Cautions on the Use of Multiple Imputation When Selecting Between Latent Categorical versus Continuous Models for Psychological Constructs

REFERENCES

• American Psychiatric Association. (2013). 5th ed.. Washington, DC: American Psychiatric Press.
   Google Scholar
• Asparouhov, T., & Muthén, B.(2010). Multiple imputation with Mplus. Retrieved from http://statmodel.com
   Google Scholar
• Barker, E. D., Vitaro, F., Lacourse, E., Fontaine, N. M., Carbonneau, R., & Tremblay, R. E. (2010). Testing the developmental distinctiveness of male proactive and reactive aggression with a nested longitudinal experimental intervention. Aggressive Behavior, 36, 127–140.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Biggs, B. K., Vernberg, E., Little, T. D., Dill, E. J., Fonagy, P., & Twemlow, S. W. (2010). Peer victimization trajectories and their association with children's affect in late elementary school. International Journal of Behavioral Development, 34, 136–146.
   Web of Science ®Google Scholar
• Brown, T., & Barlow, D. (2005). Dimensional versus categorical classification of mental disorders in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and beyond: Comment on the special section. Journal of Abnormal Psychology, 114, 551–556.
   PubMed Web of Science ®Google Scholar
• Cho, S. J., Cohen, A. S., & Kim, S.-H. (2011). Markov chain monte carlo estimation of a mixture item response theory model. Journal of Statistical Computation and Simulation, 78, 1–29.
   Google Scholar
• Clark, S., Muthén, B., Kaprio, J., D'Onofrio, M., Viken, R., & Rose, R. (2013). Models and strategies for factor mixture analysis. An example concerning the structure underlying psychological disorders. Structural Equation Modeling, 20, 681–703.
   Web of Science ®Google Scholar
• Colder, C., Mehta, P., Balanda, K., Campbell, R. T., Mayhew, K. P., Stanton, W. R., … Flay, B. R. (2001). Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling. Health Psychology, 20, 127–135.
   PubMed Web of Science ®Google Scholar
• Collins, L. M., & Lanza, S. T.(2010). Latent class and latent transition analysis with applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley.
   Google Scholar
• Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 4, 330–351.
   Web of Science ®Google Scholar
• Conway, C., Hammen, C., & Brennan, P. (2012). A comparison of latent class, latent trait, and factor mixture models of DSM–IV borderline personality disorder criteria in a community setting: Implications for DSM–V. Journal of Personality Disorders, 26, 793–803.
   PubMed Web of Science ®Google Scholar
• Costello, D., Dierker, L., Jones, B., & Rose, J. (2008). Trajectories of smoking from adolescence to early adulthood and their psychosocial risk factors. Health Psychology, 27, 811–818.
   PubMed Web of Science ®Google Scholar
• Croy, C., & Novins, D. (2005). Methods for addressing missing data in psychiatric and developmental research. Journal of the American Academy of Child & Adolescent Psychiatry, 44, 1230–1240.
   PubMed Web of Science ®Google Scholar
• Demirtas, H., Freels, S., & Yuncel (2008). Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. Journal of Statistical Computation and Simulation, 78, 69–84.
   Web of Science ®Google Scholar
• Enders, C. K.(2010). Applied missing data analysis. New York, NY: Guilford.
   Google Scholar
• Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430–457.
   Web of Science ®Google Scholar
• Enders, C. K., & Gottschall, A. (2011). Multiple imputation strategies for multiple group structural equation models. Structural Equation Modeling, 18, 35–54.
   Web of Science ®Google Scholar
• Feldman, B. J., Masyn, K. E., & Conger, R. D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45, 652–676.
   PubMed Web of Science ®Google Scholar
• Gillespie, N. A., Neale, M. C., Legrand, L. N., Iacono, W. G., & McGue, M. (2011). Are the symptoms of cannabis use disorder best accounted for by dimensional, categorical, or factor mixture models? A comparison of male and female young adults. Psychology of Addictive Behaviors, 26, 68–77.
   PubMed Web of Science ®Google Scholar
• Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80–100.
   Web of Science ®Google Scholar
• Graham, J. W., Olchowski, A., & Gilreath, T. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206–213.
   PubMed Web of Science ®Google Scholar
• Graham, J. W., & Schafer, J. L.(1999). On the performance of multiple imputation for multivariate data with small sample size. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 1–29). Thousand Oaks, CA: Sage.
   Google Scholar
• Hallquist, M., & Pilkonis, P. (2012). Refining the phenotype of borderline personality disorder: Diagnostic criteria and beyond. Personality Disorders, 3, 228–246.
   PubMedGoogle Scholar
• Helzer, J., van den Brink, W., & Guth, S. (2006). Should there be both categorical and dimensional criteria for the substance use disorders in DSM–V?Addiction, 101, 17–22.
   PubMed Web of Science ®Google Scholar
• Hirsh-Pasek, K., & Burchinal, M. (2006). Mother and caregiver sensitivity over time: Predicting language and academic outcomes with variable- and person-centered approaches. Merrill Palmer Quarterly, 52, 449–485.
   Web of Science ®Google Scholar
• Hudziak, J. J., Heath, A. C., Madden, P. F., Reich, W., Bucholz, K. K., Slutske, W., … Todd, R. D. (1998). Latent class and factor analysis of DSM–IV ADHD: A twin study of female adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 848–857.
   PubMed Web of Science ®Google Scholar
• Ingoldsby, E. M., Shaw, D. S., Winslow, E., Schonberg, M., Gilliom, M., & Criss, M. M. (2006). Neighborhood disadvantage, parent–child conflict, neighborhood peer relationships, and early antisocial behavior problem trajectories. Journal of Abnormal Child Psychology, 34, 303–319.
   PubMed Web of Science ®Google Scholar
• Jelicic, H., Phelps, E., & Lerner, R. (2009). Use of missing data methods in longitudinal studies: The persistence of bad practices in developmental psychology. Developmental Psychology, 45, 1195–1199.
   PubMed Web of Science ®Google Scholar
• Jonkmann, K., Trautwein, U., & Ludke, O. (2009). Social dominance in adolescence: The moderating role of the classroom context and behavioral heterogeneity. Child Development, 80, 338–355.
   PubMed Web of Science ®Google Scholar
• Kraemer, H. C., Shrout, P. E., & Rubio-Stipec, M. (2007). Developing the diagnostic and statistical manual V: What will “statistical” mean in DSM–V?Social Psychiatry and Psychiatric Epidemiology, 42, 259–267.
   PubMed Web of Science ®Google Scholar
• Kreuter, F., & Muthén, B. O. (2008). Analyzing criminal trajectory profiles: Bridging multilevel and group-based approaches using growth mixture modeling. Journal of Quantitative Criminology, 24, 1–31.
   Web of Science ®Google Scholar
• Krueger, R., Markon, K., Patrick, C., & Iacono, W. (2005). Externalizing psychopathology in adulthood: A dimensional spectrum conceptualization and its implications for DSM–V. Journal of Abnormal Psychology, 114, 537–550.
   PubMed Web of Science ®Google Scholar
• Lanza, S., Coffman, D., & Xu, S. (2013). Causal inference in latent class analysis. Structural Equation Modeling, 20, 361–383.
   PubMed Web of Science ®Google Scholar
• Lubke, G. (2012). Old issues in a new jacket: Power and validation in the context of mixture modeling. Measurement, 10, 212–216.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Lubke, G. H., Muthén, B., 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. American Academy of Child and Adolescent Psychiatry, 46, 1584–1593.
   PubMed Web of Science ®Google Scholar
• Lubke, G., & Neale, M. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood?Multivariate Behavioral Research, 41, 499–532.
   PubMed Web of Science ®Google Scholar
• Lubke, G., & Neale, M. (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.
   PubMed Web of Science ®Google Scholar
• Lubke, G., & Tueller, M. (2010). Latent class detection and class assignment: A comparison of the MAXEIG taxonomic procedure and factor mixture modeling approaches. Structural Equation Modeling, 17, 605–628.
   PubMed Web of Science ®Google Scholar
• Markon, K., & Krueger, R. (2006). Information-theoretic latent distribution modeling: Distinguishing discrete and continuous latent variable models. Psychological Methods, 11, 228–243.
   PubMed Web of Science ®Google Scholar
• McLachlan, G., & Peel, D.(2000). Finite mixture models. New York, NY: Wiley.
   Google Scholar
• Meng, X. L. (1994). Multiple imputation inference with uncongenial sources of input. Statistical Science, 9, 538–573.
   Web of Science ®Google Scholar
• Merkle, E. (2011). A comparison of imputation methods for Bayesian factor analysis models. Journal of Educational and Behavioral Statistics, 36, 257–276.
   Web of Science ®Google Scholar
• Missall, K., Mercer, S., Martinez, R., & Casebeer, D. (2012). Concurrent and longitudinal patterns and trends in performance on early numeracy curriculum-based measures in kindergarten through third grade. Assessment for Effective Intervention, 37, 95–106.
   Google Scholar
• Molenaar, P., & von Eye, A.(1994). On the arbitrary nature of latent variables. In A. von Eye & C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research (pp. 226–242). Thousand Oaks, CA: Sage.
   Google Scholar
• Molenberghs, G., & Kenward, M. G.(2007). Missing data in clinical studies. West Sussex, UK: Wiley.
   Google Scholar
• Muthén, B. O. (2006). Should substance use disorders be considered as categorical or dimensional?Addiction, 101, 6–16.
   PubMed Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O.(1998–2012). 6th edition. Los Angeles, CA: Muthén & Muthén.
   Google Scholar
• Nylund, K. L., Asparouhov, T., & Muthén, 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.
   Web of Science ®Google Scholar
• Regier, D., Kuhl, E., & Kupfer, D. (2013). The DSM–V: Classification and criteria changes. World Psychiatry, 12, 92–98.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B., & Shenker, N. (1986). Multiple imputation for interval estimation from simple random samples with ignorable response. Journal of the American Statistical Association, 81, 366–374.
   Web of Science ®Google Scholar
• Ruscio, J.(2006). Taxometric programs for the R computing environment: Users manual. Available from http://www.tcnj.edu/~ruscio/TaxProgManual%202012-03-10.pdf
   Google Scholar
• Schafer, J. L.(1997). Analysis of incomplete multivariate data. London, UK: Chapman & Hall.
   Google Scholar
• Schafer, J. L. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica, 57, 19–35.
   Web of Science ®Google Scholar
• Sterba, S. K.(2014). Modeling strategies in developmental psychopathology research: Prediction of individual change. In M. Lewis & K. D. Rudolph (Eds.), Handbook of developmental psychopathology (3rd ed., pp. 109–124). New York, NY: Springer.
   Google Scholar
• Sterba, S. K., Baldasaro, R. E., & Bauer, D. J. (2012). Factors affecting the adequacy and preferability of semiparametric groups-based approximations of continuous growth trajectories. Multivariate Behavioral Research, 40, 590–634.
   Web of Science ®Google Scholar
• Trull, T., & Durrett, C. (2005). Categorical and dimensional models of personality disorder. Annual Review of Clinical Psychology, 1, 355–380.
   PubMed Web of Science ®Google Scholar
• Tueller, S., & Lubke, G. (2010). Evaluation of structural equation mixture models: Parameter estimates and correct class assignment. Structural Equation Modeling, 17, 165–192.
   PubMed Web of Science ®Google Scholar
• van den Akker, A., Dekovic, M, Prinzie, P., & Asscher, J. (2010). Toddlers' temperament profiles: Stability and relations to negative and positive parenting. Journal of Abnormal Child Psychology, 38, 485–495.
   PubMed Web of Science ®Google Scholar
• Vaughn, M. G., Shook, J. J., & McMillin, J. (2008). Aging out of foster care and legal involvement: Toward a typology of risk. Social Service Review, 82, 419–446.
   Web of Science ®Google Scholar
• Walton, K., Ormel, J., & Krueger, R. (2011). The dimensional nature of externalizing behaviors in adolescence: Evidence from a direct comparison of categorical, dimensional, and hybrid models. Journal of Abnormal Child Psychology, 39, 553–561.
   PubMed Web of Science ®Google Scholar
• West, R., Hill, K., Hewison, J., Knapp, P., & House, A. (2010). Psychological disorders after stroke are an important influence on functional outcomes. Stroke, 41, 1723–1727.
   PubMed Web of Science ®Google Scholar
• Widiger, T., & Samuel, D. (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.
   PubMed Web of Science ®Google Scholar
• Witkiewitz, K., King, K., McMahon, R., Wu, J., Luk, J., Bierman, K. L., … Conduct Problems Prevention Research Group. (2013). Evidence for a multidimensional latent structural model of externalizing disorders. Journal of Abnormal Child Psychology, 41, 223–237.
   PubMed Web of Science ®Google Scholar
• Wothke, W.(2000). Longitudinal and multigroup modeling with missing data. In T. Little, K. Schnabel, & J. Baumert (Eds.), Testing structural equation models (pp. 256–293). Mahwah, NJ: Erlbaum.
   Google Scholar