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Contrasting prototypes and dimensions in the classification of personality pathology: evidence that dimensions, but not prototypes, are robust

Published online by Cambridge University Press:  22 September 2010

N. R. Eaton ,
R. F. Krueger ,
S. C. South ,
L. J. Simms  and
L. A. Clark
N. R. Eaton*
Affiliation:
University of Minnesota, Minneapolis, MN, USA
R. F. Krueger
Affiliation:
University of Minnesota, Minneapolis, MN, USA
S. C. South
Affiliation:
Purdue University, West Lafayette, IN, USA
L. J. Simms
Affiliation:
University at Buffalo, The State University of New York, Buffalo, NY, USA
L. A. Clark
Affiliation:
University of Notre Dame, Notre Dame, IN, USA
*
*Address for correspondence: N. R. Eaton, M.A., Department of Psychology, 75 East River Road, University of Minnesota, Minneapolis, MN 55455-0344, USA. (Email: nreaton@gmail.com)
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Abstract

Background

DSM-5 may mark the shift from a categorical classification of personality pathology to a dimensional system. Although dimensional and categorical conceptualizations of personality pathology are often viewed as competing, it is possible to develop categories (prototypes) from combinations of dimensions. Robust prototypes could bridge dimensions and categories within a single classification system.

Method

To explore prototype structure and robustness, we used finite mixture modeling to identify empirically derived personality pathology prototypes within a large sample (n=8690) of individuals from four settings (clinical, college, community, and military), assessed using a dimensional measure of normal and abnormal personality traits, the Schedule for Nonadaptive and Adaptive Personality (SNAP). We then examined patterns of convergent and discriminant external validity for prototypes. Finally, we investigated the robustness of the dimensional structure of personality pathology.

Results

The resulting prototypes were meaningful (externally valid) but non-robust (sample dependent). By contrast, factor analysis revealed that the dimensional structures underlying specific traits were highly robust across samples.

Conclusions

We interpret these results as further evidence of the fundamentally dimensional nature of an empirically based classification of personality pathology.

Keywords

Dimensionspersonalitypersonality disorderpersonality pathologyprototypes
Type
Original Articles
Information
Psychological Medicine , Volume 41 , Issue 6 , June 2011 , pp. 1151 - 1163
Copyright
Copyright © Cambridge University Press 2010

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References

Abdi, H (2007). RV coefficient and congruence coefficient. In Encyclopedia of Measurement and Statistics (ed. Salkind, N.), pp. 849–853. Sage: Thousand Oaks, CA.Google Scholar
Andershed, H, Köhler, D, Louden, JE, Hinrichs, G (2008). Does the three-factor model of psychopathy identify a problematic subgroup of young offenders? International Journal of Law and Psychiatry 31, 189–198.Google Scholar
APA (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association: Washington, DC.Google Scholar
Asendorpf, JB, Borkenau, P, Ostendorf, F, Van Aken, MAG (2001). Carving personality description at its joints: confirmation of three replicable personality prototypes for both children and adults. European Journal of Personality 15, 169–198.Google Scholar
Ashton, MC, Lee, K (2009). An investigation of personality types within the HEXACO personality framework. Journal of Individual Differences 30, 181–187.Google Scholar
Block, J (1971). Lives Through Time. Bancroft Books: Berkeley, CA.Google Scholar
Brown, TA (2006). Confirmatory Factor Analysis for Applied Research. Guilford Press: New York.Google Scholar
Browne, MW (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research 36, 111–150.Google Scholar
Burt, C (1948). Factor analysis and canonical correlations. British Journal of Psychology 1, 95–106.Google Scholar
Casillas, A, Clark, LA (2002) Dependency, impulsivity, and self-harm: traits hypothesized to underlie the association between Cluster B personality and substance abuse disorders. Journal of Personality Disorders 16, 424–436.CrossRefGoogle Scholar
Clark, LA (1993). Schedule for Nonadaptive and Adaptive Personality (SNAP): Manual for Administration, Scoring, and Interpretation. University of Minnesota Press: Minneapolis, MN.Google Scholar
Clark, LA (2007). Assessment and diagnosis of personality disorder: perennial issues and an emerging reconceptualization. Annual Review of Psychology 58, 227–257.CrossRefGoogle Scholar
Clark, LA, Simms, LJ, Wu, KD, Casillas, A (in press). Schedule for Nonadaptive and Adaptive Personality – Second Edition (SNAP-2). University of Minnesota Press: Minneapolis, MN.Google Scholar
Clark, LA, Vittengl, J, Kraft, D, Jarrett, RJ (2003). Separate personality traits from states to predict depression. Journal of Personality Disorders 17, 152–172.Google Scholar
Costa, PT Jr., McCrae, RR (1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual. Psychological Assessment Resources: Odessa, FL.Google Scholar
Crocker, L, Algina, J (2006). Introduction to Classical and Modern Test Theory. Thomson Custom Solutions: Mason, OH.Google Scholar
De Fruyt, F, Mervielde, I, Van Leeuwen, K (2002). The consistency of personality type classification across samples and five-factor measures. European Journal of Personality 16, S57–S72.Google Scholar
Eaton, NR, South, SC, Krueger, RF (2010). The meaning of comorbidity among common mental disorders. In Contemporary Directions in Psychopathology, 2nd edn (ed. Millon, T., Krueger, R. and Simonsen, E.), pp. 223–241. Guilford Press: New York.Google Scholar
Falkenbach, D, Poythress, N, Creevy, C (2008). The exploration of subclinical psychopathic subtypes and the relationship with types of aggression. Personality and Individual Differences 44, 821–832.CrossRefGoogle Scholar
Fiedler, ER, Oltmanns, TF, Turkheimer, E (2004). Traits associated with personality disorders and adjustment to military life: predictive validity of self and peer reports. Military Medicine 169, 207–211.Google Scholar
Fraley, C, Raftery, AE (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal 41, 578–588.Google Scholar
Fraley, C, Raftery, AE (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97, 611–631.CrossRefGoogle Scholar
Fraley, C, Raftery, A (2008). MCLUST: Multivariate Normal Mixture Modeling and Model-Based Clustering, version 3.1-1 (www.stat.washington.edu/mclust). Accessed 12 March 2010.Google Scholar
Frances, A (1993). Dimensional diagnosis of personality: not whether, but when and which. Psychological Inquiry 4, 110–111.CrossRefGoogle Scholar
Harlan, E, Clark, LA (1999). Short-forms of the Schedule for Nonadaptive and Adaptive Personality (SNAP) for self and collateral ratings: development, reliability, and validity. Assessment 6, 131–146.CrossRefGoogle ScholarPubMed
Harkness, AR, McNulty, JL (1994). The Personality Psychopathology Five (PSY-5): issue from the pages of a diagnostic manual instead of a dictionary. In Differentiating Normal and Abnormal Personality (ed. Strack, S. and Lorr, M.), pp. 291–315. Springer: New York.Google Scholar
Harkness, AR, McNulty, JL, Ben-Porath, YS (1995). The Personality Psychopathology Five (PSY-5): constructs and MMPI-2 scales. Psychological Assessment 7, 104–114.CrossRefGoogle Scholar
Hettema, JM, Neale, MC, Myers, JM, Prescott, CA, Kendler, KS (2006). A population-based twin study of the relationship between neuroticism and internalizing disorders. American Journal of Psychiatry 163, 857–864.Google Scholar
Hicks, BM, Markon, KE, Patrick, CJ, Krueger, RF, Newman, JP (2004). Identifying psychopathy subtypes on the basis of personality structure. Psychological Assessment 16, 276–288.Google Scholar
John, OP, Robins, RW (1994). Traits and types, dynamics and development: no doors should be closed in the study of personality. Psychological Inquiry 5, 137–142.Google Scholar
Kamphuis, JH, Noordhof, A (2009). On categorical diagnoses in DSM-V: cutting dimensions at useful points? Psychological Assessment 21, 294–301.Google Scholar
Kendell, R, Jablensky, A (2003). Distinguishing between the validity and utility of psychiatry diagnoses. American Journal of Psychiatry 160, 4–12.Google Scholar
Krueger, RF, Eaton, NR, South, SC, Clark, LA, Simms, LJ (in press). Dimensions, prototypes, and disorders: conceptualizing personality and personality disorders for DSM-V. In Evolution of the DSM-V Conceptual Framework: Development, Dimensions, Disability, Spectra, and Gender/Culture (ed. Regier, D. A., Narrow, W. E., Kuhl, E. A. and Kupfer, D. J.). American Psychiatric Association: Arlington, VA.Google Scholar
Krueger, RF, Skodol, AE, Livesley, WJ, Shrout, P, Huang, Y (2007). Synthesizing dimensional and categorical approaches to personality disorders: refining the research agenda for DSM-V Axis II. International Journal of Methods in Psychiatric Research 16, S65–S73.Google Scholar
Lahey, BB (2009). Public health significance of neuroticism. American Psychologist 64, 241–256.Google Scholar
Lenzenweger, MF, Clarkin, JF, Yeomans, FE, Kernberg, OF, Levy, KN (2008). Refining the borderline personality disorder phenotype through finite mixture modeling: implications for classification. Journal of Personality Disorders 22, 313–331.Google Scholar
Lenzenweger, MF, McLachlan, G, Rubin, DB (2007). Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. Journal of Abnormal Psychology 116, 16–29.CrossRefGoogle ScholarPubMed
Lubke, G, Muthén, B (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling 14, 26–47.Google Scholar
McCrae, RR, Terracciano, A, Costa, PT Jr., Ozer, DJ (2006). Person-factors in the California Adult Q-Set: closing the door on personality trait types? European Journal of Personality 20, 29–44.Google Scholar
McLachlan, G, Peel, D (2000). Finite Mixture Models. John Wiley & Sons, Inc.: New York.CrossRefGoogle Scholar
Morey, LC, Hopwood, CJ, Gunderson, JG, Skodol, AE, Shea, MT, Yen, S, Stout, RL, Zanarini, MC, Grilo, CM, Sanislow, CA, McGlashan, TH (2007). Comparison of alternative models for personality disorder. Psychological Medicine 37, 983–994.Google Scholar
Morey, LC, Warner, MB, Shea, MT, Gunderson, JG, Sanislow, CA, Grilo, C, Skodol, AE, McGlashan, TH (2003). The representation of four personality disorders by the Schedule for Nonadaptive and Adaptive Personality dimensional model of personality. Psychological Assessment 15, 326–332.CrossRefGoogle ScholarPubMed
Mun, EY, von Eye, A, Bates, ME, Vaschillo, EG (2008 a). Finding groups using model-based cluster analysis: heterogeneous emotional self-regulatory processes and heavy alcohol use risk. Developmental Psychopathology 44, 481–495.Google Scholar
Mun, EY, Windle, M, Schainker, LM (2008 b). A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes. Development and Psychopathology 20, 291–318.CrossRefGoogle ScholarPubMed
Muthén, B (2006). Should substance use disorders be considered as categorical or dimensional? Addiction 101 (Suppl. 1), 6–16.Google Scholar
Muthén, LK, Muthén, BO (2009). Mplus User's Guide, 5th edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
Oltmanns, TF, Turkheimer, E (2006). Perceptions of self and others regarding pathological personality traits. In Personality and Psychopathology (ed. Krueger, R. F. and Tackett, J. L.), pp. 71–111. Guilford Press: New York.Google Scholar
Raftery, AE (1995). Bayesian model selection in social research. Sociological Methodology 25, 111–163.CrossRefGoogle Scholar
Raftery, AE, Dean, N (2006). Variable selection for model-based clustering. Journal of the American Statistical Association 101, 168–178.CrossRefGoogle Scholar
Ready, RE, Clark, LA (2002). Correspondence of psychiatric patient and informant ratings of personality traits, temperament, and interpersonal problems. Psychological Assessment 14, 39–49.CrossRefGoogle ScholarPubMed
Ready, RE, Clark, LA, Watson, D, Westerhouse, K (2000). Self- and peer-reported personality: agreement, trait ratability, and the ‘self-based heuristic’. Journal of Research in Personality 34, 208–244.Google Scholar
Ready, RE, Stierman, L, Paulsen, JS (2001). Ecological validity of neuropsychological and personality measures of executive function. Clinical Neuropsychologist 15, 314–323.Google Scholar
Reynolds, SK, Clark, LA (2001). Predicting personality disorder dimensions from domains and facets of the five-factor model. Journal of Personality 69, 199–222.CrossRefGoogle ScholarPubMed
Robins, RW, John, OP, Caspi, A, Moffitt, TE, Stouthamer Loeber, M (1996). Resilient, overcontrolled, and undercontrolled boys: three replicable personality types. Journal of Personality and Social Psychology 70, 157–171.Google Scholar
Samuel, DB, Widiger, TA (2008). A meta-analytic review of the relationships between the five-factor model and DSM-IV-TR personality disorders: a facet level analysis. Clinical Psychology Review 28, 1326–1342.CrossRefGoogle ScholarPubMed
Simms, LJ, Casillas, A, Clark, LA, Watson, D, Doebbeling, D (2005). Psychometric evaluation of the Restructured Clinical scales of the MMPI-2. Psychological Assessment 17, 345–358.Google Scholar
Simms, LJ, Turkheimer, E, Clark, LA (2007). Novel approaches to the structure of personality disorder. In Symposium III, 21st Annual Meeting of the Society for Research in Psychopathology, Iowa City, IA (www.psychopathology.org/programs_archives_detail.cgi?abstract=187).Google Scholar
Skeem, J, Johansson, P, Andershed, H, Kerr, M, Louden, JE (2007). Two subtypes of psychopathic violent offenders that parallel primary and secondary variants. Journal of Abnormal Psychology 116, 395–409.Google Scholar
Tackett, JL, Silberschmidt, AL, Krueger, RF, Sponheim, SR (2008). A dimensional model of personality disorder: incorporating DSM Cluster A characteristics. Journal of Abnormal Psychology 117, 454–459.CrossRefGoogle Scholar
Tellegen, A, Ben-Porath, YS (1993). Code-type comparability of the MMPI and MMPI-2: analysis of recent findings and criticism. Journal of Personality Assessment 61, 489–500.Google Scholar
Tellegen, A, Ben-Porath, YS, McNulty, JL, Arbisi, PA, Graham, JR, Kaemmer, B (2003). The MMPI-2 Restructured Clinical (RC) Scales: Development, Validation, and Interpretation. University of Minnesota Press: Minneapolis, MN.Google Scholar
Trull, TJ, Durrett, CA (2005). Categorical and dimensional models of personality disorder. Annual Review of Clinical Psychology 1, 355–380.Google Scholar
Tucker, LR (1951). A Method for the Synthesis of Factor Analysis Studies. Personnel Research Section Report No. 984. Department of the Army: Washington, DC.Google Scholar
Turkheimer, E, Ford, DC, Oltmanns, TF (2008). Regional analysis of self-reported personality disorder criteria. Journal of Personality 76, 1587–1622.CrossRefGoogle ScholarPubMed
Vittengl, JR, Clark, LA, Owen-Salters, E, Gatchel, RJ (1999). Diagnostic change and personality stability following functional restoration treatment in a chronic low back pain patient sample. Assessment 6, 79–92.Google Scholar
Watson, D, Clark, LA, Chmielewski, M (2008). Structures of personality and their relevance to psychopathology: II. Further articulation of a comprehensive unified trait structure. Journal of Personality 76, 1545–1585.Google Scholar
Widiger, TA, Samuel, DB (2005). Diagnostic categories or dimensions? A question for the Diagnostic and Statistical Manual of Mental Disorders–Fifth Edition. Journal of Abnormal Psychology 114, 494–504.CrossRefGoogle ScholarPubMed
Widiger, TA, Simonsen, E (2005). Alternative dimensional models of personality disorder: finding a common ground. Journal of Personality Disorders 19, 110–130.Google Scholar
Widiger, TA, Simonsen, E, Krueger, R, Livesley, W, Verheul, R (2005). Personality disorder research agenda for DSM-V. Journal of Personality Disorders 19, 315–338.Google Scholar
Widiger, TA, Trull, TJ (2007). Plate tectonics in the classification of personality disorder: shifting to a dimensional model. American Psychologist 62, 71–83.CrossRefGoogle ScholarPubMed
Wu, KD, Clark, LA (2003). Relations between personality traits and self-reports of daily behavior. Journal of Research in Personality 37, 231–256.Google Scholar