Exploratory Factor Analysis Task
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An Exploratory Factor Analysis Task is a factor analysis task that is an exploratory analysis to uncover the underlying structure of a structured dataset.
- Context:
- output: a Factor Analysis Model.
- It can range from (typically) being an Exploratory Matrix Factor Analysis Task to being ...
- Example(s)
- What are the main factors in the 20 Newsgroups Dataset.
- …
- Counter-Example(s)
- See: Factor Analysis, Exploratory Analysis, Feature Compression Task, Exploratory Factor Analysis Algorithm.
References
2013
- http://en.wikipedia.org/wiki/Exploratory_factor_analysis
- In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.[1] It is commonly used by researchers when developing a scale (a scale is a collection of questions used to measure a particular research topic) and serves to identify a set of latent constructs underlying a battery of measured variables.[2] It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables.[3] Measured variables are any one of several attributes of people that may be observed and measured. An example of a measured variable would be the physical height of a human being. Researchers must carefully consider the number of measured variables to include in the analysis.[2] EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis. There should be at least 3 to 5 measured variables per factor.[4]
- ↑ Norris, Megan; Lecavalier, Luc (17 July 2009). "Evaluating the Use of Exploratory Factor Analysis in Developmental Disability Psychological Research". Journal of Autism and Developmental Disorders 40 (1): 8–20. doi:10.1007/s10803-009-0816-2.
- ↑ 2.0 2.1 Fabrigar, Leandre R.; Wegener, Duane T., MacCallum, Robert C., Strahan, Erin J. (1 January 1999). "Evaluating the use of exploratory factor analysis in psychological research.". Psychological Methods 4 (3): 272–299. doi:10.1037/1082-989X.4.3.272.
- ↑ Finch, J. F., & West, S. G. (1997). “The investigation of personality structure: Statistical models". Journal of Research in Personality, 31 (4), 439-485.
- ↑ Maccallum, R. C. (1990). “The need for alternative measures of fit in covariance structure modeling". Multivariate Behavioral Research, 25(2), 157-162.
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.