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31.
A latent variable modeling approach to evaluate scale reliability under realistic conditions in empirical behavioral and social research is discussed. The method provides point and interval estimation of reliability of multicomponent measuring instruments when several assumptions are violated. These assumptions include missing data, correlated errors, nonnormality, lack of unidimensionality, and data not missing at random. The procedure can be readily used to aid scale construction and development efforts in applied settings, and is illustrated using data from an educational study. 相似文献
32.
Tenko Raykov Philip B. Gorelick Anna Zajacova George A. Marcoulides 《Structural equation modeling》2017,24(6):926-935
Analysis and modeling of time to event data have been traditionally associated with nonparametric, semiparametric, or parametric statistical frameworks. Recent advances in latent variable modeling have additionally provided unique analytic opportunities to methodologists and substantive researchers interested in survival time modeling. As a consequence, discrete time survival analyses can now be readily carried out using latent variable modeling, an approach that offers substantively important extensions to conventional survival models. Using data from the Health and Retirement Study, the discussed approach is applied to the study of the increasingly prominent vascular depression hypothesis in gerontology, geriatrics, and aging research, allowing examination of the unique predictive power of depression with respect to time to stroke in middle-aged and older adults. 相似文献
33.
A latent variable modeling method for examining the difference between maximal reliability and composite reliability for homogenous multicomponent measuring instruments is outlined. The procedure allows point and interval estimation of the discrepancy between the reliability coefficients associated with the optimal linear combination and with the popular unit-weighted sum of the scale components. The approach permits a researcher to make an informed choice if needed between the maximal reliability and composite reliability coefficients and concepts in an empirical setting as indexes of quality of measurement with an instrument under consideration. The discussed method is illustrated using numerical data. 相似文献
34.
Tenko Raykov Mark Brennan Joann P. Reinhardt Amy Horowitz 《Structural equation modeling》2013,20(4):603-626
A correlation structure modeling method for comparison of mediated effects is outlined. The procedure permits point and interval estimation of differences in mediator effects, and is useful with models postulating 1 or more predictor, intervening, or response variables that may also be latent constructs. The approach allows scale-free evaluation of differences in effects of any explanatory upon criterion variables transmitted via studied mediators, and is applied on data from a study of older adults with recent vision loss. 相似文献
35.
A large-sample procedure for examining explained population variance in principal component analysis is discussed. The method yields interval estimates for the proportions of variance accounted for by the considered for retention principal components. The obtained confidence intervals can be used to make decisions about the number of selected components, based on their performance in studied populations. The approach can also be employed for ascertaining group similarity or differences in percentage explained variance by the considered principal components. The procedure is illustrated with 2 examples. 相似文献
36.
Tenko Raykov 《Structural equation modeling》2013,20(3):419-429
This article is concerned with the question of whether the missing data mechanism routinely referred to as missing completely at random (MCAR) is statistically examinable via a test for lack of distributional differences between groups with observed and missing data, and related consequences. A discussion is initially provided, from a formal logic standpoint, of the distinction between necessary conditions and sufficient conditions. This distinction is used to argue then that testing for lack of these group distributional differences is not a test for MCAR, and an example is given. The view is next presented that the desirability of MCAR has been frequently overrated in empirical research. The article is finalized with a reference to principled, likelihood-based methods for analyzing incomplete data sets in social and behavioral research. 相似文献
37.
A latent variable modeling procedure for examining specificity in any indicator of a common factor for a set of repeatedly administered measures is outlined in a longitudinal design setting with measurement invariance and specificity stability over time. The method permits one to test whether there is specificity in a given indicator and in the affirmative case allows one to point and interval estimate the specificity variance in that measure. The discussed procedure is readily applicable with popular software, is based on empirically testable conditions, and is illustrated with an example. 相似文献
38.
A latent variable modeling procedure for examining whether a studied population could be a mixture of 2 or more latent classes is discussed. The approach can be used to evaluate a single-class model vis-à-vis competing models of increasing complexity for a given set of observed variables without making any assumptions about their within-class interrelationships. The method is helpful in the initial stages of finite mixture analyses to assess whether models with 2 or more classes should be subsequently considered as opposed to a single-class model. The discussed procedure is illustrated with a numerical example. 相似文献
39.
A latent variable modeling approach to evaluation of scale reliability in complex design studies is outlined. The procedure is readily applicable in empirical research for the purpose of point and interval estimation of reliability of multicomponent measuring instruments in the presence of probability sampling and possible nesting within higher order units. The method can be used to aid scale construction and development efforts in large-scale studies of substantially heterogeneous populations. The described approach is illustrated with data from an international educational survey. 相似文献
40.
A procedure for evaluating candidate auxiliary variable correlations with response variables in incomplete data sets is outlined. The method provides point and interval estimates of the outcome-residual correlations with potentially useful auxiliaries, and of the bivariate correlations of outcome(s) with the latter variables. Auxiliary variables found in this way can enhance considerably the plausibility of the popular missing at random (MAR) assumption if included in ensuing maximum likelihood analyses, or can alternatively be incorporated in imputation models for subsequent multiple imputation analyses. The approach can be particularly helpful in empirical settings where violations of the MAR assumption are suspected, as is the case in many longitudinal studies, and is illustrated with data from cognitive aging research. 相似文献