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Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model. [ABSTRACT FROM AUTHOR]

Low test-taking effort of examinees has been considered a source of construct-irrelevant variance in item response modeling, leading to serious consequences on parameter estimation. This study aims to investigate how non-effortful response (NER) influences the estimation of item and person parameters in item-pool scale linking (IPSL) and whether differential NER for two groups has a different effect on parameter estimation under the equivalent and non-equivalent group designs. Three simulation studies and an empirical study were conducted to answer these research questions. Results show that NER on items in a certain item set (e.g. common new items, exclusive new items) have a dominant negative effect on parameter estimation of items from the same item set, while NER on items from the item pool would seriously impact the accuracy of ability parameters. Discussions and recommendations for item set orders are provided. [ABSTRACT FROM AUTHOR]

Railway workers are more likely to have an irregular work schedule, which had an effect on their circadian rhythm of sleep, and may lead to circadian rhythm sleep-wake disorders (CRSWDs). The association between CRSWDs and dyslipidemia in railway workers is poorly understood. The objective of this research is to study the association between CRSWDs and the risk of dyslipidemia. This cross-sectional study was conducted among railway workers in Southwest China. CRSWDs were assessed by the morningness-eveningness questionnaire self-assessment version (MEQ-SA). The blood samples were collected in the morning and the lipids of participants were measured. Associations of CRSWDs with dyslipidemia and its components were analyzed. A total of 8079 participants were enrolled in this study, and the results revealed that shift work sleep disorder (SWD) and advanced sleep-wake phase disorder (ASWPD) were associated with a higher risk of dyslipidemia (OR 1.17, 95%CI 1.06–1.29, P < 0.01; OR 1.68, 95%CI 1.09–2.64, P < 0.05) after adjusting for sociodemographic characteristics and lifestyles, in comparison with the control group. As for its components, the SWD group was associated with a higher risk of elevated total cholesterol, triglycerides, and low-density lipoprotein than the control group, while the ASWPD group was associated with a higher risk of elevated total cholesterol, and low-density lipoprotein (P < 0.05). In summary, SWD and ASWPD participants were associated with a higher risk of dyslipidemia in railway workers in Southwest China. Abbreviation: TG: triglyceride; TC: Total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; FPG: fasting plasma glucose. MEQ-SA: morningness-eveningness questionnaire self-assessment version; IPW: inverse-probability weighting; HDS: healthy diet scores; FFQ: food frequency; PA: physical activity; IQAP-SF: international physical activity questionnaire short form; MET-min/wk: metabolic equivalent task minutes per week; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HBP: hypertension; DM: diabetes; CVD: cerebrovascular disease; OR: odds ratios; CI: confidence intervals. [ABSTRACT FROM AUTHOR]

Students' response sequences to a technology-based problem-solving task can be treated as a discrete time stochastic process with a conditional Markov property—after conditioning on the students' abilities of problem solving, the next state only depends on the current state. This article proposes a sequential response model (SRM) with a Bayesian approach for parameter estimation that incorporates comprehensive information from the response process to infer problem-solving ability more effectively. A Monte Carlo simulation study showed that parameters were well-recovered. An illustrated example is provided to showcase additional gains using our model for understanding the response process with a real-world interactive assessment item "Tickets" in the programme for international student assessment (PISA) 2012. [ABSTRACT FROM AUTHOR]

We propose a new method, the Monte Carlo confidence interval (MCCI) method, for studying measurement invariance. This method allows researchers to examine the invariance of all items simultaneously by comparing the observed between-group differences in parameters with those obtained under the null hypothesis of invariance. We compare the performance of our method to two other methods: the multigroup confirmatory factor analysis (MG-CFA) and the forward method using confidence intervals (forward CI). The results show that our method performed better than the MG-CFA method and comparably with the forward CI method. The code to implement our method and an empirical example are provided in the . [ABSTRACT FROM AUTHOR]

The primary goals of this study are to investigate if cognitive activation increases mathematics self-efficacy, which in turn alleviates anxiety and to examine the moderating role of internal migrant status. Hierarchical linear models and multilevel structural equation models were employed to datasets of 3088 fourth graders from 57 classrooms in China. The moderated effect and the moderated mediation effect of cognitive activation at both the student and classroom levels were tested. Results show that: (a) cognitive activation was positively associated with mathematics self-efficacy and negatively associated with mathematics anxiety; (b) the association of cognitive activation with mathematics self-efficacy and anxiety was stronger in classrooms with more internal migrant children and (c) mathematics self-efficacy mediated the relationship between cognitive activation and mathematics anxiety. [ABSTRACT FROM AUTHOR]

With single-level data, Yuan, Cheng and Maxwell developed a two-level regression model for more accurate moderation analysis. This article extends the two-level regression model to a two-level moderated latent variable (2MLV) model, and uses a Bayesian approach to estimate and test the moderation effects. Monte Carlo results indicate that: 1) the new method yields more accurate estimate of the interaction effect than those via the product-indicator (PI) approach and latent variable interaction (LVI) with single-level model, both are also estimated via Bayesian method; 2) the coverage rates of the credibility interval following the 2MLV model are closer to the nominal 95% than those following the other methods; 3) the test for the existence of the moderation effect is more reliable in controlling Type I errors than both PI and LVI, especially under heteroscedasticity conditions. Moreover, a more interpretable measure of effect size is developed based on the 2MLV model, which directly answers the question as to what extent a moderator can account for the change of the coefficient between the predictor and the outcome variable. A real data example illustrates the application of the new method. [ABSTRACT FROM AUTHOR]

In longitudinal studies, missing data are ubiquitous. This article made a comparison of three model-based techniques for handling different types of missing data (i.e., missing at random (MAR)-based maximum likelihood (ML) approach, missing not at random (MNAR)-based Diggle–Kenward (DK) selection model and MNAR-based pattern mixture (PM) model) in longitudinal studies through a Monte Carlo simulation study. Two influential factors were considered: the dropout rates (5%, 10%, 20%, and 40%) and the sample sizes (100, 300, 500, and 1000) under MAR and MNAR missingness mechanisms respectively. The results indicated that the model selection was a crucial issue when researchers were dealing with missing data in longitudinal studies because under MNAR mechanism, DK method outperformed MAR-based ML approach, but PM method performed worse than MAR-based method did. The differences of the parameter estimation among three methods became more significant as the sample size and the dropout rate increased. [ABSTRACT FROM AUTHOR]

Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance. [ABSTRACT FROM AUTHOR]

AbstractResearch has indicated that mindfulness is a protective factor against suicidal ideation. However, the dynamic reciprocal relation between them has been understudied. In this study, 110 female college students with suicidal ideation completed a measure of trait mindfulness and a 28-day diary of suicidal ideation and three-dimensional state mindfulness, including acting with awareness, present-moment attention, and nonjudgmental acceptance. Dynamic structural equation modeling was used to examine the dynamic and bidirectional effects between dimensions of state mindfulness and suicidal ideation and the moderating effect of trait mindfulness. Results showed that suicidal ideation predicted lower levels of present-moment attention the next day. More importantly, there was a dynamic and bidirectional relation between nonjudgmental acceptance and suicidal ideation for people with trait mindfulness higher than the average level (i.e., M + 0.15SD). Our findings suggested that studies and interventions on suicide should pay more attention to specific dimensions of state mindfulness and trait mindfulness. [ABSTRACT FROM AUTHOR]

Objectives:The improved passive immune thrombocytopenia (ITP) mouse model has been extensively utilized for the study of ITP. However, how closely this model matches the human inflammation state and immune background is unclear. Our study aimed to explore the profile of Th cytokines and Th17/Treg cells in the model. Methods:We induced the ITP mouse model by dose-escalation injection of MWReg30. The serum levels of cytokines (IFN-γ, IL-2, IL-4, IL-10, IL-17A, and TGF-β1) were measured by enzyme-linked immunosorbent assay and the frequency of Th17 and Treg cells was measured by flow cytometry. The mRNA expression of Foxp3 and RORrt was measured by real-time PCR. Results:The serum levels of cytokines IFN-γ, TGF-β1, IL-4, and IL-10 were significantly lower in ITP mice. The secretion of serum proinflammatory cytokines IL-2 and IL-17A and the percentage of Th17 cells showed no statistically significant increase. In ITP mice the frequency of Treg cells and mRNA expression of Foxp3 was significantly lower in splenocytes. Conclusion:Our data suggest that the improved passive ITP mouse model does not mimic the autoimmune inflammatory process of human ITP. Compared with human ITP, this model has a similar change in frequency of Treg cells, which may directly or indirectly result from antibody-mediated platelet destruction due to attenuated release of TGF-β. [ABSTRACT FROM PUBLISHER]

This article concentrates on the multi-level random mediation effects model (1-1-1) and reviews the maximum likelihood (ML), restricted maximum likelihood (REML), and minimum variance quadratic unbiased estimation (MIVQUE) estimation methods provided by the SAS MIXED process. This paper uses Monte Carlo simulation to make a comparison of the performance of these estimators under a wide variety of different conditions. First, REML and ML produced equivalent results and both of them outperformed MIVQUE, no matter whether the normality assumption was satisfied. Second, the results indicated that the distribution of thedoes not influence the mediation effect. The deviation of the normal distribution ofor ‘and’ affected the mediation effect, particularly in condition that not only the magnitude of the deviation but also the covariance between these two effects were large. This thesis ends with the implications, suggestions and recommendations for the application. [ABSTRACT FROM AUTHOR]

The present study investigated the relative importance of executive functioning, parent–child verbal interactions, phonological awareness and visual skills on reading and mathematics for Chinese children from low-versus middle-socio economic status (SES) backgrounds. A total of 199 kindergarten children were assessed on executive functioning, verbal interactions, phonological awareness, visual skills, mathematics and word reading in Chinese and English. Results revealed that low-SES children exhibited lower levels of cognitive-linguistic skills, verbal interactions, reading and mathematics achievement than their middle-SES counterparts. Path analyses also indicated that executive functioning and verbal interactions made significant and direct contributions to mathematics, and indirect contributions to reading through phonological awareness. These results suggest that executive functioning and verbal interactions provide the foundation for phonological awareness and visual skills, which in turn affect reading and mathematics achievement. Overall, findings underscore the potential importance of SES inequalities, cognitive-linguistic skills and parental verbal input to their children for early reading and mathematics achievement. [ABSTRACT FROM AUTHOR]

When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation. [ABSTRACT FROM PUBLISHER]

The purpose of this investigation is to compare and evaluate 2 approaches for estimating interaction effects in latent growth models (LGMs): the unconstrained approach and the latent moderated structural equations (LMS) approach. To reduce the complexity of modeling interactions in LGMs, we created difference-product indicators to replace the traditional product indicators used in the unconstrained approach because these differences in original indicators represent changes over time. Our focus was to verify the performance of this simplified interaction model of LGMs with difference-product indicators by using the unconstrained approach and comparing it with the LMS approach. Our simulation study showed that the LMS approach generally resulted in smaller biases in the estimated parameters and was consistently more precise than the unconstrained approach under normal conditions, particularly when the sample size was small. When normality assumptions were violated, however, the unconstrained approach was shown to be more robust than the LMS approach in terms of the bias of estimation. In summary, we generally recommend both the unconstrained approach and the LMS approach if the indicators are normally distributed and when the sample size is large enough (e.g., not less than 500). Under normal conditions, the LMS approach is preferred. If normality assumptions are violated, however, the unconstrained approach is recommended. Under the most stringent conditions when normality assumptions are severely violated and the sample size is small, the results from both the unconstrained approach and the LMS approach should be treated with caution, and alternative procedures might be considered. [ABSTRACT FROM PUBLISHER]

Natural polymer polysaccharides chitosan (CS) was successfully assembled with phytic acid (PA) into {PA/CS}n layer-by-layer films. Myoglobin (Mb) could be gradually “absorbed” or loaded into {PA/CS}n films when the films were immersed into Mb solutions, forming {PA/CS}n-Mb films. The {PA/CS}n-Mb films demonstrated well-defined and quasi-reversible cyclic voltammetry (CV) responses for Mb FeIII/FeII redox couple and were used to catalyze electrochemical reduction of oxygen and hydrogen peroxide. The interaction between Mb and {PA/CS}n films was explored and discussed, which suggested that the electrostatic attraction might play a major role in loading Mb into the films. This new kind of film incorporated with redox proteins could be used to fabricate the new type of biosensors or bioreactors without using mediators. [ABSTRACT FROM AUTHOR]

To study tolerance of rice (Oryza sativa L.) to excessive copper (Cu), 167 varieties were screened for Cu tolerance or sensitivity. Based on the elongation of the root, No. 1139 and No. 1195 were chosen as Cu-tolerant and Cu-sensitive variety. Compared with the control (0.32 μ mol L- 1 Cu), treatments with 5 and 8 μ mol L- 1 Cu for 10 days had no significant effect on the root and shoot dry masses of Cu-tolerant variety No. 1139, but significantly decreased root and shoot dry masses of variety No. 1195. Copper at 5 and 8 μ mol L- 1 also significantly decreased contents of chlorophyll and carotenoid and the ratio of variable to maximal chlorophyll fluorescence (Fv/Fm) in the leaves of the Cu-sensitive variety No. 1195. Roots and shoots of variety No. 1139 contained significantly higher the concentration of Cu than variety No. 1195 exposed to 5 and 8 μ mol L- 1 Cu. However, variety No. 1139 had higher Cu proportions on the cell walls in shoots and roots than variety No. 1195. [ABSTRACT FROM AUTHOR]