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Choosing an appropriate number of factors in factor analysis with incomplete data

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  • Song, Juwon
  • Belin, Thomas R.

Abstract

When we conduct factor analysis, the number of factors is often unknown in advance. Among many decision rules for an appropriate number of factors, it is easy to find approaches that make use of the estimated covariance matrix. When data include missing values, the estimated covariance matrix using either complete cases or available cases may not accurately represent the true covariance matrix, and decision based on the estimated covariance matrix may be misleading. We discuss how to apply model selection techniques using AIC or BIC to choose an appropriate number of factors when data include missing values. In the simulation study, it is shown that the suggested methods select the correct number of factors for simulated data with known number of factors.

Suggested Citation

  • Song, Juwon & Belin, Thomas R., 2008. "Choosing an appropriate number of factors in factor analysis with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3560-3569, March.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3560-3569
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    References listed on IDEAS

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    1. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
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    1. Chai Bin Feng & Muhammad Usman Khurram & Raheel Safdar & Sultan Sikandar Mirza & Amjad Iqbal, 2023. "Corporate strategic responses, supplier concentration and sustainable growth of chinese listed firms," Operations Management Research, Springer, vol. 16(3), pages 1413-1427, September.
    2. repec:zna:indecs:v:20:y:2022:i:1:p:50-56 is not listed on IDEAS
    3. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    4. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
    5. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.

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