Exploratory factor analysis: Difference between revisions

Content deleted Content added
Citation bot (talk | contribs)
Removed URL that duplicated identifier. Removed parameters. | Use this bot. Report bugs. | #UCB_CommandLine
OAbot (talk | contribs)
m Open access bot: hdl updated in citation with #oabot.
 
Line 114:
Factor loadings are numerical values that indicate the strength and direction of a factor on a measured variable. Factor loadings indicate how strongly the factor influences the measured variable. In order to label the factors in the model, researchers should examine the factor pattern to see which items load highly on which factors and then determine what those items have in common.<ref name =Fabrigar/> Whatever the items have in common will indicate the meaning of the factor. Interpretation has long been noted as an important, but difficult, part of the analytic process.<ref>{{Cite journal |last=Copeland |first=Herman A. |date=March 1935 |title=A note on "The Vectors of Mind." |url=https://doi.apa.org/doi/10.1037/h0057026 |journal=Psychological Review |volume=42 |issue=2 |pages=216–218 |doi=10.1037/h0057026 |issn=1939-1471|url-access=subscription }}</ref>
 
However, while exploratory factor analysis is a powerful tool for uncovering underlying structures among variables, it is crucial to avoid reliance on it without adequate theorizing. Armstrong's<ref>{{cite journal |last1=Armstrong |first1=J. Scott |title=Derivation of Theory by Means of Factor Analysis or Tom Swift and His Electric Factor Analysis Machine |journal=The American Statistician |date=December 1967 |volume=21 |issue=5 |pages=17–21 |doi=10.1080/00031305.1967.10479849|hdl=1721.1/47256 |hdl-access=free }}</ref> critique highlights that EFA, when conducted without a theoretical framework, can lead to misleading interpretations. For instance, in a hypothetical case study involving the analysis of various physical properties of metals, the results of EFA failed to identify the true underlying factors, instead producing an "over-factored" model that obscured the simplicity of the relationships amongst the observed variables. Similarly, poorly designed survey items can lead to spurious factor structures.<ref>{{cite journal |last1=Maul |first1=Andrew |title=Rethinking Traditional Methods of Survey Validation |journal=Measurement: Interdisciplinary Research and Perspectives |date=3 April 2017 |volume=15 |issue=2 |pages=51–69 |doi=10.1080/15366367.2017.1348108}}</ref>
 
==See also==