Statistical Power of the Multiple Domain Latent Growth Model for Detecting Group Differences

The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological performance is very limited. Hence, the present study compared the power of the MDLGM with that of a set of univariate LGMs for detecting group differences in growth rates over time using a Monte Carlo study with a two-group and two-domain design. The results indicated that there were different scenarios where the power rates for the MDLGM were greater than that of the set of LGMs (and vice versa) due to a joint function of the two domains’ intercorrelation size and the group difference effect size.     

A Comparison of the English Vocabulary Used by Children of Non-American Ancestry in Hawaii before they Reach the Age of Seven Years with that of Kindergarten Children in Continental United States

The authors investigated 2 issues concerning the power of latent growth modeling (LGM) in detecting linear growth: the effect of the number of repeated measurements on LGM's power in detecting linear growth and the comparison between LGM and some other approaches in terms of power for detecting linear growth. A Monte Carlo simulation design was used, with 3 crossed factors (growth magnitude, number of repeated measurements, and sample size) and 1,000 replications within each cell condition. The major findings were as follows: For 3 repeated measurements, a substantial proportion of samples failed to converge in structural equation modeling; the number of repeated measurements did not show any effect on the statistical power of LGM in detecting linear growth; and the LGM approach outperformed both the dependent t test and repeated-measures analysis of variance (ANOVA) in terms of statistical power for detecting growth under the conditions of small growth magnitude and small to moderate sample size conditions. The multivariate repeated-measures ANOVA approach consistently underperformed the other tests.     

Using Design-Based Latent Growth Curve Modeling With Cluster-Level Predictor to Address Dependency

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the higher-level predictor was not included and that standard errors of the regression coefficients from the higher-level were underestimated when a regular LGCM was used. Nevertheless, random effect estimates, regression coefficients, and standard error estimates were consistent with those from the true MLGCM when the design-based LGCM included the higher-level predictor. They discussed implication for the study with empirical data illustration.     

采用明尼苏达多相人格量表(MMPI) 进行的一次心理测量

为了研究在押犯的人格特点，并提出相应的教育改造对策，本项研究选用了男性在押犯210人，用明尼苏达多相个性调查表(MMPI)进行了测试，并与明尼苏达多相个性调查表中国修订版常模样本中的男性被试820人的特征进行了比较。结果发现：在押犯具有明显的人格异常偏向，并且这种偏向具有整体性和一致性；在押犯人格异常偏向突出表现为忧郁、焦虑、对未来悲观失望等六个方面。在此基础上，提出了对在押犯教育改造的七条对策。     

基于现场测试的软弱黄土地基桥头引道路堤变形规律及控制措施研究

基于现场测试手段,深入研究分析软弱黄土地基桥头引道路堤变形的时空特征,并从技术方面系统提出变形控制技术措施,确保桥头引道的工程质量。通过现场观测,分析各类技术手段对桥头引道路堤变形控制的有效性。     

关于小组讨论形式口语考试的概化研究

小组讨论形式的口语考试既可以提高考试的效率,又能考到总结谈话等面试考试所考不到的谈话管理能力,所以被认为是可以应用在一般教学环境中的有效的口语考试方式。文章利用概化理论对小组讨论形式口语考试的总体信度进行了实证考察,考察结果表明小组讨论形式口语考试有可能被接受的信度。同时,为了最大限度地节省考试的时间和人力等资源,文章研究通过概化理论的D研究在保证考试信度的基础上科学地削减了分项评价项目的个数。     

Testing Time-Invariance of Variable Specificity in Repeated Measure Designs Using Structural Equation Modeling

A structural equation modeling method for examining time-invariance of variable specificity in longitudinal studies with multiple measures is outlined, which is developed within a confirmatory factor-analytic framework. The approach represents a likelihood ratio test for the hypothesis of stability in the specificity part of the residual term associated with repeated administration of each measure. The procedure can be used in the search for parsimonious versions of multiwave multiple-indicator models, to test for variable specificity in them, and to examine assumptions underlying particular parameter estimation procedures in repeated measure designs. The outlined method is illustrated with empirical data.     

Estimating Latent Variable Interactions with the Unconstrained Approach: A Comparison of Methods to Form Product Indicators for Large,Unequal Numbers of Items

This Monte Carlo simulation study investigated methods of forming product indicators for the unconstrained approach for latent variable interaction estimation when the exogenous factors are measured by large and unequal numbers of indicators. Product indicators were created based on multiplying parcels of the larger scale by indicators of the smaller scale, multiplying the three most reliable indicators of each scale matched by reliability, and matching items by reliability to create as many product indicators as the number of indicators of the smallest scale. The unconstrained approach was compared with the latent moderated structural equations (LMS) approach. All methods considered provided unbiased parameter estimates. Unbiased standard errors were obtained in all conditions with the LMS approach and when the sample size was large with the unconstrained approach. Power levels to test the latent interaction and Type I error rates were similar for all methods but slightly better for the LMS approach.     

Using Exploratory Bifactor Analysis to Understand the Latent Structure of Multidimensional Psychological Measures: An Example Featuring the WISC-V

Exploratory bifactor analysis (EBFA) represents a methodological advancement for implementing a bifactor model in exploratory factor analysis (EFA). However, little is known about how to properly employ the procedure. The current rotation criteria available for EBFA make it more likely to “get stuck” in local minima, contributing to possible group factor collapse, than more traditional EFA rotations. Thus, getting a proper solution is a more complex and involved process than typical EFA and may require a sensitivity analysis. This article examines EBFA through a sensitivity analysis and subsequent simulation of parameters thought to contribute to group factor collapse. Results support the use of sensitivity analysis, as the problematic variable was shown to greatly increase the likelihood of factor collapse. The hypothesis that estimation start values contribute to factor collapse was not supported. Accompanying R syntax for all analyses are provided to facilitate reproducibility.     

考生口试话语语言特征衡量指标的有效性研究

基于考生英语口试话语的研究中,不同研究者用采分析话语准确度,流利度与复杂度等语言特征的衡量指标有相似的也有差异的。为了验证这些研究中所共同采用的一些衡量指标的有效性,在一项实证研究的基础上,对六个被不同研究采用的衡量指标进行因子分析,结果表明所考察的六个衡量指标均能准确有效地反映出预期的考生口试话语语言特征。     

The Misspecification of the Covariance Structures in Multilevel Models for Single-Case Data: A Monte Carlo Simulation Study

The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted. Modeling covariance does not result in less biased between-study variance estimates as the between-study covariance estimate is biased. When the research interest lies in the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate for estimating between-study variance if fewer than 30 studies are included.     

Estimating R 2 Shrinkage in Multiple Regression: A Comparison of Different Analytical Methods

The effectiveness of various analytical formulas for estimating R ² shrinkage in multiple regression analysis was investigated. Two categories of formulas were identified: estimators of the squared population multiple correlation coefficient (ρ²) and those of the squared population cross-validity coefficient (ρ_c ²). The authors conducted a Monte Carlo experiment to investigate the effectiveness of the analytical formulas for estimating R ² shrinkage, with 4 fully crossed factors (squared population multiple correlation coefficient, number of predictors, sample size, and degree of multicollinearity) and 500 replications in each cell. The results indicated that the most widely used Wherry formula (in both SAS and SPSS) is probably not the most effective analytical formula for estimating ρ². Instead, the Pratt formula and the Browne formula outperformed other analytical formulas in estimating ρ² and ρ_c ², respectively.     

音乐团体游戏缓解音乐专业学生考试焦虑有效性的实证研究

以74名具有考试焦虑症状的高师音乐系学生为被试,采用等组前后测设计,考察音乐团体游戏对考试焦虑症所起的作用。研究结果表明,音乐团体游戏能够有效地缓解高师音乐系学生的考试焦虑情绪。     

Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study

This simulation study assesses the statistical performance of two mathematically equivalent parameterizations for multitrait–multimethod data with interchangeable raters—a multilevel confirmatory factor analysis (CFA) and a classical CFA parameterization. The sample sizes of targets and raters, the factorial structure of the trait factors, and rater missingness are varied. The classical CFA approach yields a high proportion of improper solutions under conditions with small sample sizes and indicator-specific trait factors. In general, trait factor related parameters are more sensitive to bias than other types of parameters. For multilevel CFAs, there is a drastic bias in fit statistics under conditions with unidimensional trait factors on the between level, where root mean square error of approximation (RMSEA) and χ2 distributions reveal a downward bias, whereas the between standardized root mean square residual is biased upwards. In contrast, RMSEA and χ2 for classical CFA models are severely upwardly biased in conditions with a high number of raters and a small number of targets.     

A Survey of the Social Development of the 10th, 11th,and 12th Grade Pupils in a Small High School

A potential weakness of quantitative methods is the intrusive nature of testing in general, and pretesting specifically. The Solomon four-group design ameliorates this difficulty, but because of statistical issues there are few published examples. W. Braver and Braver (1988) suggested the use of meta-analytic Stouffer's Z to combine the data from all four groups. Sawilowsky and Markman (1990a, 1990b, 1990c, 1990d) and Braver and W. Braver (1990a, 1990b) exchanged differing opinions on this approach. The present study is a Monte Carlo demonstration that the experiment-wise error rate inflates nearly triple nominal alpha. However, when not conducted as conditional tests, traditional procedures are more powerful than this meta-analytic approach, despite the ability of Stouffer's Z to combine all available data into a single statistic.