PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Psychol Addict Behav. Author manuscript; available in PMC Jul 7, 2010.
Published in final edited form as:
PMCID: PMC2898721
NIHMSID: NIHMS44368
Similarities and Differences of Longitudinal Phenotypes Across Alternate Indices of Alcohol Involvement: A Methodologic Comparison of Trajectory Approaches
Kristina M. Jackson and Kenneth J. Sher
Department of Psychological Sciences, University of Missouri—Columbia
Correspondence concerning this article should be addressed to Kristina M. Jackson, who is now at the Center for Alcohol and Addiction Studies, Brown University, Box G-BH, Providence, RI 02912. kristina_jackson/at/brown.edu
Extant developmental research distinguishing young adults who moderate versus persist in alcohol consumption has not consistently evaluated the domain of alcohol involvement being modeled, making it difficult to compare findings across studies. In the present study, the authors characterized the developmental course of 5 indices of alcohol involvement using a prospective (6-wave) sample of 377 young adults (Year 1 age = 18.52 years; 55% female; 51% with family history of alcoholism) over 11 years. Growth mixture models were applied to each measure. Despite similarity in trajectory shape, predicted prevalences varied, and the consistency of trajectory classifications across alternate indices revealed low agreement. Correlates of drinking course, however, were somewhat robust across alcohol index. The finding that trajectories are conditional on the specific indices used suggests that it may be hazardous to generalize across alternate indices of alcohol involvement.
Keywords: alcohol, drinking, trajectory, course, developmental
Early young adulthood (ages 18–25 years) represents the period of peak prevalence for alcohol use (Gallup Organization, 1987) and alcohol use disorders (AUDs) (Grant, 1997), and the transition from high school to college is a time of risk for heavy drinking (Baer, Kivlahan, & Marlatt, 1995; Johnston, O’Malley, & Bachman, 2002a, 2002b). As young adults age beyond college, however, many exhibit a tendency to moderate heavy alcohol involvement (Donovan, Jessor, & Jessor, 1983; Perkins, 1999). In the population, alcohol consumption (particularly heavy drinking), problem drinking, and AUDs tend to peak in the early 20s and then show a decline over the third decade of life (Dawson, Grant, Stinson, & Chou, 2004; Fillmore, 1988; Jessor, Donovan, & Costa, 1991; Johnston et al., 2002a, 2002b; Muthén & Muthén, 2000). Against this normative decrease, many young adults continue to drink heavily (Windle, 1988), continue to have drinking problems (Fillmore, 1988; Tubman, Vicary, von Eye, & Lerner, 1990; Windle, 1988), and manifest AUDs (Zucker, Fitzgerald, & Moses, 1995). Thus, a central question of research into the etiology of alcoholism concerns factors that distinguish young adults who moderate their alcohol consumption from those who persist in heavy drinking. Yet, until recently, relatively little research has addressed variability in typical courses of alcohol involvement during this rapidly changing period of life of early young adulthood (recently termed “emerging adulthood”; Arnett, 2000). In the present study, we addressed three central research questions: (a) whether there exist prototypical courses of alcohol involvement, (b) the extent to which these courses are consistent across operationalization of alcohol involvement, and (c) whether these different courses vary in their etiological correlates.
Recent research has begun to chart the longitudinal course of alcohol involvement during adolescence and young adulthood. Theoretical (Zucker, 1987, 1994; Zucker et al., 1995) and empirical (e.g., Bennett, McCrady, Johnson, & Pandina, 1999; Chassin, Pitts, & Prost, 2002; Colder, Campbell, Ruel, Richardson, & Flay, 2002; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996; Schulenberg, Wadsworth, O’Malley, Bachman, & Johnston, 1996; Tucker, Orlando, & Ellickson, 2003) work has suggested that several prototypical courses can be identified. Although results with respect to the specific characterization of course and associated prevalences vary somewhat from study to study, there is consensus in findings revealing the presence of a nonuser/stable low-use course, a chronic or high-use course, a “developmentally limited” course (maturing out of drinking), and some evidence for a later onset or increasing course.
Recent research on the developmental course of alcohol involvement has varied considerably with respect to the specific variables and domains assessed and includes indices of alcohol involvement such as heavy/binge drinking (e.g., Chassin et al., 2002; Schulenberg, O’Malley et al. 1996; Schulenberg, Wadsworth, et al., 1996; Tucker et al., 2003), problem drinking (e.g., Bennett et al., 1999), and quantity–frequency (e.g., Colder et al., 2002). Assuming moderate associations among various indices of alcohol involvement, it is reasonable to hypothesize that trajectories based on one type of measure may be similar to those based on others (e.g., quantity–frequency vs. binge drinking vs. problems). However, the lack of studies comparing the correspondence of trajectories based on alternate definitions of alcohol involvement makes it difficult to compare findings across studies. To the extent that researchers have begun to reify these trajectories as summary measures of drinking course, it is critical to determine the extent to which such correspondence exists. Lack of correspondence can seriously limit the types of generalizations that can be made from one investigation to another if study particulars are not adequately considered.
In the process of investigating the construct validity of drinking trajectories, several investigations have attempted to identify etiological correlates of drinking course, often looking at the possible influence of demographic variables (e.g., sex, race) as well as measures of genetic disposition to alcoholism, personality/temperament, life events, and motivations for substance use. The findings with respect to several of these covariates are summarized below.
Sex
Men generally consume more alcohol and experience more alcohol-related problems than do women (Ager et al., 1996; Baer et al., 1995; Harford & Grant, 1994; Wilsnack & Wilsnack, 1997). In terms of course of alcohol involvement, findings are inconsistent, with some studies finding gender differences (e.g., Baer et al., 1995; Bennett et al., 1999; Chassin et al., 2002; Curran, Muthén, & Harford, 1998; Hill, White, Chung, Hawkins, & Catalano, 2000; Schulenberg, Wadsworth, et al., 1996; Tucker et al., 2003) and others failing to find differences (Bates & Labouvie, 1997; Duncan et al., 1997; Pape & Hammer, 1996; Wills, McNamara, Vaccaro, & Hirky, 1996). Generally, when a difference is observed, being male is associated with increased or persistently high alcohol involvement.
Family History of Alcoholism
Positive family history of alcoholism is associated with increased risk for AUDs (Sher, 1991; West & Prinz, 1987; Windle & Searles, 1990). Although Chassin and colleagues found that paternal alcoholism was associated with positive growth in alcohol use (Chassin, Curran, Hussong, & Colder, 1996) and with likelihood of belonging to an early onset (increasing) drinking group (Chassin et al., 2002), other researchers have failed to detect a family history effect on drinking course (Baer et al., 1995; Bates & Labouvie, 1997; Bennett et al., 1999).
In a similar vein, higher parental substance use in general has been shown to be associated with a more maladaptive (persistently heavy or increasing use) course of alcohol involvement (White, Johnson, & Buyske, 2000; Wills et al., 1996).
Behavioral Undercontrol
Indices of behavioral undercontrol, such as conduct disorder and delinquency, predict alcohol use and alcohol-related problems both cross-sectionally (Moss & Kirisci, 1995; Neighbors, Kempton, & Forehand, 1992) and prospectively (Brown, Gleghorn, Schuckit, Myers, & Mott, 1996; Johnson, Arria, Borges, Ialongo, & Anthony, 1995). In addition, behavioral undercontrol is a robust predictor of course of alcohol involvement among adolescents and young adults (Bates & Labouvie, 1997; Bennett et al., 1999; Chassin et al., 2002; Colder et al., 2002; Hill et al., 2000; Stice, Myers, & Brown, 1998; Tucker et al., 2003; White, Xie, Thompson, Loeber, & Stouthamer-Loeber, 2001; Wills et al., 1996). This has been shown to be true for temperamental constructs (i.e., harm avoidance and disinhibition) as well as antisocial, externalizing, risk-taking, and delinquent behavior. In general, findings reveal that those who began drinking early and had persistently high rates of drinking, in particular, exhibited greater behavioral undercontrol than did other groups. In the present study, we examined a temperamental index of behavioral undercontrol (novelty seeking) as well as a behavioral one (conduct disorder).
Negative Affectivity
Depression and anxiety disorders are associated with alcohol use in adolescents (Galaif, Chou, Sussman, & Dent, 1998; Hussong, Hicks, Levy, & Curran, 2001) and young adults (Kushner & Sher, 1993). Although indices of negative affectivity, such as depression, have shown associations with drinking course, particularly for those courses represented by elevated drinking (Chassin et al., 2002) and rapid escalation (Colder et al., 2002), findings have been inconsistent. Stice, Myers, and Brown (1998) showed that negative affectivity and internalizing predicted abstinence (vs. escalation to moderate alcohol use) but failed to show that either construct predicted escalation to heavy drinking. Bates and Labouvie (1997) found that emotional outbursts and self-derogation predicted continued heavy use versus continued low use. In addition, both societal- and individual-level research indicate that suicidal ideation, attempted suicide, and completed suicide are associated with alcohol use and abuse (Grant & Hasin, 1999; Lester, 2000) as well as alcohol dependence (Knopik et al., 2004). To date, however, no research has examined the influence of suicidal ideation or attempt on drinking course. In the present study, we examined diagnosis with a Diagnostic and Statistical Manual of Mental Disorders (3rd edition, DSM–III; American Psychiatric Association, 1980) depression or anxiety disorder as well as lifetime suicidal thoughts.
Reasons for Drinking
Drinking to regulate negative affect (e.g., tension reduction) robustly predicts alcohol use and problems (Cooper, Frone, Russell, & Mudar, 1995; Mann, Chassin, & Sher, 1987). In the only study to examine the association between reasons for drinking and course of alcohol involvement, Bennett et al. (1999) showed that persistent and developmentally limited courses of drinking tended to have the highest escape and enhance reasons for use, followed by moderate (and low) groups. In the present study, we examined affect-regulation reasons for drinking as a predictor of drinking course.
Although there is wide variation in how alcohol involvement is defined, alcohol involvement can be categorized into distinct but correlated domains: alcohol consumption and alcohol-related consequences or problems, and syndromal alcohol abuse and dependence. Heavy (or binge) alcohol consumption is related to higher probabilities of alcohol-related consequences (Harford, Wechsler, & Muthén, 2002; O’Hare, 1990; O’Neill, Parra, & Sher, 2001; Wechsler & Nelson, 2001) and higher rates of diagnosable AUDs, both cross-sectionally and prospectively (O’Neill et al., 2001; we note that prospective analyses suggest that this relation may be more robust during late adolescence than during later young adulthood, as individuals become more experienced with drinking alcohol).
However, some research suggests only a moderate association between consumption and problems, with only a small proportion of users actually reporting alcohol problems (Bailey & Rachal, 1993; Sadava, 1985, 1990; Stice, Barrera, & Chassin, 1998). Bailey and Rachal (1993) observed only moderate intercorrelations between indices of consumption frequency, alcohol-related problems, and symptoms of dependency, and Sadava (1990) computed the average correlation between consumption and problems as r = .40 across a set of general population studies. Bailey and Rachal (1993), Sadava (1990), and Stice, Barrera, and Chassin (1998) proposed distinct risk factors for moderate drinking and alcohol problems/consequences; Bailey and Rachal’s findings (but not Stice, Barrera, & Chassin’s findings) support this. No research to date, however, has explored the congruence between different courses of drinking on the basis of indices of alcohol consumption, alcohol consequences/problems, and alcohol use disorder or examined the extent to which these different courses can be differentially predicted by risk factors.
Overview
We evaluated the extent to which trajectories based on different indices of alcohol involvement tend to identify the same people. First, developmental trajectories were identified across five indices of alcohol involvement ranging in degree of severity, including alcohol use disorder, alcohol dependence, alcohol consequences, heavy drinking, and alcohol quantity–frequency. Then, pattern and prevalence of trajectory group membership was compared across indices. In addition, etiological predictors of trajectories based on different indices of alcohol involvement were compared.
Sample and Procedure
Our sample was taken from a prospective study of college students (i.e., Sher, Walitzer, Wood, & Brent, 1991). During the 1987–1988 academic year, 3,156 incoming, first-time college freshmen at a large, midwestern university were recruited to participate in the study over the telephone. Participants were screened for family history of alcoholism with the Short Michigan Alcoholism Screening Test (F-SMAST and M-SMAST for paternal and maternal alcoholism, respectively; Selzer, Vinokur, & van Rooijen, 1975), adapted for assessing parental alcoholism (Crews & Sher, 1992), and sections of the Family History-Research Diagnostic Criteria interview (FH-RDC; Endicott, Andreasen, & Spitzer, 1978). Participants were classified as family history positive (FH+) for alcoholism if they scored a 4 or greater on the F-SMAST (in some cases, 3 was used as a cut score; see Sher et al., 1991) and if their biological father was diagnosed with alcoholism with the FH-RDC. Participants were classified as family history negative (FH-) for alcoholism if they had no biological first-degree relatives with alcoholism or drug abuse or antisocial personality disorder and no second-degree relatives with alcoholism or drug abuse. Hence, our sample consisted of only those respondents who exhibited positive or negative (but not intermediate or indeterminate) family history of paternal alcoholism; consequently, approximately half of our sample was high risk (reflecting a substantial oversampling of persons with a positive family history of alcoholism). From these participants, a final sample consisting of 489 participants (47% male) was retained, including 113 FH− men, 118 FH+ men, 124 FH− women, and 132 FH+ women.1 Baseline age was 18.5 years; 94% of the sample was Caucasian. Participants were assessed at baseline and at five subsequent occasions over the next 10 years (Years 2, 3, 4, 7, and 11). Assessment included clinical interview and self-administered questionnaire. Interviews were conducted primarily in person; telephone interviews increased in frequency as participants relocated (42% by Year 11). At Year 11, 410 participants (84% of the original 489) were reinterviewed; 33 refused further participation, 39 were not assessed (because they were never located, never scheduled, or failed to complete the survey), and 7 were deceased. A more complete portrayal of subject retention and loss over all six waves of assessment is presented in Jackson and Sher’s (2003) study. For the present study, 377 participants (77% of the original 489) provided complete data at all six waves. At all waves, informed consent was given for participation.
Measures
We used five indices of alcohol involvement, including an interview-based alcohol use disorder assessment and four questionnaire-based measures: alcohol consequences, alcohol dependence, alcohol quantity–frequency, and heavy drinking.
AUD
Past-year alcohol abuse and dependence diagnoses were assessed with the Diagnostic Interview Schedule (DIS; Robins, Helzer, Croughan, Williams, & Spitzer, 1985): DIS Version III-A (Robins et al., 1985) at Years 1 and 2; DIS Version III-R (Robins, Helzer, Cottler, & Goldring, 1989) at Years 3, 4, and 7; and DIS Version-IV (Robins, Cottler, Bucholz, & Compton, 1997) at Year 11. To maintain continuity, diagnoses were made according to criteria from the DSM–III (American Psychiatric Association, 1980). A single AUD diagnosis was assigned if a participant met criteria for alcohol abuse and/or dependence.
Alcohol dependence
A count of past-year alcohol dependence symptoms was assessed with a 13-item scale (α from .70 to .85, over six waves) developed for the present study (Sher et al., 1991). Item responses included 0(no, never), 1 (yes, but not in the past year), 2 (once in the past year), 3 (twice in the past year), and 4 (three or more times in the past year). Sample items included experiencing shakes after quitting or cutting back on alcohol, needing larger amounts of alcohol to feel an effect, and not remembering part of the previous evening after drinking.
Alcohol consequences
A count of past-year alcohol consequences was assessed with a 14-item scale (α from .72 to .75), also developed for the present study (Sher et al., 1991). Item responses were identical to alcohol dependence responses. Sample items included getting into trouble at work or school because of drinking, finding oneself in a sexual situation when drinking that was later regretted, and receiving a lower grade on a test or paper because of drinking.
Alcohol quantity–frequency
Past-year quantity–frequency of alcohol consumption was computed by taking the product of per-week drinking quantity (number of drinks per drinking day, expressed as standard drink equivalents) and past-week drinking frequency (scaled to reflect drinking occasions per week). The variable was log-transformed (after adding 1) to reduce skew.
Heavy drinking
Frequency of heavy drinking in the past month was assessed with a single item: “In the past 30 days, how many times have you had five or more drinks at a single sitting, either of beer, wine, wine coolers, liquor, or some combination of these?” We retained the ordinal scaling of the item, which ranged from 0 (didn’t drink five or more drinks at a single sitting in the past 30 days) to 7 (every day). Although we originally transformed this item to reflect weekly drinking values, we experienced model convergence problems with this highly skewed variable and consequently retained the original ordinal item.
Predictors of course
Predictors included sex, family history of alcoholism, conduct disorder symptom count, novelty seeking, lifetime diagnosis with a DSM–III depression or anxiety disorder, the presence of suicidal thoughts in lifetime, and affect-regulation reasons for drinking. All were taken from Year 1. Sex and family history of alcoholism (as described above) were assessed at screening for all respondents. Novelty seeking was assessed with the 34-item novelty-seeking subscale of the 98-item Tridimensional Personality Questionnaire (TPQ; Cloninger, 1987b). A symptom count for conduct disorder, diagnoses of lifetime generalized anxiety and depression, and the single-item measure assessing the presence of suicidal thoughts in lifetime were assessed with the DIS Version III-A (Robins et al., 1985). Finally, affect-regulation reasons for drinking (e.g., “I drink because it helps me to relax”) were assessed with 7 items from a 15-item instrument adapted from Cahalan, Cisin, and Crossley (1969).
Data Analytic Technique
To identify trajectories of drinking, we used a mixture modeling procedure (Jones, Nagin, & Roeder, 2001; Muthén, 2001a, 2001b; Muthén & Muthén, 2000; Nagin, 1999), latent growth mixture models (LGMMs). These models “mix,” in a single estimation procedure, the continuous nature of a latent growth curve model with the categorical nature of group membership. Typical latent growth curve models assume that respondents come from the same population, with identical mean levels (intercepts) and growth patterns (slopes) over time (with individual variation represented by the intercept and slope factor variances). LGMMs, however, allow for different populations to have unique intercepts and slopes. In essence, LGMMs estimate a unique latent growth curve (with individual variability) for each underlying population. For applications of this technique in the substance use area, see Chassin et al. (2002), Colder et al. (2001, 2002), and Li, Barrera, Hops, and Fisher (2002).
For our categorical measure (AUD), we used latent class growth analysis (LCGA), which is a procedure much like latent class analysis but which incorporates the time-ordered nature of the data (Muthén, 2001a). The latent classes are particular subtypes or groups of individuals who empirically display similar patterns of diagnosing with AUD, and the observed variables represent the presence (1) or absence (0) of a diagnosis with AUD at Years 1, 2, 3, 4, 7, and 11.
All models to estimate trajectories were estimated with Mplus, Version 2.13 (Muthén & Muthén, 1998–2004). The base model included intercept and linear slope. The categorical AUD model also included a quadratic slope (models with a quadratic slope would not converge for continuous alcohol measures).2 Regressions to predict group membership from baseline predictors of drinking course were also estimated with Mplus.
Identification of Trajectories
To maximize meaningful comparisons, we selected a four-class model for each measure. For most cases, this was the best-fitting model in terms of model fit and class interpretability and size (see Table 1 for fit indices). Model fit was evaluated with information criteria fit indices (Bayesian information criterion [Schwartz, 1978]; Akaike’s information criterion [Akaike, 1987]). Table 1 also presents entropy values, which represent precision of classification based on posterior probability values (Muthén & Muthén, 1998-2004); an entropy value close to 1.0 indicates clear classification (little overlap among trajectories).
Table 1
Table 1
Model Fit for Five Indices of Alcohol Involvement
Figures Figures11 through through55 show the developmental courses for alcohol use disorder, alcohol dependence, alcohol consequences, alcohol quantity–frequency, and heavy drinking, respectively. Group membership was characterized by the following courses: nondrinking or nonproblematic drinking, which ranged from 45% to 74%; developmentally limited, which decreased over time, ranging from 8% to 26%; later onset, which increased over time, ranging from 2% to 19%; and chronic (this course also often exhibited a decrease over time; however, it was the most chronic of all courses), which ranged from 3% to 22%.
Figure 1
Figure 1
Latent class growth analysis model for alcohol use disorder (AUD) at Waves 1–6, weighted by estimated class probabilities. N = 377. Later Ons. = later onset; NonDx = nondrinking or nonproblematic drinking; Dev Limited = developmentally limited. (more ...)
Figure 5
Figure 5
Mixture model for heavy drinking at Waves 1–6, weighted by estimated class probabilities. N = 377. Heavy drinking was scored as 0 = didn’t drink 5 or more drinks at a single sitting in the past 30 days, 2 = once during the past 30 days (more ...)
Comparison of Trajectories
To compare agreement between class membership for each measure, respondents were first assigned to classes on the basis of their most likely group membership (unweighted by probability of class membership), and then contingency tables were created for each pairwise comparison (10 in all).3 Agreement between indices, assessed by Cohen’s kappa (Cohen, 1960), ranged from κ = .26 to κ = .54 (see below the diagonal in Table 2). These represent moderate levels of agreement (Goodman & Kruskal, 1954). Ancillary analyses examined hand-calculated kappa estimates that were weighted by probability of group membership, which were very similar in magnitude (κ = .21 to κ = .48; see above the diagonal in Table 2). Note that these “weighted” kappas are not weighted kappas as the term is traditionally used in the literature to describe a kappa variant that takes into account gradation in level of disagreement (Cohen, 1968). Rather, they are unweighted kappas for estimates that were weighted by probability of group membership.
Table 2
Table 2
Comparison of Trajectories Using Cohen’s Kappa
One concern is that the association between any two measures is actually driven by the nonproblem groups. We recomputed the kappas after eliminating those two groups from the comparison (see Table 3). On average, the magnitude of the kappas was reduced by about two thirds (Mdn = 66%), suggesting that, overall, different patterns among users account for the majority, but not all, of the concordance between the indices. Inspection of specific pairs indicated substantial variability after eliminating nonusers (39% to 94% of the original value of the kappa remained after removing nonusers); however, there was no specific pattern suggesting that nonusers account for concordance among certain constructs.
Table 3
Table 3
Comparison of Trajectories Using Cohen’s Kappa After Removing Nonusers
Given that presenting each of the 10 pairwise contingency tables would be cumbersome, we also present a summary figure (see Figure 6) documenting the location of trajectory agreement (i.e., along the diagonal) and disagreement (i.e., off-diagonal cells). To determine the particular combinations of courses that most contribute to agreement between two trajectories, the cell chi-square statistics for each cell were plotted with bar graphs for each of the 10 comparisons. Corresponding courses were observed at a rate greater than what would be expected by chance (i.e., agreement along the diagonal). Associations between AUD and other indices were lower than those between any other comparison. This may be due to the larger negative slope and higher initial value of the developmentally limited course for AUD than for the other indices.
Figure 6
Figure 6
Summary of trajectory agreement represented by cell chi-square statistics for each of the five alcohol measures. Dark bars reflect values that are greater than would be expected by chance, and light bars indicate values that are lower than would be expected (more ...)
Correspondingly, disagreement along the off diagonal was considerable, although the chronic course was occasionally associated with the developmentally limited and later-onset courses. Although the chronic course tended to decline over time for some of the alcohol indices (AUD, alcohol dependence, alcohol consequences), this would not explain why it was associated with the developmentally limited course for other alcohol indices or why it was at times associated with the later-onset course.
Etiological Predictors
Finally, we examined third-variable prediction of group membership for each of the indices. Predictors (sex, family history of alcoholism, conduct disorder symptom count, novelty seeking, lifetime diagnosis with a DSM–III depression or anxiety disorder, presence of suicidal thoughts in lifetime, and affect-regulation reasons for drinking) were treated as exogenous to class membership. Continuous measures (conduct disorder symptom count, novelty seeking, and reasons for drinking) were standardized to provide a more meaningful metric for odds ratios. A multinomial logistic regression procedure was used (Agresti, 1990). Five comparisons of interest were examined: the three drinking groups versus the nondrinking group (consistent with the literature) and developmentally limited versus chronic (begin at the same point but diverge) and later onset versus chronic (begin at different points but converge). All models were run in Mplus, which allows for parameters to be weighted by probability of group membership. For the five psychosocial predictors, sex and family history were controlled.
In Table 4, we present odds ratios for each measure of alcohol involvement and each of the seven predictors. Relative to the nondrinking/nonproblematic drinking group, likelihood of membership in the chronic group was generally predicted by all etiological predictors, although considerably less so for family history of alcoholism, depression/anxiety, and suicidal thoughts. In general, likelihood of belonging to the developmentally limited class was significantly predicted by greater conduct disorder, novelty seeking, and reasons for drinking. Likelihood of belonging to the later onset class was positively predicted by greater reasons for drinking and, for measures of more problematic drinking (AUD, alcohol dependence, alcohol consequences), greater conduct disorder. There were few differences between the chronic group and the developmentally limited and later-onset groups. Those who remitted from the problematic alcohol involvement measures were less likely to exhibit conduct disorder or endorse affect-regulation reasons for drinking than were those who continued their drinking. In addition, those who increased their alcohol involvement over young adulthood were more likely to exhibit conduct disorder (for the consumption measures) or endorse novelty seeking (particularly for the problematic measures). In sum, with a few exceptions, prediction tended to be relatively consistent across measures of alcohol involvement, despite the differences in trajectory structure and membership and the low-to-moderate agreement among trajectories.
Table 4
Table 4
Odds Ratios and Significance Values for Univariate Prediction of Trajectories for Five Pairwise Comparisons Across Each Measure of Alcohol Involvement, for Seven Predictor Variables
The present study examined congruence of drinking trajectories over an 11-year interval across five indices of alcohol involvement, including AUD, alcohol dependence, alcohol consequences, heavy drinking, and alcohol quantity–frequency, and explored the extent to which etiological predictors of drinking trajectory varied across indices.
Agreement Across Alternate Indices of Alcohol Involvement
For the five alcohol indices, we observed four developmental courses tracking alcohol involvement across early young adulthood. Given the epidemiological data showing the decline in heavy and problematic drinking during this developmental period (e.g., Dawson et al., 2004; Johnston et al., 2002a, 2002b), it was not surprising that we observed a developmentally limited course for each measure. In fact, for most indices of alcohol involvement, even the more chronic course tended to evince a decline over time. We examined whether the slopes for the chronic and developmentally groups were in fact identical by examining the 95% confidence intervals for the slope parameter. The two slopes were identical for alcohol consequences (but not for the other measures), suggesting that, for this measure, despite different initial levels, these two groups shared a normative decrease. We do note that the developmentally limited course may be less evident in nonstudents. In fact, there is evidence that although college students surpass their noncollege student peers in heavy drinking after high school, they exhibit lower levels of heavy drinking in their 30s (Jackson, Sher, & Park, 2005), and recent analyses of the National Epidemiologic Survey on Alcohol and Related Conditions (Dawson et al., 2004) revealed that those not in college and living independently tended to have more stable heavy drinking rates than did those in college, who revealed a more developmentally limited pattern.
Despite relatively similar trajectory shapes, predicted prevalences varied considerably across alternate indices of alcohol involvement. The indices of alcohol dependence and alcohol consequences showed the greatest number of participants in the nonproblematic drinking category and the fewest in either the chronic (for alcohol consequences) or later-onset (for alcohol dependence) classes. Whereas most of the individuals who reported having had some alcohol consequences tended to show a developmentally limited pattern, alcohol dependence contained large frequencies of both chronic and developmentally limited groups. Alcohol quantity–frequency showed the fewest in the nondrinking category and the greatest in the developmentally limited category, which is consistent with the notion of abundant drinking during college. Likewise, heavy drinking showed a large group of developmentally limited individuals, although an equally large group of individuals who persistently binge drank was observed. Consistent with the idea of a Type I alcoholic (Cloninger, 1987a), there was a substantial group (19%) with a later-onset trajectory for the AUD measure; none of the other indices showed later-onset groups of such magnitude (all others < 10%).
Of greatest importance for the present study, trajectory classifications across alternate indices of alcohol involvement revealed only small-to-moderate agreement. This has profound implications for researchers who wish to generalize developmental courses among indices of alcohol involvement. Not surprisingly, more similar indices showed greater agreement, with the two consumption indices (quantity–frequency and heavy drinking) showing the greatest agreement, along with the two questionnaire-based indices of more pathological alcohol involvement (alcohol dependence and alcohol consequences). Agreement with the AUD measure tended to be lowest, which is likely due in part to the interview-based assessment and categorical nature of AUD. As would be expected, heavy drinking showed stronger agreement with the problem indices (AUD, alcohol dependence, alcohol consequences) than did alcohol quantity–frequency. Although the concordance between nonusing groups contributed to a good part of the agreement between indices, eliminating these groups did not diminish the relation entirely. Most of the correspondence was due to concordance between corresponding groups, although, at times, there was a tendency for the chronic group to be classified as developmentally limited or later onset.
Etiological Predictors
Both the temperamental and behavioral measures of behavioral undercontrol exhibited strong prediction of alcohol group membership across all indices. This is consistent with much of the literature showing effects for measures of harm avoidance and disinhibition and antisocial, externalizing, risk taking, and delinquent behavior on drinking trajectories (e.g., Bates & Labouvie, 1997; Bennett et al., 1999; Chassin et al., 2002; Colder et al., 2002; Hill et al., 2000; Stice, Myers, & Brown, 1998; Tucker et al., 2003; Wills et al., 1996). Substance use such as drinking may occur in the context of general problem behavior, whereby behavioral undercontrol and substance use are two of a number of behaviors associated with a deviant lifestyle (Farrell, Danish, & Howard, 1992; Jessor & Jessor, 1977). For the problem alcohol indices only, behavioral undercontrol was associated with relatively heavy and persistent drinking during early young adulthood (i.e., the early college years) versus drinking that remitted over time. In addition, and not surprisingly, behavioral undercontrol was greater for those whose drinking was chronic than for those who had a later onset of drinking.
In addition, affect-regulation reasons for drinking were highly predictive of group membership, with a monotonically decreasing level from chronic to developmentally limited to nondrinking trajectories (reasons for drinking in the later-onset group were greater than in the nondrinking group but were not necessarily greater than those of the other two groups). This is consistent with Bennett et al. (1999), who found that escape and enhance reasons for use were greatest for the persistent and developmentally limited drinking groups. The effects for negative affect were less robust, supporting previous work showing this to be a weaker predictor than is behavioral undercontrol (e.g., Colder et al., 2002).
Consistent with cross-sectional analyses of our own data (Sher et al., 1991), we expected to see the strongest effect of family history on more problematic measures of alcohol involvement. We did, in fact, find a difference between chronic and nondiagnosing groups for AUD and alcohol dependence. Our prior work showed that family history relates to the persistence of problematic alcohol involvement in early adulthood rather than to the initiation into heavier drinking (Jackson, Sher, Gotham, & Wood, 2001); prediction of membership in chronic but not developmentally limited drinking trajectories is consistent with this. However, surprisingly, we failed to observe differences among the diagnosing groups (e.g., chronic vs. developmentally limited or later onset).
In addition, there were fewer sex effects than might be expected. Being male did tend to predict heavy drinking course, however, which may be due in part to the convention of defining binge drinking as five drinks at a time for both men and women, despite individual differences—in body mass, body water content, and gastric metabolism of alcohol (Lieber, 1997)—associated with gender. These gender differences may not have been as readily observable with a more gender-specific definition of binge drinking (Wechsler, Dowdall, Davenport, & Rimm, 1995). However, gender effects were noted for other measures where the definitions were not gender specific (e.g., AUD; also for alcohol consequences and heavy drinking using univariate tests; results not shown).
Despite the epidemiological literature on “maturing out,” we found few risk factors that actually distinguished between individuals who moderate versus persist in their alcohol consumption. Some of the more temperamental measures (e.g., conduct disorder, novelty seeking, reasons for drinking) predicted membership in the chronic group, relative to the developmentally limited group, for the problematic alcohol indices. Following the framework suggested by Sher, Gotham, and Watson (2004), these constructs (particularly conduct disorder and novelty seeking) could be considered “stable vulnerability indicators” that distinguish course and may be indexing a severity-graded vulnerability process such that the developmentally limited course is a less severe form of the chronic course. Conversely, those in the chronic group may be vulnerable to the influence of more time-varying, situational risk factors, which were not assessed (e.g., peer influence, life stressors), that could increase their risk of problematic alcohol involvement.
In sum, despite small-to-moderate agreement in trajectory classification, we observed roughly similar patterns of prediction across trajectories based on different indices of alcohol involvement. Although we could not explicitly test this, alcohol quantity–frequency appeared to show the smallest effects, and the most severe indices of problematic drinking appeared to show the strongest effects, especially for the chronic trajectories.
Conceptualization of Developmental Course
Recent research in substance use, and in developmental psychopathology in general, has attempted to model heterogeneity in developmental course and to determine the risk and protective factors associated with a less maladaptive course. Although course can be represented by modeling trajectories, as in the present article, it is certainly not the only way to represent course. Research has explored correlates of individual growth curves representing initial level (intercept) and growth (using polynomial slope factors) of a construct using a growth curve modeling approach (Muthén & Curran, 1997). For situations where modeling growth in a process is not a study objective, chronicity can be represented as a continuous variable using state-trait modeling (e.g., Jackson & Sher, 2003), which posits that behavior over time can be captured by a single latent variable. One of the strengths of state-trait models is in the multiple assessments of a single construct. Increasing the number of assessments increases our assessment of reliability, akin to Cronbach’s alpha increasing with number of scale items (Nunnally & Bernstein, 1994). In fact, behavior averaged over a number of events has been shown to be more predictable than is a single instance of behavior (Epstein, 1979). Current work in our lab is exploring the extent to which modeling behavior as a chronic process is more powerful than exploring behavior at individual time points. Concordance between alternate indices of alcohol involvement can also be represented with the state-trait model as well as by agreement between developmental courses. Preliminary work (Sher & Jackson, 2004) has shown that aggregated (trait) correlations between alternate indices of alcohol involvement are considerably greater than are the median zero-order correlations across six waves. Variable-centered approaches such as state-trait modeling complement the more person-centered approaches such as that taken in the present study.
Final Comments
In sum, we found that longitudinal trajectories of alcohol involvement were conditional on the indices used. Recognition of this general finding should lead researchers to be cautious in generalizing longitudinal patterns across different facets of alcohol involvement, and care must be taken not to overreify empirically derived trajectories. Although person-based approaches to resolving course of alcohol involvement are almost certainly an advance in thinking about variations in alcohol involvement over the life course, we should not lose sight of the fact that trajectories need to be referenced to the indices, timing, and observation periods used.
Thus, from our perspective, characterization of trajectories represents a step forward in the study of developmental aspects of alcohol involvement in the life course. However, we are still at an early stage of research with respect to understanding the implications of these new methodologies for how well we “carve nature at her joints.” Our present findings suggest that, although carvings based on different facets of alcohol yield similar structures and show roughly similar patterns of correlates, they are proportioned differently (i.e., yield different prevalences). Also, we do not yet know how best to choose the number and timing of measurement occasions to yield optimally resolved portrayals of life-course trajectories. Progress in our understanding individual differences in alcohol use over the life course will require a careful consideration of the developmental meanings of different domains of alcohol involvement and longitudinal designs that are optimally sensitive to the underlying phenomena.
Figure 2
Figure 2
Mixture model for alcohol dependence at Waves 1–6, weighted by estimated class probabilities. N = 377. Alcohol dependence symptoms are scored as follows: 0 = no, never, 1 = yes, but not in the past year, 2 = once in the past year, 3 = twice in (more ...)
Figure 3
Figure 3
Mixture model for alcohol consequences at Waves 1–6, weighted by estimated class probabilities. N = 377. Alcohol consequence symptoms are scored as follows: 0 = no, never, 1 = yes, but not in the past year, 2 = once in the past year, 3 = twice (more ...)
Figure 4
Figure 4
Mixture model for alcohol quantity–frequency at Waves 1–6, weighted by estimated class probabilities. N = 377. Alcohol quantity–frequency is scaled as the log of (the product of per-week drinking quantity and past-week drinking (more ...)
Acknowledgments
Preparation of this article was supported by National Institute on Alcohol Abuse and Alcoholism Grants K01 AA13938, R01 AA07231, R37 AA13987, and P50 AA11998. We thank Carolyn Basinger, Jennifer Krull, Bengt Muthén, Linda Muthén, Aesoon Park, Gilbert Parra, John Schulenberg, and Phillip Wood for their assistance in analyses and for their helpful comments.
Footnotes
1Two of the 489 participants learned after study entry that they had been adopted as children.
2We note that the AUD model with a linear slope only produces trajectories and class prevalences that are very similar to the model with the quadratic and linear slopes.
3When possible, estimates that are weighted by the probability for group membership are preferred, as it ensures that individuals who are “cleanly” assigned to a group (e.g., 100% likely to be in the chronic group, 0% likely to be in any other group) are not treated identically to individuals whose profiles are less well defined (e.g., 51% likely to be in the chronic group, 49% likely to be in the later-onset group). However, it was not possible to create contingency tables with this weighted approach, and for these analyses, we assigned respondents to their most likely class.
  • Ager CR, Ferrer HP, Fillmore KM, Golding JM, Leino EV, Motoyoshi M. Aggregate-level predictors of the prevalence of selected drinking patterns in multiple studies: A research synthesis from the Collaborative Alcohol-Related Longitudinal Project. Substance Use and Misuse. 1996;31:1503–1523. [PubMed]
  • Agresti A. Categorical data analysis. Wiley; New York: 1990.
  • Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–332.
  • American Psychiatric Association Diagnostic and statistical manual of mental disorders. 3rd ed. Author; Washington, DC: 1980.
  • Arnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist. 2000;55:469–480. [PubMed]
  • Baer JS, Kivlahan DR, Marlatt GA. High-risk drinking across the transition from high school to college. Alcoholism: Clinical and Experimental Research. 1995;19:54–61. [PubMed]
  • Bailey SL, Rachal JV. Dimensions of adolescent problem drinking. Journal of Studies on Alcohol. 1993;54:555–565. [PubMed]
  • Bates ME, Labouvie EW. Adolescent risk factors and the prediction of persistent alcohol and drug use into adulthood. Alcoholism: Clinical and Experimental Research. 1997;21:944–950. [PubMed]
  • Bennett ME, McCrady BS, Johnson V, Pandina RJ. Problem drinking from young adulthood to adulthood: Patterns, predictors and outcomes. Journal of Studies on Alcohol. 1999;60:605–614. [PubMed]
  • Brown SA, Gleghorn A, Schuckit MA, Myers MG, Mott MA. Conduct disorder among adolescent alcohol and drug abusers. Journal of Studies on Alcohol. 1996;57:314–324. [PubMed]
  • Cahalan D, Cisin IH, Crossley HM. American drinking practices: A national study of drinking behavior and attitudes. Rutgers Center of Alcohol Studies; New Brunswick, NJ: 1969. Monograph No. 6.
  • Chassin L, Curran PJ, Hussong AM, Colder CR. The relation of parent alcoholism to adolescent substance use: A longitudinal follow-up study. Journal of Abnormal Psychology. 1996;105:70–80. [PubMed]
  • Chassin L, Pitts SC, Prost J. Binge drinking trajectories from adolescence to emerging adulthood in a high-risk sample: Predictors and substance abuse outcomes. Journal of Consulting and Clinical Psychology. 2002;70:67–78. [PubMed]
  • Cloninger CR. Neurogenetic adaptive mechanisms in alcoholism. Science. 1987a April 24;236:410–416. [PubMed]
  • Cloninger CR. Tridimensional Personality Questionnaire, Version 4. 1987b. Unpublished manuscript.
  • Cohen J. A coefficient of agreement for nominal scales. Educational Psychological Measurement. 1960;20:37–46.
  • Cohen J. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin. 1968;70:213–220. [PubMed]
  • Colder CR, Campbell RT, Ruel E, Richardson JL, Flay BR. A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology. 2002;70:976–985. [PubMed]
  • Colder CR, Mehta P, Balanda K, Campbell RT, Mayhew KP, Stanton WR, Pentz MA, Flay BR. Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling. Health Psychology. 2001;20:127–135. [PubMed]
  • Cooper ML, Frone MR, Russell M, Mudar P. Drinking to regulate positive and negative emotions: A motivational model of alcohol use. Journal of Personality and Social Psychology. 1995;69:990–1005. [PubMed]
  • Crews TM, Sher KJ. Using adapted Short MASTs for assessing parental alcoholism: Reliability and validity. Alcoholism: Clinical and Experimental Research. 1992;16:576–584. [PubMed]
  • Curran PJ, Muthén BO, Harford TC. The influence of changes in marital status on developmental trajectories of alcohol use in young adults. Journal of Studies on Alcohol. 1998;59:647–658. [PubMed]
  • Dawson DA, Grant BF, Stinson FS, Chou PS. Another look at heavy episodic drinking and alcohol use disorders among college and noncollege youth. Journal of Studies on Alcohol. 2004;65:477–488. [PubMed]
  • Donovan JE, Jessor R, Jessor L. Problem drinking in adolescence and young adulthood: A follow-up study. Journal of Studies on Alcohol. 1983;44:109–137. [PubMed]
  • Duncan TE, Duncan SC, Alpert A, Hops H, Stoolmiller M, Muthén B. Latent variable modeling of longitudinal and multilevel substance use data. Multivariate Behavioral Research. 1997;32:275–318.
  • Endicott J, Andreasen N, Spitzer RL. Family History-Research Diagnostic Criteria (FH-RDC) New York State Psychiatric Institute; New York: 1978.
  • Epstein S. The stability of behavior: I. On predicting most of the people much of the time. Journal of Personality and Social Psychology. 1979;37:1097–1126.
  • Farrell AD, Danish SJ, Howard CW. Relationship between drug use and other problem behaviors in urban adolescents. Journal of Consulting and Clinical Psychology. 1992;60:705–712. [PubMed]
  • Fillmore KM. Alcohol use across the life course. Addiction Research Foundation; Toronto, Ontario, Canada: 1988.
  • Galaif ER, Chou CP, Sussman S, Dent CW. Depression, suicidal ideation, and substance use among continuation high school students. Journal of Youth and Adolescence. 1998;27:275–299.
  • Gallup Organization The Gallup report: Alcohol use and abuse in America. Author; Princeton, NJ: 1987. Report No. 265.
  • Goodman LA, Kruskal WH. Measures of association for cross classifications. Journal of the American Statistical Association. 1954;49:732–769.
  • Grant BF. Prevalence and correlates of alcohol use and DSM–IV alcohol dependence in the United States: Results of the National Longitudinal Alcohol Epidemiologic Survey. Journal of Studies on Alcohol. 1997;58:464–473. [PubMed]
  • Grant BF, Hasin DS. Suicidal ideation among the United States drinking population: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Studies on Alcohol. 1999;60:422–429. [PubMed]
  • Harford TC, Grant BF. Prevalence and population validity of DSM–III–R alcohol abuse and dependence: The 1989 National Longitudinal Survey on Youth. Journal of Substance Abuse. 1994;6:37–44. [PubMed]
  • Harford TC, Wechsler H, Muthén BO. The impact of current residence and high school drinking on alcohol problems among college students. Journal of Studies on Alcohol. 2002;63:271–279. [PubMed]
  • Hill KG, White HR, Chung I, Hawkins JD, Catalano RF. Early adult outcomes of adolescent binge drinking: Person- and variable-centered analyses of binge drinking trajectories. Alcoholism: Clinical and Experimental Research. 2000;24:892–901. [PMC free article] [PubMed]
  • Hussong AM, Hicks RE, Levy SA, Curran PJ. Specifying the relations between affect and heavy alcohol use among young adults. Journal of Abnormal Psychology. 2001;110:449–461. [PubMed]
  • Jackson KM, Sher KJ. Alcohol use disorders and psychological distress: A prospective state-trait analysis. Journal of Abnormal Psychology. 2003;112:599–613. [PubMed]
  • Jackson KM, Sher KJ, Gotham HJ, Wood PK. Transitioning into and out of large-effect drinking in young adulthood. Journal of Abnormal Psychology. 2001;110:378–391. [PubMed]
  • Jackson KM, Sher KJ, Park A. Drinking among college students: Consumption and consequences. In: Galanter M, editor. Recent developments in alcoholism: Research on alcohol problems in adolescents and young adults. Vol. 17. Kluwer Academic/Plenum; New York: 2005. pp. 85–117. [PubMed]
  • Jessor R, Donovan JE, Costa FM. Beyond adolescence: Problem behavior and young adult development. Cambridge University Press; Cambridge, England: 1991.
  • Jessor R, Jessor S. Problem behavior and psychosocial development. Academic Press; New York: 1977.
  • Johnson EO, Arria AM, Borges G, Ialongo N, Anthony JC. The growth of conduct problem behaviors from middle childhood to early adolescence: Sex differences and the suspected influence of early alcohol use. Journal of Studies on Alcohol. 1995;56:661–671. [PubMed]
  • Johnston LD, O’Malley PM, Bachman JG. Monitoring the Future national survey results on drug use, 1975–2001: Vol. I. Secondary school students. National Institute on Drug Abuse; Bethesda, MD: 2002a. NIH Publication No. 02–5106.
  • Johnston LD, O’Malley PM, Bachman JG. Monitoring the Future national survey results on drug use, 1975–2001: Vol. II. College students and young adults. National Institute on Drug Abuse; Bethesda, MD: 2002b. NIH Publication No. 02–5107.
  • Jones BL, Nagin DS, Roeder KA. SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methodology Research. 2001;29:374–393.
  • Knopik VS, Heath AC, Madden PAF, Bucholz KK, Slutske WS, Nelson EC, et al. Genetic effects on alcohol dependence risk: Re-evaluating the importance of psychiatric and other heritable risk factors. Psychological Medicine. 2004;34:1519–1530. [PubMed]
  • Kushner MG, Sher KJ. Comorbidity of alcohol and anxiety disorders among college students: Effects of gender and family history of alcoholism. Addictive Behavior. 1993;18:543–552. [PubMed]
  • Lester D. Alcoholism, substance use, and suicide. In: Maris RW, Berman AL, Silverman MM, editors. Comprehensive textbook of suicidology. Guilford Press; New York: 2000. pp. 357–375.
  • Li F, Barrera MJR, Hops H, Fisher KJ. The longitudinal influence of peers on the development of alcohol use in late adolescence: A growth mixture analysis. Journal of Behavioral Medicine. 2002;25:293–315. [PubMed]
  • Lieber CS. Gender differences in alcohol metabolism and susceptibility. In: Wilsnack RW, Wilsnack SC, editors. Gender and alcohol: Individual and social perspectives. Rutgers Center of Alcohol Studies; New Brunswick, NJ: 1997. pp. 77–89.
  • Mann LM, Chassin L, Sher KJ. Alcohol expectancies and the risk for alcoholism. Journal of Consulting and Clinical Psychology. 1987;55:411–417. [PubMed]
  • Moss HB, Kirisci L. Aggressivity in adolescent alcohol abusers: Relationship with conduct disorder. Alcoholism: Clinical and Experimental Research. 1995;19:642–646. [PubMed]
  • Muthén B. Latent variable mixture modeling. In: Marcoulides GA, Schumacker RE, editors. New developments and techniques in structural equation modeling. Erlbaum; Mahwah, NJ: 2001a. pp. 1–33.
  • Muthén B. Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class-latent growth modeling. In: Collins LM, Sayer AG, editors. New methods for the analysis of change: Decade of behavior. American Psychological Association; Washington, DC: 2001b. pp. 291–322.
  • Muthén B, Curran PJ. General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods. 1997;2:371–402.
  • Muthén B, Muthén LK. Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research. 2000;24:882–891. [PubMed]
  • Muthén LK, Muthén BO. Mplus user’s guide. 3rd ed. Author; Los Angeles, CA: 1998–2004.
  • Nagin DS. Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods. 1999;4:139–157.
  • Neighbors B, Kempton T, Forehand R. Co-occurrence of substance abuse with conduct, anxiety, and depression disorders in juvenile delinquents. Addictive Behaviors. 1992;17:379–386. [PubMed]
  • Nunnally JC, Bernstein IH. Psychometric theory. McGraw-Hill; St. Louis, MO: 1994.
  • O’Hare TM. Drinking in college: Consumption patterns, problems, sex differences and legal drinking age. Journal of Studies on Alcohol. 1990;51:536–541. [PubMed]
  • O’Neill SE, Parra GR, Sher KJ. Clinical relevance of heavy drinking during the college years: Cross-sectional and prospective perspectives. Psychology of Addictive Behaviors. 2001;15:350–359. [PubMed]
  • Pape H, Hammer T. How does young people’s alcohol consumption change during the transition to early adulthood? A longitudinal study of changes at aggregate and individual level. Addiction. 1996;91:1345–1357. [PubMed]
  • Perkins HW. Stress-motivated drinking in collegiate and post-collegiate young adulthood: Life course and gender patterns. Journal of Studies on Alcohol. 1999;60:219–227. [PubMed]
  • Robins LN, Cottler L, Bucholz KK, Compton W. Diagnostic Interview Schedule for DSM–IV (DIS-IV) 1997. Unpublished manuscript.
  • Robins LN, Helzer JE, Cottler L, Goldring E. National Institute of Mental Health Diagnostic Interview Schedule, Version III—Revised. Washington University; St. Louis, MO: 1989.
  • Robins LN, Helzer JE, Croughan J, Williams JBW, Spitzer RL. National Institute of Mental Health Diagnostic Interview Schedule, Version III-A. National Institute of Mental Health; Rockville, MD: 1985. Public Health Service Publication No. ADDM-T-42–3 (4–85.
  • Sadava SW. Problem behavior theory and consumption and consequences of alcohol use. Journal of Studies on Alcohol. 1985;46:392–397. [PubMed]
  • Sadava SW. Problem drinking and alcohol problems: Widening the circle of covariation. In: Galanter M, editor. Recent developments in alcoholism. Vol. 8. Plenum Press; New York: 1990. pp. 173–201. [PubMed]
  • Schulenberg J, O’Malley PM, Bachman JG, Wadsworth KN, Johnston LD. Getting drunk and growing up: Trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol. 1996;57:289–304. [PubMed]
  • Schulenberg J, Wadsworth KN, O’Malley PM, Bachman JG, Johnston LD. Adolescent risk factors for binge drinking during the transition to young adulthood: Variable- and pattern-centered approaches to change. Developmental Psychology. 1996;32:659–674.
  • Schwartz G. Estimating the dimension of a model. Annals of Statistics. 1978;6:461–464.
  • Selzer M, Vinokur A, van Rooijen L. A self-administered Short Michigan Alcoholism Screening Test (SMAST) Journal of Studies on Alcohol. 1975;36:117–126. [PubMed]
  • Sher KJ. Children of alcoholics: A critical appraisal of theory and research. University of Chicago Press; Chicago: 1991.
  • Sher KJ, Gotham HJ, Watson AL. Trajectories of dynamic predictors of disorder: Their meanings and implications. Development and Psychopathology. 2004;16:825–856. [PubMed]
  • Sher KJ, Jackson KM. Trajectories of alcohol involvement: Implications of ages surveyed and measures used; Paper presented at the annual meeting of the Society for Research on Adolescence; Baltimore, MD. 2004, March.
  • Sher KJ, Walitzer KS, Wood PK, Brent EE. Characteristics of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology. 1991;100:427–448. [PubMed]
  • Stice E, Barrera M, Chassin L. Prospective differential prediction of adolescent alcohol use and problem use: Examining the mechanisms of effect. Journal of Abnormal Psychology. 1998;107:616–628. [PubMed]
  • Stice E, Myers MG, Brown SA. A longitudinal grouping analysis of adolescent substance use escalation and de-escalation. Psychology of Addictive Behaviors. 1998;12:14–27.
  • Tubman JG, Vicary JR, von Eye A, Lerner JV. Longitudinal substance use and adult adjustment. Journal of Substance Abuse. 1990;2:317–334. [PubMed]
  • Tucker JS, Orlando M, Ellickson PL. Patterns and correlates of binge drinking trajectories from early adolescence to young adulthood. Health Psychology. 2003;22:79–87. [PubMed]
  • Wechsler H, Dowdall GW, Davenport A, Rimm EB. A gender-specific measure of binge drinking among college students. American Journal of Public Health. 1995;85:982–985. [PubMed]
  • Wechsler H, Nelson TF. Binge drinking and the American college student: What’s five drinks? Psychology of Addictive Behaviors. 2001;15:287–291. [PubMed]
  • West MO, Prinz RJ. Parental alcoholism and childhood psychopathology. Psychological Bulletin. 1987;102:204–218. [PubMed]
  • White HR, Johnson V, Buyske S. Parental modeling and parenting behavior effects on offspring alcohol and cigarette use: A growth curve analysis. Journal of Substance Abuse. 2000;12:287–310. [PubMed]
  • White HR, Xie M, Thompson W, Loeber R, Stouthamer-Loeber M. Psychopathology as a predictor of adolescent drug use trajectories. Psychology of Addictive Behaviors. 2001;15:210–218. [PubMed]
  • Wills TA, McNamara G, Vaccaro D, Hirky AE. Escalated substance use: A longitudinal grouping analysis from early to middle adolescence. Journal of Abnormal Psychology. 1996;105:166–180. [PubMed]
  • Wilsnack RW, Wilsnack SC, editors. Gender and alcohol: Individual and social perspectives. Rutgers Center of Alcohol Studies; New Brunswick, NJ: 1997.
  • Windle M. Are those adolescent to early adulthood drinking patterns so discontinuous? A response to Temple and Fillmore. International Journal of the Addictions. 1988;23:907–912. [PubMed]
  • Windle M, Searles JS, editors. Children of alcoholics: Critical perspectives. Guilford Press; New York: 1990.
  • Zucker RA. The four alcoholisms: A developmental account of the etiologic process. In: Rivers PC, editor. Nebraska Symposium on Motivation, 1986: Alcohol and addictive behavior; Lincoln: University of Nebraska Press; 1987. pp. 27–83. [PubMed]
  • Zucker RA. Pathways to alcohol problems and alcoholism: A developmental account of the evidence for multiple alcoholisms and for contextual contributions to risk. In: Zucker RA, Howard J, Boyd GM, editors. The development of alcohol problems: Exploring the biopsychosocial matrix of risk. National Institute on Alcohol Abuse and Alcoholism; Rockville, MD: 1994. pp. 255–289. NIH Publication No. 94–3495.
  • Zucker RA, Fitzgerald HE, Moses HD. Emergence of alcohol problems and the several alcoholisms: A developmental perspective on etiologic theory and life course trajectory. In: Cicchetti D, Cohen DJ, editors. Developmental psychopathology: Vol. 2. Risk, disorder, and adaptation. Wiley; New York: 1995. pp. 677–711.