Exploratory Factor Analysis (original) (raw)
1988, Handbook of Multivariate Experimental Psychology
AI-generated Abstract
This paper presents an overview of exploratory factor analysis (EFA), explaining the underlying latent structures that can account for observed variables. The discussion includes the mathematical framework for EFA, various criteria for determining the number of factors to extract, including Kaiser's Criterion and Scree Plot analysis, and the implications of communalities and error variances. Different methodologies and rules of thumb are evaluated for practice, emphasizing the necessity for a combination of criteria to accurately identify the appropriate number of factors, guided by the underlying hypothesis and the field's standard practices.
Sign up to get access to over 50M papers
Related papers
On the Interpretation of Factor Analysis
The importance of the researcher’s interpretation of factor analysis is illustrated by means of an example. The results from this example appear to be meaningful and easily interpreted. The example omits any measure of reliability or validity. If a measure of reliability had been included, it would have indicated the worthlessness of the results. A survey of 46 recent papers from 6 journals supported the claim that the example is typical, two-thirds of the papers provide no measure of reliability. In fact, some papers did not even provide sufficient information to allow for replication. To improve the current situation some measure of factor reliability should accompany applied studies that utilize factor analysis. Three operational approaches are suggested for obtaining measures of factor reliability: use of split samples, Monte Carlo simulation, and a priori models.
Application of factor analysis....pdf
Several studies have suggested the efficacy of topological rotation as an adjunct to oblique analytical rotation in attaining improved approximation to maximum simple structure of the factor pattern matrix. Recently, using a higherorder scale factoring of the Objective Motivation Analysis Test (MAT), and the Eight State Questionnaire (8SQ), Boyle(1983) reported a 6.17% increase in the ±.10 hyperplane count after only five Rotoplot cycles. Four of the 11 extracted factors were simplified in line with Thurstone's simple structure requirements.
Confirmatory Analysis of Exploratively Obtained Factor Structures
Educational and Psychological Measurement, 2001
Factor structures obtained by exploratory factor analysis (EFA) often turn out to fit poorly in confirmative follow-up studies. In the present study, the authors assessed the extent to which results obtained in EFA studies can be replicated by confirmatory factor analysis (CFA) in the same sample. More specifically, the authors used CFA to test three different factor models on several correlation matrices of exploratively obtained factor structures that were reported in the literature. The factor models varied with respect to the role of the smaller factor pattern coefficients. Results showed that confirmatory factor models in which all low EFA pattern coefficients were fixed to zero fitted especially poorly. The authors conclude that it may be justified to use a less constrained model when testing a factor model by allowing some correlation among the factors and some of the lower factor pattern coefficients to differ from zero.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.