MOCCA-College: Preliminary Validity Evidence of a Cognitive Diagnostic Reading Comprehension Assessment.

As access to higher education increases, it is important to monitor students with special needs to facilitate the provision of appropriate resources and support. Although metrics such as the "reading readiness" ACT (formerly American College Testing) of provide insight into how many students may need such resources, they do not specify why a student may need support or how to provide that support. Increasingly, students are bringing reading comprehension struggles to college. Multiple-choice Online Causal Comprehension Assessment-College (MOCCA-College) is a new diagnostic reading comprehension assessment designed to identify who is a poor comprehender and also diagnose why they are a poor comprehender. Using reliability coefficients, receiver-operating characteristic curve analysis, and correlations, this study reports findings from the first year of a 3-year study to validate the assessment with 988 postsecondary students who took MOCCA-College, a subset of whom also provided data on other reading assessments (i.e., ACT, n = 377; Scholastic Aptitude Test [SAT], n = 192; and Nelson-Denny Reading Test [NDRT], n = 78). Despite some limitations (e.g., the sample is predominantly females from 4-year institutions), results indicate that MOCCA-College has good internal reliability, and scores are correlated with other reading assessments. Through a series of analyses of variance (ANOVAs), we also report how students identified by MOCCA-College as good and poor comprehenders differ in terms of demographics, cognitive processes used while reading, overall comprehension ability, and scores on admissions tests. Findings are discussed in terms of using MOCCA-College to help gauge which students may be at risk of reading comprehension difficulties, identify why they may be struggling, and inform directions in actionable instructional changes based on comprehension processing data.

Regression with reduced rank predictor matrices: A model of trade-offs.

A regression model of predictor trade-offs is described. Each regression parameter equals the expected change in Y obtained by trading 1 point from one predictor to a second predictor. The model applies to predictor variables that sum to a constant T for all observations; for example, proportions summing to T = 1.0 or percentages summing to T = 100 for each observation. If predictor variables sum to a constant T for all observations and if a least squares solution exists, the predicted values for the criterion variable Y will be uniquely determined, but there will be an infinite set of linear regression weights and the familiar interpretation of regression weights does not apply. However, the regression weights are determined up to an additive constant and thus differences in regression weights ßv-ßv∗ are uniquely determined, readily estimable, and interpretable. ßv-ßv∗ is the expected increase in Y given a transfer of 1 point from variable v∗ to variable v. The model is applied to multiple-choice test items that have four response categories, one correct and three incorrect. Results indicate that the expected outcome depends, not just on the student's number of correct answers, but also on how the student's incorrect responses are distributed over the three incorrect response types. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Materials Matter: An Exploration of Text Complexity and Its Effects on Middle School Readers' Comprehension Processing.

Purpose Complex features of science texts present idiosyncratic challenges for middle grade readers, especially in a post-Common Core educational world where students' learning is dependent on understanding informational text. The primary aim of this study was to explore how middle school readers process science texts and whether such comprehension processes differed due to features of complexity in two science texts. Method Thirty 7th grade students read two science texts with different profiles of text complexity in a think-aloud task. Think-aloud protocols were coded for six comprehension processes: connecting inferences, elaborative inferences, evaluative comments, metacognitive comments, and associations. We analyzed the quantity and type of comprehension processes generated across both texts in order to explore how features of text complexity contributed to the comprehension processes students produced while reading. Results Students made significantly more elaborative and connecting inferences when reading a text with deep cohesion, simple syntax, and concrete words, while students made more evaluative comments, paraphrases, and metacognitive comments when reading a text with referential cohesion, complex syntax, and abstract words. Conclusions The current study provides exploratory evidence for features of text complexity affecting the type of comprehension processes middle school readers generate while reading science texts. Accordingly, science classroom texts and materials can be evaluated for word, sentence, and passage features of text complexity in order to encourage deep level comprehension of middle school readers.

Effects of Crushed Ticagrelor Versus Eptifibatide Bolus Plus Clopidogrel in Troponin-Negative Acute Coronary Syndrome Patients Undergoing Percutaneous Coronary Intervention: A Randomized Clinical Trial.

Background After a loading dose of ticagrelor, the rate of high on-treatment platelet reactivity remains elevated, which increases periprocedural myocardial infarction and injury. This indicates that faster platelet inhibition with crushed ticagrelor (CTIC) or eptifibatide is needed to reduce high on-treatment platelet reactivity. The efficacy of CTIC versus eptifibatide bolus plus clopidogrel is unknown. Methods and Results A total of 100 P2Y12 naïve, troponin-negative patients with acute coronary syndrome were randomized to CTIC (180 mg) versus eptifibatide bolus (180 µg/kg×2 intravenous boluses) plus clopidogrel (600 mg) at the time of percutaneous coronary intervention. High on-treatment platelet reactivity was markedly higher with CTIC versus eptifibatide bolus plus clopidogrel (42% versus 0%; P<0.001) at 30 minutes and persisted up to 2 hours (12% versus 0%; P=0.01, respectively). Platelet aggregation by adenosine diphosphate dropped faster from baseline with eptifibatide bolus plus clopidogrel versus CTIC (0.5 versus 2 hours, respectively) and was higher with CTIC versus eptifibatide bolus plus clopidogrel at 0.5, 2, and 4 hours after loading dose (53±12% versus 1.3±2%; 35±11% versus 0.34±1.0%; and 23±9% versus 3.5±2%, respectively; P<0.001). Eptifibatide bolus plus clopidogrel, but not CTIC, significantly inhibited platelet aggregation induced by thrombin-receptor activating peptide. Periprocedural myocardial infarction and injury was higher with CTIC versus eptifibatide bolus plus clopidogrel (48% versus 28%, respectively; P=0.035). Post-percutaneous coronary intervention hemoglobin levels were not different between groups. Conclusions Eptifibatide bolus plus clopidogrel led to faster and more potent platelet inhibition than CTIC and reduced periprocedural myocardial infarction and injury in troponin-negative acute coronary syndrome patients undergoing percutaneous coronary intervention, with no significant hemoglobin drop after percutaneous coronary intervention. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT02925923.

Constructing Subscores That Add Validity: A Case Study of Identifying Students at Risk.

Prior research suggests that subscores from a single achievement test seldom add value over a single total score. Such scores typically correspond to subcontent areas in the total content domain, but content subdomains might not provide a sound basis for subscores. Using scores on an inferential reading comprehension test from 625 third, fourth, and fifth graders, two new methods of creating subscores were explored. Three subscores were based on the types of incorrect answers given by students. The fourth was based on temporal efficiency in giving correct answers. All four scores were reliable. The three subscores based on incorrect answers added value and validity. In logistic regression analyses predicting failure to reach proficiency on a statewide test, models including subscores fit better than the model with a single total score. Including the pattern of incorrect responses improved fit in all three grades, whereas including the comprehension efficiency score only modestly improved fit in fourth and fifth grades, but not third grade. Area under the curve (AUC) statistics from receiver operating characteristic (ROC) curves based on the various models were higher for models including subscores than those without subscores. Implications for using models with and without subscores are illustrated and discussed.

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.

BACKGROUND: Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. RESULTS: We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer's disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer's disease. CONCLUSIONS: We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.

Linguistic Markers of Inference Generation While Reading.

Words can be informative linguistic markers of psychological constructs. The purpose of this study is to examine associations between word use and the process of making meaningful connections to a text while reading (i.e., inference generation). To achieve this purpose, think-aloud data from third-fifth grade students ([Formula: see text]) reading narrative texts were hand-coded for inferences. These data were also processed with a computer text analysis tool, Linguistic Inquiry and Word Count, for percentages of word use in the following categories: cognitive mechanism words, nonfluencies, and nine types of function words. Findings indicate that cognitive mechanisms were an independent, positive predictor of connections to background knowledge (i.e., elaborative inference generation) and nonfluencies were an independent, negative predictor of connections within the text (i.e., bridging inference generation). Function words did not provide unique variance towards predicting inference generation. These findings are discussed in the context of a cognitive reflection model and the differences between bridging and elaborative inference generation. In addition, potential practical implications for intelligent tutoring systems and computer-based methods of inference identification are presented.

Psychometric properties of the Child Anxiety Life Interference Scale (CALIS).

This paper describes the development and psychometric evaluation of a parent and child report measure of life interference and impairment associated with childhood anxiety, the Child Anxiety Life Interference Scale (CALIS). The CALIS is designed to measure life interference and impairment experienced by the child from the child (9 items) and parent (16 items) point of view and also the interference experienced by the parent in their own life. A total of 622 children between 6 and 17 years of age, and their parents, completed the CALIS. Results indicated that the CALIS has good internal consistency, moderate-to-high test re-test reliability, significant inter rater reliability, good convergent and divergent validity and is sensitive to treatment change. The CALIS is a reliable and valid tool for the assessment of life interference and impairment associated with anxiety disorders in childhood.