57 Changes in student appreciation of small-group active learning Students who were willing to take part in the (optional) interview, were scheduled for a meeting via Zoom. JWG conducted the interviews. During the interview, participants were shown their Q-sort and answered questions regarding the Q-sorting process, thoughts or feelings about specific statements, and the choices they made. These interviews were used to gain a deeper insight into their perspectives. Participants were then asked to reflect on their perceived change in the last three years. For this, we used a visual aid: a list of statements on which they differed most in the two measurements (e.g., a statement which they placed at +5 in 2018, and -3 in 2021). Analyses We split the analyses into two parts. The first part was concerned with identifying factors in the new data. The second part was concerned with change: identifying how and why factors changed between 2018 and 2021. Part 1: Identifying factors We used Ken-Q Analysis Desktop Edition to analyze the quantitative data from the Q-sorting procedure (42). We followed procedures outlined by Watts and Stenner (38). We extracted factors using the Centroid Method with Varimax rotation. JWG, AC, and RAK evaluated the outcomes of the analysis in three steps. In the first step, we evaluated eigenvalues (>1.00), if factors had at least two significant loading Q-sorts, and aimed to achieve at least 40% explained variance. In the second step, we evaluated if the qualitative interview data supported the factors. In the third step, we evaluated if the factors made sense to us (coherent, differentiated, recognizable) and if they fit the two conceptual frameworks. Factor interpretation was done using an expanded version of the crib sheet suggested by Watts and Stenner (38). Factor arrays were the basis for factor interpretation. These are the weighted averages of Q-sorts in a factor, and thus how a prototypical student in a factor would sort the statements (Table 3.1). We first looked at a factor’s highest and lowest scoring items, added statistical and demographical information, and built an initial story. We then looked at items ranked higher or lower than other factors, items in the middle, distinguishing statements, and consensus statements to expand the story. Then, we added the qualitative data from the interviews and our notes to connect the different parts of the story. Finally, we checked the accuracy and clarity of factor descriptions by reviewing the factor descriptions holistically and ensuring they reflected our understanding of the factors. Part 2: Identifying how and why factors changed There are few published Q-methodological studies investigating change in subjectivity, and different authors have chosen different methods for analyzing change (43). There are no clear or accepted guidelines to follow for these types of studies. Therefore, before we started analyzing our data, we set out to formulate guidelines for the analysis. We consulted published Q-studies with comparative designs, attended the 2020 ISSS virtual Q conference 3
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