This study investigated the Outcome Questionnaire’s (OQ-45) factor structure and demonstrated the use of factor mixture modeling (FMM) for the purpose of score validation. OQ-45 scores did not fit the one-class, one- and three-factor models. Use of FMM to identify a two-class model is detailed. Implications for OQ-45 users are provided.
Akaike, H. ( 1987). Factor analysis and AIC. Psychometrika , 52, 317-332.
2.
Allua, S., Stapleton, L.M., & Beretvas, S.N. (2008). Testing latent mean differences between observed and unobserved groups using multilevel factor mixture models . Educational and Psychological Measurement, 63(3), 357-378.
3.
American Psychological Association. (2004, October). The president signs the Garrett Lee Smith Memorial Act. Retrieved June 8, 2008, from http://www.apa.org/ppo/issues/eglsbillsign.html
4.
Bauer, D.J., & Curran, P.J. ( 2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3-29.
5.
Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., & Erbaugh, J. ( 1961). An inventory for measuring depression. Archives of General Psychology, 4, 53-56.
6.
Benton, S.A., Robertson, J.M., Tseng, W.-.C., Newton, F.B., & Benton, S.L. ( 2003). Changes in counseling center client problems across 13 years. Professional Psychology: Research and Practice, 34, 66-72.
7.
Beretvas, S.N., & Kearney, L.K. ( 2003). A shortened form of the Outcome Questionnaire: A validation of scores across groups (A research report of the Research Consortium of Counseling and Psychological Services for Higher Education). Austin : University of Texas at Austin, Counseling and Mental Health Center.
8.
Celeux G., & Soromenho G. ( 1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195-212.
9.
Colder, C.R., Campbell, R.T., Ruel, E., Richardson, J.L., & Flay, B.R. ( 2002). A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology, 70, 976-985.
10.
Derogatis, L.R., & Cleary, P.A. ( 1977). Confirmation of the dimensional structure of the SCL-90: A study in construct validation. Journal of Clinical Psychology , 33, 981-989.
11.
Derogatis, L.R., Rickels, K., & Rock, A.F. ( 1976). The SCL-90 and the MMPI: A step in the validation of a new self-report scale. British Journal of Psychology, 128, 280-289.
12.
Ellickson, P.L., Martino, S.C., & Collins, R.L. ( 2004). Marijuana use from adolescence to young adulthood: Multiple developmental trajectories and their associated outcomes. Health Psychology, 23, 299-307.
13.
Floyd, F.J., & Widaman, K.F. ( 1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment , 7, 286-299.
14.
Gagné, P. ( 2006). Mean and covariance structure mixture models. In G. R. Hancock & R. D. Mueller (Eds.), Structural equation modeling: A second course (pp. 197-224). Greenwich, CT: Information Age Publishing.
Hancock, G.R., Stapleton, L.M., & Arnold-Berkovits, I. (2006). The tenuousness of invariance tests within multisample covariance and mean structure models. Unpublished manuscript.
17.
Hannan, C., Lambert, M.J., Harmon, C., Nielsen, S.L., Smart, D.W., Shimokawa, K., et al. (2005). A lab test and algorithms for identifying clients at risk for treatment failure. Journal of Clinical Psychology, 61, 155-163.
18.
Hansen, N.B., Lambert, M.J., & Forman, E.M. ( 2002). The psychotherapy dose-response effect and its implications for treatment delivery services. Clinical Psychology: Science and Practice, 9, 329-343.
19.
Hatfield, D.R., & Ogles, B.M. ( 2004). The use of outcome measures by psychologists in clinical practice. Professional Psychology: Research and Practice, 35, 485-491.
20.
Hessen, D.J., Dolan, C.V., & Wicherts, J.M. (2006). The multigroup common factor model with minimal uniqueness constraints and the power to detect uniform bias. Applied Psychological Measurement, 30, 233-246.
21.
Horowitz, L.M., Rosenberg, S.E., Baer, B.A., Ureño, G., & Vilaseñor, V.S. (1988). Inventory of interpersonal problems: Psychometric properties and clinical applications. Journal of Consulting and Clinical Psychology, 56, 885-892.
22.
Hu, L.-T., & Bentler, P.M. ( 1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 1, 1-55.
23.
Jackson, K.M., & Sher, K.J. ( 2005). Similarities and differences of longitudinal phenotypes across alternate indices of alcohol involvement: A methodologic comparison of trajectory approaches. Psychology of Addictive Behaviors , 19, 339-351.
24.
Jacobson, N.S., & Truax, P. ( 1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59, 12-19.
25.
Jedidi, K., Jagpal, H.S., & DeSarbo, W.S. ( 1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science , 16, 39-59.
26.
Lambert, M.J., Burlingame, G.M., Umphress, V., Hansen, N.B., Vermeersch, D.A., Clouse, G.C., et al. (1996). The reliability and validity of the Outcome Questionnaire. Clinical Psychology and Psychotherapy, 3, 249-258.
27.
Lambert, M.J., Gregersen, A.T., & Burlingame, G.M. (2004). The Outcome Questionnaire . In M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcome assessment: Vol. 3. Instruments for adults (3rd ed., pp. 191-234). Mahwah, NJ: Lawrence Erlbaum.
28.
Lambert, M.J., Hansen, N.B., & Finch, A.E. ( 2001). Patient-focused research: Using patient outcome data to enhance treatment effects. Journal of Consulting and Clinical Psychology, 69, 159-172.
29.
Lambert, M.J., Lunnen, K., Umphress, V., Hansen, N.B., & Burlingame, G.M. (1994). Administration and scoring manual for the Outcome Questionnaire (OQ-45.1). Salt Lake City, UT: IHC Center for Behavioral Healthcare Efficacy .
30.
Lambert, M.J., Whipple, J.L., Bishop, M.J., Vermeersch, D.A., Gray, G.V., & Finch, A.E. ( 2002). Comparison of empirically-derived and rationally-derived methods for identifying patients at risk for treatment failure. Clinical Psychology and Psychotherapy, 9, 149-164.
31.
Lo, Y., Mendell, N.R., & Rubin, D.B. ( 2001). Testing the number of components in a normal mixture . Biometrika, 88, 767-778.
32.
Lubke, G.H., & Muthén, B.O. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods , 10, 21-39.
33.
Lubke, G.H., & Muthén, B.O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14, 26-47.
34.
McLachlan, G., & Peel, D. ( 2000). Finite mixture models. New York: Wiley.
35.
Meade, A.W., Lautenschlager, G.J., & Hecht, J.E. ( 2005). Establishing measurement equivalence and invariance in longitudinal data with item response theory. International Journal of Testing, 5, 279-300.
36.
Meredith, W. ( 1993). Measurement invariance, factor analysis, and factorial invariance. Pyschometrika, 58, 525-543.
37.
Meredith, W., & Horn, J. ( 2001). The role of factorial invariance in modeling growth and change. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change: Decade of behavior (pp. 203-240). Washington, DC: American Psychological Association.
38.
Messick, S. ( 1998). Test validity: A matter of consequence. Social Indicators Research, 45, 35-44.
39.
Mueller, R.M., Lambert, M.J., & Burlingame, G.M. (1998). Construct validity of the Outcome Questionnaire: A confirmatory factor analysis. Journal of Personality Assessment, 70, 248-262.
40.
Muthén, B.O. (2004 ). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage.
41.
Muthén, B.O. ( 2006). Should substance use disorders be considered as categorical or dimensional? Addiction, 101, 6-16.
Muthén, B.O., & Shedden, K. ( 1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
45.
Nylund, K.L., Asparouhov, T., & Muthén, B.O. (2006). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Manuscript submitted for publication.
46.
Okiishi, J., Lambert, M.J., Nielsen, S.L., & Ogles, B.M. ( 2003). Waiting for supershrink: An empirical analysis of therapist effects. Clinical Psychology and Psychotherapy, 10, 361-373.
47.
Raykov, T., & Marcoulides, G.A. (2006). A first course in structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
48.
Reinecke, J. ( 2006). Longitudinal analysis of adolescents’ deviant and delinquent behavior: Applications of latent class growth curves and growth mixture models. Methodology, 2, 100-112.
49.
Schwarz, G. ( 1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
50.
Sclove, L. ( 1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333-343.
51.
Sharkin, B.S., & Coulter, L.P. ( 2005). Empirically supporting the increasing severity of college counseling center client problems: Why is it so challenging? Journal of College Counseling, 8, 165-171.
52.
Spielberger, C.D. ( 1983). Manual for the State-Trait Anxiety Inventory STAI (Form Y). Palo Alto, CA: Consulting Psychologists Press.
53.
Spielberger, C., Gorsuch, A., & Lushene, R. ( 1970). The state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologist Press.
54.
Taylor, J.A. ( 1953). A personality scale of manifest anxiety. Journal of Abnormal and Social Psychology, 48, 285-290.
55.
Tucker, J.S., Orlando, M., & Ellickson, P.L. (2003). Patterns and correlates of binge drinking trajectories from early adolescence to young adulthood. Health Psychology, 22, 79-87.
56.
Umphress, V.J., Lambert, M.J., Smart, D.W., Barlow, S.H., & Clouse, G. ( 1997). Concurrent and construct validity of the Outcome Questionnaire . Journal of Psychoeducational Assessment, 15, 40-55.
57.
Vermeersch, D.A., Lambert, M.J., & Burlingame, G.A. (2000). Outcome Questionnaire: Item sensitivity to change. Journal of Personality Assessment , 74, 242-261.
58.
Vermeersch, D.A., Whipple, J.L., Lambert, M.J., Hawkins, E.J., Burchfield, C.M., & Okiishi, J.C. ( 2004). Outcome Questionnaire: Is it sensitive to changes in counseling center clients? Journal of Counseling Psychology , 51, 38-49.
59.
Wampold, B.E., & Brown, G.S. ( 2005). Estimating variability in outcomes attributable to therapists: A naturalistic study of outcomes in managed care. Journal of Consulting and Clinical Psychology, 73, 914-923.
60.
Weissman, M.M., & Bothwell, S. ( 1976). Assessment of social-adjustment by patient self-report . Archives of General Psychiatry, 33, 1111-1115.
61.
Whipple, J.L., Lambert, M.J., Vermeersch, D.A., Smart, D.W., Nielsen, S.L., & Hawkins, E.J. ( 2003). Improving the effects of psychotherapy: The use of early identification of treatment failure and problem-solving strategies in routine practice. Journal of Counseling Psychology, 50, 59-68.
62.
White, H.R., Bates, M.E., & Buyske, S. ( 2001). Adolescence-limited versus persistent delinquency: Extending Moffitt’s hypothesis into adulthood. Journal of Abnormal Psychology, 110, 600-609.
63.
Wicherts, J.M., Dolan, C.V., & Hessen, D.J. ( 2005). Stereotype threat and group differences in test performance: A question of measurement invariance. Journal of Personality and Social Psychology, 89, 696-716.
64.
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.
65.
Zung, W.W. ( 1965). A self-rating depression scale. Archives of General Psychiatry, 12, 63-70.
66.
Zung, W.W. ( 1971). A rating instrument for anxiety disorders. Psychosomatics, 6, 371-379.
