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1.
This research uses longitudinal data from the Wechsler Adult Intelligence Scale (WAIS) and linear structural equation models (e.g., LISREL) in an evaluation of the structural, kinematic, and dynamic hypotheses of the “theory of fluid and crystallized intelligence.” In a first set of analyses we use linear dynamic models in a formal evaluation of the growth and declines of abilities through latent growth and linear dynamic models. Our first results indicate separate trends over age for different intellectual abilities including broad knowledge, spatial reasoning, perceptual speed, and immediate memory. In a second set of analyses we extend these multivariate dynamic structural equation models to explore the age-based leading and lagging indicators. These results indicate a complex system of relationships, with memory losses as an important leading indicator. In a third set of analyses we use confirmatory techniques to test specific aging hypotheses. These results indicate support for both the “general memory loss” hypothesis and the “general slowing” hypothesis, provide some support for the “investment theory” at the adult level, and also suggest a single “general” factor does not describe the complexity of cognitive aging. These result synthesize prior WAIS studies and provide methods for further research on the dynamics of the growth and decline of intellectual abilities across the adult life-span.  相似文献   

2.
In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from 1,738 adolescents in New England. The first, a mixture factor model, accounts for the zero-inflated mixture distribution underlying mixed-ancestry identification. Alternatively, a latent class model allows classification distinction between relatively ambiguous versus unambiguous mixed-ancestry responses. Comparison of individual indicators reveals that the Census 2000 survey version estimates higher prevalence of mixed ancestry but is less sensitive to relative certainty of identification than are alternate survey versions (i.e., offering a “mixed” check box option, allowing a written response). Ease of coding and missing data are also considered in discussing the relative merit of individual mixed-ancestry indicators among adolescents.  相似文献   

3.
A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression, very little has transferred to models with latent variables. By adding penalties to specific parameters in a structural equation model, researchers have a high level of flexibility in reducing model complexity, overcoming poor fitting models, and the creation of models that are more likely to generalize to new samples. The proposed method was evaluated through a simulation study, two illustrative examples involving a measurement model, and one empirical example involving the structural part of the model to demonstrate RegSEM’s utility.  相似文献   

4.
The relations between the latent variables in structural equation models are typically assumed to be linear in form. This article aims to explain how a specification error test using instrumental variables (IVs) can be employed to detect unmodeled interactions between latent variables or quadratic effects of latent variables. An empirical example is presented, and the results of a simulation study are reported to evaluate the sensitivity and specificity of the test and compare it with the commonly employed chi-square model test. The results show that the proposed test can identify most unmodeled latent interactions or latent quadratic effects in moderate to large samples. Furthermore, its power is higher when the number of indicators used to define the latent variables is large. Altogether, this article shows how the IV-based test can be applied to structural equation models and that it is a valuable tool for researchers using structural equation models.  相似文献   

5.
From the time of William James, psychologists have posited individually importance-weighted-average models (IWAMs) in which weighting specific attributes by individual measures of importance improves prediction of the global outcome measures. Because IWAMs cause much confusion, we briefly review a general taxonomic paradigm and structural equation models for testing IWAMs, and demonstrate its application for 2 simulated and 3 diverse “real” data applications (multidimensional measures of self-concept, quality of life, and job satisfaction). Consistent across the real data applications and previous research more generally, there is surprisingly little support for IWAMs when tested appropriately. In these diverse tests of IWAMs we integrate new approaches such as exploratory structural equation modeling (SEM), alternative approaches to constructing latent interactions, application of bifactor models, modeling method and item-wording effects, and the juxtaposition of model evaluation in relation to goodness of fit (typically used in SEM studies) and variance explained (typically used in multiple regression tests of IWAMs).  相似文献   

6.
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. The proposed “semi‐parametric” approach posits that the sample of endogenous observations arises from a finite mixture of components (or latent‐classes) of unknown proportions with multiple structural relations implied by the specified model for each latent‐class. We devise an Expectation‐Maximization algorithm in a maximum likelihood framework to simultaneously estimate the class proportions, the class‐specific structural parameters, and posterior probabilities of membership of each observation into each latent‐class. The appropriate number of classes can be chosen using various information‐theoretic heuristics. A data set entailing cross‐sectional observations for a diverse sample of businesses is used to illustrate the proposed approach.  相似文献   

7.
Data collected from questionnaires are often in ordinal scale. Unweighted least squares (ULS), diagonally weighted least squares (DWLS) and normal-theory maximum likelihood (ML) are commonly used methods to fit structural equation models. Consistency of these estimators demands no structural misspecification. In this article, we conduct a simulation study to compare the equation-by-equation polychoric instrumental variable (PIV) estimation with ULS, DWLS, and ML. Accuracy of PIV for the correctly specified model and robustness of PIV for misspecified models are investigated through a confirmatory factor analysis (CFA) model and a structural equation model with ordinal indicators. The effects of sample size and nonnormality of the underlying continuous variables are also examined. The simulation results show that PIV produces robust factor loading estimates in the CFA model and in structural equation models. PIV also produces robust path coefficient estimates in the model where valid instruments are used. However, robustness highly depends on the validity of instruments.  相似文献   

8.
Tetrad IV is a program designed for the specification of causal models. It is specifically designed to search for causal relations, but also offers the possibility to estimate the parameters of a structural equation model. It offers a remarkable graphical user interface, which facilitates building, evaluating, and searching for causal models. The search algorithms make it possible to find alternatives for existing models, as well as to find new models when a theoretical directive is lacking. This is illustrated by the detection of a causal model for longitudinal data, which is a viable alternative for a latent growth model.  相似文献   

9.
This study tested a structural equation model of enrollment patterns of white and Hispanic males and females in two-year institutions and the invariance of parameter estimates among the different subgroups in the study. The model represented a multiequation model with three latent endogenous variables, high school academic preparation in mathematics and science, mathematics and science attitudes, and the dependent variable, enrollment patterns in mathematics and science courses. Exogenous variables included parents' education, levels of encouragement by others, and high school grades. Structural equation modeling was used to examine the structural and measurement coefficients of the hypothesized causal model for all subgroups in the study. In summary, an examination of the direct and total effect coefficients revealed different underlying patterns of factors for white and Hispanic females. No convergence on the model was found for white and Hispanic males. Equality constraints on all structural coefficients for both white and Hispanic females were tested and results indicated that all parameter estimates in the structural models for both subgroups were significantly different from each other.  相似文献   

10.
If test scores are collected from an individual pupil at different points in time and a state-space model is available for describing latent ability development over time, the Kalman filter and smoother turn out to be the optimal procedures for estimating the pupil's latent curves. The Kalman filter is implemented in the Nijmegen Pupil Monitoring System, LISKAL. The essentials of Kalman filtering and smoothing in comparison to traditional cross-sectional factor score estimators are explained, stressing unbiasedness considerations and the initialization problem. The state-space model is represented as an SEM (structural equation model) and estimated by means of an SEM program. The value of the Kalman filter and smoother in pupil monitoring is enhanced by specifying a “structured means” instead of the traditional “zero means” SEM model and by introducing random subject effects.  相似文献   

11.
Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes and illustrates key features of Bayesian approaches to model diagnostics and assessing data–model fit of structural equation models, discussing their merits relative to traditional procedures.  相似文献   

12.
This study evaluates latent differential equation models on binary and ordinal data. Binary and ordinal data are widely used in psychology research and many statistical models have been developed, such as the probit model and the logit model. We combine the latent differential equation model with the probit model through a threshold approach, and then compare the threshold model with a naive model, which blindly treats binary and ordinal data as continuous. Simulation results suggest that the naive model leads to bias on binary data and on ordinal data with fewer than 5 levels, whereas the threshold model is unbiased and efficient for binary and ordinal data. Two example analyses on empirical binary data and ordinal data show that the threshold model also has better external validity. The R code for the threshold model is provided.  相似文献   

13.
Many mechanistic rules of thumb for evaluating the goodness of fit of structural equation models (SEM) emphasize model parsimony; all other things being equal, a simpler, more parsimonious model with fewer estimated parameters is better than a more complex model Although this is usually good advice, in the present article a heuristic counterexample is demonstrated in which parsimony as typically operationalized in indices of fit may be undesirable. Specifically, in simplex models of longitudinal data, the failure to include correlated uniquenesses relating the same indicators administered on different occasions will typically lead to systematically inflated estimates of stability. Although simplex models with correlated uniquenesses are substantially less parsimonious and may be unacceptable according to mechanistic decision rules that penalize model complexity, it can be argued a priori that these additional parameter estimates should be included. Simulated data . are used to support this claim and to evaluate the behavior of a variety of fit indices and decision rules. The results demonstrate the validity of Bollen and Long’s (1993) conclusion that “test statistics and fit indices are very beneficial, but they are no replacement for sound judgment and substantive expertise” (p. 8).  相似文献   

14.
Nonrecursive structural equation models generally take the form of feedback loops, involving 2 latent variables that are connected by 2 unidirectional paths, 1 starting with each variable and terminating in the other variable. Nonrecursive models belong to a larger class of path models that require the use of instrumental variables (IVs) to achieve model identification. Prior research has focused on SEM parameter estimation with IVs when indicators were continuous and normally distributed. Much less is known about how estimators function in the presence of categorical indicators, which are commonly used in the social sciences, such as with cognitive and affective instruments. In this study, there was specific interest in comparing the 2-stage least squares (2SLS) estimator and its categorical variant to other recommended estimators. This study compares the performance of several estimation approaches for fitting structural equation models with categorical indicator variables when IVs are necessary to obtain proper model estimates. Across conditions, 1 extension of the nonlinear 2SLS (N2SLS) approach, the nonlinear 3-stage least squares (N3SLS), which accounts for correlated errors among regressors within each model (as does the N2SLS), as well as correlations of errors across models, which N2SLS does not, appears to work the best among methods compared.  相似文献   

15.
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory factor analyses (CFA and EFA), as applied to substantively important questions based on multidimentional students' evaluations of university teaching (SETs). For these data, there is a well established ESEM structure but typical CFA models do not fit the data and substantially inflate correlations among the nine SET factors (median rs = .34 for ESEM, .72 for CFA) in a way that undermines discriminant validity and usefulness as diagnostic feedback. A 13-model taxonomy of ESEM measurement invariance is proposed, showing complete invariance (factor loadings, factor correlations, item uniquenesses, item intercepts, latent means) over multiple groups based on the SETs collected in the first and second halves of a 13-year period. Fully latent ESEM growth models that unconfounded measurement error from communality showed almost no linear or quadratic effects over this 13-year period. Latent multiple indicators multiple causes models showed that relations with background variables (workload/difficulty, class size, prior subject interest, expected grades) were small in size and varied systematically for different ESEM SET factors, supporting their discriminant validity and a construct validity interpretation of the relations. A new approach to higher order ESEM was demonstrated, but was not fully appropriate for these data. Based on ESEM methodology, substantively important questions were addressed that could not be appropriately addressed with a traditional CFA approach.  相似文献   

16.
We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth components representing the constructs of substantive interest. The corresponding matrix of structural coefficients is then computed by inverting the contrast matrix. In the third and last step, the first 2 steps are integrated into a structural equation model. The particular strength of this approach is that it permits construction of latent growth components in such a way that they represent interesting contrasts from a substantive point of view. This is illustrated using data of cancer patients obtained from 3 fatigue scales of the multidimensional fatigue inventory measured at 4 time points.  相似文献   

17.
This article is intended to complement previous research (Sivo, 1997; Sivo & Willson, 1998, in press) by discussing the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. Three practical considerations motivated this article. Unlike Marsh (1993), Sivo and Willson (2000) did not offer multiple indicator (latent order) equivalents to their autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models. Moreover, such models have yet to be discussed, despite Marsh's (1993) advocacy for multiple indicator models in general. Further motivating multiple indicator extensions of the AR, MA, and ARMA equivalent models is the fact that longitudinal studies often collect data on more than 1 related variable per occasion. Such multiple indicator models capitalize on 1 of the chief analytical advantages of structural equation modeling in that measurement error may be estimated directly.  相似文献   

18.
Philosophers of education often focus their critique on issues such as neoliberalism, consumerism, pluralism, and so on, and they typically turn for solutions to what we might call the political: democracy, the public, cosmopolitanism, dissent. These critiques and solutions remain firmly connected to what Heidegger calls “the world,” and this worldly analysis seemingly hovers above earthly issues of the environment and ecology. In this article, Clarence Joldersma employs Martin Heidegger's distinction between earth and world, drawing on Kelly Oliver's interpretation of it, to “ecologize” philosophy of education by arguing that that earth “juts” into the world. Philosophy of education needs a Derridean supplement, something that makes up for a lack, but that, in filling the lack, simultaneously supplants it. Joldersma invites philosophy of education to supplement its worldly principles (dissent, democracy, and the like) with an “earth ethics” that is characterized by three features. First, this ethics lets the earth and earthlings be, recognizing their continuing mystery as beings. Second, it acknowledges gratefulness toward the earth, an indebtedness to the earth for the reliable support it provides to our worldly projects and concerns. Third, it recognizes earth's fundamental fragility, that its seeming worldly dependability conceals an earthly vulnerability. Joldersma concludes that these three features, in tandem, give rise to an earthly ethics of responsibility. Philosophy of education needs an earth ethics to supplement, if not supplant, its worldly principles.  相似文献   

19.
西方哲学围绕“人如何理解和把握世界”问题而形成了三种哲学致思模式 :本原中介模式、理性中介模式和语言中介模式。这三种模式均遭到失败的原因在于 ,没有认识到人不是借助于某种独立于人的外在中介而是通过人自身的实践来实现对世界的理解和把握的。  相似文献   

20.
Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.  相似文献   

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