Mixture modeling methods for the assessment of normal and abnormal personality, Part I: Cross-sectional models

Journal of Personality Assessment

Volumn 96, Issue 3, 2014, Pages 256-268

  Hallquist, Michael N ^a   Wright, Aidan G C ^b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION; CROSS-SECTIONAL STUDY; FACTORIAL ANALYSIS; HUMAN; PERSONALITY; PERSONALITY ASSESSMENT; PSYCHOLOGICAL MODEL; STANDARDS; STATISTICAL MODEL;

CROSS-SECTIONAL STUDIES; FACTOR ANALYSIS, STATISTICAL; HUMANS; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; PERSONALITY; PERSONALITY ASSESSMENT;

EID: 84897984520     PISSN: 00223891     EISSN: None     Source Type: Journal    
DOI: 10.1080/00223891.2013.845201     Document Type: Article

References (92)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
2. Allport, G.W. (1927). Concepts of trait and personality. Psychological Bulletin, 24, 284-293.
3. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., revised.). Washington, DC: Author.
4. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington DC: Author.
5. Beck, S. J. (1953). The science of personality: Nomothetic or idiographic? Psychological Review, 60, 353.
6. Bem, D. J. (1983). Constructing a theory of the triple typology: Some (second) thoughts on nomothetic and idiographic approaches to personality. Journal of Personality, 51, 566-577.
7. Blair, R. J. R. (2001). Neurocognitive models of aggression, the antisocial personality disorders, and psychopathy. Journal of Neurology, Neurosurgery & Psychiatry, 71, 727-731.
8. Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-related behaviors: A meta-analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130, 887-919. doi:10.1037/0033-2909.130. 6.887
9. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
10. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford.
11. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi-model inference: A practical information-theoretic approach (2nd ed.). New York, NY: Springer.
12. Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Erlbaum.
13. Byrne, B. M., Shavelson, R. J., & Muth́en, B. O. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456-466.
14. Caspi, A., Roberts, B. W., & Shiner, R. L. (2005). Personality development: Stability and change. Annual Review of Psychology, 56, 453-484.
15. Cattell, R. B. (1946). Description and measurement of personality. Oxford, UK: World Book.
16. Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195-212.
17. Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging. New York, NY: Cambridge University Press.
18. Clark, L. A., & Livesley, W. J. (2002). Two approaches to identifying the dimensions of personality disorder: Convergence on the five-factor model. In P. T. Costa & T. A. Widiger (Eds.), Personality disorders and the Five-factor model of personality (2nd ed., pp. 161-176). Washington, DC: American Psychological Association.
19. Clark, L. A., Simms, L. J., Wu, K. D., & Cassilas, A. (in press). Schedule for Nonadaptive and Adaptive Personality: Manual for administration, scoring, and interpretation (2nd ed.). Minneapolis: University of Minnesota Press.
20. Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7, 309-319.
21. Clark, S. L., Muth́en, B. O., Kaprio, J., D'Onofrio, B. M., Viken, R., & Rose, R. J. (2009). Models and strategies for factor mixture analysis: Two examples concerning the structure underlying psychological disorders. Manuscript submitted for publication.
22. Cloninger, C. R., Przybeck, T. R., & Svrakic, D. M. (1994). The Temperament and Character Inventory (TCI): A guide to its development and use. St. Louis, MO: Center for Psychobiology of Personality.
23. Costa, P. T., & McCrae, R. R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources.
24. De Boeck, P.,Wilson, M., & Acton, G. S. (2005). A conceptual and psychometric framework for distinguishing categories and dimensions. Psychological Review, 112, 129-158. doi:10.1037/0033-295X.112.1.129
25. Eaton, N. R., Krueger, R. F., Docherty, A. R., & Sponheim, S. R. (this issue). Toward a model-based approach to the clinical assessment of personality psychopathology. Journal of Personality Assessment.
26. Eaton, N. R., Krueger, R. F., South, S. C., Simms, L. J., & Clark, L. A. (2011). Contrasting prototypes and dimensions in the classification of personality pathology: Evidence that dimensions, but not prototypes, are robust. Psychological Medicine, 41, 1151-1163. doi:10.1017/S0033291710001650
27. Eaves, L. J., Eysenck, H. J., Martin, N. G., Jardine, R., Heath, A. C., Feingold, L.,... Kendler, K. S. (1989). Genes, culture and personality: An empirical approach. London, UK: Cambridge University Press.
28. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
29. Eysenck, H. J. (1947). Dimensions of personality. New Brunswick, NJ: Transaction.
30. Eysenck, H. J., & Eysenck, S. B. G. (1976). Psychoticism as a dimension of personality. New York, NY: Crane, Russak, & Company.
31. Feng, Z. D., & McCulloch, C. E. (1996). Using bootstrap likelihood ratios in finite mixture models. Journal of the Royal Statistical Society: Series B (Methodological), 58, 609-617.
32. Fleeson, W. (2001). Toward a structure-and process-integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80, 1011-1027. doi:10.1037//0022-3514.80.6.1011
33. Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied latent class analysis. London, UK: Cambridge University Press.
34. Hallquist, M. N., & Pilkonis, P. A. (2010). Quantitative methods in psychiatric classification: The path forward is clear but complex:Commentary onKrueger and Eaton (2010). Personality Disorders: Theory, Research, and Treatment, 1, 131-134. doi:10.1037/a0020201
35. Hallquist,M. N.,&Pilkonis, P. A. (2012). Refining the phenotype for borderline personality disorder: Diagnostic criteria and beyond. Personality Disorders: Theory, Research, and Treatment, 3, 228-246.
36. Hopwood, C. J., & Donnellan, M. B. (2010). How should the internal structure of personality inventories be evaluated? Personality and Social Psychology Review: An Official. Journal of the Society for Personality and Social Psychology, Inc, 14, 332-346. doi:10.1177/1088868310361240
37. Humphreys, M. S., & Revelle, W. (1984). Personality, motivation, and performance: A theory of the relationship between individual differences and information processing. Psychological Review, 91, 153-184. doi:10.1037/0033- 295X.91.2.153
38. Hurvich,C.M.,&Tsai, C.-L. 1989). Regression and time series model selection in small samples. Biometrika 76, 297-307, doi:10.1093/biomet/76.2.297.
39. Hurvich, C. M., & Tsai, C. L. (1991). Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika, 78, 499-509.
40. Krueger, R. F., & Markon, K. E. (2006). Reinterpreting comorbidity: A modelbased approach to understanding and classifying psychopathology. Annual Review of Clinical Psychology, 2, 111-133.
41. Lahey, B. B. (2009). Public health significance of neuroticism. The American Psychologist, 64, 241-256.
42. Langbehn, D. R., Pfohl, B.M., Reynolds, S., Clark, L. A., Battaglia, M., Bellodi, L.,... Links, P. (1999). The Iowa Personality Disorder Screen: Development and preliminary validation of a brief screening interview. Journal of Personality Disorders, 13, 75-89.
43. Leary, T. (1957). Interpersonal diagnosis of personality. New York, NY: Ronald Press.
44. Lenzenweger, M. F., Clarkin, J. F., Yeomans, F. E., Kernberg, O. F., & Levy, K. N. (2008). Refining the phenotype of borderline personality disorder using finite mixturemodeling: Implications for classification. Journal of Personality Disorders, 22, 313-331.
45. Livesley,W. J.,&Jackson, D. N. (2009). Dimensional Assessment of Personality Pathology-Basic questionnaire. Port Huron, MI: Research Psychologists Press.
46. Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports, 3, 635-694.
47. Lubke, G. H., Dolan, C. V., Kelderman, H., & Mellenbergh, G. J. (2003).Weak measurement invariance with respect to unmeasured variables:An implication of strict factorial invariance. The British Journal of Mathematical and Statistical Psychology, 56(2), 231-248. doi:10.1348/000711003770480020
48. Lubke, G. H., & Muth́en, B. O. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21-39.
49. Lubke, G. H., Muth́en, B. O., Moilanen, I. K., McGough, J. J., Loo, S. K., Swanson, J. M.,... Smalley, S. L. (2007). Subtypes versus severity differences in attention-deficit/hyperactivity disorder in the Northern Finnish Birth Cohort. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1584-1593.
50. Lubke, G. H., & Neale, M. C. (2008). Distinguishing between latent classes and continuous factors with categorical outcomes: Class invariance of parameters of factor mixture models. Multivariate Behavioral Research, 43, 592-620.
51. Markon, K. E.,&Krueger, R. F. (2006). Information-theoretic latent distribution modeling: Distinguishing discrete and continuous latent variable models. Psychological Methods, 11, 228-243.
52. Markon, K. E., Krueger, R. F., & Watson, D. (2005). Delineating the structure of normal and abnormal personality: An integrative hierarchical approach. Journal of Personality and Social Psychology, 88, 139-157.
53. Masyn, K., Henderson, C., & Greenbaum, P. (2010). Exploring the latent structures of psychological constructs in social development using the dimensional-categorical spectrum. Social Development, 19, 470-493. doi:http://dx.doi.org/10.1111/j.1467-9507.2009.00573.x
54. McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model for moment structures. British Journal of Mathematical and Statistical Psychology, 37, 234-251. doi:10.1111/j.2044-8317. 1984.tb00802.x
55. McCrae, R. R., Costa, P. T., Jr., Del Pilar, G. H., Rolland, J. P., & Parker, W. D. (1998). Cross-cultural assessment of the five-factor model: The revised NEO Personality Inventory. Journal of Cross-Cultural Psychology, 29, 171-188.
56. McLachlan, G. J. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Journal of the Royal Statistical Society: Series C (Applied Statistics), 36, 318-324. doi:10.2307/2347790
57. McLachlan, G. J., Do, K.-A., & Ambroise, C. (2004). Analyzing microarray gene expression data. New York, NY: Wiley-Interscience.
58. McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.
59. Meehl, P. E., &Waller, N. G. (2002). The path analysis controversy: A new statistical approach to strong appraisal of verisimilitude. PsychologicalMethods, 7, 283-300.
60. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
61. Mischel,W. (2004). Toward an integrative science of the person. Annual Review of Psychology, 55, 1-22.
62. Mischel,W.,&Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102, 246.
63. Morey, L. C., Berghuis, H., Bender, D. S., Verheul, R., Krueger, R. F., & Skodol, A. E. (2011). Toward a model for assessing level of personality functioning in DSM-5, part II: Empirical articulation of a core dimension of personality pathology. Journal of Personality Assessment, 93, 347-353. doi:10.1080/00223891.2011.577853
64. Morse, J. Q., & Pilkonis, P. A. (2007). Screening for personality disorders. Journal of Personality Disorders, 21, 179-198.
65. Muth́en, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132.
66. Muth́en, B.O. (2008). Latent variable hybrids: Overviewof old and newmodels. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1-24). Charlotte, NC: Information Age.
67. Muth́en, B. O., & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050-1066.
68. Ng, S. K., & McLachlan, G. J. (2003). An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data. Statistics in Medicine, 22, 1097-1111. doi:10.1002/sim.1371
69. Ning, Y., & Finch, S. J. (2004). The likelihood ratio test with the Box-Cox transformation for the normalmixture problem: Power and sample size study. Communications in Statistics: Simulation and Computation, 33, 553-565. doi:10.1081/SAC-200033328
70. Nunnally, J. C., & Bernstein, I. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill Humanities/Social Sciences/Languages.
71. Nylund, K. L., Asparouhov, T., & Muth́en, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535-569.
72. Pilkonis, P. A., Hallquist, M. N., Morse, J. Q., & Stepp, S. D. (2011). Striking the (im)proper balance between scientific advances and clinical utility: Commentary on the DSM-5 proposal for personality disorders. Personality Disorders: Theory, Research, and Treatment, 2, 68-82.
73. Pilkonis, P. A., Kim, Y., Proietti, J. M., & Barkham, M. (1996). Scales for personality disorders developed from the Inventory of Interpersonal Problems. Journal of Personality Disorders, 10, 355-369.
74. Qu, Y., Tan, M., & Kutner, M. H. (1996). Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics, 52, 797-810. doi:10.2307/2533043
75. Samuel, D. B., Simms, L. J., Clark, L. A., Livesley, W. J., & Widiger, T. A. (2010). An item response theory integration of normal and abnormal personality scales. Personality Disorders, 1, 5-21. doi:10.1037/a 0018136
76. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
77. Shevlin, M., & Elklit, A. (2012). The latent structure of posttraumatic stress disorder: Different models or different populations? Journal of Abnormal Psychology, 121, 610-615. doi:10.1037/a0028591
78. Skodol, A. E., Clark, L. A., Bender, D. S., Krueger, R. F., Morey, L. C., Verheul, R.,... Oldham, J. M. (2011). Proposed changes in personality and personality disorder assessment and diagnosis for DSM-5, Part I: Description and rationale. Personality Disorders: Theory, Research, and Treatment, 2, 4-22. doi:10.1037/a0021891
79. Stepp, S. D., Yu, L., Miller, J. D., Hallquist,M. N., Trull, T. J., & Pilkonis, P. A. (2012). Integrating competing dimensional models of personality: Linking the SNAP, TCI, and NEO using item response theory. Personality Disorders: Theory, Research, and Treatment, 3, 107-126.
80. Sugiura, N. (1978). Further analysis of the data by Akaike's Information Criterion and the finite corrections. Communications in Statistics, Theory, and Methods, A7, 13-26.
81. Teh, Y. W., Seeger, M., & Jordan, M. I. (2005). Semiparametric latent factor models. In Workshop on artificial intelligence and statistics (Vol. 10, pp. 333-340). Presented at the Tenth International Workshop on Artificial Intelligence and Statistics, Barbados. Retrieved from http://www.gatsby. ucl.ac.uk/aistats/aistats2005 eproc.pdf
82. Tellegen, A., & Waller, N. G. (2008). Exploring personality through test construction: Development of the Multidimensional Personality Questionnaire. In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The Sage handbook of personality theory and assessment: Vol. II. Personality measurement and testing (pp. 261-292). London, UK: Sage.
83. Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17, 228-243. doi:10.1037/a0027127
84. Walton, K. E., Roberts, B. W., Krueger, R. F., Blonigen, D. M., & Hicks, B. M. (2008). Capturing abnormal personality with normal personality inventories: An item response theory approach. Journal of Personality, 76, 1623-1647.
85. Wang, C. P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models. Journal of the American Statistical Association, 100, 1054-1076.
86. Whiteside, S. P., & Lynam, D. R. (2001). The Five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669-689. doi:10.1016/S0191-8869(00) 00064-7
87. Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281-324). Washington, DC: American Psychological Association.
88. Widiger, T. A., & Simonsen, E. (2005). Alternative dimensional models of personality disorder: Finding a common ground. Journal of Personality Disorders, 19, 110-130.
89. Wright, A. G. C., Hallquist, M. N., Morse, J. Q., Scott, L. N., Stepp, S. D., Nolf, K. A., & Pilkonis, P. A. (2013). Clarifying interpersonal heterogeneity in borderline personality disorder using latent mixture modeling. Journal of Personality Disorders, 27, 125-143.
90. Yang, J., McCrae, R. R., Costa, P. T., Jr., Dai, X., Yao, S., Cai, T., & Gao, B. (1999). Cross-cultural personality assessment in psychiatric populations: The NEO-PI-R in the People's Republic of China. Psychological Assessment, 11, 359-368. doi:10.1037/1040-3590.11.3.359
91. Yang,Y. (2005).Can the strengths of AIC and BIC be shared?Aconflict between model identification and regression estimation. Biometrika, 92, 937-950. doi:10.1093/biomet/92.4.937
92. Zinbarg, R. E., Yovel, I., Revelle, W., & McDonald, R. P. (2006). Estimating generalizability to a latent variable common to all of a scale's indicators: A comparison of estimators for ωh. Applied Psychological Measurement, 30, 121-144. doi:10.1177/0146621605278814