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1.
Differential item functioning (DIF) may be caused by an interaction of multiple manifest grouping variables or unexplored manifest variables, which cannot be detected by conventional DIF detection methods that are based on a single manifest grouping variable. Such DIF may be detected by a latent approach using the mixture item response theory model and subsequently explained by multiple manifest variables. This study facilitates the interpretation of latent DIF with the use of background and cognitive variables. The PISA 2009 reading assessment and student survey are analyzed. Results show that members in manifest groups were not homogenously advantaged or disadvantaged and that a single manifest grouping variable did not suffice to be a proxy of latent DIF. This study also demonstrates that DIF items arising from the interaction of multiple variables can be effectively screened by the latent DIF analysis approach. Background and cognitive variables jointly well predicted latent class membership.  相似文献   

2.
Methods of latent curve analysis (latent growth modeling) have recently emerged as a versatile tool for investigating longitudinal change in measured variables. This article, using higher order factor models as suggested by McArdle (1988) and Tisak and Meredith (1990), illustrates latent curve analysis for the purpose of modeling longitudinal change directly in a latent construct. The construct of interest is assumed to be indicated by several measured variables, all of which are observed at the same multiple time points. Examples with simultaneous estimation of covariance and mean structures are provided for both a single group and a two-group scenario.  相似文献   

3.
Latent class models are often used to assign values to categorical variables that cannot be measured directly. This “imputed” latent variable is then used in further analyses with auxiliary variables. The relationship between the imputed latent variable and auxiliary variables can only be correctly estimated if these auxiliary variables are included in the latent class model. Otherwise, point estimates will be biased. We develop a method that correctly estimates the relationship between an imputed latent variable and external auxiliary variables, by updating the latent variable imputations to be conditional on the external auxiliary variables using a combination of multiple imputation of latent classes and the so-called three-step approach. In contrast with existing “one-step” and “three-step” approaches, our method allows the resulting imputations to be analyzed using the familiar methods favored by substantive researchers.  相似文献   

4.
This article shows how nonlinear latent curve models may be fitted for simultaneous analysis of multiple variables measured longitudinally using Mx statistical software. Longitudinal studies often involve observation of several variables across time with interest in the associations between change characteristics of different variables measured within individuals. Other applications involve repeated measures for distinguishable individuals nested within small groups, such as families, with interest in the associations between change characteristics in variables for individuals within groups. This article shows how Mx can be used to carry out analysis of multiple variables measured over time where at least one variable is described by a function that includes one or more parameters that enter the model nonlinearly. An example is provided.  相似文献   

5.
We consider a multivariate generalized latent variable model to investigate the effects of observable and latent explanatory variables on multiple responses of interest. Various types of correlated responses, such as continuous, count, ordinal, and nominal variables, are considered in the regression. A generalized confirmatory factor analysis model that is capable of managing mixed-type data is proposed to characterize latent variables via correlated observed indicators. In addressing the complicated structure of the proposed model, we introduce continuous underlying measurements to provide a unified model framework for mixed-type data. We develop a multivariate version of the Bayesian adaptive least absolute shrinkage and selection operator procedure, which is implemented with a Markov chain Monte Carlo (MCMC) algorithm in a full Bayesian context, to simultaneously conduct estimation and model selection. The empirical performance of the proposed methodology is demonstrated through a simulation study. An application of the proposed method to a study of adolescent substance abuse based on the National Longitudinal Survey of Youth is presented.  相似文献   

6.
This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and change (slope) of the putative mediator variable can be appropriately included in the causal chain between the independent and dependent variables in longitudinal mediation models. We further address whether the slope of the dependent variable should be controlled for the dependent variable's intercept to improve the conceptual relevance of the mediation models. The models proposed are illustrated by analyzing a longitudinal data set. We conclude that for certain research questions in developmental science, a multiple mediation model where the dependent variable's slope is controlled for its intercept can be considered an adequate analytical model. However, such models also show several limitations.  相似文献   

7.
Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.  相似文献   

8.
The primary goal of this article is to demonstrate the close relationship between 2 classes of dynamic models in psychological research: latent change score models and continuous time models. The secondary goal is to point out some differences. We begin with a brief review of both approaches, before demonstrating how the 2 methods are mathematically and conceptually related. It will be shown that most commonly used latent change score models are related to continuous time models by the difference equation approximation to the differential equation. One way in which the 2 approaches differ is the treatment of time. Whereas there are theoretical and practical restrictions regarding observation time points and intervals in latent change score models, no such limitations exist in continuous time models. We illustrate our arguments with three simulated data sets using a univariate and bivariate model with equal and unequal time intervals. As a by-product of this comparison, we discuss the use of phantom and definition variables to account for varying time intervals in latent change score models. We end with a reanalysis of the Bradway–McArdle longitudinal study on intellectual abilities (used before by McArdle & Hamagami, 2004) by means of the proportional change score model and the dual change score model in discrete and continuous time.  相似文献   

9.
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   

10.
Estimation of the direct effect of an exposure on an outcome requires adjustment for confounders of the exposure–outcome and mediator–outcome relationships. When some of the latter confounders have been affected by the exposure, then standard regression adjustment is prone to possibly severe bias. The use of inverse probability weighting under so-called marginal structural models has recently been suggested as a solution in the psychological literature. In this article, we show how progress can alternatively be made via G-estimation. We show that this estimation method can be easily embedded within the structural equation modeling framework and could in particular be used for estimating direct effects in the presence of latent variables. Moreover, by avoiding inverse probability weighting, it accommodates the typical problem of unstable weights in the alternative estimation approaches based on marginal structural models. We illustrate the approach both by simulations and by the analysis of a longitudinal study in individiduals who ended a romantic relationship. In this example we explore whether the effect of attachment anxiety during the relationship on mental distress 2 years after the breakup is mediated by rumination or not.  相似文献   

11.
Measurement burst designs, wherein individuals are measured intensively during multiple periods (i.e., bursts), have created new opportunities for studying change at multiple time scales. This article develops a model that might be useful in situations where the functional form of short-term change is unknown, might consist of multiple phases, and might change over the long term. Specifically, we combine measurement of intraindividual entropy, a latent basis growth model, a multiphase growth model, and a growth model with covariates into a unified framework that could help accommodate the complexity of patterns that emerge in multiple time-scale categorical data streams. Empirical data from a longitudinal study of young children’s behavior during laboratory tasks designed to induce frustration are used to illustrate the utility of the proposed model for simultaneously describing intratask (short-term) change in self-regulation and developmental (long-term) shifts in intratask change.  相似文献   

12.
A multiple testing approach is outlined that can be used to examine the assumption of underlying normal variables in latent variable models with categorical indicators. The method is based on an application of the increasingly popular Benjamini–Hochberg multiple testing procedure, and is readily applicable with widely circulated software. The discussed method is especially useful for ascertaining this assumption that is very often made in research based on structural equation modeling using models containing discrete outcomes. The described approach is illustrated with numerical data.  相似文献   

13.
Most theories in the social sciences involve relationships among constructs which are not directly observable. Behavioral measures exist of all constructs, such as intelligence, creativity, and other cognitive traits, aggressiveness, sociability, and other personality and affective characteristics; but these observed measures are usually assumed to be imperfect indicators of the (presumed) underlying construct. The imperfections exist because of errors of measurement and because the observed behavior may be influenced by other underlying constructs in addition to the one of primary interest. Statistical methods, called latent variable models, have been developed to provide rigorous tests of theories involving unobserved variables. This paper describes the major types of latent variable models, shows how they can be applied in educational research, and gives representative examples of their use from the literature.  相似文献   

14.
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   

15.
In this article, we propose a nonlinear dynamic latent class structural equation modeling (NDLC-SEM). It can be used to examine intra-individual processes of observed or latent variables. These processes are decomposed into parts which include individual- and time-specific components. Unobserved heterogeneity of the intra-individual processes are modeled via a latent Markov process that can be predicted by individual- and time-specific variables as random effects. We discuss examples of sub-models which are special cases of the more general NDLC-SEM framework. Furthermore, we provide empirical examples and illustrate how to estimate this model in a Bayesian framework. Finally, we discuss essential properties of the proposed framework, give recommendations for applications, and highlight some general problems in the estimation of parameters in comprehensive frameworks for intensive longitudinal data.  相似文献   

16.
A latent variable modeling method for testing criterion correlations with measurement error terms in multicomponent measuring instruments is outlined. The approach is based on an application of the Benjamini–Hochberg multiple testing procedure and can be used when assumptions of validity estimation related procedures need to be examined. The method also allows studying the extent to which criterion validity coefficients might be due to the relationship between a presumed underlying latent construct evaluated by a psychometric scale and a criterion variable, or could be a consequence of the relation between measurement error in the overall scale score and the criterion. The discussed procedure is widely applicable with popular latent variable modeling software, and is illustrated using a numerical example.  相似文献   

17.
This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome.  相似文献   

18.
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and evaluate their performance with a Monte Carlo simulation study. We found that the 4 models performed similarly with respect to model fit, bias of parameter estimates, Type I error, and power to test the treatment effect. To demonstrate a multigroup latent growth model with dummy treatment indicators, we estimated the effect of students changing schools during elementary school years on their reading and mathematics achievement, using data from the Early Childhood Longitudinal Study Kindergarten Cohort.  相似文献   

19.
Although much is known about the performance of recent methods for inference and interval estimation for indirect or mediated effects with observed variables, little is known about their performance in latent variable models. This article presents an extensive Monte Carlo study of 11 different leading or popular methods adapted to structural equation models with latent variables. Manipulated variables included sample size, number of indicators per latent variable, internal consistency per set of indicators, and 16 different path combinations between latent variables. Results indicate that some popular or previously recommended methods, such as the bias-corrected bootstrap and asymptotic standard errors had poorly calibrated Type I error and coverage rates in some conditions. Likelihood-based confidence intervals, the distribution of the product method, and the percentile bootstrap emerged as leading methods for both interval estimation and inference, whereas joint significance tests and the partial posterior method performed well for inference.  相似文献   

20.
This is the first study to test whether the stages of change of the transtheoretical model are qualitatively different through exploring discontinuity patterns in theory of planned behavior (TPB) variables using latent multigroup structural equation modeling (MSEM) with AMOS. Discontinuity patterns in terms of latent means and prediction patterns for the different stage groups were examined. Adults (n = 3,462) were assessed on their physical activity stages of change and TPB variables. The TPB was separately examined within the five stage groups. The TPB measurement model fit was acceptable. Latent mean analyses with post-hoc contrast and MSEM indicated discontinuity patterns. Results underscore the qualitative differences between the stages that may guide further research and the design of interventions integrating the approaches.  相似文献   

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