Multivariate Latent Growth Models for Mixed Data with Covariate Effects

Published on Jul 17, 2012in Communications in Statistics-theory and Methods0.893
路 DOI :10.1080/03610926.2011.609955
Silvia Bianconcini9
Estimated H-index: 9
(UNIBO: University of Bologna),
Silvia Cagnone9
Estimated H-index: 9
(UNIBO: University of Bologna)
Sources
Abstract
The paper presents an extension of a new class of multivariate latent growth models (Bianconcini and Cagnone, 2012) to allow for covariate effects on manifest, latent variables and random effects. The new class of models combines: (i) multivariate latent curves that describe the temporal behavior of the responses, and (ii) a factor model that specifies the relationship between manifest and latent variables. Based on the Generalized Linear and Latent Variable Model framework (Bartholomew and Knott, 1999), the response variables are assumed to follow different distributions of the exponential family, with item-specific linear predictors depending on both latent variables and measurement errors. A full maximum likelihood method is used to estimate all the model parameters simultaneously. Data coming from the Data WareHouse of the University of Bologna are used to illustrate the methodology.
馃摉 Papers frequently viewed together
References18
Newest
The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a cohort of students of the University of Bologna. The model proposed is innovative since it is composed by (a) multivariate growth models that allow the capt...
Source
#1Silvia Cagnone (UNIBO: University of Bologna)H-Index: 9
#2Irini Moustaki (LSE: London School of Economics and Political Science)H-Index: 23
Last. Vassilis G. S. Vasdekis (OPA: Athens University of Economics and Business)H-Index: 13
view all 3 authors...
The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for dependencies among items within time and between time. One model fits item-specific random effects which account for the between time points correlations and the second model uses a common factor. The relationships between the time-dependent latent variables are modelled with a non-stationary autoregressive ...
Source
Department of Statistical Sciences, University of Bologna, Italy.Summary. The evaluation of the formative process in the University system has been assum-ing an ever increasing importance in the European countries. Within this context the analysisof student performance and capabilities plays a fundamental role. In this work we propose amultivariate latent growth model for studying the performances of a cohort of students of theUniversity of Bologna. The model proposed is innovative since it is c...
Source
#1Irini Moustaki (OPA: Athens University of Economics and Business)H-Index: 23
#2Karl G. J枚reskogH-Index: 51
Last. Dimitris MavridisH-Index: 33
view all 3 authors...
We consider a general type of model for analyzing ordinal variables with covariate effects and 2 approaches for analyzing data for such models, the item response theory (IRT) approach and the PRELIS-LISREL (PLA) approach. We compare these 2 approaches on the basis of 2 examples, 1 involving only covariate effects directly on the ordinal variables and 1 involving covariate effects on the latent variables in addition.
Source
#1Anders SkrondalH-Index: 57
#2Sophia Rabe-Hesketh (University of California, Berkeley)H-Index: 70
METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction 'True' variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses Generating flexible distributions Combining information Summary MODELING DIFFERENT RESPONSE PROCESSES Introduction Generalized linear models Extensions of generalized linear models Latent response formulation Modeling durations or survival Summary and further reading CLASSICAL LATENT VARIABLE MODELS...
#1Irini Moustaki (OPA: Athens University of Economics and Business)H-Index: 23
Previous work on a general class of multidimensional latent variable models for analysing ordinal manifest variables is extended here to allow for direct covariate effects on the manifest ordinal variables and covariate effects on the latent variables. A full maximum likelihood estimation method is used to estimate all the model parameters simultaneously. Goodness-of-fit statistics and standard errors are discussed. Two examples from the 1996 British Social Attitudes Survey are used to illustrat...
Source
This article presents a new approach for analysis of multidimensional longitudinal data, motivated by studies using an item response battery to measure traits of an individual repeatedly over time. A general modeling framework is proposed that allows mixtures of count, categorical, and continuous response variables. Each response is related to age-specific latent traits through a generalized linear model that accommodates item-specific measurement errors. A transition model allows the latent tra...
Source
#1Jason Roy (UM: University of Michigan)H-Index: 40
#2Xihong Lin (UM: University of Michigan)H-Index: 81
Summary. Multiple outcomes are often used to properly characterize an effect of interest. This paper proposes a latent variable model for the situation where repeated measures over time are obtained on each outcome. These outcomes are assumed to measure an underlying quantity of main interest from different perspectives. We relate the observed outcomes using regression models to a latent variable, which is then modeled as a function of covariates by a separate regression model. Random effects ar...
Source
#1Irini Moustaki (LSE: London School of Economics and Political Science)H-Index: 23
#2Martin Knott (LSE: London School of Economics and Political Science)H-Index: 9
In this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. A unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be presented. We discuss in addition the scoring of individuals on the latent dimensions. The general framework presented allows, not only the analysis of manifest variables all of one type but also the simulta...
Source
#1Wim P. KrijnenH-Index: 25
#2Theo K. DijkstraH-Index: 21
Last. Richard D. Gill (UU: Utrecht University)H-Index: 55
view all 3 authors...
The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy p...
Source
Cited By4
Newest
#1Hildete Prisco Pinheiro (State University of Campinas)H-Index: 10
#2Rafael Pimentel Maia (State University of Campinas)H-Index: 6
Last. Mariana Rodrigues-Motta (State University of Campinas)H-Index: 6
view all 4 authors...
The purpose of this work is to present suitable statistical methods to study the performance of undergraduate students based on the incidence/proportion of failed courses/subjects. Three approaches are considered: first, the proportion of failed subjects is modeled considering a zero-one augmented beta distribution; second, discrete models are used to model the probability of failing subjects with logit link; third, incidence is modeled using regression for count data with log link and the logar...
Source
#1Leila Amiri (Shahid Beheshti University)H-Index: 2
#2Mojtaba Khazaei (Shahid Beheshti University)H-Index: 4
Last. Mojtaba Ganjali (Shahid Beheshti University)H-Index: 13
view all 3 authors...
Latent variable models are widely used for jointly modeling of mixed data including nominal, ordinal, count and continuous data. In this paper, we consider a latent variable model for jointly modeling relationships between mixed binary, count and continuous variables with some observed covariates. We assume that, given a latent variable, mixed variables of interest are independent and count and continuous variables have Poisson distribution and normal distribution, respectively. As such data may...
Source
#1Qian Ding (WHU: Wuhan University)H-Index: 3
#2Qiaohui Tong (WHU: Wuhan University)
Last. Hengqing Tong (WUT: Wuhan University of Technology)H-Index: 11
view all 5 authors...
ABSTRACTA definite linear algorithm based on the constraint least squares solution for the structural equation model (SEM) is proposed. First, the modular constraint least squares solution is obtained. Then, the prescription constraint (the weight coefficients are non negative and their sum is one) is added, and the last solution of SEM is obtained. This definite linear algorithm is a new and reasonable algorithm compared to traditional algorithms like linear structural relationship (LISREL) and...
Source
This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.