Making Sense of Methods and Measurement: Q-Methodology-Part II

Clinical Simulation in Nursing (2015) 11, 75-77

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Making Sense of Methods and Measurement: Q-MethodologydPart IIdMethodological Procedures Jane B. Paige, PhD, RN, CNE* Associate Professor, Milwaukee School of Engineering, Milwaukee, WI 53202, USA This is the second column on Q-methodology (Q) with a focus on Q’s methodological procedures. As introduced in a prior column, Q combines quantitative and qualitative techniques (Newman & Ramlo, 2010) to reveal what Q-methodologist call ‘‘subjectivity’’. Subjectivity describes peoples’ points of view about a particular topic of interest. A person’s point of view reveals itself as one rank orders (Q-sorts) a number of items (opinions on the topic of interest). Through the use of factor analytic techniques, the researcher is then able to reveal how groups of people share similar to divergent points of view. A study that explored perspectives nurse educators hold about simulation design illustrates the Q-sort process as experienced by a participant. A simplified explanation illustrates how the researcher analyzed and interpreted the data (Q-sorts). Terminology unique to Q (Table 1) and resources useful for researchers undertaking Q (Table 2) are shared.

Q-Sort Process (Participant’s Experience) As a participant for this Q-study, the researcher recruited me along with other nurse educators considering our varying levels of experience with simulation and our potential to hold differing points of view. Following a set of instruction provided by the researcher, each of us completed a Q-sorting process. This began by reading 60 statements on how to design simulation activities. Each statement was written on a small card and numbered from 1 to 60. The statements were opinion based rather than factual. First, I sorted the statements into three pilesdwhat I most recommend, most * Corresponding author: [email protected] (J. B. Paige).

not recommend, or what I felt indifferent to when designing simulations. I then took each pile of statements and rank ordered them into a sorting grid configured as a normal distribution (Figure). The rank-ordering process was challenging, as I had to compare statements to each other. However, by doing this comparison, I was forced to make choices based on what I felt most strongly about. I discovered in myself what I valued most or least when designing simulation activities. After the Q-sorting process was complete, I explained to the investigator why I placed the statements where I did in the distribution grid.

Factor Analysis and Interpretation (by Researcher) As the researcher employing Q, I combined quantitative (factor analysis) and qualitative (factor interpretation) methodological procedures. During factor analysis (data reduction), I accessed a software program specific to Q to calculate a correlation matrix displaying how each nurse educator’s unique 60-statement rank-ordered Q-sort correlated to each other nurse educator’s unique 60-statement arrangement. Using the correlation matrix as the raw data, I located groupings of Q-sorts where nurse educators placed statements in significantly similar fashions. Each grouping of Q-sorts (in other words, a grouping of nurse educators) represented a factor (a particular point of view). In factor analytic terms, these techniques involved factor extraction and factor loading and/or rotation calculations. In Q, five basic types of data are generated: (a) correlation matrix, (b) factor loadings, (c) rankordered list of Q-sample statements with z scores, (d) factor array scores, and (e) list of statements that distinguish each

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Q-Method Table 1

76 Unique Terminology in Q-Methodology

Term

Definition

Subjectivity Concourse

The sum of behavioral activity that constitutes a person’s current point of view. A population of statements, typically opinion based rather than fact based, about a particular phenomenon of interest. A representative subset of statements sampled from the concourse. A P-set (P stands for people or participants) is a purposely selected group of participants whose viewpoints matter in relation to the phenomena of interest. The operant process by which a participant ranks and orders the Q-sample statements. A quasi-normal distribution grid, typically numbered from a negative to a positive value, and contains the same number of placement spots as the number of Q-sample statements. The particular set of instruction, developed by the investigator, that participants are asked to follow as they rank and order the statements and place into the sorting grid. The Q-sort is the product of the sorting activity undertaken by each participant. Each Q-sort is each participant’s unique arrangement of the statements sorted based on the condition of instruction, from his or her point of view. A reconfigured Q-sort based on the composite and weighted z scores from all the participants who define a particular factor. A factor array can be displayed as a composite Q-sort in a reconfigured grid formation or as a table in which the z scores have been converted back into whole numbers within the confines of the sorting grid. A factor array characterizes a person who would load 100% on that factor. Statement(s) placed in the sorting grid in a statistically significant different position compared with all other factors. Statement(s) placed in the sorting grid in a statistically significant similar position compared with all other factors.

Q-sample P-set Q-sorting Sorting grid Condition of instructions Q-sort Factor array

Distinguishing statements Consensus statements Characterizing statements

Statements placed at the two polar ends of the sorting grid of each factor.

Sources: Brown (1980) and Watts and Stenner (2012).

factor from other factors and list of consensus statements that represent agreement among all the factors (Brown, 1980). Refer to Table 1 for definitions. During factor interpretation, I employed qualitative techniques to interpret each factor (point of view). I examined factor array scores (definition in Table 1) and paid special attention to the distinguishing, consensus,

Table 2

and characterizing statements (Table 1) and how they compared across factors. I used a constant comparative process where I set the factor arrays side by side and compared for differences and similarities. I considered the explanations provided by nurse educators about why they placed statements in the particular areas in the sorting grid. Finally, I named and described each factor (perspective). My aim,

Resources for Conducting Q-Studies

Texts

Stephenson (1953). The study of behavior: Q-technique and its methodology. Brown (1980). Political subjectivity: Applications of Q methodology in political science. Watts and Stenner (2012). Doing Q methodological research: Theory, method and interpretation. Professional International Society for the Scientific Study of Organization Subjectivity (ISSSS, 2014) Q ListServ

Q Annual Conference

Journal

Operant Subjectivity

Philosophical and theoretical focus. High-level read. Provides comprehensive discussion with detailed statistical explanations. Higher level read for first-time users of Q. Introduction to Q with guidelines helpful to first-time users. Professional organization committed to the scientific study of subjectivity (http://qmethod.org/about). As a member of ISSSS, access this ListServ is a valuable source to find answers to a variety of questions. List-serve members readily respond. No question is too basic to ask. Annual conference of researchers employing Q from a variety of disciplines. It is exciting to hear how a common research approach to study subjectivity is employed across disciplines. Journal dedicated to Q research. First issue in 1978. Recent 2014 online availability (http://www.operantsubjectivity. org/).

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Q-Method

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Figure

Card sorting grid.

as a researcher, was to capture the essence of meaning for each perspective and in so doing reveal underlying values, beliefs, and patterns of thinking as held by groups of nurse educators about simulation design.

References Brown, S. (1980). Political subjectivity: Applications of Q methodology in political science. Yale: University Press.

ISSSS. (2014). International Society for the Scientific Study of Subjectivity (ISSSS). Q-methodology: A method for modern research. Retrieved from http://qmethod.org/about. Newman, I., & Ramlo, S. (2010). Using Q methodology and Q factor analysis in mixed methods research. In A. Tashakkori, & C. Teddlie (Eds.), Sage handbook of mixed methods in social and behavioral research (2nd ed.). (pp. 505-530) Los Angeles: Sage. Stephenson, W. (1953). The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press. Watts, S., & Stenner, P. (2012). Doing Q methodological research: Theory, method and interpretation. London, UK: Sage Publications.

pp 75-77  Clinical Simulation in Nursing  Volume 11  Issue 1