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Viechtbauer, Wolfgang; López-López, José Antonio – Research Synthesis Methods, 2022
Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this…
Descriptors: Meta Analysis, Models, Predictor Variables, Computation
Caddick, Zachary A.; Rottman, Benjamin M. – Cognitive Science, 2021
The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and…
Descriptors: Motivation, Logical Thinking, Preferences, Influences
Adam N. Glynn; Miguel R. Rueda; Julian Schuessler – Sociological Methods & Research, 2024
Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and…
Descriptors: Causal Models, Statistical Inference, Error of Measurement, Least Squares Statistics
Regan Mozer; Luke Miratrix – Grantee Submission, 2024
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…
Descriptors: Artificial Intelligence, Coding, Efficiency, Statistical Inference
Joshua Weidlich; Ben Hicks; Hendrik Drachsler – Educational Technology Research and Development, 2024
Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today,…
Descriptors: Educational Research, Educational Technology, Research Design, Structural Equation Models
Saijun Zhao; Zhiyong Zhang; Hong Zhang – Grantee Submission, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Saijun Zhao; Zhiyong Zhang; Hong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Yan Xia; Selim Havan – Educational and Psychological Measurement, 2024
Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…
Descriptors: Data Interpretation, Factor Analysis, Statistical Inference, Research Problems
Virginia Clinton-Lisell; Sarah E. Carlson; Heather Ness-Maddox; Amanda Dahl; Terrill Taylor; Mark L. Davison; Ben Seipel – Journal of College Reading and Learning, 2024
The purpose of this study was to examine clusters of less-skilled college readers. College students with below average reading comprehension skills (N = 77) read and thought aloud about four texts, recalled the texts, and completed standardized assessments of reading skills. Based on the findings of cluster analyses of the cognitive processes…
Descriptors: Reading Skills, Reading Difficulties, Reading Comprehension, Cognitive Processes
Pamela Filiatrault-Veilleux; Chantal Desmarais; Caroline Bouchard; Breanne Esau; Audette Sylvestre – Journal of Speech, Language, and Hearing Research, 2024
Purpose: Using a longitudinal design, this study aimed to describe inferential comprehension abilities of neglected French-speaking preschool children from 42 to 66 months of age in comparison to non-neglected peers, to examine the association with receptive vocabulary, and to determine whether rates of change in inferential abilities over time…
Descriptors: French, Inferences, Comprehension, Child Neglect
Joshua B. Gilbert – Annenberg Institute for School Reform at Brown University, 2024
When analyzing treatment effects on test scores, researchers face many choices and competing guidance for scoring tests and modeling results. This study examines the impact of scoring choices through simulation and an empirical application. Results show that estimates from multiple methods applied to the same data will vary because two-step models…
Descriptors: Scores, Statistical Bias, Statistical Inference, Scoring
Gina Biancarosa; Patrick C. Kennedy; Sarah E. Carlson; Ben Seipel; Mark L. Davison – Intervention in School and Clinic, 2025
Reading outcomes at a national level have remained stagnant for more than two decades. One reason why is that the field has struggled with how to address poor reading comprehension when reading words is not the problem. Another is that limited insight into the causes of poor comprehension performance is offered by traditional reading comprehension…
Descriptors: Reading Comprehension, Cognitive Processes, Grade 3, Grade 4
Baharloo, Roya; Vasil, Ny; Ellwood-Lowe, Monica E.; Srinivasan, Mahesh – Developmental Science, 2023
Young children often endorse stereotypes--such as "girls are bad at math." We explore one mechanism through which these beliefs may be transmitted: via pragmatic inference. Specifically, we ask whether preschoolers and adults can learn about an unmentioned social group from what is said about another group, and if this inferential…
Descriptors: Stereotypes, Beliefs, Pragmatics, Inferences
Parkkinen, Veli-Pekka; Baumgartner, Michael – Sociological Methods & Research, 2023
In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal…
Descriptors: Robustness (Statistics), Comparative Analysis, Causal Models, Models
Valentine Hacquard – Journal of Child Language, 2023
Words have meanings vastly undetermined by the contexts in which they occur. Their acquisition therefore presents formidable problems of induction. Lila Gleitman and colleagues have advocated for one part of a solution: indirect evidence for a word's meaning may come from its syntactic distribution, via syntactic bootstrapping. But while formal…
Descriptors: Pragmatics, Syntax, Semantics, Language Acquisition

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