categories in personality disorder: would we know them if we saw them?

Categories in Personality Disorder: Would We Know Them If We Saw Them?Author(s): Leslie C. MoreySource: Psychological Inquiry, Vol. 4, No. 2 (1993), pp. 111-113Published by: Taylor & Francis, Ltd.Stable URL: http://www.jstor.org/stable/1448839 .

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COMMENTARIES

clinicians in the limitations of the categorical approach to diagnosis and to introduce prototypical and dimensional perspectives to their diagnostic practice. It will be useful to cover some of these issues in the DSM-IV introduction to the Personality Disorders section and perhaps also in the Appendix. Perhaps in time for DSM-V (in the early years of the new millennium), we will have a clearer idea of which dimensions are best, how to evaluate them, and how to educate clinicians to a readiness to apply them. If a dimensional system is introduced in DSM-V, Widiger will deserve great credit as a forerunner in its development.

Note

Allen Frances, Box 3950, Duke University Medical Center, Durham, NC 27710.

References

American PsychiatricAssociation. (1980).Diagnosticandstatistical manual of mental disorders (3rd ed.). Washington, DC: Author.

American PsychiatricAssociation. (1987). Diagnosticandstatistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.

Categories in Personality Disorder: Would We Know Them If We Saw Them?

Leslie C. Morey Vanderbilt University

Widiger provides an excellent review of the contro- versy regarding categorical and dimensional approaches to the classification of personality disorders. Furthermore, he proposes a dimensional alternative to the categorical system that has been (and almost certainly will continue to be) included in the American Psychiatric Association nomenclature, the Diagnostic and Statistical Manual of Mental Disorder, and points out that this dimensional model reflects a growing consensus among individual-differences researchers. Perhaps it is a sign of a maturing science, or perhaps it is a function of shared biases, but I find myself generally in agreement both with his conclusions and his substantive recommendations.

However, there are a few points upon which I would like to elaborate. These elaborations concern the intri- cate link between classification theories and the methods used to evaluate these theories. The aim of my commentary is to caution against premature closure on this issue-to avoid concluding that the validity of the dimensional model has been convincingly demon- strated and that further investigations are unnecessary. Although the early returns for the dimensional approach are encouraging, the field needs to encourage further, more sophisticated research than what has been conducted thus far.

The Mutual Influences of Theory and Method

The dimensional alternative proposed by Widiger reflects the current state of the art in an individual-dif-

ferences tradition that has its roots in Galton's "anthro- pometric" approach in the 19th century, and many of the statistical procedures that provide the foundation for this tradition were developed by Galton's students and followers. At the core of these procedures are regression and correlational models that assume dimensional relatedness between constructs, and also linearity in these relations. Certainly these methods have served psychological researchers well since their development; however, they have also shaped our thinking and our conceptualizations to some degree. The pri- mary purpose of my commentary is to express a caution against letting our methods predetermine our conclusions.

As Widiger notes, it is reasonable to conclude that the preponderance of empirical evidence accumu- lated thus far supports the superiority of a dimensional over a categorical model. The bulk of this research demonstrates this conclusion by using linear types of predictor models (e.g., regression or some analog) to demonstrate that dimensional information accounts for considerably more variation in some theoretically relevant external construct than does information expressed categorically. Thus, the dimensional representation provides a better "fit" of the observed data when using traditional dimensional analytic measures to represent this fit. However, it is possible that the use of analytic techniques based on somewhat different mathematical assumptions can lead to different conclusions. For example, taxometric cluster analysis methods were developed from a mathematical foundation largely categorical in its

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approach; the purpose of these methods is to identify more or less discrete categories within a given population. It is worth noting that, in at least one study, application of cluster analysis methodology to DSM- III-R (American Psychiatric Association, 1987) personality disorder data yielded conclusions that were quite consonant with the structure of personality disorders given in the DSM-III-R (Morey, 1988). That study was designed to select methodological options that were in keeping with the general ratio- nale underlying the DSM system. The first assumption was that the classification was designed to be generally atheoretical with regard to etiology; the second was that the personality disorders are concep- tualized as prototype classes represented polythetic- ally; the third was that personality disorders represent syndromes the features of which should be intercorrelated in a mixed population; and the fourth was that personality disorders can be hierarchically arranged into superordinate classes. These assumptions are quite consonant with those underlying many of the clustering techniques, and, when such techniques were applied to relevant data, the goodness of fit with the putative categorical structure of the DSM was reasonably good. Other studies using taxometric approaches have also provided support for the pres- ence of natural categories in personality data (e.g., Gangestad & Snyder, 1985).

This is not to say that the nature of any conclusions drawn from the use of correlational methods must by definition favor dimensional models. Also, it does not mean that method influences can operate only in favor of dimensional models. For example, it is generally presumed that categorical variables should manifest distributions with multiple modes (Kendell, 1975), and, as Widiger notes, several statistical techniques have been developed in attempts to identify multimodal distributions. However, multimodality provides no assurance that a construct is categorical in nature; there are several methodological artifacts that can lead to such an outcome. Obviously, sam- pling limitations can lead to bimodality if individuals who fall within certain ranges of a continuum are underrepresented, leading to apparent (but artificial) points of rarity. However, Grayson (1987) pointed out a more subtle artifact that can serve to bring about bimodality where there is none; this involves the distribution being a by-product of the characteristics of the measuring instrument. Such a result can occur when the measure provides only discriminating information at certain points along a conceptual continuum.

As an example, begin with the assumption that the construct "mathematical ability" in actuality lies on a continuum and that it is normally distributed. Now

imagine a measure of this construct that consists en- tirely of calculus problems of similar difficulty, an- swered in a multiple-choice format. The distribution resulting from administration of this test to college undergraduates is likely to be bimodal, with one mode representing students who have been exposed to calculus and the other mode representing students who haven't. A fairly clear point of rarity between these modes would be obtained. Furthermore, the internal consistency of the test within the two "populations" is likely to be quite low, meaning that analyses such as maximum covariance analysis can lead to the conclusion that there are two discrete populations in these data. However, this conclusion is an artifact of the measuring device; use of a different measure (e.g., the Mathematical subtest of the Scholastic Aptitude Test) would lead to a quite different conclusion.

Although this example may seem removed from the assessment of personality disorders and psycho- pathology, similar contrasts can be drawn between currently available measurements. For example, most of the items for the Minnesota Multiphasic Personality Inventory (MMPI: Hathaway & McKin- ley, 1957) were selected for their ability to make a categorical distinction between some criterion group (e.g., depressed patients) and some "normal" group. Viewed from an item information model (Lord, 1980), all MMPI items in effect were selected to provide information around the conceptual point of rarity between these two populations. As a result, as with the hypothetical test of mathematical ability described earlier, the MMPI item-selection strategy yields items providing information that, when summed, will tend to dichotomize subjects. In con- trast, I recently completed an assessment instrument (the Personality Assessment Inventory [PAI]; Morey, 1991) for which item selection explicitly assumed that all psychopathological constructs have meaningful dimensional variance. Hence, items were selected to provide information across the full range of the theoretical continuum of the construct. One might expect that quite different conclusions about the dimensionality of any construct would be drawn from empirical studies using different item pools selected on the basis of different theoretical assumptions, such as from the MMPI and the PAI.

Where to Go From Here?

To this point, the field has provided little guidance as to what criteria one should use in deciding whether one is dealing with a categorical or a dimensional construct. The various statistical procedures described by Widiger reflect a promising start, but

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these procedures themselves are not fully tested-for example, several simulation studies using such methods could profitably be conducted-and ultimately they are bound by the limitations of the measurements that serve as data. Eventually, more meaningful conclusions may be drawn from theoretically driven experi- ments that specifically address these models. For example, members of a class might be expected to demonstrate a qualitatively different response to some intervention than nonmembers of that class. Functional relations between categorical constructs and other theoretically related measures should not be expected to be monotone and linear in nature, and hence other metrics of fit should be applied to the examination of these data. In the few instances when such applications are explicitly made, the categorical approach does not fare badly. There is a need to specify more clearly the results to be expected under the different models. Meehl (1977) outlined several forms of potential etio- logical forms for categorical constructs; it would be useful to begin discussion of similar forms for concur- rent (e.g., diagnostic) or predictive (e.g., treatment) implications of the categorical model. Without a care- ful consideration of just how a categorical construct may be manifest in observable variables, as a discipline we might not ever be able to accurately identify such constructs.

Note

Leslie C. Morey, Department of Psychology, 301 Wilson Hall, Vanderbilt University, Nashville, TN 37240.

References

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.

Gangestad, S., & Snyder, M. (1985). "To carve nature at its joints": On the existence of discrete classes in personality. Psychologi- cal Review, 92, 317-349.

Grayson, D. A. (1987). Can categorical and dimensional views of psychiatric illness be distinguished? British Journal of Psychi- atry 151, 355-361.

Hathaway, S. R., & McKinley, J. C. (1957). MMPI manual (Rev. ed.). New York: Psychological Corporation.

Kendell, R. E. (1975). The role of diagnosis in psychiatry. Oxford, England: Blackwell Scientific.

Lord, F. M. (1980). Applications of item response theory to practical testingproblems. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Meehl, P. E. (1977). Specific etiology and other forms of strong influence: Some quantitative meanings. Journal of Medicine and Philosophy 2,33-53.

Morey, L. C. (1988). The categorical representation of personality disorder: A cluster analysis of DSM-IH-R personality features. Journal ofAbnormal Psychology, 97, 314-321.

Morey, L. C. (1991). The Personality Assessment Inventory profes- sional manuaL Odessa, FL: Psychological Assessment Re- sources, Inc.

Can Alcohol Abuse and Dependence Be Dimensionalized-and Should They Be?

Peter E. Nathan University of Iowa

Included among the relatively few premorbid factors clearly predictive of alcohol abuse and dependence are conduct disorder during childhood and juvenile delin- quency during adolescence and young adulthood (McLaughlin, Baer, Burnside, & Pokorny, 1985); the single factor most predictive of alcoholism is family history of alcohol dependence (Buydens-Branchey, Branchey, & Noumair, 1989; Schuckit, 1985). The co-morbid conditions most frequently associated with alcohol abuse and dependence are antisocial personality disorder and one or another of the depressive spec- trum disorders (Hesselbrock, Hesselbrock, & Workman-Daniels, 1986; Stabenau, 1984); less com- mon but nonetheless a frequent accompaniment of alcoholism is borderline personality disorder (Nace, Saxon, & Shore, 1986; Rounsaville, Dolinsky, Babor, & Meyer, 1987).

Recalling these relations made Widiger's thoughtful and provocative target article of even greater interest to me. I have spent most of my career studying and thinking about alcohol abuse and dependence, particularly its clinical manifestations, diagnosis, and treatment. Widiger's article, although concerned with the personality disorders and not alcoholism, raises issues and draws conclusions that also have considerable rele- vance to alcoholism.

Alcohol dependence in the third edition of the Diag- nostic and Statistical Manual of Mental Disorders (DSM-III; American Psychiatric Association [APA], 1980) and in the revision (DSM-III-R; APA, 1987) is diagnosed from distinct behavioral signs and symp- toms. These diagnostic cues describe the adverse so- cial, occupational, and health consequences of abuse, compulsive use patterns, preoccupation with acquiring

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categories in personality disorder: would we know them if we saw them?

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