Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-24T01:30:45.088Z Has data issue: false hasContentIssue false
  • English
  • Français

A comparison of the predictive abilities of dimensional and categorical models of unipolar depression in the National Comorbidity Survey

Published online by Cambridge University Press:  10 October 2008

J. J. Prisciandaro  and
J. E. Roberts
J. J. Prisciandaro
Affiliation:
University at Buffalo, The State University of New York, Amherst, NY, USA
J. E. Roberts*
Affiliation:
University at Buffalo, The State University of New York, Amherst, NY, USA
*
*Address for correspondence: Dr J. E. Roberts, University at Buffalo, The State University of New York, Department of Psychology, Park Hall, Box 604110, Amherst, NY 14260-4110, USA. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Taxometric research on depression has yielded mixed results, with some studies supporting dimensional solutions and others supporting taxonic solutions. Although supplementary tests of construct validity might clarify these mixed findings, to date such analyses have not been reported. The present study represents a follow-up to our previous taxometric study of depression designed to evaluate the relative predictive validities of dimensional and categorical models of depression.

Method

Two sets of dimensional and categorical models of depression were constructed from the depression items of the Composite International Diagnostic Interview: (1) empirically derived models obtained using latent structure analyses and (2) rationally selected models, including an additive depressive symptoms scale (dimensional) and DSM major depressive episodes (categorical). Both sets of dimensional and categorical models were compared in terms of their abilities to predict various clinically relevant outcomes (psychiatric diagnoses and impairment).

Results

Factor analyses suggested a two-factor model (‘cognitive–affective’ and ‘somatic’ symptoms) and latent class analyses suggested a three-class model (‘severe depression’, ‘moderate depression’ and ‘cognitive–affective distress’). In predictive analyses that simultaneously included dimensional and categorical models as predictors, the dimensional models remained significant unique predictors of outcomes while the categorical models did not.

Conclusions

Both dimensional models provided superior predictive validity relative to their categorical counterparts. These results provide construct validity evidence for the dimensional findings from our previous taxometric study and thus inspire confidence in dimensional conceptualizations of depression. It remains for future research to evaluate the construct validity of the taxonic solutions reported in the literature.

Keywords

CategoricaldepressiondimensionalNational Comorbidity Surveytaxometric
Type
Original Articles
Information
Psychological Medicine , Volume 39 , Issue 7 , July 2009 , pp. 1087 - 1096
Copyright
Copyright © 2008 Cambridge University Press

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

Aggen, SH, Neale, MC, Kendler, KS (2005). DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models. Psychological Medicine 35, 475–487.Google Scholar
Ambrosini, PJ, Bennett, DS, Cleland, CM, Haslam, N (2002). Taxonicity of adolescent melancholia: a categorical or dimensional construct. Journal of Psychiatric Research 36, 247–256.CrossRefGoogle ScholarPubMed
APA (1987). Diagnostic and Statistical Manual of Mental Disorders, 3rd edn, revised. American Psychological Association: Washington, DC.Google Scholar
APA (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychological Association: Washington, DC.Google Scholar
Beach, SRH, Amir, N (2006). Depression is taxonic. Journal of Psychopathology and Behavioral Assessment 28, 171–178.CrossRefGoogle Scholar
Cohen, J, Cohen, P, West, SG, Aiken, LS (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. Erlbaum: Mahwah, New Jersey.Google Scholar
Comrey, AL, Lee, HB (1992). A First Course in Factor Analysis, 2nd edn. Erlbaum: Mahwah, New Jersey.Google Scholar
Coyne, JC (1994). Self-reported distress: analog or ersatz depression? Psychological Bulletin 116, 29–45.Google Scholar
Croon, M (2002). Ordering the classes. In Applied Latent Class Analysis (ed. Hagenaars, J. A. and McCutcheon, A. L.), pp. 137–162. Cambridge University Press: Cambridge, UK.Google Scholar
Flett, GL, Vredenburg, K, Krames, L (1997). The continuity of depression in clinical and nonclinical samples. Psychological Bulletin 121, 395–416.CrossRefGoogle ScholarPubMed
Franklin, CL, Strong, DR, Greene, RL (2002). A taxometric analysis of the MMPI-2 depression scales. Journal of Personality Assessment 79, 110–121.Google Scholar
Hankin, BL, Fraley, RC, Lahey, BB, Waldman, ID (2005). Is depression best viewed as a continuum or discrete category? A taxometric analysis of childhood and adolescent depression in a population-based sample. Journal of Abnormal Psychology 114, 96–110.CrossRefGoogle ScholarPubMed
Haslam, N, Beck, AT (1994). Subtyping major depression: a taxometric analysis. Journal of Abnormal Psychology 103, 686–692.Google Scholar
Horn, JL (1965). A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185.Google Scholar
Hu, L, Bentler, PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 1–55.Google Scholar
Isometsä, ET, Henriksson, MM, Hillevi, ME, Kuoppasalmi, KI, Lönnqvist, JK (1994). Suicide in major depression. American Journal of Psychiatry 151, 530–536.Google ScholarPubMed
Kessler, RC (2002 a). National Comorbidity Survey, 1990–1992 [Data file]. Conducted by University of Michigan, Survey Research Center, 2nd ICPSR edn. Inter-University Consortium for Political and Social Research: Ann Arbor, MI.Google Scholar
Kessler, RC (2002 b). Epidemiological perspectives for the development of future diagnostic systems. Psychopathology 35, 158–161.CrossRefGoogle ScholarPubMed
Kessler, RC, Berglund, P, Demler, O, Jin, R, Koretz, D, Merikangas, KR, Rush, AJ, Walters, EE, Wang, PS (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association 289, 3095–3105.Google Scholar
Kessler, RC, Davis, CG, Kendler, KS (1997). Childhood adversity and adult psychiatric disorder in the US National Comorbidity Survey. Psychological Medicine 27, 1101–1119.Google Scholar
Kessler, RC, Foster, CL, Saunders, WB, Stang, PE (1995). Social consequences of psychiatric disorders: I. Educational attainment. American Journal of Psychiatry 152, 1026–1032.Google Scholar
Kessler, RC, McGonagle, KA, Zhao, S, Nelson, CB, Hughes, M, Eshleman, S, Wittchen, HU, Kendler, KS (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Archives of General Psychiatry 51, 8–19.CrossRefGoogle ScholarPubMed
Kessler, RC, Wittchen, H-U, Abelson, JM, McGonagle, KA, Schwarz, N, Kendler, KS, Knäuper, B, Zhao, S (1998). Methodological studies of the Composite International Diagnostic Interview (CIDI) in the US National Comorbidity Survey (NCS). International Journal of Methods in Psychiatric Research 7, 33–55.CrossRefGoogle Scholar
Krueger, RF (1999). The structure of common mental disorders. Archives of General Psychiatry 56, 921–926.Google Scholar
Markon, KE, Krueger, RF (2006). Information-theoretic latent distribution modeling: distinguishing discrete and continuous latent variable models. Psychological Methods 11, 228–243.Google Scholar
Muthén, BO (2004). Mplus Technical Appendices. Muthén & Muthén: Los Angeles, CA.Google Scholar
Muthén, LK, Muthén, BO (2007). Mplus User's Guide, 3rd edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
Nylund, KL, Asparouhov, T, Muthén, BO (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.CrossRefGoogle Scholar
O'Connor, B (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods, Instrumentation, and Computers 32, 396–402.Google Scholar
Prisciandaro, JJ, Roberts, JE (2005). A taxometric investigation of unipolar depression in the National Comorbidity Survey. Journal of Abnormal Psychology 114, 718–728.Google Scholar
Preacher, KJ, MacCallum, RC (2003). Repairing Tom Swift's Electric Factor Analysis Machine. Understanding Statistics 2, 13–43.Google Scholar
Raftery, AE (1993). Bayesian model selection in structural equation models. In Testing Structural Equation Models (ed. Bollen, K. A. and Long, J. S.), pp. 163–180. Sage Publications, Inc.: Thousand Oaks, CA.Google Scholar
Robins, LN, Wing, J, Wittchen, H-U, Helzer, JE, Babor, TF, Burke, J, Farmer, A, Jablenski, A, Pickens, R, Regier, DA, Sartorius, N, Towle, LH (1988). The Composite International Diagnostic Interview: an epidemiologic instrument suitable for use in conjunction with different diagnostic systems and in different cultures. Archives of General Psychiatry 45, 1069–1077.CrossRefGoogle ScholarPubMed
Ruscio, AM, Ruscio, J (2002). The latent structure of analogue depression: should the Beck Depression Inventory be used to classify groups? Psychological Assessment 14, 135–145.Google Scholar
Ruscio, J, Haslam, N, Ruscio, AM (2006). Introduction to the Taxometric Method: a Practical Guide. Erlbaum: Mahwah, New Jersey.Google Scholar
Ruscio, J, Ruscio, AM (2000). Informing the continuity controversy: a taxometric analysis of depression. Journal of Abnormal Psychology 109, 473–487.Google Scholar
Ruscio, J, Zimmerman, M, McGlinchey, JB, Chelminski, I, Young, D (2007). Diagnosing major depressive disorder: XI. A taxometric investigation of the categorical–dimensional debate on the structure underlying DSM-IV symptoms. Journal of Nervous and Mental Disease 195, 10–19.Google Scholar
SAS Institute, Inc. (2002). %SREGSUB macro provides additional capabilities for PROC SURVEYREG (http://support.sas.com/kb/24/985.html#ref). Accessed 5 June 2008.Google Scholar
Shafer, AB (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology 62, 123–146.Google Scholar
Slade, T, Andrews, G (2005). Latent structure of depression in a community sample: a taxometric analysis. Psychological Medicine 35, 489–497.CrossRefGoogle Scholar
Solomon, A, Ruscio, J, Seeley, JR, Lewinsohn, PM (2006). A taxometric investigation of unipolar depression in a large community sample. Psychological Medicine 36, 973–985.Google Scholar
Spitzer, RL, Williams, JBW, Gibbon, M, First, MB (1992). The Structured Clinical Interview for DSM-III-R (SCID). Archives of General Psychiatry 49, 624–636.Google Scholar
Sullivan, MD, LaCroix, AZ, Russo, JE, Walker, EA (2001). Depression and self reported physical health in patients with coronary disease: mediating and moderating factors. Psychosomatic Medicine 63, 248–256.Google Scholar
Wacker, HR, Battegay, R, Muellejans, R, Schloesser, C (1990). Using the CIDI-C in the general population. In Psychiatry: A World Perspective, vol. 1 (ed. Stefanis, C. N., Rabavilas, A. D. and Soldatos, C. R.), pp. 138–143. Elsevier Science Publishers B. V. (Biomedical Division): New York.Google Scholar
Wade, TJ, Cairney, J (2000). Major depressive disorder and marital transition among mothers: results from a national panel study. Journal of Nervous and Mental Disease 188, 741–750.CrossRefGoogle ScholarPubMed
Waller, NG, Meehl, PE (1998). Multivariate Taxometric Procedures: Distinguishing Types from Continua. Sage Publications, Inc.: Thousand Oaks, CA.Google Scholar
Watson, D (2003). Investigating the construct validity of the dissociative taxon: stability analyses of normal and pathological dissociation. Journal of Abnormal Psychology 112, 298–305.Google Scholar
Wittchen, H-U (1991). Cross-cultural feasibility, reliability and sources of variance of the Composite International Diagnostic Interview (CIDI). British Journal of Psychiatry 159, 645–653.CrossRefGoogle ScholarPubMed
Wittchen, H-U (1994). Reliability and validity studies of the WHO Composite International Diagnostic Interview: a critical review. Journal of Psychiatric Research 28, 57–84.Google Scholar
Zhao, S, Kessler, RC, Wittchen, H-U (1994). Diagnostic algorithms for NCS/DSM-III-R Di. NCS Working Paper no. 7 (http://www.hcp.med.harvard.edu/ncs/ftpdir/working_paper_7.pdf). Accessed 1 September 2006.Google Scholar