Using Cross-Classified Structural Equation Models to Examine the Accuracy of Personality Judgments
- 410 Downloads
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.
Keywordspersonality judgments accuracy lens model mixed models cross-classified structural equation models
- 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
- 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.
- 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
- 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.
- Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed.). Berkeley, CA: University of California Press.Google Scholar
- Funder, D. C. (1999). Personality judgment: A realistic approach to person perception. San Diego, CA: Academic Press.Google Scholar
- Goldstein, H. (1987). Multilevel models in educational and social research. London: Griffin.Google Scholar
- 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
- Hall, J. A., & Bernieri, F. J. (2001). Interpersonal Sensitivity: Theory and Measurement. Taylor & Francis: Psychology Press.Google Scholar
- 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
- McCulloch, C. E., Searle, S. R., & Neuhaus, J. M. (2004). Generalized, linear, and mixed models (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
- Muthén, B. O. (1990). Mean and covariance structure analysis of hierarchical data (UCLA Statistics Series No. 62). Los Angeles: University of California.Google Scholar
- Muthén, L. K., & Muthén, B. O. (1998–2014). Mplus user’s guide: Statistical analysis with latent variables (7th ed.). Los Angeles, CAGoogle Scholar
- Ostendorf, F., & Angleitner, A. (2004). NEO-PI-R-NEO Personality inventory after Costa and McCrae: Revised version. Göttingen: Hogrefe.Google Scholar
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage Publications.Google Scholar
- 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
- 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
- Verbeke, G., & Molenberghs, G. (2009). Linear mixed models for longitudinal data. Berlin: Springer.Google Scholar