共查询到20条相似文献，搜索用时 687 毫秒
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A paucity of research has compared estimation methods within a measurement invariance (MI) framework and determined if research conclusions using normal-theory maximum likelihood (ML) generalizes to the robust ML (MLR) and weighted least squares means and variance adjusted (WLSMV) estimators. Using ordered categorical data, this simulation study aimed to address these queries by investigating 342 conditions. When testing for metric and scalar invariance, Δχ2 results revealed that Type I error rates varied across estimators (ML, MLR, and WLSMV) with symmetric and asymmetric data. The Δχ2 power varied substantially based on the estimator selected, type of noninvariant indicator, number of noninvariant indicators, and sample size. Although some the changes in approximate fit indexes (ΔAFI) are relatively sample size independent, researchers who use the ΔAFI with WLSMV should use caution, as these statistics do not perform well with misspecified models. As a supplemental analysis, our results evaluate and suggest cutoff values based on previous research. 相似文献
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《Structural equation modeling》2013,20(2):186-203
This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no model misspecification. The most important results were that with 2 and 3 categories the rejection rates of the WLSMV chi-square test corresponded much more to the expected rejection rates according to an alpha level of. 05 than the rejection rates of the ML chi-square test. The magnitude of the loadings was more precisely estimated by means of WLSMV when the variables had only 2 or 3 categories. The sample size for WLSMV estimation needed not to be larger than the sample size for ML estimation. 相似文献
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Data collected from questionnaires are often in ordinal scale. Unweighted least squares (ULS), diagonally weighted least squares (DWLS) and normal-theory maximum likelihood (ML) are commonly used methods to fit structural equation models. Consistency of these estimators demands no structural misspecification. In this article, we conduct a simulation study to compare the equation-by-equation polychoric instrumental variable (PIV) estimation with ULS, DWLS, and ML. Accuracy of PIV for the correctly specified model and robustness of PIV for misspecified models are investigated through a confirmatory factor analysis (CFA) model and a structural equation model with ordinal indicators. The effects of sample size and nonnormality of the underlying continuous variables are also examined. The simulation results show that PIV produces robust factor loading estimates in the CFA model and in structural equation models. PIV also produces robust path coefficient estimates in the model where valid instruments are used. However, robustness highly depends on the validity of instruments. 相似文献
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This study evaluates latent differential equation models on binary and ordinal data. Binary and ordinal data are widely used in psychology research and many statistical models have been developed, such as the probit model and the logit model. We combine the latent differential equation model with the probit model through a threshold approach, and then compare the threshold model with a naive model, which blindly treats binary and ordinal data as continuous. Simulation results suggest that the naive model leads to bias on binary data and on ordinal data with fewer than 5 levels, whereas the threshold model is unbiased and efficient for binary and ordinal data. Two example analyses on empirical binary data and ordinal data show that the threshold model also has better external validity. The R code for the threshold model is provided. 相似文献
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This study compared diagonal weighted least squares robust estimation techniques available in 2 popular statistical programs: diagonal weighted least squares (DWLS; LISREL version 8.80) and weighted least squares–mean (WLSM) and weighted least squares—mean and variance adjusted (WLSMV; Mplus version 6.11). A 20-item confirmatory factor analysis was estimated using item-level ordered categorical data. Three different nonnormality conditions were applied to 2- to 7-category data with sample sizes of 200, 400, and 800. Convergence problems were seen with nonnormal data when DWLS was used with few categories. Both DWLS and WLSMV produced accurate parameter estimates; however, bias in standard errors of parameter estimates was extreme for select conditions when nonnormal data were present. The robust estimators generally reported acceptable model–data fit, unless few categories were used with nonnormal data at smaller sample sizes; WLSMV yielded better fit than WLSM for most indices. 相似文献
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Application of Latent Growth Curve Analysis With Categorical Responses in Social Behavioral Research
Tae Kyoung Lee Kandauda Wickrama Catherine W. O’Neal 《Structural equation modeling》2018,25(2):294-306
Latent growth modeling allows social behavioral researchers to investigate within-person change and between-person differences in within-person change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution assumptions, have rarely been used. This article describes the latent growth curve model with categorical variables, and illustrates applications using Mplus software that are applicable to social behavioral research. The illustrations use marital instability data from the Iowa Youth and Family Project. We close with recommendations for the specification and parameterization of growth models that use both logit and probit link functions. 相似文献
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Dexin Shi Christine DiStefano Heather L. McDaniel Zhehan Jiang 《Structural equation modeling》2018,25(6):924-945
This study examined the effect of model size on the chi-square test statistics obtained from ordinal factor analysis models. The performance of six robust chi-square test statistics were compared across various conditions, including number of observed variables (p), number of factors, sample size, model (mis)specification, number of categories, and threshold distribution. Results showed that the unweighted least squares (ULS) robust chi-square statistics generally outperform the diagonally weighted least squares (DWLS) robust chi-square statistics. The ULSM estimator performed the best overall. However, when fitting ordinal factor analysis models with a large number of observed variables and small sample size, the ULSM-based chi-square tests may yield empirical variances that are noticeably larger than the theoretical values and inflated Type I error rates. On the other hand, when the number of observed variables is very large, the mean- and variance-corrected chi-square test statistics (e.g., based on ULSMV and WLSMV) could produce empirical variances conspicuously smaller than the theoretical values and Type I error rates lower than the nominal level, and demonstrate lower power rates to reject misspecified models. Recommendations for applied researchers and future empirical studies involving large models are provided. 相似文献
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利用随机截尾寿命试验数据,给出了两参数Pareto分布参数．可靠度和换效率的Bayes点估计及其置信限． 相似文献
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利用随机截尾寿命试验数据,给出了两参数Pareto分布参数,可靠度和换效率的Bayes点估计及其置信限． 相似文献
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Xenia Schmalz Kristina Moll Claudio Mulatti Gerd Schulte-Körne 《Scientific Studies of Reading》2019,23(1):64-76
ABSTRACTPrevious studies found a relationship between performance on statistical learning (SL) tasks and reading ability and developmental dyslexia. Thus, it has been suggested that the ability to implicitly learn patterns may be important for reading acquisition. Causal mechanisms behind this relationship are unclear: Although orthographic sensitivity to letter bigrams may emerge through SL and facilitate reading, there is no empirical support for this link. We test 84 adults on two SL tasks, reading tests, and a bigram sensitivity task. We test for correlations using Bayes factors. This serves to test the prediction that SL and reading ability are correlated and to explore sensitivity to bigram legality as a potential mediator. We find no correlations between SL tasks and reading ability, SL and bigram sensitivity, or between the SL tasks. We conclude that correlating SL with reading ability may not yield replicable results, partly due to low correlations between SL tasks. 相似文献
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Psychometric properties of item response theory proficiency estimates are considered in this paper. Proficiency estimators based on summed scores and pattern scores include non-Bayes maximum likelihood and test characteristic curve estimators and Bayesian estimators. The psychometric properties investigated include reliability, conditional standard errors of measurement, and score distributions. Four real-data examples include (a) effects of choice of estimator on score distributions and percent proficient, (b) effects of the prior distribution on score distributions and percent proficient, (c) effects of test length on score distributions and percent proficient, and (d) effects of proficiency estimator on growth-related statistics for a vertical scale. The examples illustrate that the choice of estimator influences score distributions and the assignment of examinee to proficiency levels. In particular, for the examples studied, the choice of Bayes versus non-Bayes estimators had a more serious practical effect than the choice of summed versus pattern scoring. 相似文献
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Growth models allow for the study of within-person change and between-person differences in within-person change. Typically, these models are applied to continuous variables where the residuals are assumed to be normally distributed. With normally distributed residuals there are a variety of residual structures that can be imposed and tested, which have been shown to affect model fit and parameter estimation. This article concerns residual structures in growth models with binary and ordered categorical outcomes using the probit link function. Different residual structures and their appropriateness for growth data are discussed and their use is illustrated with longitudinal data collected as part of Head Start’s Family and Child Experiences Survey 1997 Cohort. We close with recommendations for the specification and parameterization of growth models that use the probit link. 相似文献
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Melissa McTernan 《Structural equation modeling》2013,20(2):216-226
Ordinal response scales are often used to survey behaviors, including data collected in longitudinal studies. Advanced analytic methods are now widely available for longitudinal data. This study evaluates the performance of 4 methods as applied to ordinal measures that differ by the number of response categories and that include many zeros. The methods considered are hierarchical linear models (HLMs), growth mixture mixed models (GMMMs), latent class growth analysis (LCGA), and 2-part latent growth models (2PLGMs). The methods are evaluated by applying each to empirical response data in which the number of response categories is varied. The methods are applied to each outcome variable, first treating the outcome as continuous and then as ordinal, to compare the performance of the methods given both a different number of response categories and treatment of the variables as continuous versus ordinal. We conclude that although the 2PLGM might be preferred, no method might be ideal. 相似文献
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Reading difficulties (RDs) are easily noticed by classmates, may cause frustration in the affected students, and are often accompanied by emotional, behavioral, and interpersonal problems at school. Although interviews with students with RDs have revealed bullying experiences, whether RDs actually increase the risk of bullying involvement has not been investigated before. We tested the association of self-reported RDs with peer-reported involvement in bullying in a nationally representative sample of 17,188 students (grades 3–8) from 1045 classrooms in 147 schools. Results indicated that experienced difficulties in the most fundamental learning skill seem to put students at risk especially for victimization at school (viewed by peers as victims and bully/victims), when gender, level of schooling, self-esteem, and difficulties in math were taken into account. In general, over a third of students with RDs were involved in bullying as victims, bullies, or bully/victims, compared with approximately a fifth of students without RDs. 相似文献
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Milica Miočević Oscar Gonzalez Matthew J. Valente David P. MacKinnon 《Structural equation modeling》2018,25(1):121-136
Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example. 相似文献
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Jill R. Glassman Heather M. Franks Elizabeth R. Baumler Karin K. Coyle 《Sex education》2014,14(5):497-509
Most interventions designed to prevent HIV/STI/pregnancy risk behaviours in young people have multiple components based on psychosocial theories (e.g. social cognitive theory) dictating sets of mediating variables to influence to achieve desired changes in behaviours. Mediation analysis is a method for investigating the extent to which a variable X (e.g. intervention indicator) influences an outcome variable Y (e.g. unprotected sex) by first influencing an intermediate variable M (e.g. self-efficacy to use a condom) and provides a way for empirically validating theoretical hypothesised mediators. In this way, mediation analysis is a critical tool for suggesting which components of complex interventions should be the focus of more efficient and effective interventions in the future. The present study applied multilevel mediation analysis to outcome data from the All4You2! study to begin to examine the relationships between a theory-based HIV/STI/pregnancy prevention curriculum for students attending alternative high schools who were at risk of educational failure. The study targeted psychosocial mediating variables and the primary outcome unprotected sex in the past three months. Results suggest helping young people attending alternative schools identify and avoid exposure to risky situations and improving their self-efficacy to refuse sex should be focal points of future interventions. 相似文献
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Olof Reichenberg 《Journal of Education & Work》2019,32(1):1-20
The purpose of my paper is to describe and explain the probability of staying in temporary work for young people (age 16–27) in Sweden between 1992 and 2011 and its relation to socioeconomic outcomes (low socioeconomic classification and wage). I used panel data from the Swedish Labour Force Survey (LFS) and the longitudinal integration database for health insurance and labour market studies (LISA). To analyse the data, I used a dynamic probit model, unconditional quantile regression, and a pooled bivariate probit model. My results suggest that young people who have a low education have lower probabilities of receiving temporary employment in younger cohorts. However, younger cohorts with a lower education have a substantive wage disadvantage, specifically in younger cohorts compared to older cohorts. Low-educated cohorts also have a higher probability of obtaining low socioeconomic classification (SEC) employment, which is conditional on holding temporary employment in older cohorts compared to other educational groupings. 相似文献
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Study interference (i.e., studying is interfered by enjoyable alternatives) and leisure interference (i.e., leisure time is interfered by duties) are investigated as separate mediators between students' self-control capacities and their overall functioning (N = 253). Based on the assumption that both conflict experiences are associated with domain-specific outcomes, we calculated multiple mediator models with several indicators of students' domain-specific functioning as criteria, self-control as predictor, and students' tendency to experience motivational interference during studying (TMIS) and during leisure time (TMIL) as parallel mediators. As predicted, TMIS was the strongest mediator for measures of academic functioning, whereas TMIL was the strongest mediator for leisure functioning. With regard to general well-being, TMIL was the more consistent mediator. Findings are in line with the assumption that students' self-regulation difficulties are not only important for academic contexts but also for leisure contexts, especially when concepts of successful development include students' strivings in various life domains. 相似文献
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Deborah L. Bandalos 《Structural equation modeling》2013,20(1):102-116
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate. 相似文献