Stephanie T. Lanza

Changes in Children’s Self-Competence and Values: Gender and Domain Differences across Grades One through Twelve

This study extended previous research on changes in childrens self-beliefs by documenting domain-specific growth trajectories for 761 children across grades 1 through 12 in a longitudinal study of perceptions of self-competence and task values. Hierarchical Linear Modeling was used to (1) describe changes in beliefs across childhood and adolescence within the domains of mathematics, language arts, and sports; (2) examine the impact of changes in competence beliefs on changes in values over time in the same domains; and (3) describe gender differences in mean levels and trajectories of change in competence beliefs and values. The most striking finding across all domains was that self-perceptions of competence and subjective task values declined as children got older, although the extent and rate of decline varied across domains. For example, in language arts, competence beliefs declined rapidly during the elementary school years, but then leveled off or increased to some extent; whereas the decline in self-competence beliefs in sports accelerated during the high school years. Significant gender differences in beliefs were found in most domains; however, the gender differences in developmental trajectories appeared to be domain specific rather than global. Importantly, the gender differences between boys and girls did not systematically increase with age, as predicted by some socialization perspectives. Adding competence beliefs as an explanatory variable to the model for task values revealed that changes in competence beliefs accounted for much of the age-related decline in task values. In addition, competence beliefs accounted for most of the gender differences in task values for language arts and sports.

PROC LCA: A SAS Procedure for Latent Class Analysis

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.

Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis. Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in examining higher-order interactions. An empirical example draws on N = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2) a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting future intervention resources to subgroups that promise to show the maximum treatment response.

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to 2 commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudoclass draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.

Pubertal Timing and the Onset of Substance Use in Females During Early Adolescence

The goal of this study is to examine in detail the relationship between pubertal timing and substance use onset using a sample of females from The National Longitudinal Study of Adolescent Health. The sample includes 966 females who were in 7th grade at Wave 1 and 8th grade at Wave 2. Participants in the sample are approximately 69% White, 20% African American, 4% Asian or Pacific Islander, 2% American Indian, 4% other, of Hispanic origin, and 1% other, not of Hispanic origin. Twenty percent of the females were identified as early maturers based on self-reports of body changes (increased breast size and body curviness) measured in 7th grade. These participants are hypothesized to be at increased risk for substance use onset. Important differences in substance use onset were found between early maturers and their on-time and late-maturing counterparts. During 7th grade, females in the early-maturing group are three times more likely to be in the most advanced stage of substance use (involving alcohol use, drunkenness, cigarette use, and marijuana use) than are those in the on-time/late group. Prevalence rates indicate that early maturers are more likely to have tried alcohol, tried cigarettes, been drunk, and tried marijuana. Prospective findings show that early developers are significantly more likely to transition out of the “No Substance Use” stage between 7th and 8th grade (47% for early developers vs. 22% for on-time and late developers). In addition, early developers are more likely to advance in substance use in general, regardless of their level of use at Grade 7.

Demographic and Familial Predictors of Early Executive Function Development: Contribution of a Person-Centered Perspective.

Executive function (EF) skills are integral components of young childrens growing competence, but little is known about the role of early family context and experiences in their development. We examined how demographic and familial risks during infancy predicted EF competence at 36months of age in a large, predominantly low-income sample of nonurban families from Pennsylvania and North Carolina in the United States. Using latent class analysis, six ecological risk profiles best captured the diverse experiences of these families. Profiles with various combinations of family structure, income, and psychosocial risks were differentially related to EF. Much of the influence of early risks on later EF appears to be transmitted through quality of parent-child interactions during infancy. Findings suggest that early family environments may prove to be especially fruitful contexts for the promotion of EF development.

A new SAS procedure for latent transition analysis: Transitions in dating and sexual risk behavior.

The set of statistical methods available to developmentalists is continually being expanded, allowing for questions about change over time to be addressed in new, informative ways. Indeed, new developments in methods to model change over time create the possibility for new research questions to be posed. Latent transition analysis, a longitudinal extension of latent class analysis, is a method that can be used to model development in discrete latent variables, for example, stage processes, over 2 or more times. The current article illustrates this approach using a new SAS procedure, PROC LTA, to model change over time in adolescent and young adult dating and sexual risk behavior. Gender differences are examined, and substance use behaviors are included as predictors of initial status in dating and sexual risk behavior and transitions over time.

Medical decision-making capacity in patients with mild cognitive impairment

Objectives: To empirically assess the capacity of patients with amnestic mild cognitive impairment (MCI) to consent to medical treatment under different consent standards (Ss). Methods: Participants were 56 healthy controls, 60 patients with MCI, and 31 patients with mild Alzheimer disease (AD). Each participant was administered the Capacity to Consent to Treatment Instrument (CCTI) and a comprehensive neuropsychological battery. Group differences in performance on the CCTI and neuropsychological variables were examined. In addition, the capacity status (capable, marginally capable, or incapable) of each MCI participant on each CCTI standard was examined using cut scores derived from control performance. Results: Patients with MCI performed comparably to controls on minimal consent standards requiring merely expressing a treatment choice (S1) or making the reasonable treatment choice [S2], but significantly below controls on the three clinically relevant standards of appreciation (S3), reasoning (S4), and understanding (S5). In turn, the MCI group performed significantly better than the mild AD group on [S2], S4, and S5. Regarding capacity status, patients with MCI showed a progressive pattern of capacity compromise (marginally capable and incapable outcomes) related to stringency of consent standard. Conclusions: Patients with amnestic mild cognitive impairment (MCI) demonstrate significant impairments on clinically relevant abilities associated with capacity to consent to treatment. In obtaining informed consent, clinicians and researchers working with patients with MCI must consider the likelihood that many of these patients may have impairments in consent capacity related to their amnestic disorder and related cognitive impairments. GLOSSARY: AD = Alzheimer disease; ADRC = Alzheimers Disease Research Center; CCTI = Capacity to Consent to Treatment Instrument; CVLT-II = California Verbal Learning Test, second edition; DRS-2 = Dementia Rating Scale, 2nd edition; GDS = Geriatric Depression Scale; MCI = mild cognitive impairment; MDC = medical decision-making capacity; MMSE = Mini-Mental State Examination; Ss = consent standards; WAIS-III = Wechsler Adult Intelligence Scale, third edition; WMS-III = Wechsler Memory Scale, third edition; WMS-R = Wechsler Memory Scale, revised edition; WRAT-3 = Wide Range Achievement Test, third edition.

Modeling the interplay of multilevel risk factors for future academic and behavior problems: A person-centered approach

This study identified profiles of 13 risk factors across child, family, school, and neighborhood domains in a diverse sample of children in kindergarten from four US locations (n = 750; 45% minority). It then examined the relation of those early risk profiles to externalizing problems, school failure, and low academic achievement in Grade 5. A person-centered approach, latent class analysis, revealed four unique risk profiles, which varied considerably across urban African American, urban White, and rural White children. Profiles characterized by several risks that cut across multiple domains conferred the highest risk for negative outcomes. Compared to a variable-centered approach, such as a cumulative risk index, these findings provide a more nuanced understanding of the early precursors to negative outcomes. For example, results suggested that urban children in single-parent homes that have few other risk factors (i.e., show at least average parenting warmth and consistency and report relatively low stress and high social support) are at quite low risk for externalizing problems, but at relatively high risk for poor grades and low academic achievement. These findings provide important information for refining and targeting preventive interventions to groups of children who share particular constellations of risk factors.

Latent Transition Analysis: Benefits of a Latent Variable Approach to Modeling Transitions in Substance Use:

We apply latent transition analysis (LTA) to characterize transitions over time in substance use behavior profiles among first-year college students. Advantages of modeling substance use behavior as a categorical latent variable are demonstrated. Alcohol use (any drinking and binge drinking), cigarette use, and marijuana use were assessed in a sample (N=718) of college students during the fall and spring semesters. Four profiles of 14-day substance use behavior were identified: (1) Non-Users; (2) Cigarette Smokers; (3) Binge Drinkers; and (4) Bingers with Marijuana Use. The most prevalent behavior profile at both times was the Non-Users (with over half of the students having this profile), followed by Binge Drinkers and Bingers with Marijuana Use. Cigarette Smokers were the least prevalent behavior profile. Gender, race/ethnicity, early onset of alcohol use, grades in high school, membership in the honors program, and friendship goals were all significant predictors of substance use behavior profile.