Skip to main content

Psychometrics

  • Reference work entry
  • First Online:
Encyclopedia of Personality and Individual Differences
  • 44 Accesses

Introduction

It seems that even a major professional society devoted to Psychometrics has to explain what is meant by the term. Four contemporary scholars were asked, and their somewhat similar responses were put on the society’s website (https://www.psychometricsociety.org/content/what-psychometrics). Some explanations used Galton’s (1879) definition regarding imposing measures or numbers onto “operations of the mind,” other appear somewhat circular as they seem to define the term by quantitative psychology. This is tempting, of course, to explain something by pointing towards something else that appears to be a bit more descriptive. An example is a subtitle, more specifically the subtitle one of the leading journals in the domain uses. In this sense, psychometrics is quantitative psychology, as Psychometrika is “… a journal of quantitative psychology”. The pre-1984 subtitle of the journal suggests that the field (and journal) is “…devoted to the development of psychology as a...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 3,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 5,499.99
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

  • Bock, R. D. (1997). A brief history of item response theory. Educational Measurement: Issues and Practice, 16(4), 21–33. https://doi.org/10.1111/j.1745-3992.1997.tb00605.x.

    Article  Google Scholar 

  • Cudeck, R., & MacCallum, R. C. (2007). Factor analysis at 100: Historical developments and future directions. Mahwah: Erlbaum.

    Google Scholar 

  • de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76, 179–199.

    Article  Google Scholar 

  • Galton, F. (1879). Psychometric experiments. Brain, 2, 149–162.

    Article  Google Scholar 

  • Green, B. F., Jr. (1952). Latent structure analysis and its relation to factor analysis. Journal of the American Statistical Association, 47, 71–76.

    Article  Google Scholar 

  • Henry. (1999). Latent structure analysis at fifty. Paper presented at the 1999 Joint statistical meetings, Baltimore MD, August 11, 1999. https://www.amstat.org/sections/srms/proceedings/papers/1999_102.pdf

  • Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log linear models with latent variables. Psychometrika, 74, 191–210.

    Article  Google Scholar 

  • Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258–272.

    Article  Google Scholar 

  • Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. PNAS, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lewin, K. (1936). Principles of topological psychology. New York: McGraw-Hill.

    Book  Google Scholar 

  • Macready, G. B., & Dayton, C. M. (1977). The use of probabilistic models in the assessment of mastery. Journal of Educational Statistics, 2, 99–120.

    Article  Google Scholar 

  • McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Moustaki, I., & Knott, M. (2000). Generalized latent trait models. Psychometrika, 65, 391–411.

    Article  Google Scholar 

  • Mulaik, S. A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22, 267–305. https://doi.org/10.1207/s15327906mbr2203_3.

    Article  PubMed  Google Scholar 

  • Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modelling. Psychometrika, 69, 167–190.

    Article  Google Scholar 

  • Rijmen, F., Jeon, M., von Davier, M., & Rabe-Hesketh, S. (2014). A third order item response theory model for modeling the effects of domains and subdomains in large-scale educational assessment surveys. Journal of Educational and Behavioral Statistics, 38, 32–60. https://doi.org/10.3102/1076998614531045.

    Article  Google Scholar 

  • Takane, Y., & De Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393–408.

    Article  Google Scholar 

  • Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20(4), 345–354.

    Article  Google Scholar 

  • Traub, R. (1997). Classical test theory in historical perspective. Educational Measurement: Issues and Practice, 16(4), 8–14. https://doi.org/10.1111/j.1745-3992.1997.tb00603.x.

    Article  Google Scholar 

  • von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61, 287–307.

    Article  Google Scholar 

  • von Davier, M. (2009). Some notes on the reinvention of latent structure models as diagnostic classification models. Measurement – Interdisciplinary Research and Perspectives, 7(1), 67–74.

    Article  Google Scholar 

  • von Davier, M. (2013). The DINA model as a constrained general diagnostic model – two variants of a model equivalency. British Journal of Mathematical and Statistical Psychology, 67, 49–71. http://onlinelibrary.wiley.com/doi/10.1111/bmsp.12003/abstract.

    Article  Google Scholar 

  • von Davier, M. (2014). The log-linear cognitive diagnostic model (LCDM) as a special case of the general diagnostic model (GDM). ETS Research Report Series. http://onlinelibrary.wiley.com/doi/10.1002/ets2.12043/abstract.

  • von Davier, M. (2016). Chapter 3: The Rasch model. In W. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (Vol. 1, 2nd ed.). Boca Raton: CRC Press.

    Google Scholar 

  • von Davier, M., & Haberman, S. (2014). Hierarchical diagnostic classification models morphing into unidimensional ‘diagnostic’ classification models – A commentary. Psychometrika. https://doi.org/10.1007/s11336-013-9363-z.

  • von Davier, M., & Rost, J. (2016). Chapter 23: Logistic mixture-distribution response models. In W. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (Vol. 1, 2nd ed.). Boca Raton: Chapman & Hall/CRC.

    Google Scholar 

  • von Davier, M., Naemi, B., & Roberts, R. D. (2012). Factorial versus typological models: A comparison of methods for personality data. Measurement: Interdisciplinary Research and Perspectives, 10(4), 185–208.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthias von Davier .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

von Davier, M. (2020). Psychometrics. In: Zeigler-Hill, V., Shackelford, T.K. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-24612-3_1341

Download citation

Publish with us

Policies and ethics