Measurement Invariance Across Groups in Latent Growth Modeling

This Monte Carlo study investigated the impacts of measurement noninvariance across groups on major parameter estimates in latent growth modeling when researchers test group differences in initial status and latent growth. The average initial status and latent growth and the group effects on initial status and latent growth were investigated in terms of Type I error and bias. The location and magnitude of noninvariance across groups was related to the location and magnitude of bias and Type I error in the parameter estimates. That is, noninvariance in factor loadings and intercepts was associated with the Type I error inflation and bias in the parameter estimates of the slope factor (or latent growth) and the intercept factor (or initial status), respectively. As noninvariance became large, the degree of Type I error and bias also increased. On the other hand, a correctly specified second-order latent growth model yielded unbiased parameter estimates and correct statistical inferences. Other findings and implications on future studies were discussed.     

The Role of Socioeconomic Status in SAT–Freshman Grade Relationships Across Gender and Racial Subgroups

Recent research has shown that admissions tests retain the vast majority of their predictive power after controlling for socioeconomic status (SES), and that SES provides only a slight increment over SAT and high school grades (high school grade point average [HSGPA]) in predicting academic performance. To address the possibility that these overall analyses obscure differences by race/ethnicity or gender, we examine the role of SES in the test‒grade relationship for men and women as well as for various racial/ethnic subgroups within the United States. For each subgroup, the test‒grade relationship is only slightly diminished when controlling for SES. Further, SES is a substantially less powerful predictor of academic performance than both SAT and HSGPA. Among the indicators of SES (i.e., father's education, mother's education, and parental income), father's education appears to be strongest predictor of freshman grades across subgroups, with the exception of the Asian subgroup. In general, SES appears to behave similarly across subgroups in the prediction of freshman grades with SAT scores and HSGPA.     

Measurement Invariance Testing Across Between-Level Latent Classes Using Multilevel Factor Mixture Modeling

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.     

Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling

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.     

Social Support and Socioeconomic Status Predict Secondary Students' Grades and Educational Plans Indifferently Across Immigrant Group and Gender

Social support and socioeconomic status (SES) have received considerable attention in explaining academic achievement and the achievement gap between students with ethic majority and immigrant background, and between boys and girls. Using a Structural Equation Modeling approach we examine (1) if there exist a gap in school achievements between these groups, (2) whether social support and SES is associated with achievements across these groups, (3) whether social support is associated with achievements after controlling for SES, and (4) whether achievement gaps are explained by group differences in social support and SES. Self-reported cross-sectional survey and register data from 8,574 10th graders in Norway was employed. Although we found group differences in achievements, and direct associations with social support and SES, we found no group differences in these associations. Controlling for SES and other sources of social support, teachers' support remained a significant positive influence on students' school outcomes.     

It Matters: Reference Indicator Selection in Measurement Invariance Tests

Conventional approaches for selecting a reference indicator (RI) could lead to misleading results in testing for measurement invariance (MI). Several newer quantitative methods have been available for more rigorous RI selection. However, it is still unknown how well these methods perform in terms of correctly identifying a truly invariant item to be an RI. Thus, Study 1 was designed to address this issue in various conditions using simulated data. As a follow-up, Study 2 further investigated the advantages/disadvantages of using RI-based approaches for MI testing in comparison with non-RI-based approaches. Altogether, the two studies provided a solid examination on how RI matters in MI tests. In addition, a large sample of real-world data was used to empirically compare the uses of the RI selection methods as well as the RI-based and non-RI-based approaches for MI testing. In the end, we offered a discussion on all these methods, followed by suggestions and recommendations for applied researchers.     

低社会经济地位与儿童发展

社会经济地位是根据个体获取或控制有价值资源而对其进行的层级排名，低社会经济地位会对儿童的身体健康、认知发展、情绪适应和行为问题等产生消极影响，中介模式与调节模式两种理论对这些影响的机制进行了解释。政府和全社会都要积极行动起来，以促进低社会经济地位儿童的健康成长与发展。     

Assessing Measurement Invariance in Multiple-Group Latent Profile Analysis

The study of measurement invariance in latent profile analysis (LPA) indicates whether the latent profiles differ across known subgroups (e.g., gender). The purpose of the present study was to examine the impact of noninvariance on the relative bias of LPA parameter estimates and on the ability of the likelihood ratio test (LRT) and information criteria statistics to reject the hypothesis of invariance. A Monte Carlo simulation study was conducted in which noninvariance was defined as known group differences in the indicator means in each profile. Results indicated that parameter estimates were biased in conditions with medium and large noninvariance. The LRT and AIC detected noninvariance in most conditions with small sample sizes, while the BIC and adjusted BIC needed larger sample sizes to detect noninvariance. Implications of the results are discussed along with recommendations for future research.     

Testing Measurement Invariance Across Groups in Longitudinal Data: Multigroup Second-Order Latent Growth Model

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.     

Collapsing Categorical Variables and Measurement Invariance

Cross-cultural comparisons of latent variable means demands equivalent loadings and intercepts or thresholds. Although equivalence generally emphasizes items as originally designed, researchers sometimes modify response options in categorical items. For example, substantive research interests drive decisions to reduce the number of item categories. Further, categorical multiple-group confirmatory factor analysis (MG-CFA) methods generally require that the number of indicator categories is equal across groups; however, categories with few observations in at least one group can cause challenges. In the current paper, we examine the impact of collapsing ordinal response categories in MG-CFA. An empirical analysis and a complementary simulation study suggested meaningful impacts on model fit due to collapsing categories. We also found reduced scale reliability, measured as a function of Fisher’s information. Our findings further illustrated artifactual fit improvement, pointing to the possibility of data dredging for improved model-data consistency in challenging invariance contexts with large numbers of groups.     

教育技术学实验研究工具的跨时测量恒等检测

基于跨时测量恒等视角与知识图谱分析,文章对我国教育技术学较常探讨的变量"自我效能"量表进行了工具检测,并以四川省某小学三年级的197名学生为被试,前后测时间间隔为6个月。文章采用结构方程模型的跨时测量恒等检验程序,依序针对不同恒等程度的模型进行比较,结果发现:数学自我效能量表不符合完全的度量恒等,放宽两道题项的参数限制后可达到部分的纯量恒等,但仍不及严格恒等的要求;跨时测量恒等性的结果会影响配对样本t检验的结论。基于此,文章提出建议:为了提升实验的内在效度,较长时间的实验研究应纳入工具的跨时测量恒等性检验。     

Investigating the Sensitivity of Goodness-of-Fit Indices to Detect Measurement Invariance in a Bifactor Model

A Monte Carlo simulation study was conducted to evaluate the sensitivities of the likelihood ratio test and five commonly used delta goodness-of-fit (ΔGOF) indices (i.e., ΔGamma, ΔMcDonald’s, ΔCFI, ΔRMSEA, and ΔSRMR) to detect a lack of metric invariance in a bifactor model. Experimental conditions included factor loading differences, location and number of noninvariant items, and sample size. The results indicated all ΔGOF indices held Type I error to a minimum and overall had adequate power for the study. For detecting the violation of metric invariance, only ΔGamma and ΔCFI, in addition to Δχ², are recommended to use in the bifactor model with values of ?.016 to ?.023 and ?.003 to ?.004, respectively. Moreover, in the variance component analysis, the magnitude of the factor loading differences contributed the most variation to all ΔGOF indices, whereas sample size affected Δχ² the most.     

Measurement Invariance Testing with Many Groups: A Comparison of Five Approaches

With the increasing use of international survey data especially in cross-cultural and multinational studies, establishing measurement invariance (MI) across a large number of groups in a study is essential. Testing MI over many groups is methodologically challenging, however. We identified 5 methods for MI testing across many groups (multiple group confirmatory factor analysis, multilevel confirmatory factor analysis, multilevel factor mixture modeling, Bayesian approximate MI testing, and alignment optimization) and explicated the similarities and differences of these approaches in terms of their conceptual models and statistical procedures. A Monte Carlo study was conducted to investigate the efficacy of the 5 methods in detecting measurement noninvariance across many groups using various fit criteria. Generally, the 5 methods showed reasonable performance in identifying the level of invariance if an appropriate fit criterion was used (e.g., Bayesian information criteron with multilevel factor mixture modeling). Finally, general guidelines in selecting an appropriate method are provided.     

Comparing Teachers’ Job Satisfaction across Countries: A Multiple‐Pairwise Measurement Invariance Approach

There is much interest in comparing latent traits, such as teacher job satisfaction, in large international surveys. However, different countries respond to questionnaires in different languages and interpret the questions through different cultural lenses, raising doubts about the psychometric equivalence of the measurements. Making valid comparisons depends on the latent traits displaying scalar measurement invariance. Unfortunately, this condition is rarely met across many countries at once. Different approaches that maximize the utility of such surveys, but remain faithful to the principles of measurement invariance testing, are therefore needed. This article illustrates one such approach, involving multiple‐pairwise comparisons. This enables us to compare teacher job satisfaction in England to 17 of the countries that participated in TALIS 2013. Teacher job satisfaction in England was as low, or lower, than all of the 17 comparable countries.     

An Investigation of the Alignment Method With Polytomous Indicators Under Conditions of Partial Measurement Invariance

The alignment method (Asparouhov & Muthén, 2014) is an alternative to multiple-group factor analysis for estimating measurement models and testing for measurement invariance across groups. Simulation studies evaluating the performance of the alignment for estimating measurement models across groups show promising results for continuous indicators. This simulation study builds on previous research by investigating the performance of the alignment method’s measurement models estimates with polytomous indicators under conditions of systematically increasing, partial measurement invariance. We also present an evaluation of the testing procedure, which has not been the focus of previous simulation studies. Results indicate that the alignment adequately recovers parameter estimates under small and moderate amounts of noninvariance, with issues only arising in extreme conditions. In addition, the statistical tests of invariance were fairly conservative, and had less power for items with more extreme skew. We include recommendations for using the alignment method based on these results.     

The Impact of Varying the Number of Measurement Invariance Constraints on the Assessment of Between-Group Differences of Latent Means

The objective was to offer guidelines for applied researchers on how to weigh the consequences of errors made in evaluating measurement invariance (MI) on the assessment of factor mean differences. We conducted a simulation study to supplement the MI literature by focusing on choosing among analysis models with different number of between-group constraints imposed on loadings and intercepts of indicators. Data were generated with varying proportions, patterns, and magnitudes of differences in loadings and intercepts as well as factor mean differences and sample size. Based on the findings, we concluded that researchers who conduct MI analyses should recognize that relaxing as well as imposing constraints can affect Type I error rate, power, and bias of estimates in factor mean differences. In addition, fit indexes can be misleading in making decisions about constraints of loadings and intercepts. We offer suggestions for making MI decisions under uncertainty when assessing factor mean differences.     

A Test for Cluster Bias: Detecting Violations of Measurement Invariance Across Clusters in Multilevel Data

We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings are equal to the between-level factor loadings, and whether the between-level residual variances are zero. The test is illustrated with an example from school research. In a simulation study, we show that the cluster bias test has sufficient power, and the proportions of false positives are close to the chosen levels of significance.     

Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.     

我国企业大学发展的背景、现状与前景

企业大学是企业教育发展的一种高级形态.我国企业大学发展的背景是知识经济条件下企业的学习型组织定位与企业人力资源发展需求下传统学校教育的缺位.目前,我国企业大学发展初具规模,但尚未发展成熟,还存在着诸多不足,其未来发展趋势主要体现在数量增长、教学与课程改革、逐渐成为独立实体、寻求多方合作伙伴的支持等方面.