A1: Mixed Methods with MAXQDA: Practices and Techniques

This is a practical course designed to assist junior researchers in developing and executing a mixed method research (MMR) project. Mixed methods are increasingly deployed by researchers across social science disciplines. If designed correctly, mixed methods research can significantly strengthen the validity of one’s empirical findings, offset the weaknesses of single-method studies, and answer multiple questions in a single project. This course takes students from their central research question to the selection of the correct MMR template to techniques for data collection and analysis tailored to your project. Students should come prepared for class by reading short excerpts from three to four articles each day. During class sessions, we will discuss the principles of MMR from the readings and use class exercises to give students experience in how to use these principles in practice. Students will also gain hands-on familiarity with MAXQDA—a computer-aided qualitative data analysis software program used to organize and analyze multiple forms of data. (I can also offer guidance in NVivo, if that is your preferred platform.) To gain maximum benefit from the course, students are encouraged to bring their idea for a project for which they will develop a bespoke mixed methods research design. Over a period of five days, the instructor will assist each student in developing and writing up a plan for their MMR dissertation, book, or article project.

The best contemporary social science research is interdisciplinary. This implies not only working across epistemological and ontological lines, but also deploying multiple methodological approaches for analyzing and combining qualitative and quantitative data. The leading social science journals and book publishers increasingly feature two or more different social science methodologies, widely known as mixed method research (MMR). The value of drawing on more than one methodological approach is that many ”big” research questions can only be answered by examining processes taking place across multiple temporal and spatial domains at different levels of analysis. To understand these processes, today’s researchers increasingly draw on more than one methodology to shed light on multiple facets of social science questions in hopes of achieving both greater explanatory leverage and descriptively rich empirical results.  

The course is heavily weighted toward examples, class exercises and workshopping student assignments, with extensive instructor feedback on optional written assignments. As such, the course is intended to guide students through the process of fitting an appropriately tailored MMR design to their research question, establish a plan to assess their empirical claims using a combination of research methods, and work out how to approach, collect, and analyze different types of empirical data at multiple levels of analysis and across different spatial and temporal domains using MAXQDA.

Day 1

Covers the uses and attractiveness of mixed methods research by discussing best practices and first principles of MMR and by reviewing practical examples. We break down the logic of the MMR approach and discuss research questions that are particularly well-suited for MMR. The first day also covers the limitations and challenges students are likely to face in applying MMR to their own research projects, as well as common templates that researchers can adapt to their own use. The remainder of the course introduces students to MAXQDA and discusses how to use it to execute mixed methods research using class exercises.

Day 2

Covers traditional positivist MMR in mainstream social science. This typically combines regression analysis with small-N case analysis. On this day, we look at a range of ways that positivist researchers combine qualitative and quantitative data to answer their research questions. We discuss examples of these positivist projects with a focus on logico-deductive theory development, hypothesis testing, and concept definition and measurement. We also talk about how to sequence and blend different methods components differently depending on the researcher’s unique goals. This day is heavy weighted toward developing causal mechanisms, covering how to combine statistical analysis with small-N comparative studies, and how to combine micro-level experimental or other survey research undertaken at the individual level with macro-level analysis undertaken at higher levels of analysis.

Day 3

Focuses on how to combine different qualitative research techniques in positivist research designs. For example, some researchers use historical comparative analysis to examine a small set of cases comparatives over long periods of time. This can be done using comparative analysis of two to four cases using periodization analysis or critical juncture analysis. Another variety combines comparative or medium-N (QCA) analysis of multiple cases with a single in-depth case study analysis or comparative analysis using cross-temporal or –sectional analysis of states, institutions, groups, or individuals. The focus on Day 3 is how to combine cross-historical or comparative research using periodization strategies with single or paired case studies using pattern-matching and process-tracing techniques.

Day 4

Covers hybrid MMR projects that cross epistemological lines. These combine positivist and non-positivist techniques in an approach known as ”analytical eclecticism” or “critical realism.” While less mainstream than positivist hybrids (Days 2 and 3), the combination of interpretivist research techniques with positivist research design principles or positivist methods has gained momentum across a number of social science fields. As in the previous two days, class exercises will be conducted in MAXQDA to show how interpretivist data and analysis can be combined with more mainstream positivist data and analysis in a single project.

Day 5

Is meant to pull it all together, focusing on the data analysis, data displays, and the ”write-up,” namely how to derive empirical generalizations across different analyses and how to integrate these findings in a single, readable research report. On this day, we review methods for displaying findings visually in tables, charts, and figures, and show how to use MAXQDA to visualize data in new and exciting ways, using sample corpora provided by the instructor.

This description is subject to change at the discretion of the Instructor.

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.