Longitudinal patterns of relationship adjustment among German parents.
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Although there have been many studies that have examined the trajectory of relationship adjustment among newlywed couples in the United States, less is known about the trajectory of relationship adjustment in other countries and over other developmental periods of relationships, such as among families with young children. In this study, we used latent growth curve mixture modeling to examine the trajectories of relationship adjustment among German parents across a 4-year period (N = 242). Approximately 90% of men and women could be classified as showing high relationship adjustment and a stable or increasing trajectory. The remaining 10%, were initially more distressed and showed a decline in relationship adjustment over time. In addition, latent relationship adjustment trajectory class significantly predicted change in men's depressive symptoms over the 4 years; for women, relationship-adjustment trajectory class was related to depressive symptom levels, but did not predict change over time.Keywords:
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SUMMARY Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques. KEYWORDS: Structural equation modelinglatent growth curve analysisindividual growth curve analysislongitudinal researchspiritualityreligiousness
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This research explores the usefulness of latent growth curve modeling in the study of pacing behavior and test speededness. Examinee response times from a high-stakes, computerized examination, collected before and after the examination was subjected to a timing change, were analyzed using a series of latent growth curve models to detect identifiable patterns of examinee pacing behavior. To help explain how examinees progress through the examination, the influences of two important predictor variables were tested: examinees’ native language and overall proficiency. Results illustrate how group-specific changes in the relationship between proficiency and response times and a phase-specific interaction effect would have gone unnoticed if a longitudinal perspective had not been used. The findings suggest that growth curve modeling is a useful tool for modeling change in test speed as a continuous process.
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Latent growth curve models (LGCM) are a versatile tool to model change in individual units over time. The most common application in communication research is the analysis of panel data. Univariate LGCMs describe the change of a single observed or latent variable over time. Multivariate LGCMs include predictors of growth processes, consequences of growth processes, and/or parallel growth curves, which model the influences between multiple growth processes. Latent growth curve models may also incorporate autoregressive terms, and growth mixture models distinguish different growth processes in different latent classes. The entry introduces these different kinds of models and demonstrates their application using publicly available sample data.
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The representation and measurement of change is a fundamental concern to practically all scientific disciplines. The researcher interested in demonstrating change in behavior over time must consider longitudinal studies. The models presented in this work, Latent Growth Curve Models, represented a proper technique emerged within Structural Equation Modeling to analyze longitudinal data. They not only describe a single individual developmental trajectory, but also captures individual differences in these trajectories over time have been developed. The simplest latent growth curve model involves one variable measured the same way at two time points. However, two points in time are not ideal for studying development or for using growth curve methodology: two-wave designs are appropriate only if the intervening growth process is considered irrelevant or is known to be linear. With more than two time occasions the validity of the straight-line growth model for the trajectory can be evaluated, and the precision of parameter estimates will tend to increase. For these reasons, this work will cover models for at least three-wave data, and in addition it will focus on nonlinear LCM.
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To compare two commonly used statistical approaches:the multilevel model and the latent growth curve model in analyzing longitudinal data. A longitudinal data set, obtained from the quality of life in patients with colorectal cancer after operation, was used to illustrate the similarities and differences between the two methods. Results from the study indicated that the latent growth curve modeling was equivalent to multilevel modeling with regards to longitudinal data which could yield identical results for the estimates of parameters. Multilevel model approach seemed easier for model specification. However, latent growth curve model had the advantage of providing model evaluation and was more flexible in statistical modeling by allowing the incorporation of latent variables. Both multilevel and latent growth curve models were suitable for analyzing longitudinal data with advantages on their own, they could be chosen by researchers under different situation to be chosen accordingly by researchers under different situation.
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Abstract The role of different forms of self‐esteem (i.e., global vs. domain‐specific) at work and their longitudinal changes and associations have yet to be examined. Our aim is to analyze the pattern of longitudinal changes between global self‐esteem (GSE) and organization‐based self‐esteem (OBSE). We used three‐wave longitudinal data ( N = 1616) of newcomers at the beginning of their career. Results from multivariate latent growth curve models revealed that OBSE and GSE decreased linearly. Their trajectories were positively correlated ( r slopes = 0.52), suggesting positive longitudinal associations between changes in both forms of self‐esteem. Finally, OBSE and GSE trajectories differently predicted changes in job satisfaction, commitment, work engagement, and burnout. Findings support the importance of simultaneously considering both forms of self‐esteem at work.
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A linear latent growth curve mixture model is presented which includes switching between growth curves. Switching is accommodated by means of a Markov transition model. The model is formulated with switching as a highly constrained multivariate mixture model and is fitted using the freely available Mx program. The model is illustrated by analyzing data from the National Longitudinal Survey of Youth (NLSY97). The data concern alcohol use in a sample of 737 White youths who were assessed on 4 occasions.
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Abstract In this chapter, we use an example of panel data on the longitudinal development of reading in children to introduce the latent growth curve analysis. Latent growth modeling originates in a simple mapping of the standard multilevel model for change onto the general model available for covariance structure analysis (CSA). Here, we illustrate how the latent growth modeling approach can be enacted by using the CSA Y‐measurement model to contain the level‐1 individual growth model and the CSA structural model to contain the level‐2 model for interindividual differences in change. Because this mapping is possible, all fixed and random parameters in the multilevel model for change can be estimated by fitting the corresponding CSA model to data using standard structural equation modeling software (such as LISREL). Latent growth curve analysis is important because it capitalizes on the intrinsic flexibility of the general CSA model to test interesting and complex hypotheses about change, many of which are either more difficult or impossible to test using standard multilevel modeling approaches.
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