Psychometrika

, Volume 82, Issue 2, pp 475–497 | Cite as

Using Cross-Classified Structural Equation Models to Examine the Accuracy of Personality Judgments

Article
  • 410 Downloads

Abstract

Whether, when, and why perceivers are able to accurately infer the personality traits of other individuals is a key topic in psychological science. Studies examining this question typically ask a number of perceivers to judge a number of targets with regard to a specific trait. The resulting data are then analyzed by averaging the judgments across perceivers or by computing the respective statistic for each single perceiver. Here, we discuss the limitations of the average-perceiver and single-perceiver approaches. Furthermore, we argue that and illustrate how cross-classified structural equation models can be used for the flexible analysis of accuracy data.

Keywords

personality judgments accuracy lens model mixed models cross-classified structural equation models 

References

  1. Asparouhov, T., & Muthén, B. (2007). Computationally efficient estimation of multilevel high-dimensional latent variable models. In Proceedings of the 2007 Joint Statistical Meetings, Section on Statistics in Epidemiology (pp. 2531–2535). Alexandria, VA: American Statistical Association.Google Scholar
  2. Asparouhov, T., & Muthén, B. (2012). General Random Effect Latent Variable Modeling: Random Subjects, Items, Contexts, and Parameters. Technical Report. https://www.statmodel.com/download/NCME12.pdf.
  3. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412. doi:10.1016/j.jml.2007.12.005.CrossRefGoogle Scholar
  4. Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychological Science, 21, 372–374.Google Scholar
  5. Bates, D., Maechler, M., Bolker, B. M., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. Retrieved from http://cran.r-project.org/web/packages/lme4.
  6. Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163.CrossRefPubMedGoogle Scholar
  7. Biesanz, J. C. (2010). The social accuracy model of interpersonal perception: Assessing individual differences in perceptive and expressive accuracy. Multivariate Behavioral Research, 45, 853–885.CrossRefPubMedGoogle Scholar
  8. Borkenau, P., & Liebler, A. (1992). Trait inferences: Sources of validity at zero acquaintance. Journal of Personality and Social Psychology, 62, 645–657.CrossRefGoogle Scholar
  9. Borkenau, P., & Liebler, A. (1993). Convergence of stranger ratings of personality and intelligence with self-ratings, partner ratings, and measured intelligence. Journal of Personality and Social Psychology, 65, 546–553.CrossRefGoogle Scholar
  10. Borkenau, P., Mauer, N., Riemann, R., Spinath, F. M., & Angleitner, A. (2004). Thin slices of behavior as cues of personality and intelligence. Journal of Personality and Social Psychology, 86, 599–614.CrossRefPubMedGoogle Scholar
  11. Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed.). Berkeley, CA: University of California Press.Google Scholar
  12. Funder, D. C. (1999). Personality judgment: A realistic approach to person perception. San Diego, CA: Academic Press.Google Scholar
  13. Funder, D. C. (2012). Accurate personality judgments. Current Directions in Psychological Science, 21, 177–182.CrossRefGoogle Scholar
  14. Funder, D. C., & Sneed, C. D. (1993). Behavioral manifestations of personality: An ecological approach to judgmental accuracy. Journal of Personality and Social Psychology, 64, 479–490.CrossRefPubMedGoogle Scholar
  15. Furr, R. M. (2008). A framework for profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76, 1267–1316.CrossRefPubMedGoogle Scholar
  16. Gifford, R. (1994). A lens-mapping framework for understanding the encoding and decoding of interpersonal dispositions in nonverbal behaviors. Journal of Personality and Social Psychology, 66, 398–412.CrossRefGoogle Scholar
  17. Goldstein, H. (1987). Multilevel models in educational and social research. London: Griffin.Google Scholar
  18. González, J., De Boeck, P., & Tuerlinckx, F. (2008). A double-structure structural equation model for three-mode data. Psychological Methods, 13, 337–353.CrossRefPubMedGoogle Scholar
  19. Gosling, S. D., Ko, S. J., Mannarelli, T., & Morris, M. E. (2002). A room with a cue: Personality judgments based on offices and bedrooms. Journal of Personality and Social Psychology, 82, 379–398.CrossRefPubMedGoogle Scholar
  20. Hall, J. A., Bernieri, F. J., & Carney, D. R. (2005). Nonverbal behavior and interpersonal sensitivity. In J. A. Harrigan, R. Rosenthal, & K. R. Scherer (Eds.), The new handbook of methods in nonverbal behavior research (pp. 237–281). Oxford: Oxford University Press.Google Scholar
  21. Hall, J. A., & Bernieri, F. J. (2001). Interpersonal Sensitivity: Theory and Measurement. Taylor & Francis: Psychology Press.Google Scholar
  22. Hirschmüller, S., Egloff, B., Nestler, S., & Back, M. D. (2013). The dual lens model: A comprehensive framework for understanding self-other agreement of personality judgments at zero acquaintance. Journal of Personality and Social Psychology, 104, 335–353.CrossRefPubMedGoogle Scholar
  23. Hirschmüller, S., Egloff, B., Schmukle, S. C., Nestler, S., & Back, M. D. (2015). Accurate judgments of neuroticism at zero acquaintance: A question of relevance. Journal of Personality, 83, 221–228.CrossRefPubMedGoogle Scholar
  24. Hursch, C. J., Hammond, K. R., & Hursch, J. L. (1964). Some methodological considerations in multiple-probability studies. Psychological Review, 71, 42–60.CrossRefPubMedGoogle Scholar
  25. Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103, 54–69.CrossRefPubMedGoogle Scholar
  26. Karelaia, N., & Hogarth, R. M. (2008). Determinants of linear judgment: A meta-analysis of lens-model studies. Psychological Bulletin, 134, 404–426.CrossRefPubMedGoogle Scholar
  27. Kenny, D. A., & West, T. V. (2008). Zero acquaintance: Definitions, statistical model, findings, and process. In J. Skowronski & N. Ambady (Eds.), First impressions (pp. 129–146). New York: Guilford.Google Scholar
  28. Küfner, A. C. P., Back, M. D., Nestler, S., & Egloff, B. (2010). Tell me a story and I will tell you who you are! Lens model analyses of personality and creative writing. Journal of Research in Personality, 44, 427–435.CrossRefGoogle Scholar
  29. Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  30. McCulloch, C. E., Searle, S. R., & Neuhaus, J. M. (2004). Generalized, linear, and mixed models (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  31. McDonald, R. P. (1993). A general model for two-level data with responses missing at random. Psychometrika, 58, 575–585.CrossRefGoogle Scholar
  32. McDonald, R. P. (1994). The bilevel reticular action model for path analysis with latent variables. Sociological Methods and Research, 22, 399–413.CrossRefGoogle Scholar
  33. Muthén, B. O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585.CrossRefGoogle Scholar
  34. Muthén, B. O. (1990). Mean and covariance structure analysis of hierarchical data (UCLA Statistics Series No. 62). Los Angeles: University of California.Google Scholar
  35. Muthén, L. K., & Muthén, B. O. (1998–2014). Mplus user’s guide: Statistical analysis with latent variables (7th ed.). Los Angeles, CAGoogle Scholar
  36. Naumann, L. P., Vazire, S., Rentfrow, P. J., & Gosling, S. D. (2009). Personality judgments based on physical appearance. Personality and Social Psychology Bulletin, 35, 1661–1671.CrossRefPubMedGoogle Scholar
  37. Nestler, S., & Back, M. D. (2013). Applications and extensions of the lens model to understand interpersonal judgments at zero acquaintance. Current Directions in Psychological Science, 22, 374–379.CrossRefGoogle Scholar
  38. Nestler, S., Egloff, B., Küfner, A. C. P., & Back, M. D. (2012). An integrative lens model approach to bias and accuracy in human inferences: Hindsight effects and knowledge updating in personality judgments. Journal of Personality and Social Psychology, 103, 698–717.CrossRefGoogle Scholar
  39. Nestler, S., Grimm, K., & Schönbrodt, F. D. (2015). The social consequences and mechanisms of personality: How to analyze longitudinal data from individual, dyadic, round-robin, and network designs. European Journal of Personality, 29, 272–295.CrossRefGoogle Scholar
  40. Ostendorf, F., & Angleitner, A. (2004). NEO-PI-R-NEO Personality inventory after Costa and McCrae: Revised version. Göttingen: Hogrefe.Google Scholar
  41. Preacher, K. J., & Hayes, A. F. (2008a). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.CrossRefPubMedGoogle Scholar
  42. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209–233.CrossRefPubMedGoogle Scholar
  43. Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203–212.CrossRefGoogle Scholar
  44. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage Publications.Google Scholar
  45. Rentfrow, P. J., & Gosling, S. D. (2006). Message in a Ballad: The role of music preferences in interpersonal perception. Psychological Science, 17, 236–242.CrossRefPubMedGoogle Scholar
  46. Rogosa, D., & Saner, H. (1995). Longitudinal data analysis examples with random coefficient models. Journal of Educational and Behavioral Statistics, 20, 149–170.CrossRefGoogle Scholar
  47. Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: Sage Publishers.Google Scholar
  48. Stopfer, J. M., Egloff, B., Nestler, S., & Back, M. D. (2014). Personality expression and impression formation in online social networks: An integrative approach to understanding the processes of accuracy, impression management, and meta-accuracy. European Journal of Personality, 28, 73–94.CrossRefGoogle Scholar
  49. Todorov, A., Dotsch, R., Wigboldus, D., & Said, C. P. (2011). Data-driven methods for modeling social perception. Social and Personality Psychology Compass, 5, 775–791.CrossRefGoogle Scholar
  50. Verbeke, G., & Molenberghs, G. (2009). Linear mixed models for longitudinal data. Berlin: Springer.Google Scholar
  51. Zebrowitz, L. A., & Collins, M. A. (1997). Accurate social perception at zero acquaintance: The affordances of a Gibsonian approach. Personality and Social Psychology Review, 1, 203–222.CrossRefGoogle Scholar

Copyright information

© The Psychometric Society 2015

Authors and Affiliations

  1. 1.University of MünsterMünsterGermany

Personalised recommendations