No publication has been more influential in the study and treatment of psychopathology than the Diagnostic and Statistical Manual of Mental Disorders (4^th ed.; DSM-IV) [1]. Criteria for over 300 mental disorders appear in its pages. The DSM-IV implies that mental disorders form discrete categories. Many authors have criticized this assumption [2,3]. For example, Widiger and Clark discussed the difficulty in determining threshold criteria for the various disorders and the lack of sufficient empirical support for the existence of the categories [3]. Briefly stated, the "dimensional vs. categorical" debate addresses whether psychopathology is a matter of qualitative differences or a matter of degree of pathology. Proponents of the dimensional view state that mental disorders essentially vary in degree with adaptive functioning, perhaps along multiple dimensions. In contrast, proponents of the categorical view (as represented in the DSM) present mental disorders as qualitatively distinct categories within which there is a certain amount of variation by degree. For example, in the current research context, a categorical view would posit that cannabis users who meet dependence criteria are qualitatively different from users who do not become dependent (although there is some variability within the two groups). A dimensional view would posit a continuous degree of cannabis dependence with users falling at various points along this continuum.

Recent advances in statistical methods have provided a powerful tool for determining whether psychological constructs are categorical or dimensional (i.e., determining the latent structure). Meehl and colleagues have developed a number of these statistical methods, known as taxometrics [4,5]. Taxometric procedures allow one to determine whether constructs are best conceived of as dimensional or whether they are a natural "type" (i.e., category). These procedures are different from traditional methods such as factor analysis or cluster analysis because taxometrics do not impose a structure on the data. Taxometric procedures have been applied to many psychological constructs (for a review, see [6]) such as depression [7-9], anxiety [10], psychopathic personality [11,12], posttraumatic stress disorder [13], schizotypy [14], worry [15], sexual orientation[16], and marital discord [17].

To our knowledge, however, no study has investigated the latent structure of any of the substance dependence disorders. Thus, the current study sought to obtain empirical evidence as to whether there are qualitative differences between cannabis users who are dependent on the substance and those who are not, or whether cannabis dependence is better conceptualized as a dimension. Although the DSM suggests that the latent structure of cannabis dependence is categorical, this has not been empirically examined.

Why is the latent structure of cannabis dependence important? This distinction has important implications for understanding policy, treatment, and assessment of cannabis dependence. Regarding policy, if cannabis dependence truly forms a latent category, prevention and treatment resources should be directed primarily at those users who already meet criteria for dependence or are most likely to develop dependence. On the other hand, if cannabis dependence is dimensional, these same resources should target all users or users with a single symptom.

Two recent cross-sectional population-based surveys of cannabis users found rates of dependence for young users at 7% and adult users at 21% [18,19]. Given the relatively high prevalence of the disorder among cannabis users, determining the latent structure of cannabis dependence (and other substance dependences) may be an important first step in providing improved treatment. For example, rather than waiting until a client meets full DSM criteria to initiate treatment, a dimensional view would suggest that treatment could be most helpful if initiated as soon as one develops one or two dependence symptoms and before the onset of additional symptoms.

The latent structure of cannabis dependence also has implications for assessment in research and clinical settings. If the distinction between dependent and non-dependent is a true category, then two-group comparisons between these categories might prove the most powerful and appropriate. Any variation within the dependent and non-dependent groups would likely be less relevant. In contrast, a dimensional model would suggest that continuous variables like symptom counts or severity of problems might be a more appropriate approach.