NotesFAQContact Us
Collection
Advanced
Search Tips
Laws, Policies, & Programs
No Child Left Behind Act 20011
What Works Clearinghouse Rating
Showing 1 to 15 of 95 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Jason A. Schoeneberger; Christopher Rhoads – American Journal of Evaluation, 2025
Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Multivariate Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Wendy Chan – Asia Pacific Education Review, 2024
As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…
Descriptors: Probability, Scores, Causal Models, Statistical Inference
Peer reviewed Peer reviewed
Direct linkDirect link
Marchant, Nicolás; Quillien, Tadeg; Chaigneau, Sergio E. – Cognitive Science, 2023
The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…
Descriptors: Classification, Bayesian Statistics, Causal Models, Evaluation Methods
Peer reviewed Peer reviewed
Direct linkDirect link
Sang-June Park; Youjae Yi – Journal of Educational and Behavioral Statistics, 2024
Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…
Descriptors: Interaction, Predictor Variables, Causal Models, Mathematical Models
Peer reviewed Peer reviewed
Direct linkDirect link
Baumgartner, Michael; Ambühl, Mathias – Sociological Methods & Research, 2023
Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the…
Descriptors: Causal Models, Evaluation Methods, Goodness of Fit, Scores
Peer reviewed Peer reviewed
Direct linkDirect link
Xiaohui Luo; Yueqin Hu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Reichardt, Charles S. – American Journal of Evaluation, 2022
Evaluators are often called upon to assess the effects of programs. To assess a program effect, evaluators need a clear understanding of how a program effect is defined. Arguably, the most widely used definition of a program effect is the counterfactual one. According to the counterfactual definition, a program effect is the difference between…
Descriptors: Program Evaluation, Definitions, Causal Models, Evaluation Methods
Peer reviewed Peer reviewed
Direct linkDirect link
Yuejin Zhou; Wenwu Wang; Tao Hu; Tiejun Tong; Zhonghua Liu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Vignettes
Peer reviewed Peer reviewed
Direct linkDirect link
Ping-Lin Chuang – Language Testing, 2025
This experimental study explores how source use features impact raters' judgment of argumentation in a second language (L2) integrated writing test. One hundred four experienced and novice raters were recruited to complete a rating task that simulated the scoring assignment of a local English Placement Test (EPT). Sixty written responses were…
Descriptors: Interrater Reliability, Evaluators, Information Sources, Primary Sources
Peer reviewed Peer reviewed
Direct linkDirect link
Douthwaite, Boru; Proietti, Claudio; Polar, Vivian; Thiele, Graham – American Journal of Evaluation, 2023
This paper develops a novel approach called Outcome Trajectory Evaluation (OTE) in response to the long-causal-chain problem confronting the evaluation of research for development (R4D) projects. OTE strives to tackle four issues resulting from the common practice of evaluating R4D projects based on theory of change developed at the start. The…
Descriptors: Research and Development, Change, Program Evaluation, Social Sciences
Peer reviewed Peer reviewed
Direct linkDirect link
Taylor, Jonathan E.; Sondermeyer, Elizabeth – Adult Learning, 2023
Over 2000 years ago, Aristotle wrote of four distinct causes at play in the world we know. Those causes, the material cause, the formal cause, the efficient cause, and the final cause, were meant to refer to ontological and, by extension, epistemological concerns, and were powerful enough to be seized upon and used in some form by those of very…
Descriptors: Philosophy, Causal Models, Evaluation Methods, Program Evaluation
Peer reviewed Peer reviewed
Direct linkDirect link
Antosz, Patrycja; Szczepanska, Timo; Bouman, Loes; Polhill, J. Gareth; Jager, Wander – International Journal of Social Research Methodology, 2022
Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of "integrating" diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level…
Descriptors: Causal Models, Role, Correlation, Problem Solving
Peer reviewed Peer reviewed
Direct linkDirect link
Leslie Rutkowski; David Rutkowski – Journal of Creative Behavior, 2025
The Programme for International Student Assessment (PISA) introduced creative thinking as an innovative domain in 2022. This paper examines the unique methodological issues in international assessments and the implications of measuring creative thinking within PISA's framework, including stratified sampling, rotated form designs, and a distinct…
Descriptors: Creativity, Creative Thinking, Measurement, Sampling
Peer reviewed Peer reviewed
Direct linkDirect link
Manolov, Rumen; Tanious, René; Fernández-Castilla, Belén – Journal of Applied Behavior Analysis, 2022
In science in general and in the context of single-case experimental designs, replication of the effects of the intervention within and/or across participants or experiments is crucial for establishing causality and for assessing the generality of the intervention effect. Specific developments and proposals for assessing whether an effect has been…
Descriptors: Intervention, Behavioral Science Research, Replication (Evaluation), Research Design
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4  |  5  |  6  |  7