Confirmatory factor analysis

Enns MW, Cox BJ, Parker JD, Guertin JE. (1998). Confirmatory factor analysis of the Beck anxiety and depression inventories in patients with major depression. J Affective Disord. 47(1-3) 195-200. Erdelmeier CA. (1998). Hyperforin, possibly the major non-nitrogenous secondary metabolite of Hypericum perforatum L. Pharmacopsychiatry. 31(suppl 1) 2-6. 

March, J.S., Conners, C.K., Arnold, L.E., Epstein, J., Parker, S., Hinshaw, S., Abikoff, H., Molina, B., Wells, K., Newcorn,/., Schuck, S., Pelham, W.E., and Hoza, B. (1999) The Multidimensional Anxiety Scale for Children (MASC) confirmatory factor analysis in a pediatric ADHD sample./ Attention Disord 3 85-90. 

Dickinson D, Ragland ID, Calkins ME, Gold IM, Gur RC. 2006. A comparison of cognitive structure in schizophrenia patients and healthy controls using confirmatory factor analysis. Schizophr Res 85 20-29. 

Reise, S.P., Widaman, K.F. and Pugh, R.H. 1993. Confirmatory factor analysis and item response theory Two approaches for exploring measurement invariance. Psychological Bulletin, 114, 552-6. 

Analyses of each instrument s psychometric properties has shown that exploratory factor analysis (EFA) was conducted for two measures and confirmatory factor analysis (CFA) for two measrrres. In the case of 10 measures both methods were ttsed. 

For both translations, confirmatory factor analysis showed an acceptable fit with the original 12-dimensional model. Although exploratory factor analysis resulted in a 10-dimensional and a 9-dimensional stracture for, respectively, the Dutch and French questionnaires, based on the acceptable validity and reliability scores, it was concluded that no modifications were required to the original 12-factor model in order to allow internal and external benchmarking for the psychiatric hospitals. 

The questionnaire was piloted with a sample of air traffic controllers, engineers and managers from four ANSPs (different from those already canvassed) from across Europe in 2007 and 2008. In order to test the validity of the siuvey instrament, its construct validity (to ensure it was measuring safety culture and not something else), both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) statistical techniques were applied (Gerbing and Hamilton 1996). 

Gerbing, D.W. and Hamilton, J.G. 1996. Viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Structural Equation Modelling A Multidisciplinary Journal, 3, 62-72. 

For validity evidence based on internal structure, confirmatory factor analysis was performed in Mplus 5.2 to estimate how well the designed two-factor correlated structure for the instrament fits the responses obtained with the sample (L. Muthen B. Muthen, 2007). Fit indices such as chi-square ( ), Comparative Fit Index (CFI), and the Standardized Root Mean Square Residual (SRMR) were examined to assess the fitness of the model to the data, and item loadings were also evaluated. The criteria of CFI value greater than 0.95 and SRMR value less than 0.08 were used to indicate a good model fit and CFI >0.90 as acceptable fit (Bentler, 1990 Hu Bentler, 1995). 

Firstly, researchers need to refine instruments to measure students chemistry self-efficacy. It is likely that chemistry self-efficacy is a multidimensional construct. Therefore, attempts should be made by researchers to develop subscales. However, research by Dalgety et al. (2003) indicated that it is difficult to develop a multidimensional scale to measure chemistry self-efficacy the structure of their 17-item instrument is still unclear because students responses to the items did not load on the expected factors. The work of Uzuntiryaki and apa Aydin (2009) in Turkey showed some promising results from confirmatory factor analysis, but there is a need to test their 21-item college chemistry self-efficacy scale in other contexts. 

Shirbagi, N. (2008). A confirmatory factor analysis of the Persian translation of the Fennema-Sherman mathematics attitudes scales. Pedagogy Studies (Pedagogika), 92,46-55. 

SPSS 19.0 and AMOS 18.0 statistical software packages were undertaken to assess the relations of constructs on the questionnaires. Statistical methods are used primarily as follows reliability and validity analysis, confirmatory factor analysis, and stmctural equation. 

The Effect of Institutional Pressures and Top Managers Posture. .. Table 1 Measurement model and confirmatory factor analysis... 

A clear understanding of these fit indices will be useful for the explanation of the result of analysis. We follow Bagozzi and Yi (1988) on statistics of fit (except GFI), namely CFI, NFI, TLI and the RMSEA. Table 1 shows the result of the confirmatory factor analysis. 

Using confirmatory factor analysis with LISREL, steps were undertaken tocheck (1) unidimensionality and convergent validity, (2) reliability, (3) discriminant validity, and (4) second-order constract validity of the measurement. Unidimensionality is... 

Table 7.3 Confirmatory factor analysis results for IT resources...

Table 7.5 Confirmatory factor analysis results for IQS appropriation... 

Table 7.7 Confirmatory factor analysis results for collaborative culture... 

Table 7.15 Confirmatory factor analysis results for firm performance... 

Byrne, B. M. (1989). A primer ofLISREL Basic applications and programming for confirmatory factor analysis analytic models. New York Springer-Verlog. 

Doll, W. J., Raghunathan, T., Lim, S. J., Gupta, Y. P. (1995). A confirmatory factor analysis of the user information satisfaction instrument. Information Systems Research, 6(2), 177-188. 

Marsh, H. W., Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept First-and higher-order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562—582. 

Segars, A. H., Grover, V. (1993). Re-examining perceived ease of use and usefulness A confirmatory factor analysis. MIS Quarterly, 17(4), 517-525. 

Next we determined that the nine theorized climate measures were intercorrelated and that student data could be aggregated. We then conducted a confirmatory factor analysis which yielded nine data-driven factors. We correlated these with the theorized measures to determine if the two sets of measures were related. Except for three factors from the data-driven results that did not distinctively capture specific theorized climate measures, over half (42) of the 81 zero-order correlations were at least moderate (.40 < r < 1.0) indicating that the results of the two factor analyses were comparable. For example. Factor 1 of the data-driven result included Involvement and Faculty Support of the theorized measures. 

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