Profiles of alcohol intoxication and their associated risks in young adults' natural settings: A multilevel latent profile analysis applied to daily transdermal alcohol concentration data.

OBJECTIVE: Transdermal alcohol concentration (TAC) sensors capture aspects of drinking events that self-reports cannot. The multidimensional nature of TAC data allows novel classification of drinking days and identification of associated behavioral and contextual risks. We used multilevel latent profile analysis (MLPA) to create day-level profiles of TAC features and test their associations with (a) daily behaviors and contexts and (b) risk for alcohol use disorders at baseline. METHOD: Two hundred twenty-two regularly heavy-drinking young adults (Mage = 22.3) completed the Alcohol Use Disorders Identification Test (AUDIT) at baseline and then responded to mobile phone surveys and wore TAC sensors for six consecutive days. MLPA identified day-level profiles using four TAC features (peak, rise rate, fall rate, and duration). TAC profiles were tested as correlates of daily drinking behaviors, contexts, and baseline AUDIT. RESULTS: Four profiles emerged: (a) high-fast (8.5% of days), (b) moderate-fast (12.8%), (c) low-slow (20.4%), and (d) little-to-no drinking days (58.2%). Profiles differed in the odds of risky drinking behaviors and contexts. The highest risk occurred on high-fast days, followed by moderate-fast, low-slow, and little-to-no drinking days. Higher baseline AUDIT predicted higher odds of high-fast and moderate-fast days. CONCLUSIONS: Days with high and fast intoxication are reflective of high-risk drinking behaviors and were most frequent among those at risk for alcohol use disorders. TAC research using MLPA may offer novel and important insights to intervention efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Monte Carlo confidence intervals for the indirect effect with missing data.

Missing data is a common occurrence in mediation analysis. As a result, the methods used to construct confidence intervals around the indirect effect should consider missing data. Previous research has demonstrated that, for the indirect effect in data with complete cases, the Monte Carlo method performs as well as nonparametric bootstrap confidence intervals (see MacKinnon et al., Multivariate Behavioral Research, 39(1), 99-128, 2004; Preacher & Selig, Communication Methods and Measures, 6(2), 77-98, 2012; Tofighi & MacKinnon, Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 194-205, 2015). In this manuscript, we propose a simple, fast, and accurate two-step approach for generating confidence intervals for the indirect effect, in the presence of missing data, based on the Monte Carlo method. In the first step, an appropriate method, for example, full-information maximum likelihood or multiple imputation, is used to estimate the parameters and their corresponding sampling variance-covariance matrix in a mediation model. In the second step, the sampling distribution of the indirect effect is simulated using estimates from the first step. A confidence interval is constructed from the resulting sampling distribution. A simulation study with various conditions is presented. Implications of the results for applied research are discussed.

betaDelta and betaSandwich: Confidence Intervals for Standardized Regression Coefficients in R.

The multivariate delta method was used by Yuan and Chan to estimate standard errors and confidence intervals for standardized regression coefficients. Jones and Waller extended the earlier work to situations where data are nonnormal by utilizing Browne's asymptotic distribution-free (ADF) theory. Furthermore, Dudgeon developed standard errors and confidence intervals, employing heteroskedasticity-consistent (HC) estimators, that are robust to nonnormality with better performance in smaller sample sizes compared to Jones and Waller's ADF technique. Despite these advancements, empirical research has been slow to adopt these methodologies. This can be a result of the dearth of user-friendly software programs to put these techniques to use. We present the betaDelta and the betaSandwich packages in the R statistical software environment in this manuscript. Both the normal-theory approach and the ADF approach put forth by Yuan and Chan and Jones and Waller are implemented by the betaDelta package. The HC approach proposed by Dudgeon is implemented by the betaSandwich package. The use of the packages is demonstrated with an empirical example. We think the packages will enable applied researchers to accurately assess the sampling variability of standardized regression coefficients.

FINDOUT: Using Either SPSS Commands or Graphical User Interface to Identify Influential Cases in Structural Equation Modeling in AMOS.

The results in a structural equation modeling (SEM) analysis can be influenced by just a few observations, called influential cases. Tools have been developed for users of R to identify them. However, similar tools are not available for AMOS, which is also a popular SEM software package. We introduce the FINDOUT toolset, a group of SPSS extension commands, and an AMOS plugin, to identify influential cases and examine how these cases influence the results. The SPSS commands can be used either as syntax commands or as custom dialogs from pull-down menus, and the AMOS plugin can be run from AMOS pull-down menu. We believe these tools can help researchers to examine the robustness of their findings to influential cases.

DIY bootstrapping: Getting the nonparametric bootstrap confidence interval in SPSS for any statistics or function of statistics (when this bootstrapping is appropriate).

Researchers can generate bootstrap confidence intervals for some statistics in SPSS using the BOOTSTRAP command. However, this command can only be applied to selected procedures, and only to selected statistics in these procedures. We developed an extension command and prepared some sample syntax files based on existing approaches from the Internet to illustrate how researchers can (a) generate a large number of nonparametric bootstrap samples, (b) do desired analysis on all these samples, and (c) form the bootstrap confidence intervals for selected statistics using the OMS commands. We developed these tools to help researchers apply nonparametric bootstrapping to any statistics for which this method is appropriate, including statistics derived from other statistics, such as standardized effect size measures computed from the t test results. We also discussed how researchers can extend the tools for other statistics and scenarios they encounter.

SEM-Based Methods to Form Confidence Intervals for Indirect Effect: Still Applicable Given Nonnormality, Under Certain Conditions.

A SEM-based approach using likelihood-based confidence interval (LBCI) has been proposed to form confidence intervals for unstandardized and standardized indirect effect in mediation models. However, when used with the maximum likelihood estimation, this approach requires that the variables are multivariate normally distributed. This can affect the LBCIs of unstandardized and standardized effect differently. In the present study, the robustness of this approach when the predictor is not normally distributed but the error terms are conditionally normal, which does not violate the distributional assumption of ordinary least squares (OLS) estimation, is compared to four other approaches: nonparametric bootstrapping, two variants of LBCI, LBCI assuming the predictor is fixed (LBCI-Fixed-X) and LBCI based on ADF estimation (LBCI-ADF), and Monte Carlo. A simulation study was conducted using a simple mediation model and a serial mediation model, manipulating the distribution of the predictor. The Monte Carlo method performed worst among the methods. LBCI and LBCI-Fixed-X had suboptimal performance when the distributions had high kurtosis and the population indirect effects were medium to large. In some conditions, the problem was severe even when the sample size was large. LBCI-ADF and nonparametric bootstrapping had coverage probabilities close to the nominal value in nearly all conditions, although the coverage probabilities were still suboptimal for the serial mediation model when the sample size was small with respect to the model. Implications of these findings in the context of this special case of nonnormal data were discussed.

Effects of Social and Outcome Expectancies on Hazardous Drinking among Chinese University Students: The Mediating Role of Drinking Motivations.

Background and Objectives: Based on the theory of reasoned action, the present study investigated the relative effects of drinking outcome expectancies and parental norms, as well as the mediating effect of drinking motivations, on hazardous drinking in Chinese university students. Method: A sample of Chinese university students in Hong Kong and Macao (N = 973, M = 19.82, SD = 1.57, 48.9% males), who reported drinking in the past 3 months, voluntarily completed an anonymous questionnaire. Path analysis was used to test the effects of the variables on hazardous drinking. Results: All the psychosocial variables showed positive correlations with hazardous drinking. In the path model, controlling for sex, parental norms had both direct and indirect effects on hazardous drinking through social and enhancement motivations. Courage had the strongest indirect effect on drinking behavior through social, enhancement, and coping motivations, whereas the relationship between tension reduction and hazardous drinking was mediated by enhancement and coping motivations. Sociality and sexuality only had indirect effect through social and coping motivations respectively. Negative outcome expectancies had no direct nor indirect effects on hazardous drinking. Conclusions: Perceived approval from parents and positive alcohol outcome expectancies may enhance individuals' tendency to engage in hazardous drinking by increasing their motivation to drink to be social, for enjoyment, and to cope with problems. Parents should explicitly show their disapproval of their children's drinking, and education efforts should focus on decreasing positive outcome expectancies and associated motivations for drinking among Chinese university students.

Psychometric properties of a Chinese version of the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ).

AIM: This study evaluated the psychometric properties of the Chinese version of the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ). METHOD: In this study, 1616 Chinese university students (male = 58.66%; Mage = 19.88) reporting past-year drinking experience voluntarily completed an anonymous questionnaire. Rasch analysis, reliability analysis, and linear modeling were performed to examine the psychometric properties of the Chinese version of B-YAACQ. RESULTS: Results of Rasch analysis and reliability analysis supported the assumption of uni-dimensionality, local independence, and internal consistency of the 24-item B-YAACQ in our Chinese sample. However, six items had marginal outfit statistics and/or potential gender bias; therefore, a model with 18 items was also tested after removing these items. The 18-item model showed excellent fit to the uni-dimensional model with no gender bias. However, the Pearson correlation between the 24-item and 18-item versions was r = 0.98, suggesting highly similar measurement. Both versions demonstrated concurrent validity through positive association with the Alcohol Use Disorder Identification Test (AUDIT) subscales, even after controlling for the effects of age and gender. CONCLUSION: This study is the first to validate a measurement tool for negative drinking consequences for university students in China. Despite some limitations, the original 24-item B-YAACQ was shown to have satisfactory psychometric properties when applied to Chinese university students. We recommend the shorter 18-item version without significant gender bias for testing gender differences.

Utility of the Athlete Drinking Scale for assessing drinking motives among high school athletes.

Research suggests that high school athletes are at greater risk for heavy alcohol use and alcohol-related problems than their non-athlete peers. Drinking motives unique to the athletic experience may contribute to elevated use. The Athlete Drinking Scale (ADS) was designed to assess sport-related motives for alcohol use, but has not yet been validated among high school athletes. The purpose of this study was to examine the psychometric properties of the ADS among a sample of high school athletes. Participants were 216 high school student-athlete drinkers who completed anonymous self-report surveys. A confirmatory factor analysis resulted in a revised three-factor solution with a satisfactory overall model fit. Path analyses indicated that the Positive Reinforcement motives subscale was the only ADS subscale that was significantly associated with alcohol use and alcohol-related problems when controlling for the effects of the other factors (i.e., age and gender) in this population. The ADS may be a valuable assessment tool for researchers and clinicians involved in alcohol prevention efforts for high school athletes.

Frequency of drinking games participation and alcohol-related problems in a multiethnic sample of college students: do gender and ethnicity matter?

INTRODUCTION: A drinking game (DG) is a high-risk, social drinking activity that consists of certain rules (i.e., when to drink and how much to consume) designed to promote inebriation and that requires each player to perform a cognitive and/or motor task (Zamboanga et al., 2013). Research suggests that non-White or female students who play DGs are at an increased risk of experiencing alcohol-related problems. Thus, this study examined whether the associations between DG participation and alcohol-related problems were similar for men and women and across ethnic groups. METHOD: College students (N=7409; 73% women; 64% White, 8% Black, 14% Hispanic, 14% Asian) from 30 U.S. colleges/universities completed self-report questionnaires. RESULTS: Controlling for age, site, Greek membership (i.e., membership in a fraternity or sorority), and typical alcohol consumption, results indicated that the association between DG participation and alcohol-related problems was stronger for men compared to women. With respect to ethnicity, the association between these variables was stronger among Black women than Black men. CONCLUSIONS: Findings from this large-scale study highlight the need to closely investigate how gender and ethnicity moderate the associations between DG participation and alcohol-related problems. College intervention efforts designed to address high-risk drinking behaviors such as DG participation might consider paying close attention to ethnic minority populations, perhaps particularly Black women.

Brief report: identity processes in Filipino late adolescents and young adults: parental influences and mental health outcomes.

This study focused on a process-oriented approach to identity formation using a sample of Filipino late adolescents and young adults (17-30 years; N = 779). Indirect relations between parenting and mental health via identity formation processes were examined. Two parenting dimensions (psychological control and support), two types of mental health outcomes (depression and psychological well-being), and five identity dimensions (commitment making (CM), identification with commitment (IC), exploration in breadth (EB), exploration in depth (ED), and ruminative exploration (RE)) were assessed. Recursive path analysis showed indirect relations between parenting and mental health via EB, ED, RE, and IC. Model differences between late adolescents (17-21 year olds) and young adults (22-30 year olds) were examined using multigroup path analysis. Results showed that the direct effect of psychological control on RE, and its indirect effect on depression through RE differed between the age groups. Implications and suggestions for future research are provided.