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Q-Sample Construction: A Critical Step for a Q-Methodological Study Jane B. Paige and Karen H. Morin West J Nurs Res published online 4 August 2014 DOI: 10.1177/0193945914545177 The online version of this article can be found at: http://wjn.sagepub.com/content/early/2014/08/01/0193945914545177

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WJNXXX10.1177/0193945914545177Western Journal of Nursing ResearchPaige and Morin

Article

Q-Sample Construction: A Critical Step for a Q-Methodological Study

Western Journal of Nursing Research 1–15 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0193945914545177 wjn.sagepub.com

Jane B. Paige1,2 and Karen H. Morin1

Abstract Q-sample construction is a critical step in Q-methodological studies. Prior to conducting Q-studies, researchers start with a population of opinion statements (concourse) on a particular topic of interest from which a sample is drawn. These sampled statements are known as the Q-sample. Although literature exists on methodological processes to conduct Q-methodological studies, limited guidance exists on the practical steps to reduce the population of statements to a Q-sample. A case exemplar illustrates the steps to construct a Q-sample in preparation for a study that explored perspectives nurse educators and nursing students hold about simulation design. Experts in simulation and Q-methodology evaluated the Q-sample for readability, clarity, and for representativeness of opinions contained within the concourse. The Q-sample was piloted and feedback resulted in statement refinement. Researchers especially those undertaking Q-method studies for the first time may benefit from the practical considerations to construct a Q-sample offered in this article. Keywords Q-methodology, simulation-based learning, Q-sample construction

1University 2Milwaukee

of Wisconsin–Milwaukee, USA School of Engineering, WI, USA

Corresponding Author: Jane B. Paige, 1025 North Broadway, Milwaukee, WI 53202, USA. Email: [email protected]


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Western Journal of Nursing Research

Q-methodology is a research approach designed to study subjectivity (Stephenson, 1953). Subjectivity is the sum of behavioral activity that constitutes the point of view of a person or a group of people (Watts & Stenner, 2012). Subjectivity becomes evident when people communicate their thinking, thoughts, beliefs, values, and opinions about a particular phenomenon of interest. While the term subjectivity is largely unique to Q-methodologists, understanding what comprises people’s subjectivity contributes valuable insights into human behavior (Stephenson, 1978). As opposed to conventional survey research where participants rate items in a questionnaire, in Q-methodological studies participants compare items (opinion statements) with every other item (opinion statement) in a rankordering procedure. Such a rank-ordering procedure forces participants, consciously or subconsciously, to reveal their personal choice, feelings, and underlying beliefs. Prior to conducting a Q-methodological study, the researcher must start with a population of opinion statements (known as the concourse) on the phenomenon of interest. It is from the concourse a subset of statements (known as the Q-sample) is selected for investigation (Brown, 1980). Although literature exists on the methodological techniques (Q-sort forced distribution, factor extraction/rotation, and factor array interpretation) to conduct Q-studies including the seminal works of Stephenson (1953) and Brown (1980), with recent publications by Watts and Stenner (2012), McKeown and Thomas (2013), and specific to nursing research by Dennis (1986), AkhtarDanesh, Baumann, and Cordingley (2008), and Thompson and Baker (2008), little has been published detailing the steps to construct a Q-sample from a concourse. Q-sample construction is a critical yet often overlooked and underdescribed process prior to undertaking Q-studies. Manuscript page restrictions frequently limit authors’ ability to report the details on how the Q-sample was constructed. As the statements comprised within the Q-sample become the unit of analysis, it is important that the Q-sample uphold the concept of representative design (Brunswik, 1955). In other words, the Q-sample represents the breadth and depth of opinions contained in the concourse (population). A case exemplar illustrates the practical and prerequisite steps to construct a Q-sample from a concourse of opinion statements in preparation for a Q-methodological study. Researchers, especially those undertaking Q-method studies for the first time, may find the practical considerations and guidelines for Q-sample construction beneficial. An overview of Q-methodology with a brief explanation of the case exemplar frames the discussion.


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Q-Methodology In brief, Q-methodology investigates subjectivity (Stephenson, 1953) by exploring how participants rank-order opinion statements about a particular phenomenon of interest into a normal distribution (− to +) grid. The particular arrangement each participant rank-orders the opinion statements (known as a Q-sort) undergoes correlation with all other participants’ rankordering of statements. In Q-method, people are correlated by the way they think about a topic and then through factor analysis people are clustered together who share similar ways of thinking. Thus, Q-method is considered a by-person factor analysis rather than a by-trait factor analysis as in conventional factor analysis. The interpretation of the resulting factors subsequently reveals how participants share similar or different points of view (Brown, 1980). The construction of the Q-sample occurred in preparation for a study (Paige, 2013) that explored perspectives nurse educators and nursing students hold about the design of simulation activities. Uncovering the perspectives that influence educators’ choices in simulation design is important to discover and understand given the trend of assimilating simulation into nursing programs (Ironside & Jeffries, 2010; Nehring & Lashley, 2010). During the period of growth in simulation pedagogical knowledge, educators need time to reflect how this high-technology-driven teaching strategy fits into one’s current teaching perspective(s). Moreover, as students commonly evaluate teaching methods, it is important to understand from what perspective students base their evaluative comments (Brookfield, 2006). To offer readers context for the case exemplar, simulation is conceptualized as “a dynamic process involving the creation of a hypothetical opportunity that incorporates an authentic representation of reality, facilitates active student engagement, and integrates the complexities of practical and theoretical learning with opportunity for repetition, feedback, evaluation, and reflection” (Bland, Topping, & Wood, 2010, p. 5). In the case exemplar, the National League for Nursing–Jeffries Simulation Framework (NLN-JSF; Jeffries, 2012) provided theoretical guidance by identifying five simulation design characteristics (objectives, student support, problem-solving, fidelity, and debriefing) that are influenced by three educational considerations (teacher, student, and educational practices). These eight conceptual components guided the gathering of the concourse of statements (population) and the subsequent sampling of statements (Q-sample) from the concourse. Although not a linear process, the following four steps capture the important considerations for Q-sample construction (see Figure 1).


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Step One – Populate the Concourse

Step Two – Select Preliminary Q-Sample (Inductive or Deductive Approach) Step Three – Evaluate Q-Sample with Experts

Edit & Refine statements

Step Four – Pilot Q-Sample and Refine

Retain Q-Sample and Proceed to Q-Study

Figure 1. Steps in Q-sample construction.

Constructing a Q-Sample Step 1—Populate the Concourse Prior to constructing a Q-Sample, researchers gather a concourse of opinion statements (population) about the phenomenon of interest. Populating the concourse aims to capture the breadth and depth of opinions on the topic of interest (McKeown & Thomas, 2013). Such a concourse derived from dayto-day and ordinary conversations offers researchers a vehicle to gain insights into human behavior (Stephenson, 1978). Typically, researchers gather opinion statements from ordinary conversations, commentary, interviews, and literature and include statements of opinion rather than statements of fact (Brown, 1980; Stephenson, 1978). In the case exemplar, two data sources, simulation literature and interviews of nurse educators, contributed to populating the concourse. Members from the International Association for Clinical Simulation and Learning (INACSL) served as one data source. The INACSL (2011) is an international organization that disseminates educational practice and standards for clinical simulation methodologies and learning environments. Accessing nurse educators from this organization optimized the ability to gather a concourse that


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Paige and Morin Table 1. Demographics of Nurse Educators Providing Opinion Statements on Simulation Design. Demographic Level of education BSN MSN DNP PhD EdD Simulation traininga Conference In-service Manufacture Person-to-person Years involved in simulation 5 School enrollment size 250 Collaborate with non-nursing professions No Yes Region (U.S.) Northeast Midwest South West Non-U.S. Total nurse educators

n (%) 3 (9) 20 (57) 5 (14) 5 (14) 2 (6) 31 (28) 21 (19) 32 (29) 25 (23) 6 (17) 18 (52) 11 (31) 5 (14) 11 (32) 19 (54) 19 (54) 16 (46) 6 (17) 12 (34) 8 (23) 5 (14) 4 (12), Canada 35

a. More than one can apply.

represented the breadth and depth of opinions on how to design simulation activities. To find diverse opinions, a purposeful sampling frame located nurse educators across a range of demographics (see Table 1). Thirty-five members of the INACSL organization completed open-ended questionnaires (9 members in-person and 26 members electronically) between June 2011


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and September 2011. Commentary was sought from nurse educators on their particular opinions on how, when, where, who, or what are methods/ways to design and carry out simulation activities. The simulation literature was the second data source. Databases searched included ERIC, MEDLINE, Academic Search Complete, and CINAHL with key words simulation, simulation design characteristics, features, and elements limited to the years 2006-2011. Particular attention directed at qualitative studies located opinion statements about simulation design. Collection of opinions continued until data saturation (no further new opinions) occurred and together these two data sources populated a concourse of 392 opinion statements on simulation design.

Step 2—Select a Preliminary Q-Sample Generally, a concourse of opinion statements can contain hundreds of opinion statements. As this number of statements contained in the concourse would be too unwieldy for participants to sort and rank-order, a representative subset of opinion statements is sampled from the concourse. To offer participants a range of opinions to rank-order, Brown (1980) recommends having 40 to 60 opinion statements. Typically, this number of statements is sufficient to elicit existing points of view (Brown, 1980). In general, 40 to 80 statements are considered a standard number for a Q-sample (Watts & Stenner, 2012). Certain Q-methodologists (McKeown & Thomas, 2013; Watts & Stenner, 2012) explain Q-sample selection using an inductive (unstructured) or deductive (structured) approach. In an inductive approach, the researcher selects statements when no preexisting theory exists related to the phenomenon of interest. In such a case, the selection of statements is based on themes that emerge from a review of the opinion statements. When a deductive approach is chosen, the researcher selects statements based on theoretical considerations. In such a case, the selection of statements is systematic and structured based on relevant concepts derived from a theory or framework. In the case exemplar, criteria for selection of the Q-sample were guided by the NLN-JSF in a deductive approach for Q-sample construction. A 3 × 5 factorial design bracketed by three educational considerations (student, teacher, educational practices) times five simulation design characteristics (objectives, student support, problem-solving, fidelity, and debriefing) produced 15 possible cells to categorize opinion statements (see Table 2), for example, an opinion statement that combined the concept of student (educational consideration) with the concept of objective (simulation design characteristic) or in another example, the concept of teacher (educational consideration) with the concept of problem-solving (simulation design characteristic), and so forth. Considering


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Paige and Morin Table 2. Factorial Design of Q-Sample (Statements). NLN-JSF Five Simulation Design Characteristics

Objectives

Problem Solving

Fidelity

Debriefing

Student NLN-JSF Three educational considerations Teacher

4 (actual 5) 4 statements 4 (actual 3) 4 statements statements statements 4 statements 4 statements 4 statements 4 (actual 3) statements Educational 4 statements 4 statements 4 statements 4 (actual 5) practices statements

Student Support 4 statements 4 statements 4 statements

Note. Q-sample, N = (3) × (5) × (4 repetitions) = 60 opinion statements. NLN-JSF = National League of Nursing–Jeffries Simulation Framework.

the 3 × 5 factorial design and the desire for 60 statements, it was planned to select four statements per each of the 15 cells. Statement editing began once the concourse was reduced to a workable number of 120 statements. An excel document containing the 120 statements allowed joint editing and tracking of edits with a second researcher. The 120-statement concourse eventually achieved reduction to the desired 60-statement Q-sample. Even though the aim was to select 4 statements from each of the 15 cells, in 2 cells it was difficult to choose less than 5 statements, and thus all were retained. In 2 other cells, 3 statements were sufficient to capture the diversity of opinions. This resulted in a slight imbalance in 4 of the 15 cells; however, this was considered acceptable as it permitted the Q-sample to be most representative of the opinions contained in the concourse. According to Stephenson (1953), “apportioning of statements into the cells of a design” does not mean it is “correct” to any particular theory (p. 76). Rather, the factorial design serves as a guide.

Step 3—Evaluate the Q-Sample With Experts Following the preliminary selection of the Q-sample, it is appropriate to consult experts to evaluate how closely the selected opinion statements for the Q-sample represent the concourse (Akhtar-Danesh et al., 2008). In the case exemplar, two experts in simulation and one expert in Q-method reviewed the preliminary 60-statement Q-sample along with the concourse as reduced to 120 statements. The selection of domain experts in simulation provided expertise regarding simulation design while the Q-method expert was able to offer advice in Q-sample statement construction. Domain experts in simulation reviewed each of the Q-sample statements for readability as would be read by nurse educators and as would be read by Downloaded from wjn.sagepub.com at MILWAUKEE SCHL ENGINEERING on August 5, 2014

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Table 3. Questions for Domain Experts in Q-Sample Development. 1. The statement is clear and unambiguous as would be read by a nurse educator. 2. The statement is clear and unambiguous as would be read by a nursing student. 3. The statement illustrates heterogeneity from other statements in the factorial design based on the NLN-JSF. 4. Are there other statements expressed in the literature or SBL discussions you would offer that are not represented in the concourse of statements?

1

2

3

4

1

2

3

4

1

2

3

4

Open-ended

Note. 1 = not at all; 2 = somewhat; 3 = mostly; 4 = completely. NLN-JSF = National League of Nursing–Jeffries Simulation Framework.

nursing students. Experts also evaluated whether the four statements from each of the 15 cells illustrated the breadth and depth in range in opinions from the concourse. It was important to clarify with domain experts that they did not have to evaluate the accuracy of the content contained in the statement, but rather evaluate the readability of the statement irrespective of its accuracy. It was necessary to reinforce this point to domain experts as they identified statements at odds with how they thought. Unique to this case exemplar was the use of a content validity index (CVI) to assess agreement between simulation domain experts. Three questions comprised the CVI that rated readability, clarity of statement, and breadth and depth (heterogeneity) and each of the 60 statements received a CVI score (see Table 3). Considering the use of two raters, an acceptable CVI rating was set at 0.80 or above. Results of the CVI for the 60 statements included a CVI of 1.00 for 43 statements, a CVI of 0.83 for 10 statements, and a CVI of 0.66 for 7 statements. An open-ended question asked domain experts if they were aware of any other opinions on simulation design not reflected in the concourse of statements. One simulation domain expert suggested the topic of videotaping debriefings. Although the concourse contained several opinions on videotaping, these opinion statements were in relation to videotaping the simulation and not videotaping the debriefing. As the concourse of statements may not be all-inclusive as there is always something more people can say about a topic (Simons, 2013), the authors concluded that the Q-sample reflected a comprehensive range of opinions on designing simulations and decided not to add an additional statement. A Q-method expert also reviewed the preliminary Q-sample and offered additional changes in wording of statements. For example, the Q-method


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expert recommended removal of additional sentences in an opinion statement that added a supportive argument. It is up to the sorter to impose his or her argument for that opinion statement in the context of comparing with all the other statements. In addition, the Q-method expert suggested minor changes in wording of statements to reflect similar action worded statements. Based on feedback from simulation domain and Q-method experts, authors (one novice and one experienced researcher) reviewed the seven statements with a CVI of less than 0.80 and edited six for wording while replacing one with another statement from the concourse. Minor word edits were made to 25 additional statements (even with a CVI greater than 0.80) and 28 statements were left unchanged. Examples of editing process appear in Table 3 based on simulation expert input (see Part A) and Q-method expert input (see Part B).

Step 4—Pilot Q-Sample and Rank-Ordering Procedure With Participants In addition to obtaining expert review, it was beneficial to pilot the Q-sample and the rank-ordering procedure with potential participants. When participants rank-order statements in Q-methodological studies, it is important that they are clear on what the researcher is asking them to do. Written instructions detailing the rank-ordering procedure should be clear and transparent (Watts & Stenner, 2012). Location where participants complete the rankordering procedure should be free of distraction and interruption to optimize participants’ thoughtful engagement in the activity (Killam, Timmermans, & Raymond, 2013). The time needed to complete the rank-ordering procedure should not overwhelm the participant. An excessive number of statements or length of the statements can become unwieldy to rank-order and participants can lose focus (McKeown & Thomas, 2013). The standard of 40 to 80 statements (Brown, 1980; Watts & Stenner, 2012) and an average time of 30 to 60 min to rank-order 50 statements (Akhtar-Danesh et al., 2008) are considerations when structuring the rank-ordering procedure. In the case exemplar, testing the clarity of instructions and time for completion was especially important as future participants will be administered the Q-sample without the researcher present. Furthermore, considering the future Q-study for the case exemplar plans to ask nursing students to rank-order statements that were offered by nurse educators, it was necessary to test the clarity of the statements as would be read by nursing students. A convenience sample of four nurse educators and four nursing students evaluated the directions for the sorting process, completion time, and clarity of Q-sample statements as they conducted a pilot rank-ordering of the statements into a distribution grid. Participants in follow-up phone interviews reported that


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the instructions were clear and 30 to 60 min were needed to complete the rankordering of the 60 statements. As programs are available to complete the rankordering of the statement procedure electronically, this possibility was being explored by the investigators. Uncertainty about participants’ attention and engagement in the rank-ordering procedure was a concern, and thus participants were queried on this option. In response to this query, participants consistently reported that an electronic procedure would be more difficult. One participant stated, “I liked to see all statements at one time, think about them, and move them around.” Participant feedback on the clarity of Q-sample statements elicited comments on 14 statements, all provided by the nursing educators, whereas the nursing students had no particular comments. Feedback offered by nurse educators included the following: (a) more than one idea in 4 statements, (b) depends on the situation in 6 statements, (c) uncertain in the meaning in 3 statements, and (d) 1 statement too long. Of the 14 statements, only 1 statement received comments by more than one nurse educator. Based on interview feedback received concerning the 14 statements, authors refined 4 statements to limit each statement to one idea, refined 8 to offer greater clarity in wording, and left 2 statements unchanged. For example, one nurse educator commented that she was uncertain whether the statement “students should be left to figure out problems on their own in a simulation” pertained to the debriefing or the actual simulation. Considering this feedback, authors refined this statement to “students should be left to figure out problems on their own during the actual simulation.” Two of the four nurse educators commented that their decision to rank statements “depended on the situation” for 6 of the statements. Based on these comments, the authors returned to the raw data contained in the open-ended questionnaires to gain insights into whether rewording of these six statements would offer greater clarity to the situation at hand. Five statements were subsequently refined with examples of refinements made to statements depicted in Table 4 (see Part C). The refined 60-statement Q-Sample was now complete and retained for the Q-study.

Discussion As nurse researchers consider Q-methodology as a promising approach to study subjectivity (Akhtar-Danesh et al., 2008), what cannot be discounted is the construction of the Q-sample. The process to select a representative sample (Q-sample) from the concourse of opinion statements using Brunswik’s (1955) concept of representative design is an important consideration in Q-methodological studies (Brown, 1980; McKeown & Thomas, 2013). Upholding this concept becomes evident if repeating the same sampling scheme with a different set of statements from the same concourse results in


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Paige and Morin Table 4. Examples of Edited Q-Sample Statements. Part A: Original Statement “Utilize a ‘ticket to enter’ to get the students prepared to take care of the simulated patient. Students who work though modules are better prepared for the simulation.”

Edited Statement Based on Input From Simulation Domain Experts “Assign students pre-simulation modules to help students be more prepared to take care of the simulated patient.”

“Do not use the word ‘pretend’ “Do not use the word during simulations. Instead, ‘pretend.’ During pre-briefing instruct students to carry out instruct students if they are actions, i.e., washing hands, going to do something, then administering medication.” do it, i.e., give medications, wash hands, etc.”

Part B: Original Statement

Edited Statement Based on Input From Q-Methodologist

“Prior to the first simulation, “Prior to the first simulation, students should observe a students should observe a simulation and then have simulation and then have hands-on orientation with the hands-on orientation with manikin.” the manikin. This allows time to express fears and anxieties relating to the simulation experience.” “Use simulation for one-on-one “Simulation can be used learning/evaluation of students for one-on-one learning/ who are struggling or possibly evaluation for students who unsafe in clinical.” are struggling or possibly unsafe in clinical.”

Part C: Original Statement

Edited Statement Based on Input From Trial With Participants

Rationale for Editing By editing the wording from “ticket to enter” to “pre-simulation modules,” the statement was clearer but retained the original point of view. Grammatical rewording offered clearer sentence structure.

Rationale for Editing Removal of the second sentence that added a supportive argument. This permits the sorter to assign his or her meaning to why or why not this activity is necessary. Rewording to have statement phased as an action. This is similar to other statements and promotes a clearer sorting process.

Rationale for Editing

Reduce to one idea. “Design and keep objectives “Limit objectives to 3 to 4 general so students are not and keep them general so informed of the specific focus of students are not informed the simulation.” of the specific focus of the simulation.” Offer greater clarity to “End a simulation, for example, “End a simulation when students situation. Reviewed are not actively providing care, when the patient has been original statement for example when the patient has transferred to another unit, in raw data to gain been transferred to another unit, the patient has recovered, or insight for rewording the patient has recovered, or the student team has reached the statement. consensus reached by the team.” consensus.”


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similar factors (Brown, 1980; Stephenson, 1953). With this said, if researchers use a scheme to sample statements from the concourse that captures a balance of its breadth, depth, and comprehensiveness, the construction of the Q-sample becomes less forbearing. Individual statement selection often is negated when future Q-study participants impose their own meaning on the statements as they compare each statement with every other statement during the rank-ordering procedure (Brown, 1980). In the case exemplar, the construction of the Q-sample entailed an iterative process that spanned 3 months. Based on the authors’ experience, the particular techniques to select statements from a concourse and the acceptable degree by which to edit statements were unclear in the literature. As authors delved into this process, more questions arose than answers found in the literature. To help prospective researchers using a Q-method research approach, the following are practical considerations for Q-sample construction. Limitations in Q-sample construction particular to the exemplified case offer addition information. First, when evaluating a concourse of opinion statements for breadth, depth, and comprehensiveness, it is useful to organize raw data using some tool that allows for the visualization of the statements captured within the concourse. Hundreds of opinion statements may exist that need deliberation. In the case exemplar, “post-it” notes displayed on a large poster board helped organize this process. Such a strategy provided a gestalt view of the entire concourse as decisions on statement saturation and selection for the Q-sample construction occurred. A limitation with this strategy is the ability to share electronically the statements displayed on the poster board. Second, as minimal detail exists in the literature on how to select and edit Q-sample statement composition, the guidelines in Table 5 serve as a helpful and collective resource to other researchers. Even though recommendations on the process to select and edit statements are offered by Stephenson (1953), Brown (1980), Akhtar-Danesh et al. (2008and Watts and Stenner (2012), questions remain on statement selection and the appropriate degree to which to edit statement composition. An important strategy to uphold is to have a clear and definitive research question before starting Q-sample construction (Watts & Stenner, 2012) as well as a pre-determined process to categorize the opinions that represent the phenomena of interest. In the case exemplar, the NLN-JSF provided structural guidance for statement selection about simulation design. Nevertheless, once the concourse was reduced to 120 statements, it became more difficult to select statements, as they all seemed important. At this point, a second researcher’s input as well as domain experts helped distinguish the statements that provided the most breadth and depth about choices in simulation design. For example, a variety of statements existed on grading simulation, both in favor and not in favor. However, the context


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Table 5. Guidelines for Selecting and Editing Q-Sample Statements. Selection of Q-sample statements 1. Avoid selecting statements too difficult to understand, mere opposite of another statement, or ones that could be “picked out for special regard on extraneous or incidental grounds” (Stephenson, 1953, p. 76). 2. Avoid double-barreled statements containing two or more proposition (Watts & Stenner, 2012), for example, “simulation is fun but anxiety provoking” or double negative statement such as “I do not find simulations unenjoyable.” 3. Avoid statements with two opinions as this can make it difficult for the sorter if he or she agrees with one part but not the other (Watts & Stenner, 2012). 4. Retain statements that invite a range of emotional reactions. The intent following completion of a Q-sort is for participants to feel they were given ample opportunity to articulate their viewpoints (Watts & Stenner, 2012). Editing of Q-sample statements 5. Edit grammar to offer clarity in wording of statements and reduce ambiguity of meaning. However, avoid removal of any emotional response evoked by the statement (Akhtar-Danesh, Baumann, & Cordingley, 2008). 6. Avoid the urge to correct illogical properties of a statement (Brown, 1980).

under which grading occurred varied within the opinion statements. Thus, the challenge was to select statements that were most representative of the breadth of the grading topic. As far as statement editing goes, the challenge is to retain the essence of the statement and remain faithful to the phrasing as provided by the original source. Statement editing should be limited to grammar and syntax corrections. Furthermore, it is important that researchers distinguish the means by which statements constructed for a Q-sample differ from statements in a questionnaire. As opposed to survey research where the researcher determines a priori the meaning of an item, in Q-methodological studies the researcher looks for participants to impose their own meaning on a statement. In other words, participants assign a score rather than receive a score as in a questionnaire (Brown, 1980). The retention of statements that contain language in use (ordinary conversations) is expected and actually desired in a Q-sample. Therefore, resources on scale development may not be as beneficial for a researcher using a Q-methodological approach. Third, it is important to avoid a Q-sample structure that is “biased” toward a particular viewpoint (Watts & Stenner, 2012, p. 58). Such a structure would be unbalanced and restrict a future participant’s opportunity to fully express his or her views through the rank-ordering procedure. For example, in the case exemplar, it was also important to select opinions even if they were incongruent with the emerging best practices in simulation design. These opinions do exist, are held by nurse educators, and consequently influence how simulations are designed. Downloaded from wjn.sagepub.com at MILWAUKEE SCHL ENGINEERING on August 5, 2014

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Fourth, consulting experts in both simulation and Q-method was valuable as each offered different advice on statement construction. Likewise, even with revisions suggested by the experts, there remained statements that still needed refinement, and thus piloting the Q-sample with potential participants proved additionally beneficial. Conducting follow-up phone conversations with participants after they completed a pilot rank-ordering of statements provided useful information for statement refinement. Maintaining an audit trail of the edits made to statements, along with the rationale for decisions, was extremely valuable. In the case exemplar, it was necessary to return to the original statement to reconsider the decisions made in statement refinement. As the unit of analysis, statements in a Q-sample are the heart of any Q-study. Considering this statement, researchers cannot minimize the process to construct a Q-sample that represents the breadth and depth of opinions about the phenomenon of interest. Doing so provides future Q-study participants the opportunity to express their point of view. The value of accessing experts for Q-sample construction cannot be overstated. As the details about how to select a Q-sample from a concourse along with how to edit the Q-sample statements is an area not well elucidated in the literature, a case exemplar offers researchers using Q-method four steps to consider in Q-sample construction. Based on the case exemplar, researchers considering Q-method as a research approach should allot sufficient time to construct a Q-sample for their Q-study. Acknowledgments Special thanks are offered to Suzie Kardong-Edgren, Jeffrey Groom, and Steven Brown serving as domain experts. Additional thanks go to Steven Brown for critical review of manuscript.

Authors’ Note This article was a component of a doctoral dissertation by Jane B. Paige titled “Simulation Design Characteristics: Perspectives Held by Nurse Educators and Nursing Students.” This study was completed and submitted as requirement for dissertation to the University of Wisconsin–Milwaukee in December 2013.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by funding from the Harriet Werley Doctoral Student Research Award from the


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University of Wisconsin–Milwaukee College of Nursing and Sigma Theta Tau International–Eta Nu Chapter Graduate Student Scholarship Award.

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