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All about variable selection in factor analysis and structural equation modeling

Explore the methods and importance of variable selection in factor analysis and structural equation modeling, covering SEFA and SCoFA, including statistical derivations, composite scale construction, and improving model fit. Learn about theoretical properties and recent literature on variable selection. Discover practical examples and program resources for SEFA and SCoFA. Understand the implications of variable removal, identification of inconsistent variables, and testing model fit for accurate results in your research. Benefit from the insights shared to enhance your understanding of variable selection in factor analysis and SEM.

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All about variable selection in factor analysis and structural equation modeling

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  1. IMPS2001, July 15-19,2001Osaka, Japan All about variable selection in factor analysis and structural equation modeling Yutaka Kano Osaka University School of Human Sciences

  2. Today’s talk • Motivation for variable selection • How SEFA (and SCoFA) works • Derivation of the statistics • Theoretical property • What does variable selection with model fit mean? • Summary

  3. Needs for variable selection • Variable selection in EFA is an important but time-consuming process • Composite scale construction • Reliability analysis • Variable selection in SEM should be less important but … • Indicator selection • Improvement of model fit

  4. Recent literature • Little et. al. (1999). On selecting indicators for multivariate measurement and modeling with latent variables. Psychological Methods, 4, 192-211. • Fabrigar et. al. (1999). Evaluating the use of EFA in psychological research. Psychological Methods, 4, 272-299. • Kano et. al. (in press, 2000, 1994).

  5. Procedures for variable selection in EFA • Usual procedure • Magnitude of communalities • Interpretability • Towards simple structures • Our approach • Model fit

  6. Programs for variable selection in factor analysis • Exploratory analysis • SEFA(Stepwise variable selection in EFA) • http://koko15.hus.osaka-u.ac.jp/~harada/sefa2001/stepwise/ • Confirmatory analysis • SCoFA(Stepwise Confirmatory FA) • http://koko16.hus.osaka-u.ac.jp/~harada/scofa/input.html

  7. Example_1 • A questionnaire on perception on physical exercise • n=653, p=15, one-factor model • Data was collected by Dr Oka (Waseda U.) • Conclusion • Remove X2, X9, X13, X14

  8. Example_2

  9. Example_3

  10. Example_4

  11. Example_5

  12. Example_6

  13. SCoFa:24 Pschological variable

  14. Original Model (p=24)

  15. Obtain estimates for a current model Construct predicted chi-square for each one-variable-deleted model using the estimates, without tedious iterations Take a sort of LM approach Theory of SEFA and SCoFA

  16. Known quantities and goal_1

  17. Known quantities and goal_2

  18. Basic idea We construct T02’ as LM test

  19. Final formula for T2 Note: This is Browne’s (Browne 1982) statistic of goodness-of-fit using general estimates

  20. Properties_1

  21. Question 1 • Can T2 work even if X1 is inconsistent? • Estimate for Θ is biased.

  22. Properties_2

  23. Question 2 • Can SEFA identify an uncorrelated variable? • Unfortunately, no • We have developed a way of testing zero communality in SEFA (see Harada-Kano, IMPS)

  24. Question 3 • What is the actual meaning of variable selection with model fit? • The following shows an illustrative example:

  25. Answer 3_1: Example again • X2, X9, X13, X14 are to be removed

  26. Answer 3_2: Example again • Best fitted model with correlated errors • SEFA conclusion: X2, X9, X13, X14 are to be removed

  27. Answer 3_3: Example again • Variables to be deleted are identified so as to break up the correlated errors • Correlated errors may cause • Different interpretation of FA results • Common factors considered are not enough to explain correlations between observed variables • Such variables are not good indicators (e.g., in SEM) • Inaccurate reliability estimates • Green-Hershberger (2000), Raykov (2001) • Kano-Azuma (2001, IMPS)

  28. Question 4 • How one should do if SEFA or SCoFA identifies a variable with large factor loading estimate as inconsistent?

  29. Answer 4_1: Reliability • If one employs the alpha coefficient or(s)he has to delete it to have a good-fit model.

  30. Answer 4_2: Reliability • If one employs(s)he can remain it, and compare reliability between models.

  31. Bad-fitted One-factor Model based ρ 0.76 Answer 4_3: Example ρ' 0.64α 0.74

  32. Answer 4_4: Example ρ' 0.64 0.63α 0.74 0.63

  33. Answer 4_5: Example ρ' 0.60 0.63α 0.78 0.63

  34. Summary_1 • A new option for variable selection was introduced, which is based on model fit. • You can easily access the programs on the internet • SEFA(Stepwise variable selection in EFA) • http://koko15.hus.osaka-u.ac.jp/~harada/sefa2001/stepwise/ • SCoFA(Stepwise Confirmatory FA) • http://koko16.hus.osaka-u.ac.jp/~harada/scofa/input.html

  35. Summary_2 • It enjoys preferable theoretical properties • Testing null communality is important • Uncorrelated variables cannot be identified • Variable selection with model fit can find out error correlations • Traditional reliability coefficients based on a poor-fit model have serious bias

  36. Summary_3 • High communality variables can be inconsistent • Whether such variables should be removed depends • Reliability has to be figured out using nonstandard factor model

  37. Harada, A. and Kano, Y. (2001) Variable selection and test of communality in EFA. IMPS2001, Osaka Kano, Y. (in press).Variable selection for structural models. Journal of Statistical Inference and Planning. Kano, Y. and Harada, A. (2000).Stepwise variable selection in factor analysis. Psychometrika, 65, 7-22. Kano, Y. and Ihara, M. (1994). Identification of inconsistent variates in factor analysis. Psychometrika, Vol.59, 5-20 References

  38. Thank you for coming to Osaka and being at my talk • TakoYaki performance will start soon • You can understand how octopus relates to Osaka, if you see and taste it

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