Skip to main content
SpringerLink
Log in

A Confirmatory Factor Model for the Investigation of Cognitive Data Showing a Ceiling Effect: An Example

  • Conference paper
  • First Online:
Quantitative Psychology Research

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 167))

Abstract

A method for addressing the consequences of ceiling effects on model misfit in confirmatory factor analysis of cognitive data is proposed. This method focuses on the reduction of variance as a major ingredient of the ceiling effect. The model of the covariance matrix is modified in such a way that it reflects the impact of the ceiling effect on variances and covariances. The method applies to models including theory-based constraints of factor loadings for investigating cognitive data. The effectiveness of the method is demonstrated in data collected by means of a measure of working memory capacity. The application of the method in combination with a confirmatory factor model that assumes working memory capacity as the major source of performance yields the expected increase in the degree of model fit.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Allen, M. J., & Yen, W. M. (2001). Introduction to measurement theory (2nd ed.). Monterey CA: Brooks/Cole.

    Google Scholar 

  • Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 395–479). Reading, MA: Addison-Wesley.

    Google Scholar 

  • Hessling, R. M., Traxel, N. M., & Schmidt, T. J. (2004). Ceiling effect. In M. S. Lewis-Beck, A. Bryman, & T. F. Liao (Eds.), The SAGE encyclopedia of social science research methods (p. 107). Thousand Oaks: Sage.

    Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi:10.1080/10705519909540118.

    Article  Google Scholar 

  • Jöreskog, K. G. (1970). A general method for analysis of covariance structure. Biometrika, 57, 239–257. doi:10.2307/2334833.

    Article  MathSciNet  MATH  Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.80.. Lincolnwood, IL: Scientific Software International.

    Google Scholar 

  • Kubinger, K. D. (2009). Applications of the linear logistic test model in psychometric research. Educational and Psychological Measurement, 69, 232–244. doi:10.1177/0013164408322021.

    Article  MathSciNet  Google Scholar 

  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading: Addison-Wesley.

    MATH  Google Scholar 

  • Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156–166. doi:10.1037/0033-2909.105.1.156.

    Article  Google Scholar 

  • Muthen, B. (1989). TOBIT factor analysis. British Journal of Mathematical and Statistical Psychology, 42, 241–250. doi:10.1111/j.2044-8317.1989.tb00913.x.

    Article  MathSciNet  MATH  Google Scholar 

  • Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.

    Google Scholar 

  • Satorra, A., & Bentler, P. M. (1994). Corrections to the test statistics and standard errors on covariance structure analysis. In A. von Eye & C. C. Glogg (Eds.), Latent variable analysis (pp. 399–419). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Schweizer, K. (1996). The speed-accuracy transition due to task complexity. Intelligence, 22, 115–128. doi:10.1016/S0160-2896(96)90012-4.

    Article  Google Scholar 

  • Schweizer, K. (2011). Scaling variances of latent variables by standardizing loadings: Applications to working memory and the position effect. Multivariate Behavioral Research, 46, 938–955. doi:10.1080/00273171.2011.625312.

    Article  Google Scholar 

  • Tobit, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26, 24–36. doi:10.2307/1907382.

    Article  MathSciNet  Google Scholar 

  • Uttl, B. (2005). Measurements of individual differences: Lessons from memory assessment in research and clinical practice. Psychological Science, 16, 460–467. doi:10.1111/j.0956-7976.2005.01557.x.

    Google Scholar 

  • Van den Oord, E. J. C. G., & Van der Ark, L. A. (1997). A note on the use of Tobit approach for tests scores with floor or ceiling effects. British Journal of Mathematical and Statistical Psychology, 50, 351–364. doi:10.1111/j.2044-8317.1997.tb01150.x.

    Article  MATH  Google Scholar 

  • Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organisational research. Organisational Research Methods, 16, 4–70.

    Article  Google Scholar 

  • Vogt, W. P. (2005). Dictionary of statistics & methodology: A nontechnical guide for the social sciences (3rd ed.). Thousand Oaks: Sage.

    Book  Google Scholar 

  • Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A. (2009). Investigating ceiling effects in longitudinal data analysis. Multivariate Behavioral Research, 43, 476–496. doi:10.1080%2F00273170802285941.

    Article  Google Scholar 

  • Wesnes, K., & Warburton, D. M. (1983). The effect of smoking on rapid visual information processing performance. Neuropsychobiology, 9, 223–229. doi:10.1016/j.neurobiolaging.2014.01.145.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, Frankfurt a. M., 60323, Germany

    Karl Schweizer

Authors
  1. Karl Schweizer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karl Schweizer .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    L. Andries van der Ark

  2. University of Wisconsin, Madison, Wisconsin, USA

    Daniel M. Bolt

  3. Education University of Hong Kong, Hong Kong, China

    Wen-Chung Wang

  4. University of Illinois, Champaign, Illinois, USA

    Jeffrey A. Douglas

  5. Umeå University, Umeå, Sweden

    Marie Wiberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Schweizer, K. (2016). A Confirmatory Factor Model for the Investigation of Cognitive Data Showing a Ceiling Effect: An Example. In: van der Ark, L., Bolt, D., Wang, WC., Douglas, J., Wiberg, M. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 167. Springer, Cham. https://doi.org/10.1007/978-3-319-38759-8_14

Download citation

Publish with us

Policies and ethics

Search

Navigation