Findings from the 1999 National Household Survey on Drug Abuse

Earlier chapters, using descriptive statistics and simple odds ratios (ORs), presented the prevalence of risk and protective factors and the associations of those factors with past year marijuana use. This chapter presents the strength of the relationship between risk and protective factors and marijuana use using multiple logistic regression models, in which the associations with past year marijuana use are adjusted for both demographic variables and other risk and protective factors included in the models. This chapter addresses the following issues:

- the relative importance of each risk and protective domain in predicting past year marijuana use;
- the importance of demographic factors in predicting past year marijuana use;
- how much risk and protective factors from each domain add to the prediction of past year marijuana use beyond the demographic factors;
- the importance of demographic variables combined with the full set of risk and protective factors in explaining the variation in past year marijuana use; and
- the usefulness of hierarchical modeling techniques in explaining the variation in past year marijuana use.

The word "prediction" is used not to imply that events have occurred in a certain sequence, but to describe a statistical question: "How well does statistical information about one characteristic improve one's ability to guess what happened to a different characteristic?" For example, if knowing the employment status of each person in a group would improve how well early initiation of marijuana use could be estimated, employment status would be called a "predictor," without necessarily meaning that employment status came first. Moreover, there are statistical methods for determining just how strong a predictor employment status may prove to be in any given group of people. When a number of predictors are used together in a statistical analysis of this kind, the combination of predictors is referred to as a "prediction model."

Because of the complex survey design of the National Household Survey on Drug Abuse (NHSDA), the regression analyses were performed using the LOGISTIC procedure in SUrvey DAta ANalysis (SUDAAN), a statistical program employing variance estimation
calculations that take into account this complexity (Shah, Barnwell, & Bieler, 1998). Note that the initial analyses use simple individual (person-level) logistic regression models that adjust for the effects of clustering on the estimates but otherwise ignore the true hierarchical structure of the data, namely, the fact that youths aged 12 to 17 are nested within families that are, in turn, nested in neighborhoods. Therefore, these analyses treat variables at the higher levels of hierarchy as being individual (youth) variables.^{17} Analyses presented later in the chapter address the hierarchical structure of the data and the utility of including this structure in prediction models.

Multiple logistic regression determines the importance of individual predictor variables by testing whether these factors account for a statistically significant amount of variation in the dependent variable after controlling for other predictor variables included in the model. Multiple logistic regression can also determine the relative importance of groups of variables by measuring how much (additional) variation in the dependent variable that one group of predictor variables can explain beyond another group of variables. The lack of statistical significance of a predictor variable does not imply that the variable is unimportant in the epidemiology of substance use. For example, the variable may have a significant indirect relationship to the dependent variable through another independent variable in a path analysis. Other analysis techniques, such as structural equation modeling, may be more appropriate for analyzing those relationships.

First, results are presented for individual-level models predicting past year use of marijuana. This involves a comparison of the explained variation of each of the four domains as well as a "full model" that contains a set of demographic variables and factors from all four domains. Second, results are presented for individual-level models predicting past year use of cigarettes and alcohol. Third, simple hierarchical models are used to highlight the difference between hierarchical models and ordinary least squares models.

In this section, three separate multiple regression models of past year marijuana use are presented for each of the four domains discussed in **Chapter 1**.^{18} The first regression model (Model 1) includes only a set of demographic variables: race/ethnicity, gender, age, number of parents in the home, household income, geographic region, and county type. The second model (Model 2) includes all the risk and protective factors that comprise the domain. The third model (Model 3) includes both the set of demographic variables as well as the risk and protective factors that comprise the domain. Comparisons of Model 2 with Model 1 assess whether the set of factors that make up each domain are more or less predictive of past year marijuana use than the set of demographic variables. Comparisons of Model 3 with Model 2 assess the extent to which the addition of the set of demographic factors improves the predictiveness of the set of risk and protective factors that comprise the domain.

The results of these models are presented in **Tables 4.1** through **4.4**, with each domain presented in a separate table. For each model, these tables present the regression coefficient (or ) and OR for each predictor, a significance test for each predictor, and two measures that summarize the explanatory power for the model as a whole. The OR is easier to understand than the regression coefficient, both of which are measures that describe the strength and direction of the relationship between the predictors and past year marijuana use. For example in **Table 4.1**, the OR for gender indicates that the odds of past year marijuana use were 1.18 times higher for males than for females, after controlling for other demographic variables.^{19} The *p* value for this is less than 0.05, indicating that gender is a significant variable in Model 1 after controlling for the other demographic variables. With the exception of the comparison between Hispanic youths and white youths, there were significant associations between each demographic variable and past year marijuana use in Model 1.

The summary measures in **Table 4.1** indicate that the set of community domain factors (Model 2) accounted for significantly more variance (R^{2} = 0.17; R_{N}^{2} = 0.31) than the demographic variables in Model 1 (R^{2} = 0.09; R_{N}^{2} = 0.15).^{20} The addition of the demographic variables to the model with the community domain factors (Model 3) resulted in only a slight improvement in explanatory power (R^{2} = 0.19; R_{N}^{2} = 0.34) compared with Model 2. The results were similar for the peer/individual domain (**Table 4.3**) and the school domain (**Table 4.4**).
In both of these domains, the risk and protective factors that comprised the domains (Model 2) accounted for significantly more variance than the demographic variables (Model 1), and the addition of the demographic variables to the factors in these domains (Model 3) did little to improve the model. In the family domain (**Table 4.2**), the set of risk and protective factors accounted for a similar amount of variance (R^{2} = 0.10; R_{N}^{2} = 0.17) compared with the set of demographics. In addition, the model that included the set of family risk and protective factors and the set of demographic variables accounted for significantly more variation (R^{2} = 0.15; R_{N}^{2} = 0.25) than the model that included only the set of family domain factors.

Of the four domains, the factors in the peer/individual domain accounted for the most variation in past year marijuana use by youths (R^{2} = 0.30; R_{N}^{2} = 0.53). Following this were the community domain (R^{2} = 0.19; R_{N}^{2} = 0.34) and the school domain (R^{2} = 0.18; R_{N}^{2} = 0.32). The family domain accounted for the least amount of variation in past year marijuana use (R^{2} = 0.15; R_{N}^{2} = 0.25). However, it should be noted that these estimates of relative contribution are based only on the items used to measure these constructs and the methodology of the 1999 NHSDA. It could be that other measures of these constructs, or other research methodologies, would result in different relative contributions for these domains.

In this section, tables are presented in which certain risk and protective factors from all four domains were combined into a single model. **Table 4.5** presents a "combined reduced" model that includes the set of demographic variables as well as all of the risk and protective factors that were significant predictors of past year marijuana use in Model 3 of **Tables 4.1** through **4.4**. Collectively, this set of variables accounted for more variation in past year marijuana use (R^{2} = 0.33; R_{N}^{2} = 0.56) than any of the domains individually. However, this combined reduced model improved only slightly on the variance accounted for by the model that contained only demographics and the factors in peer/individual domain (see Model 3 of **Table 4.3**).

The combined reduced model presented in **Table 4.5** included all of the risk and protective factors that were significant in the test of the different domains. As a result, some of the factors in the combined reduced model were not significant. In an effort to obtain a more parsimonious model, a "final" model was created that included the set of demographic variables as well as the risk and protective factors that were significant in the combined reduced model (**Table 4.6**). This final model accounted for the same amount of variation as the combined reduced model. These results indicate that the variables in this model accounted for a significant percentage of the total variation in whether a youth used marijuana in the past year. To the extent that the model includes risk and protective factors that have been demonstrated in well-designed prevention programs to reduce marijuana use, application of such programs has the potential
of reducing youth marijuana use. By contrast, if the prevention factors had only accounted for a small percentage of the total variation, this could raise concern that programs aimed at reducing the levels of the variables in the model might not reduce usage of marijuana among youths in a significant way. It is worth emphasizing that the NHSDA is an annual cross-sectional survey that provides a snapshot of the relationship between these risk and protective factors and marijuana use for youths who have been surveyed at some point during 1999. A number of youths aged 12 to 17 reported that they used marijuana in the past year and indicated the presence of various risk or protective factors. However, the use of marijuana may have preceded the presence of the risk factor for some youths, resulting in a somewhat "inflated" R_{N}^{2}. Therefore, one should be cautious in drawing conclusions about youth marijuana use from the set of risk and protective factors reported in the NHSDA.

In the final model, the strongest associations with past year marijuana were found with the risk factors in the peer/individual domain; youths were more likely to have used marijuana in the past year if they reported higher levels of antisocial behavior (OR = 2.13), had friends who used marijuana (OR = 2.07), perceived low risks from marijuana use (OR = 1.79), and had more positive individual attitudes toward marijuana use (OR = 1.71) (**Table 4.6**). Among the protective factors, youths were less likely to have used marijuana in the past year if they listed their parents as a source of social support (OR = 0.71) and if they had been exposed to prevention messages in the media (OR = 0.81).

There were some variables that had ORs that were counterintuitive. One reason this can occur is the cross-sectional nature of the survey. For example, the final model indicated that youths were more likely to have used marijuana in the past year if their parents had talked with them about the dangers of substance use in the past year (OR = 1.55). This association does not necessarily indicate that parental communication with youths about the dangers of substance use increases the likelihood that they will use marijuana; it is possible that this association is the result of increased communication about the dangers of substance use among parents who know or suspect that their children are using, or are in danger of using, marijuana. Another reason for ORs that are counterintuitive to expectations is that the association between a given variable and marijuana use can be affected by the inclusion of other variables in the model. For example, Model 1 in **Table 4.1** indicated that males were more likely to have used marijuana in the past year (OR = 1.18) compared with females. The final model, however, indicated that after controlling for risk and protective factors from all domains, males were less likely to have used marijuana in the past year (OR = 0.85) than females.

A small number of the risk factors in the final model were highly correlated with each other (see **Tables A.9** to **A.11** in **Appendix A** for intercorrelations between factors). For example, friends' use of marijuana was highly correlated (*r* = 0.67) with perceived prevalence of marijuana at school. This type of "multicollinearity" of predictors can be problematic, as it
can reduce the ability of each individual predictor to make a unique contribution to explained variation in the outcome measure (Cohen & Cohen, 1983). To test whether these high intercorrelations had a sizable effect on the final model, the model was repeated after eliminating three variables: friends' use of marijuana, friends' attitude toward marijuana use, and perceived prevalence of marijuana use at school. Eliminating these three variables acted to eliminate all correlations higher than *r* = 0.50 from the set of predictors. The removal of these factors, all of which were significant predictors of past year marijuana use in the final model (**Table 4.6**), had little effect; the adjusted *R*^{2} of this reduced model was only slightly lower (*R _{N}*

Models predicting past year use of cigarettes are presented in **Tables 4.7** through **4.9**. **Table 4.7** presents the results of four models; each model contained the risk and protective factors from one domain,^{21} in addition to the set of demographic variables. The factors that were significant in these models, along with the demographic variables were then included in the combined reduced model (**Table 4.8**). The risk and protective factors that were significant in the combined reduced model were then included in the final model (**Table 4.9**). Similar models predicting any past year use of alcohol are presented in **Tables 4.10** through **4.12**.^{22}

In terms of explained variation as measured by the Nagelkerke R^{2}, the final models for past year cigarette use (R^{2} = 0.29; R_{N}^{2} = 0.43) and past year alcohol use (R^{2} = 0.34; R_{N}^{2} = 0.46) accounted for less variation than the final model for past year marijuana use (R^{2} = 0.33; R_{N}^{2} = 0.56).^{23} As was the case for the final model of past year use of marijuana, the strongest predictors of past year cigarette and alcohol use were the peer/individual risk factors. Friends' use of cigarettes and friends' use of alcohol were the strongest predictors in these models.

The following discussion provides some general background to hierarchical modeling and some simple models. Raudenbush and Bryk (2002) provide further information on the diversity and advantages of hierarchical models.

Hierarchical modeling has been described under a variety of names historically: mixed-effects models, random-effects models, random-coefficient regression models, and covariance components models. Raudenbush and Bryk (2002, pp. 5–6) give the following description for these types of mixed models:

The models discussed in this book appear in diverse literatures under a variety of titles. In sociological research, they are often referred to asmultilevel linear models(cf. Goldstein, 1995; Mason et al., 1983). In biometric applications, the termsmixed-effectsmodelsandrandom-effects modelsare common (cf. Elston & Grizzle, 1962; Laird & Ware, 1982; Singer, 1998). They are also calledrandom-coefficient regression modelsin the econometrics literature (cf. Rosenberg, 1973; Longford, 1993) and in the statistical literature have been referred to ascovariance components models(cf. Dempster, Rubin, & Tsutakawa, 1981; Longford, 1987).

In this report, the above models are referred to collectively as* hierarchical models* in order to emphasize the nested and clustered nature of the data that has a direct impact on assumptions about dependence of observations within and across hierarchical levels. There has been a significant amount of analysis in areas such as education (Bock, 1989; Bryk, Thum, Easton, & Luppescu, 1998; Morris, 1995). In elementary and secondary education, one typical structure consists of students nested within classrooms, which are in turn nested within schools, which are nested within school districts. Another type of structure is repeated measures, where observations over time are nested within an individual. The focus of much of that analysis has been on the effects of school administration and quality of teachers, or teaching, on student achievement. Although there has been some application of these models to the field of substance use (i.e., Duncan, Duncan, Hops, & Alpert, 1997; Kreft, 1994; Novak & Clayton, 2001), their application has not been as prevalent in the field of substance use as in the field of education.

In the current study, the focus regarding hierarchical models is the effect of family and community characteristics on the use of marijuana by youths aged 12 to17. The prevention literature includes numerous risk and protective factors for youth substance use that are a function of family or community characteristics; those included in the 1999 NHSDA are listed in **Tables A.1** and **A.2** in **Appendix A**.

Historically, analyses in a variety of areas treated clustered observations as independent—failing to account for the fact that units within the same cluster tend to be more similar to each other than to units outside the cluster.^{24} For example, members of the same family or persons in the same neighborhood tend to share characteristics that make them more similar to each other than to other persons. One result of assuming independent observations at the person level when that is not true is that the researcher may conclude that certain explanatory variables are significant (i.e., significantly different from 0), when in fact, they are not. Because within-cluster correlation tends to be positive, a realistic effective sample size is typically smaller than the nominal sample size. Hence, variances estimated under the independence assumption tend to be too small. Another result of assuming independent observations at the person level is that it has "fostered an impoverished conceptualization, discouraging the formation of explicit multilevel models with hypotheses about effects occurring at each level and across levels" (Raudenbush & Bryk, 2002, p. 5). In the case of continuous data, the classical assumptions are that the observations are independently normally distributed and the model residuals have a common mean and variance. It is not necessary, however, to make these restrictive assumptions if they are unrealistic.

In the case of a model in which the dependent variable of interest is dichotomous (e.g., used or did not use marijuana in the past year), the observations are conditionally Bernoulli distributed (a special case of the binomial) given the explanatory variables, and the predicted probabilities of "success" are typically transformed by taking the log of the odds (the logit function). However, in this form there are difficulties in describing how much of the total variation in the dependent variable has been "explained" by the model because the measures of variance and explained variation are also in the log odds metric. Some of the issues involved in accurately estimating the parameters of a hierarchical model when the dependent variable is binary are discussed in Rodriguez and Goldman (1995) and Goldstein and Rasbash (1996).

In a nested hierarchical design, when the original data are normally distributed, the total variation in the dependent variable can be broken down into components at each level of the hierarchy. For example, if the dependent variable were the student math achievement score on a test and those scores followed a normal distribution, the total variation could be partitioned into the part deriving from student variation (within schools) and the part from school variation (between schools). The first part would be determined by the variation *among students within a school*, averaged over all schools. The second part would be characterized by the variation in
the average student score *between schools*. The percentage of total variation that is between schools then is an indication of the magnitude of influence in student scores that is determined by school characteristics. The interest then might be in identifying what those school characteristics are that lead to higher math achievement scores given the same set of students.

When the dependent variable is dichotomous, as it is for past year use of marijuana, and the predicted probabilities of "success" have been transformed into the log odds metric, the predicted probabilities of success can be retransformed into the original metric, which can be used in predicting prevalences.^{25} From such analysis, the overall variance for past year use of marijuana can be partitioned into three parts corresponding to variation accounted for by the person level, the family level, and the neighborhood level. The person level refers to the individual choices that a youth makes to either use or not use a substance. The family level refers to the degree of influence the family with whom a youth lives has on the youth's substance use. The neighborhood level refers to the degree of influence the neighborhood in which a youth lives has on the youth's substance use. The partitioning described in this report assumes a nested structure in which youths live in households (referred to as families), and the households are situated in neighborhoods (defined by groups of contiguous Census blocks, which are the first stage of sampling for the NHSDA). Analyses using the 1999 NHSDA have indicated that the person level accounts for 78 percent of the total variation in past year marijuana use among youths, the family accounts for 16 percent of the total, and the neighborhood accounts for the remaining 6 percent.^{26} One way to interpret this information is that youth reports about using marijuana in the past year appear to be mostly influenced by their own choices (78 percent) and not by the family (16 percent) or neighborhood (6 percent). Experience with these percentages for different NHSDA years confirms that the percentages have remained fairly constant.

Another way to better understand this information is to consider what the estimates would have been under other circumstances. If youths in each neighborhood (group of contiguous Census blocks) included in the survey reported the same percentage of marijuana use in the past year (e.g., 10 percent of youths in every sampled neighborhood reported using marijuana in the past year), the variation accounted for by the neighborhood level would have been 0 percent. If, on the other hand, there was a large amount of variation between neighborhoods in the youth reports of marijuana use (e.g., a small percentage of youths in some of the sampled neighborhoods reported use whereas a large percentage of youths in othersampled neighborhoods reported use), the neighborhood level would have accounted for a large percentage of the total variation. At the family level, if youth marijuana use was completely controlled by factors that exist within the household in which a youth lives (e.g., the influence of parents and siblings), all youths living in the same household would report the same level of marijuana use. In this case, the total variation in youth marijuana use accounted for by the family level would be larger, and variation accounted for by the person level would be smaller.

It is important to state that the contributions of the family and neighborhood presented in this report are overall results for the United States for youths aged 12 to 17. It is likely that the actual impact by the family (e.g., the impact of parents) or the neighborhood differ for different demographic groups within the overall youth population. For example, some cross-sectional research has suggested that the influence of parents on the behavior of youths decreases as youths get older (Kandel, 1996; Krosnick & Judd, 1982). To the extent that this true, family-level variables may account for more variation in the substance use of youths aged 12 to 14 than youths aged 15 to 17. Because of this perception of greater parental influence during early adolescence, and because most youths do not initiate substance use before age 12 (Gfroerer, Wu, & Penne, 2002), most family-based prevention programs labeled as "model programs" by the Center for Substance Abuse Prevention (CSAP, 2001) are targeted toward youths in their preteen or early-teenage years.

To simplify the discussion of the advantages of hierarchical modeling, the analysis presented below focuses on a continuous measure of perceived risk of marijuana use (RSKMJUSE) rather than the dichotomous measure of past year marijuana use that was employed in previous models. The use of a scaled continuous variable that is assumed to be normally distributed simplifies the discussion by rendering the interpretation of explained variation easier to understand. Perceived risk is a scaled variable based on the average of responses to two questions: "How much do people risk harming themselves physically and in other ways when they smoke marijuana once a month?" and "How much do you think people risk harming themselves physically and in other ways when they smoke marijuana once or twice a week?" The response options for both questions are (1) great risk, (2) moderate risk, (3) slight risk, and (4) no risk. Perceived risk of marijuana use is typically closely associated with marijuana use among youths. For example, the 1999 NHSDA indicated that 52.2 percent of youths who perceived no risk of using marijuana once a month had used marijuana in the past year compared with 24.7 percent among those who perceived slight risk, 9.0 percent among those who perceived moderate risk, and only 4.6 percent among those who perceived great risk.

A series of models were fit in which three covariates that might reasonably be expected to have explanatory power at the community, family, and individual levels were introduced sequentially. The definition of community used in the present study is the segment, which is a Census block or group of contiguous Census blocks (where the blocks are those defined by the U.S. Bureau of the Census). The *community-level variable* was a dichotomous measure (yes/no) asking whether the youth had been approached by a drug seller in the past 30 days. As a perceived community-level variable, the responses to this question were "averaged up" to the community (segment) level. Put another way, the mean value of all respondents in a given segment was calculated, and all respondents in that segment were assigned this mean value for this variable. The *family-level* variable was how often parents had helped the youth with homework during the past 12 months. The response options for this question were (1) never, (2) seldom, (3) sometimes, or (4) always. A maximum of two youths from the same family could be included in the 1999 NHSDA; in cases where two youths from the same family were interviewed, each youth was assigned the average of the responses for the two youths in the family. The *person-level variable* was a scaled score measuring the youths' attitude toward youth substance use, assessed using three questions asking "How do you feel about someone your age trying (marijuana/hashish once or twice) (smoking one or two packs of cigarettes per day) (having one or more drinks of an alcoholic beverage nearly every day)?" The response options for each question were (1) strongly disapprove, (2) somewhat disapprove, or (3) neither approve nor disapprove.

**Exhibit 4.1**, shown on the facing page, presents the estimates of variance components for each level, the total variance estimates, the fixed effects estimates, and the standard errors of five models involving these variables. For completeness, both fixed effects as well as random effects are included. The discussion centers on the estimates of variance components because these illustrate the main points of interest. The model assumptions are summarized below as model equations of the form *Y _{ijk}* =

Model | Estimates of Variance Components | Estimates of Fixed Effects | ||||||
---|---|---|---|---|---|---|---|---|

Community Level (SE) | Family Level (SE) | Person Level (SE) | Total^{1} (SE) |
Community Level (SE) | Family Level (SE) | Person Level (SE) | Intercept (SE) | |

1. Random Effects (RE) Only | .026 (.004) | .125 (.011) | .556 (.011) | .707 (.016) | — | — | — | 1.831 (.006) |

2. (RE) & Community (C) | .013 (.003) | .121 (.011) | .557 (.011) | .691 (.016) | .247 (.011) | — | — | 1.830 (.006) |

3. (RE) & (C) & Family (F) | .011 (.003) | .092 (.011) | .551 (.011) | .654 (.016) | .209 (.011) | .204 (.006) | — | 1.830 (.006) |

4. (RE) & (C) & (F) & Person (P) | .011 (.003) | .075 (.009) | .491 (.010) | .577 (.016) | .138 (.010) | .098 (.006) | .300 (.005) | 1.830 (.005) |

5. Fixed Effects (FE) Only | — | — | .576 (.005) | .576 (.005) | .139 (.010) | .098 (.006) | .302 (.005) | 1.829 (.005) |

^{1} The total column is the sum of the community-, family-, and person-level columns and indicates the total variation left unexplained by the model. The total variance in Model 1 (.707), which contains only random effects, represents the total unexplained variation in the perceived risk of marijuana use. For Models 2 to 4, the total column indicates how much of the explainable variation in the perceived risk of marijuana use is left unexplained after adding fixed effects to the random effects. For example, Model 4, which includes the random effects of Model 1 plus three fixed effects (one variable for each of the levels), indicates that 82 percent (.577/.707 x 100) of the total variation is still unexplained; however, the hierarchical model indicates how much is unexplained (equal to 1 minus the percentage of explained variation) at each of the three levels. Model 5 is a single-level (person-level) model that treats each of the fixed effect variables as person-level variables. The total unexplained variation in Model 5 is the same (approximately) as that for Model 4, but in Model 5 there is no information about the variance components at each of the levels. Also, the standard error of the total variance in Model 5 is understated because the clustering of persons is not taken into account.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

** Model 1** simply contained a constant and a random effect for each level of the hierarchy. This model can be represented using the following notation:

*RSKMJUSE _{ijk}* =

In this notation, *RSKMJUSE _{ijk}* denotes the

** Model 2** contained the random effects included in Model 1, and also included the fixed effect for the community-level variable (COMMUNITY) asking about being approached by a drug seller in the past 30 days. The model then became

*RSKMJUSE _{ijk}* =

The error terms are now residual variances. The results indicate that compared with Model 1, the community-level variation remaining (to be explained) dropped by half from .026 to .013. The family variation dropped slightly, from .125 to .121. The person-level variation was similar to Model 1's.

** Model 3** contained the effects included in Model 2 (random effects and the fixed effects for the community-level), as well as the fixed effect for the family-level variable (FAMILY) asking how often parents help youths with their homework. The model then became

*RSKMJUSE _{ijk}* =

Compared with Model 2, the family-level variation dropped from .121 to .092. The community-level variation dropped slightly from .013 to .011. The person-level variation remaining dropped slightly from .557 to .551.

** Model 4** contained the effects included in Model 3 (random effects as well as fixed effects at the community and family levels), and it also included the fixed effects for the person-level variable (PERSON) asking about positive attitudes toward drug use. The model then became

*RSKMJUSE _{ijk}* =

Compared with Model 3, the person-level variation fell from .551 to .491. The family-level variation also dropped slightly from .092 to .075. The community-level variation remained unchanged.

In Model 4, approximately 18 percent of the total variation ([1 - (.577 / .707)] H 100 = 18.4 percent) has been explained. Among the variables at the different levels, approximately 12 percent of the person-level variation has been explained ([1 - (.491 / .556)] H100 = 11.7 percent);40 percent of the family-level variation has been explained ([1 - (.075 / .125)] H 100 = 40.0 percent); and 58 percent of the community-level variation has been explained ([1 - (.011 / .026)] H 100 = 57.7 percent).

** Model 5**, for comparison purposes, contained only the individual-level regression model (i.e., fixed effects for the community, family, and person-level variables). This model can be represented using the following notation:

*RSKMJUSE _{ijk}* =

This indicates that the overall total variation is similar (.576 for Model 5 and .577 for Model 4), but Model 5 does not include information on how much of the variation has been explained at each level. In addition, the standard errors for the estimates of the fixed effects of the variables *B*_{0}, *B*_{1}, *B*_{2}, and *B*_{3} from Model 5 would typically be somewhat smaller (underestimates) than those reported in Models 2 to 4 because they would assume independence within the family and within the neighborhood. However, there is little difference between these standard errors in this case because of the magnitude of the individual-level variation (relative to the family and neighborhood components) and the large overall sample size.

The examples above are meant to clarify some of the differences between hierarchical models and ordinary least squares individual-level regression models, especially the incorporation of the correct assumptions about dependence among observations and the improved understanding of explained variation based on multiple levels of variation. It should be noted that there are numerous additional advantages to hierarchical modeling, such as the ability to build separate regression models at each level of the hierarchy, and to further relax assumptions so that both the individual coefficients (slopes) can vary across units at the same level as can the variances of those units (Raudenbush & Bryk, 2002).

Model 1: Demographics | Model 2: Community Risk/Protective Factors | Model 3: Demographics + Community Risk/Protective Factors | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
||||

Intercept | — | — | <.0001 | — | — | <.0001 | — | — | <.0001 | |||

Demographics |
||||||||||||

Race/ethnicity | ||||||||||||

Black vs. white | -0.43 | 0.65 | (0.55, 0.78) | <.0001 | — | — | — | — | -0.72 | 0.49 | (0.40, 0.60) | <.0001 |

Hispanic vs. white | -0.10 | 0.90 | (0.76, 1.06) | .2155 | — | — | — | — | -0.10 | 0.90 | (0.75, 1.09) | .2767 |

Other vs. white | -0.39 | 0.67 | (0.50, 0.91) | .0095 | — | — | — | — | -0.03 | 0.97 | (0.68, 1.37) | .5120 |

Gender - male vs. female | 0.16 | 1.18 | (1.07, 1.29) | .0008 | — | — | — | — | 0.15 | 1.16 | (1.05, 1.29) | .0480 |

Age (continuous - 12 to 17) | 0.52 | 1.68 | (1.63, 1.72) | <.0001 | — | — | — | — | 0.30 | 1.35 | (1.30, 1.40) | <.0001 |

Number of parents in home (2 vs. others) | -0.67 | 0.51 | (0.46, 0.57) | <.0001 | — | — | — | — | -0.44 | 0.65 | (0.57, 0.73) | <.0001 |

Economic deprivation (household income under $20,000) | -0.16 | 0.85 | (0.74, 0.98) | .0242 | — | — | — | — | -0.25 | 0.78 | (0.65, 0.92) | .0038 |

Geographic region | ||||||||||||

Northeast vs. West | -0.20 | 0.82 | (0.70, 0.96) | .0119 | — | — | — | — | -0.11 | 0.89 | (0.75, 1.06) | .2007 |

North Central vs. West | -0.17 | 0.84 | (0.73, 0.96) | .0127 | — | — | — | — | -0.13 | 0.88 | (0.75, 1.03) | .1161 |

South vs. West | -0.26 | 0.77 | (0.68, 0.88) | .0001 | — | — | — | — | -0.15 | 0.86 | (0.75, 1.00) | .0442 |

County type | ||||||||||||

Large MSA vs. non-MSA | 0.19 | 1.21 | (1.07, 1.36) | .0023 | — | — | — | — | 0.16 | 1.17 | (1.03, 1.34) | .0190 |

Small MSA vs. non-MSA | 0.21 | 1.24 | (1.09, 1.41) | .0008 | — | — | — | — | 0.17 | 1.19 | (1.03, 1.36) | .0149 |

Community Domain^{2} |
||||||||||||

Community disorganization and crime | — | — | — | — | -0.15 | 0.86 | (0.79, 0.95) | .0017 | 0.00 | 1.00 | (0.91, 1.10) | .9587 |

Neighborhood cohesiveness | — | — | — | — | 0.01 | 1.01 | (0.94, 1.09) | .7262 | 0.03 | 1.03 | (0.96, 1.11) | .4024 |

Community attitudes toward marijuana use | — | — | — | — | 0.37 | 1.44 | (1.33, 1.56) | <.0001 | 0.29 | 1.34 | (1.23, 1.45) | <.0001 |

Community norms toward marijuana use | — | — | — | — | 1.00 | 2.71 | (2.48, 2.97) | <.0001 | 0.99 | 2.70 | (2.46, 2.96) | <.0001 |

Availability of marijuana | — | — | — | — | 0.82 | 2.26 | (2.12, 2.41) | <.0001 | 0.68 | 1.97 | (1.84, 2.12) | <.0001 |

Exposed to prevention messages in the media | — | — | — | — | -0.25 | 0.78 | (0.67, 0.90) | .0006 | -0.26 | 0.77 | (0.67, 0.89) | .0006 |

Sample size | 25,357 | 23,031 | 23,031 | |||||||||

R^{2} (see footnote 3) |
0.09 | 0.17 | 0.19 | |||||||||

R_{N}^{2} (see footnote 4) |
0.15 | 0.31 | 0.34 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from multiple logistic regression models and adjusted for other variables included in each model. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Table A.1**). The coding and distribution of the responses for each factor are provided in **Table 2.1**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

Model 1: Demographics | Model 2: Family Risk/Protective Factors | Model 3: Demographics + Family Risk/Protective Factors | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
||||

Intercept | — | — | <.0001 | — | — | <.0001 | — | — | <.0001 | |||

Demographics |
||||||||||||

Race/ethnicity | ||||||||||||

Black vs. white | -0.43 | 0.65 | (0.55, 0.78) | <.0001 | — | — | — | — | -0.34 | 0.71 | (0.58, 0.88) | .0014 |

Hispanic vs. white | -0.1 | 0.90 | (0.76, 1.06) | .2155 | — | — | — | — | 0.09 | 1.09 | (0.89, 1.34) | .3856 |

Other vs. white | -0.39 | 0.67 | (0.50, 0.91) | .0095 | — | — | — | — | -0.39 | 0.67 | (0.47, 0.96) | .0312 |

Gender - male vs. female | 0.16 | 1.18 | (1.07, 1.29) | .0008 | — | — | — | — | 0.13 | 1.14 | (1.02, 1.28) | .0189 |

Age (continuous - 12 to 17) | 0.52 | 1.68 | (1.63, 1.72) | <.0001 | — | — | — | — | 0.44 | 1.56 | (1.50, 1.61) | <.0001 |

Number of parents in home (2 vs. others) | -0.67 | 0.51 | (0.46, 0.57) | <.0001 | — | — | — | — | -0.56 | 0.57 | (0.50, 0.65) | <.0001 |

Economic deprivation (household income under $20,000) | -0.16 | 0.85 | (0.74, 0.98) | .0242 | — | — | — | — | -0.21 | 0.81 | (0.68, 0.97) | .0205 |

Geographic region | ||||||||||||

Northeast vs. West | -0.2 | 0.82 | (0.70, 0.96) | .0119 | — | — | — | — | -0.14 | 0.87 | (0.72, 1.04) | .1285 |

North Central vs. West | -0.17 | 0.84 | (0.73, 0.96) | .0127 | — | — | — | — | -0.16 | 0.85 | (0.73, 1.00) | .0481 |

South vs. West | -0.26 | 0.77 | (0.68, 0.88) | .0001 | — | — | — | — | -0.17 | 0.85 | (0.73, 0.98) | .0282 |

County type | ||||||||||||

Large MSA vs. non-MSA | 0.19 | 1.21 | (1.07, 1.36) | .0023 | — | — | — | — | 0.20 | 1.22 | (1.06, 1.41) | .0057 |

Small MSA vs. non-MSA | 0.21 | 1.24 | (1.09, 1.41) | .0008 | — | — | — | — | 0.13 | 1.14 | (0.98, 1.32) | .0925 |

Family Domain^{2} |
||||||||||||

Parental monitoring | — | — | — | — | 0.77 | 2.16 | (1.96, 2.38) | <.0001 | 0.50 | 1.65 | (1.49, 1.83) | <.0001 |

Parental encouragement | — | — | — | — | -0.21 | 0.81 | (0.75, 0.87) | <.0001 | -0.21 | 0.81 | (0.75, 0.87) | <.0001 |

Parental attitudes toward marijuana use | — | — | — | — | 0.95 | 2.57 | (2.35, 2.82) | <.0001 | 0.88 | 2.42 | (2.20, 2.67) | <.0001 |

Parents communicate about substance use | — | — | — | — | 0.45 | 1.57 | (1.39, 1.77) | <.0001 | 0.40 | 1.50 | (1.32, 1.70) | <.0001 |

Parents are source of social support | — | — | — | — | -0.67 | 0.51 | (0.45, 0.58) | <.0001 | -0.67 | 0.51 | (0.45, 0.58) | <.0001 |

Sample size | 25,357 | 18,896 | 18,896 | |||||||||

R^{2} (see footnote 3) |
0.09 | 0.10 | 0.15 | |||||||||

R_{N}^{2} (see footnote 4) |
0.15 | 0.17 | 0.25 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from multiple logistic regression models and adjusted for other variables included in each model. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Table A.2**). The coding and distribution of the responses for each factor are provided in **Table 2.2**.

^{3} Indicates *X*^{2} comparison -2log-likelihood of Model 2 vs. Model 3 is significant.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

Model 1: Demographics | Model 2: Peer/Individual Risk/Protective Factors | Model 3: Demographics + Peer Individual Risk/Protective Factors | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
||||

Intercept | — | — | <.0001 | — | — | <.0001 | — | — | <.0001 | |||

Demographics |
||||||||||||

Race/ethnicity | ||||||||||||

Black vs. white | -0.43 | 0.65 | (0.55, 0.78) | <.0001 | — | — | — | — | -0.31 | 0.73 | (0.59, 0.91) | .0044 |

Hispanic vs. white | -0.10 | 0.90 | (0.76, 1.06) | .2155 | — | — | — | — | -0.04 | 0.96 | (0.77, 1.19) | .7172 |

Other vs. white | -0.39 | 0.67 | (0.50, 0.91) | .0095 | — | — | — | — | -0.03 | 0.97 | (0.69, 1.38) | .8716 |

Gender - male vs. female | 0.16 | 1.18 | (1.07, 1.29) | .0008 | — | — | — | — | -0.24 | 0.79 | (0.69, 0.90) | .0003 |

Age (continuous - 12 to 17) | 0.52 | 1.68 | (1.63, 1.72) | <.0001 | — | — | — | — | 0.31 | 1.36 | (1.30, 1.43) | <.0001 |

Number of parents in home (2 vs. others) | -0.67 | 0.51 | (0.46, 0.57) | <.0001 | — | — | — | — | -0.37 | 0.69 | (0.60, 0.79) | <.0001 |

Economic deprivation (household income under $20,000) | -0.16 | 0.85 | (0.74, 0.98) | .0242 | — | — | — | — | -0.24 | 0.79 | (0.65, 0.95) | .0115 |

Geographic region | ||||||||||||

Northeast vs. West | -0.20 | 0.82 | (0.70, 0.96) | .0119 | — | — | — | — | -0.24 | 0.78 | (0.63, 0.98) | .0315 |

North Central vs. West | -0.17 | 0.84 | (0.73, 0.96) | .0127 | — | — | — | — | -0.02 | 0.98 | (0.81, 1.17) | .8103 |

South vs. West | -0.26 | 0.77 | (0.68, 0.88) | .0001 | — | — | — | — | -0.07 | 0.94 | (0.78, 1.12) | .4642 |

County type | — | — | — | — | ||||||||

Large MSA vs. non-MSA | 0.19 | 1.21 | (1.07, 1.36) | .0023 | — | — | — | — | 0.09 | 1.09 | (0.93, 1.27) | .2792 |

Small MSA vs. non-MSA | 0.21 | 1.24 | (1.09, 1.41) | .0008 | — | — | — | — | 0.06 | 1.06 | (0.90, 1.24) | .4840 |

Peer/Individual Domain^{2} |
||||||||||||

Antisocial behavior | — | — | — | — | 0.60 | 1.82 | (1.50, 2.21) | <.0001 | 0.82 | 2.26 | (1.83, 2.80) | <.0001 |

Individual attitudes toward marijuana use | — | — | — | — | 0.58 | 1.79 | (1.63, 1.97) | <.0001 | 0.56 | 1.74 | (1.58, 1.92) | <.0001 |

Friends' attitudes toward marijuana use | — | — | — | — | 0.38 | 1.46 | (1.34, 1.59) | <.0001 | 0.35 | 1.42 | (1.31, 1.55) | <.0001 |

Friends' marijuana use | — | — | — | — | 1.15 | 3.17 | (2.90, 3.46) | <.0001 | 1.03 | 2.79 | (2.55, 3.06) | <.0001 |

Perceived risk of marijuana use | — | — | — | — | 0.55 | 1.74 | (1.61, 1.88) | <.0001 | 0.54 | 1.72 | (1.59, 1.86) | <.0001 |

Risk-taking proclivity | — | — | — | — | 0.29 | 1.34 | (1.22, 1.47) | <.0001 | 0.33 | 1.38 | (1.25, 1.53) | <.0001 |

Participation in two or more extracurricular activities | — | — | — | — | -0.09 | 0.91 | (0.80, 1.04) | .1773 | -0.08 | 0.92 | (0.81, 1.05) | .2384 |

Religiosity | — | — | — | — | -0.11 | 0.9 | (0.82, 0.98) | .0149 | -0.06 | 0.95 | (0.87, 1.04) | .2279 |

Sample size | 25,357 | 23,487 | 23,487 | |||||||||

R^{2} (see footnote 3) |
0.09 | 0.29 | 0.30 | |||||||||

R_{N}^{2} (see footnote 4) |
0.15 | 0.51 | 0.53 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from multiple logistic regression models and adjusted for other variables included in each model. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Table A.3**). The coding and distribution of the responses for each factor are provided in **Table 2.3**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

Model 1: Demographics | Model 2: School Risk/Protective Factors | Model 3: Demographics + School Risk/Protective Factors | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
OR^{1} |
95% CI | p value |
||||

Intercept | — | — | <.0001 | — | — | <.0001 | — | — | <.0001 | |||

Demographics |
||||||||||||

Race/ethnicity | ||||||||||||

Black vs. white | -0.43 | 0.65 | (0.55, 0.78) | <.0001 | — | — | — | — | -0.66 | 0.52 | (0.41, 0.65) | <.0001 |

Hispanic vs. white | -0.10 | 0.90 | (0.76, 1.06) | .2155 | — | — | — | — | -0.05 | 0.95 | (0.78, 1.16) | .6092 |

Other vs. white | -0.39 | 0.67 | (0.50, 0.91) | .0095 | — | — | — | — | -0.16 | 0.85 | (0.60, 1.21) | .3656 |

Gender - male vs. female | 0.16 | 1.18 | (1.07, 1.29) | .0008 | — | — | — | — | 0.11 | 1.12 | (0.99, 1.26) | .0695 |

Age (continuous - 12 to 17) | 0.52 | 1.68 | (1.63, 1.72) | <.0001 | — | — | — | — | 0.34 | 1.41 | (1.35, 1.47) | <.0001 |

Number of parents in home (2 vs. others) | -0.67 | 0.51 | (0.46, 0.57) | <.0001 | — | — | — | — | -0.55 | 0.57 | (0.50, 0.66) | <.0001 |

Economic deprivation (household income under $20,000) | -0.16 | 0.85 | (0.74, 0.98) | .0242 | — | — | — | — | -0.11 | 0.90 | (0.75, 1.07) | .2349 |

Geographic region | ||||||||||||

Northeast vs. West | -0.20 | 0.82 | (0.70, 0.96) | .0119 | — | — | — | — | -0.20 | 0.82 | (0.67, 0.99) | .0371 |

North Central vs. West | -0.17 | 0.84 | (0.73, 0.96) | .0127 | — | — | — | — | -0.15 | 0.86 | (0.73, 1.02) | .0757 |

South vs. West | -0.26 | 0.77 | (0.68, 0.88) | .0001 | — | — | — | — | -0.18 | 0.84 | (0.71, 0.99) | .0323 |

County type | ||||||||||||

Large MSA vs. non-MSA | 0.19 | 1.21 | (1.07, 1.36) | .0023 | — | — | — | — | 0.12 | 1.13 | (0.98, 1.31) | .1039 |

Small MSA vs. non-MSA | 0.21 | 1.24 | (1.09, 1.41) | .0008 | — | — | — | — | 0.02 | 1.03 | (0.88, 1.20) | .7517 |

School Domain^{2} |
||||||||||||

Commitment to school | — | — | — | — | -0.39 | 0.68 | (0.62, 0.74) | <.0001 | -0.37 | 0.69 | (0.63, 0.76) | <.0001 |

Sanctions against marijuana use at school | — | — | — | — | -0.11 | 0.89 | (0.75, 1.07) | .2087 | -0.11 | 0.90 | (0.75, 1.07) | .2255 |

Perceived prevalence of marijuana use | — | — | — | — | 1.43 | 4.17 | (3.82, 4.55) | <.0001 | 1.26 | 3.52 | (3.20, 3.87) | <.0001 |

Academic performance | — | — | — | — | 0.32 | 1.38 | (1.29, 1.48) | <.0001 | 0.33 | 1.39 | (1.30, 1.50) | <.0001 |

Exposed to prevention messages in school | — | — | — | — | -0.23 | 0.79 | (0.69, 0.90) | .0006 | -0.14 | 0.87 | (0.76, 1.00) | .0511 |

Sample size | 25,357 | 17,679 | 17,679 | |||||||||

R^{2} (see footnote 3) |
0.09 | 0.16 | 0.18 | |||||||||

R_{N}^{2} (see footnote 4) |
0.15 | 0.27 | 0.32 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from multiple logistic regression models and adjusted for other variables included in each model. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Table A.4**). The coding and distribution of the responses for each factor are provided in **Table 2.4**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{1} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | — | — | <.0001 | |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.56 | 0.57 | (0.44, 0.74) | <.0001 |

Hispanic vs. white | -0.01 | 0.99 | (0.77, 1.28) | .9264 |

Other vs. white | 0.01 | 1.01 | (0.65, 1.58) | .9619 |

Gender - male vs. female | -0.15 | 0.86 | (0.74, 1.00) | .0494 |

Age (continuous - 12 to 17) | 0.25 | 1.29 | (1.22, 1.36) | <.0001 |

Number of parents in home (2 vs. others) | -0.28 | 0.76 | (0.64, 0.90) | .0017 |

-0.21 | 0.81 | (0.65, 1.02) | .0762 | |

Geographic region | ||||

Northeast vs. West | -0.18 | 0.83 | (0.65, 1.06) | .1377 |

North Central vs. West | -0.01 | 0.99 | (0.80, 1.23) | .9084 |

South vs. West | 0.02 | 1.03 | (0.83, 1.27) | .8171 |

County type | ||||

Large MSA vs. non-MSA | 0.15 | 1.16 | (0.96, 1.40) | .1274 |

Small MSA vs. non-MSA | 0.00 | 1.00 | (0.82, 1.22) | .9836 |

Community Domain^{2} |
||||

Community attitudes toward marijuana use | -0.13 | 0.88 | (0.79, 0.99) | .0285 |

Community norms toward marijuana use | 0.35 | 1.42 | (1.25, 1.60) | <.0001 |

Availability of marijuana | 0.25 | 1.28 | (1.18, 1.39) | .0001 |

Exposed to prevention messages in the media | -0.21 | 0.81 | (0.67, 0.99) | .0434 |

Family Domain^{2} |
||||

Parental monitoring | 0.10 | 1.11 | (0.97, 1.26) | .1345 |

Parental encouragement | -0.02 | 0.98 | (0.88, 1.09) | .7285 |

Parental attitudes toward marijuana use | 0.17 | 1.19 | (1.03, 1.38) | .0197 |

Parents communicate about substance use | 0.47 | 1.59 | (1.35, 1.88) | <.0001 |

Parents are source of social support | -0.32 | 0.73 | (0.62, 0.85) | .0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.75 | 2.11 | (1.63, 2.73) | <.0001 |

Individual attitudes toward marijuana use | 0.54 | 1.71 | (1.53, 1.91) | .0001 |

Friends' attitudes toward marijuana use | 0.34 | 1.40 | (1.26, 1.55) | <.0001 |

Friends' marijuana use | 0.73 | 2.07 | (1.80, 2.37) | <.0001 |

Perceived risk of marijuana use | 0.58 | 1.78 | (1.63, 1.95) | <.0001 |

Risk-taking proclivity | 0.24 | 1.27 | (1.12, 1.44) | .0002 |

School Domain^{2} |
||||

Commitment to school | 0.35 | 1.42 | (1.25, 1.61) | <.0001 |

Perceived prevalence of marijuana use | 0.35 | 1.42 | (1.24, 1.62) | <.0001 |

Academic performance | 0.18 | 1.20 | (1.09, 1.32) | .0003 |

Sample size | 16,402 | |||

R^{2} (see footnote 3) |
0.33 | |||

R_{N}^{2} (see footnote 4) |
0.56 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{1} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | — | — | <.0001 | |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.57 | 0.57 | (0.44, 0.74) | <.0001 |

Hispanic vs. white | -0.01 | 0.99 | (0.77, 1.28) | .9312 |

Other vs. white | 0.01 | 1.01 | (0.65, 1.58) | .9502 |

Gender - male vs. female | -0.16 | 0.85 | (0.73, 0.99) | .0405 |

Age (continuous - 12 to 17) | 0.26 | 1.30 | (1.22, 1.37) | <.0001 |

Number of parents in home (2 vs. others) | -0.28 | 0.75 | (0.64, 0.90) | .0014 |

Economic deprivation (household income under $20,000) | -0.21 | 0.81 | (0.65, 1.02) | .0749 |

Geographic region | ||||

Northeast vs. West | -0.18 | 0.84 | (0.66, 1.07) | .1517 |

North Central vs. West | -0.01 | 0.99 | (0.80, 1.23) | .9575 |

South vs. West | 0.03 | 1.03 | (0.84, 1.27) | .7656 |

County type | ||||

Large MSA vs. non-MSA | 0.15 | 1.16 | (0.96, 1.40) | .1263 |

Small MSA vs. non-MSA | 0.00 | 1.00 | (0.82, 1.21) | .9725 |

Community Domain^{2} |
||||

Community attitudes toward marijuana use | -0.12 | 0.88 | (0.79, 0.99) | .0323 |

Community norms toward marijuana use | 0.35 | 1.42 | (1.25, 1.61) | <.0001 |

Availability of marijuana | 0.25 | 1.28 | (1.18, 1.40) | <.0001 |

Exposed to prevention messages in the media | -0.21 | 0.81 | (0.66, 0.99) | .0423 |

Family Domain^{2} |
||||

Parental attitudes toward marijuana use | 0.18 | 1.19 | (1.03, 1.38) | .0186 |

Parents communicate about substance use | 0.44 | 1.55 | (1.32, 1.82) | <.0001 |

Parents are source of social support | -0.34 | 0.71 | (0.61, 0.83) | <.0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.76 | 2.13 | (1.65, 2.75) | <.0001 |

Individual attitudes toward marijuana use | 0.54 | 1.71 | (1.53, 1.91) | <.0001 |

Friends' attitudes toward marijuana use | 0.34 | 1.40 | (1.26, 1.56) | <.0001 |

Friends' marijuana use | 0.73 | 2.07 | (1.81, 2.38) | <.0001 |

Perceived risk of marijuana use | 0.58 | 1.79 | (1.64, 1.95) | <.0001 |

Risk-taking proclivity | 0.24 | 1.27 | (1.12, 1.44) | .0002 |

School Domain^{2} |
||||

Commitment to school | 0.33 | 1.39 | (1.22, 1.58) | <.0001 |

Perceived prevalence of marijuana use | 0.35 | 1.42 | (1.25, 1.63) | <.0001 |

Academic performance | 0.18 | 1.20 | (1.09, 1.32) | .0004 |

Sample size | 16,411 | |||

R^{2} (see footnote 3) |
0.33 | |||

R_{N}^{2} (see footnote 4) |
0.56 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year marijuana use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of marijuana use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against marijuana use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

Demographics^{1} + Risk/Protective Factors^{2} |
||||
---|---|---|---|---|

OR^{3} |
95% CI | p value |
||

Community Domain^{2} + Demographics^{1} |
||||

Community disorganization and crime | 0.05 | 1.05 | (0.98, 1.13) | .1439 |

Neighborhood cohesiveness | 0.01 | 1.01 | (0.95, 1.07) | .7155 |

Community attitudes toward cigarette use | 0.39 | 1.47 | (1.40, 1.56) | <.0001 |

Community norms toward cigarette use | 0.70 | 2.00 | (1.87, 2.14) | <.0001 |

Exposed to prevention messages in the media | -0.22 | 0.80 | (0.72, 0.89) | .0001 |

Family Domain^{2} + Demographics^{1} |
||||

Parental monitoring | 0.37 | 1.45 | (1.32, 1.60) | <.0001 |

Parental encouragement | -0.19 | 0.83 | (0.77, 0.89) | <.0001 |

Parental attitudes toward cigarette use | 0.76 | 2.14 | (1.96, 2.34) | <.0001 |

Parents communicate about substance use | 0.30 | 1.34 | (1.22, 1.48) | <.0001 |

Parents are source of social support | -0.76 | 0.47 | (0.42, 0.52) | <.0001 |

Peer/Individual Domain^{2} + Demographics^{1} |
||||

Antisocial behavior | 0.68 | 1.97 | (1.66, 2.34) | <.0001 |

Individual attitudes toward cigarette use | 0.45 | 1.56 | (1.47, 1.67) | <.0001 |

Friends' attitudes toward cigarette use | 0.21 | 1.24 | (1.16, 1.32) | <.0001 |

Friends' cigarette use | 0.92 | 2.52 | (2.34, 2.71) | <.0001 |

Perceived risk of cigarette use | 0.18 | 1.19 | (1.12, 1.27) | <.0001 |

Risk-taking proclivity | 0.46 | 1.59 | (1.47, 1.71) | <.0001 |

Participation in two or more extracurricular activities | -0.17 | 0.85 | (0.76, 0.94) | .0015 |

Religiosity | -0.17 | 0.85 | (0.79, 0.91) | <.0001 |

School Domain^{2} + Demographics^{1} |
||||

Commitment to school | -0.42 | 0.65 | (0.60, 0.71) | <.0001 |

Sanctions against cigarette use at school | -0.23 | 0.79 | (0.73, 0.86) | <.0001 |

Perceived prevalence of cigarette use | 0.74 | 2.10 | (1.93, 2.29) | <.0001 |

Academic performance | 0.41 | 1.51 | (1.43, 1.60) | <.0001 |

Exposed to prevention messages in school | -0.12 | 0.88 | (0.78, 1.00) | .0542 |

OR = odds ratio; CI = confidence interval.

Note: No question was asked about availability of cigarettes.

^{1} Demographic variables included in models were race/ethnicity, gender, age, number of parents in home, household income, geographic region, and county type.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} ORs are derived from multiple logistic regression models, run separately for each domain, and adjusted for the demographic variables as well as the other factors within each domain. ORs > 1.0 indicate that the odds of past year cigarette use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of cigarette use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against cigarette use.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{1} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | -9.90 | — | — | <.0001 |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.57 | 0.56 | (0.46, 0.69) | <.0001 |

Hispanic vs. white | -0.30 | 0.74 | (0.61, 0.90) | .0021 |

Other vs. white | -0.22 | 0.80 | (0.59, 1.09) | .1594 |

Gender - male vs. female | -0.26 | 0.77 | (0.68, 0.87) | <.0001 |

Age (continuous - 12 to 17) | 0.25 | 1.29 | (1.24, 1.34) | <.0001 |

Number of parents in home (2 vs. others) | -0.24 | 0.79 | (0.69, 0.90) | .0003 |

-0.22 | 0.80 | (0.68, 0.95) | .0119 | |

Geographic region | ||||

Northeast vs. West | -0.11 | 0.89 | (0.73, 1.10) | .2918 |

North Central vs. West | 0.00 | 1.00 | (0.84, 1.18) | .9626 |

South vs. West | 0.09 | 1.09 | (0.93, 1.29) | .2910 |

County type | ||||

Large MSA vs. non-MSA | -0.10 | 0.90 | (0.78, 1.04) | .1612 |

Small MSA vs. non-MSA | -0.01 | 0.99 | (0.85, 1.15) | .8923 |

Community Domain^{2} |
||||

Community attitudes toward cigarette use | 0.03 | 1.03 | (0.95, 1.11) | .4500 |

Community norms toward cigarette use | 0.11 | 1.12 | (1.02, 1.23) | .0161 |

Exposed to prevention messages in the media | -0.02 | 0.98 | (0.85, 1.13) | .7816 |

Family Domain^{2} |
||||

Parental monitoring | 0.09 | 1.10 | (0.97, 1.23) | .1290 |

Parental encouragement | -0.01 | 0.99 | (0.91, 1.08) | .8356 |

Parental attitudes toward cigarette use | 0.25 | 1.29 | (1.16, 1.43) | <.0001 |

Parents communicate about substance use | 0.38 | 1.46 | (1.30, 1.64) | <.0001 |

Parents are source of social support | -0.49 | 0.62 | (0.54, 0.70) | <.0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.53 | 1.69 | (1.39, 2.06) | <.0001 |

Individual attitudes toward cigarette use | 0.46 | 1.58 | (1.46, 1.70) | <.0001 |

Friends' attitudes toward cigarette use | 0.13 | 1.14 | (1.05, 1.24) | .0015 |

Friends' cigarette use | 0.82 | 2.28 | (2.08, 2.49) | <.0001 |

Perceived risk of cigarette use | 0.17 | 1.18 | (1.09, 1.28) | <.0001 |

Risk-taking proclivity | 0.39 | 1.48 | (1.34, 1.63) | <.0001 |

Participation in two or more extracurricular activities | -0.11 | 0.90 | (0.78, 1.03) | .1298 |

Religiosity | -0.17 | 0.85 | (0.78, 0.92) | .0001 |

School Domain^{2} |
||||

Commitment to school | 0.03 | 1.03 | (0.93, 1.14) | .5952 |

Sanctions against cigarette use at school | 0.01 | 1.01 | (0.92, 1.11) | .8681 |

Perceived prevalence of cigarette use | 0.16 | 1.17 | (1.06, 1.31) | .0029 |

Academic performance | 0.17 | 1.19 | (1.11, 1.28) | <.0001 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

Note: No question was asked about availability of cigarettes.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year cigarette use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of cigarette use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against cigarette use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{1} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | — | — | <.0001 | |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.58 | 0.56 | (0.46, 0.68) | <.0001 |

Hispanic vs. white | -0.25 | 0.78 | (0.64, 0.94) | .0079 |

Other vs. white | -0.25 | 0.78 | (0.58, 1.05) | .1031 |

Gender - male vs. female | -0.26 | 0.77 | (0.68, 0.87) | <.0001 |

Age (continuous - 12 to 17) | 0.26 | 1.30 | (1.25, 1.35) | <.0001 |

Number of parents in home (2 vs. others) | -0.25 | 0.78 | (0.68, 0.88) | .0001 |

Economic deprivation (household income under $20,000) | -0.22 | 0.80 | (0.68, 0.95) | .0115 |

Geographic region | ||||

Northeast vs. West | -0.12 | 0.89 | (0.72, 1.09) | .2506 |

North Central vs. West | -0.02 | 0.98 | (0.84, 1.16) | .8539 |

South vs. West | 0.09 | 1.09 | (0.93, 1.28) | .2826 |

County type | ||||

Large MSA vs. non-MSA | -0.11 | 0.90 | (0.78, 1.03) | .1316 |

Small MSA vs. non-MSA | -0.02 | 0.98 | (0.85, 1.14) | .8305 |

Community Domain^{2} |
||||

Community's norms toward cigarette use | 0.13 | 1.14 | (1.04, 1.25) | .0052 |

Family Domain^{2} |
||||

Parental attitudes toward cigarette use | 0.28 | 1.32 | (1.19, 1.46) | <.0001 |

Parents communicate about substance use | 0.36 | 1.43 | (1.29, 1.60) | <.0001 |

Parents are source of social support | -0.50 | 0.61 | (0.54, 0.69) | <.0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.55 | 1.72 | (1.42, 2.09) | <.0001 |

Individual attitudes toward cigarette use | 0.45 | 1.57 | (1.46, 1.70) | <.0001 |

Friends' attitudes toward cigarette use | 0.15 | 1.16 | (1.07, 1.26) | .0003 |

Friends' cigarette use | 0.82 | 2.28 | (2.09, 2.49) | <.0001 |

Perceived risk of cigarette use | 0.17 | 1.18 | (1.09, 1.28) | <.0001 |

Risk-taking proclivity | 0.38 | 1.46 | (1.33, 1.60) | <.0001 |

Religiosity | -0.19 | 0.83 | (0.77, 0.90) | <.0001 |

School Domain^{2} |
||||

Perceived prevalence of cigarette use | 0.15 | 1.17 | (1.05, 1.29) | .0035 |

Academic performance | 0.18 | 1.20 | (1.12, 1.28) | <.0001 |

Sample size | 17,410 | |||

R^{2} (see footnote 3) |
0.29 | |||

R_{N}^{2} (see footnote 4) |
0.43 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

Note: No question was asked about availability of cigarettes.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year cigarette use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of cigarette use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against cigarette use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

Demographics^{1} + Risk/Protective Factors^{2} |
||||
---|---|---|---|---|

OR^{3} |
95% CI | p value |
||

Community Domain^{2} + Demographics^{1} |
||||

Community disorganization and crime | -0.05 | 0.95 | (0.89, 1.02) | .1610 |

Neighborhood cohesiveness | 0.01 | 1.01 | (0.96, 1.07) | .6212 |

Community attitudes toward alcohol use | 0.27 | 1.31 | (1.24, 1.39) | <.0001 |

Community norms toward alcohol use | 0.92 | 2.51 | (2.35, 2.68) | <.0001 |

Exposed to prevention messages in the media | -0.07 | 0.93 | (0.84, 1.02) | .1336 |

Family Domain^{2} + Demographics^{1} |
||||

Parental monitoring | 0.49 | 1.64 | (1.51, 1.77) | <.0001 |

Parental encouragement | -0.13 | 0.88 | (0.82, 0.93) | <.0001 |

Parental attitudes toward alcohol use | 0.65 | 1.91 | (1.73, 2.11) | <.0001 |

Parents communicate about substance use | 0.27 | 1.31 | (1.19, 1.44) | <.0001 |

Parents are source of social support | -0.65 | 0.52 | (0.47, 0.58) | <.0001 |

Peer/Individual Domain^{2} + Demographics^{1} |
||||

Antisocial behavior | 0.50 | 1.65 | (1.35, 2.01) | <.0001 |

Individual attitudes toward alcohol use | 0.38 | 1.46 | (1.37, 1.57) | <.0001 |

Friends' attitudes toward alcohol use | 0.08 | 1.08 | (1.01, 1.16) | .0310 |

Friends' alcohol use | 0.99 | 2.69 | (2.50, 2.89) | <.0001 |

Perceived risk of alcohol use | 0.19 | 1.21 | (1.14, 1.30) | <.0001 |

Risk-taking proclivity | 0.64 | 1.89 | (1.76, 2.03) | <.0001 |

Participation in two or more extracurricular activities | 0.11 | 1.12 | (1.01, 1.24) | .0292 |

Religiosity | -0.26 | 0.77 | (0.72, 0.82) | <.0001 |

School Domain^{2} + Demographics^{1} |
||||

Commitment to school | -0.46 | 0.63 | (0.58, 0.68) | <.0001 |

Sanctions against alcohol use at school | 0.11 | 1.11 | (0.99, 1.25) | .0676 |

Perceived prevalence of alcohol use | 0.96 | 2.62 | (2.42, 2.83) | <.0001 |

Academic performance | 0.21 | 1.23 | (1.16, 1.30) | <.0001 |

Exposed to prevention messages in school | -0.03 | 0.97 | (0.87, 1.08) | .5494 |

Note: No question was asked about availability of alcohol.

^{1} Demographic variables included in models were race/ethnicity, gender, age, number of parents in home, household income, geographic region, and county type.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} ORs are derived from multiple logistic regression models, run separately for each domain, and adjusted for the demographic variables as well as the other factors within each domain. ORs > 1.0 indicate that the odds of past year alcohol use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of alcohol use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against alcohol use.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{1} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | — | — | <.0001 | |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.63 | 0.53 | (0.44, 0.65) | <.0001 |

Hispanic vs. white | 0.00 | 1.00 | (0.83, 1.22) | .9681 |

Other vs. white | -0.28 | 0.75 | (0.56, 1.01) | .0567 |

Gender - male vs. female | -0.41 | 0.67 | (0.60, 0.74) | <.0001 |

Age (continuous - 12 to 17) | 0.36 | 1.43 | (1.38, 1.48) | <.0001 |

Number of parents in home (2 vs. others) | -0.18 | 0.83 | (0.74, 0.94) | .0021 |

Economic deprivation (household income under $20,000) | -0.17 | 0.84 | (0.72, 0.99) | .0329 |

Geographic region | ||||

Northeast vs. West | 0.01 | 1.01 | (0.85, 1.20) | .8829 |

North Central vs. West | -0.06 | 0.94 | (0.81, 1.09) | .4278 |

South vs. West | -0.03 | 0.97 | (0.83, 1.13) | .6693 |

County type | ||||

Large MSA vs. non-MSA | 0.09 | 1.09 | (0.96, 1.24) | .1935 |

Small MSA vs. non-MSA | 0.08 | 1.08 | (0.95, 1.24) | .2480 |

Community Domain^{2} |
||||

Community attitudes toward alcohol use | -0.06 | 0.94 | (0.87, 1.02) | .1238 |

Community norms toward alcohol use | 0.27 | 1.31 | (1.19, 1.43) | <.0001 |

Family Domain^{2} |
||||

Parental monitoring | 0.16 | 1.17 | (1.07, 1.29) | .0012 |

Parental encouragement | 0.03 | 1.03 | (0.96, 1.11) | .4043 |

Parental attitudes toward alcohol use | 0.17 | 1.18 | (1.03, 1.35) | .0139 |

Parents communicate about substance use | 0.26 | 1.30 | (1.17, 1.46) | <.0001 |

Parents are source of social support | -0.34 | 0.71 | (0.63, 0.80) | <.0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.52 | 1.69 | (1.32, 2.16) | <.0001 |

Individual attitudes toward alcohol use | 0.41 | 1.50 | (1.38, 1.63) | <.0001 |

Friends' attitudes toward alcohol use | -0.01 | 0.99 | (0.91, 1.08) | .8207 |

Friends' alcohol use | 0.85 | 2.34 | (2.12, 2.59) | <.0001 |

Perceived risk of alcohol use | 0.22 | 1.24 | (1.15, 1.35) | <.0001 |

Risk-taking proclivity | 0.59 | 1.81 | (1.65, 1.98) | <.0001 |

Participation in two or more extracurricular activities | 0.04 | 1.04 | (0.92, 1.19) | .5205 |

Religiosity | -0.24 | 0.79 | (0.73, 0.85) | <.0001 |

School Domain^{2} |
||||

Commitment to school | 0.07 | 1.07 | (0.97, 1.18) | .1598 |

Perceived prevalence of alcohol use | 0.13 | 1.14 | (1.03, 1.27) | .0118 |

Academic performance | 0.03 | 1.03 | (0.97, 1.11) | .3341 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

Note: No question was asked about availability of alcohol.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year alcohol use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of alcohol use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against alcohol use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

OR^{3} |
95% CI | p value |
||
---|---|---|---|---|

Intercept | — | — | <.0001 | |

Demographics |
||||

Race/ethnicity | ||||

Black vs. white | -0.61 | 0.54 | (0.45, 0.66) | <.0001 |

Hispanic vs. white | 0.01 | 1.01 | (0.84, 1.21) | .9453 |

Other vs. white | -0.28 | 0.76 | (0.57, 1.01) | .0580 |

Gender - male vs. female | -0.40 | 0.67 | (0.61, 0.75) | <.0001 |

Age (continuous - 12 to 17) | 0.35 | 1.41 | (1.36, 1.47) | <.0001 |

Number of parents in home (2 vs. others) | -0.20 | 0.82 | (0.73, 0.92) | .0007 |

Economic deprivation (household income under $20,000) | -0.18 | 0.83 | (0.71, 0.97) | .0203 |

Geographic region | ||||

Northeast vs. West | 0.02 | 1.02 | (0.86, 1.21) | .8179 |

North Central vs. West | -0.06 | 0.94 | (0.81, 1.09) | .4085 |

South vs. West | -0.03 | 0.97 | (0.83, 1.13) | .7133 |

County type | ||||

Large MSA vs. non-MSA | 0.06 | 1.06 | (0.93, 1.21) | .3661 |

Small MSA vs. non-MSA | 0.05 | 1.05 | (0.92, 1.20) | .4733 |

Community Domain^{2} |
||||

Community norms toward alcohol use | 0.27 | 1.31 | (1.20, 1.43) | <.0001 |

Family Domain^{2} |
||||

Parental monitoring | 0.13 | 1.14 | (1.04, 1.25) | .0038 |

Parental attitudes toward alcohol use | 0.16 | 1.17 | (1.04, 1.33) | .0114 |

Parents communicate about substance use | 0.26 | 1.30 | (1.17, 1.45) | <.0001 |

Parents are source of social support | -0.33 | 0.72 | (0.64, 0.81) | <.0001 |

Peer/Individual Domain^{2} |
||||

Antisocial behavior | 0.48 | 1.61 | (1.29, 2.03) | <.0001 |

Individual attitudes toward alcohol use | 0.39 | 1.48 | (1.38, 1.58) | <.0001 |

Friends' alcohol use | 0.84 | 2.31 | (2.09, 2.54) | <.0001 |

Perceived risk of alcohol use | 0.21 | 1.24 | (1.14, 1.33) | <.0001 |

Risk-taking proclivity | 0.56 | 1.75 | (1.60, 1.90) | <.0001 |

Religiosity | -0.24 | 0.79 | (0.73, 0.85) | <.0001 |

School Domain^{2} |
||||

Perceived prevalence of alcohol use | 0.16 | 1.17 | (1.06, 1.29) | .0020 |

Sample size | 17,265 | |||

R^{2} (see footnote 3) |
0.34 | |||

R_{N}^{2} (see footnote 4) |
0.46 |

OR = odds ratio; CI = confidence interval; MSA = metropolitan statistical area.

Note: No question was asked about availability of alcohol.

^{1} ORs are derived from a single multiple logistic regression model that included the set of demographic variables as well as all of the risk and protective factors included in the table. ORs > 1.0 indicate that the odds of past year alcohol use increased with each unit increase in the predictor. For risk factors, each unit increase in the predictor generally indicates an increased risk of alcohol use. For protective factors, each unit increase in the predictor generally indicates a higher level of protection against alcohol use.

^{2} The questions used to measure each of the factors are provided in **Appendix A** (**Tables A.1** to **A.4**). The coding and distribution of the responses for each factor are provided in **Tables 2.1** to **2.4**.

^{3} Cox and Snell (1989) R^{2} is a measure of the fit of the model, defined as where *L*(*O*) is the likelihood of the intercept-only model, is the likelihood of the full model, and *n* is the sample size.

^{4} Recognizing that the Cox and Snell R^{2} reaches a maximum for models that depend on the value of the estimated percentage, Nagelkerke (1991) proposed dividing the Cox and Snell measure by the maximum. In this sense, R_{N}^{2} measures the absolute percentage of variation explained by the model.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999.

This page was last updated on July 17, 2008. |