共查询到20条相似文献，搜索用时 663 毫秒
1.
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
2.
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
3.
A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or simple hypotheses about these coefficients. The proposed method is illustrated with a numerical example. 相似文献
4.
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
5.
Siegfried Gabler 《Structural equation modeling》2017,24(1):104-111
A latent variable modeling method for studying maximal reliability of unidimensional multicomponent measuring instruments with correlated errors is outlined. In the presence of correlation between 2 residual terms, the procedure allows one to point and interval estimate the reliability of the linear combination of the scale components that possesses the highest possible reliability coefficient. The approach is readily applicable with popular latent variable modeling software and also provides an alternative scoring rule to the widely used overall sum score for homogeneous psychometric scales. The discussed method is illustrated with a numerical example. 相似文献
6.
Tenko Raykov George A. Marcoulides Siegfried Gabler 《Structural equation modeling》2017,24(5):755-767
In repeated measure studies with unidimensional scales, measurement invariance, and specificity stability over time, the specificity variance in each instrument component can be identified. This article describes for that setting an improved point and interval estimation procedure for the maximal reliability coefficient associated with a given set of homogeneous measures. The method is developed within the framework of latent variable modeling and can also be readily used in longitudinal studies for improved point and interval estimation of individual measure reliability and scale reliability at each assessment occasion. The procedure is based on empirically testable conditions and is illustrated with an example. 相似文献
7.
《Structural equation modeling》2013,20(1):124-141
Structural equation modeling (SEM) techniques provide us with excellent tools for conducting preliminary evaluation of differential validity and reliability of measurement instruments among a comprehensive selection of population groups. This article demonstrates empirically an SEM technique for group comparison of reliability and validity. Data are from a study of 495 mothers' attitudes toward pregnancy. Proportions of African American and White, married and unmarried, and Medicaid and non-Medicaid mothers provided sample sizes large enough for group comparisons. Four hypotheses are tested: that factor structures are invariant between subgroups, that factor loadings are invariant between subgroups, that measurement error is invariant between subgroups, and that means of the latent variable are invariant between subgroups. Discussion of item distributions, sample size issues, and appropriate estimation techniques is included. 相似文献
8.
Walter L. Leite 《Structural equation modeling》2013,20(4):581-610
A multiple testing approach is outlined that can be used to examine the assumption of underlying normal variables in latent variable models with categorical indicators. The method is based on an application of the increasingly popular Benjamini–Hochberg multiple testing procedure, and is readily applicable with widely circulated software. The discussed method is especially useful for ascertaining this assumption that is very often made in research based on structural equation modeling using models containing discrete outcomes. The described approach is illustrated with numerical data. 相似文献
9.
《Structural equation modeling》2013,20(2):252-263
A structural equation modeling based method is outlined that accomplishes interval estimation of individual optimal scores resulting from multiple-component measuring instruments evaluating single underlying latent dimensions. The procedure capitalizes on the linear combination of a prespecified set of measures that is associated with maximal reliability and validity. The approach is useful when one is interested in evaluating plausible ranges for subject scores on the composite exhibiting highest measurement consistency and strongest linear relation with a given criterion. The method is illustrated with a numerical example. 相似文献
10.
In latent growth modeling, measurement invariance across groups has received little attention. Considering that a group difference is commonly of interest in social science, a Monte Carlo study explored the performance of multigroup second-order latent growth modeling (MSLGM) in testing measurement invariance. True positive and false positive rates in detecting noninvariance across groups in addition to bias estimates of major MSLGM parameters were investigated. Simulation results support the suitability of MSLGM for measurement invariance testing when either forward or iterative likelihood ratio procedure is applied. 相似文献
11.
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
12.
A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits examining the latent structure of tentative versions of multiple-component measuring instruments. The discussed procedure is straightforwardly utilized with the increasingly popular latent variable modeling software Mplus, and is illustrated on a numerical example. 相似文献
13.
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
14.
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
15.
《Structural equation modeling》2013,20(1):59-72
A non-arbitrary method for the identification and scale setting of latent variables in general structural equation modeling is introduced. This particular technique provides identical model fit as traditional methods (e.g., the marker variable method), but it allows one to estimate the latent parameters in a nonarbitrary metric that reflects the metric of the measured indicators. This technique, therefore, is particularly useful for mean and covariance structures (MACS) analyses, where the means of the indicators and latent constructs are of key interest. By introducing this alternative method of identification and scale setting, researchers are provided with an additional tool for conducting MACS analyses that provides a meaningful and nonarbitrary scale for the estimates of the latent variable parameters. Importantly, this tool can be used with single-group single-occasion models as well as with multiple-group models, multiple-occasion models, or both. 相似文献
16.
Eun Sook Kim Seang-Hwane Joo Philseok Lee Yan Wang Stephen Stark 《Structural equation modeling》2016,23(6):870-887
This simulation study examines the efficacy of multilevel factor mixture modeling (ML FMM) for measurement invariance testing across unobserved groups when the groups are at the between level of multilevel data. To this end, latent classes are generated with class-specific item parameters (i.e., factor loading and intercept) across the between-level classes. The efficacy of ML FMM is evaluated in terms of class enumeration, class assignment, and the detection of noninvariance. Various classification criteria such as Akaike’s information criterion, Bayesian information criterion, and bootstrap likelihood ratio tests are examined for the correct enumeration of between-level latent classes. For the detection of measurement noninvariance, free and constrained baseline approaches are compared with respect to true positive and false positive rates. This study evidences the adequacy of ML FMM. However, its performance heavily depends on the simulation factors such as the classification criteria, sample size, and the magnitude of noninvariance. Practical guidelines for applied researchers are provided. 相似文献
17.
Larry R. Price Daniel P. Gonzalez Tiffany A. Whittaker 《Structural equation modeling》2019,26(1):12-23
A problem central to structural equation modeling is measurement model specification error and its propagation into the structural part of nonrecursive latent variable models. Full-information estimation techniques such as maximum likelihood are consistent when the model is correctly specified and the sample size large enough; however, any misspecification within the model can affect parameter estimates in other parts of the model. The goals of this study included comparing the bias, efficiency, and accuracy of hypothesis tests in nonrecursive latent variable models with indirect and direct feedback loops. We compare the performance of maximum likelihood, two-stage least-squares and Bayesian estimators in nonrecursive latent variable models with indirect and direct feedback loops under various degrees of misspecification in small to moderate sample size conditions. 相似文献
18.
Yan Wang Eunsook Kim John M. Ferron Robert F. Dedrick Tony X. Tan Stephen Stark 《Educational and psychological measurement》2021,81(1):61
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed. 相似文献
19.
Audrey L. Quails-Payne 《Journal of Educational Measurement》1992,29(3):213-225
Numerous methods have been proposed and investigated for estimating · the standard error of measurement (SEM) at specific score levels. Consensus on the preferred method has not been obtained, in part because there is no standard criterion. The criterion procedure in previous investigations has been a single test occasion procedure. This study compares six estimation techniques. Two criteria were calculated by using test results obtained from a test-retest or parallel forms design. The relationship between estimated score level standard errors and the score scale was similar for the six procedures. These relationships were also congruent to findings from previous investigations. Similarity between estimates and criteria varied over methods and criteria. For test-retest conditions, the estimation techniques are interchangeable. The user's selection could be based on personal preference. However, for parallel forms conditions, the procedures resulted in estimates that were meaningfully different. The preferred estimation technique would be Feldt's method (cited in Gupta, 1965; Feldt, 1984). 相似文献
20.
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