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Factor loadings were calculated using principal axis factoring (PAF), and varimax rotation was used to investigate the construct validity of the Instructional Design Factors Scale. In the PAF solution, factors with eigenvalues greater than one were extracted. In the extraction phase, items were used if they had a factor loading of at least 0.40 Galunisertib (Pett et?al., 2003). Multiple regression analysis with a stepwise method was performed using the extracted factors to investigate which of these factors influenced overall satisfaction with TBL. The general characteristics of the participants are presented in Table?2. Of the 402 nursing students, 91% (n?=?364) were women. Their ages ranged from 19 to 43?years, with a mean age of 21.6?years (standard deviation [SD]?=?3.26). Nursing students in 2011 showed significantly higher satisfaction with school life than nursing students in 2010 (��2?=?14.10, P?=?0.007). Learner satisfaction with TBL and perceptions regarding the instructional design factors are presented in Table?3. On a seven point scale, the total mean learner satisfaction score was 4.96 (SD?=?1.06). This indicated that students were generally satisfied with TBL. The highest score was in ��A deeper understanding of the subject through team activity�� (mean?=?5.15, SD?=?1.14), and the lowest score was in ��Interest as compared with lecture formats�� (mean?=?4.84, SD?=?1.43). Nursing students in 2011 reported higher overall satisfaction than nursing students in 2010 (t?=?3.08, P?=?0.002). Furthermore, nursing students RhoC in 2011 reported more positive responses for pre-assignment (t?=?2.14, P?=?0.033), team activity (t?=?2.12, P?=?0.034), learning environment (t?=?2.63, P?=?0.009), orientation (t?=?5.18, P?Selleck Z VAD FMK results of the multiple linear regression model summary and overall fit statistics using the stepwise method are shown in Table?4. Assumptions of normality, independence of errors, and multicollinearity of data were examined to test the assumptions of multiple regression analysis. With a Durbin�CWatson test statistic of 1.958, which is between the two critical values of 1.5 and 2.5, we can assume that there was no first-order linear autocorrelation in our multiple linear regression data. The plot also indicated that in our multiple linear regression analysis, there was no tendency in the error terms. With a tolerance of >?0.1 or a variance inflation factor