How qualitative research methods can be leveraged to strengthen mixed methods research in public policy and public administration?
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Abstract Recently, there have been a variety of arguments voiced to encourage that more attention be given to the role qualitative methods can play in mixed methods research in public policy and public administration. This article discusses these claims and describes the benefits of qualitative approaches, and how qualitative research methods can be leveraged to strengthen mixed methods research in public administration. We also provide a guide for improving the credibility of mixed methods research through increasing transparency and discussions of all methodological decisions. This study is based on a systematic content analysis of 186 mixed methods studies published in public policy and public administration journals between 2010 and 2018. We found that findings from the quantitative methods dominated the mixed methods studies, little diversity in data collection and analysis methods, and frequent failure to integrate insights from both methods. We also analyzed the 36 qualitative‐dominant studies in the sample, and illuminated seven different ways that authors of qualitative‐dominant studies leveraged the qualitative strand to strengthen mixed methods research. We developed lessons from our analysis of the qualitative‐dominant articles on how to incorporate qualitative methods in a thoughtful manner, articulate a role for each strand, and effectively support findings with one or more strands.Keywords:
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The purpose of this study was to assess students’ satisfaction with the practices and implementation of non-regular education programs (NREPs) with particular regard to Haramaya University (HU), Ethiopia. To achieve the aim of the study, an explanatory sequential mixed methods research design, which initially allows collecting quantitative data and then qualitative data for elaboration on the quantitative data, was used. The study used a 5-point Likert scale questionnaire for quantitative inquiries from 741-students belonging to different centres, and follow-up with 20 interview participants purposefully selected to elaborate those results in more detail. In the quantitative phase, four features were considered as predictors of students’ satisfaction with service quality: (a) academic issues, (b) administrative issues, (c) resources/facilities, and (d) assessment and feedback issues. In the qualitative follow-up, the semi-structured interviews outlined three major themes: (a) overall teaching-learning, (b) administrative and management issues, and (c) learning support facilities. The paper used descriptive statistics to interpret the quantitative data and thematic content analysis to interpret the qualitative data. The findings are presented sequentially following the order of the analysis of quantitative and qualitative data presented in the paper. The conclusions and relevant recommendations are also stated at the end of the paper.
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Background: A core assumption of mixed methods research is that it combines the strengths of both quantitative and qualitative research. Simply collecting quantitative and qualitative data and separate reporting of the results does not leverage this strength. Although the use of mixed methods research in music therapy is steadily growing, quantitative and qualitative findings tend to be presented separately in study reports and true integration of data sets is often lacking.Objective: To explore various strategies for effective integration of quantitative and qualitative data in order to optimize understanding of research phenomena.Methods: Integration of quantitative and qualitative data is particularly challenging for new mixed methods researchers. This session will illustrate different strategies for data integration including merging data in joint displays, transforming data, connecting data in sequential fashion, and spiralling. The presenter will share several data integration examples of her mixed ...
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This chapter focuses on how mixed-methods researchers can conceptualise and analyse time and the life-course when reusing longitudinal qualitative and quantitative data sources. Specifically, it addresses the methodological and analytical challenges involved in undertaking a mixed-method, longitudinal, research project that reused qualitative and quantitative secondary data to investigate individual attitudes towards voluntarism between 1981 and 2012. Discussing the project’s research design, its mixed-method analyses, and the key learning points of this mixed-method process, the chapter poses a series of key questions. Were the longitudinal qualitative and quantitative datasets used compatible and able to be mixed? What were the roles and relationships between the qualitative and quantitative analyses, did one facilitate the other? Does a mixed-method approach work when researching time and the life-course? The chapter examines some of the challenges involved in longitudinal mixed-method research, notably in ensuring a good fit between data sources, and between analyses. However it also highlights the value of using this approach, where the respective weaknesses of each analytical methodology were offset by their joint strengths to enable a multi-dimensional, comprehensive understanding of time and the life-course in the context of understanding British voluntarism
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Engineering education researchers are increasingly integrating qualitative and quantitative research methods to study learning and retention in engineering. While quantitative methods can provide generalisable results, qualitative methods generate rich, descriptive understanding of the investigated phenomenon. On the other hand, a mixed methods approach provides benefits of the two approaches by incorporating them in a single study. However, engineering faculty often faces difficulty in integrating qualitative and quantitative methods and designs in their education research. This article discusses mixed methods in the context of an actual ongoing engineering education research project investigating student resistance to active learning. We describe the research design in phases that show the integration of quantitative and qualitative results, and how these data sources can help influence the direction of the research and triangulate findings. Our mixed method research experience highlights the importance of thinking iteratively between qualitative and quantitative data sources during the instrument development process.
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In this article, we provide a typology of mixed analysis techniques, namely the Mixed Analysis Matrix, that helps researchers select a data analysis technique given the number of (a) data types collected (i.e. quantitative or qualitative; or quantitative and qualitative) and (b) analysis types used (i.e. quantitative or qualitative; or quantitative and qualitative)—yielding a 2 X 2 representation involving four cells that each contain specific analytical techniques, with two of these cells containing a total of 15 mixed analysis techniques. Furthermore, we describe the fundamental principle of mixed analysis, describe the steps in a mixed analysis, and delineate the rationale and purpose for conducting mixed analyses. For each technique, readers are directed to published studies that serve as illustrative examples. Outlining the mixed-analysis techniques available for researchers hopefully will increase awareness of the number of choices for analyzing data from mixed studies.
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The main aim of this article is to discuss the factors a researcher should take into account when selecting the appropriate research design or method (i.e. qualitative, quantitative, and mixed-methods). The article also discusses sample size determination and sampling procedures in qualitative and quantitative research. Further, the paper has provided an examination of qualitative and quantitative data collection and analysis methods. The study used online desk research to collect data from online public access scholarly databases such as Google Scholar, ResearchGate, Academia, and many more freely accessible electronic books in research methods. Search terms that were used included, among many, the following: qualitative research, quantitative research, mixed-methods, qualitative and quantitative data collection, qualitative and quantitative data analysis; descriptive and inferential statistics. A wealth of relevant and timely scholarly literature was downloaded, read, and used to write this paper, and draw the conclusion provided. Keywords: Qualitative Research; Quantitative Research; Mixed-Methods Research; Qualitative Data Collection Techniques; Quantitative Data Collection Techniques; Qualitative Data Analysis Techniques; Quantitative Analysis Techniques; Descriptive Statistics; Inferential Statistics. DOI: 10.7176/JMCR/87-06 Publication date: November 30 th 2022
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Although the mixing of quantitative and qualitative data is an essential component of mixed methods research, the process of integrating both types of data in meaningful ways can be challenging. The purpose of this article is to describe the use of data labels in mixed methods research as a technique for the integration of qualitative and quantitative data within equivalent-status mixed methods research designs. The music education study described utilized a combination of focus group, survey, and case study data to provide a fuller view of instrumental music teaching within the urban context. Using this study as an exemplar, the author discusses the application of data labels to points of convergence between qualitative and quantitative strands of mixed methods studies in order to achieve integration between both data sets.
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The aim of this study is; determining the relationship between teachers’ education beliefs and their teaching-learning conceptions. A mixed method has been used to reach the overall aim of the research. In the study, explanatory sequential design was used as mixed method design. In this context, quantitative data were collected and analysed at the first stage. In the second stage, qualitative data were collected to help explain the findings obtained after the analysis of the quantitative data. The study group of the quantitative dimension of the study constitutes 301 high school teachers who are working in secondary education institutions in the provincial centers of Mersin province during the 2015-2016 educational year and voluntarily participating in the research. The qualitative study group consists of 45 teachers from the quantitative dimension. Education Beliefs Scale, Teaching-Lerning Conceptions Questionnaire and Interview Form were used in collecting research data. In the study, the analysis of the data was carried out in two stages: analysis of quantitative data and analysis of qualitative data. Anova was used to examine whether the educational beliefs of teachers and their teaching-learning conceptions differed according to the service year variable and t-test was used to see if it varied according to the gender variable. The relationship between teachers' educational beliefs and teaching-learning conceptions was examined by Pearson's correlation coefficient. Multiple linear regression analysis has been used to determine how teachers perceive their teaching-learning conception of educational beliefs. Content analysis was used for the analysis of qualitative data. It has been determined in the research that there is a significant relationship between teachers' educational beliefs and teaching-learning conceptions. In the study, it was determined that teachers' educational beliefs were a significant predictor of constructivist and traditional teaching-learning conceptions.
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