Latent Variable Growth Curve Modeling Research Articles

Pathways from Cyberbullying Victimization to Negative Health Outcomes among Elementary School Students: a Longitudinal Investigation

Growing concern about the negative effects experienced by youth exposed to cyberbullying victimization has led to a rapid increase of scientific inquiry in this area in recent years. However, few studies have focused on younger children despite some evidence suggesting substantial technology use among children in elementary school. To that end, the purpose of the present study was to explore change in cyberbullying victimization across time among a sample of elementary school students and to examine how such change impacted later health-related outcomes. A total of 660 and 960 3rd through 5th grade students participated in year one and year two of the study respectively. Latent-variable growth curve modeling (LGM) was used to examine changes in cyberbullying victimization across four waves of data and to explore the impact of these changes on later social, emotional and academic outcomes. Results revealed no consistent pattern of growth in victimization among participants over the course of the study. However, initial levels of cyberbullying victimization were significantly different from zero, suggesting that a notable number of participants reported exposure to this form of victimization at baseline data collection. Further, initial levels of cyberbullying victimization were found to have a significant impact on later health outcomes, including self-esteem and school connection with depressive symptoms approaching significance.

False Identification Use Among College Students Increases the Risk for Alcohol Use Disorder: Results of a Longitudinal Study

It is well known that using false identification (ID) is a common method by which underage youth in the United States obtain alcohol. While false ID use is associated with high-risk drinking patterns, its association with alcohol use disorder (AUD), independent of other risk factors, has not been firmly established. Participants were 1,015 college students recruited from 1 university and assessed annually during their first 4years of college. Latent variable growth curve modeling was used to identify significant predictors of false ID use and test the hypothesis that false ID use increased the risk for AUD, by increasing the frequency and/or quantity of alcohol use. Several other hypothesized risk factors for AUD were accounted for, including demographics (sex, race, living situation, religiosity, socioeconomic status), individual characteristics (childhood conduct problems, sensation-seeking, age at first drink), high school behaviors (high school drinking frequency, drug use), family factors (parental monitoring, parental alcohol problems), perception of peer drinking norms, and other factors related to false ID use. False IDs were used by almost two-thirds (66.1%) of the sample. False ID use frequency was positively associated with baseline quantity and frequency of alcohol use, independent of all other factors tested. False ID use was not directly related to AUD risk, but indirectly predicted increases in AUD risk over time through its association with greater increases in alcohol use frequency over time. Several predictors of false ID use frequency were also identified. False ID use may contribute to AUD risk by facilitating more frequent drinking. If replicated, these findings highlight the potential public health significance of policies that enforce sanctions against false ID use. Students who use false IDs represent an important target population for alcohol prevention activities.

Growth mixture models in longitudinal research

Latent growth curve models as structural equation models are extensively discussed in various research fields (Curran and Muthen in Am. J. Community Psychol. 27:567–595, 1999; Duncan et al. in An introduction to latent variable growth curve modeling. Concepts, issues and applications, 2nd edn., Lawrence Earlbaum, Mahwah, 2006; Muthen and Muthen in Alcohol. Clin. Exp. Res. 24(6):882–891, 2000a; in J. Stud. Alcohol. 61:290–300, 2000b). Recent methodological and statistical extension are focused on the consideration of unobserved heterogeneity in empirical data. Muthen extended the classic structural equation approach by mixture components, i.e. categorical latent classes (Muthen in Marcouldies, G.A., Sckumacker, R.E. (eds.), New developments and techniques in structural equation modeling, pp. 1–33, Lawrance Erlbaum, Mahwah, 2001a; in Behaviometrika 29(1):81–117, 2002; in Kaplan, D. (ed.), The SAGE handbook of quantitative methodology for the social sciences, pp. 345–368, Sage, Thousand Oaks, 2004). The paper discusses applications of growth mixture models with data on delinquent behavior of adolescents from the German panel study Crime in the modern City (CrimoC) (Boers et al. in Eur. J. Criminol. 7:499–520, 2010; Reinecke in Delinquenzverlaufe im Jugendalter: Empirische Uberprufung von Wachstums- und Mischverteilungsmodellen, Institut fur sozialwissenschaftliche Forschung e.V., Munster, 2006a; in Methodology 2:100–112, 2006b; in van Montfort, K., Oud, J., Satorra, A. (eds.), Longitudinal models in the behavioral and related sciences, pp. 239–266, Lawrence Erlbaum, Mahwah, 2007). Observed as well as unobserved heterogeneity will be considered with growth mixture models. Special attention is given to the distribution of the outcome variables as counts. Poisson and negative binomial distributions with zero inflation are considered in the proposed growth mixture models variables. Different model specifications will be emphasized with respect to their particular parameterizations.

Impact of School-Based Health Center Use on Academic Outcomes

The purpose of this study was twofold: (1) to examine the effects of School-Based Health Center (SBHC) use on academic outcomes for high school students, using a well-controlled, longitudinal model, and (2) to examine whether SBHC medical and mental health service use differentially impacts academic outcomes. Analyses used a latent variable growth curve modeling approach to examine longitudinal outcomes over five school semesters for ninth grade SBHC users and nonusers from Fall 2005 to Fall 2007 (n = 2,306). Propensity score analysis was used to control for self-selection factors in the SBHC user and nonuser groups. Results indicated a significant increase in attendance for SBHC medical users compared to nonusers. Grade point average increases over time were observed for mental health users compared to nonusers. Discipline incidents were not found to be associated with SBHC use. SBHC use was associated with academic improvements over time for a high-risk group of users. The moderating effect of type of use (medical and mental health) reinforces the importance of looking at subgroups when determining the impact of SBHC use on outcomes.

Effects of Growth in Family Conflict in Adolescence on Adult Depressive Symptoms: Mediating and Moderating Effects of Stress and School Bonding

PurposeTo (1) examine growth in family conflict during adolescence as a predictor of depressive symptoms in early adulthood, (2) examine adult stressful life events as a possible mechanism linking prior family conflict with depressive symptoms, and (3) examine adolescent school bonding as a possible moderator of family conflict and stressful life events in relation to later depression. MethodsAnalyses used a latent variable growth curve modeling approach to examine longitudinal patterns in data from the Seattle Social Development Project. Assessments of a gender-balanced and ethnically diverse sample of 754 participants were conducted from age 10 to age 27. ResultsResults show an increase in conflict each year between ages 14 and 18 for participating youth. High initial levels and growth in family conflict predicted adult stressful life events, which, in turn, predicted adult depressive symptoms, controlling for earlier risks of poverty and internalizing problems. Bonding to school in mid-adolescence did not significantly change the associations among the variables in the model. ConclusionsInhibiting the growth in family conflict over the course of adolescence may reduce the risk for some life stresses linked to depression in early adulthood. Although a test of the protective influence of school bonding during mid-adolescence was not significant, the study offers an example of how longitudinal patterns and protective factors can be analyzed.

Mental Ability in Childhood and Cognitive Aging

Background: Identifying the determinants of cognitive aging is a research priority; however, few studies are able to examine the influence of pre-morbid cognitive ability on later changes in cognitive function. Objective: To examine the association between childhood cognitive ability and cognitive change from age 79 to 83 in the presence of other demographic and lifestyle indicators. Methods: The participants took a test of mental ability when aged 11 as part of the Scottish Mental Survey 1932. Cognitive ability based on Raven’s Matrices, Verbal Fluency, and Logical Memory was assessed at ages 79 and 83. We used both linear regression and latent variable growth curve modeling to compare methods and results. Results: Using linear regression, childhood mental ability was a significant predictor of cognitive change from 79 to 83, accounting for about 1.4% of the variance. Sex, education, social class, smoking status and alcohol intake were non-significant. In contrast, using latent variable growth curve modeling, there was no association between childhood mental ability and cognitive change. Sex (male), years of education, drinking status (positive), and childhood IQ were associated with better cognitive ability at age 79. The difference in results was due to the inability of linear regression to account completely for test-specific variance. Conclusion: Within a group of non-demented older people, greater childhood mental ability was associated with level of cognitive ability at age 79, but not with change in cognitive ability to age 83. To obtain accurate results regarding covariates of change, it is important to use methodology that can appropriately allocate all measured sources of variance.

Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials

Typical methods of analyzing data from clinical trials have shortcomings, notably comparisons of group means, use of change scores from pre- and post-treatment assessments, ignoring intervening assessments, and focusing on direct effects of treatment. A comparison of group means disregards the likelihood that individuals have different trajectories of change. Moreover, change scores ignore intervening assessments that may provide useful information about change. This paper compares results from traditional regression-based methods for analyzing data from a clinical trial (e.g., regression with change scores) with those of latent growth curve modeling (LGM). LGM is a method that uses structural equation modeling techniques to model individual change, assess treatment effects and the relationship among multiple outcomes simultaneously, and model measurement error. The consequence is more precise parameter estimates while using data from all available time points. Results demonstrate that LGM can yield stronger parameter estimates than the traditional regression-based approach and explain more variance in the outcome. In trials where there is a true effect, but it is non-significant or marginally significant using the traditional methods, LGM may provide evidence of this effect. Analysts are encouraged to consider LGM as an additional and informative tool for analyzing clinical trial or other longitudinal data.

An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications, 2nd edition by T. E. DUNCAN, S. C. DUNCAN, and L. A. STRYCKER

An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications, 2nd edition by T. E. DUNCAN, S. C. DUNCAN, and L. A. STRYCKER

Verbal Learning and Everyday Functioning in Dementia: An Application of Latent Variable Growth Curve Modeling

This study used latent variable growth curve modeling to identify predictors and correlates of verbal learning over trials on a list-learning task in patients with dementia. Data from 116 patients evaluated at the Detroit satellite of the Michigan Alzheimer's Disease Research Center were incorporated in the present analyses. Patients were administered the Fuld Object Memory Evaluation, examined independently by a geriatrician, and, if appropriate, given a diagnosis of probable Alzheimer's disease according to criteria from the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association. The presence of dementia significantly predicted both the intercept (i.e., level of performance) and the slope (i.e., learning over trials), with dementia patients demonstrating lower overall levels of performance and less verbal learning over trials. Rate of verbal learning over trials was a significant predictor of everyday functioning (instrumental activities of daily living) above and beyond general cognitive impairment and demographics.

An Extension of the General Latent Variable Growth Modeling Framework to Four Levels of the Hierarchy

In recent years, a number of statistical techniques for the analysis of longitudinal multilevel data have become available, allowing researchers the opportunity to study behavior at different levels (e.g., individual, family, school, neighborhood). Although many of these statistical approaches are widely available, they are still relatively underused, and there is uncertainty among researchers as to the appropriateness and usefulness of different approaches for analyzing longitudinal data. The purpose of this article is to present an extension of the general latent variable growth curve modeling framework to 4 levels of the hierarchy. This 4-level extension merges 2 common analytic approaches used in answering longitudinal multilevel research questions: (a) a full information maximum likelihood (ML) latent growth modeling approach using an extension of a factor-of-curves model and (b) a limited information multilevel latent growth modeling (LGM) approach using Muthén's ML-based estimator. Data comprised repeated observations over 4 time points from 250 adolescents in 125 households and 61 geographical sampling areas. Results demonstrate comparable outcomes and interpretations derived from the hierarchical LGM extension and regression-based techniques. Discussion includes a comparison of techniques, encompassing advantages and limitations.

Book Review: An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and applications

Book Review: An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and applications