A Confirmatory Factor Analysis of the Student Adaptation to College Questionnaire

Abstract

The construct validity of scores on the Student Adaptation to College Questionnaire (SACQ) was examined using confirmatory factor analysis (CFA). The purpose of this study was to test the fit of the SACQ authors' proposed four-factor model using a sample of university students. Results indicated that the hypothesized model did not fit. Additional CFAs specifying one-factor models for each subscale were performed to diagnose areas of misfit, and results also indicated lack of fit. Exploratory factor analyses were then conducted and a four-factor model, different from the model proposed by the authors, was examined to provide information for future instrument revisions. It was concluded that researchers need to return to the first stage of instrument development, which would entail examining not only the theories behind adjustment to college in greater detail, but also how the current conceptualization of the SACQ relates to such theories.

Adjustment to College as Measured by the Student Adaptation to College Questionnaire: A Quantitative Review of its Structure and Relationships with Correlates and Consequences

“…Such findings are supportive of the argument (McCornack 1956) that two highly correlated variables should not necessarily be formed into a composite because even very highly correlated variables (r>0.95) can still exhibit substantially different relationships with a third variable. The high correlation between social adjustment and institutional attachment is also strongly inflated by the fact that up to eight items of the SACQ are used in the computation of both the social adjustment and institutional adjustment subscales in one version of the SACQ (Taylor and Pastor 2007)-although this number was reduced to one common item in other versions of the SACQ (e.g., Baker and Siryk 1989). In aggregate, our judgment is that the theoretical distinction between social adjustment and institutional attachment remains warranted.…”

“…Although the process by which this measure was developed was non-optimal (see Taylor and Pastor 2007 for a discussion), the only known examination of the factor structure of the SACQ (Taylor and Pastor), found adequate fit (RMSEA=0.089, CFI=0.91), despite the use of a non-optimal estimation method that would have resulted in a substantial under-estimation of fit due to very serious violations of multivariate normality. 1 1 Taylor and Pastor (2007) use maximum likelihood (ML) estimation techniques despite very severe violations of multivariate normality that would suggest that weighted least squares estimation methods would be more appropriate. Relying on ML estimation for data that does not satisfy the assumptions of multivariate normality results in artificially low fit indexes (Curran et al 1996).…”

Torn between study and leisure: How motivational conflicts relate to students’ academic and social adaptation.

“…In order to assess domain-specific effects of conflict experience, we used the academic and social adaptation facets of the German version of the SACQ (Bmnstein et al, 2008). Due to matters of economy and to a substantial overlap that has been reported, especially for the different facets of academic adaptation (Taylor & Pastor, 2007), we only used selected subscales for both measures. We used the subscales Performance (nine items, e.g., "I am satisfied with my academic performance").…”

Validación de la Versión en Español del Mathematics Value Inventory (MVI) entre Estudiantes Universitarios Uruguayos

“…GFI y CFI se recomiendan por su sensibilidad a errores de especifi cación en modelos complejos, siendo .95 el punto de corte recomendado para ambos (Hu & Bentler, 1999). Puesto que existe controversia respecto al uso de puntos de corte estrictos para aceptar o desestimar un modelo (Marsh, Hau, & Grayson, 2005;Marsh, Hau, & Wen, 2004;Taylor & Pastor, 2007), otro insumo fueron los residuos estandarizados. Estos representan para cada par de ítems la diferencia entre la covarianza muestral y la covarianza estimada y son útiles para identifi car un ajuste pobre local cuando > 3 (Byrne, 1988).…”