Latent Growth Modeling Approach Research Articles

The ABC’s of LGM: An Introductory Guide to Latent Variable Growth Curve Modeling

In recent years, we have witnessed an increase in the complexity of theoretical models that attempt to explain behavior from both contextual and developmental perspectives. This increase in the complexity of our theoretical propositions regarding behavior parallels recent methodological advances for the analysis of change.These new analysis techniques have fundamentally altered how we conceptualize and study change. Researchers have begun to identify larger frameworks to integrate our knowledge regarding the analysis of change. One such framework is latent growth modeling, perhaps the most important and influential statistical revolution to have recently occurred in the social and behavioral sciences. This paper presents a basic introduction to a latent growth modeling approach for analyzing repeated measures data. Included is the specification and interpretation of the growth factors, primary extensions such as the analysis of growth in multiple populations, and structural models including both precursors of growth, and subsequent outcomes hypothesized to be influenced by the growth functions.

Change in Newcomers' Supervisor Support and Socialization Outcomes After Organizational Entry

Using a four-wave longitudinal research design and a latent growth modeling approach, we modeled change in newcomers' perceived supervisor support and socialization outcomes (role clarity, work mastery, job satisfaction, and salary). Further, the role of perceived supervisor support in socialization outcomes was examined. The results showed that, on average, newcomers' perceived supervisor support declined during the period 6–21 months after organizational entry. The results showed further that the steeper the decline in perceived supervisor support, the greater the rate of decrease in role clarity and job satisfaction, and the slower the increase in salary over time.

Evaluating Mediation in Longitudinal Multivariate Data: Mediation Effects for the Aban Aya Youth Project Drug Prevention Program

This study illustrates a method to evaluate mediational mechanisms in a longitudinal prevention trial, the Aban Aya Youth Project (AAYP). In previous studies, interventions of AAYP were found to be effective in reducing the growth of violence, substance use and unsafe sex among African American adolescents. In this article, we hypothesized that the effects of the interventions in reducing the growth of substance use behavior were achieved through their effects in changing intermediate processes such as behavioral intentions, attitudes toward the behavior, estimates of peers' behaviors, best friends' behaviors, and peer group pressure. In evaluating these mediational mechanisms, difficulties arise because the growth trajectories of the substance use outcome variable and some of the mediating variables were curvilinear. In addition, all of the multivariate mediational measures had planned missing data so that a score from the multiple items for a mediator could not be formed easily. In this article, we introduce a latent growth modeling (LGM) approach; namely, a two-domain LGM mediation model, in which the growth curves of the outcome and the mediator are simultaneously modeled and the mediation effects are evaluated. Results showed that the AAYP intervention effects on adolescent drug use were mediated by normative beliefs of prevalence estimates, friends' drug use behavior, perceived friends' encouragement to use, and attitudes toward the behavior.

Effects of selection and training on unit-level performance over time: A latent growth modeling approach.

Surprisingly few data exist concerning whether and how utilization of job-related selection and training procedures affects different aspects of unit or organizational performance over time. The authors used longitudinal data from a large fast-food organization (N = 861 units) to examine how change in use of selection and training relates to change in unit performance. Latent growth modeling analyses revealed significant variation in both the use and the change in use of selection and training across units. Change in selection and training was related to change in 2 proximal unit outcomes: customer service performance and retention. Change in service performance, in turn, was related to change in the more distal outcome of unit financial performance (i.e., profits). Selection and training also affected financial performance, both directly and indirectly (e.g., through service performance). Finally, results of a cross-lagged panel analysis suggested the existence of a reciprocal causal relationship between the utilization of the human resources practices and unit performance. However, there was some evidence to suggest that selection and training may be associated with different causal sequences, such that use of the training procedure appeared to lead to unit performance, whereas unit performance appeared to lead to use of the selection procedure.

Genetic Influences on Change in BMI from Middle to Old Age: A 29-Year Follow-up Study of Twin Sisters

Weight gain through middle age is a common phenomenon that increases the risk for different types of metabolic diseases and functional limitations later in life. This study examined genetic and environmental influences on the evolution of body mass index (BMI) in women from middle to old age. BMI was evaluated in 102 monozygotic and 114 dizygotic pairs of twin sisters from the year 1975, when they were 42.6 +/- 3.4 years-old, and thereafter in 1981, 1990, 2001 and 2004, in a total 29-year follow-up period. We examined genetic and environmental influences explaining BMI overall level and its rate of change using a latent growth modeling approach. The results showed that mean (+/-SD) BMI increased from 24.1 +/- 3.1 to 28.2 +/- 5.1 kg/m(2) during the 29-year period. The heritability of BMI showed a consistent increment across occasions, from 54% in 1975, to 72% in 2004. Genetic influences accounted for both overall BMI level (60%) and BMI rate of change (64%). Genetic and environmental correlations between BMI level and rate of change were: r(g) = 0.40 and r(e) = -0.24, respectively. We conclude that in relatively healthy women, genes affecting level of BMI may differ from those affecting change in BMI with age. These results provide a basis for identifying genetic variants for change in BMI.

Changes in physical activity in adolescent girls: a latent growth modelling approach

To examine: (1) the developmental trajectory of physical activity as well the influence of body mass index (BMI) and friend support on initial physical activity and change of physical activity; and (2) the stability of physical activity in adolescent girls. Participants were 193 urban adolescent girls and their best friends. Physical activity was measured using the 3-day physical activity recall. Best friend social support was assessed using a questionnaire. BMI was calculated based on height and body mass of the participants. Data were collected on four occasions over a 1.75-year period. The stability coefficients for moderate, vigorous and total physical activity were statistically significant and ranged from 0.25 to 0.62. There was a curvilinear decrease in physical activity and a curvilinear increase in BMI and friend social support across four measurement occasions. Latent growth modelling (LGM) revealed that initially higher level of friend social support was associated with higher physical activity, and higher level of BMI was associated with lower levels of physical activity. With the physical activity change factor, there were statistically significant and positive direct effect from friend social support change factor, and statistically significant and negative direct effect from BMI change factor. Change in friend social support was positively and change in BMI was inversely associated with a change in adolescent girls' physical activity.

Improvement and decline of cognitive function in schizophrenia over one year: a longitudinal investigation using latent growth modelling

BackgroundLong-term follow-up studies of people with schizophrenia report stability of cognitive performance; less is known about any shorter-term changes in cognitive function.MethodsThis longitudinal study aimed to establish whether there was stability, improvement or decline in memory and executive functions over four assessments undertaken prospectively in one year. Cognitive performance was assessed during randomized controlled trials of first- and second-generation antipsychotic medication. Analyses used a latent growth modeling approach, so that individuals who missed some testing occasions could be included and trajectories of cognitive change explored despite missing data.ResultsOver the year there was significant decline in spatial recognition but no change in pattern recognition or motor speed. Improvement was seen in planning and spatial working memory tasks; this may reflect improved strategy use with practice. There were significant individual differences in the initial level of performance on all tasks but not in rate of change; the latter may have been due to sample size limitations. Age, sex, premorbid IQ and drug class allocation explained significant variation in level of performance but could not predict change. Patients randomized to first-generation drugs improved more quickly than other groups on the planning task.ConclusionWe conclude that cognitive change is present in schizophrenia but the magnitude of change is small when compared with the large differences in cognitive function that exist between patients. Analyses that retain patients who drop out of longitudinal studies, as well as those who complete testing protocols, are important to our understanding of cognition in schizophrenia.

The Role of Change in the Relationship Between Commitment and Turnover: A Latent Growth Modeling Approach.

Through the use of affective, normative, and continuance commitment in a multivariate 2nd-order factor latent growth modeling approach, the authors observed linear negative trajectories that characterized the changes in individuals across time in both affective and normative commitment. In turn, an individual's intention to quit the organization was characterized by a positive trajectory. A significant association was also found between the change trajectories such that the steeper the decline in an individual's affective and normative commitments across time, the greater the rate of increase in that individual's intention to quit, and, further, the greater the likelihood that the person actually left the organization over the next 9 months. Findings regarding continuance commitment and its components were mixed.

An introduction to latent growth curve modeling

Over the past 3 decades we have witnessed an increase in the complexity of theoretical models that attempt to explain development in a number of behavioral domains. The conceptual movement to examine behavior from both developmental and contextual perspectives parallels recent methodological advances in the analysis of change. These new analysis techniques have fundamentally altered how we conceptualize and study change, and have prompted researchers to identify larger frameworks to integrate knowledge. One such framework is latent growth modeling. This article presents a basic latent growth modeling approach for analyzing repeated measures data and delineates several of its extensions, including analyses for multiple populations, accelerated designs, multivariate associative models, and a framework for sample size selection and power estimation.

Power of Latent Growth Modeling for Detecting Group Differences in Linear Growth Trajectory Parameters

This simulation study focused on the power for detecting group differences in linear growth trajectory parameters within the framework of structural equation modeling (SEM) and compared the latent growth modeling (LGM) approach to the more traditional repeated-measures analysis of variance (ANOVA) approach. Several patterns of group differences in linear growth trajectories were considered. SEM growth modeling consistently showed higher statistical power for detecting group differences in the linear growth slope than repeated-measures ANOVA. For small group differences in the growth trajectories, large sample size (e.g., N > 500) would be required for adequate statistical power. For medium or large group differences, moderate or small sample size would be sufficient for adequate power. Some future research directions are discussed.

Metabolic Syndrome

Case Presentation: E.C. is a 53-year-old postmenopausal female, referred for treatment of hypertension, with a family history of type 2 diabetes, hypertension, and coronary heart disease (CHD). Until learning that her blood pressure was “too high” during a routine physical examination, she felt well, and her postmenopausal symptoms had responded to hormone replacement therapy. She was not overweight (her body mass index [BMI] was 23.7 kg/m2), and the only abnormality on physical examination was a blood pressure of 145/95 RAR. Laboratory results revealed a normal blood count and urinalysis, with the following fasting plasma concentrations of relevant metabolic variables (in mg/dL): glucose 102, triglycerides (TG) 238, low-density lipoprotein cholesterol (LDL-C) 147, and high-density lipoprotein cholesterol (HDL-C) 52. E.C. is hypertensive and hypertriglyceridemic and at increased risk for CHD. Less obvious is that these metabolic abnormalities are highly likely to be the manifestations of a more fundamental defect—resistance to insulin-mediated glucose disposal and compensatory hyperinsulinemia, changes that greatly increase CHD risk.1,2⇓ The importance of insulin resistance as a CHD risk factor was first explicated in 1998, and the cluster of abnormalities likely to appear as manifestations of the defect in insulin action designated as syndrome X.1 Support for this notion has grown almost as fast as the names used to describe the phenomenon. The Adult Treatment Panel III (ATP III) has recently3 recognized the importance as CHD risk factors of a “constellation of lipid and nonlipid risk factors of metabolic origin,” designated this cluster of abnormalities as “the metabolic syndrome,” and indicated that “this syndrome is closely linked to insulin resistance.” Table 1 lists the criteria the ATP III stipulated be used to diagnose the metabolic syndrome, and a recent report4 has applied these criteria to the database of the Third National Health and Nutrition …

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.

Qualitative and quantitative shifts in adolescent problem behavior development: A cohort-sequential multivariate latent growth modeling approach.

The aim of this study was to examine the nature of problem behavior development from late childhood through adolescence, to assess the quantitative development of problem behavior (alcohol use, marijuana use, deviance, academic failure) as well as potential qualitative shifts in problem behavior over time. Multivariate latent growth curve modeling (LGM) analyses and a cohort-sequential design were employed. Data were from the National Youth Survey and included 770 youth from four cohorts (11, 12, 13, 14 years old), assessed annually for 5 years. Results showed significant growth in problem behavior from ages 11 to 18. Alcohol use and marijuana use contributed most, and academic failure contributed least to the problem behavior latent construct. Results of the variant model revealed that the contribution of all four behaviors to the overall problem behavior construct increased similarly as children aged.

Interindividual differences in intraindividual changes in proactivity during organizational entry: a latent growth modeling approach to understanding newcomer adaptation.

Intraindividual change over time is the essence of the change phenomenon hypothesized to occur in the individual newcomer adaptation process. Many important adaptation questions cannot be answered without an adequate conceptualization and assessment of intraindividual change. Using a latent growth modeling approach to data collected from 146 doctoral program newcomers over 4 repeated measurements spaced at 1-month intervals, the authors explicitly modeled interindividual differences in intraindividual changes in newcomer proactivities (information seeking, relationship building) and proximal adaptation outcomes (task mastery, role clarity, social integration) during organizational entry. Results indicated that changes in proactivity may be related to newcomer characteristics and adaptation outcomes in interesting ways that have not been previously examined.

Interindividual differences in intraindividual changes in proactivity during organizational entry: A latent growth modeling approach to understanding newcomer adaptation.

Interindividual differences in intraindividual changes in proactivity during organizational entry: A latent growth modeling approach to understanding newcomer adaptation.

Contributions of the social context to the development of adolescent substance use: a multivariate latent growth modeling approach

This article demonstrates a latent growth curve methodology for analyzing longitudinal data of adolescent substance use. Hypotheses concerning the form of growth in alcohol, cigarette, and marijuana use, and covariates influencing the form of growth, were tested. Participants were male and female adolescents ( n=664) assessed at three time points. A common trajectory existed across the developmental period with significant increases in all three substances. Second-order multivariate extensions of the basic latent growth modeling framework suggested that associations among the individual differences parameters, representing growth or change in the various substance use behaviors, could be adequately modeled by a higher-order substance use construct. Inept parental monitoring, parent-child conflict, peer deviance, academic failure, gender, and age, were significant predictors of initial levels and the trajectory of substance use.

Alcohol Use Among African American and White Siblings: A Multilevel Latent Growth Modeling Approach

This study examined alcohol use and correlates of alcohol use among African American and White siblings over a 4-year period. A multiple-group multilevel latent growth model of family alcohol use across the two ethnic groups was specified which explicitly modeled the within-level and between-level covariance matrices in sibling alcohol use over time. The White sample comprised 700 siblings (292 families), and the African American sample comprised 157 siblings (59 families). Correlates of sibling alcohol use included parent income, marital status and family involvement. Results showed homogeneity in alcohol use within families, and heterogeneity across families and ethnic groups. Siblings from White families had higher average levels of alcohol use, and a greater increase in alcohol use over time than did African American siblings. The between-level effects of income, marital status, family involvement, and an interaction of family involvement with marital status, were consistent across African American and White families.