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Cross-loadings are common in multiple-factor confirmatory factor analysis (CFA) but often ignored in measurement invariance testing. This study examined the impact of ignoring cross-loadings on the sensitivity of fit measures (CFI, RMSEA, SRMR, SRMRu, AIC, BIC, SaBIC, LRT) to measurement noninvariance. The manipulated design factors included the magnitude and percentage of cross-loadings, the magnitude and percentage of noninvariance, location of measurement noninvariance, model size, and sample size. Results suggested that the ignored cross-loadings affected the sensitivity of all fit measures but LRT to metric noninvariance to varying degrees, whereas they did not affect the sensitivity of fit measures to scalar noninvariance except for RMSEA. RMSEA was impacted by the magnitude of cross-loadings in both metric and scalar invariance testing. In the largest model size, CFI failed to detect metric noninvariance when there were no cross-loadings in the population model but detected the metric noninvariance of.30 with ignored cross-loadings. [ABSTRACT FROM AUTHOR]

To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse cross-loading structures, focusing on ordered categorical data. A sensitivity analysis was conducted by assigning eight choices of small-variance priors on all potential cross-loadings. Results indicated that variable selection was performed well in a sparse loading structure in which the number of essential cross-loadings was small and the magnitudes were relatively large. Characteristics of ordinal items did not seem to have a sizable impact on parameter estimation. If the number of cross-loading estimates were small and centered around zero, BSEM-N may serve more efficiently as a tool for parameter estimation. [ABSTRACT FROM AUTHOR]

Among human and non-human animals, the ability to respond rapidly to biologically significant events in the environment is essential for survival and development. Research has confirmed that human adult listeners respond emotionally to environmental sounds by relying on the same acoustic cues that signal emotionality in speech prosody and music. However, it is unknown whether young children also respond emotionally to environmental sounds. Here, we report that changes in pitch, rate (i.e. playback speed), and intensity (i.e. amplitude) of environmental sounds trigger emotional responses in 3- to 6-year-old American and Chinese children, including four sound types: sounds of human actions, animal calls, machinery, and natural phenomena such as wind and waves. Children's responses did not differ across the four types of sounds used but developed with age – a finding observed in both American and Chinese children. Thus, the ability to respond emotionally to non-linguistic, non-music environmental sounds is evident at three years of age – an age when the ability to decode emotional prosody in language and music emerges. We argue that general mechanisms that support emotional prosody decoding are engaged by all sounds, as reflected in emotional responses to non-linguistic acoustic input such as music and environmental sounds. [ABSTRACT FROM AUTHOR]

The present study examined the mediating role of text reading fluency in reading comprehension in English and Kiswahili utilizing longitudinal data from 628 first- and second-grade children from multilingual contexts in Kenya. We employed path analysis to explore the mediating role of text reading fluency on decoding and reading comprehension in English and Kiswahili. The results demonstrated that decoding and text reading fluency bridges the gap between word recognition skills and reading comprehension in English and Kiswahili. Over time, as children improve their reading skills, the direct influence of decoding on reading comprehension diminishes; hence, text reading fluency explains the unique variance in reading comprehension. The present findings highlight the importance of text reading fluency in the developmental phases of reading comprehension among multilingual children learning to read across multiple languages. [ABSTRACT FROM AUTHOR]

Model size has been shown to influence model fit measures in structural equation modeling, whereas its impact on measurement invariance testing using recommended cutoff criteria of fit measures is not clear. This study examined the impact of model size on the sensitivity of fit measures (CFI, RMSEA, AIC, BIC, SaBIC, LRT) to measurement noninvariance. Results suggested that CFI and RMSEA failed to detect metric noninvariance when model size was large (e.g., 6 factors with 8 items per factor). AIC and LRT were minimally affected by model size. Recommendations for empirical researchers were provided based on the findings of this simulation study. [ABSTRACT FROM AUTHOR]

Exploratory structural equation modeling (ESEM) allows for the estimation of all cross-loadings, which leads to the number of parameters estimated substantially greater than that in conventional SEM. This study examined the sensitivity of fit measures (CFI, RMSEA, AIC, BIC, SaBIC, LRT) to measurement noninvariance in ESEM. Results suggested that the sensitivity of the fit measures depended on the magnitude of noninvariance, model complexity, and sample size. The sensitivity of LRT to both metric and scalar noninvariance was high. Comparative fit index tended to filter out small noninvariance with large sample sizes. Root mean squared error of approximation failed to detect even a large measurement noninvariance when the model was complex and sample size was below 300 per group. Information criteria were more sensitive to scalar noninvariance than to metric noninvariance. Implications and recommendations based on the findings were discussed. [ABSTRACT FROM AUTHOR]

The property of item parameter invariance in item response theory (IRT) plays a pivotal role in the applications of IRT such as test equating. The scope of parameter invariance when using estimates from finite biased samples in the applications of IRT does not appear to be clearly documented in the IRT literature. This article provides information on the extent to which item parameter invariance is observed in samples with the Rasch and 2-parameter model calibrations through simulations, where the behaviors of item parameter estimates were examined under 12 different types of convenient sampling scenarios. The results indicated that the property of item invariance in IRT for dichotomously scored data could hold for the sample item parameter estimates, regardless of biased samples, when the model holds in the data, the number of items in a test is not small, and the sample size is large. [ABSTRACT FROM AUTHOR]

Recent developments allow for incorporating exploratory features into structural equation models (SEM). Two approaches, exploratory SEM (ESEM) and Bayesian SEM (BSEM), have been shown flexible of estimating complex SEM. This simulation study compared the performance of ESEM and BSEM for estimating structural regression models with ordinal indicators where cross-loadings were present in selected factors. Data were generated under conditions including various categorical data distributions, similarity of categorical distributions across indicators, and sample sizes. ESEM with Geomin rotation and BSEM with four small-variance normal priors on cross-loadings were used. Results indicated that ESEM may be prioritized over BSEM when sample sizes were large, distributions of ordinal indicators were symmetric or moderately asymmetric, and cross-loadings were non-ignorable. When sample sizes were relatively small, we recommend using one approach to complement the other. For BSEM, a sensitivity test is recommended to evaluate the impact of various prior choices on the estimation outcomes. [ABSTRACT FROM AUTHOR]

Correct detection of measurement bias could help researchers revise models or refine psychological scales. Measurement bias detection can be viewed as a variable-selection problem, in which biased items are optimally selected from a set of items. This study investigated a number of regularization methods: ridge, lasso, elastic net (enet) and adaptive lasso (alasso), in comparison with maximum likelihood estimation (MLE) for detecting various forms of measurement bias in regard to a continuous violator using restricted factor analysis. Particularly, complex structural equation models with relatively small sample sizes were the study focus. Through a simulation study and an empirical example, results indicated that the enet outperformed other methods in small samples for identifying biased items. The alasso yielded low false positive rates for non-biased items outside of a high number of biased items. MLE performed well for the overall estimation of biased items. [ABSTRACT FROM AUTHOR]

This study compared the performance of (1) model selection methods based on a point estimate (MSM-P), (2) partially Bayesian methods, and (3) fully Bayesian methods, for testing factorial invariance. The MSM-P considered included the likelihood ratio test (LRT) and a number of information criteria (IC). The Bayesian methods tested included the deviance information criterion (DIC), widely available information criterion (WAIC), and leave-one-out cross-validation (LOO). We investigated true positive and false positive rates of each model selection method under conditions varying in sample size, proportion of non-invariant items, and pattern, location and magnitude of non-invariance. The impact of various prior choices on Bayesian model selection results was also examined. Results showed that DIC, WAIC, and LOO performed comparably well, had a good balance between true and false positive rates, were flexible given the prior choices, and may complement MSM-P in factorial invariance testing. Implications and recommendations based on the findings were discussed. [ABSTRACT FROM AUTHOR]

Current methods that simultaneously model differential testlet functioning (DTLF) and differential item functioning (DIF) constrain the variances of latent ability and testlet effects to be equal between the focal and the reference groups. Such a constraint can be stringent and unrealistic with real data. In this study, we propose a multigroup one-parameter testlet model (M1PLTM) that relaxes this equal variance assumption and also allows simultaneous modeling of DTLF and DIF. M1PLTM is a more generalized version of the extant models. The results of a simulation study show that parameters of M1PLTM could be accurately recovered via a Markov chain Monte Carlo (MCMC) method; when data were generated based on M1PLTM, falsely assuming equal variances of latent ability and testlet effects resulted in inferior parameter recovery. [ABSTRACT FROM AUTHOR]

To infer longitudinal relationships among latent factors, traditional analyses assume that the measurement model is invariant across measurement occasions. Alternative to placing cross-occasion equality constraints on parameters, approximate measurement invariance (MI) can be analyzed by specifying informative priors on parameter differences between occasions. This study evaluated the estimation of structural coefficients in multiple-indicator autoregressive cross-lagged models under various conditions of approximate MI using Bayesian structural equation modeling. Design factors included factor structures, conditions of non-invariance, sizes of structural coefficients, and sample sizes. Models were analyzed using two sets of small-variance priors on select model parameters. Results showed that autoregressive coefficient estimates were more accurate for the mixed pattern than the decreasing pattern of non-invariance. When a model included cross-loadings, an interaction was found between the cross-lagged estimates and the non-invariance conditions. Implications of findings and future research directions are discussed. [ABSTRACT FROM AUTHOR]

This study addressed the argument that online courses rarely use discussion strategies that are specifically designed and constructed for soliciting learners' cognitive presence and higher-order thinking. Prompted by this argument, the authors conducted a meta-analysis of the empirical studies that examined the effectiveness of discussion strategies in online learning. More specifically, the meta-analysis was to reveal whether learners perform better in a strategic discussion session than when they engage in a conventional online discussion and under what conditions the learners' differences in performance are greater or smaller. Furthermore, the investigation examined the extent to which the online discussion strategies that included instructional elements and the pedagogical recommended by the literature make a difference in learners' performance. [ABSTRACT FROM AUTHOR]