Confirmatory Factor Analysis Task
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A Confirmatory Factor Analysis Task is a factor analysis task that accepts a factor analysis model.
- Context:
- It can be used to test whether a Observations Dataset fit a Hypothesized Measurement Model.
- …
- Counter-Example(s):
- See: Latent Variable Model, Confirmatory Data Analysis, Construct Validity, Multitrait-Multimethod Matrix, Hypothesis Testing.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/confirmatory_factor_analysis Retrieved:2015-3-8.
- In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social research.[1] It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. [2] CFA was first developed by Jöreskog [3] and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959). [4] In confirmatory factor analysis, the researcher first develops a hypothesis about what factors s/he believes are underlying the measures s/he has used (e.g., “Depression” being the factor underlying the Beck Depression Inventory and the Hamilton Rating Scale for Depression) and may impose constraints on the model based on these a priori hypotheses. By imposing these constraints, the researcher is forcing the model to be consistent with his/her theory. For example, if it is posited that there are two factors accounting for the covariance in the measures, and that these factors are unrelated to one another, the researcher can create a model where the correlation between factor A and factor B is constrained to zero. Model fit measures could then be obtained to assess how well the proposed model captured the covariance between all the items or measures in the model. If the constraints the researcher has imposed on the model are inconsistent with the sample data, then the results of statistical tests of model fit will indicate a poor fit, and the model will be rejected. If the fit is poor, it may be due to some items measuring multiple factors. It might also be that some items within a factor are more related to each other than others. For some applications, the requirement of "zero loadings" (for indicators not supposed to load on a certain factor) has been regarded as too strict. A newly developed analysis method, "exploratory structural equation modeling", specifies hypotheses about the relation between observed indicators and their supposed primary latent factors while allowing for estimation of loadings with other latent factors as well. [5]
- ↑ Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, New York: Guilford Press.
- ↑ Preedy, V. R., & Watson, R. R. (2009) Handbook of Disease Burdens and Quality of Life Measures. New York: Springer.
- ↑ Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183-202.
- ↑ Campbell, D. T. & Fisk, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
- ↑ Asparouhov, T. & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397-438
2004
- (Thompson, 2004) ⇒ Bruce Thompson. (2004). “Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications." American Psychological Association, ISBN:1591470935
- BOOK OVERVIEW: Investigation of the structure underlying variables (or people, or time) has intrigued social scientists since the early origins of psychology. Conducting one's first factor analysis can yield a sense of awe regarding the power of these methods to inform judgment regarding the dimensions underlying constructs. This book presents the important concepts required for implementing two disciplines of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The book may be unique in its effort to present both analyses within the single rubric of the general linear model. Throughout the book canons of best factor analytic practice are presented and explained. The book has been written to strike a happy medium between accuracy and completeness versus overwhelming technical complexity. An actual data set, randomly drawn from a large-scale international study involving faculty and graduate student perceptions of academic libraries, is presented in Appendix A. Throughout the book different combinations of these variables and participants are used to illustrate EFA and CFA applications.