Of the data from 30 respondents, 28 were used for the analysis as two of the Q sorts had errors in them (such as double entry of a MK-0457 research buy statement number) and had to be rejected. 57 % of the final respondents were male (n = 16) and 43 % were female (n = 12). Results Factor extraction The Q sorts were subjected
to factor analysis using the PQ method software selleck screening library that is available for free download from the internet. Brown (1980), Watts and Stenner (2005) and Watts and Stenner (2012) were consulted during the analysis. The factors were extracted using centroid analysis (Horst’s centroid). The data generated eight factors of which the first three were selected for the analysis due to the following reasons: first, it is a standard procedure to consider factors with Eigen values greater than 1 and having at least two respondents (that is, have at least two defining Q sorts) load on the factor (Brown 1980; Watts and Stenner 2012). Second,
together the three factors explained 51 % of the total variance and had minimal BVD-523 price correlation within them, whereas the latter factors had stronger correlation with the first three factors as well as with one another. Finally, the difference in error in residual variance did not change significantly when considering four factors versus three factors. Each factor had a few Q sorts that especially contributed to defining that particular factor. The respondents corresponding to these defining Q sorts for each factor have been mentioned in the
following section on factor interpretation. The three chosen factors were then subjected to varimax rotation before the software conducted the final analysis. The three factors Florfenicol together had 26 defining Q sorts (two Q sorts loaded individually on two other factors that did not meet the criteria of selecting a factor). The software also presented the factor array table (or a model Q sort). A factor array table contains the statement scores for each factor based on the weighted average of its defining Q sorts (Table 1). Simply put, a factor array represents the statement scores on a factor that a Q sort would assign if it were to load a hundred percent on that factor. The statement scores in this table were used in the final interpretation. Taking a conservative approach, distinguishing statements (that is, statements which were highlighted in the analysis as being significant to the interpretation of a particular factor) at p < 0.01 were also used in the interpretation, even though they might have had lower statement scores. Following the same logic, consensus statements (that is, statements that did not help in distinguishing among the three factors) at p < 0.01 were excluded from the interpretation of individual factors, even though some of them had higher statement score.