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. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: Assessment. 2022 Apr 18;30(4):1125–1139. doi: 10.1177/10731911221089201

Measurement Invariance and Application of an Alcohol-Related Consequence Scale for American Indian Adolescents

Silvi C Goldstein 1, Nichea S Spillane 1, Melissa R Schick 1, Joseph S Rossi 1
PMCID: PMC9576817  NIHMSID: NIHMS1823480  PMID: 35435000

Abstract

American Indian (AI) adolescents experience disproportionate alcohol-related consequences. The present study evaluated the psychometric properties and application of the American Drug and Alcohol Survey (ADAS™) alcohol-related consequence scale for AI adolescents through a secondary analysis of a large population-based sample of adolescents living on or near AI reservations. We found support for the ADAS alcohol-related consequence scale as a one-factor model, invariant discretely across race, sex assigned at birth, and age, and with good internal consistency. Evidence for construct validity was found through significant positive correlations between frequency of past 12 months of drinking, frequency of past 12 months of intoxication, and lifetime alcohol-related consequences. AI adolescents were significantly more likely to report more alcohol-related consequences than their non-Hispanic White peers. Race significantly interacted with frequency of drinking in predicting alcohol-related consequences such that these associations were stronger for AI adolescents. However, race did not significantly interact with frequency of intoxication in predicting alcohol-related consequences. Results from this study demonstrate the utility of the ADAS alcohol-related consequence scale for use across demographic groups with little risk of measurement bias.

Keywords: American Indian, adolescents, alcohol use, alcohol-related consequences, psychometrics

American Indian (AI) communities have identified alcohol use as a primary concern (Radin et al., 2015; Spillane et al., 2020). It is important that researchers and consumers of research appreciate the likely role that history has played with respect to alcohol in many AI communities. Historical trauma stemming from a history of genocide, forced removal from lands and the destruction of culture, has been hypothesized to have contributed to substance use and mental health concerns within AI communities (Substance Abuse and Mental Health Services Administration [SAMHSA], 2019). Indeed, empirical studies have found high rates of alcohol use resulting directly from acculturative stress and indirectly from historical trauma (Lane & Simmons, 2011; Myhra, 2011; Whitesell, Kaufman, et al., 2012). With that, there is a high degree of variability in prevalence rates of alcohol use across AI populations. Some research suggests that AI adolescents may not be at higher risk for engaging in past-month alcohol use as compared with their non-AI peers (Lynne-Landsman et al., 2016). However, other studies have found that adolescent alcohol use is a concern among AI adolescents, with rates of use higher than what is seen in other racial/ethnic groups (Stanley et al., 2014; Swaim & Stanley, 2018; Whitbeck et al., 2014). For example, AI adolescents have been found to have higher rates of alcohol use disorder (AUD; SAMHSA, 2019) and higher rates of past-month binge drinking compared with their non-AI counterparts (Swaim & Stanley, 2018). Given this health inequity, it is important to further investigate alcohol among AI adolescents.

Although there is variability in rates of alcohol use, it is well established that AIs suffer disproportionate negative effects associated with alcohol use compared with the general population (Gonzales et al., 2014; Indian Health Service, 2019; Landen et al., 2014; Szlemko et al., 2006). AIs tend to initiate drinking at young ages compared with their non-AI counterparts (Friese et al., 2011; Henry et al., 2011; Stanley et al., 2014; Yu & Stiffman, 2007), with one study finding initiation is 2.3 times that of comparable White youth (Stanley & Swaim, 2015). Average age of initiation among AI and First Nations are 14 and 13, respectively (Spillane et al., 2015; Whitesell, Kaufman, et al., 2012), which is of concern given that intoxication before age 14 is associated with experiencing more alcohol-related consequences, heavier drinking, and increased risk for being diagnosed with an AUD later in life (Henry et al., 2011). Moreover, compared with those who do not drink, AI adolescents who drink alcohol are more likely to smoke, use more drugs, be convicted of a crime, and run away from home (Mitchell et al., 2008). In addition, research suggests a strong association between binge drinking and suicidal behavior among AI adolescents (Cwik et al., 2018). Although recent research comparing alcohol-related consequences between AI and adolescents from other racial/ethnic groups is limited, Beauvais (1992) found that various alcohol-related consequences, such as driving (e.g., car accidents) and relationship-related consequences (e.g., fights with parents and peers), were more likely for reservation-dwelling AI youth compared with their White peers (Beauvais, 1992). Furthermore, AI adolescents are more likely to continue to face alcohol-related health inequities into adulthood. For instance, the rate of alcohol-related mortality for AIs was 6.6 times higher than the general population. Specifically, death from chronic liver disease and cirrhosis was 4.6 times higher, suicide was 1.7 times higher, alcohol-related hypothermia was 14.2 times higher, alcohol poisoning was 7.6 times higher, and alcohol-related psychosis was 5.0 times higher (Indian Health Service, 2019; Landen et al., 2014). There is a clear alcohol-related health disparity with experiences of alcohol-related consequences that begin in adolescence and continue through adulthood.

Alcohol-related health disparities also likely exist across sex assigned at birth and gender groups. Studies reporting on alcohol-related consequences across sex and gender find that men generally consume more alcohol and experience more alcohol-related consequences than women (A. M. White, 2020), whereas women experience more alcohol-related consequences than men at the same levels of alcohol use (Clarke et al., 2013; Skidmore et al., 2012), including faster progression to AUD and greater risk for alcohol-induced hangovers, liver inflammation, cardiovascular diseases, and certain cancers compared with men (Kirpich et al., 2017; Nolen-Hoeksema, 2004; van Lawick van Pabst et al., 2019; Vatsalya et al., 2018). Although sex and gender differences with respect to AI adolescent alcohol use and related consequences are understudied, results suggest the possibility of underlying alcohol-related health disparities, with one study finding that higher lifetime alcohol use for girls than boys (De Ravello et al., 2014). Given the likelihood of this alcohol-related health disparity, it is important to assess whether measures work equally across sex assigned at birth to allow for further investigations and comparisons of alcohol-related consequences. Taken together, the lack of studies examining the psychometric properties of alcohol-related consequence scales necessitates the prioritization of examining the validity of measurement tools that are used with this population to ensure accurate assessment.

Despite the importance of investigating the psychometric properties when utilizing instruments among populations for whom they were not initially developed, many have not yet been validated for use with AI adolescents (Ferreira et al., 2014; Pilatti et al., 2014; Read et al., 2006; Verster et al., 2009; Winters, 1999), with a few exceptions (Hirchak et al., 2021; Noel et al., 2010, Goldstein, Spillane, Tate, et al., in press). For example, the Short Inventory of Problems (SIP; Miller, 1995) is a 15-item self-report measure designed to assess the effects of alcohol use on quality of life and treatment outcomes. Participants respond with respect to alcohol-related consequences across five domains (i.e., physical, interpersonal, intrapersonal, impulse, and social). Hirchak et al. (2021) examined the applicability and validity of the SIP in a sample of urban AI treatment-seeking adults, finding support for its use to assess alcohol-related consequences in this population and its association with alcohol use. In addition, Goldstein, Spillane, Tate, et al. (in press) found the SIP-2R to be psychometrically valid for use among North American Indigenous (NAI) adults experiencing homelessness with AUD. Similarly, the Rutgers Alcohol Problem Index (RAPI; H. R. White & Labouvie, 1989) is a 23-item self-report measure designed to assess adolescent experiences of alcohol-related consequences across six domains (i.e., delinquency, family life, physical problems, social relationships, psychological functioning, and neuropsychological functioning). Participants respond with how many times in a given timeframe that alcohol use has caused them to have experienced various consequences (e.g., going to school drunk, noticing a change in personality). Noel et al. (2010) examined the utility of the RAPI to distinguish between problem and non-problem drinkers in NAI adolescents versus non-NAI adolescents from lower socio-economic backgrounds. Their results found evidence for criterion validity for the RAPI across racial groups. Cross-cultural compassions of cutoff scores revealed that NAI adolescents are experiencing more harmful alcohol use at lower scores versus their non-NAI counterparts. This finding is important to understand the cross-cultural clinical utility of this measure, given that use of higher cutoffs could have led to missing the detection of NAI youth who were experiencing more alcohol-related consequences. Thus, these findings underscore the importance of examining the psychometric properties of measures specifically for use with AI populations.

One measure that has not yet been validated for use with adolescents is the American Drug and Alcohol Survey™ (ADAS). This measure has been widely used to study trends and correlates of alcohol and other drug use among AI adolescents living on or near a reservation as part of the Our Youth Our Future Project conducted by the Colorado State University Tri-Ethnic Center for Prevention Research since 1974 (Oetting & Beauvais, 1990; Oetting et al., 1985; Schick et al., 2020, 2021; Stanley et al., 2014; Stanley & Swaim, 2015, 2018, Swaim et al., 1997). However, despite wide use in large-scale epidemiologic research, there is a dearth of literature examining the psychometric properties of the ADAS’s scales, including the alcohol-related consequence scale. This scale has 13 items that ask about whether drinking alcohol has ever caused a series of consequences in an adolescent’s lifetime (e.g., getting a traffic ticket, being in a car accident, getting arrested, having money problems). Given the existence of alcohol-related health disparities, it is vital to accurately report on differences across race when drawing comparisons. By not testing the psychometric properties of a measure, we risk introducing measurement bias when drawing conclusions across racial groups. One approach is to test the psychometric properties of the ADAS alcohol-related consequence scale to establish measurement invariance across racial groups. Finding invariance between racial groups across the scale argues that latent means (the scale’s item constructs) are similar across groups, thus allowing meaningful comparisons to be drawn about alcohol-related consequence across racial groups (Milfont & Fischer, 2010; Widaman & Reise, 1997).

Purpose of the Study

The purpose of this study is to examine the factor structure of the ADAS alcohol-related consequence scale, evaluate the extent to which the ADAS alcohol-related consequence scale is invariant across race (AI vs. non-Hispanic White), sex assigned at birth (female vs. male), race combined with sex assigned at birth (AI female vs. AI male vs. non-Hispanic White female vs. non-Hispanic White male), and age (15–17 vs. 18–21). In addition, we aim to specifically evaluate the effect of race (AI and non-Hispanic White) in the association between alcohol consumption and alcohol-related consequences. If invariance is found across race, we hypothesize that race will moderate the association between alcohol consumption and alcohol-related consequences. Specifically, AIs will experience more alcohol-related consequences at higher levels of drinking and intoxication over the past 12 months compared with the non-Hispanic White adolescents.

Method

Participants

Participants were drawn from a larger parent study (N = 5,744) of adolescents aged 10 to 21 years old living on or near an AI reservation. This data set is a nationally representative sample of reservation-dwelling AI adolescents.1 For the purposes of the present study, we excluded any participants who were under the age of 15 given that the alcohol-related consequence scale of the ADAS included questions that may be less relevant for younger participants2 (e.g., related to driving; remaining n = 3,395). Next, we excluded any participants who reported never having drank alcohol (remaining n = 2,460), and any participant with missing data to allow for complete case analysis (remaining n = 2,214). See Table 1 for demographic characteristics of the full sample following exclusions (n = 2,214). Of note, we examined demographic characteristics, frequency of drinking, and alcohol-related consequences and largely did not find significant differences between those with and without missing data. Although we did find a significant difference by gender, such that those with missing data were more likely to be males, the effect size was very small (Cramer’s V = .069) suggesting it may be due to the large sample size.

Table 1.

Sociodemographic Information for the Full Sample.

Participants demographics (n = 2,214) Frequency (%)
Race American Indian 1,139 (51.4)
White 899 (40.6)
Black 113 (5.1)
Alaska Native 23 (1)
Hawaiian or Pacific Islander 8 (0.4)
Asian American 18 (0.8)
Other 80 (3.6)
Hispanic/Latino/a 93 (4.2)
Sex assigned at birth Female 1,153 (52.1)
Male 1,061 (47.9)
Age 15 653 (29.5)
16 707 (31.9)
17 571 (25.8)
18 242 (10.9)
19 29 (1.3)
20 5 (0.2)
21 7 (0.3)
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Procedures

The parent study has collected these data in waves for the past three decades; the present study made use of data collected between 2009 and 2013 (i.e., the most recent wave to be made publicly available through the National Addiction and HIV Data Archive program). Recruitment was stratified across six geographic regions based on the 2000 U.S. Census in which reservation-based AIs live (Snipp, 2005); schools in these regions were invited to participate if at least 20% of their student body were AI. Study procedures were reviewed and approved by the Colorado State University Institutional Review Board (IRB), and appropriate tribal and/or school board authority approval was obtained prior to beginning data collection. Self-report pencil-and-paper surveys were administered during classes by school staff at 33 participating schools. Parents were able to opt their children out of participation by contacting the school, and students could decline to participate by leaving their survey blank; <1% of children at the schools declined to participate or were opted out by their parents (Stanley et al., 2014). Procedures for secondary data analysis were classified as exempt by the University of Rhode Island’s Institutional Review Board.

Measures

Participants were administered the ADAS, a widely used measure of child and adolescent substance use (Oetting & Beauvais, 1990). The ADAS includes questions assessing types, frequencies, and experiences of substance use, as well as questions regarding normative influences to use substances, outcome expectancies related to substance use, family support, and other psychosocial characteristics, including personality factors.

Demographic characteristics including age, sex assigned at birth, grade, and race were collected.

Past-Year Alcohol Use.

Frequency of alcohol use and frequency of intoxication over the past year were each assessed by one question asking participants how frequently they “had alcohol to drink” or “got drunk” over the past 12 months. Participants respond to each item on a 5-point scale (1 = none, 5 = 50 or more times; Frequency of drinking: M = 1.564, SD = 1.133; Frequency of getting intoxication: M = 1.040, SD = 1.156).

Alcohol-Related Consequences.

Alcohol-related consequences were measured with 13 items assessing whether or not participants have experienced specific consequences as a result of their alcohol use over their lifetime on a 4-point scale (0 = no, 3 = 10 or more times). For the purpose of the present study, items were dichotomized such that 0 (no) reflected never experiencing a given alcohol-related consequence and 1 (yes) indicating having experienced a given alcohol-related consequence at least once in their lifetime. For moderation analyses, a total score reflecting the number of alcohol-related consequences endorsed was created by adding up the number of individual items endorsed with total scores ranging from 0 to 13. See Table 2 for descriptive statistics for endorsement of alcohol-related consequence items.

Table 2.

Descriptive Statistics for Endorsement of Alcohol-Related Consequence Items.

Frequency of endorsement of alcohol-related consequences
Item Full sample (N = 2,214) AI (n = 1,084) Non-Hispanic White (n = 837) Test statistic (n = 1,921)
Traffic ticket 2.4 (54) 2.8 (30) 1.6 (13) χ2(1) = 3.183, p = .074, V = .041
Car accident 5.6 (125) 7.6 (82) 2.4 (20) χ2(1) = 25.159, p < .001, V = .114
Got arrested 13.1 (291) 20.7 (224) 4.5 (38) χ2(1) = 104.253 (1), p < .001, V = .233
Money problems 10.7 (238) 15.6 (169) 4.8 (40) χ2(1) = 56.938 (1), p < .001, V = .172
Trouble at school 9.9 (219) 13.4 (145) 6.1 (51) χ2(1) = 27.345 (1), p < .001, V = .119
Hurt your school work 13.0 (288) 18.1 (196) 7.4 (62) χ2(1) = 46.281 (1), p < .001, V = .155
Fought with other kids 21.7 (480) 29.3 (320) 11.9 (100) χ2(1) = 85.376 (1), p < .001, V = .211
Fought with your parents 16.8 (371) 21.7 (235) 11.5 (96) χ2(1) = 34.519 (1), p < .001, V = .134
Damaged a friendship 15.6 (346) 21.0 (228) 9.7 (81) χ2(1) = 45.123 (1), p < .001, V = .153
Passed out 37.7 (835) 45.6 (494) 28.9 (242) χ2(1) = 55.464 (1), p < .001, V = .170
Could not remember what happened while drinking 41.4 (916) 48.5 (526) 35.2 (295) χ2(1) = 34.032 (1), p < .001, V = .133
Broke something 25.8 (571) 30.7 (333) 20.8 (174) χ2(1) = 23.978 (1), p < .001, V = .112
Lost your job 1.9 (43) 2.0 (22) 1.6 (13) χ2(1) = .599 (1), p = .439, V = .018
Total number of consequences endorsed (M [SD]) 2.158 (2.68) 2.771 (2.820) 1.464 (2.255) t(1,919) = 10.971, p <.001, d = 0.505
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Note. Test statistics are between AI and non-Hispanic White only. AI = American Indian.

Data Analysis Plan

Study analyses were conducted using SPSS v25.0, R 1.2 Lavaan (latent variable analysis) package, and Mplus. As recommended by Tabachnick and Fidell (2007), all variables of interest were assessed for assumptions of normality, homoscedasticity, and multicollinearity. To evaluate the factor structure of the ADAS alcohol-related consequence scale, a split-half, cross-validation approach was adopted, using SPSS v25.0, to provide more stable and precise parameter estimates for the model due to the larger sample size. First, the structure of the measure on one half of the sample was confirmed. Then, a confirmatory factor analysis (CFA) on the combined sample was conducted to provide a baseline for subsequent multigroup CFAs (Redding et al., 2006).

As such, the full sample (N = 2,214) was randomly split into two random halves for exploratory (n = 1,107) and confirmatory factor analyses (n = 1,107). First, the structure of the 13 alcohol-related consequence items was investigated using a series of exploratory principal component analysis (PCA) with principal axis factoring and varimax orthogonal rotation. The number of components to extract and retain was based on an examination of the eigenvalues, the scree test (Cattell, 1966; Zwick & Velicer, 1986) and parallel analysis (Lautenschlager, 1989). Replication and confirmation of the structure based on the exploratory analyses were evaluated with a CFA using R 1.2 conducted on the second half of the sample using weighted least squares estimation (WLSMV) due to the categorical nature of item responses. WLSMV has been shown to be less biased and more accurate than robust maximum likelihood in estimating factor loadings for categorical data (Li, 2016). Overall model fit was assessed using the likelihood ratio test, based on the Satorra–Bentler adjusted chi-square value to account for uneven distribution. Model fit was assessed using the comparative fit index (CFI; cutoff ≥ .95), the root mean square error of approximation (RMSEA; cutoff < .06), and the standardized root mean square residual (SRMR; cutoff < .08; Hu & Bentler, 1999). Next, a second CFA was conducted on the full sample (n = 2,214) to confirm good fit using the same criteria as was used for the first CFA.

To evaluate the extent to which the ADAS alcohol-related consequence scale is invariant across race (AI vs. non-Hispanic White), sex assigned at birth (female vs. male), combined four groups (AI females, AI males, non-Hispanic White females, and non-Hispanic White males), and age (15–17 vs. 18–21), four multigroup CFA (MGCFA) were examined using R 1.2. The first MGCFA for race was conducted across AI (n = 1,0843) and non-Hispanic White (n = 837) participants. A second MGCFA was conducted across sex assigned at birth on female (n = 1,153) and male participants (n = 1,061). A third MGCFA was conducted across four groups: AI Female (n = 582), AI Male (n = 502), Non-Hispanic White Female (n = 440), and Non-Hispanic White Male (n = 397) adolescents. Finally, a fourth MGCFA was conducted across age comparing 15- to 17-year-olds (n = 1,931) to 18- to 21-year-olds (n = 283). The test was conducted based on a three-step approach for ordered categorical indicators (Kite et al., 2018; Putnick & Bornstein, 2016). For categorical variables, parameters include thresholds rather than intercepts, and scale factors are used in the estimation of residuals (Sass, 2011). Most commonly, the three-step approach entails testing increasingly strict definitions of invariance across groups: (a) configural invariance (i.e., all groups share the same factor structure), (b) metric invariance (i.e., factor loadings constrained to be equal across groups), and (c) scalar invariance (i.e., factor loadings and thresholds constrained to be equal across groups). However, given the binary nature of the alcohol-related consequence items, metric and scalar invariance could not be tested separately (Millsap & Yun-Tein, 2004; Wu & Estabrook, 2016). Therefore, measurement invariance was tested by first assessing configural invariance and then scalar invariance. Meeting these requirements allows us to assume latent means (the scale’s item constructs) are similar across groups, thus allowing meaningful comparisons about alcohol-related consequences to be drawn across racial groups (Milfont & Fischer, 2010; Widaman & Reise, 1997). Fit for the configural models was assessed based on the same criterion as for the CFAs. Change in model fit from configural to the more restrictive scalar models was assessed using the following criteria: ΔCFI >.01, ΔRMSEA > .015, and ΔSRMR > .015 (Chen, 2007).

Finally, we evaluated whether racial differences (AI and non-Hispanic White) exist in the magnitude and/or direction of the association between alcohol consumption and alcohol-related consequences (as measured by the ADAS alcohol-related consequence scale). Bivariate correlations were calculated between racial group, frequency of drinking and of intoxication over the past 12 months, and alcohol-related consequences. To examine whether race moderated the association between frequency of drinking and intoxication over the past 12 months and alcohol-related consequences, multilevel zero-inflated negative binomial regression analyses were conducted using Mplus to evaluate the effects of age, sex assigned at birth, alcohol use, and race (Level 1 variables) as well as community location (Level 2), on alcohol-related consequences. To allow for easier interpretation of parameter estimates, and to lessen the correlation between the interaction term and its components, continuous variables were centered around the mean. First, controlling for sex assigned at birth, we examined the main and interactive effects of race and frequency of drinking over the past 12 months on alcohol-related consequences. Second, controlling for sex assigned at birth, we examined the main and interactive effects of race and frequency of intoxication over the past 12 months on alcohol-related consequences. Following the methods described by Aiken et al. (1991), we followed up significant interactions by plotting regression slopes of differences in frequency of past-year drinking and intoxication across racial groups (AI vs. non-Hispanic White) to examine whether the slopes of the regression lines differed significantly from zero.

Results

Principal Component Analysis

A PCA with varimax rotation on the exploratory half of the sample (n = 1,107) revealed three factors with eigenvalues >1. The scree plot and parallel analyses using Lautenschlager’s (1989) tables of average eigenvalues of random correlation matrices assuming independence showed support for a two-factor solution. However, individual items did not load clearly on either a two- or three-factor model, such that no grouping of items with similar constructs loaded >.40 on any of the two/three factors. Therefore, we then explored a one-factor model and found that all items loaded well (>.40) on a single factor (see Table 3).

Table 3.

One-Factor Component Matrix for PCA and CFA on Random Halves of the Sample.

Factor loadings
Item PCA CFA
Traffic ticket .419 .846
Car accident .514 .727
Got arrested .661 .787
Money problems .622 .714
Trouble at school .546 .717
Hurt your school work .645 .776
Fought with other kids .690 .756
Fought with your parents .689 .806
Damaged a friendship .608 .751
Passed out .614 .852
Could not remember what happened while drinking .594 .889
Broke something .632 .747
Lost your job .459 .870
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Note. PCA = principal component analysis; CFA = confirmatory factor analysis.

Confirmatory Factor Analysis

A one-factor solution with all 13 items imposed on the second half of the sample (n = 1,107) demonstrated good fit to the data (CFI = .966, RMSEA = .057, 90% confidence interval [CI] = [0.051, 0.064], SRMR = .082, χ2(65) = 298.677, p < .001). Next, a second CFA performed on the full sample (N = 2,214) found that a one-factor solution again demonstrated good fit (CFI = .961, RMSEA = .060, 90% CI = [0.056, 0.065], SRMR = .077, χ2(65) = 583.495, p < .001).

Reliability Analyses and Descriptive Statistics

Overall, the scale had good internal consistency in the overall sample (Factor Rho Coefficient = .954; Cronbach’s α = .834). In addition, good internal consistency for the scale was found for AI (Cronbach’s α = .820), non-Hispanic White (Cronbach’s α = .836), females (Cronbach’s α = .826), and males (Cronbach’s α = .845). See Table 2 for statistics describing the frequency of alcohol-related consequence endorsement. AI adolescents were significantly more likely than their non-Hispanic White peers to report more alcohol-related consequences on 11 of the 13 items: car accidents, arrests, money problems, trouble in school, hurt schoolwork, fights with other kids, fights with parents, damaged friendships, being passed out from alcohol, couldn’t remember what happened while drinking, and breaking something. For the remaining two items, AI reported more alcohol-related traffic tickets and loss of jobs, but these were not significantly different from non-Hispanic White adolescents. The overall total number of consequences endorsed was significantly greater for AI (M = 2.771) than for non-Hispanic Whites (M = 1.464), t(1,919) = 10.971, p < .001, d = 0.505, 95% CI = [0.413, 0.596].

Multiple-Group Confirmatory Factor Analysis

Configural and scalar invariance of the 13-item, one-factor model across four different grouping factors (race, sex assigned at birth, race, and sex assigned at birth, and age) was examined using multiple-group confirmatory factor analysis (MGCFA). Results for all models are shown in Table 4.

Table 4.

Multiple Group Confirmatory Factor Analysis for Race, Sex Assigned at Birth, and Age.

Model invariance χ2(df) CFI RMSEA (90CI) SRMR Model comp Δχ2(Δdf) ΔCFI ΔRMSEA ΔSRMR Decision
Race (AI [n = 1,084] and non-Hispanic White) [n = 837]
 M1: Configural 519.272 (130)* .962 .060 [0.051, 0.061] .091 — — — — — —
 M2: Scalar 581.116 (141)* .957 .057 [0.052, 0.062] .093 M1a 61.844 (11)* .005 .003 .002 Accept
Sex assigned at birth (female [n = 1,153] and male [n = 1,061])
 M1: Configural 591.644 (130)* .966 .057 [0.052, 0.061] .085 — — — — — —
 M2: Scalar 682.022 (141)* .960 .059 [0.055, 0.063] .089 M1b 90.378 (11)* .006 .002 .004 Accept
Age (15–27 [n = 1,931] and 18–21 [n = 283])
 M1: Configural 572.645 (130)* .964 .055 [0.051, 0.060] .080 — — — — — —
 M2: Scalar 570.326 (141)* .965 .052 [0.048, 0.057] .080 M1c 2.319 (11) .001 .003 0 Accept
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Note. CFI = comparative fit index; RMSEA = the root-mean square error of approximation; CI = confidence interval; SRMR = standardized root mean square residual; AI = American Indian; M1a = Model 1; M2 = Model 2.

*

p ≤ .001.

Race: AI Versus Non-Hispanic White.

Configural invariance was found across the AI (n = 1,084) and non-Hispanic White (n = 837) groups (CFI = .962, RMSEA = .060, SRMR = .091). The more restrictive scalar invariance model also fits the data well (CFI = .957, RMSEA = .057, SRMR = .093) and showed no significant loss in fit compared with the less restrictive configural model (ΔCFI = .005, ΔRMSEA = .003, and ΔSRMR = .002).

Sex Assigned at Birth: Female Versus Male.

Configural invariance across sex assigned at birth was established for the female (n = 1,153) and male (n = 1,061) groups (CFI = .966, RMSEA = .057, SRMR = .085). The more restrictive scalar invariance model also fits the data well (CFI = .960, RMSEA = .059, SRMR = .089) and showed no significant loss in fit compared with the less restrictive configural model (ΔCFI = .001, ΔRMSEA = .003, and ΔSRMR = .000).

Four Groups: Race and Sex Assigned at Birth.

A four-group MGCFA was conducted for race and sex assigned at birth. Included in the analyses were AI females (n = 582), AI males (n = 502), non-Hispanic White females (n = 440), and non-Hispanic White males (n = 397) adolescents. After several attempts to assess invariance across the four groups, the model was unable to converge.

Age: 15–17 Versus 18–21.

Configural invariance was established across the two age groups: 15–17 (n = 1,931) and 18–21 (n = 283; CFI = .964, RMSEA = .055, SRMR = .080). Again, the more restrictive scalar invariance model also fits the data well (CFI = .965, RMSEA = .052, SRMR = .080) and showed no significant loss in fit compared with the less restrictive configural model (ΔCFI = .006, ΔRMSEA = .002, and ΔSRMR = .004).

Bivariate Correlations and Construct Validity

As shown in Table 5, evidence for construct validity was found through bivariate correlations, which revealed significant positive correlations between frequency of past 12 months of drinking, frequency of past 12 months of intoxication, and lifetime alcohol-related consequences. Bivariate correlations revealed significant positive associations between alcohol-related consequences and frequency of drinking in the past 12 months, r(2,078) = .435, p < .001, and frequency of intoxication in the past 12 months, r(2,019) = .535, p < .001. AI racial group was significantly positively related to number of alcohol-related consequences experienced, r(2,214) = .224, p < .001, and with frequency of intoxication over the past 12 months, r(2,019) = .093, p < .001, but was not significantly related to frequency of drinking over the past 12 months. Non-Hispanic White racial group was significantly negatively correlated with number of alcohol-related consequences experienced, r(2,214) = −.199, p < .001, and with frequency of intoxication over the past 12 months, r(2,019) = −.072, p = .001), but was not significantly associated with frequency of endorsing drinking over the past 12 months.

Table 5.

Correlations Among Variables of Interest.

Variable 4 5 6
1. AI racial group — — — .224** .011 .093**
2. Non-Hispanic White racial group — — −.199** .017 −.072*
3. Female sex assigned at birth — .034 −.002 −.035
4. Alcohol-related consequences — .435** .535**
5. Past 12 months drinking — .778**
6. Past 12 months intoxicated —
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Note. AI = American Indian.

*

p < .01.

**

p < .001 (two-tailed).

Frequency of Drinking.

To examine the main and interactive effects of frequency of drinking over the past 12 months and race (non-Hispanic White and AI), while controlling for sex assigned at birth and age, we examined a multilevel zero-inflated negative binomial regression model. These analyses are summarized in Table 6. A significant main effect was found for race (b = −0.23, SE = .049, p <.001, 95% CI = [−0.329, −0.138]), and frequency of drinking over 12 months (b = .521, SE = .022, p < .001, 95% CI = [0.477, 0.565]), on alcohol-related consequences. In addition, there was a significant interaction between frequency of drinking over 12 months by race on alcohol-related consequences (b = −.148, SE = .017, p < .001, 95% CI = [−0.148, −0.083]). As illustrated in Figure 1, analysis of simple slopes revealed that the association between past 12-month drinking and alcohol-related consequences is significant for both AI (b = .970, SE = .040, p < .001) and non-Hispanic White (b = .934, SE = .066, p < .001) adolescents; however, the relationship is stronger among AI adolescents (see Figure 1).

Table 6.

Moderation Analyses Examining Main and Interactive Effects of Race on Alcohol-Related Consequences.

Variable b SE p 95% CI
Model 1: frequency of past 12-month drinking
Non-Hispanic White a -.233 .049 <.001 [−0.329, −0.138]
Female sex assigned at birthb .060 .025 .017 [0.011, 0.108]
Age .001 .016 .965 [−0.031, 0.033]
Frequency of drinking .521 .022 <.001 [0.477, 0.565]
Frequency of Drinking × Race -.116 .017 <.001 [−0.148, −0.083]
Model 2: frequency of past 12-month intoxication
Non-Hispanic Whitea −0.175 .066 .008 [−0.304, −0.045]
Female sex assigned at birth b .077 .020 <.001 [0.037, 0.117]
Age −.005 .023 −.228 [−0.051, 0.040]
Frequency of intoxication .575 .034 <.001 [0.509, 0.641]
Frequency of Intoxication × Race −.072 .042 .090 [−0.155, 0.011]
Open in a new tab

Note. AI = American Indian.

a

AI was the reference group.

b

Male was the reference group; bolded typeface indicates significance at the p < .001 level.

Figure 1.

Figure 1.

Open in a new tab

Frequency of Drinking by Race on Alcohol-Related Consequences.

Frequency of Intoxication.

To examine the main and interactive effects of frequency of intoxication over the past 12 months and race (non-Hispanic White and AI), while controlling for sex assigned at birth, we examined a multilevel zero-inflated negative binomial regression. These analyses are summarized in Table 6. Analyses for alcohol-related consequences, controlling for sex assigned at birth, found a significant main effect for race (b = −0.175, SE = .066, p = .008, 95% CI = [−0.304, −0.045]), and frequency of intoxication over 12 months on alcohol-related consequences (b = .575, SE = .034, p < .001, 95% CI = [0.509, 0.641]). However, there was no significant interaction between frequency of intoxication over 12 months by race for alcohol-related consequences (b = −0.072, SE = .042, p = .090, 95% CI = [−0.155, 0.011]).

Discussion

The purpose of this study was threefold: (a) to evaluate the factor structure of the alcohol-related consequence scale from the ADAS, (b) to evaluate whether the ADAS alcohol-related consequence scale was invariant across race (AI vs. non-Hispanic White), sex assigned at birth (female vs. male) combined four groups (AI females vs. AI males vs. non-Hispanic White females vs. non-Hispanic White males), and age (15–17 vs. 18–21), and (c) to examine race as a moderator in the relationship between alcohol consumption and alcohol-related consequences. Results from this study supported a one-factor model, consistent with literature on other alcohol-related consequence measures that finds alcohol-related consequences to be unifactorial (López Núñez et al., 2012; Marra et al., 2014). Results from the PCA revealed a one-factor model, providing evidence that one latent variable exists across the 13 items (Warner, 2012). Additionally, results from the CFA provide evidence for good fit among the items and reliability of the scale (evidenced by Cronbach’s α), indicating that the alcohol-related consequence scale is appropriate to administer as a single-factor scale, as it has been used in extant literature (e.g., Kirk-Provencher et al., 2020; Schick et al., 2020, 2021). Next, this study found the alcohol-related consequence scale to be invariant discretely across race, sex assigned at birth, and age, for adolescents aged 15 to 21, indicating that this scale can be used to compare meaningfully across these groups; caution should be taken if comparing across race and sex assigned at birth combined (four groups). Our findings are important for research aiming to assess alcohol-related health disparities among AI adolescents given the imperative need to identify alcohol-related inequities across racial groups to target interventions appropriately. To accomplish this goal, there is a need for valid and reliable measures.

The ADAS has been widely used among diverse populations, including AIs living on or near reservations to draw comparisons with non-AI youth (Stanley et al., 2014; Stanley & Swaim, 2015, 2018; Swaim & Stanley, 2018). Thus, investigations such as the present study are imperative to understand whether measurement bias may be influencing the ability to draw conclusions. Our findings suggest that there is little to no measurement bias when comparing across racial groups, sex assigned at birth, and age (Milfont & Fischer, 2010). However, caution is still warranted given that the four-group model (AI female vs. AI male vs. non-Hispanic White female vs. non-Hispanic White male) failed to converge. Researchers may need to proceed with caution when making comparisons across certain groups, for example, AI females to non-Hispanic males, and AI males to non-Hispanic females. There may be several reasons for this inconclusive result. First, covariance matrices were examined to assess for differences between the four groups. We found that AI females and non-Hispanic White males had relatively high covariances among the alcohol-related consequence items, while AI males and non-Hispanic White females had fairly low covariances among the alcohol-related consequence items. Although this study found support for a one-factor model and found this measure to be reliable for use among AI and non-Hispanic White adolescents, and among female and male adolescents, it is possible that this more complex four-group model did not converge due to between-group differences. For example, perhaps when AI and non-Hispanic White adolescents and female and male adolescents are combined and separated into four groups, new factor structures emerge among the different combination of groups. Thus, it is possible that the model would not converge if each subgroup produced its own underlying factor structure. Furthermore, perhaps AI males and non-Hispanic White females are simply not endorsing enough of the same consequences as AI females and non-Hispanic White males; therefore, the model may be strained. Another possible explanation for the model not converging may be due to lack of variance. Perhaps the dichotomous treatment of the items does not provide sufficient variance, limiting items’ ability to correlate with other variables, and thus the ability of the model to converge. Future research should investigate these possible explanations further.

Thereafter, we found that race significantly moderated the associations between drinking and alcohol-related consequences, but not with drinking until intoxicated and alcohol-related consequences. Specifically for frequency of drinking, while the association was significant for both AI and non-Hispanic White adolescents, it was stronger for AI adolescents. Our finding that AI adolescents experience significantly more alcohol-related consequences than their non-Hispanic White peers, and that at higher frequency of drinking the relationship to alcohol-related consequences was stronger for AI, aligns with literature that suggests AI adolescents drink alcohol at higher rates (Friese et al., 2011; Swaim & Stanley, 2018) and experienced more alcohol-related consequences than their White peers (Beauvais, 1992). However, our finding that there is no difference between AI and their non-Hispanic White peers in the association between drinking until intoxicated and experiencing alcohol-related problems indicates that perhaps when drinking to the point of intoxication, there is no difference across racial groups. Future work should continue to explore this finding. Nonetheless, our results emphasize the alcohol-related health disparity experienced by AI adolescents. A nationally representative study of AI youth found that rates of lifetime alcohol use were 72.5% for 12th graders (Swaim & Stanley, 2018). Thus, perhaps exploring harm reduction treatment approaches, where tracking incremental reductions in alcohol-related problems is foundational to its underlying theory and practice (Collins et al., 2019, 2012, 2021) may prove beneficial. Although taking a harm reduction approach with AI adolescents may be controversial, these approaches may be especially well-suited for this population given earlier age of alcohol initiation that is associated with experiencing more alcohol-related consequences, heavier drinking, and increased risk for being diagnosed with an AUD later in life (Henry et al., 2011, Swaim & Stanley, 2020). Therefore, targeting and reducing harm experienced by AI youth is of great importance and the alcohol-related consequence measure of the ADAS may be one useful tool to assist clinicians in targeting and reducing alcohol-related consequence experienced by their clients, thus reducing the alcohol-related health disparity experienced by AI adolescents.

We believe that when studying alcohol use, consequences, and disparities, it is important to consider contemporary and historical contexts to meaningfully contextualize results of the present study. Our finding that the association between drinking, intoxication and alcohol-related consequences is stronger for AI adolescents than for their non-Hispanic White counterparts may be best understood against the backdrop of systematic and racial inequalities in the United States. AI adolescents may be at greater risk for experiencing alcohol-related consequences when drinking. For example, the item asking respondents if their drinking alcohol had ever caused them to be arrested may reflect literature that finds AI adolescents as nearly 4 times as likely as White youth to be committed to juvenile facilities (Rovner, 2016). It is possible that the items used in this now validated alcohol-related consequences scale also reflect harms related to systemic racial discrimination. Furthermore, it is imperative to acknowledge the history that led to the development of these alcohol-related health inequities for Indigenous peoples to avoid over pathologizing and further stigmatization. Research suggests that historical trauma related to colonization is significantly associated with AI past month alcohol use (Wiechelt, 2012) and offers etiological explanations for substance use and disorders (Whitesell, Beals, et al., 2012). Indeed, Indigenous people did not have distilled, potent forms of alcohol prior to European contact (Beauvais, 1998). In fact, there is no evidence of alcohol use disorders among AIs prior to contact with European settlers (Hawkins & Blume, 2002), suggesting a link between alcohol use and the devastating effects of colonization (Skewes & Blume, 2019). A number of studies show that lasting intergenerational effects experienced by AI communities from social and cultural disturbance related to the colonization of North America—including forced removal from their tribal lands, broken treaties, and enforced placement of AI/AN children in boarding schools—are strongly linked to alcohol and other drug use, as well as psychosocial issues, such as poor mental health and poverty (Brave Heart, 2003; Evans-Campbell, 2008; Ross et al., 2015; Wunder & Hu-DeHart, 1992). Thus, to fully appreciate the results as a movement toward acknowledging the alcohol-related health disparity experienced by AI adolescents, we must also acknowledge the history that likely plays a role.

Future Directions and Limitations

Findings of this study should be considered within the context of its limitations. First, given the relevance of some of the questions asked on the ADAS alcohol-related consequence scale (e.g., questions about driving or employment), we decided to only include adolescents 15 years and older. Given this, our psychometric evaluation is limited to this age group. Future research should consider developing a measure for kids and adolescents younger than 15 years old that assesses developmentally appropriate problems they may experience.

Second, given that these data were zero-inflated and due to the sparseness in responses between the answer choices “none,” versus “1 to 2 times,” “3 to 9 times,” and “10 or more times,” we decided to dichotomize our variables, which may have resulted in a loss of data. In addition, we used traditional fit index cutoffs (Hu & Bentler, 1999). Future research efforts may incorporate new and emerging methods, including dynamic fit index cutoffs, which are tailored to the data and model (McNeish & Wolf, 2021). Moreover, when examining measurement invariance for the four-group analysis, the model did not converge. Given this, future work should consider refinement of this alcohol-related consequences scale through qualitative data methods and focus groups to tease apart meaningful differences and consider alternative underlying factor structures. This may allow for a more thorough, four-group psychometric validation of the scale to rule out measurement bias. In addition, assessing simple slopes for race by consumption interpreted across sex would be beneficial after further psychometrics are conducted for a four-group comparison of race and sex across this measure.

Third, this study consisted of secondary data analysis from surveys administered within schools. Therefore, procedures to identify random or careless responding were not available. In addition, students who did not attend school or who dropped out were not included in the study. Given that alcohol use among truant adolescents varies compared with those who attend school (Henry & Thornberry, 2010), future research should consider collecting data from adolescents both attending and not attending school.

Fourth, it should be noted that the variables used from the ADAS utilize different timeframes: alcohol use outcomes asked about past consumption, and alcohol-related consequences asked about lifetime experiences. Future research should consider matching the timeline for alcohol use outcomes and alcohol-related consequences measures to be more specific with these measurements. Finally, though this sample was a nationally representative sample of reservation-based AI adolescents, the sample did not include a nationally representative sample of non-Hispanic White adolescents. Future research should consider replicating this study using a nationally representative sample of both AI and non-Hispanic White adolescents.

Furthermore, while we found that the ADAS alcohol-related consequence scale is psychometrically reliable and valid for use with AI adolescents, it may be that culturally specific alcohol-related consequences are not captured on this scale, such as cultural practices that require sobriety (Goldstein, Spillane, Nalven, & Weiss, et al., in press). Furthermore, several of the items on the scale (e.g., Had money problems? Got a traffic ticket?) do not inherently align with AI collectivist cultural values and norms (i.e., more interdependent, interconnected with one another, and focused on the needs of others within one’s community; Beckstein, 2014) and may not represent unique consequences faced by AI living on or near a reservation. For example, some studies have found AI adolescents who drink alcohol experience alcohol-related consequences not captured on the scale including sexual assault (Kirk-Provencher et al., 2020), suicidality (Cwik et al., 2018), likeliness to run away, to smoke, to use more drugs, and to have sex (Mitchell et al., 2008). Other studies have reported AI who live on reservations to experience alcohol-related consequences shaped by a constellation of social-ecological conditions including kinship, housing, public/social service capacity, the supply of alcohol in nearby off-reservation areas, as well as inter-governmental relationship and the spiritual life on reservation residents (Lee et al., 2018). AI adolescents may be facing unique alcohol-related consequences that are not measured in the ADAS alcohol-related consequences scale given that it was not created within a reservation-based AI community. Thus, further underscoring the need for qualitative work to develop measures that are grounded in the experiences of AI adolescents.

Conclusion

Overall, our results contribute to better understanding alcohol-related consequences among AI adolescents by aiding in more thorough examination of alcohol-related health disparities among this population. In sum, our results find the ADAS’s alcohol-related consequences scale as valid for use when comparing alcohol-related consequences discretely among AI and non-Hispanic White, female and male adolescents, and across adolescents aged 15 to 21.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by grants 1F31AA029274 awarded to Silvi C. Goldstein, as well as R01DA003371 and G20RR030883 awarded by the National Institutes of Health. Opinions, findings, and conclusions expressed are those of the authors and do not necessarily reflect those of the National Institutes of Health.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

1.

This sample does not include a nationally representative sample of Alaska Natives, thus the term AI/AN is not used in this article.

2.

When assessing frequency of responses for kids and adolescents younger than 15 (n = 2,337), 0.7% responded yes to the question “got a traffic ticket,” 2.5% responded yes to the question “had a car accident,” and 0.9% responded yes to “lost your job.” Given the content of the measure (e.g., questions regarding employment and about driving a car), we decided the application of this measure and target population should be aged 15 years and above.

3.

This subsample of AI (for the MGCFA and moderation analyses) excluded any AI who also identified as White, and vice versa, to create two mutually exclusive groups for the purpose of these analyses. We chose to include Hispanic-AI, but not Hispanic-White, given the history and crossover between Hispanic ethnic heritage and Indigenous peoples (e.g., American Indians) across the Americas before colonialism.

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