Academic journal article Journal of Marriage and Family

The Hazards of Predicting Divorce without Crossvalidation

Academic journal article Journal of Marriage and Family

The Hazards of Predicting Divorce without Crossvalidation

Article excerpt

Divorce prediction studies (e.g., Gottman, Coan, Carrere, & Swanson, 1998) suggest that couples' eventual divorce can be very accurately predicted from a number of different variables. Recent attention to these studies has failed to consider the need to crossvalidate prediction equations and to consider the prevalence of divorce in the population. We analyze archival data to demonstrate that accuracy and predictive value drops precipitously during crossvalidation. We conclude that results of studies without crossvalidation analyses should be interpreted with extreme caution, no matter how impressive the initial results appear to be.

Key Words: crossvalidation, divorce, overfitting, prediction


Can we really predict who will divorce from premarital data? The possibilities of such findings are staggering. The negative effects of marital decline and divorce are substantial and far-reaching on physical health (e.g., Burman & Margolin, 1992), psychological problems (e.g., Richards, Hardy, & Wadsworth, 1997), children's well-being (Grych & Fincham, 1990), and worker productivity (Forthofer, Markman, Cox, Stanley, & Kessler, 1996). Knowing who will eventually divorce would allow professionals, clergy, lay practitioners, and couples themselves to take steps to identify and ameliorate the factors that put them at risk before these factors take their toll. Accurate premarriage prediction of a couple's eventual divorce is truly the "Holy Grail" of marital and family preventionists.

Although a large number of studies have identified risk factors for divorce, only 15 published studies have predicted who will get divorced. These studies have used a variety of questionnaire, interview, and observational methods (Buehlman, Gottman, & Katz, 1992; Carrere, Buehlman, Gottman, Coan & Ruckstuhl, 2000; Crane, Soderquist, & Frank, 1995; Edwards, Johnson, & Booth, 1987; Fowers & Olson, 1986; Gottman, 1994; Gottman et al., 1998; Gottman & Levenson, 1999; Hill & Peplau, 1998; Jacobson, Gottman, Gortner, Berns & Shortt, 1996; Kurdek, 1993; Larsen & Olson, 1989; Lindahl, Clements & Markman, 1998; Matthews, Wickrama, & Conger, 1996; Rogge & Bradbury, 1999). The level of prediction accuracy is quite impressive, ranging from 67% to 95%. Most studies were prospective, following couples for 2-15 years. Sample sizes ranged from 54 to 286.

The six studies of Gottman and colleagues-- Gottman et al. (1998) in particular-have received an extraordinary degree of professional and popular attention (e.g., Weiss, 2000), and thus a consonant degree of scrutiny is appropriate. We should note that Gottman et al. (1998) not only included estimates of prediction accuracy but also tested several theoretical models of functional and dysfunctional couple behavior. Because a recent article in Journal of Marriage and the Family by Stanley, Bradbury, and Markman (2000) discussed problems with Gottman et al. (1998; many of which were disputed in a reply by Gottman, Carrere, Swanson, & Coan, 2000), the current article will be confined to an issue that Stanley et al. did not address: that the level of accuracy with which divorce can be predicted is being accepted and generalized without the necessary supporting statistical tests. That is, predictive equations must be crossvalidated for their true value to be known. No published study predicting divorce with general population couples has done this to date. (Crane et al. (1995) crossvalidated their predictive equation; however, they (a) used a marital therapy sample and (b) one of their two scales asked about cognitive and behavioral steps already taken toward divorce.) Two issues in particular-overfitting and the difficulty of predicting low prevalence events-plague the 15 prediction studies and make it imprudent to inform the public and clinicians that researchers can accurately predict who will divorce.


Discriminant function analyses and logistic regression can be used to predict a categorical outcome (e. …

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