Search

Starting from an appreciation of history, this article considers aspects on developing cognitive excellence in the context of core tensions and themes surrounding gifted education that continue to be relevant to present scholarship and society. We discuss some of the knowns and unknowns regarding cognitive talent development in the context of history, research methods, policy, and politics with a focus on the intersection between the fields of gifted education and cognitive aptitudes in the U.S. with novel considerations for advances in methodology.

A study was conducted to implement the use of a standardized effect size and corresponding classification guidelines for polytomous data with the POLYSIBTEST procedure and compare those guidelines with prior recommendations. Two simulation studies were included. The first identifies new unstandardized test heuristics for classifying moderate and large differential item functioning (DIF) for polytomous response data with three to seven response options. These are provided for researchers studying polytomous data using POLYSIBTEST software that has been published previously. The second simulation study provides one pair of standardized effect size heuristics that can be employed with items having any number of response options and compares true-positive and false-positive rates for the standardized effect size proposed by Weese with one proposed by Zwick et al. and two unstandardized classification procedures (Gierl; Golia). All four procedures retained false-positive rates generally below the level of significance at both moderate and large DIF levels. However, Weese's standardized effect size was not affected by sample size and provided slightly higher true-positive rates than the Zwick et al. and Golia's recommendations, while flagging substantially fewer items that might be characterized as having negligible DIF when compared with Gierl's suggested criterion. The proposed effect size allows for easier use and interpretation by practitioners as it can be applied to items with any number of response options and is interpreted as a difference in standard deviation units.

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.

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.

A simulation study was conducted to investigate the heuristics of the SIBTEST procedure and how it compares with ETS classification guidelines used with the Mantel-Haenszel procedure. Prior heuristics have been used for nearly 25 years, but they are based on a simulation study that was restricted due to computer limitations and that modeled item parameters from estimates of ACT and ASVAB tests from 1987 and 1984, respectively. Further, suggested heuristics for data fitting a two-parameter logistic model (2PL) have essentially went unused since their original presentation. This simulation study incorporates a wide range of data conditions to recommend heuristics for both 2PL and three-parameter logistic (3PL) data that correspond with ETS's Mantel-Haenszel heuristics. Levels of agreement between the new SIBTEST heuristics and Mantel-Haenszel heuristics were similar for 2PL data and higher than prior SIBTEST heuristics for 3PL data. The new recommendations provide higher true-positive rates for 2PL data. Conversely, they displayed decreased true-positive rates for 3PL data. False-positive rates, overall, remained below the level of significance for the new heuristics. Unequal group sizes resulted in slightly larger false-positive rates than balanced designs for both prior and new SIBTEST heuristics, with rates less than alpha levels for equal ability distributions and unbalanced designs versus false-positive rates slightly higher than alpha with unequal ability distributions and unbalanced designs.

Using all Arkansas correctional releases from 2005-2016 of adults who at intake lacked high school diplomas or GEDs, the authors examined how correctional education and its moderating factors affect recidivism (N = 32,174) and post-release employment (N = 40,667). Completion of GED and vocational programs were associated with financially viable reductions in three-year recidivism rates. Quarterly post-release earnings were higher for GED completers than non-completers. Effects were strengthened when GED programs were combined with auxiliary educational programs and completed by more serious and habitual inmates, as well as by Black and older inmates. Stakeholders may use these findings to further substantiate correctional education's effects and construct programming that targets those inmates and programs with which correctional education works best.

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.

One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance hyperparameters for estimating the treatment effect in a two-group randomized controlled trial. Assuming a random sample of treatment effect sizes is obtained from past studies, the hyper priors can be constructed based on the sampling distributions of the effect size mean and precision. The performance of the hierarchical Bayes approach was compared with the empirical Bayes approach (hyperparameters are fixed values or point estimates) and the ordinary least squares (OLS) method via simulation. The design factors for data generation included the sample treatment effect size, treatment/control group size ratio, and sample size. Each generated data set was analyzed using the hierarchical Bayes approach with three hyper priors, the empirical Bayes approach with twelve priors (including correct and inaccurate priors), and the OLS method. Results indicated that the proposed hierarchical Bayes approach generally outperformed the empirical Bayes approach and the OLS method, especially with small samples. When more sample effect sizes were available, the treatment effect was estimated more accurately regardless of the sample sizes. Practical implications and future research directions are discussed.

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both variable selection and model estimation. The prior sensitivity in BSEM-N was explored in factor analysis models with sparse loading structures through a simulation study (Study 1) and an empirical example (Study 2). Study 1 examined the prior sensitivity in BSEM-N based on the model fit, population model recovery, true and false positive rates, and parameter estimation. Seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters were examined. Study 2 provided a real data example to illustrate the impact of various priors on model fit and parameter selection and estimation. Results indicated that when the 95% credible intervals of shrinkage priors barely covered the population cross-loading values, it resulted in the best balance between true and false positives. If the goal is to perform variable selection, a sparse cross-loading structure is required, preferably with a minimal number of nontrivial cross-loadings and relatively high primary loading values. To improve parameter estimates, a relatively large prior variance is preferred. When cross-loadings are relatively large, BSEM-N with zero-mean priors is not recommended for the estimation of cross-loadings and factor correlations.

The demand for diagnostic feedback has triggered extensive research on cognitive diagnostic models (CDMs), such as the deterministic input, noisy output "and" gate (DINA) model. This study explored two Q-matrix specifications with the DINA model in a statewide large-scale mathematics assessment. The first Q-matrix was developed based on five predefined content reporting categories, and the second was based on the post hoc coding of 15 attributes by test-development experts. Total raw scores correlated strongly with the number of skills mastered, using both Q-matrices. Correlations between the DINA-model item statistics and those from the item response theory analyses were moderate to strong, but were always lower for the 15-skill model. Results highlighted the trade-off between finer-grained modeling and less precise model estimation.

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.

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]

International students pursuing graduate education in U.S. institutes have been rapidly increasing in recent years. Students from all over the world remarkably contribute to the advancement of U.S. economy and technology. This article addresses the challenges and opportunities international students face during and after graduate education. The challenges include overcoming the barriers with regard to language, culture, and employment. Along with these challenges are numerous opportunities such as introducing a different culture to U.S. college campuses, facilitating long-term academic and business collaboration, and obtaining enriched working experiences in the global job market. Strategies and recommendations to attain academic and occupational success are also discussed.

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]

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.

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]

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]

Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model evaluation tool, which allows researchers to detect Q‐matrix misspecification. However, model selection methods using posterior predictive checking (PPC) for Bayesian DCM are not well investigated. Thus, this research aims to propose a novel model selection approach using posterior predictive checking with limited‐information statistics for selecting the correct Q‐matrix. A simulation study was conducted to examine the performance of the proposed method. Furthermore, an empirical example was provided to illustrate how it can be used in real scenarios. [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]