mixing methods qualitative and quantitative research

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Combining qualitative and quantitative research within mixed method research designs: A methodological review

Ulrika Östlund.

a Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

b Institute for Applied Health Research/School of Health, Glasgow Caledonian University, United Kingdom

Yvonne Wengström

c Division of Nursing, Department or Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

Neneh Rowa-Dewar

d Public Health Sciences, University of Edinburgh, United Kingdom

It has been argued that mixed methods research can be useful in nursing and health science because of the complexity of the phenomena studied. However, the integration of qualitative and quantitative approaches continues to be one of much debate and there is a need for a rigorous framework for designing and interpreting mixed methods research. This paper explores the analytical approaches (i.e. parallel, concurrent or sequential) used in mixed methods studies within healthcare and exemplifies the use of triangulation as a methodological metaphor for drawing inferences from qualitative and quantitative findings originating from such analyses.

This review of the literature used systematic principles in searching CINAHL, Medline and PsycINFO for healthcare research studies which employed a mixed methods approach and were published in the English language between January 1999 and September 2009.

In total, 168 studies were included in the results. Most studies originated in the United States of America (USA), the United Kingdom (UK) and Canada. The analytic approach most widely used was parallel data analysis. A number of studies used sequential data analysis; far fewer studies employed concurrent data analysis. Very few of these studies clearly articulated the purpose for using a mixed methods design. The use of the methodological metaphor of triangulation on convergent, complementary, and divergent results from mixed methods studies is exemplified and an example of developing theory from such data is provided.

A trend for conducting parallel data analysis on quantitative and qualitative data in mixed methods healthcare research has been identified in the studies included in this review. Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings, help researchers to clarify their theoretical propositions and the basis of their results. This can offer a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and develop new theory.

What is already known about the topic?

• • Mixed methods research, where quantitative and qualitative methods are combined, is increasingly recognized as valuable, because it can potentially capitalize on the respective strengths of quantitative and qualitative approaches.
• • There is a lack of pragmatic guidance in the research literature as how to combine qualitative and quantitative approaches and how to integrate qualitative and quantitative findings.
• • Analytical approaches used in mixed-methods studies differ on the basis of the sequence in which the components occur and the emphasis given to each, e.g. parallel, sequential or concurrent.

What this paper adds

• • A trend for conducting parallel analysis on quantitative and qualitative data in healthcare research is apparent within the literature.
• • Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings and help researchers to clearly present both their theoretical propositions and the basis of their results.
• • Using triangulation as a methodological metaphor may also support a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and aid the development of new theory.

1. Introduction

Mixed methods research has been widely used within healthcare research for a variety of reasons. The integration of qualitative and quantitative approaches is an interesting issue and continues to be one of much debate ( Bryman, 2004 , Morgan, 2007 , Onwuegbuzie and Leech, 2005 ). In particular, the different epistemological and ontological assumptions and paradigms associated with qualitative and quantitative research have had a major influence on discussions on whether the integration of the two is feasible, let alone desirable ( Morgan, 2007 , Sale et al., 2002 ). Proponents of mixed methods research suggest that the purist view, that quantitative and qualitative approaches cannot be merged, poses a threat to the advancement of science ( Onwuegbuzie and Leech, 2005 ) and that while epistemological and ontological commitments may be associated with certain research methods, the connections are not necessary deterministic ( Bryman, 2004 ). Mixed methods research can be viewed as an approach which draws upon the strengths and perspectives of each method, recognising the existence and importance of the physical, natural world as well as the importance of reality and influence of human experience ( Johnson and Onquegbuzie, 2004 ). Rather than continue these debates in this paper, we aim to explore the approaches used to integrate qualitative and quantitative data within healthcare research to date. Accordingly, this paper focuses on the practical issues of conducting mixed methods studies and the need to develop a rigorous framework for designing and interpreting mixed methods studies to advance the field. In this paper, we will attempt to offer some guidance for those interested in mixed methods research on ways to combine qualitative and quantitative methods.

The concept of mixing methods was first introduced by Jick (1979) , as a means for seeking convergence across qualitative and quantitative methods within social science research ( Creswell, 2003 ). It has been argued that mixed methods research can be particularly useful in healthcare research as only a broader range of perspectives can do justice to the complexity of the phenomena studied ( Clarke and Yaros, 1988 , Foss and Ellefsen, 2002 , Steckler et al., 1992 ). By combining qualitative and quantitative findings, an overall or negotiated account of the findings can be forged, not possible by using a singular approach ( Bryman, 2007 ). Mixed methods can also help to highlight the similarities and differences between particular aspects of a phenomenon ( Bernardi et al., 2007 ). Interest in, and expansion of, the use of mixed methods designs have most recently been fuelled by pragmatic issues: the increasing demand for cost effective research and the move away from theoretically driven research to research which meets policymakers’ and practitioners’ needs and the growing competition for research funding ( Brannen, 2009 , O’Cathain et al., 2007 ).

Tashakkori and Creswell (2007) broadly define mixed methods research as “research in which the investigator collects and analyses data, integrates the findings and draws inferences using both qualitative and quantitative approaches” (2007:3). In any mixed methods study, the purpose of mixing qualitative and quantitative methods should be clear in order to determine how the analytic techniques relate to one another and how, if at all, the findings should be integrated ( O’Cathain et al., 2008 , Onwuegbuzie and Teddlie, 2003 ). It has been argued that a characteristic of truly mixed methods studies are those which involve integration of the qualitative and quantitative findings at some stage of the research process, be that during data collection, analysis or at the interpretative stage of the research ( Kroll and Neri, 2009 ). An example of this is found in mixed methods studies which use a concurrent data analysis approach, in which each data set is integrated during the analytic stage to provide a complete picture developed from both data sets after data has been qualitised or quantitised (i.e. where both forms of data have been converted into either qualitative or quantitative data so that it can be easily merged) ( Onwuegbuzie and Teddlie, 2003 ). Other analytic approaches have been identified including; parallel data analysis, in which collection and analysis of both data sets is carried out separately and the findings are not compared or consolidated until the interpretation stage, and finally sequential data analysis, in which data are analysed in a particular sequence with the purpose of informing, rather than being integrated with, the use of, or findings from, the other method ( Onwuegbuzie and Teddlie, 2003 ). An example of sequential data analysis might be where quantitative findings are intended to lead to theoretical sampling in an in depth qualitative investigation or where qualitative data is used to generate items for the development of quantitative measures.

When qualitative and quantitative methods are mixed in a single study, one method is usually given priority over the other. In such cases, the aim of the study, the rationale for employing mixed methods, and the weighting of each method determine whether, and how, the empirical findings will be integrated. This is less challenging in sequential mixed methods studies where one approach clearly informs the other, however, guidance on combining qualitative and quantitative data of equal weight, for example, in concurrent mixed methods studies, is rather less clear ( Foss and Ellefsen, 2002 ). This is made all the more challenging by a common flaw which is to insufficiently and inexplicitly identify the relationships between the epistemological and methodological concepts in a particular study and the theoretical propositions about the nature of the phenomena under investigation ( Kelle, 2001 ).

One approach to combining different data of equal weight and which facilitate clear identification of the links between the different levels of theory, epistemology, and methodology could be to frame triangulation as a ‘methodological metaphor’, as argued by Erzberger and Kelle (2003) . This can help to; describe the logical relations between the qualitative and quantitative findings and the theoretical concepts in a study; demonstrate the way in which both qualitative and quantitative data can be combined to facilitate an improved understanding of particular phenomena; and, can also be used to help generate new theory ( Erzberger and Kelle, 2003 ) (see Fig. 1 ). The points of the triangle represent theoretical propositions and empirical findings from qualitative and quantitative data while the sides of the triangle represent the logical relationships between these propositions and findings. The nature and use of the triangle depends upon the outcome from the analysis, whether that be convergent , where qualitative and quantitative findings lead to the same conclusion; complementary, where qualitative and quantitative results can be used to supplement each other or; divergent , where the combination of qualitative and quantitative results provides different (and at times contradictory) findings. Each of these outcomes requires a different way of using the triangulation metaphor to link theoretical propositions to empirical findings ( Erzberger and Kelle, 2003 ).

Illustrating the triangulation triangle ( Erzberger and Kelle, 2003 )

1.1. Purpose of this paper

In the following paper, we identify the analytical approaches used in mixed methods healthcare research and exemplify the use of triangulation ( Erzberger and Kelle, 2003 ) as a methodological metaphor for drawing inferences from qualitative and quantitative findings. Papers reporting on mixed methods studies within healthcare research were reviewed to (i) determine the type of analysis approach used, i.e. parallel, concurrent, or sequential data analysis and, (ii) identify studies which could be used to illustrate the use of the methodological metaphor of triangulation suggested by Erzberger and Kelle (2003) . Four papers were selected to illustrate the application of the triangulation metaphor on complementary, convergent and divergent outcomes and to develop theory.

This literature review has used systematic principles ( Cochrane, 2009 , Khan, 2001 ) to search for mixed methods studies within healthcare research. The first search was conducted in September 2009 in the data bases CINAHL, Medline and PsycINFO on papers published in English language between 1999 and 2009. To identify mixed methods studies, the search terms (used as keywords and where possible as MeSH terms) were: “mixed methods”, “mixed research methods”, “mixed research”, “triangulation”, “complementary methods”, “concurrent mixed analysis” and “multi-strategy research.” These terms were searched individually and then combined (with OR). This resulted in 1896 hits in CINAHL, 1177 in Medline and 1943 in PsycINFO.

To focus on studies within, or relevant to, a healthcare context the following search terms were used (as keywords or as MeSH terms and combined with OR): “health care research”; “health services research”; and “health”. These limits applied to the initial search (terms combined with AND) resulted in 205 hits in Medline and 100 hits in PsycINFO. Since this combination in CINAHL only limited the search results to 1017; a similar search was conducted but without using the search term triangulation to capture mixed methods papers; resulting in 237 hits. In CINAHL the search result on 1017 papers was further limited by using “interventions” as a keyword resulting in 160 papers also selected to be reviewed. Moreover; in Medline the mixed methods data set was limited by the MeSH term “research” resulting in 218 hits and in PsycINFO with “intervention” as keyword or MeSH term resulting in 178 hits.

When duplicates were removed the total numbers of papers identified were 843. The abstracts were then reviewed by each author and those identified as relevant to the review were selected to be retrieved and reviewed in full text. Papers were selected based on the following inclusion criteria: empirical studies; published in peer review journals; healthcare research (for the purpose of this paper defined as any study focussing on participants in receipt, or involved in the delivery, of healthcare or a study conducted within a healthcare setting, e.g. different kinds of care, health economics, decision making, and professionals’ role development); and using mixed methods (defined as a study in which both qualitative and quantitative data were collected and analysed ( Halcomb et al., 2009b ). To maintain rigour, a random sample (10%) of the full text papers was reviewed jointly by two authors. Any disagreements or uncertainties that arose between the reviewers regarding their inclusion or in determining the type of analytic approach used were resolved through discussion between the authors.

In addition to the criteria outlined above, papers were excluded if the qualitative element constituted a few open-ended questions in a questionnaire, as we would agree with previous authors who have argued such studies do not strictly constitute a mixed methods design ( Kroll and Neri, 2009 ). Papers were also excluded if they could not be retrieved in full text via the library services at the University of Edinburgh, Glasgow Caledonian University or the Karolinska Institutet, or did not adequately or clearly describe their analytic strategy, for example, failing to report how the qualitative and quantitative data sets were analysed individually and, where relevant, how these were integrated. See Table 1 for reasons for the exclusion of subsequent papers.

Reasons for exclusion.

A second search was conducted within the databases of Medline, PsychInfo and Cinahl to identify studies which have specifically used Erzberger and Kelle's (2003) triangulation metaphor to frame the description and interpretation of their findings. The term ‘triangulation metaphor’ (as keywords) and author searches on ‘Christian Erzberger’ and ‘Udo Kelle’ were conducted. Three papers, published by Christian Erzberger and Udo Kelle, were identified in the PsychInfo databases but none of these were relevant to the purpose of this review. There were no other relevant papers identified in the other two databases.

168 Papers were included in the final review and reviewed to determine the type of mixed analysis approach used, i.e. parallel, concurrent, or sequential mixed analysis. Four of these papers (identified from the first search on mixed methods studies and healthcare research) were also used to exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ). Data was extracted from included papers accordingly in relation to these purposes.

In total, 168 papers were included in our review. The majority of these studies originated in the USA ( n  = 63), the UK ( n  = 39) and Canada ( n  = 19), perhaps reflecting the considerable interest and expertise in mixed methods research within these countries. The focus of the studies included in the review varied significantly and the populations studied included both patients and healthcare professionals.

3.1. Analytic approaches

Table 2 illustrates the types of analytic approaches adopted in each of the studies included in the review. The most widely used analytic approach ( n  = 98) was parallel analysis ( Creswell and Plano Clark, 2007 ). However, very few of the studies employing parallel analysis clearly articulate their purpose for mixing qualitative and quantitative data, the weighting (or priority) given to the qualitative and quantitative data or the expected outcomes from doing so, mirroring previous research findings ( O’Cathain et al., 2008 ). The weighting, or priority, of the qualitative and quantitative data in a mixed methods study is dependent upon various factors including; the aims of the study and whether the purpose is, for example, to contextualise quantitative data using qualitative data or to use qualitative data to inform a larger quantitative approach such as a survey. Nonetheless, the omission of this statement makes it difficult to determine which data set the conclusions have been drawn from and the role of, or emphasis on, each approach. Therefore, is of importance for authors to clearly state this in their papers ( Creswell and Plano Clark, 2007 ). A number of studies had also used sequential data analysis ( n  = 46), where qualitative approaches were visibly used to inform the development of both clinical tools (e.g. Canales and Rakowski, 2006 ) and research measures and surveys (e.g. Beatty et al., 2004 ) or where quantitative surveys were supplemented by and issues further explored using qualitative approaches (e.g. Abadia and Oviedo, 2009 , Cheng, 2004 , Halcomb et al., 2008 ).

Included papers illustrating their analytical approach and country of origin.

Most notably, with only 20 included studies using a concurrent approach to data analysis, this was the least common design employed. Compared to the studies using a parallel or sequential approach, the authors of concurrent studies more commonly provided an explanation for their purpose of using a mixed methods design in their study, e.g. how it addressed a gap or would facilitate and advance the state of knowledge (e.g. Bussing et al., 2005 , Kartalova-O’Doherty and Tedstone Doherty, 2009 ). Despite this, there remained a lack of clarity within these studies about the weighting given to, and priority of, each method. Consequently, the importance and relevance of the findings produced by each approach and how these have informed their conclusions and interpretation is lacking. In four of the included papers a combination of approaches to data analysis (i.e. sequential and concurrent, parallel and concurrent, or sequential and parallel) were used. In the next section, we have selected papers to illustrate the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ).

3.2. Using the methodological metaphor of triangulation

We have selected four papers from our review ( Lukkarinen, 2005 , Midtgaard et al., 2006 , Shipman et al., 2008 , Skilbeck et al., 2005 ) to illustrate how the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) can be applied to mixed methods studies. Each of these studies has been used to illustrate how the metaphor of triangulation can be applied to studies producing: (i) complementary findings, (ii) convergent findings, and (iii) divergent findings. In the following section, we demonstrate how the application of the metaphor can be used as a framework both to develop theory and to facilitate the interpretation of the findings from mixed methods studies and their conclusions in each of these scenarios, and how using the metaphor can help to promote greater clarity of the study's purpose, its theoretical propositions, and the links between data sets.

3.2.1. Triangulating complementary results

To exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from complementary results, we have drawn on the results of a UK based study by Shipman et al. (2008) ( Fig. 2 ). In the UK, members of district nursing teams (DNs) provide most nursing care to people at home in the last year of life. Following concerns that inadequate education might limit the confidence of some DNs to support patients and their carers’ at home, and that low home death rates may in part be related to this, the Department of Health (DH) identified good examples of palliative care educational initiatives for DNs and invested in a 3-year national education and support programme in the principles and practice of palliative care. Shipman et al.’s study evaluates whether the programme had measurable effects on DN knowledge and confidence in competency in the principles and practice of palliative care. The study had two parts, a summative (concerned with outcomes) quantitative component which included ‘before and after’ postal questionnaires which measured effects on DNs’ ( n  = 1280) knowledge, confidence and perceived competence in the principles and practice of palliative care and a formative (concerned with process) qualitative component which included semi-structured focus groups and interviews with a sub-sample of DNs ( n  = 39).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on complementary results in the study by Shipman et al. (2008) .

While their theoretical proposition may not be explicitly stated by the authors, there is clearly an implicit theoretical proposition that the educational intervention would improve DNs knowledge and confidence (theoretical proposition 1, Fig. 2 ). This was supported by the quantitative findings which showed significant improvement in the district nurses confidence in their professional competence post intervention. Qualitative results supported and complemented the quantitative findings as the district nurses described several benefits from the program including greater confidence in tackling complex problems and better communication with patient and carers’ because of greater understanding of the reasons for symptoms. Thus, a complementary theoretical proposition (theoretical proposition 2, Fig. 2 ) can be deduced from the qualitative findings: the DN's better understanding of factors contributing to complex problems and underlying reasons for symptoms led to improved confidence in competence raised from district nurses increased understanding.

Fig. 2 illustrates the theoretical propositions, the empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 is supported by the quantitative findings. From qualitative findings, a complementary theoretical proposition (theoretical proposition 2) can be stated explaining the process that led to the DNs improved confidence in competence.

3.2.2. Triangulating convergent results

To illustrate how the methodological metaphor of triangulation can be used to draw inferences from convergent findings, we have drawn on the example of a Danish study by Midtgaard et al. (2006) ( Fig. 3 ). This study was conducted to explore experiences of group cohesion and changes in quality of life (QoL) among people ( n  = 55) who participated in a weekly physical exercise intervention (for six weeks) during treatment for cancer. The study, conducted in a Danish hospital, involved the use of structured QoL questionnaires, administered at baseline and post intervention (at six weeks) to determine changes in QoL and health status, and qualitative focus groups, conducted post intervention (at six weeks), to explore aspects of cohesion within the group. With regards to the theoretical proposition of the study ( Fig. 3 ), group cohesion was seen as essential to understand the processes within the group that facilitated the achievement of desired outcomes and the satisfaction of affective needs as well as promoting a sense of belonging to the group itself.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on convergent results in the study by Midtgaard et al. (2006) .

This proposition was deductively tested in an intervention where patients exercised in mixed gender groups of seven to nine members during a nine hour weekly session over a six week period and was supported by both the empirical quantitative and qualitative findings. The quantitative data showed significant improvements in peoples’ emotional functioning, social functioning and mental health. The qualitative data showed how the group setting motivated the individuals to pursue personal endeavors beyond physical limitations, that patients used each others as role models during ‘down periods’ and how exercising in a group made individuals feel a sense of obligation to train and to do their best. This subsequently helped to improve their social functioning which in turn satisfied their affective needs, improving their improved emotional functioning and mental health.

Fig. 3 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Both the quantitative and qualitative findings, demonstrating improvements in participants’ emotional and social functioning and their mental health, can be attributed to the nature of group cohesion within the programme as expected.

3.2.3. Triangulating divergent results

Qualitative and quantitative results that seem to contradict each other are often explained as resulting from methodological error. However, instead of a methodological flaw, a divergent result could be a consequence of the inadequacy of the theoretical concepts used. This may indicate the need for changing or developing the theoretical concepts involved ( Erzberger and Kelle, 2003 ). The following example of using the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from divergent results is intended as an example rather than an attempt to change the theoretical concept involved. In a study by Skilbeck et al. (2005) ( Fig. 4 ), some results were found to be divergent which was explained as resulting from the use of inadequate questionnaires. We do not wish to critique their conclusion; rather we intend to simply offer an alternative interpretation for their findings.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on divergent results using the study by Skilbeck et al. (2005) .

The study aimed to explore family carers’ expectations and experiences of respite services provided by one independent hospice in North England. This hospice provides inpatient respite beds specifically for planned respite admission for a two-week period. Referrals were predominated from general practitioners and patients and their carers were offered respite care twice a year, during the study this was reduced to once a year for each patient. Data was collected prior to respite admission and post respite care by semi-structured interviews and using the Relative Stress Scale inventory (RSSI), a validated scale to measure relative distress in relation to caring. Twenty-five carers were included but pre- and post-data were completed by 12 carers. Qualitative data was analysed by using a process of constant comparison and quantitative data by descriptive and comparative statistical analysis.

No clear theoretical proposition was stated by the authors, but from the definition of respite care it is possible to deduce that ‘respite care is expected to provide relief from care-giving to the primary care provider’ (theoretical proposition 1, Fig. 4 ). This proposition was tested quantitatively by pre- and post-test using the RSSI showing that the majority of carers experienced either a negative or no change in scores following the respite stay (no test of significance was stated). Accordingly, the theoretical proposition was not supported by the quantitative empirical data. The qualitative empirical results, however, were supportive in showing that most of the carers considered respite care to be important as it enabled them to have a break and a rest from ongoing care-responsibilities. From this divergent empirical data it could be suggested changing or developing the original theoretical proposition. It seems that respite care gave the carers relief from their care-responsibilities but not from the distress carers experienced in relation to caring (measured by the used scale). We would therefore suggest that in order to lessen distress related to caring, other types of support is also needed which would change the theoretical proposition as suggested (theoretical proposition 2).

Fig. 4 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 was not supported by the quantitative findings (indicated in Fig. 4 by the broken arrow), but the qualitative findings supported this proposition. From these divergent empirical findings, the theoretical proposition could accordingly be changed and developed. Respite care seemed to provide relief from carers’ on-going care-responsibilities, but other types of support need to be added to provide relief from distress experienced (theoretical proposition 2).

3.2.4. Triangulation to produce theoretical propositions

Methodological triangulation has also been applied to illustrate how theoretical propositions can be produced by drawing on the findings from a Finnish study by Lukkarinen (2005) ( Fig. 5 ). The purpose of this longitudinal study was to describe, explain and understand the subjective health related quality of life (QoL) and life course of people with coronary artery disease (CAD). A longitudinal quantitative study was undertaken during the year post treatment and 19 individuals also attended thematic interviews one year after treatment. This study is one of the few studies that clearly defines theoretical underpinnings for both the selected methods and their purpose, namely “to obtain quantitatively abundant average information about the QoL of CAD patients and the changes in it as well as the patients’ individual, unique experiences of their respective life situations” ( Lukkarinen, 2005 :622).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) to develop theory from the study by Lukkarinen (2005) .

The results of the quantitative analysis showed that the male and female CAD patients in the youngest age group had the poorest QoL. While patients’ QoL improved in the dimensions of pain, energy and mobility it deteriorated on dimensions of social isolation, sleep and emotional reactions. From the viewpoint of methodological triangulation used in the study the aim of the quantitative approach was to observe changes in QoL at the group level and also explore correlations of background factors to QoL. The qualitative approach generated information concerning both QoL in the individuals’ life situation and life course and the individuals’ rehabilitation. Both the quantitative and the qualitative analysis showed the youngest CAD patients to have the poorest psychosocial QoL. The results obtained using qualitative methods explained the quantitative findings and offered new insight into the factors related to poor psychosocial QoL, which could be used to help develop theoretical propositions around these. Patients at risk of poorer QoL were those with an acute onset of illness at a young age that led to an unexpected termination of career, resulting in financial problems, and worries about family. This group also experienced lack of emotional support (especially the females with CAD) and were concerned for the illness that was not alleviated by treatment. The interviews and the method of phenomenological psychology therefore helped to gain insight into the participants’ situational experience of QoL and life course, not detectable by the use of a questionnaire.

Fig. 5 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the relationships between these. The use of the mixed methods approach enabled a clearer understanding to emerge in relation to the lived experience of CAD patients and the factors that were related to poor QoL. This understanding allows new theoretical propositions about these issues to be developed and further explored, as depicted at the theoretical level.

4. Discussion

As the need for, and use of, mixed methods research continues to grow, the issue of quality within mixed methods studies is becoming increasingly important ( O’Cathain et al., 2008 , O’Cathain et al., 2007 ). Similarly, the need for guidance on the analysis and integration of qualitative and quantitative data is a prominant issue ( Bazeley, 2009 ). This paper firstly intended to review the types of analytic approaches (parallel, concurrent or sequential data analysis) that have been used in mixed methods studies within healthcare research. As identified in previous research ( O’Cathain et al., 2008 ), we found that the majority of studies included in our review employed parallel data analysis in which the different analyses are not compared or consolidated until the full analysis of both data sets have been completed. A trend to conduct separate analysis on quantitative and qualitative data is apparent in mixed methods healthcare studies, despite the fact that if the data were correlated, a more complete picture of a particular phenomenon may be produced ( Onwuegbuzie and Teddlie, 2003 ). If qualitative and quantitative data are not integrated during data collection or analysis, the findings may be integrated at the stage of interpretation and conclusion.

Although little pragmatic guidance exists within the wider literature, Erzberger and Kelle (2003) have published some practical advice, on the integration of mixed methods findings. For mixed methodologists, the ‘triangulation metaphor’ offers a framework to facilitate a description of the relationships between data sets and theoretical concepts and can also assist in the integration of qualitative and quantitative data ( Erzberger and Kelle, 2003 ). Yet despite the fact that the framework was published in 2003 within Tashakkori and Teddlie's (2003) seminal work, the Handbook for Mixed Methods in Social and Behavioural Research, our search revealed that it has received little application within the published body of work around mixed methods studies since its publication. This is surprising since mixed methodologists are acutely aware of the lack of guidance with regards to the pragmatics and practicalities of conducting mixed methods research ( Bryman, 2006 , Leech et al., 2010 ). Furthermore, there have been frequent calls to move the field of mixed methods away from the “should we or shouldn’t we” debate towards the practical application, analysis and integration of mixed methods and its’ findings and what we can learn from each other's work and advice. Consequently, we have a state of ambiguity and instability in the field of mixed methods in which mixed methodologists find themselves lacking appropriate sources or evidence to draw upon with which to facilitate the future design, conduct and interpretation of mixed methods studies. It is for these reasons that we, in this paper, also intended to identify and select studies that could be used as examples for the application of Erzberger and Kelle's (2003) triangulation metaphor.

When reviewing the studies it was clear that the majority of theoretical assumptions were implicit, rather than explicitly stated by authors. Wu and Volker (2009) previously acknowledged that while studies undoubtedly have a theoretical basis in their literature reviews and the nature of their research questions, they often fail to clearly articulate a particular theoretical framework. This is unfortunate as theory can help researchers to clarify their ideas and also help data collection, analysis and to improve the study's rigour ( Wu and Volker, 2009 ). When using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ), researchers are encouraged to articulate their theoretical propositions and to validate their conclusions in relation to the chosen theories. Theory can also guide researchers when defining outcome measures . Should the findings not support the chosen theory, as shown in our examples on complementary and divergent results, researchers can modify or expand their theory accordingly and new theory may be developed ( Wu and Volker, 2009 ). It is therefore our belief that using triangulation as a methodological metaphor in mixed methods research can also benefit the design of mixed method studies.

Like other researchers ( O’Cathain et al., 2008 ), we have also found that most of the papers reviewed lacked clarity in whether the reported results primarily stemmed from qualitative or quantitative findings. Many of the papers were even less clear when discussing their results and the basis of their conclusions. The reporting of mixed methods studies is notoriously challenging, but clarity and transparency are, at the very least, crucial in such reports ( O’Cathain, 2009 ). Using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) may be one way of addressing this lack of clarity by explicitly showing the types of data that researchers have based their interpretations on. It may even help address some of the issues raised in the debate on the feasibility of integrating research methods and results stemming from different epistemological and ontological assumptions and paradigms ( Morgan, 2007 , Sale et al., 2002 ). In order to carry out methodological triangulation researchers also need to identify and observe the consistency and adequacy of the two methods, positivistic and phenomenological regarding the research questions, data collection, methods of analysis and conclusions.

While we used systematic principles in our search for mixed methods studies in healthcare research, we cannot claim to have included all such studies. In many cases, reports of mixed methods studies are subjected to ‘salami slicing’ by researchers and hence the conduct of, and findings from, individual approaches are addressed in separate papers. Since these papers are often not indexed as a ‘mixed method’ study, they are undoubtedly more difficult to identify. Furthermore, different terminologies are used to describe and index mixed methods studies within the electronic databases ( Halcomb and Andrew, 2009a ), making it challenging to be certain that all relevant studies were captured in this review. However, the studies included in this review should give a sufficient overview of the use of mixed analysis in healthcare research and most importantly, they enable us to make suggestions for the future design, conduct, interpretation and reporting of mixed methods studies. It is also important to emphasise that we have based our triangulation examples on the data published but have no further knowledge of the analysis and findings undertaken by the authors. The examples should thus be taken as examples and not alternative explanations or interpretations.

Mixed methods research within healthcare remains an emerging field and its use is subject to much debate. It is therefore particularly important that researchers clearly describe their use of the approach and the conclusions made to improve transparency and quality within mixed methods research. The use of triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) can help researchers not only to present their theoretical propositions but also the origin of their results in an explicit way and to understand the links between theory, epistemology and methodology in relation to their topic area. Furthermore it has the potential to make valid inferences, challenge existing theoretical assumptions and to develop or create new ones.

Conflict of interest

None declared.

Ethical approval

Not required.

• Abadia C.E., Oviedo D.G. Bureaucratic itineraries in Colombia. A theoretical and methodological tool to assess managed-care health care systems. Social Science & Medicine. 2009; 68 (6):1153–1160. [ PubMed ] [ Google Scholar ]
• Bazeley P. Analysing mixed methods data. In: Andrew S., Halcomb E.J., editors. Mixed Methods Research for Nursing and the Health Sciences. Wiley-Blackwell; Chichester: 2009. pp. 84–118. [ Google Scholar ]
• Beatty P.W., Neri M.T., Bell K., DeJong G. Use of outcomes information in acute inpatient rehabilitation. American Journal of Physical Medicine & Rehabilitation. 2004; 83 (6):468–478. [ PubMed ] [ Google Scholar ]
• Bernardi L., Kleim S., von der Lippe H. Social influences on fertility: a comparative mixed methods study in Eastern and Western Germany. Journal of Mixed Methods Research. 2007; 1 (1):23–47. [ Google Scholar ]
• Brannen J. Prologue: mixed methods for novice researchers: reflections and themes. International Journal of Multiple Research Approaches. 2009; 3 (1):8–12. [ Google Scholar ]
• Bryman A. University Press; Oxford: 2004. Social Research Methods. [ Google Scholar ]
• Bryman A. Integrating quantitative and qualitative research: how is it done? Qualitative Research. 2006; 6 (1):97–113. [ Google Scholar ]
• Bryman A. Barriers to integrating quantitative and qualitative research. Journal of Mixed Methods Research. 2007; 1 (1):8–22. [ Google Scholar ]
• Bussing R., Koro-Ljungberg M.E., Gary F., Mason D.M., Garvan C.W. Exploring help-seeking for ADHD symptoms: a mixed-methods approach. Harvard Review of Psychiatry. 2005; 13 (2):85–101. [ PubMed ] [ Google Scholar ]
• Canales M.K., Rakowski W. Development of a culturally specific instrument for mammography screening: an example with American Indian women in Vermont. Journal of Nursing Measurement. 2006; 14 (2):99–115. [ PubMed ] [ Google Scholar ]
• Cheng G.Y. A study of clinical questions posed by hospital clinicians. Journal of the Medical Library Association. 2004; 92 (4):445–458. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Clarke P.N., Yaros P.S. Research blenders: commentary and response. Transitions to new methodologies in nursing sciences. Nursing Science Quarterly. 1988; 1 (4):147–151. [ PubMed ] [ Google Scholar ]
• Cochrane, 2009. Cochrane Handbook for Systematic Reviews of Interventions. http://www.cochrane-handbook.org/ . The Cochrane Collaboration.
• Creswell J.W. Sage; Thousand Oaks: 2003. Research Design: Qualitative, Quantitative and Mixed Methods Approaches. [ Google Scholar ]
• Creswell J.W., Plano Clark V.L. Sage; Thousand Oaks: 2007. Designing and Conducting Mixed Methods Research. [ Google Scholar ]
• Erzberger C., Kelle U. Making inferences in mixed methods: The rules of integration. In: Tashakkori A., Teddlie C., editors. Handbook of Mixed Methods in Social & Behavioural Research. Sage; Thousand Oaks: 2003. pp. 457–488. [ Google Scholar ]
• Foss C., Ellefsen B. The value of combining qualitative and quantitative approaches in nursing research by means of method triangulation. Journal of Advanced Nursing. 2002; 40 (2):242–248. [ PubMed ] [ Google Scholar ]
• Halcomb E.J., Andrew A. Managing mixed methods projects. In: Andrew S., Halcomb E.J., editors. Mixed Methods Research for Nursing and the Health Sciences. Wiley-Blackwell; Chichester: 2009. pp. 50–64. [ Google Scholar ]
• Halcomb E.J., Andrew A., Brannen J. Introduction to mixed methods research for nursing and the health sciences. In: Andrew S., Halcomb E.J., editors. Mixed Methods Research for Nursing and the Health Sciences. Wiley-Blackwell; Chichester: 2009. pp. 3–12. [ Google Scholar ]
• Halcomb E.J., Davidson P.M., Griffiths R., Daly J. Cardiovascular disease management: time to advance the practice nurse role? Australian Health Review. 2008; 32 (1):44–53. [ PubMed ] [ Google Scholar ]
• Jick T.D. Mixing qualitative and quantitative methods: triangulation in action. Administrative Science Quarterly. 1979; 24 :602–611. [ Google Scholar ]
• Johnson R.B., Onquegbuzie A.J. Mixed methods research: a paradigm whose time has come. Educational Researcher. 2004; 33 (7):14–26. [ Google Scholar ]
• Kartalova-O’Doherty Y., Tedstone Doherty D. Satisfied carers of persons with enduring mental illness: who and why? The International Journal of Social Psychiatry. 2009; 55 (3):257–271. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kelle U. Sociological explanations between micro and macro and the integration of qualitative and quantitative methods. Forum: Qualitative Social Research. 2001; 2 (1):5. Art. [ Google Scholar ]
• Khan K.S. Centre for Review and Dissemination; York: 2001. Undertaking Systematic Reviews of Research on Effectiveness: CRD's Guidance for those Carrying Out or Commissioning Reviews. [ Google Scholar ]
• Kroll T., Neri M. Designs for mixed methods research. In: Andrew S., Halcomb E.J., editors. Mixed Methods Research for Nursing and the Health Sciences. Wiley-Blackwell; Chichester: 2009. pp. 31–49. [ Google Scholar ]
• Leech N.L., Dellinger A.M., Brannagan K.B., Tanaka H. Evaluating mixed research studies: a mixed methods approach. Journal of Mixed Methods Research. 2010; 4 (1):17–31. [ Google Scholar ]
• Lukkarinen H. Methodological triangulation showed the poorest quality of life in the youngest people following treatment of coronary artery disease: a longitudinal study. International Journal of Nursing Studies. 2005; 42 (6):619–627. [ PubMed ] [ Google Scholar ]
• Midtgaard J., Rorth M., Stelter R., Adamsen L. The group matters: an explorative study of group cohesion and quality of life in cancer patients participating in physical exercise intervention during treatment. European Journal of Cancer Care. 2006; 15 (1):25–33. [ PubMed ] [ Google Scholar ]
• Morgan D.L. Paradigms lost and pragmatism regained: methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research. 2007; 1 (1):48–76. [ Google Scholar ]
• O’Cathain A. Reporting mixed methods projects. In: Andrew S., Halcomb E.J., editors. Mixed Methods Research for Nursing and the Health Sciences. Wiley-Blackwell; Chichester: 2009. pp. 135–158. [ Google Scholar ]
• O’Cathain A., Murphy E., Nicholl J. Why, and how, mixed methods research is undertaken in health services research in England: a mixed methods study. BMC Health Services Research. 2007; 7 :85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• O’Cathain A., Murphy E., Nicholl J. The quality of mixed methods studies in health services research. Journal of Health Services Research & Policy. 2008; 13 (2):92–98. [ PubMed ] [ Google Scholar ]
• Onwuegbuzie A., Teddlie C. A framework for analysing data in mixed methods research. In: Tashakkori A., Teddlie C., editors. Handbook of Mixed Methods in socIal & Behavioural Research. Sage; Thousands Oak: 2003. pp. 351–383. [ Google Scholar ]
• Onwuegbuzie A.J., Leech N.L. On becoming a pragmatic researcher: the importance of combining quantitative and qualitative methodologies. International Journal of Social Research Methodology. 2005; 8 (5):375–387. [ Google Scholar ]
• Sale J.E.M., Lohfeld L.H., Brazil K. Revisiting the quantitative-qualitative debate: implications for mixed methods research. Quality and Quantity. 2002; 36 :43–53. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shipman C., Burt J., Ream E., Beynon T., Richardson A., Addington-Hall J. Improving district nurses’ confidence and knowledge in the principles and practice of palliative care. Journal of Advanced Nursing. 2008; 63 (5):494–505. [ PubMed ] [ Google Scholar ]
• Skilbeck J.K., Payne S.A., Ingleton M.C., Nolan M., Carey I., Hanson A. An exploration of family carers’ experience of respite services in one specialist palliative care unit. Palliative Medicine. 2005; 19 (8):610–618. [ PubMed ] [ Google Scholar ]
• Steckler A., McLeroy K.R., Goodman R.M., Bird S.T., McCormick L. Toward integrating qualitative and quantitative methods: an introduction. Health Education Quarterly. 1992; 19 (1):1–8. [ PubMed ] [ Google Scholar ]
• Tashakkori A., Creswell J.W. Editorial: the new era of mixed methods. Journal of Mixed Methods Research. 2007; 1 (1):3–7. [ Google Scholar ]
• Tashakkori A., Teddlie C. Sage; Thousands Oak: 2003. Handbook of Mixed Methods in Social & Behavioural Research. [ Google Scholar ]
• Wu H.L., Volker D.L. The use of theory in qualitative approaches to research: application in end-of-life studies. Journal of Advanced Nursing. 2009; 65 (12):2719–2732. [ PubMed ] [ Google Scholar ]

Introduction: Considering Qualitative, Quantitative and Mixed Methods Research

• First Online: 24 December 2020

Cite this chapter

• Alistair McBeath 2 &
• Sofie Bager-Charleson 2  

4021 Accesses

1 Citations

In this introduction we will explore some of the differences and similarities between quantitative and qualitative research, and dispel some of the perceived mysteries within research. We will briefly introduce some of the advantages and disadvantages of both approaches. There will also be an introduction to some of the philosophical assumptions that underpin quantitative and qualitative research methods, with specific mention made of ontological and epistemological considerations. These about the nature of existence (ontology) and how we might gain knowledge about the nature of existence (epistemology). We will explore the difference between positivist and interpretivist research, idiographic versus nomothetic, and inductive and deductive perspectives. Finally, we will also distinguish between qualitative, quantitative and mixed method s research, gaining familiarity with attempts to bridge divides between disciplines and research approaches. Throughout this book, the issue of research-supported practice will remain an underlying theme. This chapter aims to support a research-based practice, aided by considering the multiple routes into research. The chapter encourages you to familiarise yourself with approaches ranging from phenomenological experiences to more nomothetic, generalising and comparing foci like outcome measuring and random control trials (RCTs), understood with a basic knowledge of statistics. The book introduces you to a range of research, guided by interest in separate approaches but also inductive—deductive combinations, as in grounded theory together with pluralistic and mixed methods approaches, all with a shared interest in providing support in the field of mental health and emotional wellbeing. Primarily, we hope that the chapter will encourage you to start considering your own research. Enjoy!

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

An Introduction to Qualitative and Mixed Methods Study Designs in Health Research

The Nature of Mixed Methods Research

Bager-Charleson, S., McBeath, A. G., & du Plock, S. (2019). The relationship between psychotherapy practice and research: A mixed-methods exploration of practitioners’ views. Counselling and Psychotherapy Research, 19 (3), 195–205. https://doi.org/10.1002/capr.12196 .

Article   Google Scholar  

Bager-Charleson, S. du Plock, S and McBeath, A.G. (2018) Therapists Have a lot to Add to the Field of Research, but Many Don’t Make it There: A Narrative Thematic Inquiry into Counsellors’ and Psychotherapists’ Embodied Engagement with Research. Psychoanalysis and Language, 7 (1), 4–22.

Google Scholar  

Bhaskar, R. (1975). A realist theory of science . Hassocks, England: Harvester Press.

Bhaskar, R. (1998). The possibility of naturalism . London: Routledge.

Crotty, M. (1998). The foundations of social research . London: Sage Publications.

Danermark, B., Ekstrom, M., Jakobsen, L., & Karlsson, J. C. (2002). Explaining society: Critical realism in the social sciences . New York: Routledge.

Denscombe, M. (1998). The good research for small –Scale social research project . Philadelphia: Open University Press.

Department of Health and Social Care (2017). A Framework for mental health research.

Ellis, D., & Tucker, I. (2015). Social psychology of emotions. London, United Kingdom: Sage.

Evered, R., & Louis, R. (1981). Alternative Perspectives in the Organizational Sciences: ‘Inquiry from the Inside’ and ‘Inquiry from the Outside. Academy of Management Review, 6 (3), 385–395.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . New York: Aldine de Gruyter.

Landrum, B., & Garza, G. (2015). Mending fences: Defining the domains and approaches of quantitative and qualitative research. Qualitative Psychology, 2 (2), 199–209. https://doi.org/10.1037/qup0000030 .

Malterud, K. (2001). The art and science of clinical knowledge: Evidence beyond measures and numbers. The Lancet., 358 , 397–400. https://doi.org/10.1016/S0140-6736(01)05548-9 .

McBeath, A. G. (2019). The motivations of psychotherapists: An in-depth survey. Counselling and Psychotherapy Research, 19 (4), 377–387. https://doi.org/10.1002/capr.12225 .

McBeath, A. G., Bager-Charleson, S., & Abarbanel, A. (2019). Therapists and Academic Writing: ‘Once upon a time psychotherapy practitioners and researchers were the same people. European Journal for Qualitative Research in Psychotherapy, 19 , 103–116.

McEvoy, P., & Richards, D. (2006). A critical realist rationale for using a combination of quantitative and qualitative methods. Journal of Research in Nursing, 11 , 66–78. https://doi.org/10.1177/1744987106060192 .

Rukeyser, M. (1968). The speed of darkness . New York: Random House.

Sandelowski, M. (2001). Real qualitative researchers do not count: The use of numbers in qualitative research. Research in Nursing and Health, 24 (3), 230–240. https://doi.org/10.1002/nur.1025 .

Scotland, J. (2012). Exploring the philosophical underpinnings of research: Relating ontology and epistemology to the methodology and methods of the scientific, interpretive and critical research paradigms. English Language Teaching, 5 (9), 9–16. https://doi.org/10.5539/elt.v5n9p9 .

Smith, J. A., & Osborn, S. (2008). Interpretative phenomenological analysis . In J. A. Smith (Ed.), Qualitative psychology (pp. 53–80). London: Sage.

Ukpabi, D. C., Enyindah, C. W., & Dapper, E. M. (2014). Who is winning the paradigm war? The futility of paradigm inflexibility in Administrative Sciences Research. IOSR Journal of Business and Management, 16 (7), 13–17.

Download references

Author information

Authors and affiliations.

Metanoia Institute, London, UK

Alistair McBeath & Sofie Bager-Charleson

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Alistair McBeath .

Editor information

Editors and affiliations.

Sofie Bager-Charleson  & Alistair McBeath  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s)

About this chapter

McBeath, A., Bager-Charleson, S. (2020). Introduction: Considering Qualitative, Quantitative and Mixed Methods Research. In: Bager-Charleson, S., McBeath, A. (eds) Enjoying Research in Counselling and Psychotherapy. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-55127-8_1

Download citation

DOI : https://doi.org/10.1007/978-3-030-55127-8_1

Published : 24 December 2020

Publisher Name : Palgrave Macmillan, Cham

Print ISBN : 978-3-030-55126-1

Online ISBN : 978-3-030-55127-8

eBook Packages : Behavioral Science and Psychology Behavioral Science and Psychology (R0)

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Three techniques for...

Three techniques for integrating data in mixed methods studies

• Related content
• Peer review
• Alicia O’Cathain , professor 1 ,
• Elizabeth Murphy , professor 2 ,
• Jon Nicholl , professor 1
• 1 Medical Care Research Unit, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
• 2 University of Leicester, Leicester, UK
• Correspondence to: A O’Cathain a.ocathain{at}sheffield.ac.uk
• Accepted 8 June 2010

Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis

Health researchers are increasingly using designs that combine qualitative and quantitative methods, and this is often called mixed methods research. 1 Integration—the interaction or conversation between the qualitative and quantitative components of a study—is an important aspect of mixed methods research, and, indeed, is essential to some definitions. 2 Recent empirical studies of mixed methods research in health show, however, a lack of integration between components, 3 4 which limits the amount of knowledge that these types of studies generate. Without integration, the knowledge yield is equivalent to that from a qualitative study and a quantitative study undertaken independently, rather than achieving a “whole greater than the sum of the parts.” 5

Barriers to integration have been identified in both health and social research. 6 7 One barrier is the absence of formal education in mixed methods research. Fortunately, literature is rapidly expanding to fill this educational gap, including descriptions of how to integrate data and findings from qualitative and quantitative methods. 8 9 In this article we outline three techniques that may help health researchers to integrate data or findings in their mixed methods studies and show how these might enhance knowledge generated from this approach.

Triangulation protocol

Researchers will often use qualitative and quantitative methods to examine different aspects of an overall research question. For example, they might use a randomised controlled trial to assess the effectiveness of a healthcare intervention and semistructured interviews with patients and health professionals to consider the way in which the intervention was used in the real world. Alternatively, they might use a survey of service users to measure satisfaction with a service and focus groups to explore views of care in more depth. Data are collected and analysed separately for each component to produce two sets of findings. Researchers will then attempt to combine these findings, sometimes calling this process triangulation. The term triangulation can be confusing because it has two meanings. 10 It can be used to describe corroboration between two sets of findings or to describe a process of studying a problem using different methods to gain a more complete picture. The latter meaning is commonly used in mixed methods research and is the meaning used here.

The process of triangulating findings from different methods takes place at the interpretation stage of a study when both data sets have been analysed separately (figure ⇓ ). Several techniques have been described for triangulating findings. They require researchers to list the findings from each component of a study on the same page and consider where findings from each method agree (convergence), offer complementary information on the same issue (complementarity), or appear to contradict each other (discrepancy or dissonance). 11 12 13 Explicitly looking for disagreements between findings from different methods is an important part of this process. Disagreement is not a sign that something is wrong with a study. Exploration of any apparent “inter-method discrepancy” may lead to a better understanding of the research question, 14 and a range of approaches have been used within health services research to explore inter-method discrepancy. 15

Point of application for three techniques for integrating data in mixed methods research

• Download figure
• Open in new tab
• Download powerpoint

The most detailed description of how to carry out triangulation is the triangulation protocol, 11 which although developed for multiple qualitative methods, is relevant to mixed methods studies. This technique involves producing a “convergence coding matrix” to display findings emerging from each component of a study on the same page. This is followed by consideration of where there is agreement, partial agreement, silence, or dissonance between findings from different components. This technique for triangulation is the only one to include silence—where a theme or finding arises from one data set and not another. Silence might be expected because of the strengths of different methods to examine different aspects of a phenomenon, but surprise silences might also arise that help to increase understanding or lead to further investigations.

The triangulation protocol moves researchers from thinking about the findings related to each method, to what Farmer and colleagues call meta-themes that cut across the findings from different methods. 11 They show a worked example of triangulation protocol, but we could find no other published example. However, similar principles were used in an iterative mixed methods study to understand patient and carer satisfaction with a new primary angioplasty service. 16 Researchers conducted semistructured interviews with 16 users and carers to explore their experiences and views of the new service. These were used to develop a questionnaire for a survey of 595 patients (and 418 of their carers) receiving either the new service or usual care. Finally, 17 of the patients who expressed dissatisfaction with aftercare and rehabilitation were followed up to explore this further in semistructured interviews. A shift of thinking to meta-themes led the researchers away from reporting the findings from the interviews, survey, and follow-up interviews sequentially to consider the meta-themes of speed and efficiency, convenience of care, and discharge and after care. The survey identified that a higher percentage of carers of patients using the new service rated the convenience of visiting the hospital as poor than those using usual care. The interviews supported this concern about the new service, but also identified that the weight carers gave to this concern was low in the context of their family member’s life being saved.

Morgan describes this move as the “third effort” because it occurs after analysis of the qualitative and the quantitative components. 17 It requires time and energy that must be planned into the study timetable. It is also useful to consider who will carry out the integration process. Farmer and colleagues require two researchers to work together during triangulation, which can be particularly important in mixed methods studies if different researchers take responsibility for the qualitative and quantitative components. 11

Following a thread

Moran-Ellis and colleagues describe a different technique for integrating the findings from the qualitative and quantitative components of a study, called following a thread. 18 They state that this takes place at the analysis stage of the research process (figure ⇑ ). It begins with an initial analysis of each component to identify key themes and questions requiring further exploration. Then the researchers select a question or theme from one component and follow it across the other components—they call this the thread. The authors do not specify steps in this technique but offer a visual model for working between datasets. An approach similar to this has been undertaken in health services research, although the researchers did not label it as such, probably because the technique has not been used frequently in the literature (box)

An example of following a thread 19

Adamson and colleagues explored the effect of patient views on the appropriate use of services and help seeking using a survey of people registered at a general practice and semistructured interviews. The qualitative (22 interviews) and quantitative components (survey with 911 respondents) took place concurrently.

The researchers describe what they call an iterative or cyclical approach to analysis. Firstly, the preliminary findings from the interviews generated a hypothesis for testing in the survey data. A key theme from the interviews concerned the self rationing of services as a responsible way of using scarce health care. This theme was then explored in the survey data by testing the hypothesis that people’s views of the appropriate use of services would explain their help seeking behaviour. However, there was no support for this hypothesis in the quantitative analysis because the half of survey respondents who felt that health services were used inappropriately were as likely to report help seeking for a series of symptoms presented in standardised vignettes as were respondents who thought that services were not used inappropriately. The researchers then followed the thread back to the interview data to help interpret this finding.

After further analysis of the interview data the researchers understood that people considered the help seeking of other people to be inappropriate, rather than their own. They also noted that feeling anxious about symptoms was considered to be a good justification for seeking care. The researchers followed this thread back into the survey data and tested whether anxiety levels about the symptoms in the standardised vignettes predicted help seeking behaviour. This second hypothesis was supported by the survey data. Following a thread led the researchers to conclude that patients who seek health care for seemingly minor problems have exceeded their thresholds for the trade-off between not using services inappropriately and any anxiety caused by their symptoms.

Mixed methods matrix

A unique aspect of some mixed methods studies is the availability of both qualitative and quantitative data on the same cases. Data from the qualitative and quantitative components can be integrated at the analysis stage of a mixed methods study (figure ⇑ ). For example, in-depth interviews might be carried out with a sample of survey respondents, creating a subset of cases for which there is both a completed questionnaire and a transcript. Cases may be individuals, groups, organisations, or geographical areas. 9 All the data collected on a single case can be studied together, focusing attention on cases, rather than variables or themes, within a study. The data can be examined in detail for each case—for example, comparing people’s responses to a questionnaire with their interview transcript. Alternatively, data on each case can be summarised and displayed in a matrix 8 9 20 along the lines of Miles and Huberman’s meta-matrix. 21 Within a mixed methods matrix, the rows represent the cases for which there is both qualitative and quantitative data, and the columns display different data collected on each case. This allows researchers to pay attention to surprises and paradoxes between types of data on a single case and then look for patterns across all cases 20 in a qualitative cross case analysis. 21

We used a mixed methods matrix to study the relation between types of team working and the extent of integration in mixed methods studies in health services research (table ⇓ ). 22 Quantitative data were extracted from the proposals, reports, and peer reviewed publications of 75 mixed methods studies, and these were analysed to describe the proportion of studies with integrated outputs such as mixed methods journal articles. Two key variables in the quantitative component were whether the study was assessed as attempting to integrate qualitative or quantitative data or findings and the type of publications produced. We conducted qualitative interviews with 20 researchers who had worked on some of these studies to explore how mixed methods research was practised, including how the team worked together.

Example of a mixed methods matrix for a study exploring the relationship between types of teams and integration between qualitative and quantitative components of studies* 22

• View inline

The shared cases between the qualitative and quantitative components were 21 mixed methods studies (because one interviewee had worked on two studies in the quantitative component). A matrix was formed with each of the 21 studies as a row. The first column of the matrix contained the study identification, the second column indicated whether integration had occurred in that project, and the third column the score for integration of publications emerging from the study. The rows were then ordered to show the most integrated cases first. This ordering of rows helped us to see patterns across rows.

The next columns were themes from the qualitative interview with a researcher from that project. For example, the first theme was about the expertise in qualitative research within the team and whether the interviewee reported this as adequate for the study. The matrix was then used in the context of the qualitative analysis to explore the issues that affected integration. In particular, it helped to identify negative cases (when someone in the analysis doesn’t fit with the conclusions the analysis is coming to) within the qualitative analysis to facilitate understanding. Interviewees reported the need for experienced qualitative researchers on mixed methods studies to ensure that the qualitative component was published, yet two cases showed that this was neither necessary nor sufficient. This pushed us to explore other factors in a research team that helped generate outputs, and integrated outputs, from a mixed methods study.

Themes from a qualitative study can be summarised to the point where they are coded into quantitative data. In the matrix (table ⇑ ), the interviewee’s perception of the adequacy of qualitative expertise on the team could have been coded as adequate=1 or not=2. This is called “quantitising” of qualitative data 23 ; coded data can then be analysed with data from the quantitative component. This technique has been used to great effect in healthcare research to identify the discrepancy between health improvement assessed using quantitative measures and with in-depth interviews in a randomised controlled trial. 24

We have presented three techniques for integration in mixed methods research in the hope that they will inspire researchers to explore what can be learnt from bringing together data from the qualitative and quantitative components of their studies. Using these techniques may give the process of integration credibility rather than leaving researchers feeling that they have “made things up.” It may also encourage researchers to describe their approaches to integration, allowing them to be transparent and helping them to develop, critique, and improve on these techniques. Most importantly, we believe it may help researchers to generate further understanding from their research.

We have presented integration as unproblematic, but it is not. It may be easier for single researchers to use these techniques than a large research team. Large teams will need to pay attention to team dynamics, considering who will take responsibility for integration and who will be taking part in the process. In addition, we have taken a technical stance here rather than paying attention to different philosophical beliefs that may shape approaches to integration. We consider that these techniques would work in the context of a pragmatic or subtle realist stance adopted by some mixed methods researchers. 25 Finally, it is important to remember that these techniques are aids to integration and are helpful only when applied with expertise.

Summary points

Health researchers are increasingly using designs which combine qualitative and quantitative methods

However, there is often lack of integration between methods

Three techniques are described that can help researchers to integrate data from different components of a study: triangulation protocol, following a thread, and the mixed methods matrix

Use of these methods will allow researchers to learn more from the information they have collected

Cite this as: BMJ 2010;341:c4587

Funding: Medical Research Council grant reference G106/1116

Competing interests: All authors have completed the unified competing interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare financial support for the submitted work from the Medical Research Council; no financial relationships with commercial entities that might have an interest in the submitted work; no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and no non-financial interests that may be relevant to the submitted work.

Contributors: AOC wrote the paper. JN and EM contributed to drafts and all authors agreed the final version. AOC is guarantor.

Provenance and peer review: Not commissioned; externally peer reviewed.

• ↵ Lingard L, Albert M, Levinson W. Grounded theory, mixed methods and action research. BMJ 2008 ; 337 : a567 . OpenUrl FREE Full Text
• ↵ Creswell JW, Fetters MD, Ivankova NV. Designing a mixed methods study in primary care. Ann Fam Med 2004 ; 2 : 7 -12. OpenUrl Abstract / FREE Full Text
• ↵ Lewin S, Glenton C, Oxman AD. Use of qualitative methods alongside randomised controlled trials of complex healthcare interventions: methodological study. BMJ 2009 ; 339 : b3496 . OpenUrl Abstract / FREE Full Text
• ↵ O’Cathain A, Murphy E, Nicholl J. Integration and publications as indicators of ‘yield’ from mixed methods studies. J Mix Methods Res 2007 ; 1 : 147 -63. OpenUrl CrossRef Web of Science
• ↵ Barbour RS. The case for combining qualitative and quantitative approaches in health services research. J Health Serv Res Policy 1999 ; 4 : 39 -43. OpenUrl PubMed
• ↵ O’Cathain A, Nicholl J, Murphy E. Structural issues affecting mixed methods studies in health research: a qualitative study. BMC Med Res Methodol 2009 ; 9 : 82 . OpenUrl CrossRef PubMed
• ↵ Bryman A. Barriers to integrating quantitative and qualitative research. J Mix Methods Res 2007 ; 1 : 8 -22. OpenUrl CrossRef
• ↵ Creswell JW, Plano-Clark V. Designing and conducting mixed methods research . Sage, 2007 .
• ↵ Bazeley P. Analysing mixed methods data. In: Andrew S, Halcomb EJ, eds. Mixed methods research for nursing and the health sciences . Wiley-Blackwell, 2009 :84-118.
• ↵ Sandelowski M. Triangles and crystals: on the geometry of qualitative research. Res Nurs Health 1995 ; 18 : 569 -74. OpenUrl CrossRef PubMed Web of Science
• ↵ Farmer T, Robinson K, Elliott SJ, Eyles J. Developing and implementing a triangulation protocol for qualitative health research. Qual Health Res 2006 ; 16 : 377 -94. OpenUrl Abstract / FREE Full Text
• ↵ Foster RL. Addressing the epistemologic and practical issues in multimethod research: a procedure for conceptual triangulation. Adv Nurs Sci 1997 ; 20 : 1 -12. OpenUrl PubMed
• ↵ Erzerberger C, Prein G. Triangulation: validity and empirically based hypothesis construction. Qual Quant 1997 ; 31 : 141 -54. OpenUrl CrossRef Web of Science
• ↵ Fielding NG, Fielding JL. Linking data . Sage, 1986 .
• ↵ Moffatt S, White M, Mackintosh J, Howel D. Using quantitative and qualitative data in health services research—what happens when mixed method findings conflict? BMC Health Serv Res 2006 ; 6 : 28 . OpenUrl CrossRef PubMed
• ↵ Sampson FC, O’Cathain A, Goodacre S. Is primary angioplasty an acceptable alternative to thrombolysis? Quantitative and qualitative study of patient and carer satisfaction. Health Expectations (forthcoming).
• ↵ Morgan DL. Practical strategies for combining qualitative and quantitative methods: applications to health research. Qual Health Res 1998 ; 8 : 362 -76. OpenUrl Abstract / FREE Full Text
• ↵ Moran-Ellis J, Alexander VD, Cronin A, Dickinson M, Fielding J, Sleney J, et al. Triangulation and integration: processes, claims and implications. Qualitative Research 2006 ; 6 : 45 -59. OpenUrl Abstract / FREE Full Text
• ↵ Adamson J, Ben-Shlomo Y, Chaturvedi N, Donovan J. Exploring the impact of patient views on ‘appropriate’ use of services and help seeking: a mixed method study. Br J Gen Pract 2009 ; 59 : 496 -502. OpenUrl Web of Science
• ↵ Wendler MC. Triangulation using a meta-matrix. J Adv Nurs 2001 ; 35 : 521 -5. OpenUrl CrossRef PubMed Web of Science
• ↵ Miles M, Huberman A. Qualitative data analysis: an expanded sourcebook . Sage, 1994 .
• ↵ O’Cathain A, Murphy E, Nicholl J. Multidisciplinary, interdisciplinary or dysfunctional? Team working in mixed methods research. Qual Health Res 2008 ; 18 : 1574 -85. OpenUrl Abstract / FREE Full Text
• ↵ Sandelowski M. Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies. Res Nurs Health 2000 ; 23 : 246 -55. OpenUrl CrossRef PubMed Web of Science
• ↵ Campbell R, Quilty B, Dieppe P. Discrepancies between patients’ assessments of outcome: qualitative study nested within a randomised controlled trial. BMJ 2003 ; 326 : 252 -3. OpenUrl FREE Full Text
• ↵ Mays N, Pope C. Assessing quality in qualitative research. BMJ 2000 ; 320 : 50 -2. OpenUrl FREE Full Text

• What is mixed methods research?

Last updated

20 February 2023

Reviewed by

Miroslav Damyanov

By blending both quantitative and qualitative data, mixed methods research allows for a more thorough exploration of a research question. It can answer complex research queries that cannot be solved with either qualitative or quantitative research .

Analyze your mixed methods research

Dovetail streamlines analysis to help you uncover and share actionable insights

Mixed methods research combines the elements of two types of research: quantitative and qualitative.

Quantitative data is collected through the use of surveys and experiments, for example, containing numerical measures such as ages, scores, and percentages. 

Qualitative data involves non-numerical measures like beliefs, motivations, attitudes, and experiences, often derived through interviews and focus group research to gain a deeper understanding of a research question or phenomenon.

Mixed methods research is often used in the behavioral, health, and social sciences, as it allows for the collection of numerical and non-numerical data.

• When to use mixed methods research

Mixed methods research is a great choice when quantitative or qualitative data alone will not sufficiently answer a research question. By collecting and analyzing both quantitative and qualitative data in the same study, you can draw more meaningful conclusions. 

There are several reasons why mixed methods research can be beneficial, including generalizability, contextualization, and credibility. 

For example, let's say you are conducting a survey about consumer preferences for a certain product. You could collect only quantitative data, such as how many people prefer each product and their demographics. Or you could supplement your quantitative data with qualitative data, such as interviews and focus groups , to get a better sense of why people prefer one product over another.

It is important to note that mixed methods research does not only mean collecting both types of data. Rather, it also requires carefully considering the relationship between the two and method flexibility.

You may find differing or even conflicting results by combining quantitative and qualitative data . It is up to the researcher to then carefully analyze the results and consider them in the context of the research question to draw meaningful conclusions.

When designing a mixed methods study, it is important to consider your research approach, research questions, and available data. Think about how you can use different techniques to integrate the data to provide an answer to your research question.

• Mixed methods research design

A mixed methods research design  is   an approach to collecting and analyzing both qualitative and quantitative data in a single study.

Mixed methods designs allow for method flexibility and can provide differing and even conflicting results. Examples of mixed methods research designs include convergent parallel, explanatory sequential, and exploratory sequential.

By integrating data from both quantitative and qualitative sources, researchers can gain valuable insights into their research topic . For example, a study looking into the impact of technology on learning could use surveys to measure quantitative data on students' use of technology in the classroom. At the same time, interviews or focus groups can provide qualitative data on students' experiences and opinions.

• Types of mixed method research designs

Researchers often struggle to put mixed methods research into practice, as it is challenging and can lead to research bias. Although mixed methods research can reveal differences or conflicting results between studies, it can also offer method flexibility.

Designing a mixed methods study can be broken down into four types: convergent parallel, embedded, explanatory sequential, and exploratory sequential.

Convergent parallel

The convergent parallel design is when data collection and analysis of both quantitative and qualitative data occur simultaneously and are analyzed separately. This design aims to create mutually exclusive sets of data that inform each other. 

For example, you might interview people who live in a certain neighborhood while also conducting a survey of the same people to determine their satisfaction with the area.

Embedded design

The embedded design is when the quantitative and qualitative data are collected simultaneously, but the qualitative data is embedded within the quantitative data. This design is best used when you want to focus on the quantitative data but still need to understand how the qualitative data further explains it.

For instance, you may survey students about their opinions of an online learning platform and conduct individual interviews to gain further insight into their responses.

Explanatory sequential design

In an explanatory sequential design, quantitative data is collected first, followed by qualitative data. This design is used when you want to further explain a set of quantitative data with additional qualitative information.

An example of this would be if you surveyed employees at a company about their satisfaction with their job and then conducted interviews to gain more information about why they responded the way they did.

Exploratory sequential design

The exploratory sequential design collects qualitative data first, followed by quantitative data. This type of mixed methods research is used when the goal is to explore a topic before collecting any quantitative data.

An example of this could be studying how parents interact with their children by conducting interviews and then using a survey to further explore and measure these interactions.

Integrating data in mixed methods studies can be challenging, but it can be done successfully with careful planning.

No matter which type of design you choose, understanding and applying these principles can help you draw meaningful conclusions from your research.

• Strengths of mixed methods research

Mixed methods research designs combine the strengths of qualitative and quantitative data, deepening and enriching qualitative results with quantitative data and validating quantitative findings with qualitative data. This method offers more flexibility in designing research, combining theory generation and hypothesis testing, and being less tied to disciplines and established research paradigms.

Take the example of a study examining the impact of exercise on mental health. Mixed methods research would allow for a comprehensive look at the issue from different angles. 

Researchers could begin by collecting quantitative data through surveys to get an overall view of the participants' levels of physical activity and mental health. Qualitative interviews would follow this to explore the underlying dynamics of participants' experiences of exercise, physical activity, and mental health in greater detail.

Through a mixed methods approach, researchers could more easily compare and contrast their results to better understand the phenomenon as a whole.  

Additionally, mixed methods research is useful when there are conflicting or differing results in different studies. By combining both quantitative and qualitative data, mixed methods research can offer insights into why those differences exist.

For example, if a quantitative survey yields one result while a qualitative interview yields another, mixed methods research can help identify what factors influence these differences by integrating data from both sources.

Overall, mixed methods research designs offer a range of advantages for studying complex phenomena. They can provide insight into different elements of a phenomenon in ways that are not possible with either qualitative or quantitative data alone. Additionally, they allow researchers to integrate data from multiple sources to gain a deeper understanding of the phenomenon in question.  

• Challenges of mixed methods research

Mixed methods research is labor-intensive and often requires interdisciplinary teams of researchers to collaborate. It also has the potential to cost more than conducting a stand alone qualitative or quantitative study . 

Interpreting the results of mixed methods research can be tricky, as it can involve conflicting or differing results. Researchers must find ways to systematically compare the results from different sources and methods to avoid bias.

For example, imagine a situation where a team of researchers has employed an explanatory sequential design for their mixed methods study. After collecting data from both the quantitative and qualitative stages, the team finds that the two sets of data provide differing results. This could be challenging for the team, as they must now decide how to effectively integrate the two types of data in order to reach meaningful conclusions. The team would need to identify method flexibility and be strategic when integrating data in order to draw meaningful conclusions from the conflicting results.

• Advanced frameworks in mixed methods research

Mixed methods research offers powerful tools for investigating complex processes and systems, such as in health and healthcare.

Besides the three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent parallel—you can use one of the four advanced frameworks to extend mixed methods research designs. These include multistage, intervention, case study , and participatory. 

This framework mixes qualitative and quantitative data collection methods in stages to gather a more nuanced view of the research question. An example of this is a study that first has an online survey to collect initial data and is followed by in-depth interviews to gain further insights.

Intervention

This design involves collecting quantitative data and then taking action, usually in the form of an intervention or intervention program. An example of this could be a research team who collects data from a group of participants, evaluates it, and then implements an intervention program based on their findings .

This utilizes both qualitative and quantitative research methods to analyze a single case. The researcher will examine the specific case in detail to understand the factors influencing it. An example of this could be a study of a specific business organization to understand the organizational dynamics and culture within the organization.

Participatory

This type of research focuses on the involvement of participants in the research process. It involves the active participation of participants in formulating and developing research questions, data collection, and analysis.

An example of this could be a study that involves forming focus groups with participants who actively develop the research questions and then provide feedback during the data collection and analysis stages.

The flexibility of mixed methods research designs means that researchers can choose any combination of the four frameworks outlined above and other methodologies , such as convergent parallel, explanatory sequential, and exploratory sequential, to suit their particular needs.

Through this method's flexibility, researchers can gain multiple perspectives and uncover differing or even conflicting results when integrating data.

When it comes to integration at the methods level, there are four approaches.

Connecting involves collecting both qualitative and quantitative data during different phases of the research.

Building involves the collection of both quantitative and qualitative data within a single phase.

Merging involves the concurrent collection of both qualitative and quantitative data.

Embedding involves including qualitative data within a quantitative study or vice versa.

• Techniques for integrating data in mixed method studies

Integrating data is an important step in mixed methods research designs. It allows researchers to gain further understanding from their research and gives credibility to the integration process. There are three main techniques for integrating data in mixed methods studies: triangulation protocol, following a thread, and the mixed methods matrix.

Triangulation protocol

This integration method combines different methods with differing or conflicting results to generate one unified answer.

For example, if a researcher wanted to know what type of music teenagers enjoy listening to, they might employ a survey of 1,000 teenagers as well as five focus group interviews to investigate this. The results might differ; the survey may find that rap is the most popular genre, whereas the focus groups may suggest rock music is more widely listened to. 

The researcher can then use the triangulation protocol to come up with a unified answer—such as that both rap and rock music are popular genres for teenage listeners. 

Following a thread

This is another method of integration where the researcher follows the same theme or idea from one method of data collection to the next. 

A research design that follows a thread starts by collecting quantitative data on a specific issue, followed by collecting qualitative data to explain the results. This allows whoever is conducting the research to detect any conflicting information and further look into the conflicting information to understand what is really going on.

For example, a researcher who used this research method might collect quantitative data about how satisfied employees are with their jobs at a certain company, followed by qualitative interviews to investigate why job satisfaction levels are low. They could then use the results to explore any conflicting or differing results, allowing them to gain a deeper understanding of job satisfaction at the company. 

By following a thread, the researcher can explore various research topics related to the original issue and gain a more comprehensive view of the issue.

Mixed methods matrix

This technique is a visual representation of the different types of mixed methods research designs and the order in which they should be implemented. It enables researchers to quickly assess their research design and adjust it as needed. 

The matrix consists of four boxes with four different types of mixed methods research designs: convergent parallel, explanatory sequential, exploratory sequential, and method flexibility. 

For example, imagine a researcher who wanted to understand why people don't exercise regularly. To answer this question, they could use a convergent parallel design, collecting both quantitative (e.g., survey responses) and qualitative (e.g., interviews) data simultaneously.

If the researcher found conflicting results, they could switch to an explanatory sequential design and collect quantitative data first, then follow up with qualitative data if needed. This way, the researcher can make adjustments based on their findings and integrate their data more effectively.

Mixed methods research is a powerful tool for understanding complex research topics. Using qualitative and quantitative data in one study allows researchers to understand their subject more deeply. 

Mixed methods research designs such as convergent parallel, explanatory sequential, and exploratory sequential provide method flexibility, enabling researchers to collect both types of data while avoiding the limitations of either approach alone.

However, it's important to remember that mixed methods research can produce differing or even conflicting results, so it's important to be aware of the potential pitfalls and take steps to ensure that data is being correctly integrated. If used effectively, mixed methods research can offer valuable insight into topics that would otherwise remain largely unexplored.

What is an example of mixed methods research?

An example of mixed methods research is a study that combines quantitative and qualitative data. This type of research uses surveys, interviews, and observations to collect data from multiple sources.

Which sampling method is best for mixed methods?

It depends on the research objectives, but a few methods are often used in mixed methods research designs. These include snowball sampling, convenience sampling, and purposive sampling. Each method has its own advantages and disadvantages.

What is the difference between mixed methods and multiple methods?

Mixed methods research combines quantitative and qualitative data in a single study. Multiple methods involve collecting data from different sources, such as surveys and interviews, but not necessarily combining them into one analysis. Mixed methods offer greater flexibility but can lead to differing or conflicting results when integrating data.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 13 April 2023

Last updated: 14 February 2024

Last updated: 27 January 2024

Last updated: 18 April 2023

Last updated: 8 February 2023

Last updated: 23 January 2024

Last updated: 30 January 2024

Last updated: 7 February 2023

Last updated: 18 May 2023

Last updated: 31 January 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 3: Developing a Research Question

3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches

Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods). In the following sections we look at quantitative, qualitative, and mixed methodological approaches to undertaking research. Table 2.3 synthesizes the differences between quantitative and qualitative research approaches.

Quantitative Research Approaches

A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.

Qualitative Research Approaches

On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.

Table 3.3 Differences between quantitative and qualitative approaches (from Adjei, n.d).

Note: Researchers in emergency and safety professions are increasingly turning toward qualitative methods. Here is an interesting peer paper related to qualitative research in emergency care.

Qualitative Research in Emergency Care Part I: Research Principles and Common Applications by Choo, Garro, Ranney, Meisel, and Guthrie (2015)

Interview-based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Introduction.

• Sampling Strategies
• Sample Size
• Qualitative Design Considerations
• Discipline Specific and Special Considerations
• Sampling Strategies Unique to Mixed Methods Designs

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Mixed Methods Research
• Qualitative Research Design
• Quantitative Research Designs in Educational Research

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• Black Women in Academia
• Girls' Education in the Developing World
• History of Education in Europe
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.

Sampling in Qualitative Research

Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Education »
• Meet the Editorial Board »
• Academic Achievement
• Academic Audit for Universities
• Academic Freedom and Tenure in the United States
• Action Research in Education
• Adjuncts in Higher Education in the United States
• Administrator Preparation
• Adolescence
• Advanced Placement and International Baccalaureate Courses
• Advocacy and Activism in Early Childhood
• African American Racial Identity and Learning
• Alaska Native Education
• Alternative Certification Programs for Educators
• Alternative Schools
• American Indian Education
• Animals in Environmental Education
• Art Education
• Artificial Intelligence and Learning
• Assessing School Leader Effectiveness
• Assessment, Behavioral
• Assessment, Educational
• Assessment in Early Childhood Education
• Assistive Technology
• Augmented Reality in Education
• Beginning-Teacher Induction
• Bilingual Education and Bilingualism
• Black Undergraduate Women: Critical Race and Gender Perspe...
• Blended Learning
• Case Study in Education Research
• Changing Professional and Academic Identities
• Character Education
• Children’s and Young Adult Literature
• Children's Beliefs about Intelligence
• Children's Rights in Early Childhood Education
• Citizenship Education
• Civic and Social Engagement of Higher Education
• Classroom Learning Environments: Assessing and Investigati...
• Classroom Management
• Coherent Instructional Systems at the School and School Sy...
• College Admissions in the United States
• College Athletics in the United States
• Community Relations
• Comparative Education
• Computer-Assisted Language Learning
• Computer-Based Testing
• Conceptualizing, Measuring, and Evaluating Improvement Net...
• Continuous Improvement and "High Leverage" Educational Pro...
• Counseling in Schools
• Critical Approaches to Gender in Higher Education
• Critical Perspectives on Educational Innovation and Improv...
• Critical Race Theory
• Crossborder and Transnational Higher Education
• Cross-National Research on Continuous Improvement
• Cross-Sector Research on Continuous Learning and Improveme...
• Cultural Diversity in Early Childhood Education
• Culturally Responsive Leadership
• Culturally Responsive Pedagogies
• Culturally Responsive Teacher Education in the United Stat...
• Curriculum Design
• Data Collection in Educational Research
• Data-driven Decision Making in the United States
• Deaf Education
• Desegregation and Integration
• Design Thinking and the Learning Sciences: Theoretical, Pr...
• Development, Moral
• Dialogic Pedagogy
• Digital Age Teacher, The
• Digital Citizenship
• Digital Divides
• Disabilities
• Distance Learning
• Distributed Leadership
• Doctoral Education and Training
• Early Childhood Education and Care (ECEC) in Denmark
• Early Childhood Education and Development in Mexico
• Early Childhood Education in Aotearoa New Zealand
• Early Childhood Education in Australia
• Early Childhood Education in China
• Early Childhood Education in Europe
• Early Childhood Education in Sub-Saharan Africa
• Early Childhood Education in Sweden
• Early Childhood Education Pedagogy
• Early Childhood Education Policy
• Early Childhood Education, The Arts in
• Early Childhood Mathematics
• Early Childhood Science
• Early Childhood Teacher Education
• Early Childhood Teachers in Aotearoa New Zealand
• Early Years Professionalism and Professionalization Polici...
• Economics of Education
• Education For Children with Autism
• Education for Sustainable Development
• Education Leadership, Empirical Perspectives in
• Education of Native Hawaiian Students
• Education Reform and School Change
• Educational Statistics for Longitudinal Research
• Educator Partnerships with Parents and Families with a Foc...
• Emotional and Affective Issues in Environmental and Sustai...
• Emotional and Behavioral Disorders
• English as an International Language for Academic Publishi...
• Environmental and Science Education: Overlaps and Issues
• Environmental Education
• Environmental Education in Brazil
• Epistemic Beliefs
• Equity and Improvement: Engaging Communities in Educationa...
• Equity, Ethnicity, Diversity, and Excellence in Education
• Ethical Research with Young Children
• Ethics and Education
• Ethics of Teaching
• Ethnic Studies
• Evidence-Based Communication Assessment and Intervention
• Family and Community Partnerships in Education
• Family Day Care
• Federal Government Programs and Issues
• Feminization of Labor in Academia
• Finance, Education
• Financial Aid
• Formative Assessment
• Future-Focused Education
• Gender and Achievement
• Gender and Alternative Education
• Gender, Power and Politics in the Academy
• Gender-Based Violence on University Campuses
• Gifted Education
• Global Mindedness and Global Citizenship Education
• Global University Rankings
• Governance, Education
• Grounded Theory
• Growth of Effective Mental Health Services in Schools in t...
• Higher Education and Globalization
• Higher Education and the Developing World
• Higher Education Faculty Characteristics and Trends in the...
• Higher Education Finance
• Higher Education Governance
• Higher Education Graduate Outcomes and Destinations
• Higher Education in Africa
• Higher Education in China
• Higher Education in Latin America
• Higher Education in the United States, Historical Evolutio...
• Higher Education, International Issues in
• Higher Education Management
• Higher Education Policy
• Higher Education Research
• Higher Education Student Assessment
• High-stakes Testing
• History of Early Childhood Education in the United States
• History of Education in the United States
• History of Technology Integration in Education
• Homeschooling
• Inclusion in Early Childhood: Difference, Disability, and ...
• Inclusive Education
• Indigenous Education in a Global Context
• Indigenous Learning Environments
• Indigenous Students in Higher Education in the United Stat...
• Infant and Toddler Pedagogy
• Inservice Teacher Education
• Integrating Art across the Curriculum
• Intelligence
• Intensive Interventions for Children and Adolescents with ...
• International Perspectives on Academic Freedom
• Intersectionality and Education
• Knowledge Development in Early Childhood
• Leadership Development, Coaching and Feedback for
• Leadership in Early Childhood Education
• Leadership Training with an Emphasis on the United States
• Learning Analytics in Higher Education
• Learning Difficulties
• Learning, Lifelong
• Learning, Multimedia
• Learning Strategies
• Legal Matters and Education Law
• LGBT Youth in Schools
• Linguistic Diversity
• Linguistically Inclusive Pedagogy
• Literacy Development and Language Acquisition
• Literature Reviews
• Mathematics Identity
• Mathematics Instruction and Interventions for Students wit...
• Mathematics Teacher Education
• Measurement for Improvement in Education
• Measurement in Education in the United States
• Meta-Analysis and Research Synthesis in Education
• Methodological Approaches for Impact Evaluation in Educati...
• Methodologies for Conducting Education Research
• Mindfulness, Learning, and Education
• Motherscholars
• Multiliteracies in Early Childhood Education
• Multiple Documents Literacy: Theory, Research, and Applica...
• Multivariate Research Methodology
• Museums, Education, and Curriculum
• Music Education
• Narrative Research in Education
• Native American Studies
• Nonformal and Informal Environmental Education
• Note-Taking
• Numeracy Education
• One-to-One Technology in the K-12 Classroom
• Online Education
• Open Education
• Organizing for Continuous Improvement in Education
• Organizing Schools for the Inclusion of Students with Disa...
• Outdoor Play and Learning
• Outdoor Play and Learning in Early Childhood Education
• Pedagogical Leadership
• Pedagogy of Teacher Education, A
• Performance Objectives and Measurement
• Performance-based Research Assessment in Higher Education
• Performance-based Research Funding
• Phenomenology in Educational Research
• Philosophy of Education
• Physical Education
• Podcasts in Education
• Policy Context of United States Educational Innovation and...
• Politics of Education
• Portable Technology Use in Special Education Programs and ...
• Post-humanism and Environmental Education
• Pre-Service Teacher Education
• Problem Solving
• Productivity and Higher Education
• Professional Development
• Professional Learning Communities
• Program Evaluation
• Programs and Services for Students with Emotional or Behav...
• Psychology Learning and Teaching
• Psychometric Issues in the Assessment of English Language ...
• Qualitative Data Analysis Techniques
• Qualitative, Quantitative, and Mixed Methods Research Samp...
• Queering the English Language Arts (ELA) Writing Classroom
• Race and Affirmative Action in Higher Education
• Reading Education
• Refugee and New Immigrant Learners
• Relational and Developmental Trauma and Schools
• Relational Pedagogies in Early Childhood Education
• Reliability in Educational Assessments
• Religion in Elementary and Secondary Education in the Unit...
• Researcher Development and Skills Training within the Cont...
• Research-Practice Partnerships in Education within the Uni...
• Response to Intervention
• Restorative Practices
• Risky Play in Early Childhood Education
• Scale and Sustainability of Education Innovation and Impro...
• Scaling Up Research-based Educational Practices
• School Accreditation
• School Choice
• School Culture
• School District Budgeting and Financial Management in the ...
• School Improvement through Inclusive Education
• School Reform
• Schools, Private and Independent
• School-Wide Positive Behavior Support
• Science Education
• Secondary to Postsecondary Transition Issues
• Self-Regulated Learning
• Self-Study of Teacher Education Practices
• Service-Learning
• Severe Disabilities
• Single Salary Schedule
• Single-sex Education
• Single-Subject Research Design
• Social Context of Education
• Social Justice
• Social Network Analysis
• Social Pedagogy
• Social Science and Education Research
• Social Studies Education
• Sociology of Education
• Standards-Based Education
• Statistical Assumptions
• Student Access, Equity, and Diversity in Higher Education
• Student Assignment Policy
• Student Engagement in Tertiary Education
• Student Learning, Development, Engagement, and Motivation ...
• Student Participation
• Student Voice in Teacher Development
• Sustainability Education in Early Childhood Education
• Sustainability in Early Childhood Education
• Sustainability in Higher Education
• Teacher Beliefs and Epistemologies
• Teacher Collaboration in School Improvement
• Teacher Evaluation and Teacher Effectiveness
• Teacher Preparation
• Teacher Training and Development
• Teacher Unions and Associations
• Teacher-Student Relationships
• Teaching Critical Thinking
• Technologies, Teaching, and Learning in Higher Education
• Technology Education in Early Childhood
• Technology, Educational
• Technology-based Assessment
• The Bologna Process
• The Regulation of Standards in Higher Education
• Theories of Educational Leadership
• Three Conceptions of Literacy: Media, Narrative, and Gamin...
• Tracking and Detracking
• Traditions of Quality Improvement in Education
• Transformative Learning
• Transitions in Early Childhood Education
• Tribally Controlled Colleges and Universities in the Unite...
• Understanding the Psycho-Social Dimensions of Schools and ...
• University Faculty Roles and Responsibilities in the Unite...
• Using Ethnography in Educational Research
• Value of Higher Education for Students and Other Stakehold...
• Virtual Learning Environments
• Vocational and Technical Education
• Wellness and Well-Being in Education
• Women's and Gender Studies
• Young Children and Spirituality
• Young Children's Learning Dispositions
• Young Children's Working Theories
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [66.249.64.20|81.177.182.136]
• 81.177.182.136

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• For authors
• Browse by collection
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 14, Issue 5
• Protocol for a multicentre prospective exploratory mixed-methods study investigating the modifiable psychosocial variables influencing access to and outcomes after kidney transplantation in children and young people in the UK
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0001-6090-1650 Ji Soo Kim 1 , 2 ,
• http://orcid.org/0000-0002-4769-1211 Jo Wray 3 ,
• Deborah Ridout 4 ,
• Lucy Plumb 5 , 6 ,
• Dorothea Nitsch 5 , 7 ,
• Matthew Robb 8 ,
• Stephen D Marks 1 , 2
• 1 Paediatric Nephrology , Great Ormond Street Hospital for Children NHS Foundation Trust , London , UK
• 2 NIHR Great Ormond Street Hospital Biomedical Research Centre , London , UK
• 3 Centre for Outcomes and Experience Research in Children's Health, Illness and Disability , Great Ormond Street Hospital for Children NHS Foundation Trust , London , UK
• 4 Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health , UCL , London , UK
• 5 UK Renal Registry , Bristol , UK
• 6 Population Health Sciences , University of Bristol Medical School , Bristol , UK
• 7 Non-communicable disease epidemiology , London School of Hygiene & Tropical Medicine , London , UK
• 8 Statistics and Clinical Studies , NHS Blood and Transplant , Bristol , UK
• Correspondence to Dr Ji Soo Kim; jisoo.kim{at}nhs.net

Introduction Kidney transplantation is the preferred therapy for children with stage 5 chronic kidney disease (CKD-5). However, there is a wide variation in access to kidney transplantation across the UK for children. This study aims to explore the psychosocial factors that influence access to and outcomes after kidney transplantation in children in the UK using a mixed-methods prospective longitudinal design.

Methods Qualitative data will be collected through semistructured interviews with children affected by CKD-5, their carers and paediatric renal multidisciplinary team. Recruitment for interviews will continue till data saturation. These interviews will inform the choice of existing validated questionnaires, which will be distributed to a larger national cohort of children with pretransplant CKD-5 (n=180) and their carers. Follow-up questionnaires will be sent at protocolised time points regardless of whether they receive a kidney transplant or not. Coexisting health data from hospital, UK renal registry and National Health Service Blood and Transplant registry records will be mapped to each questionnaire time point. An integrative analysis of the mixed qualitative and quantitative data will define psychosocial aspects of care for potential intervention to improve transplant access.

Analysis Qualitative data will be analysed using thematic analysis. Quantitative data will be analysed using appropriate statistical methods to understand how these factors influence access to transplantation, as well as the distribution of psychosocial factors pretransplantation and post-transplantation.

Ethics and dissemination This study protocol has been reviewed by the National Institute for Health Research Academy and approved by the Wales Research Ethics Committee 4 (IRAS number 270493/ref: 20/WA/0285) and the Scotland A Research Ethics Committee (ref: 21/SS/0038). Results from this study will be disseminated across media platforms accessed by affected families, presented at conferences and published in peer-reviewed journals.

• Renal transplantation
• Paediatric transplant surgery
• Paediatric nephrology
• Health Services Accessibility
• Quality of Life
• Social Support

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2023-078150

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

STRENGTHS AND LIMITATIONS OF THIS STUDY

Prospective, longitudinal study design allows for a more detailed understanding of delays in transplantation associated with different psychosocial factors.

Combination of qualitative and quantitative data enables in-depth exploration of how psychosocial factors influence access to transplantation and outcomes thereafter.

Involvement of the majority of UK paediatric nephrology units ensuring representation of paediatric chronic kidney disease-5 population

Limited follow-up timeline will capture only short-term to medium-term rather than long-term outcomes.

Utilisation of interpreters in qualitative interviews increases involvement of non-English-speaking families; however, the lack of validated translations of questionnaires may limit involvement of non-English-speaking families in capturing quantitative data.

Introduction

Around 1000 children (aged 0–17 years) with stage 5 chronic kidney disease (CKD-5) in the UK receive kidney replacement therapy (KRT) in the form of either peritoneal dialysis, haemodialysis or kidney transplantation. 1 2 Kidney transplantation is the gold-standard therapy for reducing mortality and improving outcomes for children. 3 Minimising time on dialysis in favour of transplantation has been shown to reduce CKD-5-related complications and morbidity. 4 Compared with dialysis, transplantation is also presumed to improve patients’ health-related quality of life (HR-QoL). 5 Furthermore in the UK, for every year that the patient’s kidney transplant functions, transplantation is three times more cost-effective than dialysis for the National Health Service (NHS). 6

However, not every child with CKD-5 can access kidney transplantation. There are approximately 193–217 prevalent children on dialysis each year. 7 8 Annually only 130–160 paediatric kidney transplants are performed in the UK. 9 There appears to be variation in transplantation access, practice and outcomes between UK paediatric nephrology units. 1 2 9 A cross-sectional survey conducted by the British Association for Paediatric Nephrology examined reasons for paediatric kidney transplantation delay in the UK. The survey showed that psychosocial factors make up 19% of the barriers, although specific factors were not identified in this study. 10 Compared with paediatrics, considerable research has been done in adult patients, regarding psychosocial factors implicated in transplant access. Formalised pretransplant and post-transplant psychosocial assessments have been widely researched for adult solid organ transplant recipients. 11–14 The UK-wide study, ‘Access to Transplantation and Transplant Outcome Measures’ specifically investigated psychosocial barriers in adult kidney transplant recipients. 15 Researchers found inequities in transplant access, in spite of a universal healthcare system, based on socioeconomic status, education level, health literacy and racial background. 16 17 In terms of outcomes, they found no difference in post-transplant HR-QoL between living and deceased donor recipients and that recipient expectations influenced post-transplant recovery. 15–19 However, for the UK children, it is less clear what these ‘psychosocial factors’ are and how they influence unit-specific decisions, access to kidney transplantation and outcomes. 10 20 21 We acknowledge the recent progress being made in exploring these psychosocial factors in some countries. 22–24 However, psychosocial studies for children with CKD-5 are still limited by retrospective or cross-sectional design or by single-centre or small study cohorts. There are no UK studies that prospectively explore how these factors impact paediatric kidney transplantation access over time.

Aims and objectives

This study aims to prospectively evaluate the psychosocial factors that are actual or perceived barriers to paediatric kidney transplantation which may be associated with poor transplant outcome. We anticipate these psychosocial factors to be broad at an individual, family and societal level and that they will encompass mental health and social determinants of health as defined by Marmot et al : ‘the conditions in which people are born, grow, live, work and age’ 25 —we hypothesise that psychosocial factors implicated in transplant access can be quantified using formal, validated measures and that these factors may influence outcomes in the short-term period following transplantation.

This will be achieved through the following research objectives:

Describe the psychosocial factors perceived by clinicians as barriers to kidney transplantation.

Describe current interventions (if any) implemented to address these psychosocial factors.

Explore the experiences and beliefs of children and their families regarding any psychosocial challenges or facilitators in accessing a kidney transplant.

Quantify the identified psychosocial factors implicated in CKD management.

Measure the prevalence of identified psychosocial factors (positive and negative) in the national cohort of patients being pre-emptively worked up for transplant, on dialysis or listed for transplant.

Examine these psychosocial factors regarding their association with time to transplant and their changes following transplantation.

Synthesise findings from each phase to inform recommendations about which psychosocial factors are potential barriers or facilitators to accessing kidney transplantation.

Findings synthesised from this study will inform the development of a complex intervention to improve uptake of kidney transplantation in children with CKD-5 who would benefit most from one.

Methods and analysis

Study design overview.

This prospective study has three phases, commencing with a sequential exploratory mixed-methods design, followed by a sequential explanatory mixed-methods design ( figure 1 ). 26 This approach was chosen to first gain new insights by becoming familiar with the range of potential psychosocial factors and then longitudinally observing the influence of these factors on kidney transplantation access and outcomes for children. 27 Phase 1 will consist of exploratory interviews with purposively selected participants. These interviews will inform which questionnaires will be distributed at baseline and follow-up to the wider cohort of children with CKD-5 and their carer(s) in phase 2 stage A. Using the interviews to inform questionnaire selection will ensure questionnaires are relevant to, and resonate with, families. Participant families with outlier findings from phase 2 stage A will then be invited for explanatory interviews in phase 2 stage B. Finally, in phase 3, the qualitative and quantitative data from the previous phases will be analysed together to develop an integrated understanding of psychosocial factors that influence access to kidney transplantation.

• Download figure
• Open in new tab
• Download powerpoint

Flow diagram of sequential mixed-method study design. CYP, children and young people; NHS, National Health Service.

Patient and public involvement

All elements of the study design were codeveloped and approved by our Research Partner Family and study steering group. Our Research Partner Family (a young person and parent dyad) and steering group members have lived experience of CKD, dialysis and kidney transplantation in childhood either as a patient or carer. The Great Ormond Street Hospital Young Persons Advisory Group has also approved the research question and study design. The Young Persons Advisory Group consists of children affiliated with Great Ormond Street Hospital for Children NHS Foundation Trust as a patient or family member of a patient and therefore who have the lived experience needed to advise researchers designing studies involving children.

This study aims to include children, carer and staff participants from all 13 UK paediatric nephrology units, of which 10 units offer kidney transplantation surgery on site and 3 units which do not offer transplantation surgery at their own site but offer shared transplant care with nearby units.

In phase 1 and phase 2 stage A, children (aged 0–17 years inclusive) with CKD-5 on chronic dialysis, being worked up for pre-emptive kidney transplantation or on the waiting list for deceased donation (active or suspended) or awaiting living donation, will be invited to participate with their carer(s). Where appropriate, children aged 5 years and above will be invited to directly participate. For children aged under 5 years or those who are unable to assent or consent, their carers will be consulted and offered an opportunity to participate through proxy measures. Members of the multidisciplinary team (MDT) involved in pretransplantation workup at their local unit (eg, paediatric nephrologists, transplant surgeons, nurse specialists, social workers, family therapists, play therapists and members of the psychosocial team) will be invited for interviews in phase 1 only. Patients or carer(s) who are unable to give informed consent or patients who have been deemed too unwell to participate or had a recent acute hospital admission in the last 14 days will not be approached to participate in the study.

Phase 1 interviews

A purposive sampling matrix will be used to maximise the diversity of views. Criteria for sampling will include age, sex and ethnicity of children, modality of KRT, whether the kidney unit offers transplantation surgery or not and role in the MDT. Completed interviews will be analysed in parallel with ongoing recruitment. Participant recruitment will stop when no new themes are generated from the data.

Analysis of these interviews will contribute towards selecting questionnaires for use in phase 2 stage A.

Phase 2 stage A questionnaires

In this phase, we will aim to recruit every child with CKD-5 who meets the inclusion criteria at pretransplant baseline with an anticipated recruitment rate of 60%–70%.

At the time of study design, the predicted annual numbers across all 13 UK paediatric nephrology units as per UK Renal Registry (UKRR) reports were as follows 1 2 :

Number of dialysis patients on the transplant waiting-list in a year; n=70.

Number of patients starting dialysis in a year; n=125.

Number of patients not on dialysis but pre-emptively on the transplant waiting list; n=50.

Transplant rate; 130–160 patients per year.

Based on these numbers, the predicted maximum number of eligible participants in a 1-year period would be 245. We chose a 2-year period for our sample size calculation, which would be a population size of 490, to include children who experience delays and receive their transplant after 1 year (see figure 2 ). We plan to recruit 180 patients and after allowing for a 20% drop-out, we expect to include 141 families. Assuming a prevalence of pertinent psychosocial factors in the national cohort of patients being pre-emptively worked up for transplant, on dialysis or listed for transplant of 15%, then with 141 families we will be able to estimate this with 5% precision. At the time of sample size calculation, there were no paediatric studies specifically measuring this nor studies encompassing all psychosocial factors. Therefore, we used the assumed prevalence of 15% based on a study measuring psychological distress in potential adult transplant candidates. 28

Flow diagram illustrating predicted number of CYP who are eligible for transplantation and receive one across 2 years. CYP, children and young people.

We expect some overlap in the cohort between phase 1 and phase 2 stage A and appreciate some families may develop research fatigue. Families who already took part in phase 1 will be asked whether they would like to also take part in phase 2 stage A or prefer to opt-out.

Phase 2 stage B

Results from phase 2 stage A will be reviewed for negative and positive cases. Families who have outlier findings, in terms of their answers to the validated questionnaires, will be invited to interview in phase 2 stage B, with the aim of reviewing and refining themes to develop additional theoretical explanation for the influence of psychosocial factors on transplant access and outcomes. The exact parameters to define outliers and sample size will be dependent on findings from phase 2 stage A.

Outcomes measured

Using a Topic Guide ( online supplemental material 1 ), qualitative data will be collected in phase 1 to address research objectives 1–4 through semistructured, in-depth interviews undertaken by the investigator (JSK). This format was chosen over focus groups to enable participation of younger or less verbal children and avoid further inconveniencing families through multiple research appointments by having separate interviews for young children and focus groups for carers. The family interview format will be based on the child’s decision to either interview with or separately from their carers, depending on which setting they find more comfortable. To ensure no participant is unfairly excluded from interviews due to English not being their first language, interpreters will be present to support their participation. The same principle of minimising communication barriers will be applied to younger children or young people who may prefer communicating through other creative outputs such as drawing or Talking Mats. The research goals for family interviews will include exploring what families feel their life is like now living with CKD-5, what good HR-QoL looks like and what they believe delays or enables how soon they receive a kidney transplant. Similarly, research goals for MDT member interviews will include exploring what the professionals think matters most to families whose child has CKD-5 when it comes to a good HR-QoL and what they, as professionals, believe impacts how soon children access a kidney transplant in terms of psychosocial factors.

Supplemental material

The Topic Guide has been developed from the investigator’s systematic literature review on the subject matter and in consultation with the Young Persons Advisory Group and the Research Partner Family. 29 To minimise any inconvenience in joining the study, all participants will be interviewed using their preferred modality (telephone, video-call or face-to-face consultations) in keeping with COVID-19 safety recommendations. 30 Interview time with young children will be kept to less than half an hour to minimise interview fatigue. All interviews will be audio or video recorded depending on participant preference.

Participants may become distressed as they reflect on their experiences due to the sensitive nature of some of the interview topics around their mental health or transplant delays. Therefore, the participant and JSK will agree on a ‘stop signal’ before commencing the interview for use should they feel uncomfortable. If the participant uses the ‘stop signal’, they will be offered a break. The participant and JSK will then discuss whether they would like to continue, reschedule or withdraw from the interview altogether. Once the interview recording stops, there will be an opportunity to discuss the participant’s feelings and, if appropriate, they will be signposted to their local support services.

The acceptability of existing validated age-appropriate questionnaires that measure outcomes relevant to the preliminary themes will be discussed with the steering group. Potential validated questionnaires that capture the preliminary themes will be identified from the systematic literature review and a wider search of the literature. These questionnaires will be checked in terms of their psychometric properties such as internal consistency (Cronbach alpha of at least 0.7) and test–retest reliability and aspects such as availability of the measures, respondent type (parent, child or other respondent) and age range for which the questionnaire has been validated, to enable the most appropriate questionnaire to be chosen to measure each theme. The selected questionnaires will then be discussed with the steering group, considering the language of the questionnaire, acceptability and level of burden for the participant. If the list of questionnaires is too onerous for the participating family, a consensus will be reached with the steering group on which preliminary themes and therefore which questionnaires should be prioritised. Once the final list of questionnaires is agreed on, these will be submitted to the Health Research Authority for final approval.

Phase 2 stage A

In phase 2 stage A, research objectives 5–6 will be addressed by measuring the following primary outcome variables: HR-QoL, psychosocial functioning and time taken to receive a kidney transplant since the date confirming CKD-5. To understand changes in psychosocial factors over time, their associations with health burden or short-term allograft deterioration must be accounted for, Therefore, we will measure the following secondary outcome: the child’s estimated glomerular filtration rate (eGFR) over time. The eGFR will be calculated with their height and serum creatinine using the CkiD U25 formula. 31 32

Questionnaires measuring HR-QoL and psychosocial functioning, selected from phase 1, will be distributed to a larger, national cohort of children with CKD-5 and their carers at their pretransplant baseline. Follow-up questionnaires will be sent post-transplant at 3, 6 and 12 months later or 12 months after their first questionnaire if they still have not received a kidney transplant in that time frame. These follow-up time points were chosen to reflect the initial period of post-transplant adaptation, which is comparable with similar studies in adults and children at the time of protocol-writing. 33–35 For children who have not received a transplant, a 12-month interval was advised by our steering group to avoid distress triggered by frequent questionnaires reminding them of their non-transplanted state. As families are more likely to participate if they can choose which questionnaire modality is most suited to their lifestyle, participant families will be offered either paper or online questionnaires. 36 Health morbidity and coexisting disease data, including their underlying primary kidney diagnosis, as described in table 1 , will be mapped to each questionnaire time point.

• View inline

Coexisting health data to be collected and mapped against questionnaire time points

An additional questionnaire (see online supplemental material 2 ) has been codesigned with the steering group to retrieve information on the participating family’s demographic background. Data on medication burden will also be collected due to its’ possible confounding effect HR-QoL as described in current literature. 37

The Interview Topic Guide for phase 2 stage B has been designed with flexibility since the interview will depend on the nature of the outlier findings from phase 2 stage A.

Data analysis plan

To address research objectives 1–4, JSK will thematically analyse interview transcripts, following the approach of Braun and Clarke. 38 Deidentified transcripts will be managed using NVivo software. 39 For the purposes of qualitative rigour, JSK will maintain a reflexivity journal after interviews and throughout data analysis, to ensure potential researcher biases and insights are noted. Maintaining a reflexivity journal is a gold-standard practice in qualitative research, which increases the credibility and deepens the understanding of the findings by describing the context in which data were collected and analysed. 40 41 A sample of transcripts will be examined by JW and discussed with JSK. Throughout all stages of data collection and analysis, findings will be shared and discussed by JSK, JW and SM.

Participant demographics will be described using descriptive statistics. It will also be used to address research objective 4 by describing the prevalence of psychosocial factors.

Research objective 6 will be addressed through the following:

First, descriptive statistics will again be used to describe how participants’ psychosocial functioning and HR-QoL variables change before and after transplantation at each data collection time point.

Second, to describe the impact waiting to access a kidney transplant and receiving one has on psychosocial functioning and HR-QoL over time, repeated measure analysis of variance will be undertaken.

Third, the association between clinical, demographic and psychosocial factors with accessing a kidney transplant will be measured using logistic regression modelling and Kaplan-Meier statistics.

Fourth, the association of clinical, demographic and psychosocial factors with failing transplant allograft in short-term follow-up between 1 and 2 years post-transplantation will be assessed. Where the outcome variable is the child’s eGFR, linear regression modelling will be used. Where the outcome variable defines a failing post-transplant kidney as reaching an eGFR equivalent to stage IV CKD (eGFR 15–29 mL/min/1.73 m 2 ), logistic regression modelling and Kaplan-Meier statistics will be used.

Separately, the level of agreement between child and carer responses will be assessed. Choice of statistical tests will depend on the type of data, for example, Cohen’s kappa for binary data, weighted kappa for ordinal data or interclass correlation coefficients for continuous data.

Where relevant, all statistical analyses will be adjusted for (but not limited to) child’s age, gender, ethnicity, socioeconomic status and KRT status at baseline.

Interview data will be analysed using thematic analysis underpinned by the same principles as in phase 1.

An integrative analysis of data from phases 1 and 2 will be undertaken to address research objective 7 and create a conceptual model of how psychosocial factors influence transplant access and outcomes. The quantitative and qualitative data will be reviewed together to understand where they converge, complement or diverge from the other dataset or have findings that are novel. A final lay report will be cowritten with the steering group to ensure it is meaningful, relevant and accessible to families whose child has CKD.

This study protocol has been peer reviewed by the National Institute for Health Research Academy and has been approved under IRAS number 270493 by the Wales Research Ethics Committee 4 (ref: 20/WA/0285) and the Scotland A Research Ethics Committee (ref: 21/SS/0038).

Coercive pressure of joining the study will be minimised. Research participants will not be paid for participation. Participant information sheets will indicate that there will be no added benefit or disruption to their medical care.

Informed written consent will be obtained from all participants aged ≥16 years old and written assent from participants aged 5–15 years old. Consent will be obtained for all research activities including interviews, questionnaires and retrieving health information from national databases and hospital records.

Participant confidentiality will be upheld by fully adhering to the Data Protection Act. Participant identifiers will be handled with appropriate pseudonymisation and all data will be kept on General Data Protection Regulation (GDPR) compliant encrypted devices or stored on hard drives with restricted access with the relevant encryption and password protection. The only instance where confidentiality will be breached is if a participant discloses information that has direct implications for child or adult safeguarding. Potential participants will be made aware of this as part of informed consent. All interviews will be digitally recorded, transcribed verbatim and have identifiable data redacted. Audio files will be transcribed either by JSK or by a third-party interview transcription company (Take Note). To minimise data handling breaches, all engagement with Take Note will only be through their secure web platform. All video files will be transcribed only by JSK to remove third-party involvement in the deidentification process. Once deidentified, only quotes that cannot lead to participant identification will be used in reports. Care will be taken in reporting findings to ensure individuals cannot be identified by their role, diagnosis, gender, age or geographic locality.

Dissemination

Research participants will be updated about the findings through newsletters. Lay summaries approved by the steering group will be disseminated across charity websites accessed by children and families affected by CKD. To ensure that professionals who work with families affected by CKD are being reached, findings will be disseminated widely in relevant peer-reviewed journals and at national and international conferences. Finally, if appropriate, a dissemination strategy will be cocreated between JSK and the steering group for other professionals (eg, teachers) who may encounter vulnerable families with CKD and need early referral for psychosocial intervention.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

We would like to thank the Great Ormond Street Hospital Young Person’s Advisory Group and members of our Research Partner Family Steering Group—Emma Beeden, Katy Beeden, Angela Watt and Heather Davis from KDARS (Kidney Disease and Renal Support) for kids, for their expertise, support and advice. We would also like to thank members of the National Kidney Federation and Kidney Care UK for their continued support of this study. Finally, we would like to thank the British Association for Paediatric Nephrology, whose support and contribution towards this study has been instrumental to its launch.

• Pyart R , et al
• Casula A , et al
• Milford EL , et al
• Inward C , et al
• Cho MH , et al
• Medcalf J , et al
• Marlais M ,
• Balasubramanian R , et al
• Feurer ID ,
• Speroff T , et al
• Goetzmann L ,
• Stamm M , et al
• Maldonado JR ,
• Lolak S , et al
• Sohal GK , et al
• Watson CJE , et al
• Oniscu GC , et al
• Taylor DM ,
• Bradley JA ,
• Bradley C , et al
• Forsythe JLR , et al
• Gibbons A ,
• Bayfield J ,
• Cinnirella M , et al
• Killian MO ,
• Schuman DL ,
• Mayersohn GS , et al
• Dunson J , et al
• Freiberger D ,
• Kimball B ,
• Traum AZ , et al
• Dorste A , et al
• Marmot MA ,
• Black J , et al
• Creswell JW
• Bonfiglio DBV
• Pierce CB ,
• Ng DK , et al
• Gottlieb J ,
• Warnecke G , et al
• Wightman A ,
• Bradford MC ,
• Morton RL ,
• Hayen A , et al
• Pulham RA ,
• Feinstein Y , et al
• Díaz-González de Ferris ME ,
• Gipson DS , et al
• Russell GM ,

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

X @dr_jowray

Collaborators British Association for Paediatric Nephrology

Contributors JSK wrote the study protocol and coordinated the entire manuscript. DR contributed towards sample size calculation and statistical methods of study protocol. LP contributed towards statistical analysis of protocol and advised regarding UK Renal Registry involvement. DN contributed towards statistical analysis of protocol and advised regarding UK Renal Registry involvement. MR contributed towards statistical analysis of protocol and advised regarding NHS Blood and Transplant involvement. JW supervised JSK, study protocol development with regular input to initial drafts and final manuscript approval. SM supervised JSK, study protocol development with regular input to initial drafts and final manuscript approval.

Funding This work is supported by the National Institute for Health Research Academy, as a Doctoral Fellowship, grant number NIHR300727.

Competing interests JSK is the National Institute for Health Research Fellowship grant recipient, which funds this study. LP reports grants from the National Institute for Health Research and Kidney Research UK. She is also the paediatric research lead for the UK Renal Registry.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Read the full text or download the PDF:

Graduate Centre

• doctoral degree & programs
• Graduate Center
• News & Announcements

Free slots available: Online-Workshop „The best of two worlds? Combining qualitative and quantitative methods in Mixed Methods research“ on 6 & 7 June 2024

KU Eichstätt-Ingolstadt offers free places in the

Online-Workshop „The best of two worlds? Combining qualitative and quantitative methods in Mixed Methods research“

Trainer: Andreas Müller, https://www.muellermixedmethods.com/

Date and time: Thursday, June 06, 2024, 9:00 am – 3:30 pm and Friday, June 07, 2024, 9:00 am – 3:30 pm

Format: Zoom (Login credentials will be sent after registration)

Audience: PhD Students and Postdocs

Registration: [email protected] (first come, first served)

Course description

This two-day webinar is targeted at beginners who consider Mixed Methods approaches for their research or have recently set out to do so. The combination of qualitative and quantitative methods requires careful considerations: What data should I combine and how? How will I be able to integrate my results? Is this the right approach for me?

This online workshop aims at preparing researchers for the exciting and sometimes challenging journey of becoming a master of more than one method. You will learn what Mixed Methods is, what to use it for and what challenges to expect. In this, the two-day workshop will address both: The initial planning and data collecting, as well as the data analysis and integration of results. In this way, we will simulate the various steps of a Mixed Methods research project with interactive exercises on example data ranging from qualitative interviews to standardized surveys.

Content • What is Mixed Methods research and why would you want to combine qualitative and quantitative methods? • What research questions and projects benefit from a Mixed Methods approach? • What types of data work for Mixed Methods project? • What do you need to consider when planning your project and gathering data? • What data analysis methods are commonly used in Mixed Methods Research? • How do you analyze data in a Mixed Methods project? • How do you integrate the results from your qualitative and quantitative analysis? • What potentials, limitations and hardships await you in Mixed Methods Research?