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Quantitative study designs: Case Studies/ Case Report/ Case Series

Quantitative study designs.

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Case Study / Case Report / Case Series

Some famous examples of case studies are John Martin Marlow’s case study on Phineas Gage (the man who had a railway spike through his head) and Sigmund Freud’s case studies, Little Hans and The Rat Man. Case studies are widely used in psychology to provide insight into unusual conditions.

A case study, also known as a case report, is an in depth or intensive study of a single individual or specific group, while a case series is a grouping of similar case studies / case reports together.

A case study / case report can be used in the following instances:

• where there is atypical or abnormal behaviour or development
• an unexplained outcome to treatment
• an emerging disease or condition

The stages of a Case Study / Case Report / Case Series

Which clinical questions does Case Study / Case Report / Case Series best answer?

Emerging conditions, adverse reactions to treatments, atypical / abnormal behaviour, new programs or methods of treatment – all of these can be answered with case studies /case reports / case series. They are generally descriptive studies based on qualitative data e.g. observations, interviews, questionnaires, diaries, personal notes or clinical notes.

What are the advantages and disadvantages to consider when using Case Studies/ Case Reports and Case Series ?

What are the pitfalls to look for.

One pitfall that has occurred in some case studies is where two common conditions/treatments have been linked together with no comprehensive data backing up the conclusion. A hypothetical example could be where high rates of the common cold were associated with suicide when the cohort also suffered from depression.

Critical appraisal tools 

To assist with critically appraising Case studies / Case reports / Case series there are some tools / checklists you can use.

JBI Critical Appraisal Checklist for Case Series

JBI Critical Appraisal Checklist for Case Reports

Real World Examples

Some Psychology case study / case report / case series examples

Capp, G. (2015). Our community, our schools : A case study of program design for school-based mental health services. Children & Schools, 37(4), 241–248. A pilot program to improve school based mental health services was instigated in one elementary school and one middle / high school. The case study followed the program from development through to implementation, documenting each step of the process.

Cowdrey, F. A. & Walz, L. (2015). Exposure therapy for fear of spiders in an adult with learning disabilities: A case report. British Journal of Learning Disabilities, 43(1), 75–82. One person was studied who had completed a pre- intervention and post- intervention questionnaire. From the results of this data the exposure therapy intervention was found to be effective in reducing the phobia. This case report highlighted a therapy that could be used to assist people with learning disabilities who also suffered from phobias.

Li, H. X., He, L., Zhang, C. C., Eisinger, R., Pan, Y. X., Wang, T., . . . Li, D. Y. (2019). Deep brain stimulation in post‐traumatic dystonia: A case series study. CNS Neuroscience & Therapeutics. 1-8. Five patients were included in the case series, all with the same condition. They all received deep brain stimulation but not in the same area of the brain. Baseline and last follow up visit were assessed with the same rating scale.

References and Further Reading  

Greenhalgh, T. (2014). How to read a paper: the basics of evidence-based medicine. (5th ed.). New York: Wiley.

Heale, R. & Twycross, A. (2018). What is a case study? Evidence Based Nursing, 21(1), 7-8.

Himmelfarb Health Sciences Library. (2019). Study design 101: case report. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casereports.cfm

Hoffmann T., Bennett S., Mar C. D. (2017). Evidence-based practice across the health professions. Chatswood, NSW: Elsevier.

Robinson, O. C., & McAdams, D. P. (2015). Four functional roles for case studies in emerging adulthood research. Emerging Adulthood, 3(6), 413-420.

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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This chapter reviews the strengths and limitations of case study as a research method in social sciences. It provides an account of an evidence base to justify why a case study is best suitable for some research questions and why not for some other research questions. Case study designing around the research context, defining the structure and modality, conducting the study, collecting the data through triangulation mode, analysing the data, and interpreting the data and theory building at the end give a holistic view of it. In addition, the chapter also focuses on the types of case study and when and where to use case study as a research method in social science research.

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Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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

Qualitative vs. Quantitative Research | Differences, Examples & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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• Published: 01 April 2024

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

• Imran Raza 1 ,
• Muhammad Hasan Jamal 1 ,
• Rizwan Qureshi 1 ,
• Abdul Karim Shahid 1 ,
• Angel Olider Rojas Vistorte 2 , 3 , 4 ,
• Md Abdus Samad 5 &
• Imran Ashraf 5  

Scientific Reports volume  14 , Article number:  7635 ( 2024 ) Cite this article

158 Accesses

Metrics details

• Computational biology and bioinformatics
• Machine learning

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

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Introduction

The advancement of technology has facilitated the accumulation of vast amounts of data from various sources such as databases, web repositories, and files, necessitating robust tools for analysis and decision-making 1 , 2 . Data mining, employing techniques such as support vector machine (SVM), decision trees, neural networks, clustering, fuzzy logic, and genetic algorithms, plays a pivotal role in extracting information and uncovering hidden patterns within the data 3 , 4 . However, the complexity of the data landscape, characterized by high dimensionality, heterogeneity, and non-traditional structures, renders the data mining process inherently challenging 5 , 6 . To tackle these challenges effectively, a combination of complementary and cooperative intelligent techniques, including SVM, fuzzy logic, probabilistic reasoning, genetic algorithms, and neural networks, has been advocated 7 , 8 .

Hybrid intelligent systems, amalgamating various intelligent techniques, have emerged as a promising approach to enhance the efficacy of data mining. Adaptive neuro-fuzzy inference systems (ANFIS) have laid the groundwork for intelligent systems in data mining techniques, providing a foundation for exploring complex data relationships 7 , 8 . Moreover, the theory of rough sets has found practical application in tasks such as attribute selection, data reduction, decision rule generation, and pattern extraction, contributing to the development of intelligent systems for knowledge discovery 7 , 8 . Extracting meaningful knowledge from hybrid data, which encompasses both categorical and numerical data, presents a significant challenge. Two predominant strategies have emerged to address this challenge 9 , 10 . The first strategy involves employing numerical data processing techniques such as Principal Component Analysis (PCA) 11 , 12 , Neural Networks 13 , 14 , 15 , 16 , and SVM 17 . However, this approach necessitates converting categorical data into numerical equivalents, leading to a loss of contextual meaning 18 , 19 . The second strategy leverages rough set theory alongside methods tailored for categorical data. Nonetheless, applying rough set theory to numerical data requires a discretization process, resulting in information loss 20 , 21 . Numerous hybrid data processing methods have been proposed, combining rough sets and fuzzy sets to handle uncertainty 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 . However, selecting an appropriate rough set model for a given dataset necessitates exploring the inherent relationships among existing models, presenting a challenge for users. The selection and utilization of an appropriate model in data mining thus demand qualitative and quantitative comparisons of existing hybrid data processing models.

This research endeavors to present a comprehensive analysis of hybrid data processing models, with a specific focus on those rooted in neighborhood rough sets (NRS). By investigating the inherent interconnections among these models, this study aims to elucidate their complex dynamics. To address the challenges posed by hybrid data, a novel hybrid model founded on NRS is introduced. This model enhances the efficiency of the data mining process without discretization mitigating information loss and ambiguity in data interpretation. Notably, the adaptability of the proposed model, particularly in adjusting the threshold value governing the neighborhood approximation space, ensures optimal performance aligned with dataset characteristics while maintaining high accuracy. A dedicated testbed tailored for Parkinson’s patients is developed to evaluate the real-world effectiveness of the proposed approach. Furthermore, a rigorous evaluation of the proposed model is conducted, encompassing both accuracy and overall effectiveness. Encouragingly, the results demonstrate that the proposed scheme surpasses alternative approaches, adeptly managing both numerical and categorical data through an adaptive framework.

The major contributions, listed below, collectively emphasize the innovative hybrid data processing model, the adaptive nature of its thresholding mechanism, and the empirical validation using a Parkinson’s patient testbed, underscoring the relevance and significance of the study’s findings.

Novel Hybrid Data Processing Model: This research introduces a novel hybrid data processing model based on NRS, preserving the practical meaning of both numerical and categorical data types. Unlike conventional methods, it minimizes information loss while optimizing interpretability. The proposed distance function combines Euclidean and Levenshtein distances with weighted calculations and dynamic selection mechanisms to enhance accuracy and realism in neighborhood approximation spaces.

Adaptive Thresholding Mechanism: Another key contribution is the integration of an adaptive thresholding mechanism within the hybrid model. This feature dynamically adjusts the threshold value based on dataset characteristics, ensuring optimal performance and yielding more accurate and contextually relevant results.

Empirical Validation through Parkinson’s Testbed: This research provides a dedicated testbed for analyzing behavioral data from Parkinson’s patients, allowing rigorous evaluation of the proposed hybrid data processing model. Utilizing real-world datasets enhances the model’s practical applicability and advances knowledge in medical data analysis and diagnosis.

The subsequent structure of the paper unfolds as follows: section “ Related work ” delves into the related work. The proposed model is introduced in section “ Adaptive neighborhood rough set model ”, Section “ Instrumentation ” underscores the instrumentation aspect, section “ Result and discussion ” unfolds the presentation of results and ensuing discussions, while section “ Conclusion and future work ” provides the concluding remarks for the paper. A list of notations used in this study is provided in Table  1 .

Related work

Rough set-based approaches have been utilized in various applications like bankruptcy prediction 42 , attribute/feature subset selection 43 , 44 , cancer prediction 45 , 46 , etc. In addition, recently, several innovative hybrid models have emerged, blending the realms of fuzzy logic and non-randomized systems (NRSs). One such development is presented by Yin et al. 47 , who introduce a parameterized hybrid fuzzy similarity relation. They apply this relation to the task of granulating multilabel data, subsequently extending it to the domain of multilabel learning. To construct a noise-tolerant multilabel fuzzy NRS model (NT-MLFNRS), they leverage the inclusion relationship between fuzzy neighborhood granules and fuzzy decisions. Building upon NT-MLFNRS, Yin et al. also devise a noise-resistant heuristic multilabel feature selection (NRFSFN) algorithm. To further enhance the efficiency of feature selection and address the complexities associated with handling large-scale multilabel datasets, they culminate their efforts by introducing an efficient extended version of NRFSFN known as ENFSFN.

Sang et al. 48 explore incremental feature selection methodologies, introducing a novel conditional entropy metric tailored for dynamic ordered data robustness. Their approach introduces the concept of a fuzzy dominance neighborhood rough set (FDNRS) and defines a conditional entropy metric with robustness, leveraging the FDNRS model. This metric serves as an evaluation criterion for features, and it is integrated into a heuristic feature selection algorithm. The resulting incremental feature selection algorithm is built upon this innovative model

Wang et al. 19 introduced the Fuzzy Rough Iterative Computational (FRIC) model, addressing challenges in hybrid information systems (HIS). Their framework includes a specialized distance function for object sets, enhancing object differentiation precision within HIS. Utilizing this function, they establish fuzzy symmetric relations among objects to formulate fuzzy rough approximations. Additionally, they introduce evaluation functions like fuzzy positive regions, dependency functions, and attribute importance functions to assess classification capabilities of attribute sets. They developed an attribute reduction algorithm tailored for hybrid data based on FRIC principles. This work contributes significantly to HIS analysis, providing a robust framework for data classification and feature selection in complex hybrid information systems.

Xu et al. 49 introduced a novel Fitting Fuzzy Rough Set (FRS) model enriched with relative dependency complement mutual information. This model addresses challenges related to data distribution and precision enhancement of fuzzy information granules. They utilized relative distance to mitigate the influence of data distribution on fuzzy similarity relationships and introduced a fitting fuzzy neighborhood radius optimized for enhancing the precision of fuzzy information granules. Within this model, the authors conducted a comprehensive analysis of information uncertainty, introducing definitions of relative complement information entropy and formulating a multiview uncertainty measure based on relative dependency complement mutual information. This work significantly advances our understanding of managing information uncertainty within FRS models, making a valuable contribution to computational modeling and data analysis.

Jiang et al. 50 presented an innovative approach for multiattribute decision-making (MADM) rooted in PROMETHEE II methodologies. Building upon the NRS model, they introduce two additional variants of covering-based variable precision fuzzy rough sets (CVPFRSs) by applying fuzzy logical operators, specifically type-I CVPFRSs and type-II CVPFRSs. In the context of MADM, their method entails the selection of medicines using an algorithm that leverages the identified features.

Qu et al. 51 introduced the concept of Adaptive Neighborhood Rough Sets (ANRSs), aiming for effective integration of feature separation and linkage with classification. They utilize the mRMR-based Feature Selection Algorithm (FSRMI), demonstrating outstanding performance across various selected datasets. However, it’s worth noting that FSRMI may not consistently outperform other algorithms on all datasets.

Xu et al. 52 introduced the Fuzzy Neighborhood Joint Entropy Model (FNSIJE) for feature selection, leveraging fuzzy neighborhood self-information measures and joint entropy to capture combined feature information. FNSIJE comprehensively analyzes the neighborhood decision system, considering noise, uncertainty, and ambiguity. To improve classification performance, the authors devised a new forward search method. Experimental results demonstrated the effectiveness of FNSIJE-KS, efficiently selecting fewer features for both low-dimensional UCI datasets and high-dimensional gene datasets while maintaining optimal classification performance. This approach advances feature selection techniques in machine learning and data analysis.

In 53 , the authors introduced a novel multi-label feature selection method utilizing fuzzy NRS to optimize classification performance in multi-label fuzzy neighborhood decision systems. By combining the NRS and FRS models a Multi-Label Fuzzy NRS model is introduced. They devised a fuzzy neighborhood approximation accuracy metric and crafted a hybrid metric integrating fuzzy neighborhood approximate accuracy with fuzzy neighborhood conditional entropy for attribute importance evaluation. Rigorous evaluation of their methods across ten diverse multi-label datasets showcased significant progress in multi-label feature selection techniques, promising enhanced classification performance in complex multi-label scenarios.

Sanget et al. 54 introduced the Fuzzy Dominance Neighborhood Rough Set (NRS) model for Interval-Valued Ordered Decision Systems (IvODS), along with a robust conditional entropy measure to assess monotonic consistency within IvODS. They also presented two incremental feature selection algorithms. Experimental results on nine publicly available datasets showcased the robustness of their proposed metric and the effectiveness and efficiency of the incremental algorithms, particularly in dynamic IvODS updates. This research significantly advances the application of fuzzy dominance NRS models in IvODS scenarios, providing valuable insights for data analysis and decision-making processes.

Zheng et al. 55 generalized the FRSs using axiomatic and constructive approaches. A pair of dual generalized fuzzy approximation operators is defined using arbitrary fuzzy relation in the constructive approach. Different classes of FRSs are characterized using different sets of axioms. The postulates governing fuzzy approximation operators ensure the presence of specific categories of fuzzy relations yielding identical operators. Using a generalized FRS model, Hu et al. 18 introduced an efficient algorithm for hybrid attribute reduction based on fuzzy relations constructing a forward greedy algorithm for hybrid attribute reduction resulting in optimal classification performance with lesser selected features and higher accuracy. Considering the similarity between two objects, Wang et al. 36 redefine fuzzy upper and lower approximations. The existing concepts of knowledge reduction are extending fuzzy environment resulting in a heuristic algorithm to learn fuzzy rules.

Gogoi et al. 56 use rough set theory for generating decision rules from inconsistent data. The proposed scheme uses indiscernibility relation to find inconsistencies in the data generating minimized and non-redundant rules using lower and upper approximations. The proposed scheme is based on the LEM2 algorithm 57 which performs the local covering option for generating minimum and non-redundant sets of classification rules and does not consider the global covering. The scheme is evaluated on a variety of data sets from the UCI Machine Learning Repository. All these data sets are either categorical or numerical having variable feature spaces. The proposed scheme performs consistently better for categorical data sets, as it is designed to handle inconsistencies in the data having at least one inconsistency. Results show that the proposed scheme generates minimized rule without reducing the feature space unlike other schemes, which compromise the feature space.

In 58 , the authors introduced a novel NRS model to address attribute reduction in noisy systems with heterogeneous attributes. This model extends traditional NRS by incorporating tolerance neighborhood relation and probabilistic theory, resulting in more comprehensive information granules. It evaluates the significance of heterogeneous attributes by considering neighborhood dependency and aims to maximize classification consistency within selected feature spaces. The feature space reduction algorithm employs an incremental approach, adding features while preserving maximal dependency in each round and halting when a new feature no longer increases dependency. This approach selects fewer features than other methods while achieving significantly improved classification performance, demonstrating its effectiveness in attribute reduction for noisy systems.

Zhu et al. 59 propose a fault tolerance scheme combining kernel method, NRS, and statistical features to adaptively select sensitive features. They employ a Gaussian kernel function with NRS to map fault data to a high-dimensional space. Their feature selection algorithm utilizes the hyper-sphere radius in high-dimensional feature space as the neighborhood value, selecting features based on significance measure regardless of the classification algorithm. A wrapper deploys a classification algorithm to evaluate selected features, choosing a subset for optimal classification. Experimental results demonstrate precise determination of the neighborhood value by mapping data into a high-dimensional space using the kernel function and hyper-sphere radius. This methodology proficiently selects sensitive fault features, diagnoses fault types, and identifies fault degrees in rolling bearing datasets.

A neighborhood covering a rough set model for the fuzziness of decision systems is proposed that solves the problem of hybrid decision systems having both fuzzy and numerical attributes 60 . The fuzzy neighborhood relation measures the indiscernibility relation and approximates the universe space using information granules, which deal with fuzzy attributes directly. The experimental results evaluate the influence of neighborhood operator size on the accuracy and attribute reduction of fuzzy neighborhood rough sets. The attribute reduction increases with the increase in the threshold size. A feature will not distinguish any samples and cannot reduce attributes if the neighborhood operator exceeds a certain value.

Hou et al. 61 applied NRS reduction techniques to cancer molecular classification, focusing on gene expression profiles. Their method introduced a novel perspective by using gene occurrence probability in selected gene subsets to indicate tumor classification efficacy. Unlike traditional methods, it integrated both Filters and Wrappers, enhancing classification performance while being computationally efficient. Additionally, they developed an ensemble classifier to improve accuracy and stability without overfitting. Experimental results showed the method achieved high prediction accuracy, identified potential cancer biomarkers, and demonstrated stability in performance.

Table  2 gives a comparison of existing rough set-based schemes for quantitative and qualitative analysis. The comparative parameters include handling hybrid data, generalized NRS, attribute reduction, classification, and accuracy rate. Most of the existing schemes do not handle hybrid data sets without discretization resulting in information loss and a lack of practical meanings. Another parameter to evaluate the effectiveness of the existing scheme is the ability to adapt the threshold value according to the given data sets. Most of the schemes do not adapt threshold values for neighborhood approximation space resulting in variable accuracy rates for different datasets. The end-user has to adjust the value of the threshold for different datasets without understanding its impact in terms of overfitting. Selecting a large threshold value will result in more global rules resulting in poor accuracy. There needs to be a mechanism to adaptively choose the value of the threshold considering both the global and local information without compromising on the accuracy rate. The schemes are also evaluated for their ability to attribute reduction using NRS. This can greatly improve processing time and accuracy by not considering insignificant attributes. The comparative analysis shows that most of the NRS-based existing schemes perform better than many other well-known schemes in terms of accuracy. Most of these schemes have a higher accuracy rate than CART, C4.5, and k NN. This makes the NRS-based schemes a choice for attribute reduction and classification.

Adaptive neighborhood rough set model

The detailed analysis of existing techniques highlights the need for a generalized NRS-based classification technique to handle both categorical and numerical data. The proposed NRS-based techniques not only handle the hybrid information granules but also dynamically select the threshold \(\delta \) producing optimal results with a high accuracy rate. The proposed scheme considers a hybrid tuple \(HIS=\langle U_h,\ Q_h,\ V,\ f \rangle \) , where \(U_h\) is nonempty set of hybrid records \(\{x_{h1},\ x_{h2},\ x_{h3},\ \ldots ,\ x_{hn}\}\) , \(Q_h=\left\{ q_{h1},\ q_{h2},\ \ q_{h3},\ \ldots \,\ q_{hn}\right\} \) is the non-empty set of hybrid features. \( V_{q_h}\) is the domain of attribute \(q_h\) and \(V=\ \cup _{q_h\in Q_h}V_{q_h}\) , and \(f=U_h\ x\ Q_h\rightarrow V\) is a total function such \(f\left( x_h,q_h\right) \in V_{q_h}\) for each \(q_h\in Q_h, x_h\in U_h\) , called information function. \(\langle U_h,\ Q_h,\ V,\ f\rangle \) is also known as a decision table if \(Q_h=C_h\cup D\) , where \(C_h\) is the set of hybrid condition attributes and D is the decision attribute.

A neighborhood relation N is calculated using this set of hybrid samples \(U_h\) creating the neighborhood approximation space \(\langle U_h,\ N\rangle \) which contains information granules \( \left\{ \delta ({x_h}_i)\big |{x_h}_i\in U_h\right\} \) based on some distance function \(\Delta \) . For an arbitrary sample \({x_h}_i\in U_h\) and \(B \subseteq C_h\) , the neighborhood \(\delta _B({x_h}_i)\) of \({x_h}_i\) in the subspace B is defined as \(\delta _B\left( {x_h}_i\right) =\{{x_h}_j\left| {x_h}_j\right. \in U_h,\ \Delta B(x_i,x_j) \le \delta \}\) . The scheme proposes a new hybrid distance function to handle both the categorical and numerical features in an approximation space.

The proposed distance function uses Euclidean distance for numerical features and Levenshtein distance for categorical features. The distance function also takes care of the significant features calculating weighted distance for both the categorical and numerical features. The proposed algorithm dynamically selects the distance function at the run time. The use of Levenshtein distance for categorical features provides precise distance for optimal neighborhood approximation space providing better results. Existing techniques add 1 to distance if two strings do not match in calculating the distance for categorical data and add 0 otherwise. This may not result in a realistic neighborhood approximation space.

The neighborhood size depends on the threshold \(\delta \) . The neighborhood will contain more samples if \(\delta \) is greater and results in more rules not considering the local information data. The accuracy rate of the NRS greatly depends on the selection of threshold values. The proposed scheme dynamically calculates the threshold value for any given dataset considering both local and global information. The threshold calculation formula is given below where \({min}_D\) is the minimum distance between the set of training samples and the test sample containing local information and \(R_D\) is the range of distance between the set of training samples and the test sample containing the global information.

The proposed scheme then calculates the lower and upper approximations given a neighborhood space \(\langle U_h, N\rangle \) for \(X \subseteq U_h\) , the lower and upper approximations of X are defined as:

Given a hybrid neighborhood decision table \(HNDT=\langle U_h,\ C_h\cup \ D, V, f\rangle \) , \(\{ X_{h1},X_{h2},\ \ldots ,\ X_{hN} \}\) are the sample hybrid subjects with decision 1 to N , \(\delta _B\left( x_{hi}\right) \) is the information granules generated by attributes \(B \subseteq C_h\) , then the lower and upper approximation is defined as:

and the boundary region of D is defined as:

The lower and upper approximation spaces are the set of rules, which are used to classify a test sample. A test sample forms its neighborhood using a lower approximation having all the rules with a distance less than a dynamically calculated threshold value. The majority voting is used in the neighborhood of a test sample to decide the class of a test sample. K-fold cross-validation is used to measure the accuracy of the proposed scheme where the value k is 10. The algorithm 1 of the proposed scheme has a time complexity of \(O(nm^{2})\) where n is the number of clients and m is the size of the categorial data.

Instrumentation

The proposed generalized rough set model has been rigorously assessed through the development of a testbed designed for the classification of Parkinson’s patients. It has also been subjected to testing using various standard datasets sourced from the University of California at Irvine machine learning data repository 63 . This research underscores the increasing significance of biomedical engineering in healthcare, particularly in light of the growing prevalence of Parkinson’s disease, which ranks as the second most common neurodegenerative condition, impacting over 1% of the population aged 65 and above 64 . The disease manifests through distinct motor symptoms like resting tremors, bradykinesia (slowness of movement), rigidity, and poor balance, with medication-related side effects such as wearing off and dyskinesias 65 .

In this study, to address the need for a reliable quantitative method for assessing motor complications in Parkinson’s patients, the data collection process involves utilizing a home-monitoring system equipped with wireless wearable sensors. These sensors were specifically deployed to closely monitor Parkinson’s patients with severe tremors in real time. It’s important to note that all patients involved in the study were clinically diagnosed with Parkinson’s disease. Additionally, before data collection, proper consent was obtained from each participant, and the study protocol was approved by the ethical committee of our university. The data collected from these sensors is then analyzed, yielding reliable quantitative information that can significantly aid clinical decision-making within both routine patient care and clinical trials of innovative treatments.

Testbed for Parkinson’s patients.

Figure  1 illustrates a real-time Testbed designed for monitoring Parkinson’s patients. This system utilizes a tri-axial accelerometer to capture three signals, one for each axis \((x,\ y,\ and\ z)\) , resulting in a total of 18 channels of data. The sensors employed in this setup employ ZigBee (IEEE 802.15.4 infrastructure) protocol to transmit data to a computer at a sampling rate of 62.5 Hz. To ensure synchronization of the transmitted signals, a transition protocol is applied. These data packets are received through the Serial Forwarder using the TinyOS platform ( http://www.tinyos.net ). The recorded acceleration data is represented as digital signals and can be visualized on an oscilloscope. The frequency domain data is obtained by applying the Fast Fourier Transform (FFT) to the signal, resulting in an ARFF file format that is then employed for classification purposes. The experimental flowchart is shown in Fig.  2 .

Experimental flowchart.

The real-time testbed includes various components to capture data using the Unified Parkinson’s Disease Rating Scale (UPDRS). TelosB MTM-CM5000-MSP and MTM-CM3000-MSP sensors are used to send and receive radio signals from the sensor to the PC. These sensors are based on an open-source TelosB/Tmote Sky platform, designed and developed by the University of California, Berkeley.

TelosB sensor uses the IEEE 802.15.4 wireless structure and the embedded sensors can measure temperature, relative humidity, and light. In CM3000, the USB connector is replaced with an ERNI connector that is compatible with interface modules. Also, the Hirose 51-pin connector makes this more versatile as it can be attachable to any sensor board family, and the coverage area is increased using SMA design by a 5dBi external antenna 66 . These components can be used for a variety of applications such as low-power Wireless Sensor Networks (WSN) platforms, network monitoring, and environment monitoring systems.

MTS-EX1000 sensor board is used for the amplification of the voltage/current value from the accelerometer. The EX1000 is an attachable board that supports the CMXXXX series of wireless sensors network Motes (Hirose 51-pin connector). The basic functionality of EX1000 is to connect the external sensors with CMXX00 communication modules to enhance the mote’s I/O capability and support different kinds of sensors based on the sensor type and its output signal. ADXL-345 Tri-accelerometer sensor is used to calculate body motion along x, y, and z-axis relative to gravity. It is a small, thin, low-power, 3-axis accelerometer that calculates high resolution (13-bit) measurements at up to ±16g. Its digital output, in 16-bit twos complement format, is accessible through either an SPI (3- or 4-wire) or I2C digital interface. A customized main circuit board is used having a programmed IC, registers, and transistors. Its basic functionality is to convert the digital data, accessed through the ADXL-345 sensor, into analog form and send it to MTS1000.

Result and discussion

The proposed generalized and ANRS is evaluated against different data sets taken from the machine learning data repository, at the University of California at Irvine. In addition to these common data sets, a real-time Testbed for Parkinson’s patients is also used to evaluate the proposed scheme. The hybrid data of 500 people was collected using the Testbed for Parkinson’s patients including 10 Parkinson’s patients, 20 people have abnormal and uncontrolled hand movements, and the rest of the samples were taken approximating the hand movements of Parkinson’s patients. The objective of this evaluation is to compare the accuracy rate of the proposed scheme with CART, k NN, and SVM having both simple and complex datasets containing numerical and hybrid features respectively. The results also demonstrate the selection of radius r for dynamically calculating the threshold value.

Table  3 provides the details of the datasets used for the evaluation of the proposed scheme including the training and test ratio used for evaluation in addition to data type, total number of instances, total feature, a feature considered for evaluation, and number of classes. The hybrid datasets are also selected to evaluate to performance of the proposed scheme against the hybrid feature space without discretization preventing information loss.

The accuracy of the NRS is greatly dependent on the threshold value. Most of the existing techniques do not dynamically adapt the threshold \(\delta \) value for different hybrid datasets. This results in the variant of NRS suitable for specific datasets with different threshold values. A specific threshold value may produce better results for one dataset and poor results for others requiring a more generic threshold value catering to different datasets with optimal results. The proposed scheme introduces an adaptable threshold calculation mechanism to achieve optimal results regardless of the datasets under evaluation. The radius value plays a pivotal role in forming a neighborhood, as the threshold values consider both the local and global information of the NRS to calculate neighborhood approximation space. Table  4 shows the accuracy rate having different values of the radius of the NRS. The proposed threshold mechanism provides better results for all datasets if the value of the radius is 0.002. Results also show that assigning no weight to the radius produces poor results, as it will then only consider the local information for the approximation space. Selecting other weights for radius may produce better results for one dataset but not for all datasets.

Table  5 presents the comparative analysis of the proposed scheme with k NN, Naive Bayes, and C45. The results show that the proposed scheme performs well against other well-known techniques for both the categorical and numerical features space. Naive Bayes and C45 also result in information loss, as these techniques cannot process the hybrid data. So the proposed scheme handles the hybrid data without compromising on the information completeness producing acceptable results. K-fold cross-validation is used to measure the accuracy of the proposed scheme. Each dataset is divided into 10 subsets to use one of the K subsets as the test set and the other K-1 subsets as training sets. Then the average accuracy of all K trials is computed with the advantage of having results regardless of the dataset division.

Conclusion and future work

This work evaluates the existing NRS-based scheme for handling hybrid data sets i.e. numerical and categorical features. The comparative analysis of existing NRS-based schemes shows that there is a need for a generic NRS-based approach to adapt the threshold selection forming neighborhood approximation space. A generalized and ANRS-based scheme is proposed to handle both the categorical and numerical features avoiding information loss and lack of practical meanings. The proposed scheme uses a Euclidean and Levenshtein distance to calculate the upper and lower approximation of NRS for numerical and categorical features respectively. Euclidean and Levenshtein distances have been modified to handle the impact of outliers in calculating the approximation spaces. The proposed scheme defines an adaptive threshold mechanism for calculating neighborhood approximation space regardless of the data set under consideration. A Testbed is developed for real-time behavioral analysis of Parkinson’s patients evaluating the effectiveness of the proposed scheme. The evaluation results show that the proposed scheme provides better accuracy than k NN, C4.5, and Naive Bayes for both the categorical and numerical feature space achieving 95% accuracy on the Parkinson’s dataset. The proposed scheme will be evaluated against the hybrid data set having more than two classes in future work. Additionally, in future work, we aim to explore the following areas; (i) conduct longitudinal studies to track the progression of Parkinson’s disease over time, allowing for a deeper understanding of how behavioral patterns evolve and how interventions may impact disease trajectory, (ii) explore the integration of additional data sources, such as genetic data, imaging studies, and environmental factors, to provide a more comprehensive understanding of Parkinson’s disease etiology and progression, (iii) validate our findings in larger and more diverse patient populations and investigate the feasibility of implementing our proposed approach in clinical settings to support healthcare providers in decision-making processes, (iv) investigate novel biomarkers or physiological signals that may provide additional insights into Parkinson’s disease progression and motor complications, potentially leading to the development of new diagnostic and monitoring tools, and (v) conduct patient-centered outcomes research to better understand the impact of Parkinson’s disease on patients’ quality of life, functional abilities, and overall well-being, with a focus on developing personalized treatment approaches.

Data availability

The datasets used in this study are publicly available at the following links:

Bupa 67 : https://doi.org/10.24432/C54G67 , Sonar 68 : https://doi.org/10.24432/C5T01Q , Mammographic Mass 69 : https://doi.org/10.24432/C53K6Z , Haberman’s Survival 70 : https://doi.org/10.24432/C5XK51 , Credit-g 71 : https://doi.org/10.24432/C5NC77 , Lymmography 73 : https://doi.org/10.24432/C54598 , Splice 74 : https://doi.org/10.24432/C5M888 , Optdigits 75 : https://doi.org/10.24432/C50P49 , Pendigits 76 : https://doi.org/10.1137/1.9781611972825.9 , Pageblocks 77 : https://doi.org/10.24432/C5J590 , Statlog 78 : https://doi.org/10.24432/C55887 , Magic04 79 : https://doi.org/10.1609/aaai.v29i1.9277 .

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Imran Raza, Muhammad Hasan Jamal, Rizwan Qureshi & Abdul Karim Shahid

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Imran Raza: Conceptualization, Formal analysis, Writing—original draft; Muhammad Hasan Jamal: Conceptualization, Data curation, Writing—original draft; Rizwan Qureshi: Data curation, Formal analysis, Methodology; Abdul Karim Shahid: Project administration, Software, Visualization; Angel Olider Rojas Vistorte: Funding acquisition, Investigation, Project administration; Md Abdus Samad: Investigation, Software, Resources; Imran Ashraf: Supervision, Validation, Writing —review and editing. All authors reviewed the manuscript and approved it.

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Creating culturally-informed protocols for a stunting intervention using a situated values-based approach ( WeValue InSitu ): a double case study in Indonesia and Senegal

• Annabel J. Chapman 1 ,
• Chike C. Ebido 2 , 3 ,
• Rahel Neh Tening 2 ,
• Yanyan Huang 2 ,
• Ndèye Marème Sougou 4 ,
• Risatianti Kolopaking 5 , 6 ,
• Amadou H. Diallo 7 ,
• Rita Anggorowati 6 , 8 ,
• Fatou B. Dial 9 ,
• Jessica Massonnié 10 , 11 ,
• Mahsa Firoozmand 1 ,
• Cheikh El Hadji Abdoulaye Niang 9 &
• Marie K. Harder 1 , 2  

BMC Public Health volume  24 , Article number:  987 ( 2024 ) Cite this article

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International development work involves external partners bringing expertise, resources, and management for local interventions in LMICs, but there is often a gap in understandings of relevant local shared values. There is a widespread need to better design interventions which accommodate relevant elements of local culture, as emphasised by recent discussions in global health research regarding neo-colonialism. One recent innovation is the concept of producing ‘cultural protocols’ to precede and guide community engagement or intervention design, but without suggestions for generating them. This study explores and demonstrates the potential of an approach taken from another field, named WeValue InSitu , to generate local culturally-informed protocols. WeValue InSitu engages stakeholder groups in meaning-making processes which ‘crystallize’ their envelope of local shared values, making them communicable to outsiders.

Our research context is understanding and reducing child stunting, including developing interventions, carried out at the Senegal and Indonesia sites of the UKRI GCRF Action Against Stunting Hub. Each national research team involves eight health disciplines from micro-nutrition to epigenetics, and extensive collection of samples and questionnaires. Local culturally-informed protocols would be generally valuable to pre-inform engagement and intervention designs. Here we explore generating them by immediately following the group WeValue InSitu crystallization process with specialised focus group discussions exploring: what local life practices potentially have significant influence on the environments affecting child stunting, and which cultural elements do they highlight as relevant. The discussions will be framed by the shared values, and reveal linkages to them. In this study, stakeholder groups like fathers, mothers, teachers, market traders, administrators, farmers and health workers were recruited, totalling 83 participants across 20 groups. Themes found relevant for a culturally-informed protocol for locally-acceptable food interventions included: specific gender roles; social hierarchies; health service access challenges; traditional beliefs around malnutrition; and attitudes to accepting outside help. The concept of a grounded culturally-informed protocol, and the use of WeValue InSitu to generate it, has thus been demonstrated here. Future work to scope out the advantages and limitations compared to deductive culture studies, and to using other formative research methods would now be useful.

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Although progress has been made towards the SDG of ‘Zero Hunger by 2025’, the global rates of malnutrition and stunting are still high [ 1 ]. Over the past 20 years, researchers have implemented interventions to reduce undernutrition, specifically focussing on the first 1000 days of life, from conception to 24 months [ 2 ]. However, due to both differing determinants between countries [ 3 , 4 ] as well as varying contextual factors, it is clear that no single fixed approach or combination of approaches can be relied on when implementing stunting interventions [ 5 , 6 , 7 ]. Furthermore, when external researchers design interventions for local areas in Low- and Middle-Income Countries (LMICs) they can often overlook relevant local cultural factors that consequently act as barriers to intervention uptake and reduce their effectiveness, such as geographical factors and the levels of migration in certain populations [ 8 , 9 ], or social norms or perceptions relating to accepting outside help, and power dynamics related to gender [ 10 , 11 , 12 ]. The inclusion of cultural level factors in behaviour change interventions has been proposed as a requirement for effective interventions [ 13 ]. However, despite the breadth of literature highlighting the negative impacts from failing to do this, the lack of integration or even regard of local culture remains a persistent problem in Global Health Research [ 14 ], possibly hindering progress towards the SDGs. Thus, there is a need for approaches to integrate local cultural elements into intervention design.

This lack of understanding of relevant local culture, social norms and shared values also has ethical implications. The field of Global Health Ethics was predominantly developed in the Global North, in High Income Countries (HICs), embedding values common in those countries such as the prominence of individual autonomy [ 15 , 16 ]. Researchers from HICs carrying out research in LMICs may wrongly assume that values held in the Global North are universal [ 14 ] and disregard some local values, such as those related to family and collective decision making, which are core to many communities in LMICs. It is therefore important for outside researchers to have an understanding of relevant local values, culture and social norms before conducting research in LMICs so as not to impose values that do not align with local culture and inadvertently cause harm or offence [ 16 , 17 ]. The importance of this is compounded by the colonial history that is often present in relationships between research communities in HICs and LMICs, and the fact that the majority of the funding and leading institutions are still located in the Global North [ 18 , 19 ]. Thus, conscious steps must be taken to avoid neo-colonialism in Global Health Research [ 20 ]. From a health-equity perspective, it is essential to ensure that those in vulnerable communities are not hindered from involvement in interventions to improve nutrition. Encouraging uptake by such communities could be provided if salient local shared values, norms and culture were taken into account [ 21 ].

In a recent paper, Memon et al., (2021) highlight the usefulness of first creating a cultural protocol that can precede and guide subsequent stages of community engagement or intervention design to ensure that salient local values are known to external researchers coming into the community [ 16 ]. We adopt the use of the concept of a cultural protocol, referring to locally-generated guidance about key values, norms, behaviours and customs relevant to working with the local community. However, we prefer the term, ‘culturally-informed protocol’ since this relates to only cultural elements deemed salient by the researchers, and locally, rather than any comprehensive notion of culture, nor extending beyond the research context.

Memon et al. (2021), point out links between the creation of such a protocol and existing codes of practice that have already been created for some cultures such as the Te Ara Tika, a Guideline for Māori Research Ethics [ 22 ]. Currently, research and interventions in Global Health can be informed by a stage of formative research involving one-to-one interviews, focus groups or direct observations, which can sometimes be ethnographic in nature such as within Focussed Ethnographic Studies or Rapid Assessment Procedures [ 23 , 24 , 25 ]. Although these methods can be effective to inform intervention designs, they have disadvantages like: can take long periods to complete [ 26 ], can be resource intensive [ 26 ] and can lack cultural acceptability [ 27 ]. These limitations may account for the frequent neglect of their use generally, highlighted by Aubel and Chibanda (2022) [ 14 ]. Additionally, none of these methods work towards making explicit local values, or towards the creation of a culturally-informed protocol. In brief, the literature suggests a need to develop alternative methods of Formative Research for understanding locally relevant cultural elements, that are less time-consuming and can generate data that is more easily translatable to intervention design. In addition, these approaches must be applicable in different cultures. Additionally, the protocols produced must be actionable and practical not only for guiding interactions between research teams but also for guiding the initial stages of intervention design.

The work presented here aims to address several of these needs. It includes an exploration of the usefulness of the WeValue InSitu ( WVIS ) approach because that has previously been shown, in environmental management domains, to offer a way to gather in-depth values-based perspectives from a target population [ 28 , 29 ] It was first created through action research, and co-designed to enable civil society organisations to better understand and measure the values-based aspects of their work [ 30 ]. The core WeValue InSitu process (detailed in Table 1 ) involves the crystallization of shared values, with a facilitator guiding a group of participants with shared experiences, through cycles of tacit meaning-making (using a stage of photo-elicitation and triggering) [ 31 ], until they can articulate more explicitly their shared values, in concise and precise statements. These statements are then linked together in a framework by the participants. In an example case in Nigeria, the results of the WVIS approach hinted at the creation of a culturally-informed protocol through an analysis of the shared values frameworks to find cultural themes for the creation of an indicator tool that was used to evaluate several development scenarios based on their social acceptability [ 29 ].

Furthermore, it has been found that if a group of WVIS participants take part in a specialised focus group discussion (FGD), named Perspectives EXploration (PEX:FGD) immediately afterward the main workshop, then they easily and articulately express their perspectives on the topics raised for discussion - and with allusions to the shared values they had crystallised just prior. In an example from Shanghai, the PEX:FGDs focussed on eliciting perspectives on climate change, which were shown to be closely linked with the cultural themes existing within the shared values frameworks produced immediately prior [ 32 ]. In that case, the PEX:FGDs allowed the cultural themes generated during the main WVIS workshop to be linked more closely to the research question. Those results suggested that the WVIS plus PEX:FGD approach could be used to create a specialised culturally-informed protocol for improved intervention design.

In the study presented here, the WVIS approach was explored for the purpose of creating culturally-informed protocols to inform the planning of interventions within two localities of the UKRI GCRF Action Against Stunting Hub [ 33 ]. The work was carried out in two parts. Firstly, the WVIS main workshop was used to elicit cultural themes within the target communities, indicating key elements to consider to ensure ethical engagement. Secondly, the PEX focus group discussions focussed on life practices related to stunting which we explored for the purpose of tailoring the culturally-informed protocols to the specific purpose of improving the design of an example intervention. The Action Against Stunting Hub works across three sites where stunting is highly prevalent but via different determinants: East Lombok in Indonesia (estimated 36% of under-fives stunted), Kaffrine in Senegal (estimated 16% of under-fives stunted) and Hyderabad in India (estimated 48% of under-fives stunted) [ 34 ]. We propose that, the information about local shared values in a given site could be used to inform the design of several interventions, but for our specific exploration the focus here is a proposed ‘egg intervention’, in which pregnant women would be provided with an egg three times per week as supplement to their diet. This study proposes that identifying shared values within a community, alongside information about local life practices, provides critical cultural information on the potential acceptability and uptake of this intervention which can be used to generate culturally-informed protocols consisting of recommendations for improved intervention design.

In this paper we aim to explore the use of the WVIS approach to create culturally-informed protocols to guide engagement and inform the design of localised egg interventions to alleviate stunting in East Lombok, Indonesia and Kaffrine, Senegal. We do this by analysing data about local shared values that are crystallized using the WeValue InSitu ( WVIS ) process to provide clear articulation of local values, followed by an analysis of life practices discussed during PEX:FGD to tailor the culturally-informed protocols for the specific intervention design.

Study setting

This research was exploratory rather than explanatory in nature. The emphasis was on demonstrating the usefulness of the WeValue InSitu ( WVIS ) approach to develop culturally-informed protocols of practical use in intervention design, in different cultural sites. This study was set within a broader shared-values workstream within the UKRI GCRF Action Against Stunting Hub project [ 33 ]. The Hub project, which was co-designed and co-researched by researchers from UK, Indonesia, Senegal and India, involves cohorts of 500 women and their babies in each site through pregnancy to 24 months old, using cross-disciplinary studies across gut health, nutrition, food systems, micro-nutrition, home environment, WASH, epigenetics and child development to develop a typology of stunting. Alongside these health studies are studies of the shared values of the communities, obtained via the WVIS approach described here, to understand the cultural contexts of that diverse health data. In this study the data from East Lombok, Indonesia and Kaffrine, Senegal were used: India’s data were not yet ready, and these two countries were deemed sufficient for this exploratory investigation.

The WVIS approach

The WVIS approach is a grounded scaffolding process which facilitates groups of people to make explicit their shared values in their own vocabulary and within their own frames (details in Fig. 1 and activities in Table 1 ). The first stage of the WVIS is Contextualisation, whereby the group identifies themselves and set the context of their shared experiences, for example, as ‘mothers in East Lombok, Indonesia’. Subsequently, there is a stage of Photo Elicitation, in which the group are first asked to consider what is important, meaningful or worthwhile to them about their context (e.g., ‘being mothers in East Lombok, Indonesia’) and then asked to choose photos from a localised set that they can use as props to help describe their answer to the group [ 29 ]. After this, a localised Trigger List is used. This Trigger List consists of 109 values statements that act as prompts for the group. Examples of these values statements are included below but all the statements begin with “it is important to me/us that…”. The group are asked to choose which statements within the trigger list resonate with them, and those are taken forward for group intersubjective discussion. After a topic of their shared values has been explored, the group begin to articulate and write down their own unique statements of them. These also all begin with “It is important to me/us that…”. After discussing all pressing topics, the group links the written statements on the table into a unique Framework, and one member provides a narrative to communicate it to ‘outsiders’. The WVIS provides a lens of each group’s local shared values, and it is through this lens that they view the topics in the focus group discussions which immediately follow, termed Perspectives EXplorations (PEX:FGDs).

Schematic of the macro-level activities carried out during the WeValue InSitu ( WVIS ) main workshop session

This results in very grounded perspectives being offered, of a different nature to those obtained in questionnaires or using external frameworks [ 31 ]. The specific PEX:FGD topics are chosen as pertinent to stunting contextual issues, including eating habits, food systems and environments, early educational environments, and perceptions of stunting. The local researchers ensured that all topics were handled sensitively, with none that could cause distress to the participants. The data for this study were collected over 2 weeks within December 2019–January 2020 in workshops in East Lombok, Indonesia, and 2 weeks within December 2020 in Kaffrine, Senegal.

The PEX:FGDs were kept open-ended so that participants could dictate the direction of the discussion, which allowed for topics that may not have been pre-considered by the facilitators to arise. Sessions were facilitated by local indigenous researchers, guided in process by researchers more experienced in the approach, and were carried out in the local languages, Bahasa in East Lombok, Indonesia and French or Wolof in Kaffrine, Senegal.

Development of localised WVIS materials

Important to the WVIS approach is the development of localised materials (Table 1 ). The main trigger list has been found applicable in globalised places where English is the first language, but otherwise the trigger lists are locally generated in the local language, incorporating local vocabulary and ways of thinking. To generate these, 5–8 specific interviews are taken with local community members, by indigenous university researchers, eliciting local phrases and ways of thinking. This is a necessary step because shared tacit values cannot be easily accessed without using local language. Examples of localised Trigger Statements produced this way are given below: (they all start with: “It is important to me/us that…”):

…there is solidarity and mutual aid between the people

…I can still be in communication with my children, even if far away

…husbands are responsible for the care of their wives and family

…the town council fulfils its responsibility to meet our needs

…people are not afraid of hard, and even manual work

Study participants

The group participants targeted for recruitment, were selected by local country Hub co-researchers to meet two sets of requirements. For suitability for the WVIS approach they should be between 3 and 12 in number; belong to naturally existing groups that have some history of shared experiences; are over 18 years old; do not include members holding significantly more power than others; and speak the same native language. For suitability in the PEX:FGD to offer life practices with relevance to the research topic of stunting, the groups were chosen to represent stakeholders with connections to the food or learning environment of children (which the Action Against Stunting Hub refer to as the Whole Child approach) [ 33 ]. The university researchers specialising in shared values from the UK, and Senegal and Indonesia respectively, discussed together which stakeholder groups might be appropriate to recruit. The local researchers made the final decisions. Each group was taken through both a WVIS workshop and the immediately-subsequent PEX:FGD.

Data collection and analysis

Standard data output from the WeValue session includes i) the jointly-negotiated bespoke Statements of shared values, linked together in their unique Framework, and ii) an oral recording of a descriptive Narrative of it, given by the group. These were digitized to produce a single presentation for each group as in Fig. 2 . It represents the synthesised culmination of the crystallisation process: a portrait of what was ‘important’ to each stakeholder group. Separately, statements from the group about the authenticity/ownership of the statements are collected.

An illustrative example of one digitized Shared Values Framework and accompanying Narrative from a teacher’s group in East Lombok, Indonesia. The “…” refers to each statement being preceded by “It is important to us that…”

When these Frameworks of ‘Statements of Shared Values’ are viewed across all the groups from one locality (Locality Shared Values Statements), they provide portraits of ‘what is important’ to people living there, often in intimate detail and language. They can be used to communicate to ‘outsiders’ what the general cultural shared values are. In this work the researchers thematically coded them using Charmaz constructionist grounded theory coding [ 35 ] to find broad Major Cultural Themes within each separate locality.

The second area of data collection was in the post- WVIS event: the PEX:FGD for each group. A translator/interpreter provided a running commentary during these discussions, which was audio recorded and then transcribed. The specific topics raised for each group to discuss varied depending on their local expertise. This required completely separate workstreams of coding of the dataset with respect to each topic. This was carried out independently by two researchers: one from UK (using NVivo software (Release 1.3.1)) and one from the local country, who resolved any small differences. All the transcripts were then collated and inductively, interpretively analysed to draw out insights that should be relayed back to the Action Against Stunting Hub teams as contextual material.

The extracts of discussion which were identified as relevant within a particular Hub theme (e.g. hygiene) were then meta-ethnographically synthesised [ 36 ] into ‘Hub Theme Statements’ on each topic, which became the core data for later communication and interrogation by other researchers within the Action Against Stunting Hub. These statements are interpretations of participants’ intended meanings, and links from each of them to data quotes were maintained, enabling future interpretations to refer to them for consistency checks between received and intended meaning.

In this investigation, those Hub Theme Statements (derived from PEX:FGD transcripts) were then deductively coded with respect to any topics with potential implications of the egg intervention. Literature regarding barriers and facilitators to nutrition interventions indicated the following topics could be relevant: attitudes to accepting help; community interactions; cooking and eating habits; traditional beliefs about malnutrition; sharing; social hierarchies [ 12 , 37 , 38 ] to which we added anything related to pregnancy or eggs. This analysis produced our Egg Intervention Themes from the data.

The Major Cultural Themes and Egg Intervention Themes were then used to create a set of culture-based recommendations and intervention specific recommendations respectively for each locality. These recommendations were then combined to form specialized culturally-informed protocols for the egg intervention in each locality: East Lombok, Indonesia and Kaffrine, Senegal. The process is displayed schematically in Fig.  3 .

Schematic representation of the method of production of the culturally-informed protocol for each locality

The preparation of the localised WVIS materials at each site took 6 hours of interview field work, and 40 person hours for analysis. The 10 workshops and data summaries were concluded within 10 workdays by two people (80 person hours). The analysis of the PEX:FGD data took a further 80 person hours. Thus, the total research time was approximately 200 person hours.

The stakeholder group types are summarised in Table 2 . The data is presented in three parts. Firstly, the Major Cultural Themes found in East Lombok, Indonesia and in Kaffrine, Senegal are described – the ones most heavily emphasised by participants. Then, the Egg Intervention Themes and finally, the combined set of Recommendations to comprise a culturally-informed protocol for intervention design for each location. Quotations are labelled INDO or SEN for East Lombok, Indonesia and Kaffrine, Senegal, respectively.

Major cultural themes from frameworks and narratives

These were derived from the Locality Shared Values Statements produced in the WVIS .

East Lombok, Indonesia

Religious values.

Islamic values were crucially important for participants from East Lombok, Indonesia and to their way of life. Through living by the Quran, participating in Islamic community practices, and teaching Islamic values to their children, participants felt they develop their spirituality and guarantee a better afterlife for themselves and their children. Participants stated the Quran tells them to breastfeed their children for 2 years, so they do. Despite no explicit religious official curriculum in Kindergarten, the teachers stated that it was important to incorporate religious teaching.

“East Lombok people always uphold the religious values of all aspects of social life.”

“It is important for me to still teach religious values even though they are not clearly stated in the curriculum.” – Workshop 1 INDO (teachers).

“In Quran for instance, we are told to breastfeed our kids for 2 years. We can even learn about that ” – Workshop 3 INDO (mothers).

Related to this was the importance of teaching manners to children and preventing them from saying harsh words. Teachers stated that it was important to create a happy environment for the children and to ensure that they are polite and well-behaved. Similarly, mothers emphasised the need to teach their children good religious values to ensure they will be polite and helpful to their elders.

“Children don’t speak harsh words.”

“My children can help me like what I did to my parents”.

– Workshop 8 INDO (mothers).

Togetherness within families and the community

The Locality Shared Values Frameworks stressed the importance of togetherness, both within family and community. Comments mentioned it being important that people rely heavily on their family and come together in times of need to support each other and provide motivation. This was also important more broadly, in that people in society should support each other, and that children grow up to contribute to society. This was also reflected in comments around roles within the family. Despite women being primary care givers, and men working to finance the family, participants stated that they follow a process of consultation to make decisions, and when facing hardships.

“that we have the sense of kinship throughout our society”.

“We have togetherness as mothers”.

“For the family side, whatever happens we need to be able to be united as a whole family. We need to have the [sense of] forgiveness for the sake of the children” – Workshop 2 INDO (mothers).

Attitudes about extra-marital pregnancy

In East Lombok, Indonesia, it was essential to both mothers and fathers that pregnancy happened within a marriage, this was to ensure that the honour of the family was upheld and that the lineage of the child was clear. The potential danger to health that early pregnancies can cause was also acknowledged.

“If they don’t listen to parents’ advice, there will be the possibility of pre-marital pregnancy happening, which will affect the family [so much].

The affect is going to be ruining the good name, honour and family dignity. When the children [are] born outside [of] marriage, she or he will have many difficulties like getting a birth certificate [and] having a hard time when registering to school or family” - Workshop 4 INDO (mothers).

“ To make sure that our children avoid getting married at a very young age and moreover [avoid] having free sex so that they will not get pregnant before the marriage” - Workshop 9 INDO (fathers).

Kaffrine, Senegal

The Major Cultural Themes which emerged from the Kaffrine data are described below. As these are grounded themes, they are different than those seen in East Lombok, Indonesia.

Access to healthcare

A recurring theme amongst the groups in Kaffrine were aspirations of affordable and easy-to-access healthcare. Community health workers stated the importance of encouraging women to give birth in hospitals and spoke of the importance of preventing early pregnancy which result from early marriages. Giving birth in hospitals was also a concern for Public Office Administrators who highlighted that this leads to subsequent issues with registering children for school. Mothers and fathers stated the importance of being able to afford health insurance and access healthcare so that they could take care of themselves.

“That the women give birth in the hospital” – Workshop 11 SEN (CHWS).

“To have affordable health insurance ” – Workshop 10 SEN (mothers).

“To have access to health care ” – Workshop 3 SEN (fathers).

“It is important that women give birth in the hospital in order to be able to have a certificate that allows us to establish the civil status” – Workshop 9 SEN (administrators).

Additionally, Community health workers spoke of their aspiration to have enough supplements to provide to their community so as to avoid frustration at the lack of supply, and mothers spoke of their desire to be provided with supplements.

“To have dietary supplements in large quantities to give them to all those who need them, so as not to create frustration” – Workshop 11 SEN (CHWS).

Another aspect of access to healthcare, was mistrust between fathers and community health workers. Community health workers explained that sometimes men can blame them when things go wrong in a pregnancy or consider their ideas to be too progressive. Thus, to these community health workers the quality of endurance was very important.

“Endurance (Sometimes men can accuse us of influencing their wives when they have difficulties in conceiving)” – Workshop 5 SEN (CHWs).

Another recurring theme was the importance of having secure employment and a means to support themselves; that there were also jobs available for young people, and that women had opportunities to make money to help support the family. This included preventing early marriages so girls could stay in school. Having jobs was stated as essential for survival and important to enable being useful to the community and society.

“To have more means of survival (subsistence) to be able to feed our families”.

“To have a regular and permanent job”.

“We assure a good training and education for our children so that they will become useful to us and the community”.

“ Our women should have access to activities that will support us and lessen our burden” – Workshop 3 SEN (fathers).

It was considered very important to have a religious education and respect for religious elders. Moreover, living by, and teaching, religious values such as being hard working, humble and offering mutual aid to others, was significant for people in Kaffrine.

“Have an education in the Islamic Culture (Education that aligns with the culture of Islam)”.

“Respect toward religious leaders” – Workshop 3 SEN (fathers).

“ To organize religious discussions to develop our knowledge about Islam ” - Workshop 10 SEN (mothers).

“ Have belief and be prayerful and give good counselling to people ” - Workshop 4 SEN (grandmothers).

Egg intervention themes from each country from perspectives EXplorations focus group discussion data

Below are results of analyses of comments made during the PEX:FGDs in East Lombok, Indonesia and Kaffrine, Senegal. The following codes were used deductively: attitudes to accepting outside help, traditional gender roles, food sharing, traditional beliefs, social hierarchies and understanding of stunting and Other. These topics were spoken about during open discussion and were not the subject of direct questions. For example, topics relating to traditional gender roles came up in East Lombok, during conversations around the daily routine. Thus, in order to more accurately reflect the intended meaning of the participants, these were labelled food practices, under the “Other” theme. If any of the themes were not present in the discussion, they are not shown below.

Attitudes to accepting outside help

Few mentions were made that focussed on participants attitudes to accepting outside help, but participants were sure that they would not make changes to their menus based on the advice of outside experts. Additionally, teachers mentioned that they are used to accepting help from local organisations that could to help them to identify under-developed children.

“ We don’t believe that [the outsiders are] going to change our eating habits or our various menus ” – Workshop 3 INDO (Mothers).

Traditional gender roles

In East Lombok, mothers spoke about how their husbands go to work and then provide them with daily money to buy the food for the day. However, this was discussed in relation to why food is bought daily and is thus discussed below in the topics Other – Food practices.

Food sharing

In East Lombok, Indonesia, in times when they have extra food, they share it with neighbours, in the hope that when they face times of hardship, their neighbours will share with them. Within the household, they mentioned sharing food from their plate with infants and encouraging children to share. Some mothers mentioned the importance of weekly meetings with other mothers to share food and sharing food during celebrations.

“ Sometimes we share our food with our family. So, when we cook extra food, we will probably send over the food to our neighbour, to our families. So, sometimes, with the hope that when we don’t have anything to eat, our neighbour will pay for it and will [share with] us.” – Workshop 3 INDO (Mothers).

“Even they serve food for the kids who come along to the house. So, they teach the kids to share with their friends. They provide some food. So, whenever they play [at their] house, they will [eat] the same.” – Workshop 2 INDO (Mothers).

Understanding of stunting

The teachers in East Lombok were aware of child stunting through Children’s Development Cards provided by local healthcare organizations. They stated that they recognise children with nutrition problems as having no patience period, no expression, no energy for activities and less desire to socialise and play with other children. The teachers said that stunted children do not develop the same as other children and are not as independent as children who are the proper height and weight for their development. They also stated that they recognise stunted children by their posture, pale faces and bloated stomachs. They explained how they usually use the same teaching methods for stunting children, but will sometimes allow them to do some activities, like singing, later, once the other children are leaving.

“ They have no patience period, don’t have any energy to do any of the activities. No expression, only sitting down and not mingling around with the kids. They are different way to learn. They are much slower than the other kids .” – Workshop 1 INDO (teachers).

“ When they are passive in singing, they will do it later when everyone else is leaving, they just do it [by] themselves ” – Workshop 1 INDO (teachers).

Specific views on eggs

In East Lombok, Indonesia, there were no superstitions or traditional beliefs around the consumption of eggs. When asked specifically on their views of eggs, and if they would like to be provided with eggs, women in East Lombok said that they would be happy to accept eggs. They also mentioned that eggs were a food they commonly eat, feed to children and use for convenience. Eggs were considered healthy and were common in their house.

“ We choose eggs instead. If we don’t have time, we just probably do some omelettes or sunny side up. So, it happens, actually when we get up late, we don’t have much time to be able to escort our kids to the school, then we fry the eggs or cook the instant noodles. And it happens to all mothers. So, if my kids are being cranky, that’s what happens, I’m not going to cook proper meals so, probably just eggs and instant noodles.” – Workshop 3 INDO (Mothers).

Other important topics – food practices

Some detailed themes about food practices were heard in East Lombok, Indonesia. The women were responsible for buying and preparing the food, which they purchased daily mainly due to the cost (their husbands were paid daily and so provided them with a daily allowance) and lack of storage facilities. They also bought from mobile vendors who came to the street, because they could buy very small amounts and get occasional credit. The mother decided the menu for the family and cooked once per day in the morning: the family then took from this dish throughout the day. Mothers always washed their fruits and vegetables and tried to include protein in their meals when funds allowed: either meat, eggs, tofu or tempeh.

“ One meal a day. They [the mothers] cook one time and they [the children] can eat it all day long. Yes, they can take it all day long. They find that they like [to take the food], because they tend to feel hungry.” – Workshop 6 INDO (Mothers).

“ They shop every day because they don’t have any storage in their house and the other factor is because the husband has a daily wage. They don’t have monthly wage. In the morning, the husband gives the ladies the money and the ladies go to the shop for the food. ” – Workshop 4 INDO (Mothers).

In Kaffrine, the following themes emerged relating to an egg intervention: they were different in content and emphasis to Lombok and contained uniquely local cultural emphases.

Mothers were welcoming of eggs as a supplement to improve their health during pregnancy and acknowledged the importance of good nutrition during pregnancy. However, they also mentioned that their husbands can sometimes be resistant to accepting outside help and provided an example of a vaccination programme in which fathers were hesitant to participate. However, participants stated that the Government should be the source of assistance to them (but currently was not perceived to be so).

“But if these eggs are brought by external bodies, we will hesitate to take it. For example, concerning vaccination some fathers hesitate to vaccinate their children even if they are locals who are doing it. So, educating the fathers to accept this is really a challenge” – Workshop 11 SEN (CHWs).

Some traditional gender roles were found to be strong. The participants emphasised that men are considered the head of the household, as expected in Islam, with the mother as primary caregiver for children. This is reflected in the comments from participants regarding the importance of Islam and living their religious values. The men thus made the family decisions and would need to be informed and agree to any family participation in any intervention – regardless of the education level of the mother. The paternal grandmother also played a very important role in the family and may also make decisions for the family in the place of the father. Community Health Workers emphasised that educating paternal grandmothers was essential to improve access to healthcare for women.

“There are people who are not flexible with their wives and need to be informed. Sometimes the mother-in-law can decide the place of the husband. But still, the husband’s [permission] is still necessary.” – Workshop 1 SEN (CHWs).

“[We recommend] communication with mothers-in-law and the community. Raise awareness through information, emphasizing the well-being of women and children.” – Workshop 1 SEN (CHWs).

“The [grand]mothers take care of the children so that the daughters in-law will take care of them in return So it’s very bad for a daughter in law not to take care of her mother in-law. Society does not like people who distance themselves from children.” – Workshop 4 SEN (grandmothers).

Social hierarchies

In addition to hierarchies relating to gender/position in the family such as grandmothers have decision making power, there was some mention of social hierarchies in Kaffrine, Senegal. For example, during times of food stress it was said that political groups distribute food and elected officials who choose the neighbourhoods in which the food will be distributed. Neighbourhood leaders then decide to whom the food is distributed, meaning there is a feeling that some people are being left out.

“ It’s political groups that come to distribute food or for political purposes…organizations that often come to distribute food aid, but in general it is always subject to a selection on the part of elected officials, in particular the neighbourhood leaders, who select the people they like and who leave the others ” – Workshop 11 SEN (CHWs).

Participants explained that during mealtimes, the family will share food from one large plate from which the father will eat first as a sign of respect and courtesy. Sometimes, children would also eat in their neighbour’s house to encourage them to eat.

“ Yes, it happens that we use that strategy so that children can eat. Note that children like to imitate so that’s why we [send them to the neighbour’s house]” – Workshop 11 SEN (CHWs)”.

Traditional beliefs about malnutrition

In Kaffrine, Senegal, some participants spoke of traditional beliefs relating to malnutrition, which are believed by fewer people these days. For example, uncovered food might attract bad spirits, and any person who eats it will become ill. There were a number of food taboos spoken of which were thought to have negative consequences for the baby, for example watermelon and grilled meat which were though to lead to birth complications and bleeding. Furthermore, cold water was thought to negatively impact the baby. Groups spoke of a tradition known as “bathie” in which traditional healers wash stunted children with smoke.

“ There are traditional practices called (Bathie) which are practiced by traditional healers. Parents are flexible about the practice of Bathie ” – Workshop 1 SEN (CHWs).

Causes of malnutrition and stunting were thought to be a lack of a balanced diet, lack of vitamin A, disease, intestinal worms, poor hygiene, socio-cultural issues such as non-compliance with food taboos, non-compliance with exclusive breastfeeding and close pregnancies. Malnutrition was also thought by some to be hereditary. Numerous signs of malnutrition were well known amongst the groups in Kaffrine. For example, signs of malnutrition were thought to be a big bloated belly, diarrhoea, oedema of the feet, anaemia, small limbs and hair loss as well as other symptoms such as red hair and a pale complexion. Despite this, malnutrition was thought to be hard to identify in Kaffrine as not all children will visit health centres, but mothers do try to take their babies heights and weights monthly. The groups were aware of the effect of poverty on the likelihood of stunting as impoverished parents cannot afford food. Furthermore, the groups mentioned that there is some stigma towards stunted children, and they can face mockery from other children although most local people feel pity and compassion towards them. Malnourished children are referred to as Khiibon or Lonpogne in the local language of Wolof.

“ It is poverty that is at the root of malnutrition, because parents do not have enough money [and] will have difficulty feeding their families well, so it is the situation of poverty that is the first explanatory factor of malnutrition here in Kaffrine” – Workshop 9 SEN (administrators).

“It can happen that some children are the victim of jokes for example of mockery from children of their same age, but not from adults and older ” – Workshop 9 SEN (administrators).

Pregnancy beliefs

In Kaffrine, Senegal, there were concerns around close pregnancies, and pregnancies in women who were too young, and for home births. Within the communities there was a stigma around close pregnancies, which prevented them from attending antenatal appointments. Similarly, there were superstitions around revealing early pregnancies, which again delayed attendance at health centres.

Groups acknowledged the role of good nutrition, and mentioned some forbidden foods such as salty foods, watermelon and grilled meat (which sometimes related back to a traditional belief that negative impacts would be felt in the pregnancy such as birth complications and bleeding). Similarly, drinking cold water was thought to negatively affect the baby. Beneficial foods mentioned included vegetables and meat, during pregnancy.

“ Often when a woman has close pregnancies, she can be ashamed, and this particularly delays the time of consultation” – Workshop 5 SEN (CHWs).

“Yes, there are things that are prohibited for pregnant women like salty foods” – Workshop 11 SEN (CHWs).

In Kaffrine, Senegal, some participants spoke of a traditional belief that if a pregnant woman consumes eggs then her baby might be overweight, or have problems learning how to talk. Despite this, mothers in Kaffrine said that they would be happy to accept eggs as a supplement, although if supplements are provided that require preparation (such as powdered supplements), they would be less likely to accept them.

“These restrictions are traditional, and more women no longer believe that eggs will cause a problem to the child. But if these eggs are brought by external bodies, we will hesitate to take it.” – Workshop 11 SEN (CHWs).

“They don’t eat eggs before the child starts speaking (the child only eats eggs when he starts talking). This is because it’s very heavy and can cause bloating and may also lead to intestinal problems.” – Workshop 4 SEN (grandmothers).

Other important topics – access to health services

For the participants in Kaffrine, Senegal, accessing health services was problematic, particularly for pre- and post-natal appointments, which faced frequent delays. Some women had access due to poor roads and chose to give birth at home. Access issues were further compounded by poverty and social factors, as procedures in hospitals can be costly, and women with close pregnancies (soon after an earlier one) can feel shame from society and hide their pregnancy.

“Women really have problems of lack of finances. There are social services in the hospital; but those services rarely attend to women without finances. Even when a child dies at birth they will require money to do the necessary procedure ” – Workshop 11 SEN (CHWs).

Creation of the culturally-informed protocols

Recommendations that comprise a culturally-informed protocol for intervention design in each locality are given in Table 3 .

The Major Cultural Themes, and specific Egg Intervention Themes drawn out from only 9–11 carefully planned group sessions in each country provided a rich set of recommendations towards a culturally-informed protocol for the localised design of a proposed Egg Intervention for both East Lombok, Indonesia and Kaffrine, Senegal. A culturally-informed protocol designed in this way comprises cultural insights which are worthy of consideration in local intervention design and should guide future stages of engagement and provide a platform from which good rapport and trust can be built between researchers and the community [ 16 ]. For example, in Kaffrine, Senegal, the early involvement of husbands and grandmothers is crucial, which reflects values around shared decision making within families that are noted to be more prevalent in LMICs, in contrast to individualistic values in HICs [ 16 , 39 ]. Similarly, due to strong religious values in both East Lombok, Indonesia and Kaffrine, Senegal, partnerships with Islamic leaders is likely to improve engagement. Past studies show the crucial role that religious leaders can play in determining social acceptability of interventions, particularly around taboo topics such as birth spacing [ 40 ].

The WVIS plus PEX:FGD method demonstrated here produced both broad cultural themes from shared values, which were in a concise and easy-to-understand format which could be readily communicated with the wider Action Against Stunting Hub, as well as life practices relevant to stunting in Kaffrine, Senegal and in East Lombok, Indonesia. Discussions of shared values during the WVIS main workshop provided useful cultural background within each community. PEX:FGD discussion uncovered numerous cultural factors within local life practices that could influence on the Egg Intervention engagement and acceptability. Combining themes from the WVIS workshop and PEX:FGDs allowed for specific recommendations to be made towards a culturally-informed protocol for the design of an Egg Intervention that included both broad cultural themes and specific Intervention insights (Table 3 ). For example, in Kaffrine, Senegal, to know that the husband’s authoritative family decision-making for health care (specific) is rooted in Islamic foundations (wider cultural) points to an Intervention Recommendation within the protocol, involving consultations with Islamic Leaders to lead community awareness targeting fathers. Similarly, in East Lombok, Indonesia the (specific) behaviour of breastfeeding for 2 years was underpinned by (wider cultural) shared values of living in Islam. This understanding of local values could prevent the imposition of culturally misaligned values, which Bernal and Adames (2017) caution against [ 17 ].

There are a number of interesting overlaps between values seen in the WVIS Frameworks and Narratives and the categories of Schwartz (1992) and The World Values Survey (2023) [ 41 , 42 ]. For example, in both Kaffrine, Senegal and East Lombok, Indonesia, strong religious values were found, and the groups spoke of the importance of practicing their religion with daily habits. This would align with traditional and conservation values [ 41 , 43 ]. Furthermore, in Kaffrine, Senegal participants often mentioned the importance of mutual aid within the community, and similar values of togetherness and respect in the community were found in East Lombok, Indonesia. These would seem to align with traditional, survival and conservation values [ 41 , 43 ]. However, the values mentioned by the groups in the WVIS workshops are far more specific, and it is possible that through asking what is most worthwhile, valuable and meaningful about their context, the participants are able to prioritise which aspects of their values are most salient to their daily lives. Grounded shared values such as these are generally neglected in Global Health Research, and values predominant in the Global North are often assumed to be universal [ 14 ]. Thus, by excluding the use of a predefined external framework, we minimized the risk of imposing our own ideas of values in the community, and increased the relevance, significance and local validity of the elicited information [ 28 ].

Participatory methods of engagement are an essential step in conducting Global Health Research but there is currently a paucity of specific guidance for implementing participatory methods in vulnerable communities [ 16 , 44 ]. In addition, there is acknowledgement in the literature that it is necessary to come into communities in LMICs without assumptions about their held values, and to use bottom-up participatory approaches to better understand local values [ 14 , 16 ]. The WVIS plus PEX:FGD methodology highlighted here exemplifies a method that is replicable in multiple country contexts [ 28 , 32 ] and can be used to crystallize local In Situ Shared Values which can be easily communicated to external researchers. Coupled with the specialised FGD (PEX:FGD), values-based perceptions of specific topics (in this case stunting) can be elicited leading to the creation of specific Culture-based recommendations. This therefore takes steps to answer the call by Memon and colleagues (2021) for the creation of cultural protocols ahead of conducting research in order to foster ethical research relationships [ 16 ]. We believe that the potential usefulness of the WVIS approach to guide engagement and inform intervention design is effectively demonstrated in this study and WVIS offers a method of making explicit local values in a novel and valuable way.

However, we acknowledge that our approach has several limitations. It has relied heavily on the local university researchers to debate and decide which participant stakeholder groups should be chosen, and although they did this in the context of the Whole Child approach, it would have been advantageous to have involved cultural researchers with a deeper understanding of cultural structures, to ensure sufficient opportunities for key cultural elements to emerge. This would have in particular strengthened the intervention design derived from the PEX:FGD data. For example, we retrospectively realised that our study could have been improved if grandmothers had been engaged in East Lombok. Understanding this limitation leads to suggestion for further work: to specifically investigate the overlap of this approach with disciplinary studies of culture, where social interactions and structures are taken into account via formal frameworks.

There are more minor limitations to note. For example, the WVIS approach can only be led by a trained and experienced facilitator: not all researchers can do this. A training programme is currently under development that could be made more widely available through online videos and a Handbook. Secondly, although the groups recruited do not need to be representative of the local population, the number recruited should be increased until theoretical saturation is achieved of the themes which emerge, which was not carried out in this study as we focussed on demonstrating the feasibility of the tool. Thirdly, there is a limit to the number of topics that can be explored in the PEX:FGDs within the timeframe of one focus group (depending on the stamina of the participants), and so if a wider range of topics need formative research, then more workshops are needed. Lastly, this work took place in a large, highly collaborative project involving expert researchers from local countries as well as international experts in WVIS : other teams may not have these resources. However, local researchers who train in WVIS could lead on their own (and in this Hub project such training was available).

The need for better understanding, acknowledgement and integration of local culture and shared values is increasing as the field of Global Health Research develops. This study demonstrates that the WVIS plus PEX:FGD shared values approach provides an efficient approach to contextualise and localise interventions, through eliciting and making communicable shared values and local life practices which can be used towards the formation of a culturally-informed protocols. Were this method to be used for intervention design in future, it is possible that more focus should be given to existing social structures and support systems and a greater variety of stakeholders should be engaged. This study thus contributes to the literature on methods to culturally adapt interventions. This could have significant implications for improving the uptake of nutrition interventions to reduce malnutrition through improved social acceptability, which could help progression towards the goal of Zero Hunger set within the SDGs. The transferability and generalisability of the WVIS plus PEX:FGD approach should now be investigated further in more diverse cultures and for providing formative research information for a wider range of research themes. Future studies could also focus on establishing its scaling and pragmatic usefulness as a route to conceptualising mechanisms of social acceptability, for example a mechanism may be that in communities with strong traditional religious values, social hierarchies involving religious leaders and fathers exist and their buy-in to the intervention is crucial to its social acceptability. Studies could also focus on the comparison or combination of WVIS plus PEX:FGD with other qualitative methods used for intervention design and implementation.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request [email protected], Orcid number 0000–0002–1811-4597. These include deidentified Frameworks of Shared Values and Accompanying Narrative from each Group; deidentified Hub Insight Statements of relevant themes.

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Acknowledgements

We thank the Hub PI, Claire Heffernan, for feedback on a late draft of the manuscript.

The Action Against Stunting Hub is funded by the Medical Research Council through the UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF), Grant No.: MR/S01313X/1.

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Annabel J. Chapman, Mahsa Firoozmand & Marie K. Harder

Department of Environmental Science and Engineering, Fudan University, Shanghai, People’s Republic of China

Chike C. Ebido, Rahel Neh Tening, Yanyan Huang & Marie K. Harder

Department of Zoology and Environmental Biology, University of Nigeria, Nsukka, Nigeria

Chike C. Ebido

Preventive Medicine and Public Health, Université Cheikh Anta Diop (UCAD), Dakar, Senegal

Ndèye Marème Sougou

Faculty of Psychology, Universitas Islam Negeri Syarif Hidayatullah, Jakarta, Indonesia

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Southeast Asian Ministers of Education Organization Regional Centre for Food and Nutrition (SEAMEO RECFON) Universitas Indonesia, Jakarta, Indonesia

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Contributions

MKH formulated the initial research question and study design. AJC developed the specific research question. Data collection in Senegal involved CCE, NMS, AHD, FBD, RNT, CEHAN and JM. Data collection in Indonesia involved RA, RK, YH and MKH. Cultural interpretation in Senegal Involved AHD, FBD, NMS, RNT and JM. Analysis involved AJC and MF. AJC and MKH wrote the paper.

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Correspondence to Marie K. Harder .

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The research was conducted in accordance with the Declaration of Helsinki and has been approved by the Ethics Review Board of the University of Brighton, and national ethics committees for research in Indonesia and Senegal. Informed consent was obtained in the local vernacular language, Bahasa, French or Wolof. Participants retained a copy of the informed consent document for reference.

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Chapman, A.J., Ebido, C.C., Tening, R.N. et al. Creating culturally-informed protocols for a stunting intervention using a situated values-based approach ( WeValue InSitu ): a double case study in Indonesia and Senegal. BMC Public Health 24 , 987 (2024). https://doi.org/10.1186/s12889-024-18485-y

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DOI : https://doi.org/10.1186/s12889-024-18485-y

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Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.)

Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation.

Chapter 5 case study quantitative data findings.

• Introduction

This chapter provides the results of the analysis of quantitative data from three different sources:

• Staff activity: task time distribution. Observations of staff activities were undertaken in each study ward to understand the types of tasks undertaken by staff and the proportion of time spent on each. Staff were shadowed by a researcher who logged their activities.
• Staff travel distances. These were collected by staff wearing pedometers. These data were collected before and after the shadowing sessions.
• Staff experience surveys. Staff surveys on each ward were conducted before and after the move to the new hospital and these data provide a comparison of perceptions of the ward environment in the old and new wards.

The survey probed perceptions of many aspects of the ward environment before and after the move. As discussed in Chapter 3 , the trust, the designers and stakeholders held various expectations about the benefits of the 100% single room design. We examined whether or not these expectations (or hypotheses about the effect of the move) were fulfilled. Specifically, the new hospital was designed to increase patient comfort, prevent infections, reduce numbers of patient falls, reduce patient stress, increase patient-centred care and increase the time spent by nurses on direct care (see Appendix 16 ). Concerns were raised about the possible reduction in staff observing and monitoring patients, increased travel distances and patient isolation.

This chapter primarily addresses the following two research questions:

• What are the advantages and disadvantages of a move to all single rooms for staff?
• Does the move to all single rooms affect staff experience and well-being and their ability to deliver effective and high-quality care?
• Staff activity: task time distribution results

Preliminary analysis showed that five activity categories accounted for 78% of observation data before the move and 83% of observation data after the move. This meant that numbers in the remaining categories were too low for analysis, so all subsequent analyses were confined to these five categories: direct care, indirect care, professional communication, medication tasks and ward-related activities. Proportion of time was derived by calculating the duration of each event from its start and end time, and then aggregating duration by activity for each observation session. The number of events for each activity was also counted ( Table 23 ).

Observations (events) per session before and after new build

Proportion of time spent in each type of activity was analysed using a general linear model with proportion of time as the dependent variable. The first model consisted of a single independent variable for before and after the new build and was used to ascertain the effect of the move to a new build, prior to adjusting for other variables. To this model were added ward (maternity, surgical, older people, AAU), staff group (midwife, RN, HCA) and day of the week. This second model was used to ascertain the effect of the move to the new build having adjusted for these variables.

Events were defined as a switch of activity (either to a new activity or to continue a previously interrupted activity) and were captured by a new entry in the PDA. The number of events (new or continuation of a previous activity) per hour was modelled in the same way except that a generalised linear model with a Poisson distribution and shift length in hours specified as offset (equivalent to modelling the hourly rate) was fitted to the data. An unadjusted analysis (before and after the new build only) and adjusted analysis (before and after the new build, ward, staff group and day of week) were performed.

Analysis of medication tasks was confined to RNs only. The fact that RMs work only on the postnatal ward means that it would not be possible to interpret whether any obtained results were due to the effect of the professional group or the ward. Therefore, staff group (i.e. midwives) was dropped from this model. On average the number of events (either new or continuations of previous activities) observed per session was higher before the move than after (177 vs. 153).

However, the move to the new build did not result in a significant change to the proportion of time spent on different activities ( Table 24 ). Although there was an increase in the proportion of direct care, indirect care, professional communication and medication tasks and a decrease in ward-related activities such as cleaning, bed making and stocking the utility room in adjusted analyses, none of these changes was statistically significant (see Table 24 ).

Mean proportion of time spent in each type of activity before and after move

Table 25 shows results for the analysis of the number of events per hour. The adjusted number of recorded events per hour decreased significantly for direct care ( p  = 0.039) and professional communication ( p  = 0.002), and increased significantly for medication tasks. A decrease in the number of events per hour for an activity, and no change in the proportion of time spent on that activity, suggests that there were fewer interruptions during these tasks and work was, therefore, less fragmented. This interpretation is supported by qualitative data showing that nurses could focus on direct care and communication tasks more easily in the single room environment. Staff had difficulty locating each other and also felt reluctant to interrupt a colleague providing direct care in a single room, and there were more frequent structured opportunities for professional communication within the small nursing teams.

Number of events per hour by type of activity before and after move

The number of events per hour increased significantly for medication tasks ( p  = 0.001), showing increased fragmentation for this task. Again, this interpretation is supported by the qualitative data showing that when staff entered a patient room to administer medication they were likely to engage in other direct care activities; thus medication administration was not carried out in a single medication round, but integrated into patient care activities generally.

We also assessed the changes in patients’ contact time per patient-day to check if nurses spent more time with the patient instead of doing other activities. The analysis draws on day shift observation data (based on 118.5 hours of staff shadowing before the move and 254.5 hours after the move). The proportion of contact time was applied to the total NHPPD to provide an estimate of the patients’ contact time per patient-day (see Table 26 ).

Patients’ contact time per patient-day before and after move in the case study wards

After the move, the contact time per patient-days increased in all units, apart from surgery, where there was a decrease in direct care and an increase in indirect care activities, for example medication activities and professional communication, and essential ward/patient care activities.

These changes are the result of a combination of two factors: a change in the proportion of care (i.e. an increase/decrease in the time spent with the patient) and a change in NHPPD (i.e. an increase/decrease in the number of nurses working full-time during a day).

• Staff travel distances results

Statistical analysis

The data were analysed using a repeated measures general linear mixed model (GLMM) with steps per hour as the dependent variable and pre/post new build, ward (maternity, surgical, older people, AAU), observation session (repeated measure), staff group (midwife, RN, HCA) and day of the week as independent variables. The first GLMM analysis investigated the main effects of ward, pre/post move, staff group and day of the week. The second GLMM analysis investigated the interactions between pre/post move and ward, and between pre/post move and staff group. Because midwives were employed only on the maternity ward, there was potential confounding between the effects of ward and staff type. Initial analyses confirmed that removing maternity from the analyses improved the fit of the models. The first sensitivity analysis added a variable to the model that indicated whether or not a member of staff contributed to both the pre- and post-build samples. Only five staff contributed to both. The effect on the overall results was minor. A second sensitivity analysis fitted a model to first observation session data only, but allowed data to repeat across individual staff before and after the build. We report the results below, including where sensitivity analyses identified differences.

The data set contains information on 140 sessions collected on 53 staff (49%) prior to and 56 staff (51%) after the new build. A number of staff contributed more than one observation session: 85 provided one session, 18 provided two sessions, five provided three sessions and one provided four sessions. There were 73 sessions (52%) collected prior to the new build and 67 sessions (48%) after the new build. The average numbers of sessions per member of staff were 1.38 and 1.20, respectively. A small number of staff ( n  = 5, 4%) were observed at both times (one RN and four HCAs). Table 27 shows descriptive data for ward and staff group.

Steps per hour before and after new build

The unadjusted means (see Table 27 ) show an increase in the number of steps per hour for all wards and staff groups. Staff working on the older people’s ward (from 664 to 845) and RNs (from 639 to 827) have seen the biggest increases.

Table 28 shows results for the main effects of ward, pre/post move, staff group and day of the week. The number of steps per hour increased significantly from a mean of 715 before the move to a mean of 839 [ F (1,83) = 10.36; p  = 0.002] after the move. HCAs took significantly more steps per hour than nurses [ F (1,83) = 8.01; p  = 0.006]. There were also significant differences between days of the week [ F (4,21) = 3.40; p  = 0.027]. There was no significant difference between wards in the distances travelled ( Table 29 ).

F -tests on main effects

Mean steps per hour by wards, pre-/post move, staff group and day of the week

Table 30 shows results for the interactions between pre/post move and ward, and between pre/post move and staff group. Neither of the two interactions was statistically significant.

F -tests on interaction effects

The estimated marginal means ( Table 31 ) showed that there was an increase from pre to post build across all wards. Although the size of this increase did not differ significantly between wards, the increases in the surgical and older people’s wards were larger than for the AAU. RNs experienced a larger increase (from 624 to 811) in the number of steps per hour (from 3.74 to 4.86 miles) than HCAs (from 828 to 862 steps; from 4.96 to 5.17 miles).

Mean steps per hour for the interactions

The estimated marginal means from the second sensitivity analysis suggested a decrease in the number of steps per hour for the AAU from 901 to 836 and for HCAs from 876 to 855, rather than an increase as shown in Table 31 . The change in means for the remaining two wards and for RNs, from pre to post build, were in the same direction, and of the same order of magnitude (see Table 31 ).

• Staff experience survey

Because of staff leave, shift patterns and staff turnover during the course of the study, it was not possible to use a completely within-subjects design, in which the pre- and post-move surveys were completed by the same people. Despite this, 19 participants did complete surveys at both times, which meant a mixed within- and between-subjects design. One potential problem with this is that the subgroup who completed both surveys could have been sensitised to the research questions and, therefore, could have been more likely to report differences after the move than those who completed only one survey; that would bias our results. We addressed this by treating the design as a between-subjects design and checking for bias by comparing the results of our analyses for the whole group with separate within-subjects analyses on the subgroup who completed both surveys. The results were identical except for a small difference: perceptions of the effect of the accommodation on the delivery of care approached significance (0.099) in the within-subjects analysis whereas for the whole group this effect was significant (0.011). This can be attributed to lack of power in the subsample of 19. On this basis we proceeded with the analysis by treating the ‘before’ and ‘after’ samples as independent groups.

There were 152 items in the staff survey. Our approach to analysis was multifaceted. First, we explored the potential for grouping questions into subscales that would summarise a topic area. We thematically analysed the questions to determine those that were likely to be measuring attitudes to related aspects of the ward design, and then tested these subscales using statistical reliability analysis. Where reliability was not adequate we revised the items in the subscales until we had identified coherent subscales. These were then analysed using independent sample t -tests to determine if post-move responses were significantly different from the pre-move scores for each subscale. Similar analyses were undertaken for the teamwork and safety climate scales. Qualitative open-ended questions were analysed thematically using a content analytic approach. The well-being and stress items were compared before and after the move using the Pearson chi-squared test and Fisher’s exact test when expected frequencies were less than 5.

One of the aims of the study was to investigate if there were differences between the case study wards in their perceptions of the positives and negatives of the new single room accommodation. However, the relatively small number of staff in each of the case study wards meant that it was not possible to explore this question statistically. We therefore used correspondence analysis and perceptual mapping to examine the interaction between ward attributes and case study wards. Correspondence analysis is an exploratory mapping tool that allows visualisation of relationships in the data that would be difficult to identify if presented in a table. 114 It is related to other techniques such as factor analysis and multidimensional scaling. It does not rely on significance testing and is best viewed as an exploratory technique that provides insights into the similarities and differences between two variables. 115 Correspondence analysis does not address questions of whether or not there were differences in ratings between the attributes (e.g. whether or not privacy for patients was rated more highly than staff teamwork). Instead, it focuses on the differences between case study wards and the interaction between ratings and wards. It allows an examination of to what extent which wards are associated with particular ratings. In this way it allows us to qualitatively explore the quantitative data.

Ward environment survey subscales

Ten reliable subscales were formed. Table 32 shows the subscales and example items from each.

Description of subscales

Appendix 19 contains a complete list of all items used for each subscale.

Table 33 summarises the statistical analysis of the subscales showing means, Cronbach’s alpha and the number of items for each subscale before and after the move. According to accepted criteria, 115 alpha above 0.60 is acceptable for exploratory analyses, above 0.70 is acceptable for confirmatory purposes and above 0.80 is good for confirmatory purposes. Obtained coefficients were generally good, ranging mostly between 0.67 and 0.92. The lowest alpha, of 0.53, was obtained for the family/visitors subscale after the move, suggesting that this subscale is not internally consistent. However, the pre-move alpha was good (0.70), so it was decided to retain this subscale for exploratory purposes.

Mean subscale scores and reliability analysis before and after the move

Table 34 shows the results of independent sample t -tests comparing subscale scores before and after the move. Staff perceived significant improvements in the efficiency of the physical environment, the patient amenity, the effect of the environment on infection control, patient privacy, and family and visitors. The largest increases were found for perceptions of infection control and patient privacy. Perceptions of the effect of the ward environment on teamwork and care delivery were significantly more negative after the move. There were no significant differences in staff perceptions of staff facilities, patient safety and staff safety.

Results of t -tests comparing perceptions of the ward environment before and after the move

Although all subscales showed moderate to very good reliability, changes were not uniform for all items in every subscale; there were some exceptions to the overall trend. Overall ratings for the subscale ‘efficiency of physical environment’ increased, but ratings for the item ‘ward design/layout minimises walking distances for staff’ decreased. These perceptions were confirmed by our findings from the analysis of travel distances showing that staff took significantly more steps after than before the move. Some aspects of the design increased the amenity of the ward for staff but others did not. For example, staff toilet facilities, locker facilities and space at staff bases were rated more highly but ratings for social interaction and natural light decreased. These positive and negative aspects meant there was no significant difference in staff amenity before and after the move. The new ward was rated as much more positive for patients but there were reduced scores for three items after the move: social contact between patients, ability of patients to see staff and way finding. All aspects of teamwork and training were rated less positively, except for the item ‘discussing patient care with colleagues’, which increased. This finding is supported by our analysis of observation data showing that professional communication activities were less fragmented.

Although there were no significant differences in the effect of the ward layout on perceptions of patient safety, examination of the items showed that ratings for two items increased (‘minimising risk to patients of physical/verbal abuse from other patients/visitors’ and ‘minimising the risk of medication errors’) while ratings for two items decreased (‘responding to patient calls for assistance’ and ‘minimising the risk of falls/injury to patients’). This suggests that, although staff thought some risks to safety were reduced, they perceived an increased risk of falls and delays in responding to calls for assistance. Staff perceptions of a rise in risk of falls are detailed in Chapter 6 . Staff also reported being unable to hear calls for assistance when in a single room with a patient.

There were five items that did not fit into any of the subscales. These items were analysed singly using Fisher’s exact test and the results are shown in Table 35 . There was a significant relationship between the move and ratings for the number and location of hand basins, ease of keeping patient areas clean and quiet, and the overall comfort of patients, which all increased after the move. There was no relationship between the move and judgements of whether or not the location of the dirty utility room (where bedpans are stored and disposed of) reduces cross-contamination.

Results of single-item analyses

The distribution of responses for the four significant items showed that significantly more staff rated these aspects of single room accommodation as more positive after the move than before ( Tables 36 – 39 ).

Distribution of responses for the item ‘Number and location of CHWBs supports good hand hygiene’

Distribution of responses for the item ‘Easy to keep patient care areas clean’

Distribution of responses for the item ‘Overall comfort of patients’

Distribution of responses for the item ‘Easy to keep patient care areas quiet’

Expectations before the move and reality after the move

Before the move, staff were asked to rate on a five-point scale whether they thought single rooms would be better or worse for different aspects of clinical work (e.g. minimising the risk of patient falls, maintaining patient confidentiality, knowing when other staff might need help). After the move they again rated whether single rooms were better or worse for clinical work, thus providing a measure of whether or not their expectations about single rooms were met in reality. The questions were a subset of 23 questions from the first part of the survey and were analysed using Fisher’s exact test.

Results ( Table 40 ) showed that staff perceptions of whether or not single rooms were better than multibedded wards changed after the move for five items. Staff perceptions of whether or not single rooms were better for responding to calls for assistance, knowing when other staff might need help and minimising walking distances were rated as worse or much worse by significantly more staff after than before the move. Staff rated single rooms as positive for patient sleep and rest and for interactions between patients and visitors after the move.

Relationship between expectations before the move and reality after the move

Tables 41 – 45 show the distribution of significant responses.

Distribution of responses for the item ‘Responding to patient calls for assistance’

Distribution of responses for the item ‘Minimising staff walking distances’

Distribution of responses for the item ‘Patient sleep and rest’

Distribution of responses for the item ‘Knowing when other staff might need a helping hand’

Distribution of responses for the item ‘Patient interaction with visitors’

Teamwork and safety climate survey

To take into account our changes to the survey, we combined the four items about the quality of communication with doctors, nurses, nursing assistants and AHPs with the items in the information handover subscale to form a new subscale of seven items. Although this is different from the scales reported by Hutchinson et al. , 98 reliability analysis confirmed the original factor structure of the survey. There were two teamwork subscales and three safety climate subscales with good to high reliability ( Table 46 ). See Appendix 20 for a list of the items contained in each subscale.

Mean scores for all subscales decreased following the move. Independent sample t -tests showed that ratings for information handover and communication decreased significantly following the move [ t  = 3.23, degrees of freedom (df) = 108, p  = 0.002], indicating that information exchange and sharing within teams was perceived to be worse after the move. There were no other significant differences.

Correspondence analysis

Correspondence analysis transforms cross-tabulated data into a biplot showing distances between variables. In this study, case study ward was a column variable and mean questionnaire subscale score was a row variable (see Table 33 ). As appropriate when analysing mean scores, Euclidean distance was used and standardisation by removing row means was used. 114 , 116 This means that differences between the subscale means were not represented in the perceptual map, as we were not interested in whether or not, for example, infection control was rated more highly than privacy. Differences between wards, contained in the columns, were of interest and are represented in the perceptual map. Separate analyses were conducted for before and after the move and for the ward attributes and teamwork/safety climate survey.

Figure 11 shows perceptual maps of the association between ward attributes and wards before and after the move. The pre-move map shows that the points on the map were dispersed, indicating that the ratings were not strongly associated with particular wards. There was one exception in that ratings for the efficiency of the physical environment, privacy and infection control were higher for the older people’s ward than for the other wards. The post-move map shows that the highest ratings for the efficiency of the physical environment, the delivery of care, the staff facilities and teamwork were obtained in the older people’s and surgical wards, indicated by proximity on the map. Ratings for patient amenity, infection control, privacy and family/visitors were highest for the surgical ward. High ratings for patient safety were obtained in maternity and the surgical ward. Ratings for staff safety were similar in the older people’s, surgical and maternity wards. The acute assessment ward was not associated with any particular ward attributes, as was the case before the move.

Perceptual maps of (a) pre- and (b) post-move ward attributes by ward.

Figure 12 shows perceptual maps before and after the move of the association between teamwork/safety climate ratings and wards. The teamwork/safety climate survey consisted of two teamwork subscales – team input into decisions, and information handover and communication – and three safety climate subscales – attitudes to safety within own team, overall confidence in safety of organisation and perceptions of management attitudes to safety. The pre-move map shows that ratings of input into decisions, information and handover, and overall confidence in safety of the organisation were highest for the acute assessment ward. Ratings of safety attitudes within the team and management attitudes to safety were highest for the surgical ward. After the move, the surgical ward had the highest ratings for safety attitudes within the team, overall attitudes to safety and management; ratings for team input into decisions and information handover and communication were highest for the older people’s ward. Ratings for all safety climate subscales decreased in the acute assessment ward, which is indicated on the perceptual map by its location in a quadrant by itself. Maternity scores did not show a consistent pattern.

Perceptual map of (a) pre- and (b) post-move ratings of teamwork/safety climate by ward. Att., attitude; mgt., management.

These maps reveal some differences between wards in perceptions of the ward environment and show that perceptions were different before and after the move.

Staff ward preferences

Nursing staff were asked to indicate whether they would prefer single rooms, multibedded accommodation or a combination. There was a range of views ( Figure 13 ). In each phase, fewer than 18% of staff indicated a preference for 100% single rooms. The most common preference in each phase was a combination of 50% of beds in single rooms and 50% in bays (see Figure 13 ). In the pre-move survey, more staff reported a preference for more beds in bays ( n  = 20) than in the post-move phase ( n  = 12).

Nurse preferences for single room or multibedded accommodation.

Staff stress and well-being

There were five categorical questions about staff well-being that investigated whether or not they had experienced injuries and harassment in the previous 12 months ( Table 47 ). There were three items about job stress that asked participants to rate their stress on a five-point Likert scale ( Table 48 ) . Results showed no differences in staff well-being and stress before and after the move.

Relationship between move and staff well-being

Relationship between move and staff stress

Staff were asked 10 questions about their satisfaction with their own performance of various tasks during their last shift, and one question about their overall job satisfaction. Results ( Table 49 ) showed no significant effect for any of the job satisfaction items.

Relationship between job satisfaction and move

Qualitative survey data

Four open-ended questions were used to gain qualitative data about staff attitudes. The questions were:

• What two things do you think would most improve the current ward environment for staff?
• What two things do you think would most improve the current ward environment for patients?
• What two things are you most looking forward to in relation to the move to 100% single rooms in the new hospital?
• What two things are you most concerned about in relation to the move to 100% single rooms in the new hospital?
• What two things do you like the most about single room wards in the new hospital?
• What two things do you dislike most about single room wards in the new hospital?

In the following sections we present the results of the thematic analysis with frequency data (almost equal numbers of staff responded before and after the move, n  = 55 and n  = 54 respectively) and examples from participants’ written responses where appropriate. Table 50 shows that staff identified a number of things that would improve the ward accommodation for patients. The need for more space, improved patient facilities, privacy, and rest and sleep were largely met, since there were fewer people identifying these as needs after the move. However, the need for improved patient–staff ratios and a day room to provide patient social interaction were still reported after the move.

What would improve the current ward environment for patients ? Response frequencies

The need that staff perceived before the move for space around patient beds and staffing levels had decreased after the move ( Table 51 ). However, ventilation/heating/lighting, access to equipment and supplies and facilities for staff, including staff bases, were identified as needing improvement after move. In addition there was a need for improvements in monitoring patients, keeping track of colleagues, reducing isolation and reducing walking distances. These have all been identified by other parts of our results (see Chapter 6 ).

What would improve the current ward environment for staff ? Response frequencies

Staff were asked about the features of the ward they were most looking forward to in the pre-move phase, and most liked in the post-move phase ( Table 52 ). Results showed that staff most liked the increased patient privacy, patient sleep and rest, increased space, working in a modern environment and improved patient bathroom facilities.

What are you most looking forward to/do you most like about 100% single room accommodation? Response frequencies

Table 53 shows that staff were most concerned about being able to monitor patients, patient isolation and the risk of falls. Being unable to find staff and increased walking distances also emerged as features staff disliked about single rooms.

What are you most concerned about/do you most dislike about 100% single room accommodation? Response frequencies

• Most staff would prefer a mix of single rooms and multibedded rooms on wards.
• Staff activity events observed per session were higher after the move and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.
• A significant increase in medication tasks among recorded events suggests medication administration was integrated into patient care activities and was not undertaken as a medication ‘round’.
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards and for RNs/RMs.
• efficiency in carrying out tasks
• patient amenity, including comfort, space, sleep, light and ventilation
• infection control
• patient privacy
• patient interaction with family/visitors and their involvement in care.
• In open comments, staff most liked the increased patient privacy, working in a modern environment, improved patient sleep and rest, and space around the bedside.
• delivery of care, including factors such as spending time with patients, communication with patients, monitoring patients and remaining close to patients, responding to calls for assistance, minimising the risks to staff, minimising walking distances and staff spending time with patients
• teamwork, including being able to locate staff, obtain assistance from colleagues, informal learning, keeping team members updated, discussing care with colleagues and knowing when other staff might need help.
• In addition, in open comments staff were most concerned about patient isolation, the risk of falls and staff isolation.
• There were no perceived differences in staff amenity and patient and staff safety.
• Ratings for information handover and communication decreased significantly following the move. This suggests that information exchange and sharing within teams – and between professions – was perceived to be worse after the move.
• Different wards valued different aspects of the ward environment.
• Ratings for staff toilet facilities, locker facilities and space at staff bases were rated more highly but ratings for social interaction and natural light decreased.
• No differences were found in staff job satisfaction, well-being or stress before and after the move.
• The need for improved patient–staff ratios and a day room to provide patient social interaction was still reported after the move.

Included under terms of UK Non-commercial Government License .

• Cite this Page Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings.
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