Physics

USING Q METHODOLOGY AND Q FACTOR ANALYSIS IN MIXED
METHODS RESEARCH

The purpose of this chapter is to identify two
multivariate techniques that can be used to
facilitate the interpretation of mixed methods
research. These techniques can aid researchers
in answering their research questions by
demonstrating how to disaggregate or aggregate
their data. More specifically, our purpose
is to introduce two multivariate techniques,
Q methodology and Q factor analysis, to
readers by describing these techniques as well
as by giving examples that should assist readers
in performing their own Q methodology
and Q factor analysis studies. Finally, we discuss
how researchers can take their research a
step farther by answering more-sophisticated
research questions that include groups of
people. For instance, these groups, whether
derived empirically such as via Q factor analysis
or theoretically such as psychosocial stages,
can be used as variables within other types of
multivariate analyses.

Q methodology (Q) is a powerful tool for determining consensus and perspectives of a group. Yet, Q is not often used within education research. This study demonstrates an application of Q where the perspectives and consensus of a college committee were determined relative to a proposed organizational change. The development of the statement concourse sorted by the participants and the subsequent analysis of the Q sorts are described. The article details the analyses of the factors, each representing a different perspective (Brown, 1980). Committee members responded positively to the Q methodology process and results. Individuals commented that the methodology and its results left them feeling empowered because Q revealed all of the group’s perspectives. In addition, as Focht (2004) suggested, the Q methodology results facilitated dialogue and collaboration among the group membership.

This study demonstrates how Q methodology can be used to investigate college physics students' views of their learning, also known as epistemology, and how those views affect their learning of important physics concepts in a first semester physics course. This study contributes to the already large literature on students' learning of force and motion concepts, which has established students' difficulty gaining Newtonian based understanding of force and motion concepts (Ramlo, research has typically used time intensive qualitative methods or Likert scale surveys which can result in loss of meaning (McKeown, 2001), as this study will show. Q methodology allowed the researcher to identify four different factors, representing four differing epistemologies, and their correlations with posttest scores on the Force and Motion Conceptual Evaluation (FMCE). The positive correlation of only one of these views with the FMCE posttest scores further demonstrates the strength of Q methodology to investigate views in this research area.

William Stephenson specifically developed Q methodology, or Q, as a means of measuring subjectivity. Q has been used to determine perspectives / views in a wide variety of fields from marketing research to political science but less frequently in education. In higher education, the author has used Q methodology to determine views about a variety of situations, from students' views about a newly developed bioinformatics course to faculty members' views of reading circles as a professional development experience to improve teaching and learning in their classrooms. The purpose of this article is to introduce Q methodology and demonstrate its versatility in addressing research purposes in higher education, especially where the focus is on determining student or faculty perceptions about a topic. Such determinations can be helpful for program evaluation and improved teaching/learning in higher education.

Typical Q methodology studies include either tables of statements or sorting grids with statement numbers representing the views of each factor that has emerged within the study.  Yet it could be useful to Q methodologists to have a simpler, more visual way to present this data to present the “big ideas” of perspectives and consensus determined in these studies.  In this article, the benefits of visual representations of data, especially in mixed research methods like Q methodology, are discussed.  Word clouds are a method for visually presenting text data, typically keywords on websites and in this paper we present a data set as a series of word clouds and also tables and sort-grids that are more typically seen in Q methodology presentations within this journal and elsewhere.

Teacher attitudes affect their instruction such that positive teacher attitudes enhance the teaching and learning process. The purpose of this study was to explore inservice science teachers' views of learning physics within the context of a professional development experience and to investigate the relationship between those views and the teachers' understanding of force and motion concepts. The conceptual understanding pretest results indicate the need for inservice science teacher professional development that focuses on conceptual understanding. The relationship between participants' views and their conceptual understanding at posttest has additional implications for the curriculum for these science teachers' professional development experiences.

In volume 32 of this journal, Paul Stenner suggests that Stephenson was resistant to Q methodology being placed within other theoretical frameworks. Yet in this same piece, Stenner states that it is time for Q methodology to be brought into a greater dialogue with contemporary social theory and research practice. This article seeks to demonstrate how Q fits into the contemporary research practice of mixed methods and argues that this perspective is not in conflict with Stephenson’s positions on Q as a methodology. Further, our position reflects recent calls for the development of new techniques and procedures to be used in mixed-methods research. Those making the call will find interest in what Q has to offer the social and behavioral sciences now, 75 years after it emerged in Stephenson’s 1935 letter to Nature, and even though the term mixed-methods research has only emerged in last couple of decades. Q methodology is shown to fit well methodologically into the mixed-methods continuum as described by prominent mixed-methods scholars, which further supports a position that Q represents a mixed research methodology.

We are grateful for Paul Stenner’s response to our article “Q Methodology and its Position in the Mixed-Methods Continuum” (this issue), and we appreciate his mostly positive view of our manuscript. We agree with many of his points although we are perplexed by some of his perceptions regarding our position on the mixed-methods continuum. We will detail these accordingly. However, we will focus on the methodological mixture of qualitative and quantitative research methods rather than the philosophical position on Q methodology specifically and mixed-methods research in general. Although we certainly made philosophical references, we did so with the purpose of explanation rather than a main focus for the article. Certainly the term is mixed-methods research, not mixed research philosophy. The concept of mixing philosophies is often considered more complex than the mixing of methods (Creswell, 2010; Newman & Benz, 1998). And we are only tackling the latter here, related to Q methodology, not the former. Thus, our response focuses solely on describing the mixing of methods (even though we strongly agree one cannot and should not separate the philosophy from the methods). The emphasis is on the methods and the description of the methods within Q methodology, which is the purpose of our article.