共查询到20条相似文献，搜索用时 78 毫秒
1.
Application of Latent Growth Curve Analysis With Categorical Responses in Social Behavioral Research
Tae Kyoung Lee Kandauda Wickrama Catherine W. O’Neal 《Structural equation modeling》2018,25(2):294-306
Latent growth modeling allows social behavioral researchers to investigate within-person change and between-person differences in within-person change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution assumptions, have rarely been used. This article describes the latent growth curve model with categorical variables, and illustrates applications using Mplus software that are applicable to social behavioral research. The illustrations use marital instability data from the Iowa Youth and Family Project. We close with recommendations for the specification and parameterization of growth models that use both logit and probit link functions. 相似文献
2.
Sonya K. Sterba 《Structural equation modeling》2013,20(4):630-647
Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary. 相似文献
3.
This study examined whether Matthew effects were evident in developmental patterns of reading and mathematics skills from middle childhood to adolescence. We obtained standardized reading and mathematics scores at Grades 3, 5, 7 and 9 for full cohorts of students in two Australian states, NSW (N = 88,958, 48% female) and Victoria (N = 65,984, 49% female). Latent growth curve models were used to identify the best-fitting longitudinal trajectories of reading and mathematics, and to examine whether cumulative (i.e. a Matthew effect), compensatory, or stable interindividual differences characterized development in each domain. For both reading and mathematics, and in both samples, growth decelerated as grade levels increased, with latent basis models fitting the data better than linear models. Negative intercept-slope covariances, and decreasing variances at increasing grades in both domains indicated compensatory growth patterns, rather than cumulative patterns or Matthew effects. These results indicate that students with below average achievement at Grade 3 make greater gains to Grade 9 than their initially higher-achieving peers. Modeling growth trajectories in two longitudinal population datasets allows strong tests of theorized growth patterns for both reading and mathematics, and presents insights about developmental change in academic skills from middle childhood to adolescence. 相似文献
4.
Growth mixture models combine latent growth curve models and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. Analyses based on these models are becoming quite common in social and behavioral science research because of recent advances in computing, the availability of specialized statistical programs, and the ease of programming. In this article, we show how mixture models can be fit to examine the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The approach is illustrated using empirical longitudinal data along with an easy to use computerized implementation. 相似文献
5.
Although many studies have documented developmental change in mathematics motivation, little is known about how these trends predict math performance. A sample of 288 participants from the United States reported their perceived math ability, math utility value and math interest in 5th, 7th and 9th grades. Latent growth curve models estimated developmental trajectories in each of these constructs. Mathematics interest and utility value decreased across time, but there was no significant change in self-perceived math ability. Slopes and intercepts of all mathematics motivation variables correlated with one another. Even when controlling for prior mathematics performance, students who self-reported high math ability in 5th grade had higher standardised test scores than their peers in high school five years later. Neither math utility value nor math interest intercepts or slopes predicted later performance. Understanding the predictors of math performance is important for supporting students’ success in science, technology, engineering and mathematics careers. 相似文献
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Latent growth modeling (LGM) is a popular and flexible technique that may be used when data are collected across several different measurement occasions. Modeling the appropriate growth trajectory has important implications with respect to the accurate interpretation of parameter estimates of interest in a latent growth model that may impact educational policy decisions. A Monte Carlo simulation study was conducted to examine the accuracy of six information-based criteria (i.e., AIC, CAIC, AICC, BIC, nBIC, and HQIC) when selecting among various growth trajectories modeled using LGM under different sample size, number of time points, and growth trajectory scenarios. The accuracy of the information criteria generally improved as sample size increased. The cubic and linear growth models were distinguished most accurately by the information criteria. All of the nonlinear models were more easily distinguished as the number of time points increased. The comparative performance of the six information criteria was dependent upon the manipulated conditions. Implications of the findings are discussed. 相似文献
8.
Chueh-An Hsieh Alexander von Eye Kimberly Maier Hsin-Jung Hsieh Shi-Hsiung Chen 《Structural equation modeling》2013,20(4):592-615
Applying item response theory models to repeated observations has demonstrated great promise in developmental research. By allowing the researcher to take account of the characteristics of both item response and measurement error in longitudinal trajectory analysis, it improves the reliability and validity of latent growth curve analysis. This has enabled the study, to differentially weigh individual items and examine developmental stability and change over time, to propose a comprehensive modeling framework, combining a measurement model with a structural model. Despite a large number of components requiring attention, this study focuses on model formulation, evaluates the performance of the estimators of model parameters, incorporates prior knowledge from Bayesian analysis, and applies the model using an illustrative example. It is hoped that this fundamental study can demonstrate the breadth of this unified latent growth curve model. 相似文献
9.
Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known as latent curve models), however, person-level covariates separately predict continuously distributed latent growth factors (e.g., an intercept vs. slope factor). This article first explains how complex and nonlinear interactions between predictors and time are recovered in different ways via LCGM versus RCGM specifications. Then a simulation comparison illustrates that, aside from some modest efficiency differences, such predictor relationships can be recovered approximately equally well by either model—regardless of which model generated the data. Our results also provide an empirical rationale for integrating findings about prediction of individual change across LCGMs and RCGMs in practice. 相似文献
10.
Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models
Davood Tofighi Yu-Yu Hsiao Eric S. Kruger David P. MacKinnon M. Lee Van Horn Katie Witkiewitz 《Structural equation modeling》2019,26(1):94-109
Latent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that confounds indirect effects. One way to address this untestable assumption is to conduct sensitivity analysis to assess whether the inference about an indirect effect would change under varying degrees of confounding bias. We developed a sensitivity analysis technique for a latent growth curve mediation model. We compute the biasing effect of confounding on point and confidence interval estimates of the indirect effects in a structural equation modeling framework. We illustrate sensitivity plots to visualize the effects of confounding on each indirect effect and present an empirical example to illustrate the application of the sensitivity analysis. 相似文献
11.
Latent basis curve models (LBCMs) have been popular in modeling change when the change trajectories are unknown or nonlinear. The estimated change trajectories from LBCMs are often viewed as optimal and used as reference points against which other change trajectories are tested. However, there is a proportionality assumption underlying LBCMs that has received little attention from researchers. This study uses a Monte Carlo simulation to show that violation of this assumption can potentially result in substantially biased estimates of the means and variances of changes and covariate effects on these changes, leading to incorrect statistical inference. The implications of the simulation study are discussed and alternatives to LBCMs are suggested for use when the proportionality assumption is likely to be violated. 相似文献
12.
Nonlinear models are effective tools for the analysis of longitudinal data. These models provide a flexible means for describing data that follow complex forms of change. Exponential and logistic functions that include a parameter to represent an asymptote, for instance, are useful for describing responses that tend to level off with time. There are forms of nonlinear latent curve models and nonlinear mixed-effects model that are equivalent, and so given the same set of data, growth function, distributional assumptions, and method of estimation, the 2 models yield equivalent results. There are also forms that are strikingly different and can yield different interpretations for a given set of data. This article discusses cases in which nonlinear mixed-effects models and nonlinear latent curve models are equivalent and those in which they are different and clarifies the estimation needs of the different models. Examples based on empirical data help to illustrate these points. 相似文献
13.
Rens van de Schoot Marit Sijbrandij Sonja D. Winter Sarah Depaoli Jeroen K. Vermunt 《Structural equation modeling》2017,24(3):451-467
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS is to present criteria that should be included when reporting the results of latent trajectory analysis across research fields. We have gone through a systematic process to identify key components that, according to a panel of experts, are necessary when reporting results for trajectory studies. We applied GRoLTS to 38 papers where LGMM or LCGA was used to study trajectories of posttraumatic stress after a traumatic event. 相似文献
14.
《The Journal of educational research》2012,105(4):283-300
ABSTRACT The author explored the developmental courses of deep learning approach and critical thinking over a 2-year period. Latent growth curve modeling (LGM) procedures were used to test and trace the trajectories of both theoretical frameworks over time. Participants were 264 (119 women, 145 men) university undergraduates. The Deep Learning subscale of Biggs's (1987) Study Process Questionnaire and the Critical Thinking subscale of the Reflective Thinking Questionnaire (Kember et al., 2000) were administered to the participants across four waves of data collection. Results of the LGM analyses indicated the growth of change of deep learning approach increased over time, whereas critical thinking practice decreased. Further multivariate growth curve analysis revealed an interactive, dynamic association between the intercept of critical thinking and the slope of deep learning approach. This evidence supports previous research findings, indicating that critical thinking may serve as an informational source in students’ engagement in deep learning approach. 相似文献
15.
A Second-Order Conditionally Linear Mixed Effects Model With Observed and Latent Variable Covariates
Jeffrey R. Harring Nidhi Kohli Rebecca D. Silverman Deborah L. Speece 《Structural equation modeling》2013,20(1):118-136
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a nonlinear manner are common to all subjects. In this article we describe how a variant of the Michaelis–Menten (M–M) function can be fit within this modeling framework using Mplus 6.0. We demonstrate how observed and latent covariates can be incorporated to help explain individual differences in growth characteristics. Features of the model including an explication of key analytic decision points are illustrated using longitudinal reading data. To aid in making this class of models accessible, annotated Mplus code is provided. 相似文献
16.
When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed by the template model between classes. 相似文献
17.
《The Journal of educational research》2012,105(2):132-136
ABSTRACT Utilizing latent transition analysis and multidimensional scaling growth analysis, the authors studied the emerging developmental trajectories in word literacy (i.e., word-reading competence) of a group of 1,503 kindergarteners. Specifically, 3 hypotheses with respect to growth patterns in word literacy from kindergarten to Grade 2 were examined: (a) children come into kindergarten with different word literacy levels, and emerging differences would be likely to remain stable over time; (b) the differences in word literacy latent status would lead to differences in word literacy trajectories over time; and (c) students with a low growth level would lead to lower achievement in reading achievement at a later time. The results of the dynamic analyses support the hypotheses and are discussed in the context of word literacy development. 相似文献
18.
Martha A. O’Daniel Rinsland 《Journal of Experimental Education》2013,81(3):307-317
Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture modeling, as well as the application to complex multilevel sampling designs. The goal of this study was to investigate—through a Monte Carlo simulation study—the performance of several methods for parameterizing multilevel latent class analysis. Of particular interest was the comparison of several such models to adequately fit Level 1 (individual) data, given a correct specification of the number of latent classes at both levels (Level 1 and Level 2). Results include the parameter estimation accuracy as well as the quality of classification at Level 1. 相似文献
19.
The present study focuses on the development of two sub-concepts necessary for a complete mathematical understanding of rational numbers, a) representations of the magnitudes of rational numbers and b) the density of rational numbers. While difficulties with rational number concepts have been seen in students' of all ages, including educated adults, little is known about the developmental trajectories of the separate sub-concepts. We measured 10- to 12-year-old students' conceptual knowledge of rational numbers at three time points over a one-year period and estimated models of their conceptual knowledge using latent variable mixture models. Knowledge of magnitude representations is necessary, but not sufficient, for knowledge of density concepts. A Latent Transition Analysis indicated that few students displayed sustained understanding of rational numbers, particularly concepts of density. Results confirm difficulties with rational number conceptual change and suggest that latent variable mixture models can be useful in documenting these processes. 相似文献
20.
Hefei Liu 《Structural equation modeling》2018,25(1):41-55
Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people. 相似文献