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Journal ArticleDOI

Estimation and testing of simplex models

01 Nov 1970-British Journal of Mathematical and Statistical Psychology (Blackwell Publishing Ltd)-Vol. 23, Iss: 2, pp 121-145
TL;DR: In this paper, various statistical models for simplex structures are formulated in terms of the well-known Wiener and Markov stochastic processes, and a distinction is made between a perfect simplex and a quasi-simplex.
Abstract: Various statistical models for simplex structures are formulated in terms of the well-known Wiener and Markov stochastic processes. A distinction is made between a perfect simplex and a quasi-simplex. For each model the problems of identification and estimation of the parameters and that of testing the goodness of fit of the model are considered. All models may be estimated by a general method for covariance structures developed by Joreskog (1970), but in some cases simpler methods may be used, in which case these are presented.
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Journal ArticleDOI
TL;DR: In this paper, a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth is presented as a method for representing development, and a numerical illustration using data initially collected by Nesselroade and Baltes is presented.
Abstract: As a method for representing development, latent trait theory is presented in terms of a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth. Maximum likelihood parameter estimates and associated asymptotic tests follow directly. These procedures may be viewed as an alternative to standard repeated measures ANOVA and to first-order auto-regressive methods. As formulated, the model encompasses cohort sequential designs and allow for period or practice effects. A numerical illustration using data initially collected by Nesselroade and Baltes is presented.

1,379 citations

Journal ArticleDOI
TL;DR: A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM) that allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions.
Abstract: This report uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM). When merged with repeated-measures data, this technique permits the estimation of parameters representing both individual and group dynamics. The statistical basis of this model allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions. Aspects of these latent growth models are illustrated with a set of longitudinal WISC data from young children and by using the LISREL V computer program.

867 citations

Journal ArticleDOI
TL;DR: In this paper, a general approach to the analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest.
Abstract: A general approach to the analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest. The statistical problems of identification, estimation and testing of such covariance or correlation structures are discussed. Several different types of covariance structures are considered as special cases of the general model. These include models for sets of congeneric tests, models for confirmatory and exploratory factor analysis, models for estimation of variance and covariance components, regression models with measurement errors, path analysis models, simplex and circumplex models. Many of the different types of covariance structures are illustrated by means of real data.

864 citations

Journal ArticleDOI
TL;DR: In this article, the developmental dynamics of mathematical performance during children's transition from preschool to grade 2 and the cognitive antecedents of this development were investigated, and the results indicated that math performance showed high stability and increasing variance over time.
Abstract: This study investigated the developmental dynamics of mathematical performance during children's transition from preschool to Grade 2 and the cognitive antecedents of this development. 194 Finnish children were examined 6 times according to their math performance, twice during each year across a 3-year period. Cognitive antecedents, that is, counting ability, visual attention, metacognitive knowledge, and listening comprehension, were tested at the first measurement point. The results indicated that math performance showed high stability and increasing variance over time. Moreover, the growth of math competence was faster among those who entered preschool with an already high level of mathematical skills. The initial level of math performance, as well as its growth, was best predicted by counting ability.

703 citations


Cites methods from "Estimation and testing of simplex m..."

  • ...First, a simplex model for the observed math performance variables was tested to examine the stability of math performance and changes in its variance over time (Jöreskog, 1970)....

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Book ChapterDOI
01 Jan 1988
TL;DR: The authors examined multivariate psychological change data using the 20th century developments of latent variable structural equation modeling, and used this dynamic equation, but here they also used this simple dynamic equation to examine multivariate psychology change data.
Abstract: The term “dynamic” is broadly defined as a pattern of change. Many scientists have searched for dynamics by calculating df/dt: the ratio of changes or differences d in a function f relative to changes in time t.This simple dynamic equation was used in the 16th and 17th century motion experiments of Galileo, in the 17th and 18th century gravitation experiments of Newton, and in the 19th century experiments of many physicists and chemists (see Morris, 1985). I also use this dynamic equation, but here I examine multivariate psychological change data using the 20th century developments of latent variable structural equation modeling.

643 citations