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1.
This article outlines an interval estimation procedure that can be used in a 3-level setting to evaluate the proportion of outcome variance attributable to the second level of clustering. The method is useful for examining the necessity of including a possibly omitted intermediate level of nesting in analyses of data from a multilevel study, and represents an informative addendum to current statistical tests of second-level variance. The approach is developed within the framework of latent variable modeling and can be used as an aid in the process of choosing between 2-level and 3-level models in a hierarchical design. The discussed procedure is illustrated with an empirical example.  相似文献   
2.
A didactic discussion of a procedure for interval estimation of change in scale reliability due to revision is provided, which is developed within the framework of covariance structure modeling. The method yields ranges of plausible values for the population gain or loss in reliability of unidimensional composites, which results from deletion or addition of scale components. The obtained confidence intervals can also be used for testing conventional as well as minimum effect hypotheses about the associated increment or drop in reliability. The approach yields as a by-product point and interval estimates for reliability of any instrument version, and is illustrated with two examples.  相似文献   
3.
Relevant aspects of the example provided by Raykov and Marcoulides (2001) Raykov, T. and Marcoulides, G. A. 2001. Can there be infinitely many models equivalent to a given covariance structure model?. Structural Equation Modeling, 8: 142149. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] are emphasized, specifically the distinctiveness of infinitely many members of its sequence of equivalent structural equation models. This emphasis appears to be needed in light of recent statements by Markus (2002) Markus, K. A. 2002. Statistical equivalence, semantic equivalence, eliminative induction and the Raykov–Marcoulides proof of infinite equivalence. Structural Equation Modeling, 9: 503522. [Taylor & Francis Online], [Web of Science ®] [Google Scholar], whose intended counterexamples do not present a disconfirmation of any of the developments of Raykov and Marcoulides (2001) Raykov, T. and Marcoulides, G. A. 2001. Can there be infinitely many models equivalent to a given covariance structure model?. Structural Equation Modeling, 8: 142149. [Taylor & Francis Online], [Web of Science ®] [Google Scholar]. Issues pertaining to differentiation between equivalent models are also discussed.  相似文献   
4.
This article is concerned with the difference in noncentrality parameters of nested structural equation models and their utility in evaluating statistical power associated with the pertinent restriction test. Based on the seminal work by Browne and Du Toit (1992), Steiger (1989, 1990), Steiger and Lind (1980), and Steiger, Shapiro, and Browne (1985), asymptotic confidence intervals for that difference are discussed. The intervals represent a useful adjunct to widely employed goodness‐of‐fit indexes and test statistics when assessing plausibility of constraints in nested models. The approach also permits estimating power of the test of validity of the nesting restrictions (cf. MacCallum, Browne, & Sugawara, 1996). It is illustrated on data from a 2‐group cognitive training study.  相似文献   
5.
A multiple testing procedure for examining the assumption of normality that is often made in analyses of incomplete data sets is outlined. The method is concerned with testing normality within each missingness pattern and arriving at an overall statement about normality using the available data. The approach is readily applied in empirical research with missing data using the popular software Mplus, Stata, and R. The procedure can be used to ascertain a main assumption underlying frequent applications of maximum likelihood in incomplete data modeling with continuous outcomes. The discussed approach is illustrated with numerical examples.  相似文献   
6.
A latent variable modeling method for testing criterion correlations with measurement error terms in multicomponent measuring instruments is outlined. The approach is based on an application of the Benjamini–Hochberg multiple testing procedure and can be used when assumptions of validity estimation related procedures need to be examined. The method also allows studying the extent to which criterion validity coefficients might be due to the relationship between a presumed underlying latent construct evaluated by a psychometric scale and a criterion variable, or could be a consequence of the relation between measurement error in the overall scale score and the criterion. The discussed procedure is widely applicable with popular latent variable modeling software, and is illustrated using a numerical example.  相似文献   
7.
This article is concerned with a latent variable modeling approach to discrete time survival analysis that includes both time-invariant and time-varying covariates. The approach is illustrated with data from the Health and Retirement Study, which are utilized to study further the relationship of depression to stroke in middle and late life. Employing lag-1 depression scores as time-varying covariates, in addition to a set of relevant medical and demographic variables as time-invariant covariates collected at baseline, the article addresses a particular aspect of the prominent vascular depression hypothesis representing an important area in aging research, gerontology, geriatrics, and medicine. The results indicate considerable links of immediately prior depression levels to subsequent occurrences of stroke in middle-aged and older adults. The findings complement those reported by Raykov, Gorelick, Zajacova, and Marcoulides (2017), and are consistent with that hypothesis implying depression as a potential warning sign of an impending stroke.  相似文献   
8.
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   
9.
This article studies the difference between the criterion validity coefficient of the widely used overall scale score for a unidimensional multicomponent measuring instrument and the maximal criterion validity coefficient that is achievable with a linear combination of its components. A necessary and sufficient condition of their identity is presented in the case of measurement errors being uncorrelated among themselves and with a used criterion. An upper bound of the difference in these validity coefficients is provided, indicating that it cannot exceed the discrepancy between the maximal reliability and composite reliability indexes. A readily applicable latent variable modeling procedure is discussed that can be used for point and interval estimation of the difference between the maximal and scale criterion validity coefficients. The outlined method is illustrated with a numerical example.  相似文献   
10.
The population discrepancy between unstandardized and standardized reliability of homogeneous multicomponent measuring instruments is examined. Within a latent variable modeling framework, it is shown that the standardized reliability coefficient for unidimensional scales can be markedly higher than the corresponding unstandardized reliability coefficient, or alternatively substantially lower than the latter. Based on these findings, it is recommended that scholars avoid estimating, reporting, interpreting, or using standardized scale reliability coefficients in empirical research, unless they have strong reasons to consider standardizing the original components of utilized scales.  相似文献   
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