TPC-Journal-V5-Issue4

The Professional Counselor /Volume 5, Issue 4 519 students, 305 males (51.6%) and 286 females (48.4%). The data consisted of PFI assessments from 24 kindergarten students (4.1%), 106 first-grade students (17.9%), 116 second-grade students (19.6%), 115 third-grade students (19.5%), 118 fourth-grade students (20.0%), and 112 fifth-grade students (19.0%). Procedures Classroom teachers completed PFI assessments for all students in their class at the close of each marking period using the rubrics described above. Extracting the data from the district’s electronic student data management system was orchestrated by the district’s information technology specialist in collaboration with members of the research team. This process included establishing mechanisms to ensure confidentiality, and identifying information was extracted from student records. Data Analyses The PFI report card data was analyzed in three phases. The first phase involved conducting an EFA at the conclusion of the first marking period. The second phase was to randomly select half of the data compiled during the second marking period and perform a confirmatory factor analysis. Finally, the remaining half of the data from the second marking period was analyzed through another CFA. Phase 1. Exploratory factor analysis. An initial EFA of the 13 items on the survey instrument was conducted using the weighted least squares mean adjusted (WLSM) estimation with the oblique rotation of Geomin. The WLSM estimator appropriately uses tetrachoric correlation matrices if items are categorical (Muthén, du Toit, & Spisic, 1997). The EFA was conducted using Mplus version 5 (Muthén & Muthén, 1998–2007). Model fit was assessed using several goodness-of-fit indices: comparative fit index (CFI), Tucker- Lewis Index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). We assessed model fit based on the following recommended cutoff values from Hu and Bentler (1999): CFI and TLI values greater than 0.95, RMSEA value less than 0.06, and SRMR value less than 0.08. Phase 2. First confirmatory factor analysis. An initial CFA was conducted on the 13 items from the instrument survey to assess a three-factor measurement model that was based on theory and on the results yielded through the exploratory analysis. Figure 1 provides the conceptual path diagram for the measurement model. Six items (3, 4, 6, 7, 11 and 13) loaded on factor one (C1), which is named “ academic temperament .” Three items (8, 9 and 12) loaded on factor two (C2), which is referred to as “s elf-knowledge .” Four items (1, 2, 5 and 10) loaded on factor three (C3), which is titled “ motivation .” All three latent variables were expected to be correlated in the measurement model. This CFA was used to assess the measurement model with respect to fit as well as convergent and discriminant validity. Large standardized factor loadings, which indicate strong inter-correlations among items associated with the same latent variable, support convergent validity. Discriminant validity is evidenced by correlations among the latent variables that are less than the standardized factor loadings; that is, the latent variables are distinct, albeit correlated (see Brown, 2006; Kline, 2011; Schumacker & Lomax, 2010). The computer program Mplus 5 (Muthén & Muthén, 1998-2007) was used to conduct the CFA with weighted least square mean and variance adjusted (WLSMV) estimation. This is a robust estimator for categorical data in a CFA (Brown, 2006). For the CFA, Mplus software provides fit indices of a given dimensional structure that can be interpreted in the same way as they are interpreted when conducting an EFA.