Biomedical subjects
Publications and source records attributed to T Raykov.
Covariance structure analysis methods for testing invariance in reliability and stability coefficients in multi-wave, multi-indicator models are discussed. Alternative approaches are outlined, which are straightforward and simpler than the reparameterization recently proposed by Alanen, Leskinen and Kuusinen.
A method of composite reliability estimation using covariance structure analysis with nonlinear constraints is outlined. To motivate the developments, initially a short overview of research is presented, demonstrating that in many cases the widely used coefficient alpha is an unsatisfactory index of scale reliability already at the population level. As an alternative, the proposed covariance structure analysis procedure is based on the theoretical formula of the scale reliability coefficient in terms of parameters pertaining to a given set of congeneric components. The described approach is illustrated with several numerical examples and its performance compared with that of coefficient alpha.
Whereas measures of explained variance in a regression and an equation of a recursive structural equation model can be simply summarized by a standard R2 measure, this is not possible in nonrecursive models in which there are reciprocal interdependencies among variables. This article provides a general approach to defining variance explained in latent dependent variables of nonrecursive linear structural equation models. A new method of its estimation, easily implemented in EQS or LISREL and available in EQS 6, is described and illustrated.
This article focuses on two-wave change measurement and initial value dependence. Initially, some general methodological limitations associated with individual change measurement in a two assessment context are discussed. The method of "intra-individual and group-differential measurement of change" (Rueckert, 1989) is next evaluated. Use of a structural equation modeling approach is advocated in two-wave designs where the intention is to estimate the degree of interrelationship between growth or decline in a repeatedly assessed psychological construct and other variables. This approach is applied with the recently developed program RAMONA to the study of practice effects in a cognitive intervention study of plasticity in fluid intelligence of aged adults (Baltes, Dittmann-Kohli, & Kliegl, 1986).
Structural equation modeling (SEM) is now widely used in social and behavioral science research. SEM provides the possibility of fitting, and evaluating the fit, of well-specified, theoretical models to empirical data--more generally, of testing elaborated psychological theories. The options available to users of these approaches are many and varied. Popular SEM computational software packages, such as LISREL and EQS, provide a large amount of information, and there is some uncertainty as to what should be routinely reported. A series of guidelines are proposed for reporting SEM results in articles submitted to Psychology and Aging. The suggested guidelines ask authors using SEM methodology to provide important analysis information that will enable readers to evaluate the findings.
In the last decade there has been a growing interest among developmental psychologists to investigate whether the cognitive performance of older adults can be improved by means of training programs. A number of cognitive training studies involving aging sensitive abilities of fluid intelligence have been performed with healthy older adults (Willis et al. 1981; Baltes et al., 1984/1986). In this paper we reanalyse data from Baltes et al. (1986) concerning the ADEPT Induction, ADEPT Figural Relations, Induction Standard and the Raven's Advanced Progressive Matrices Tests. In contrast to the above study, where the data analysis was based on a MANOVA approach, usually carried out when experimental data were gathered, this discussion implements an approach to change measurement for which the structural equation of different aspects of change in means as manifested in the moment matrices. The results here confirm these by Baltes et al. (1986), and suggest conclusions concerning change in means over time in the experimental and control groups, which are not implied by their study.