Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-17T14:46:38.237Z Has data issue: false hasContentIssue false

Addendum: Statistical Analyses and Computer Programming in Personality

Published online by Cambridge University Press:  18 September 2020

Edited by
Philip J. Corr  and
Gerald Matthews
Philip J. Corr
Affiliation:
City, University London
Gerald Matthews
Affiliation:
University of Central Florida
Get access
Type
Chapter
Information
The Cambridge Handbook of Personality Psychology , pp. 495 - 534
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aiken, L. S., & West, S. G. (1991). Multiple regression testing and interpretation. London: Sage.Google Scholar
Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen’s standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317–328.CrossRefGoogle Scholar
Atkinson, J. W., & Birch, D. (1970). The dynamics of action. New York: Wiley.Google ScholarPubMed
Atkinson, J. W., Bongort, K., & Price, L. (1977). Explorations using computer simulation to comprehend thematic apperceptive measurement of motivation. Motivation and Emotion, 1, 1–27.CrossRefGoogle Scholar
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815–824.Google Scholar
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1–48. R package version 1.1–8.CrossRefGoogle Scholar
Bechtoldt, H. (1961). An empirical study of the factor analysis stability hypothesis. Psychometrika, 26, 405–432.Google Scholar
Becker, R. A., Chambers, J. M., & Wilks, A. R. (1988). The new S language. Pacific Grove, CA: Wadsworth & Brooks.Google Scholar
Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software, Inc.Google Scholar
Bentler, P. M. (2017). Specificity-enhanced reliability coefficients. Psychological Methods, 22, 527–540.CrossRefGoogle ScholarPubMed
Bernaards, C., & Jennrich, R. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65, 676–696.CrossRefGoogle Scholar
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–404.Google Scholar
Bivand, R., & Piras, G. (2015). Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software, 63, 1–36.Google Scholar
Bleidorn, W., Schönbrodt, F., Gebauer, J. E., Rentfrow, P. J., Potter, J., & Gosling, S. D. (2016). To live among like-minded others: Exploring the links between person-city personality fit and self-esteem. Psychological Science, 27, 419–427.Google Scholar
Bliese, P. (2016). multilevel: Multilevel functions. R package version 2.6.Google Scholar
Bock, R. D. (2007). Rethinking Thurstone. In Cudeck, R. & MacCallum, R. C. (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 35–45). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616.Google Scholar
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.Google Scholar
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605–634.Google Scholar
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 203–219.CrossRefGoogle ScholarPubMed
Brogden, H. E. (1946). On the interpretation of the correlation coefficient as a measure of predictive efficiency. Journal of Educational Psychology, 37, 65–76.CrossRefGoogle ScholarPubMed
Bromley, A. G. (1982). Charles Babbage’s Analytical Engine, 1838. IEEE Annals of the History of Computing, 4, 196–217.Google Scholar
Brown, A. D. (2017). The Dynamics of Affect: Using Newtonian Mechanics, Reinforcement Sensitivity Theory, and the Cues-Tendencies-Actions Model to Simulate Individual Differences in Emotional Experience. PhD thesis, Northwestern University, Evanston, IL.Google Scholar
Brown, W. (1910). Some experimental results in the correlation of mental abilities. British Journal of Psychology, 3, 296–322.Google Scholar
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111–150.Google Scholar
Butcher, J. N., Dahlstrom, W., Graham, J., Tellegen, A., & Kaemmer, B. (1989). MMPI-2: Manual for administration and scoring. Minneapolis, MN: University of Minnesota Press.Google Scholar
Cattell, R. B. (1946). Personality structure and measurement. I. The operational determination of trait unities. British Journal of Psychology, 36, 88–102.Google Scholar
Cattell, R. B. (1966a). The data box: Its ordering of total resources in terms of possible relational systems. In Cattell, R. B. (Ed.), Handbook of multivariate experimental psychology (pp. 67–128). Chicago, IL: Rand-McNally.Google Scholar
Cattell, R. B. (1966b). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276.Google Scholar
Champely, S. (2018). pwr: Basic functions for power analysis. R package version 1.2-2.Google Scholar
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., … Li, Y. (2018). xgboost: Extreme Gradient Boosting. R package version 0.71.1.Google Scholar
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.Google Scholar
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. The Journal of Abnormal and Social Psychology, 65, 145–153.CrossRefGoogle ScholarPubMed
Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70, 213–220.Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Cohen, J. (1992). A power primer. Psychological bulletin, 112, 155–159.Google Scholar
Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49, 997–1003.CrossRefGoogle Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.Google Scholar
Cole, D. A., Martin, N. C., & Steiger, J. H. (2005). Empirical and conceptual problems with longitudinal trait-state models: Introducing a trait-state-occasion model. Psychological Methods, 10, 3–20.CrossRefGoogle ScholarPubMed
Crawford, C. B., & Ferguson, G. A. (1970). A general rotation criterion and its use in orthogonal rotation. Psychometrika, 35, 321–332.Google Scholar
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.Google Scholar
Danielson, J. R., & Clark, J. H. (1954). A personality inventory for induction screening. Journal of Clinical Psychology, 10, 137–143.Google Scholar
Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571–582.Google Scholar
Dixon, W. J., & Brown, M. B. (1979). BMDP-79: Biomedical computer programs P-series. University of California Press.Google Scholar
Eckart, C., & Young, G. (1936). The approximation of one matrix by another of lower rank. Psychometrika, 1, 211–218.Google Scholar
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7, 1–26.Google Scholar
Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37, 36–48.Google Scholar
Elleman, L. G., Condon, D. M., Russin, S. E., & Revelle, W. (2018). The personality of U.S. states: Stability from 1999 to 2015. Journal of Research in Personality, 72, 64–72. Special issue of Replication of Critical Findings in Personality Psychology.Google Scholar
Erceg-Hurn, D. M., & Mirosevich, V. M. (2008). Modern robust statistical methods: An easy way to maximize the accuracy and power of your research. American Psychologist, 63, 591–601.Google Scholar
Eysenck, H. J. (1944). Types of personality: A factorial study of seven hundred neurotics. The British Journal of Psychiatry, 90, 851–861.Google Scholar
Fisher, A. J. (2015). Toward a dynamic model of psychological assessment: Implications for personalized care. Journal of Consulting and Clinical Psychology, 83, 825–836.Google Scholar
Fisher, R. A. (1921). On the “probable error” of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.Google Scholar
Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh, UK: Oliver and Boyd.Google Scholar
Fox, J., Nie, Z., & Byrnes, J. (2013). sem: Structural equation models. R package version 3.1-3.Google Scholar
Fox, J., Nie, Z., & Byrnes, J. (2016). sem: Structural equation models. R package version 3.1-7.Google Scholar
Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.Google Scholar
Galton, F. (1888). Co-relations and their measurement. Proceedings of the Royal Society. London Series, 45, 135–145.Google Scholar
Garcia, D. M., Schmitt, M. T., Branscombe, N. R., & Ellemers, N. (2010). Women’s reactions to ingroup members who protest discriminatory treatment: The importance of beliefs about inequality and response appropriateness. European Journal of Social Psychology, 40, 733–745.CrossRefGoogle Scholar
Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59, 93–104.CrossRefGoogle ScholarPubMed
Gray, J. A. (1991). The neuropsychology of temperament. In Strelau, J. & Angleitner, A. (Eds.), Explorations in temperament: International perspectives on theory and measurement (pp. 105–128). New York: Plenum.Google Scholar
Gray, J. A., & McNaughton, N. (2000). The Neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system (2nd ed.). Oxford, UK: Oxford University Press.Google Scholar
Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Oxford, UK: John Wiley.Google Scholar
Greenwell, B., Boehmke, B., Cunningham, J., & GBM Developers (2019). gbm: Generalized boosted regression models. R package version 2.1.5. https://CRAN.R-project.org/package=gbmGoogle Scholar
Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6, 430–450.Google Scholar
Guo, J., Klevan, M., & McAdams, D. P. (2016). Personality traits, ego development, and the redemptive self. Personality and Social Psychology Bulletin, 42, 1551–1563.Google Scholar
Guttman, L. (1945). A basis for analyzing test-retest reliability. Psychometrika, 10, 255–282.Google Scholar
Hamaker, E. L., Ceulemans, E., Grasman, R., & Tuerlinckx, F. (2015). Modeling affect dynamics: State of the art and future challenges. Emotion Review, 7, 316–322.Google Scholar
Hamaker, E. L., & Wichers, M. (2017). No time like the present. Current Directions in Psychological Science, 26, 10–15.Google Scholar
Harman, H. H. (1976). Modern factor analysis (3rd ed., rev.) Chicago, IL: University of Chicago Press.Google Scholar
Harman, H. H., & Jones, W. (1966). Factor analysis by minimizing residuals (minres). Psychometrika, 31, 351–368.Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.Google Scholar
Hathaway, S., & McKinley, J. (1943). Manual for administering and scoring the MMPI. Minneapolis, MN: University of Minnesota Press.Google Scholar
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.Google Scholar
Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17, 65–70.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 61–83.Google Scholar
Hofmann, R. J. (1978). Complexity and simplicity as objective indices descriptive of factor solutions. Multivariate Behavioral Research, 13, 247–250.Google Scholar
Holzinger, K., & Swineford, F. (1937). The bi-factor method. Psychometrika, 2, 41–54.Google Scholar
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.Google Scholar
Horn, J. L., & Engstrom, R. (1979). Cattell’s scree test in relation to Bartlett’s chi-square test and other observations on the number of factors problem. Multivariate Behavioral Research, 14, 283–300.Google Scholar
Isaacson, W. (2014). The innovators: How a group of inventors, hackers, geniuses and geeks created the digital revolution. London: Simon and Schuster.Google Scholar
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.Google Scholar
James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67, 219–229.Google Scholar
Jennrich, R. I. (1979). Admissible values of γ in direct oblimin rotation. Psychometrika, 44, 173–177.Google Scholar
Jokela, M., Bleidorn, W., Lamb, M. E., Gosling, S. D., & Rentfrow, P. J. (2015). Geographically varying associations between personality and life satisfaction in the London metropolitan area. Proceedings of the National Academy of Sciences, 112, 725–730.Google Scholar
Jöreskog, K. G. (1977). Structural equation models in the social sciences: Specification, estimation, and testing. In Krishnaish, P. R. (Ed.), Applications of statistics: Proceedings of the symposium held at Wright State University (pp. 265–287). Amsterdam: North Holland Publishing Company.Google Scholar
Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443–477.Google Scholar
Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70, 631–639.Google Scholar
Jöreskog, K. G., & Sorbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Lisrel 8: Lawrence Erlbaum.Google Scholar
Judd, C. M., & McClelland, G. H. (1989). Data analysis: A model-comparison approach. San Diego, CA: Harcourt Brace Jovanovich.Google Scholar
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200.Google Scholar
Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35, 401–415.Google Scholar
Kaiser, H. F., & Caffrey, J. (1965). Alpha factor analysis. Psychometrika, 30, 1–14.Google Scholar
Keesling, W. (1972). Maximum likelihood approaches to causal flow analysis. Ph.D. thesis, University of Chicago.Google Scholar
Kelley, K. (2017). MBESS: The MBESS R Package. R package version 4.4.1.Google Scholar
Kievit, R. A., & Epskamp, S. (2012). Simpsons: Detecting Simpson’s Paradox. R package version 0.1.0.Google Scholar
Kievit, R. A., Frankenhuis, W. E., Waldorp, L. J., Borsboom, D. (2013). Simpson’s paradox in psychological science: A practical guide. Frontiers in Psychology, 4, 1–14.Google Scholar
Kuder, G., & Richardson, M. (1937). The theory of the estimation of test reliability. Psychometrika, 2, 151–160.Google Scholar
Lawley, D. N., & Maxwell, A. E. (1962). Factor analysis as a statistical method. The Statistician, 12, 209–229.Google Scholar
Lawley, D. N., & Maxwell, A. E. (1963). Factor analysis as a statistical method. London: Butterworths.Google Scholar
Lee, T., MacCallum, R. C., & Browne, M. W. (2018). Fungible parameter estimates in structural equation modeling. Psychological Methods, 23, 58 –75.Google Scholar
Liaw, A., & Wiener, M. (2002). Classification and regression by randomforest. R news, 2, 18–22.Google Scholar
Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis (4th ed.). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Loehlin, J. C., & Beaujean, A. (2017). Latent variable models: An introduction to factor, path, and structural equation analysis (5th ed.). Mahwah, NJ: Routledge.Google Scholar
Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports Monograph Supplement, 9, 635–694.Google Scholar
Lovelace, A. A. (1842). Sketch of the analytical engine invented by Charles Babbage, by L. F. Menabrea, officer of the military engineers, with notes upon the memoir by the translator. Taylor’s Scientific Memoirs, 3, 666–731.Google Scholar
MacCallum, R. C., Browne, M. W., & Cai, L. (2007). Factor analysis models as approximations. In Cudeck, R. & MacCallum, R. C. (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 153–175). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185–199.Google Scholar
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum.Google Scholar
Mair, P., Schoenbrodt, F., & Wilcox, R. (2017). WRS2: Wilcox robust estimation and testing. R package 0.9-2.Google Scholar
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519–530.Google Scholar
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11, 320–341.Google Scholar
McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605.Google Scholar
McArdle, J. J., & Bell, R. Q. (2000). Recent trends in modeling longitudinal data by latent growth curve methods. In Little, T. D., Schnabel, K. U. & Baumert, J. (Eds.), Modeling longitudinal and multiple-group data: Practical issues, applied approaches, and scientific examples (pp. 69–107). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
McCrae, R. R., & Terracciano, A. (2008). The Five-Factor Model and its correlates in individuals and cultures. In Multilevel analysis of individuals and cultures (pp. 249–283). New YorkLawrence Erlbaum.Google Scholar
McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64–82.Google Scholar
Merkle, E. C., & Rosseel, Y. (2016). blavaan: Bayesian structural equation matrix models via parameter expansion. arXiv, 1511.05604.Google Scholar
Mulaik, S. A. (2009). Linear causal modeling with structural equations. Chapman & Hall/CRC statistics in the social and behavioral sciences series. Boca Raton: CRC Press.Google Scholar
Muthén, L., & Muthén, B. (2007). Mplus user’s guide. (5th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kickpatrick, R. M., … Boker, S. M. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81, 535–549.Google Scholar
Nesselroade, J. R., & Molenaar, P. C. M. (2016). Some behavioral science measurement concerns and proposals. Multivariate Behavioral Research, 51, 396–412.CrossRefGoogle ScholarPubMed
Neuhaus, J., & Wrigley, C. (1954). The quartimax method: An analytical approach to orthogonal simple structure. British Journal of Statistical Psychology, 7, 81–91.Google Scholar
Ozer, D. J. (2007). Evaluating effect size in personality research. In Robins, R. W., Fraley, R. C. & Krueger, R. F. (Eds.), Handbook of research methods in personality psychology (pp. 495–501). New York: Guilford Press.Google Scholar
Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society. London Series, LVIII, 240–242.Google Scholar
Pearson, K. (1896). Mathematical contributions to the theory of evolution. iii. regression, heredity, and panmixia. Philisopical Transactions of the Royal Society of London. Series A, 187, 254–318.Google Scholar
Pearson, K. (1920). Notes on the history of correlation. Biometrika, 13, 25–45.Google Scholar
Pearson, K., & Heron, D. (1913). On theories of association. Biometrika, 9, 159–315.Google Scholar
Pek, J., & Flora, D. B. (2018). Reporting effect sizes in original psychological research: A discussion and tutorial. Psychological Methods, 25, 208–225.Google Scholar
Pickering, A. D. (2008). Formal and computational models of reinforcement sensitivity theory. In Corr, P. J. (Ed.), The reinforcement sensivity theory (pp. 453–481). Cambridge, NY: Cambridge University Press.Google Scholar
Plato, (1892). The Republic: The complete and unabridged Jowett translation (3rd ed.). Oxford, UK: Oxford Univeristy Press.Google Scholar
Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825–852.Google Scholar
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227.Google Scholar
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2018 ). The virtual personalities neural network model: Neurobiological underpinnings. Personality Neuroscience, 1(e10), 1–11.CrossRefGoogle ScholarPubMed
Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra, G., & Miller, L. C. (2010). A neural network model of the structure and dynamics of human personality. Psychological Review, 117, 61–92.CrossRefGoogle ScholarPubMed
Read, S. J., Vanman, E. J., & Miller, L. C. (1997). Connectionism, parallel constraint satisfaction processes, and gestalt principles: (Re)Introducing cognitive dynamics to social psychology. Personality and Social Psychology Review, 1, 26–53.Google Scholar
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696.Google Scholar
Rentfrow, P. J. (Ed.) (2014). Geographical psychology: Exploring the interaction of environment and behavior. Washington, DC: American Psychological Association.Google Scholar
Rentfrow, P. J., Gosling, S. D., & Potter, J. (2008). A theory of the emergence, persistence, and expression of geographic variation in psychological characteristics. Perspectives on Psychological Science, 3, 339–369.Google Scholar
Rentfrow, P. J., & Jokela, M. (2016). Geographical psychology: The spatial organization of psychological phenomena. Current Directions in Psychological Science, 25, 393–398.Google Scholar
Revelle, W. (1986). Motivation and efficiency of cognitive performance. In Brown, D. R. & Veroff, J. (Eds.), Frontiers of motivational psychology: Essays in honor of J. W. Atkinson (pp. 105–131). New York: Springer.Google Scholar
Revelle, W. (2007). Experimental approaches to the study of personality. In Robins, R., Fraley, R. C. & Krueger, R. F. (Eds.), Handbook of research methods in personality psychology (pp. 37–61). New York: Guilford Press.Google Scholar
Revelle, W. (2018). psych: Procedures for personality and psychological research. Northwestern University, Evanston. https://CRAN.r-project.org/package=psych. R package version 1.8.12.Google Scholar
Revelle, W., Condon, D., Wilt, J., French, J. A., Brown, A. D., & Elleman, L. G. (2016). Web and phone based data collection using planned missing designs. In Nigel, G. B., Fielding, G. & Lee, Raymond M. (Eds.), The Sage handbook of online research methods (2nd ed., pp. 578–595). London: Sage.Google Scholar
Revelle, W., & Condon, D. M. (2015). A model for personality at three levels. Journal of Research in Personality, 56, 70–81.Google Scholar
Revelle, W., & Condon, D. M. (2018a). Reliability. In Irwing, P., Booth, T. & Hughes, D. J. (Eds.), The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale and test development. London: Wiley.Google Scholar
Revelle, W., & Condon, D. M. (2019). Reliability from α to ω: A tutorial. Psychological Assessment, 31(12), 1395–1411.Google Scholar
Revelle, W., & Rocklin, T. (1979). Very Simple Structure: Alternative procedure for estimating the optimal number of interpretable factors. Multivariate Behavioral Research, 14, 403–414.Google Scholar
Revelle, W., & Wilt, J. (2016). The data box and within subject analyses: A comment on Nesselroade and Molenaar. Multivariate Behavioral Research, 51, 419–421.Google Scholar
Revelle, W., & Wilt, J. A. (2019). Analyzing dynamic data: A tutorial. Personality and Individual Differences, 136, 38–51.Google Scholar
Revelle, W., & Zinbarg, R. E. (2009). Coefficients alpha, beta, omega and the glb: Comments on Sijtsma. Psychometrika, 74, 145–154.Google Scholar
Rindskopf, D., & Rose, T. (1988). Some theory and applications of confirmatory second-order factor analysis. Multivariate Behavioral Research, 23, 51–67.Google Scholar
Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357.Google Scholar
Rocklin, T., & Revelle, W. (1981). The measurement of extraversion: A comparison of the Eysenck Personality Inventory and the Eysenck Personality Questionnaire. British Journal of Social Psychology, 20, 279–284.Google Scholar
Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 1–12.Google Scholar
Rosenthal, R. (1994). Parametric measures of effect size. In Cooper, H. & Hedges, L. V. (Eds.), The handbook of research synthesis (pp. 231–244). New York: Russell Sage Foundation.Google Scholar
Rosenthal, R., & Rubin, D. B. (1982). A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74, 166–169.Google Scholar
Rosnow, R. L., & Rosenthal, R. (2003). Effect sizes for experimenting psychologists. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 57, 221–237.Google Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.Google Scholar
semTools Contributors (2016). semTools: Useful tools for structural equation modeling. R package version 0.4-13.Google Scholar
Shapiro, A., & ten Berge, J. M. (2002). Statistical inference of minimum rank factor analysis. Psychometrika, 67, 79–94.Google Scholar
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86, 420–428.Google Scholar
Shrout, P. E., & Lane, S. P. (2012). Psychometrics. In Mehl, M. R. & Conner, T. S. (Eds.), Handbook of research methods for studying daily life (pp. 302–320). New York: Guilford Press.Google Scholar
Simpson, E. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society. Series B (Methodological), 13, 238–241.Google Scholar
Spearman, C. (1904a). “General Intelligence,” objectively determined and measured. American Journal of Psychology, 15, 201–292.Google Scholar
Spearman, C. (1904b). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.Google Scholar
Spearman, C. (1910). Correlation calculated from faulty data. British Journal of Psychology, 3, 271–295.Google Scholar
Spearman, C. (1927). The abilities of man. Oxford, UK: Macmillan.Google Scholar
SPSS (2008). Version 17.0. Chicago: SPSS Inc.Google Scholar
Streiner, D. L. (2003). Unicorns Do exist: A tutorial on “proving” the null hypothesis. The Canadian Journal of Psychiatry, 48, 756–761.Google Scholar
Strong, E. K. (1927). Vocational interest test. Educational Record, 8, 107–121.Google Scholar
Student (1908). The probable error of a mean. Biometrika, 6, 1–25.Google Scholar
Tal-Or, N., Cohen, J., Tsfati, Y., & Gunther, A. C. (2010). Testing causal direction in the influence of presumed media influence. Communication Research, 37, 801–824.Google Scholar
Tarka, P. (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52, 313–354.Google Scholar
Taylor, H. C., & Russell, J. T. (1939). The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables. Journal of Applied Psychology, 23, 565–578.Google Scholar
Therneau, T., & Atkinson, B. (2018). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-13.Google Scholar
Thurstone, L. L. (1933). The theory of multiple factors. Ann Arbor, MI: Edwards Brothers.Google Scholar
Thurstone, L. L. (1934). The vectors of mind. Psychological Review, 41, 1–32.Google Scholar
Thurstone, L. L. (1935). The vectors of mind: Multiple-factor analysis for the isolation of primary traits. Chicago, IL: University of Chicago Press.Google Scholar
Thurstone, L. L. (1947). Multiple-factor analysis: A development and expansion of the vectors of the mind. Chicago, IL: University of Chicago Press.Google Scholar
Thurstone, L. L., & Thurstone, T. G. (1941). Factorial studies of intelligence. Chicago, IL: University of Chicago Press.Google Scholar
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). mediation: R package for causal mediation analysis. Journal of Statistical Software, 59, 1–38.Google Scholar
Tukey, J. W. (1958). Bias and confidence in not-quite large samples (preliminary report) (abstract). Annals of Mathematical Statististics, 29, 614.Google Scholar
Velicer, W. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.Google Scholar
Wainer, H. (1976). Estimating coefficients in linear models: It don’t make no nevermind. Psychological Bulletin, 83, 213–217.Google Scholar
Waller, N. G. (2008). Fungible weights in multiple regression. Psychometrika, 73, 691–703.Google Scholar
Waller, N. G., & Jones, J. A. (2010). Correlation weights in multiple regression. Psychometrika, 75, 58–69.Google Scholar
Wiggins, J. S. (1973). Personality and prediction: Principles of personality assessment. Reading, MA: Addison-Wesley.Google Scholar
Wilcox, R. R. (2001). Modern insights about Pearson’s correlation and least squares regression. International Journal of Selection and Assessment, 9, 195–205.Google Scholar
Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing. Statistical modeling and decision science (2nd ed.). Amsterdam/Boston: Elsevier.Google Scholar
Wilcox, R. R., & Keselman, H. J. (2003). Modern robust data analysis methods: Measures of central tendency. Psychological Methods, 8, 254–274.CrossRefGoogle ScholarPubMed
Wiley, D. E. (1973). The identification problem for structural equation models with unmeasured variables. Structural equation models in the social sciences (pp. 69–83). New York: Seminar Press.Google Scholar
Wilt, J., Bleidorn, W., & Revelle, W. (2016). Finding a life worth living: Meaning in life and graduation from college. European Journal of Personality, 30, 158–167.Google Scholar
Wilt, J., Bleidorn, W., & Revelle, W. (2017). Velocity explains the links between personality states and affect. Journal of Research in Personality, 69, 86–95.Google Scholar
Wilt, J., Funkhouser, K., & Revelle, W. (2011). The dynamic relationships of affective synchrony to perceptions of situations. Journal of Research in Personality, 45, 309–321.Google Scholar
Wilt, J., & Revelle, W. (2019). The Big Five, situational context, and affective experience. Personality and Individual Differences, 136, 140–147.Google Scholar
Wothke, W. (1993). Nonpositive definite matrices in structural modeling. In Bollen, K. A. & Long, J. S. (Eds.), Testing structural equation models (pp. 256–293). Newbury Park, CA: Sage.Google Scholar
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences, 6, 320–332.Google Scholar
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar
Yang, Y., Read, S. J., Denson, T. F., Xu, Y., Zhang, J., & Pedersen, W. C. (2014). The key ingredients of personality traits: Situations, behaviors, and explanations. Personality and Social Psychology Bulletin, 40, 79–91.Google Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122.Google Scholar
Yates, A. (1988). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. Albany, NY: Suny Press.Google Scholar
Yule, G. U. (1903). Notes on the theory of association of attributes in statistics. Biometrika, 2, 121–134.Google Scholar
Yule, G. U. (1912). On the methods of measuring association between two attributes. Journal of the Royal Statistical Society, LXXV, 579–652.Google Scholar
Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s α, Revelle’s β, and McDonald’s ωH : Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×