Overview

The Mixed Methods Research Special Interest Group Outstanding Dissertation Award is an AERA-sanctioned award intended to recognize an individual whose dissertation makes an outstanding contribution to the field of mixed methods research.  The winning application receives recognition by the SIG in the form of an honorarium presented to the recipient at the AERA Mixed Methods SIG Business Meeting held at the annual AERA Meeting.

Empirical studies and methodological topics pertinent to conducting mixed methods research are considered appropriate submissions.  To be eligible for consideration, the dissertation must be completed and defended successfully during a two-year time period before nominations are due.  Currently the award is given out every other year (e.g., 2019, 2021).

Information about how to apply is distributed to SIG members during the Fall before the applications are due.

2023 Dissertation Award Winners

Dr. Sinem Toraman Turk, University of Cincinnati

Dr. Toraman Turk’s dissertation entitled “How Recent Doctorates Learned About Mixed Methods Research Through Sources: A Mixed Methods Social Network Analysis Study” contributed to and advanced the field of mixed methods research substantively and methodologically. Dr. Toraman Turk applied a cutting-edge mixed methods social network analysis approach to understand how a national sample of recent doctorates learned mixed methods research within their doctoral programs. Informed by the theories of diffusion of innovations, ecological systems, and social network, her research described the connections among the use of different sources for learning and identified a set of learner profiles based on these connections. In addition, Dr. Toraman Turk’s dissertation offers a creative use of various methodological techniques when combining mixed methods and social network analysis into a hybrid design, including the adaption of constant comparison technique and theoretical sampling to accomplish fully integrated analysis, description of the methodological considerations of mixed methods and social network analysis in a procedural diagram; and creation of joint displays that integrate quantitative, qualitative, and social network analysis results.

Dr. Kathleen McCallops, University of Delaware

Dr. McCallops’s dissertation entitled “Exploring the Intergenerational Schooling Experiences and Attitudes Toward Schooling Among Street-Identified Black American Girls and Women Through a Critical Race Feminist Lens: An Explanatory Sequential Mixed Methods Study” was conducted to understand the self-reported attitudes toward schooling and lived schooling experiences of street-identified Black American girls and women in Wilmington, Delaware. Dr. McCallops employed an explanatory sequential mixed methods design using only secondary data from the Street Participatory Action Research (PAR) Health Project. Her dissertation contributed to the field of mixed methods research by serving as an exemplar of how mixed methods research can be used to expand the boundaries of PAR and how to design a mixed methods study using only secondary data collected within the context of a PAR project. Furthermore, it is a model of how PAR and transformative mixed methods approaches can be employed together.

2021 Dissertation Award Winner

Dr. Michael Thier, University of Oregon

Dr. Thier's dissertation entitled "A Global Set of Dispositions? Applying Discrete-Choice Method to Measure Global Citizenship Dispositions of Secondary-School Students in Two Nations" used an explanatory sequential design to develop a discrete-choice measure that could identify and scale up secondary schools' promising practices for Global Citizenship Education (GCE). GCE is a fast-growing reform of crucial domestic and international importance. Prior to this dissertation, secondary schools aiming to offer GCE measures were designed for universities or multinational corporations; absent rigorous psychometric testing; without accounting for multidimensionality; and as self-reports, which can invite social desirability bias. This study benefited from several prior applications of mixed methods designs and yielded a novel application of the nominal group technique, revealing that tactic to be a mixed method within itself.

2021 Runners-Up

First Runner-Up: Dr. Kirstie L. Bash, University of Nebraska-Lincoln

Dr. Bash's dissertation entitled "Intersecting Advanced Quantitative Designs with Mixed Methods Research: A Case for Integrating Latent Transition Analysis" was conducted to detect changes over time in the psychological health of women who participated in equine-assisted psychotherapy (EAP) during an in-patient substance abuse treatment program. The study used a retrospective longitudinal concurrent mixed methods design to (a) illustrate the integration of latent transition analysis with qualitative case study and (b) develop a new, innovative visual joint display for integrating two person-centered analyses. The study also provided recommendations on the integration process and added evidence to the literature that supports equine-assisted psychotherapy as a therapeutic supplement. Dr. Bash's dissertation demonstrates how latent transition analysis and qualitative case study are a valuable partnership for exploring complex phenomena.

Second Runner-Up: Dr. Heather L. Walter, The George Washington University

Dr. Walter's dissertation, entitled "Exploring Early Childhood Special Education Teachers' Wellbeing Through a Multidimensional Framework: A Mixed-Methods Study" aimed to understand and investigate profiles of Early Childhood Special Education (ECSE) teacher wellbeing. Dr. Walter used an explanatory sequential mixed methods design. She used latent class analysis to identify four quantitative profiles using previously established measures. Qualitative interviews with participants from each profile helped to clarify quantitative results and suggested two profiles of ECSE teacher wellbeing rather than the four identified through LCA. The study illustrated how mixed methods approaches resulted in insights that would not have been apparent using one method alone.

2019 Dissertation Award Winner

Dr. Elisabeth Kutscher, George Washington University

Dr. Kutscher’s dissertation entitled “A Mixed Methods Exploration of Persistence in Postsecondary Education Among Young Adults with Disabilities or Learning Differences” was conducted to understand the K-12 and postsecondary factors influencing college persistence among young adults with disabilities. The study used a transformative, convergent mixed methods design and applied multiple correspondence analysis as a crossover analysis to integrate and investigate relationships among theory-driven, quantitative characteristics and emergent, qualitative themes. The study illustrates how crossover analyses a) can be used to support integration and b) may reveal unexpected relationships among variables and themes.

2017 Dissertation Award Winner

Dr. Marcia Gail Headley, University of Cincinnati

Dr. Headley’s dissertation entitled “What is Symbolic Mathematics Language Literacy? A Concurrent Mixed Methods Study of Adolescents in a Middle School” was conducted to understand symbolic mathematics language literacy (SMaLL) among middle school students learning under the Common Core State Standards with implication for instructional practice. Using the theory of developmental bio-cultural co-constructivism, Dr. Headley adapted data collection tools and implemented a multilevel concurrent mixed methods research design by which she contributed to the field of mixed methods research providing an exemplar and introducing multilevel variants of mixed methods research designs with theoretically defined levels and within-person investigations.

2015 Dissertation Award Winner

Dr. Elise M. St. John, University of Washington

Dr. St. John’s dissertation entitled “Understanding the Factors that Influence the Grouping and Assignment of Students to Elementary Classrooms” was supported in part by the US Department of Education, Institute of Education Sciences (#R305B090012). The study employed a four phase sequential mixed methods design to examine the grouping and assignment of students to elementary school classrooms.She specifically explored the grouping of students by ability across grade-level classrooms by linking classroom assignment outcomes to assignment practices to investigate the factors that contribute to varying outcomes, which had important implications for the accuracy of value-added measurement techniques. Dr. St. John’s dissertation also provides an example of how to be explicit in your descriptions of the purposes and goals of each data source, analysis strategy, and study phase.

2013 Dissertation Award Winner

Dr. Michelle C. Howell Smith, University of Nebraska-Lincoln

Dr. Howell Smith’s dissertation entitled “Factors That Facilitate or Inhibit Interest of Domestic Students in the Engineering PhD: A Mixed Methods Study” was supported in part by the National Science Foundation (award EEC-0935108). This dissertation was conducted as mixed methods sequential exploratory instrument design in four phases to identify factors that facilitate interest in engineering PhD programs among domestic engineering undergraduate students in the United States. This study contributed to the literature with a testable theory for how domestic students become interested in engineering PhD programs and a measure of that process.It is a good example of mixed methods research with a multi-phase approach that includes grounded theory, instrument development, and testing the instrument.

2011 Dissertation Award Winner

Dr. Pamela M. Wesely, University of Minnesota

Dr. Weseley’s dissertation entitled “The Language Learning Motivation of Early Adolescent French and Spanish Elementary Immersion Program Graduates” used explanatory “sequential mixed design.”She investigated the L2 learning motivation of elementary immersion school graduates.This study contributed to the mixed methods literature by incorporating qualitative research into a quantitative work with expanding a theoretical framework beyond the components of a socio-educational model.