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1.
This paper presents a strategy for specifying latent variable regressions in the hierarchical modeling framework (LVR-HM). This model takes advantage of the Structural Equation Modeling (SEM) approach in terms of modeling flexibility—regression among latent variables—and of the HM approach in terms of allowing for more general data structures. A fully Bayesian approach via Markov Chain Monte Carlo (MCMC) techniques is applied to the LVR-HM. Through analyzing the data from a longitudinal study of educational achievement, gender difference are explored in the growth of mathematical achievement across grade 7 through grade 10. Allowing for the fact that initial status effect to rates of change may differ for girls and boys, the LVR-HM is specified in a way that rates of change parameters are modeled as a function of initial status parameters and the interaction between initial status and gender.  相似文献   

2.
The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible and effective tool for relating observed and latent variables. The data were simulated in a hierarchical framework (e.g., individuals nested in schools) so that a multilevel modeling approach would be appropriate. Analyses were conducted accounting for and not accounting for the nested data to determine the impact of ignoring such multilevel data structures in full structural equation models. Results highlight the differences in modeling results when the analytic strategy is congruent with the data structure and what occurs when this congruency is absent. Type I error rates and power for the standard and multilevel methods were similar for within-cluster variables and for the multilevel model with between-cluster variables. However, Type I error rates were inflated for the standard approach when modeling between-cluster variables.  相似文献   

3.
The hierarchical rater model (HRM) re‐cognizes the hierarchical structure of data that arises when raters score constructed response items. In this approach, raters’ scores are not viewed as being direct indicators of examinee proficiency but rather as indicators of essay quality; the (latent categorical) quality of an examinee's essay in turn serves as an indicator of the examinee's proficiency, thus yielding a hierarchical structure. Here it is shown that a latent class model motivated by signal detection theory (SDT) is a natural candidate for the first level of the HRM, the rater model. The latent class SDT model provides measures of rater precision and various rater effects, above and beyond simply severity or leniency. The HRM‐SDT model is applied to data from a large‐scale assessment and is shown to provide a useful summary of various aspects of the raters’ performance.  相似文献   

4.
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   

5.
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   

6.
The hierarchical generalized linear model (HGLM) is presented as an explicit, two-level formulation of a multilevel item response model. In this paper, it is shown that the HGLM is equivalent to the Rasch model and that, characteristic of the HGLM, person ability can be expressed in the form of random effects rather than parameters. The two-level item analysis model is presented as a latent regression model with person-characteristic variables. Furthermore, it is shown that the two-level HGLM model can be extended to a three-level latent regression model that permits investigation of the variation of students' performance across groups, such as is found in classrooms and schools, and of the interactive effect of person-and group-characteristic variables.  相似文献   

7.
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   

8.
A model is proposed for identifying latent predictor score patterns associated with a latent outcome variable. The model employs 2 new devices: (a) a path coefficient vector of contrast coefficients to describe a configural pattern in a structural model, and (b) a new type of latent variable with values that quantify the match of the person's latent predictor variable profile pattern to a theoretical pattern associated with the factor. The model is illustrated using data on perceptions and evaluations of political candidates during a debate. Findings suggest a pattern of scores on the perceptual variables associated with perceived debate success for female observers but not for male observers.  相似文献   

9.
Valuable methods have been developed for incorporating ordinal variables into structural equation models using a latent response variable formulation. However, some model parameters, such as the means and variances of latent factors, can be quite difficult to interpret because the latent response variables have an arbitrary metric. This limitation can be particularly problematic in growth models, where the means and variances of the latent growth parameters typically have important substantive meaning when continuous measures are used. However, these methods are often applied to grouped data, where the ordered categories actually represent an interval-level variable that has been measured on an ordinal scale for convenience. The method illustrated in this article shows how category threshold values can be incorporated into the model so that interpretation is more meaningful, with particular emphasis given to the application of this technique with latent growth models.  相似文献   

10.
An empirical approach is adopted in this article to explore a possible model for the prediction of students' science achievement in China and the United States. The construction of the model was based on the ninth-grade data base from Phase II of the Second IEA Science Study (SISS) in the United States, and the SISS Extension Study in the Hubei province of China. The common independent variables of the students' science achievement are classified into five categories: students' gender, attitude, home background, classroom experience, and personal effort, according to distinction between visible and latent characteristics, and scree plots from principal component analyses. Latent factors are represented by the first principal components in each of the four latent categories: students' attitudes, home background, classroom experience, and personal effort. Predictors of the model are constructed by polynomials of the visible and latent factors and their interactions in a multivariate Taylor series. Significant predictors at α = .05 were selected through a backward elimination procedure using the Statistical Analysis System. The structure of the four latent factors and the model complexity are compared between the two countries in terms of their educational, political, social, and cultural contexts. © 1996 John Wiley & Sons, Inc.  相似文献   

11.
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12.
In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly and should be measured by 2 or more manifest variables. Longitudinal latent variables occur when the corresponding manifest variables are measured at multiple time points. Our primary interests are in studying the dynamic change of longitudinal latent variables and exploring the possible interactive effect among the latent variables.

Much of the existing research in longitudinal studies focuses on studying change in a single observed variable at different time points. In this article, we propose a novel latent curve model (LCM) for studying the dynamic change of multivariate manifest and latent variables and their linear and interaction relationships. The proposed LCM has the following useful features: First, it can handle multivariate variables for exploring the dynamic change of their relationships, whereas conventional LCMs usually consider change in a univariate variable. Second, it accommodates both first- and second-order latent variables and their interactions to explore how changes in latent attributes interact to produce a joint effect on the growth of an outcome variable. Third, it accommodates both continuous and ordered categorical data, and missing data.  相似文献   

13.
Longitudinal studies offer unique opportunities to identify the specificity variance in the components of a psychometric scale that is administered repeatedly. This article discusses a procedure for evaluation of the relationship between true scale scores and criterion variables uncorrelated with measurement errors in longitudinally presented measures comprising unidimensional multicomponent instruments. The approach provides point and interval estimates of the true scale criterion validity with respect to a criterion that is assessed once or repeatedly, as well as a means for testing temporal stability in this validity. The outlined method is based on an application of the latent variable modeling methodology, is readily applicable with popular software, and is illustrated using empirical data.  相似文献   

14.
This article is based on an exploratory study that examines factors which predict children's performance on the numeracy component of the Australian National Assessment Program—Literacy and Numeracy (NAPLAN). Utilizing an ecological theoretical model, this study examines child, home and school variables which may enable or constrain NAPLAN numeracy performance. Data are presented from a nationally‐representative sample of 2450 children participating in the Longitudinal Study of Australian Children (LSAC). Twenty‐four children, home and school variables are tested as predictors of performance on the Year 3 NAPLAN numeracy assessment. The analysis includes an investigation of bivariate relationships between the outcome variable and each of the predictor variables. Following this a series of linear regression models are used to analyse the relation between child, home and school‐related variables and NAPLAN numeracy performance. The results support the ecological model and point to the importance of a supportive home–school relationship on children's numeracy performance.  相似文献   

15.
A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or simple hypotheses about these coefficients. The proposed method is illustrated with a numerical example.  相似文献   

16.
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   

17.
The purpose of this study was to evaluate Zippy’s Friends, a universal school programme that aims at strengthening children’s coping skills. The sample consisted of 1483 children (aged 7–8?years) from 91 second-grade classes in 35 schools. The schools were matched and randomly assigned to intervention or control conditions. Coping was assessed by the Kidcope checklist for children and an adapted version for parents. Parents and teachers reported mental health outcomes using the Strengths and Difficulties Questionnaire. Controlling for the hierarchical structure of the data, latent variable regression analysis indicated that the programme had a significant positive effect on coping and on the impact of mental health difficulties in daily life. Subgroup analyses suggested that coping was improved in girls and children from the low socio-economic subgroup, whereas the impact of mental health difficulties was reduced in boys.  相似文献   

18.
The validity of family background variables instrumenting education in income regressions has been much criticized. In this paper, we use data from the 2004 German Socio-Economic Panel and Bayesian analysis to analyze to what degree violations of the strict validity assumption affect the estimation results. We show that, in case of moderate direct effects of the instrument on the dependent variable, the results do not deviate much from the benchmark case of no such effect (perfect validity of the instrument's exclusion restriction). In many cases, the size of the bias is smaller than the width of the 95% posterior interval for the effect of education on income. Thus, a violation of the strict validity assumption does not necessarily lead to results which are strongly different from those of the strict validity case. This finding provides confidence in the use of family background variables as instruments in income regressions.  相似文献   

19.
INTRODUCTION Landslide is one of the most serious geological hazards in mountain areas. Globally, they cause hundreds of billions of dollars in damage, and hun- dreds of thousands of deaths and injuries each year (Aleotti and Chowdhury, 1999). Over the past fewdecades, scientists have shown an ever increasing interest in this natural hazard. One of the study fields is to produce landslide susceptibility map, i.e. a map portraying the spatial distribution of the future susceptibility of s…  相似文献   

20.
In this article, we present an approach for comprehensive analysis of the effectiveness of interventions based on nonlinear structural equation mixture models (NSEMM). We provide definitions of average and conditional effects and show how they can be computed. We extend the traditional moderated regression approach to include latent continous and discrete (mixture) variables as well as their higher order interactions, quadratic or more general nonlinear relationships. This new approach can be considered a combination of the recently proposed EffectLiteR approach and the NSEMM approach. A key advantage of this synthesis is that it gives applied researchers the opportunity to gain greater insight into the effectiveness of the intervention. For example, it makes it possible to consider structural equation models for situations where the treatment is noneffective for extreme values of a latent covariate but is effective for medium values, as we illustrate using an example from the educational sciences.  相似文献   

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