRR, we propose that the parameter values representing the effects of processes on PRR in the PRR dynamical models should be determined by quantifying the roles of different processes in the regulation of PPR in the field using a structural equation model ( Figure 1 ).

Conclusions

PRR has been a subject of extensive debate and research in ecology. Over time, research on PRR has progressed through distinct stages, including the identification of different PRR shapes, investigations of influencing processes, and integrative studies involving vegetation analysis, manipulation experiments, and theoretical analysis. The central focus of the debate has been on determining the dominant shapes of PRR and understanding the underlying mechanisms.

Recent integrative research, which involves analyzing and integrating the effects of respective processes influencing PRR, has revealed that the humped, asymptotic, positive, negative, and irregular shapes of PRR are interconnected. These shapes are not fixed, and one shape of PRR can transition into another. The balance between the positive and negative effects of different processes plays a crucial role in determining the various shapes of PRR. Furthermore, this balance leads to plant diversity having a globally positive effect on plant productivity and other ecosystem functions.

Respective and integrative research represent two types of methods employed to study the ecological processes influencing PRR. Respective research focuses on testing the effects of individual processes on PRR and uncovering the underlying mechanisms. Integrative research, on the other hand, examines the relative roles and interactions of processes in regulating PRR in real-world settings, as well as the relationships between different PRR shapes. PRR is considered a fundamental ecological issue that spans populations, communities, ecosystems, and landscapes. Ecologists have long been interested in PRR and the ecological processes that affect it, which has led to the development of various ecological theories.

Future studies on PRR should emphasize the relationships between metabolic rates related to resource availability and productivity, gene mutation rates, and increasing plant diversity, as these factors are evolutionarily significant. It is essential to identify the relative importance of each process and understand their interactions for the advancement of integrative studies. While significant progress has been made in understanding PRR, it is crucial for ecologists to carefully differentiate between the two types of PRR influenced by respective and integrative processes. Confusion between these types of PRR and different research methods can contribute to additional debates and challenges in the field.

Author contributions

ZW: Conceptualization, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. JA: Software, Validation, Writing – review & editing. TY: Data curation, Methodology, Software, Writing – review & editing. CZ: Data curation, Formal Analysis, Writing – original draft. AC: Conceptualization, Supervision, Validation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work was supported by the Fundamental Research Funds for the central Universities (No.300111230018; No. 300102292902).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Abrams P. A. (1995). Monotonic or unimodal diversity-productivity gradients: what does competition theory predict? Ecology 76, 2019–2027. doi: 10.2307/1941677

CrossRef Full Text | Google Scholar

Adler P. B., Seabloom E. W., Borer E. T., Hillebrand H., Hautier Y., Hector A., et al. (2011). Productivity is a poor predictor of plant species richness. Science 333, 1750–1752. doi: 10.1126/science.1204498

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen A. P., Brown J. H., Gillooly J. F. (2002). Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548. doi: 10.1126/science.1072380

Amarasekare P. (2003). Competitive coexistence in spatially structured environments: a synthesis. Ecol. Lett. 6, 1109–1122. doi: 10.1046/j.1461-0248.2003.00530.x

Axmanová I., Chytrý M., Zelený D., Li C. F., Vymazalová M., Danihelka J., et al. (2012). The species richness-productivity relationship in the herb layer of European deciduous forests. Global Ecol. Biogeography 21, 657–667. doi: 10.1111/j.1466-8238.2011.00707.x

Balvanera P., Pfisterer A. B., Buchmann N., He J. S., Raffaelli D., Schmid B. (2006). Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156. doi: 10.1111/j.1461-0248.2006.00963.x

Bezemer T. M., van der Putten W. H. (2007). Diversity and stability in plant communities. Nature 446, E6–E7. doi: 10.1038/nature05749

Cardinale B. J., Hillebrand H., Harpole W. S., Gross K., Ptacnik R. (2009). Separating the influence of resource “availability” from resource “imbalance” on productivity-diversity relationships. Ecol. Lett. 12, 475–487. doi: 10.1111/j.1461-0248.2009.01317.x

Cardinale B. J., Wright J. P., Cadotte M. W., Carroll I. T., Hector A., Srivastava D. S., et al. (2007). Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl. Acad. Sci. 104, 18123–18128. doi: 10.1073/pnas.0709069104

Chen Y., Huang Y. Y., Niklaus P. A., Castro-Izaguirre N., Clark A. T., Bruelheide H., et al. (2020). Directed species loss reduces community productivity in a subtropical forest biodiversity experiment. Nat. Ecol. Evol. 4, 550–559. doi: 10.1038/s41559-020-1127-4

Chen S. P., Wang W. T., Xu W. T., Wang Y., Wan H. W., Chen D. M., et al. (2018). Plant diversity enhances productivity and soil carbon storage. Proc. Natl. Acad. Sci. 115, 4027–4032. doi: 10.1073/pnas.1700298114

Chiarucci A., Viciani D., Winter C., Diekmann M. (2006). Effects of productivity on species area curves in herbaceous vegetation: evidence from experimental and observational data. Oikos 115, 475–483. doi: 10.1111/j.2006.0030-1299.15116.x

De Souza F. C., Dexter K. G., Phillips O. L., Pennington R. T., Neves D., Sullivan M. J. P., et al. (2019). Evolutionary diversity is associated with wood productivity in Amazonian forests. Nat. Ecol. Evol. 3, 1754–1761. doi: 10.1038/s41559-019-1007-y

Duffy J., Godwin C., Cardinale B. (2017). Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264. doi: 10.1038/nature23886

Fichtner A., Härdtle W., Li Y., Bruelheide H., Kunz M., von Oheimb G. (2017). From competition to facilitation: how tree species respond to neighborhood diversity. Ecol. Lett. 20, 892–900. doi: 10.1111/ele.12786

Forrester D. I., Bauhus B. J. (2016). A review of processes behind diversity-productivity relationships in Forests. Curr. Forestry Rep. 2, 45–61. doi: 10.1007/s40725-016-0031-2

Foster B. L., Dickson T. L., Murphy C. A., Karel I. S., Smith V. H. (2004). Propagule pools mediate community assembly and diversity-ecosystem regulation along a grassland productivity gradient. J. Ecol. 92, 435–449. doi: 10.1111/j.0022-0477.2004.00882.x

Fraser L. H., Pither J., Jentsch A., Sternberg M., Zobel M., Skarizadeh D., et al. (2015). Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 349, 302–305. doi: 10.1126/science.aab3916

Gillman L. N., Wright S. D. (2006). The influence of productivity on the species richness of plants: a critical assessment. Ecology 87, 1234–1243. doi: 10.1890/0012-9658(2006)87[1234:TIOPOT]2.0.CO;2

Goldberg D. E., Miller T. E. (1990). Effects of different resource additions on species diversity in an annual plant community. Ecology 71, 213–225. doi: 10.2307/1940261

Grace J. B., Adler P. B., Harpole W. S., Borer E. T., Seabloom E. W. (2014). Causal networks clarify productivity-richness interrelations, bivariate plots do not. Funct. Ecol. 28, 787–798. doi: 10.1111/1365-2435.12269

Grace J. B., Anderson T. M., Seabloom E. W., Borer E. T., Adler P. B., Harpole W. S., et al. (2016). Integrative modeling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393. doi: 10.1038/nature16524

Grace J. B., Anderson T. M., Smith M. D., Seabloom E., Andelman S. J., Meche G., et al. (2007). Does species diversity limit productivity in natural grassland communities? Ecol. Lett. 10, 680–689. doi: 10.1111/j.1461-0248.2007.01058x

Grime J. P. (1974). Vegetation classification by reference to strategies. Nature 250, 26–31. doi: 10.1038/250026a0

Grossman J. J., Cavender-Bares J., Hobbie S. E., Reich P. B., Montgomery R. A. (2017). Species richness and traits predict overyielding in stem growth in an early-successional tree diversity experiment. Ecology 98, 2601–2614. doi: 10.1002/ecy.1958

Hastings A. (1980). Disturbance, coexistence, history, and competition for space. Theor. Population Biol. 18, 363–373. doi: 10.1016/0040-5809(80)90059-3

Hector A., Schmid B., Beierkuhnlein C., Caldeira M. C., Diemer M., Dimitrakopoulos P. G., et al. (1999). Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127. doi: 10.1126/science.286.5442.1123

Hodapp D., Borer E. T., Harpole W. S., Lind E. M., Seabloom E. W., Adler P. B., et al. (2018). Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization. Ecol. Lett. 21, 1364–1371. doi: 10.1111/ele.13102

Hodapp D., Hillebrand H., Blasius B., Ryabov A. B. (2016). Environmental and trait variability constrain community structure and the biodiversity-productivity relationship. Ecology 97, 1463–1474. doi: 10.1890/15-0730.1

Hooper D. U., Chapin F. S. III, Ewel J. J., Hector A., Inchausti P., Lavorel S., et al. (2005). Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35. doi: 10.1890/04-0922

Huang Y. Y., Chen Y. X., Castro-Izaguirre N., Baruffol M., Brezzi M., Lang A., et al. (2018). Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83. doi: 10.1126/science.aat6405

Hughes A. R., Byrnes J. E., Kimbro D. L., Stachowicz J. J. (2007). Reciprocal relationships and potential feedbacks between biodiversity and disturbance. Ecol. Lett. 10, 849–864. doi: 10.1111/j.1461-0248.2007.01075.x

Humborg C., Ittekkot V., Cociasu A., Bodungen B. V. (1997). Effect of Danube River dam on Black Sea biogeochemistry and ecosystem structure. Nature 386, 385–388. doi: 10.1038/386385a0

Huston M. A. (1979). A general hypothesis of species diversity. Am. Nat. 113, 81–101. doi: 10.1086/283366

Huston M. A., DeAngelis D. L. (1994). Competition and coexistence: the effects of resource transport and supply rates. Am. Nat. 144, 954–977. doi: 10.1086/285720

Kadmon R., Benjamini Y. (2001). Effects of productivity and disturbance on species richness: a neutral model. Am. Nat. 167, 939–946. doi: 10.1086/504602

Laforest-Lapointe I., Paquette A., Messier C., Kembel S. W. (2017). Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147. doi: 10.1038/nature22399

LaManna J. A., Belote R. T., Burkle L. A., Catano C. P., Myers J. A. (2017a). Negative density dependence mediates biodiversity-productivity relationships across scales. Nat. Ecol. Evol. 1, 1107–1115. doi: 10.1038/s41559-017-0225-4

LaManna J. A., Mangan S. A., Alonso A., Bourg N. A., Brockelman W. Y., Bunyavejchewin S., et al. (2017b). Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392. doi: 10.1126/science.aar3824

Lasky J. R., Uriarte M., Boukili V. K., Erickson D. L., Kress W. J., Chazdon R. L. (2014). The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession. Ecol. Lett. 17, 1158–1167. doi: 10.1111/ele.12322

Leclère D., Obersteiner M., Barrett M., Butchart S. H. M., Chaudhary A., Palma A. D., et al. (2020). Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556. doi: 10.1038/s41586-020-2705-y

Liang J. J., Crowther T. W., Picard N., Wiser S., Zhou M., Alberti G., et al. (2016b). Positive biodiversity-productivity relationship predominant in global forests. Science 354, 1–12. doi: 10.1126/science.aaf8957

Liang J. J., Watson J. V., Zhou M., Lei X. (2016a). Effects of productivity on biodiversity in forest ecosystems across the United States and China. Conserv. Biol. 30, 308–317. doi: 10.1111/cobi.12636

Loreau M. (1998). Biodiversity and ecosystem functioning: A mechanistic model. Proc. Natl. Acad. Sci. 95, 5632–5636. doi: 10.1073/pnas.95.10.5632

Loreau M., Naeem S., Inchasusti P., Bengtsson J., Grime J. P., Hector A., et al. (2001). Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808. doi: 10.1126/science.1064088

Marquard E., Weigelt A., Roscher C., Gubsch M., Lipowsky A., Schmid B. (2009). Positive biodiversity–productivity relationship due to increased plant density. J. Ecol. 97, 696–704. doi: 10.1111/j.1365-2745.2009.01521.x

Michalet R., Brooker R. W., Cavieres L. A., Kikvidze Z., Lortie C. J., Pugnaire F. I., et al. (2006). Do biotic interactions shape both sides of the humped-back model of species richness in plant communities? Ecol. Lett. 9, 767–773. doi: 10.1111/j.1461-0248.2006.00935.x

Mittelbach G. G., Steiner C. F., Scheiner S. M., Steiner C. H. (2003). What is the observed relationship between species richness and productivity? Reply. Ecol. 84, 3390–3395. doi: 10.1890/03-3077

Pierce S. (2014). Implications for biodiversity conservation of the lack of consensus regarding the humped-back model of species richness and biomass production. Funct. Ecol. 28, 253–257. doi: 10.1111/1365-2435.12147

Schmid B. (2002). The species richness–productivity controversy. Trends Ecol. Evol. 17, 113–114. doi: 10.1016/S0169-5347(01)02422-3

Stegen J. C., Enquist B. J., Ferriere R. (2009). Advancing the metabolic theory of biodiversity. Ecol. Lett. 12, 1001–1015. doi: 10.1111/j.1461-0248.2009.01358.x

Steudel B., Hector A., Friedl T., Löffler C., Lorenz M., Wesche M., et al. (2012). Biodiversity effects on ecosystem functioning change along environmental stress gradients. Ecol. Lett. 15, 1397–1405. doi: 10.1111/j.1461-0248.2012.01863.x

Stevens M. H. H., Carson W. P. (1999). Plant density determines species richness along experimental gradients. Ecology 80, 455–465. doi: 10.1890/0012-9658(1999)080[0455:PDDSRA]2.0.CO;2

Sugden A. M. (2018). Tree diversity improves forest productivity. Science 362, 41–43. doi: 10.1126/science.2018.362.6410.twis

Tilman D. (1982). Resource competition and community structure (New Jersey, USA: Princeton University Press). doi: 10.1515/9780691209654

Tilman D., Lehman C. L., Thomson K. T. (1997). Plant diversity and ecosystem productivity: Theoretical considerations. Proc. Natl. Acad. Sci. 94, 1857–1861. doi: 10.1073/pnas.94.5.1857

Tilman D., Reich P. B., Knops J., Wedin D., Mielke T., Lehman C. (2001). Diversity and productivity in a long-term grassland experiment. Science 294, 843–845. doi: 10.1126/science.1060391

Wang Z. H. (2017). Process strengths determine the forms of the relationship between plant species richness and primary productivity. PloS One 12, e0185884. doi: 10.1371/journal.pone.0185884

Wang Z. H., Chiarucci A., Arratia F. J. (2019). Integrative models explain the relationships between species richness and productivity in plant communities. Sci. Rep. 9, 13730. doi: 10.1038/s41598-019-50016-3

Whittaker R. J. (2010). Meta-analyses and mega-mistakes: calling time on meta-analysis of the species richness-productivity relationship. Ecology 91, 2522–2533. doi: 10.1890/08-0968.1

Whittaker R. J., Heegaard E. (2003). What is the observed relationship between species richness and productivity? Comment. Ecol. 84, 3384–3390. doi: 10.1890/02-3128

Willig M. R. (2011). Biodiversity and productivity. Science 333, 1709–1710. doi: 10.1126/science.1212453

Wu J. H., Li M., Sebastian F., Ma W. L., Wang X. T., Zhang X. Z., et al. (2019). Impacts of grazing exclusion on productivity partitioning along regional plant diversity and climatic gradients in Tibetan alpine grasslands. J. Environ. Manage. 231, 635–645. doi: 10.1016/j.jenvman.2018.10.097

Yenni G., Adler P. B., Ernest S. K. M. (2012). Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461. doi: 10.1890/11-1087.1

Zobel M., van der Maarel E., Dupré C. (1998). Species pool: the concept, its determination and significance for community restoration. Appl. Vegetation Sci. 1, 55–66. doi: 10.2307/1479085

Keywords: plant diversity, productivity, dynamical models, structural equation model, ecological processes, ecosystem functions, integrative research

Citation: Wang Z, Arratia J, Yan T, Zhang C and Chiarucci A (2024) Integrative framework of multiple processes to explain plant productivity–richness relationships. Front. Ecol. Evol. 12:1332985. doi: 10.3389/fevo.2024.1332985

Received: 04 November 2023; Accepted: 15 March 2024; Published: 04 April 2024.

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Copyright © 2024 Wang, Arratia, Yan, Zhang and Chiarucci. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhenhong Wang, [email protected]

This article is part of the Research Topic

Insect Conservation Behavior