Caregiver Report of Children's Exposure to Adverse Life Events: Concordance Between Questionnaire and Interview Approaches.

Assessment practices for measuring adverse life events (ALEs) are often characterized by considerable variability, which is associated with inconsistency and reproducibility issues when conducting research on children with ALE exposure. One aspect of assessment variability for caregiver report of children's ALE history that has received minimal attention is assessment format. To address this issue, the current study evaluated concordance between two main ALE assessment formats: interviews and questionnaires. This involved examining overall endorsement of ALEs and concordance among multiple characteristics of ALE exposure, including type, polyvictimization, frequency, severity, and age of onset. Fifty-eight caregivers (Mage = 33.72; 60% Black; 55% below the federal poverty line) of preschool and school-age children were administered an ALE assessment in both a questionnaire and interview format across two sessions. The sum scores and concordance rates between format responses were compared based on ALE type, polyvictimization, frequency, severity, and age of onset of exposure. Results indicated that most total or sum scores were similar between formats, with the exception of ALE severity scores. However, there was most often low-to-moderate concordance across the 50 types of ALEs examined in the current study, suggesting that a different constellation of events comprised each sum or total score. This was also the case across all characteristics of the ALEs and most notably for the severity of ALE. Based on these findings, the format of assessment may be associated with inconsistent reporting of children's ALE exposure across multiple characteristics of ALE. Researchers may need to utilize multiple types of ALE assessments when relying on caregiver report of a child's ALEs.

Growth Modeling in a Diagnostic Classification Model (DCM) Framework-A Multivariate Longitudinal Diagnostic Classification Model.

A multivariate longitudinal DCM is developed that is the composite of two components, the log-linear cognitive diagnostic model (LCDM) as the measurement model component that evaluates the mastery status of attributes at each measurement occasion, and a generalized multivariate growth curve model that describes the growth of each attribute over time. The proposed model represents an improvement in the current longitudinal DCMs given its ability to incorporate both balanced and unbalanced data and to measure the growth of a single attribute directly without assuming that attributes grow in the same pattern. One simulation study was conducted to evaluate the proposed model in terms of the convergence rates, the accuracy of classification, and parameter recoveries under different combinations of four design factors: the sample size, the growth patterns, the G matrix design, and the number of measurement occasions. The results revealed the following: (1) In general, the proposed model provided good convergence rates under different conditions. (2) Regarding the classification accuracy, the proposed model achieved good recoveries on the probabilities of attribute mastery. However, the correct classification rates depended on the cut point that was used to classify individuals. For individuals who truly mastered the attributes, the correct classification rates increased as the measurement occasions increased; however, for individuals who truly did not master the attributes, the correct classification rates decreased slightly as the numbers of measurement occasions increased. Cohen's kappa increased as the number of measurement occasions increased. (3) Both the intercept and main effect parameters in the LCDM were recovered well. The interaction effect parameters had a relatively large bias under the condition with a small sample size and fewer measurement occasions; however, the recoveries were improved as the sample size and the number of measurement occasions increased. (4) Overall, the proposed model achieved acceptable recoveries on both the fixed and random effects in the generalized growth curve model.

Using Bayesian Nonparametric Item Response Function Estimation to Check Parametric Model Fit.

Previous studies indicated that the assumption of logistic form of parametric item response functions (IRFs) is violated often enough to be worth checking. Using nonparametric item response theory (IRT) estimation methods with the posterior predictive model checking method can obtain significance probabilities of fit statistics in a Bayesian framework by accounting for the uncertainty of the parameter estimation and can indicate the location and magnitude of misfit for an item. The purpose of this study is to check the performance of the Bayesian nonparametric method to assess the IRF fit of parametric IRT models for mixed-format tests and compare it with the existing bootstrapping nonparametric method under various conditions. The simulation study results show that the Bayesian nonparametric method can detect misfit items with higher power and lower type I error rates when the sample size is large and with lower type I error rates compared with the bootstrapping method for the conditions with nonmonotonic items. In the real-data study, several dichotomous and polytomous misfit items were identified and the location and magnitude of misfit were indicated.

How Well Do Simulation Studies Inform Decisions About Multistage Testing?

Most research on multistage testing (MST) uses simulated data. This study adds to the literature by using both operational test data and simulated data to compare two different MST designs with regard to proficiency estimation accuracy and module exposure rates and by investigating whether simulation studies and operational test studies yield similar results. Two MST designs (1-2 and 1-3-4 designs) from one state's sixth-grade summative mathematics assessment across two years were compared in this study. Both simulation and operational test studies demonstrate similar results: the two MST designs yield no significant performance differences with regard to estimation accuracy and module exposure. These results provide evidence that simulation studies can provide adequate results to inform decisions about MST designs.

Adaptive Testing With a Hierarchical Item Response Theory Model.

The hierarchical item response theory (H-IRT) model is very flexible and allows a general factor and subfactors within an overall structure of two or more levels. When an H-IRT model with a large number of dimensions is used for an adaptive test, the computational burden associated with interim scoring and selection of subsequent items is heavy. An alternative approach for any high-dimension adaptive test is to reduce dimensionality for interim scoring and item selection and then revert to full dimensionality for final score reporting, thereby significantly reducing the computational burden. This study compared the accuracy and efficiency of final scoring for multidimensional, local multidimensional, and unidimensional item selection and interim scoring methods, using both simulated and real item pools. The simulation study was conducted under 10 conditions (i.e., five test lengths and two H-IRT models) with a simulated sample of 10,000 students. The study with the real item pool was conducted using item parameters from an actual 45-item adaptive test with a simulated sample of 10,000 students. Results indicate that the theta estimations provided by the local multidimensional and unidimensional item selection and interim scoring methods were relatively as accurate as the theta estimation provided by the multidimensional item selection and interim scoring method, especially during the real item pool study. In addition, the multidimensional method required the longest computation time and the unidimensional method required the shortest computation time.

The association of folate and depression: A meta-analysis.

BACKGROUND: Previous research suggested that folate levels play an important role in the etiology and course of depression. However, the literature has been inconsistent with regard to differences in folate level between individuals with and without depression. The present meta-analysis synthesized the results of previous studies to examine whether individuals with depression had lower levels of folate than individuals without depression. METHODS: Meta-analytic procedures were conducted in accordance with PRISMA guidelines. Studies evaluating folate levels in individuals with and without depression via red blood cell folate, serum folate, or dietary intake of folate methods were identified via PsycINFO and PubMed. Random-effects meta-analysis was conducted using Hedge's g, and moderation analysis was used for both folate measurement method and population type. Study heterogeneity was assessed with I2 and publication bias was qualitatively assessed via funnel plot and quantitatively assessed with the trim-and-fill method and Begg's adjusted rank test. RESULTS: We found a significant, small effect size, such that individuals with depression had lower folate levels than those without depression, Hedge's g = -0.24 (95% CI = -0.31, -0.16), p < 0.001. Study heterogeneity was high (I2 = 84.88%), and neither folate measurement method nor population accounted for study heterogeneity. CONCLUSIONS: Individuals with depression have lower serum levels of folate and dietary folate intake than individuals without depression. Given that previous literature suggested folate supplementation improved the efficacy of traditional antidepressant medications, future research on folate supplementation in depression is warranted and clinicians may wish to consider folate supplementation for patients with depression.

The relationship among self-determination, self-concept, and academic achievement for students with learning disabilities.

Research suggests that self-determination skills are positively correlated with factors that have been shown to improve academic achievement, but the direct relationship among self-determination, self-concept, and academic achievement is not fully understood. This study offers an empirical explanation of how self-determination and self-concept affect academic achievement for adolescents with learning disabilities after taking into consideration the covariates of gender, income, and urbanicity. In a nationally representative sample (N = 560), the proposed model closely fit the data, with all proposed path coefficients being statistically significant. The results indicated that there were significant correlations among the three latent variables (i.e., self-determination, self-concept, and academic achievement), with self-determination being a potential predictor of academic achievement for students with learning disabilities.