Bayesian regularization in multiple-indicators multiple-causes models.

Integrating regularization methods into structural equation modeling is gaining increasing popularity. The purpose of regularization is to improve variable selection, model estimation, and prediction accuracy. In this study, we aim to: (a) compare Bayesian regularization methods for exploring covariate effects in multiple-indicators multiple-causes models, (b) examine the sensitivity of results to hyperparameter settings of penalty priors, and (c) investigate prediction accuracy through cross-validation. The Bayesian regularization methods examined included: ridge, lasso, adaptive lasso, spike-and-slab prior (SSP) and its variants, and horseshoe and its variants. Sparse solutions were developed for the structural coefficient matrix that contained only a small portion of nonzero path coefficients characterizing the effects of selected covariates on the latent variable. Results from the simulation study showed that compared to diffuse priors, penalty priors were advantageous in handling small sample sizes and collinearity among covariates. Priors with only the global penalty (ridge and lasso) yielded higher model convergence rates and power, whereas priors with both the global and local penalties (horseshoe and SSP) provided more accurate parameter estimates for medium and large covariate effects. The horseshoe and SSP improved accuracy in predicting factor scores, while achieving more parsimonious models. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Children across cultures respond emotionally to the acoustic environment.

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.

Implementing a Standardized Effect Size in the POLYSIBTEST Procedure.

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.

The potential role of beef and nutrients found in beef on outcomes of wellbeing in healthy adults 50 years of age and older: A systematic review of randomized controlled trials.

Shifts in wellbeing and health occur as we age. As life expectancy increases, maintenance of wellbeing and health becomes increasingly important. Nutrients found in beef are associated with outcomes of wellbeing such as physical and cognitive function, lean body mass, and mood in older adults and individuals with chronic disease. However, it is unclear how beef and nutrients found in beef impact wellbeing in healthy adults ≥50 years of age. This study systematically reviewed evidence linking the intake of beef and nutrients found in beef to markers of wellbeing in healthy adults. PubMed, CINAHL, and Web of Science were searched up to August 31, 2021 for eligible randomized controlled trials (RCTs). Nutrients included in the analysis were beef, red meat, dietary protein, essential amino acids, branched chain amino acids, tryptophan, arginine, cysteine, glycine, glutamate, vitamin B6, vitamin B12, choline, zinc, and iron. We identified nine RCTs with results from 55 measurements of markers of wellbeing. An overall positive effect was found of beef and beef's nutrients on wellbeing. There was an overall positive effect of amino acids and protein on wellbeing, with no effect of arginine, vitamin B-12, leucine, and zinc. Physical function was also influenced by beef and nutrients found in beef. Eight of the studies found focused on specific nutrients found in beef, and not beef itself in older adults with one or more chronic diseases. This study identified a need for further research regarding the effect of beef and nutrients found in beef on defined functional outcomes of wellbeing in healthy adults ≥50 years of age.

Reevaluating the SIBTEST Classification Heuristics for Dichotomous Differential Item Functioning.

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.

Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures.

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.

Hierarchical Bayes Approach to Estimate the Treatment Effect for Randomized Controlled Trials.

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.

Learning how the electron transport chain works: independent and interactive effects of instructional strategies and learners' characteristics.

In order to develop an expert-like mental model of complex systems, causal reasoning is essential. This study examines the differences between forward and backward instructional strategies' in terms of efficiency, students' learning and progression of their mental models of the electronic transport chain in an undergraduate metabolism course (n = 151). Additionally, the participants' cognitive flexibility, prior knowledge, and mental effort in the learning process are also investigated. The data were analyzed using a series of general linear models to compare the strategies. Although the two strategies did not differ significantly in terms of mental model progression and learning outcomes, both groups' mental models progressed significantly. Mental effort and prior knowledge were identified as significant predictors of mental model progression. An interaction between instructional strategy and cognitive flexibility revealed that the backward instruction was more efficient than the conventional (forward) strategy for students with lower cognitive flexibility, whereas the conventional instruction was more efficient for students with higher cognitive flexibility. The results are discussed and suggestions for future research on the possible moderating role of cognitive flexibility in the area of health education are presented.

Mental model progression in learning the electron transport chain: effects of instructional strategies and cognitive flexibility.

This study investigated the effect of two instructional strategies, segmented and holistic, on the progression over time of learners' mental models toward that of an expert with the moderator of cognitive flexibility. Sixty-four juniors and seniors in a college metabolism course were randomly assigned to one of the two strategies for instruction on the electron transport chain. The data were analyzed with a repeated measures general linear model. Mental models progressed significantly for both strategies (p < .001), and a significant interaction was found between cognitive flexibility and instructional strategy on mental model progression (p = .02). The segmented strategy was superior for learners with higher cognitive flexibility but inferior to the holistic strategy for lower cognitive flexibility learners. Results have important implications for differentiating instruction on the basis of learner characteristics.