Mixed Methods Research in the Social Sciences

Author(s):  
Jessica T. DeCuir–Gunby
Author(s):  
Manfredi Valeriani ◽  
Vicki L. Plano Clark

This chapter examines mixed-methods research, which is an approach that involves the integration of quantitative and qualitative methods at one or more stages of a research study. The central idea behind mixed-methods research is that the intentional combination of numeric-based methods with narrative-based methods can best provide answers to some research questions. The ongoing attempts to construct a simple and common conceptualization of mixed-methods provide a good indicator of the status of mixed-methods itself. mixed-methods research has emerged as a formalized methodology well suited to addressing complex problems, and is currently applied throughout the social sciences and beyond. Nowadays, researchers interested in combining quantitative and qualitative methods can benefit from the growing knowledge about the epistemological foundations, essential considerations, and rigorous designs that have been advanced for mixed-methods research.


2019 ◽  
Vol 8 (4) ◽  
pp. 747-763
Author(s):  
Matthew C Ingram ◽  
Imke Harbers

AbstractMixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.


2016 ◽  
Vol 11 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Kai M. Thaler

The study of political and social violence and conflict has expanded in recent decades, concurrent with a rise in the use of mixed methods research (MMR) throughout the social sciences. This article examines how methods are best integrated in studies of violence and conflict, critically reviewing examples from previous prominent works and suggesting directions for future research. I explore the benefits of MMR for understanding structures, agency, and processes related to violence and conflict, and the opportunity MMR offers to influence a broader academic and policy audience. MMR can improve the accordance of theories and empirical studies with the complexities of social reality and enhance understanding of the causes, consequences, and potential remedies of violence and conflict.


2019 ◽  
Vol 14 (3) ◽  
pp. 288-304 ◽  
Author(s):  
Leanne M. Kallemeyn ◽  
Jori N. Hall ◽  
Emily Gates

While the field of mixed methods has discussed complexity theory, more clarification regarding its conceptualization is needed. Accordingly, we first review how various fields have interpreted and applied the central ideas of complexity theory in the social sciences. We then analyze two empirical studies that used complexity theory and mixed methods. We highlight how both studies examined complex systems, used elements of complexity theory as their theoretical framework, and used complexity–congruent methodologies and methods. To conclude, we offer conceptual and methodological implications for using complexity theory for mixed methods research. We view the clarification provided an important contribution to the field of mixed methods as it assists researchers in studying complex systems, theorizing complex phenomena, and using complex methods.


2019 ◽  
Vol 42 (1) ◽  
pp. 20-30
Author(s):  
Catherine Corr ◽  
Melinda R. Snodgrass ◽  
Jennifer C. Greene ◽  
Hedda Meadan ◽  
Rosa Milagros Santos

Mixed methods approaches to research are gaining popularity in the social sciences. Although these approaches may be unfamiliar to many in our field, they can uniquely contribute to and enhance early childhood special education (ECSE) research. The purpose of this article is to orient ECSE researchers to the field of mixed methods social inquiry. We offer two examples of mixed methods. We define mixed methods and how mental models and paradigms influence these efforts, including a discussion of the distinctive purposes for applying mixed methods. Finally, we identify challenges to determining rigor and quality of mixed methods research and offer preliminary guidance to mitigate these challenges. Throughout, we encourage integrating rigorous mixed methods into ECSE scholarship.


2016 ◽  
Vol 13 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Emma Uprichard ◽  
Leila Dawney

This article extends the debates relating to integration in mixed methods research. We challenge the a priori assumptions on which integration is assumed to be possible in the first place. More specifically, following Haraway and Barad, we argue that methods produce “cuts” which may or may not cohere and that “diffraction,” as an expanded approach to integration, has much to offer mixed methods research. Diffraction pays attention to the ways in which data produced through different methods can both splinter and interrupt the object of study. As such, it provides an explicit way of empirically capturing the mess and complexity intrinsic to the ontology of the social entity being studied.


2017 ◽  
Vol 10 (2) ◽  
pp. 205979911770311 ◽  
Author(s):  
Reginald Deschepper ◽  
Stefaan Six ◽  
Nicole Vandeweghe ◽  
Marijke De Couck ◽  
Yori Gidron ◽  
...  

Today, more and more problems that scientists need to tackle are complex problems. Many examples of these can be found in the health sciences, medicine and ecology. Typical features of complex problems are that they cannot be studied by one discipline and that they need to take into account subjective data as well as objective data. Two promising responses to deal with complex problems are Transdisciplinary and Mixed Method approaches. However, there is still a lacuna to fill, with transdisciplinary studies bridging the social sciences and biomedical sciences. More specifically, we need more and better studies that combine qualitative data about subjective experiences, perception and so on with objective, quantitative, neurophysiological data. We believe that the combination of qualitative and neurophysiological data is a good example of what we would like to call transdisciplinary mixed methods. In this article, we aim to explore the opportunities of transdisciplinary mixed-methods studies in which qualitative and neurophysiological data are used. We give a brief overview of what is characteristic for this kind of studies and illustrate this with examples; we point out strengths and limitations and propose an agenda for the future. We conclude that transdisciplinary mixed-methods studies in which qualitative and neurophysiological data are used have the potential to improve our knowledge about complex problems. A main obstacle seems to be that most scientists from the biomedical sciences are not familiar with the (qualitative) methods from the social sciences and vice versa. To end this ‘clash of paradigms’™, we urgently need to cultivate transdisciplinary thinking.


Methodology ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. 231-249
Author(s):  
Anaïd Lindemann ◽  
Jörg Stolz

The Titanic quantitative dataset has long been used to teach statistics. However, combining the quantitative dataset with a qualitative dataset of survivor testimonies shows that the Titanic case is an even better example to teach mixed methods. This article offers practical tools to teach mixed methods to undergraduate or postgraduate students in the social sciences, using the Titanic datasets. Based on an empirical analysis of the survival probabilities on the Titanic, we show how mixed methods lead to superior explanations than mono-method strategies. This paper has two goals: 1) to introduce the freely available linked Titanic datasets; and 2) to present a three-hour step-by-step exercise with the Titanic datasets that can be used to learn and teach mixed methods.


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