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1.
Confirmatory factor analytic tests of measurement invariance (MI) require a referent indicator (RI) for model identification. Although the assumption that the RI is perfectly invariant across groups is acknowledged as problematic, the literature provides relatively little guidance for researchers to identify the conditions under which the practice is appropriate. Using simulated data, this study examined the effects of RI selection on both scale- and item-level MI tests. Results indicated that while inappropriate RI selection has little effect on the accuracy of conclusions drawn from scale-level tests of metric invariance, poor RI choice can produce very misleading results for item-level tests. As a result, group comparisons under conditions of partial invariance are highly susceptible to problems associated with poor RI choice. 相似文献
2.
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. 相似文献
3.
近年来关于DINA模型的相关研究显示,样本量、先验分布、经验贝叶斯或完全贝叶斯估计方法、样本的代表性、项目功能差异和Q阵误指等,均可能是导致DINA项目参数估计发生偏差的原因。使用Monte Carlo模拟试验,对DINA项目参数(猜测参数和失误参数)的组合变化类型和偏差量进行考察,通过条件极大似然估计法估计知识状态,发现项目参数估计值与真值偏差不大时,对知识状态估计的精度影响不大;但是项目参数偏离真值较大时,尤其是在三种组合类型上,对属性掌握存在明显的高估或低估现象。研究结果对于诊断测验等值有一定的启示:若两个测验上锚题的项目参数出现了较大的偏差(0.1),则需要考虑等值的必要性。 相似文献
4.
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. 相似文献
5.
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
6.
Testing for factorial invariance of the Modified Leadership Scale for Sports: using a Japanese version 总被引：1，自引：1，他引：0
The objective of this study was to provide empirical evidence to support psychometric properties of a modified four-dimensional model of the Leadership Scale for Sports (LSS). The study tested invariance of all parameters (i.e., factor loadings, error variances, and factor variances–covariances) in the four-dimensional measurement model between two groups of student-athletes. For testing multi-group invariance of the proposed scale, 335 middle school and 320 high school student-athletes in Japan participated in this study. The modified version of the LSS consists of 35 items representing training instruction, democratic behaviour, positive feedback, and social support. A chi-square difference test was employed for model comparisons. The results supported configural, metric, scalar and factor variance–covariance invariance in the modified LSS across the two student-athlete groups. 相似文献
7.
Studies investigating invariance have often been limited to measurement or prediction invariance. Selection invariance, wherein the use of test scores for classification results in equivalent classification accuracy between groups, has received comparatively little attention in the psychometric literature. Previous research suggests that some form of selection bias (lack of selection invariance) will exist in most testing contexts, where classification decisions are made, even when meeting the conditions of measurement invariance. We define this conflict between measurement and selection invariance as the invariance paradox. Previous research has found test reliability to be an important factor in minimizing selection bias. This study demonstrates that the location of maximum test information may be a more important factor than overall test reliability in minimizing decision errors between groups. 相似文献
8.
Multigroup confirmatory factor analysis (MCFA) is a popular method for the examination of measurement invariance and specifically, factor invariance. Recent research has begun to focus on using MCFA to detect invariance for test items. MCFA requires certain parameters (e.g., factor loadings) to be constrained for model identification, which are assumed to be invariant across groups, and act as referent variables. When this invariance assumption is violated, location of the parameters that actually differ across groups becomes difficult. The factor ratio test and the stepwise partitioning procedure in combination have been suggested as methods to locate invariant referents, and appear to perform favorably with real data examples. However, the procedures have not been evaluated through simulations where the extent and magnitude of a lack of invariance is known. This simulation study examines these methods in terms of accuracy (i.e., true positive and false positive rates) of identifying invariant referent variables. 相似文献
9.
《Structural equation modeling》2013,20(3):471-492
We illustrate testing measurement invariance in a second-order factor model using a quality of life dataset (n = 924). Measurement invariance was tested across 2 groups at a set of hierarchically structured levels: (a) configural invariance, (b) first-order factor loadings, (c) second-order factor loadings, (d) intercepts of measured variables, (e) intercepts of first-order factors, (f) disturbances of first-order factors, and (g) residual variances of observed variables. Given that measurement invariance at the factor loading and intercept levels was achieved, the latent factor mean difference on the higher order factor between the groups was also estimated. The analyses were performed on the mean and covariance structures within the framework of the confirmatory factor analysis using the LISREL 8.51 program. Implications of second-order factor models and measurement invariance in psychological research were discussed. 相似文献
10.
Cognitive diagnosis models (CDMs) typically assume skill attributes with discrete (often binary) levels of skill mastery, making the existence of skill continuity an anticipated form of model misspecification. In this article, misspecification due to skill continuity is argued to be of particular concern for several CDM applications due to the lack of invariance it yields in CDM skill attribute metrics, or what in this article are viewed as the “thresholds” applied to continuous attributes in distinguishing masters from nonmasters. Using the deterministic input noisy and (DINA) model as an illustration, the effects observed in real data are found to be systematic, with higher thresholds for mastery tending to emerge in higher ability populations. The results are shown to have significant implications for applications of CDMs that rely heavily upon the parameter invariance properties of the models, including, for example, applications toward the measurement of growth and differential item functioning analyses. 相似文献
11.
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。 相似文献
12.
《Structural equation modeling》2013,20(3):343-367
In previous research (Hu & Bentler, 1998, 1999), 2 conclusions were drawn: standardized root mean squared residual (SRMR) was the most sensitive to misspecified factor covariances, and a group of other fit indexes were most sensitive to misspecified factor loadings. Based on these findings, a 2-index strategy-that is, SRMR coupled with another index-was proposed in model fit assessment to detect potential misspecification in both the structural and measurement model parameters. Based on our reasoning and empirical work presented in this article, we conclude that SRMR is not necessarily most sensitive to misspecified factor covariances (structural model misspecification), the group of indexes (TLI, BL89, RNI, CFI, Gamma hat, Mc, or RMSEA) are not necessarily more sensitive to misspecified factor loadings (measurement model misspecification), and the rationale for the 2-index presentation strategy appears to have questionable validity. 相似文献
13.
《Structural equation modeling》2013,20(2):233-255
Measurement invariance is usually tested using Multigroup Confirmatory Factor Analysis, which examines the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model. Although many studies have examined the properties of GFI as indicators of overall model fit for single-group data, there have been none to date that examine how GFIs change when between-group constraints are added to a measurement model. The lack of a consensus about what constitutes significant GFI differences places limits on measurement invariance testing. We examine 20 GFIs based on the minimum fit function. A simulation under the two-group situation was used to examine changes in the GFIs (ΔGFIs) when invariance constraints were added. Based on the results, we recommend using Δcomparative fit index, ΔGamma hat, and ΔMcDonald's Noncentrality Index to evaluate measurement invariance. These three ΔGFIs are independent of both model complexity and sample size, and are not correlated with the overall fit measures. We propose critical values of these ΔGFIs that indicate measurement invariance. 相似文献
14.
Measurement bias can be detected using structural equation modeling (SEM), by testing measurement invariance with multigroup factor analysis (Jöreskog, 1971;Meredith, 1993;Sörbom, 1974) MIMIC modeling (Muthén, 1989) or restricted factor analysis (Oort, 1992,1998). In educational research, data often have a nested, multilevel structure, for example when data are collected from children in classrooms. Multilevel structures might complicate measurement bias research. In 2-level data, the potentially “biasing trait” or “violator” can be a Level 1 variable (e.g., pupil sex), or a Level 2 variable (e.g., teacher sex). One can also test measurement invariance with respect to the clustering variable (e.g., classroom). This article provides a stepwise approach for the detection of measurement bias with respect to these 3 types of violators. This approach works from Level 1 upward, so the final model accounts for all bias and substantive findings at both levels. The 5 proposed steps are illustrated with data of teacher–child relationships. 相似文献
15.
Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed. 相似文献
16.
Factorial invariance assessment is central in the development of educational and psychological assessments. Establishing invariance of factor structures is key for building a strong score and inference validity argument and assists in establishing the fairness of score use. Fit indices and guidelines for judging a lack of invariance is an ongoing line of research. In this study, the authors examined the performance of the root mean squared error of approximation equivalence testing approach described by Yuan and Chan in the context of measurement invariance assessment. This investigation was completed through a simulation study in which several factors were varied, including sample size, type of invariance tested, and magnitude and percent of a lack of invariance. The findings generally support the use of equivalence testing for situations in which the indicator variables were normally distributed, particularly for total sample sizes of 200 or more. 相似文献
17.
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
18.
Measurement invariance of the five-factor Servant Leadership Questionnaire between female and male K-12 principals was tested using multi-group confirmatory factor analysis. A sample of 956 principals (56.9% were females and 43.1% were males) was analysed in this study. The hierarchical multi-step measurement invariance test supported the measurement invariance of the five-factor model across gender. Latent factor means were compared between females and males when measurement invariance was established. Results showed that females were significantly higher than males on emotional healing, wisdom, persuasive mapping and organisational stewardship, and they were not statistically different on altruistic calling. 相似文献
19.
《Studies in Educational Evaluation》2005,31(2-3):192-206
This article compares the invariance properties of two methods of psychometric instrument calibration for the development of a measure of wealth among families of Grade 5 pupils in five provinces in Vietnam. The measure is based on self-reported lists of possessions in the home. Its stability has been measured over two time periods. The concept of fundamental measurement, and the properties of construct and measurement invariance have been outlined. Item response modelling (IRM) and confirmatory factor modelling (CFM) as comparative methodologies, and the processes used for evaluating these, have been discussed. Each procedure was used to calibrate a 23-item instrument with data collected from a probability sample of Grade 5 pupils in a total of 60 schools. The two procedures were compared on the basis of their capacity to provide evidence of construct and measurement invariance, stability of parameter estimates, bias for or against sub samples, and the simplicity of the procedures and their interpretive powers. Both provided convincing evidence of construct invariance, but only the Rasch procedure was able to provide firm evidence of measurement invariance, parameter stability and a lack of bias across samples. 相似文献
20.
As a prerequisite for meaningful comparison of latent variables across multiple populations, measurement invariance or specifically factorial invariance has often been evaluated in social science research. Alongside with the changes in the model chi-square values, the comparative fit index (CFI; Bentler, 1990) is a widely used fit index for evaluating different stages of factorial invariance, including metric invariance (equal factor loadings), scalar invariance (equal intercepts), and strict invariance (equal unique factor variances). Although previous literature generally showed that the CFI performed well for single-group structural equation modeling analyses, its applicability to multiple group analyses such as factorial invariance studies has not been examined. In this study we argue that the commonly used default baseline model for the CFI might not be suitable for factorial invariance studies because (a) it is not nested within the scalar invariance model, and thus (b) the resulting CFI values might not be sensitive to the group differences in the measurement model. We therefore proposed a modified version of the CFI with an alternative (and less restrictive) baseline model that allows observed variables to be correlated. Monte Carlo simulation studies were conducted to evaluate the utility of this modified CFI across various conditions including varying degree of noninvariance and different factorial invariance models. Results showed that the modified CFI outperformed both the conventional CFI and the ΔCFI (Cheung & Rensvold, 2002) in terms of sensitivity to small and medium noninvariance. 相似文献