Agrawal, A., Heath, A. C., & Lynskey, M. T. (2011). DSM-IV to DSM-5: The impact of proposed revisions on diagnosis of alcohol use disorders. Addiction, 106, 1935–1943.
Agrawal, A., & Lynskey, M. T. (2007). Does gender contribute to heterogeneity in criteria for cannabis abuse and dependence? Results from the National Epidemiological Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence, 88, 300–307.
Ahmed, S. H. (2012). The science of making drug-addicted animals. Neuroscience, 211, 107–125.
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.
American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.
American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders: DSM-III-R (3rd Revised ed.). Washington, DC: Author.
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders: DSM-IV (4th ed.). Washington, DC: Author.
Baillie, A. J., & Teesson, M. (2010). Continuous, categorical and mixture models of DSM-IV alcohol and cannabis use disorders in the Australian community. Addiction, 105, 1246–1253.
Barron, A. R., & Cover, T. M. (1991). Minimum complexity density estimation. IEEE Transactions on Information Theory, 1, 1034–1054.
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.
Beseler, C. L., Taylor, L. A., & Leeman, R. F. (2010). An item-response theory analysis of DSM-IV alcohol-use disorder criteria and ‘binge’ drinking in undergraduates. Journal of Studies on Alcohol and Drugs, 71, 418–423.
Bock, R. D., & Aitken, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of the EM algorithm. Psychometrika, 46, 443–460.
Bond, J., Ye, Y., Cherpitel, C. J., Borges, G., Cremonte, M., Moskalewicz, J., & Swiatkiewicz, G. (2012). Scaling properties of the combined ICD-10 dependence and harms criteria and comparisons with DSM-5 alcohol use disorder criteria among patients in the emergency department. Journal of Studies on Alcohol and Drugs, 73
Borges, G., Ye, Y., Bond, J., Cherpitel, C. J., Cremonte, M., Moskalewicz, J., . . . Rubio-Stipec, M. (2010). The dimensionality of alcohol use disorders and alcohol consumption in a cross-national perspective. Addiction, 105, 240–254.
Cherpitel, C. J., Borges, G., Ye, Y., Bond, J., Cremonte, M., Moskalewicz, J., & Swiatkiewicz, G. (2010). Performance of a craving criterion in DSM alcohol use disorders. Journal of Studies on Alcohol and Drugs, 71
Clark, S., Muthen, B. O., Kaprio, J., D'Onofrio, B., Vike, R., & Rose, R. (2014). Models and strategies for factor mixture analysis: An example concerning the structure underlying psychological disorders. Structural Equation Modeling, 20, 681–703.
Compton, W. M., Saha, T. D., Conway, K. P., & Grant, B. F. (2009). The role of cannabis use within a dimensional approach to cannabis use disorders. Drug and Alcohol Dependence, 100, 221–227.
Crabbe, J. C., Metten, P., Cameron, A. J., & Wahlsten, D. (2005). An analysis of the genetics of alcohol intoxication in inbred mice. Neuroscience & Biobehavioral Reviews, 28, 785–802.
Crabbe, J. C., Phillips, T. J., Buck, K. J., Cunningham, C. L., & Belknap, J. K. (1999). Identifying genes for alcohol and drug sensitivity: Recent progress and future directions. Trends in Neurosciences, 22, 173–179.
Dennis, M., Babor, T. F., Roebuck, M. C., & Donaldson, J. (2002). Changing the focus: The case for recognizing and treating cannabis use disorders. Addiction, 97(Suppl. 1), 4–15.
Dolan, C. V., & Maas, H. L. J. v. d. (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika, 63, 227–253.
Edwards, G., Arif, A., & Hadgson, R. (1981). Nomenclature and classification of drug- and alcohol-related problems: A WHO memorandum. Bulletin of the World Health Organization, 59, 225–242.
Edwards, A. C., Gillespie, N. A., Aggen, S. H., & Kendler, K. S. (2013). Assessment of a modified DSM-5 diagnosis of alcohol use disorder in a genetically informative population. Alcoholism, Clinical and Experimental Research, 37, 443–451.
Everitt, B. S. (1988). A finite mixture model for the clustering of mixed-mode data. Statistics & Probability Letters, 6, 305–309.
Feingold, A., & Rounsaville, B. (1995a). Construct validity of the abuse-dependence distinction as measured by DSM-IV criteria for different psychoactive substances. Drug and Alcohol Dependence, 39
Feingold, A., & Rounsaville, B. (1995b). Construct validity of the dependence syndrome as measure by DSM-IV for different psychoactive substances. Addiction, 90, 1661–1669.
Gillespie, N. A., Henders, A. K., Davenport, T. A., Hermens, D. F., Wright, M. J., Martin, N. G., & Hickie, I. B. (2012). The Brisbane Longitudinal Twin Study: Pathways to cannabis use, abuse, and dependence project-current status, preliminary results, and future directions. Twin Research and Human Genetics, 16
Gillespie, N. A., Kendler, K. S., & Neale, M. C. (2011a). Psychometric modeling of cannabis initiation and use and the symptoms of cannabis abuse, dependence and withdrawal in a sample of male and female twins. Drug and Alcohol Dependence, 118, 166–172.
Gillespie, N. A., Kendler, K. S., & Neale, M. C. (2011b). Psychometric modeling of initiation and use and the symptoms of cannabis abuse, dependence and withdrawl in a sample of male and female twins. Drug and Alcohol Dependence, 118, 166–172.
Gillespie, N. A., & Neale, M. C. (2006). A finite mixture model for genotype and environment interactions: Detecting latent population heterogeneity. Twin Research and Human Genetics, 9, 412–423.
Gillespie, N. A., Neale, M. C., Legrand, L. N., Iacono, W. G., & McGue, M. (2012). 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.
Gillespie, N. A., Neale, M. C., Prescott, C. A., Aggen, S. H., & Kendler, K. S. (2007). Factor and item-response analysis DSM-IV criteria for abuse of and dependence on cannabis, cocaine, hallucinogens, sedatives, stimulants and opioids. Addiction, 102, 920–930.
Glockner-Rist, A., Lemenager, T., & Mann, K. (2013). Reward and relief craving tendencies in patients with alcohol use disorders: Results from the PREDICT study. Addictive Behaviors, 38, 1532–1540.
Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.
Hall, W., Johnston, L., & Donnelly, N. (1999). The epidemiology of cannabis use and its consequences. In Kalant, H., Corrigal, W., Hall, W. & Smart, R. (Eds.), The health effects of cannabis (pp. 69–125). Toronto, Canada: Addiction Research Foundation.
Hartman, C. A., Gelhorn, H., Crowley, T. J., Sakai, J. T., Stallings, M., Young, S. E., . . . Hopfer, C. J. (2008). Item response theory analysis of DSM-IV cannabis abuse and dependence criteria in adolescents. Journal of the American Academy of Child and Adolescent Psychiatry
Hasin, D. S., Fenton, M. C., Beseler, C., Park, J. Y., & Wall, M. M. (2012). Analyses related to the development of DSM-5 criteria for substance use related disorders: 2. Proposed DSM-5 criteria for alcohol, cannabis, cocaine and heroin disorders in 663 substance abuse patients. Drug and Alcohol Dependence, 122, 28–37.
Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36–53.
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.
Kendler, K. S., Aggen, S. H., Prescott, C. A., Crabbe, J., & Neale, M. C. (2012). Evidence for multiple genetic factors underlying the DSM-IV criteria for alcohol dependence. Molecular Psychiatry, 17, 1306–1315.
Keyes, K. M., Krueger, R. F., Grant, B. F., & Hasin, D. S. (2011). Alcohol craving and the dimensionality of alcohol disorders. Psychological Medicine, 41, 629–640.
Kubarych, T. S., Aggen, S. H., Hettema, J. M., Kendler, K. S., & Neale, M. C. (2005). Endorsement frequencies and factor structure of DSM-III-R and DSM-IV generalized anxiety disorder symptoms in women: Implications for future research, classification, treatment and comorbidity. International Journal of Methods in Psychiatric Research, 14
Langenbucher, J. W., Labouvie, E., Martin, C. S., Sanjuan, P. M., Bavly, L., Kirisci, L., & Chung, T. (2004). An application of item response theory analysis to alcohol, cannabis, and cocaine criteria in DSM-IV. Journal of Abnormal Psychology, 113, 72–80.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.
Leoutsakos, J. M., Zandi, P. P., Bandeen-Roche, K., & Lyketsos, C. G. (2010). Searching for valid psychiatric phenotypes: Discrete latent variable models. International Journal of Methods in Psychiatric Research, 19, 63–73.
Lynskey, M. T., & Agrawal, A. (2007). Psychometric properties of DSM assessments of illicit drug abuse and dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Psychological Medicine, 37, 1345–1355.
Markon, K. E., & Krueger, R. F. (2004, October). A modeling approach to distinguishing between discrete and continuous forms of psychopathology. Paper presented at the 19th Annual Meeting of the Society for Research in Psychopathology, St Louis, MO.
Markon, K. E., & Krueger, R. F. (2005). Categorical and continuous models of liability to externalizing disorders: A direct comparison in NESARC. Archives of General Psychiatry, 62, 1352–1359.
McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.
Mewton, L., Slade, T., McBride, O., Grove, R., & Teesson, M. (2011). An evaluation of the proposed DSM-5 alcohol use disorder criteria using Australian national data. Addiction, 106, 941–950.
Mewton, L., Slade, T., & Teeson, M. (2013). An evaluation of the proposed DSM-5 cannabis use disorder criteria using Australian national survey data. Journal of Studies on Alcohol and Drugs, 74, 614–621.
Muthen, B. (2006). Should substance use disorders be considered as categorical or dimensional?
Addiction, 101(Suppl. 1), 6–16.
Muthen, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463–469.
Neale, M. C., Aggen, S. H., Maes, H. H., Kubarych, T. S., & Schmitt, J. E. (2006). Methodological issues in the assessment of substance use phenotypes. Addictive Behaviors, 31, 1010–1034.
Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2006). Mx: Statistical modeling (7th ed.). Richmond, VA: Department of Psychiatry, Medical College of Virginia.
Nelson, C. B., Rehm, J., Ustun, T. B., Grant, B., & Chatterji, S. (1999). Factor structures for DSM-IV substance disorder criteria endorsed by alcohol, cannabis, cocaine and opiate users: Results from the WHO reliability and validity study. Addiction, 94, 843–855.
Nylund, K. L., Asparouhov, T., & Muthén, B. (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.
Russo, S. J., Murrough, J. W., Han, M. H., Charney, D. S., & Nestler, E. J. (2012). Neurobiology of resilience. Nature Neuroscience, 15, 1475–1484.
Saha, T. D., Chou, S. P., & Grant, B. F. (2006). Toward an alcohol use disorder continuum using item response theory: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychological Medicine, 36, 931–941.
SAS. (2011). SAS/STAT® 9.3 user's guide. Cary, NC: SAS Institute.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
Spearman, C. (1904). General intelligence, objectively determined and measured. American Journal of Psychology, 4, 201–293.
Steiger, J. (1985). On the multivariate asymptotic distribution of sequential chi-square tests. Psychometrika, 50, 253–264.
Stinson, F. S., Grant, B. F., Dawson, D. A., Ruan, W. J., Huang, B., & Saha, T. (2005). Comorbidity between DSM-IV alcohol and specific drug use disorders in the United States: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence, 80, 105–116.
Takane, Y., & Leeuw, J. D. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393–408.
Teesson, M., Lynskey, M., Manor, B., & Baillie, A. (2002). The structure of cannabis dependence in the community. Drug and Alcohol Dependence, 68, 255–262.
Vereshchagin, N. K., & Vitanyi, P. M. B. (2004). Kolmogorov's structure functions and model selection. IEEE Transactions on Information Theory, 50, 3265–3290.
Verweij, K. J., Zietsch, B. P., Lynskey, M. T., Medland, S. E., Neale, M. C., Martin, N. G., . . . Vink, J. M. (2010). Genetic and environmental influences on cannabis use initiation and problematic use: A meta-analysis of twin studies. Addiction, 105, 417–430.
von Sydow, K., Lieb, R., Pfister, H., Hofler, M., Sonntag, H., & Wittchen, H. U. (2001). The natural course of cannabis use, abuse and dependence over four years: A longitudinal community study of adolescents and young adults. Drug and Alcohol Dependence, 64, 347–361.
Wright, M. J., & Martin, N. G. (2004). The Brisbane Adolescent Twin Study: Outline of study methods and research projects. Australian Journal of Psychology, 56, 65–78.
Yung, Y. F. (1997). Finite mixtures in confirmatory factor-analysis models. Psychometrika, 62, 297–330.