Publication Date
In 2025 | 1 |
Since 2024 | 3 |
Since 2021 (last 5 years) | 7 |
Since 2016 (last 10 years) | 10 |
Since 2006 (last 20 years) | 24 |
Descriptor
Source
Author
Publication Type
Reports - Descriptive | 29 |
Journal Articles | 28 |
Opinion Papers | 1 |
Tests/Questionnaires | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Kindergarten | 1 |
Audience
Researchers | 5 |
Practitioners | 1 |
Teachers | 1 |
Location
Canada (Montreal) | 1 |
Indiana | 1 |
Laws, Policies, & Programs
Assessments and Surveys
SAT (College Admission Test) | 1 |
What Works Clearinghouse Rating
Kylie Anglin; Qing Liu; Vivian C. Wong – Asia Pacific Education Review, 2024
Given decision-makers often prioritize causal research that identifies the impact of treatments on the people they serve, a key question in education research is, "Does it work?". Today, however, researchers are paying increasing attention to successive questions that are equally important from a practical standpoint--not only does it…
Descriptors: Educational Research, Program Evaluation, Validity, Classification
Garret J. Hall; Sophia Putzeys; Thomas R. Kratochwill; Joel R. Levin – Educational Psychology Review, 2024
Single-case experimental designs (SCEDs) have a long history in clinical and educational disciplines. One underdeveloped area in advancing SCED design and analysis is understanding the process of how internal validity threats and operational concerns are avoided or mitigated. Two strategies to ameliorate such issues in SCED involve replication and…
Descriptors: Research Design, Graphs, Case Studies, Validity
Nicholas D. Myers; Ahnalee M. Brincks; Seungmin Lee – Measurement in Physical Education and Exercise Science, 2025
Physical activity (PA) promotion is an ideal intervention target for public health because it has the potential to help individuals feel better, sleep better, and perform daily tasks more easily, in addition to providing disease prevention benefits. There is strong evidence that individual-level theory-based behavioral interventions are effective…
Descriptors: Physical Activity Level, Intervention, Program Effectiveness, Adults
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
Thiem, Alrik – Sociological Methods & Research, 2022
Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal…
Descriptors: Comparative Analysis, Causal Models, Inferences, Risk
Weidlich, Joshua; Gaševic, Dragan; Drachsler, Hendrik – Journal of Learning Analytics, 2022
As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…
Descriptors: Causal Models, Inferences, Learning Analytics, Comparative Analysis
York, Richard – International Journal of Social Research Methodology, 2018
A common motivation for adding control variables to statistical models is to reduce the potential for spurious findings when analyzing non-experimental data and to thereby allow for more reliable causal inferences. However, as I show here, unless "all" potential confounding factors are included in an analysis (which is unlikely to be…
Descriptors: Inferences, Control Groups, Correlation, Experimental Groups
Ding, Peng; Van der Weele, Tyler; Robins, James M. – Grantee Submission, 2017
Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this…
Descriptors: Causal Models, Inferences, Outcomes of Treatment, Interaction
Troy, Jesse D.; Neely, Megan L.; Pomann, Gina-Maria; Grambow, Steven C.; Samsa, Gregory P. – Journal of Curriculum and Teaching, 2022
Student evaluation is a key consideration for educational program administrators because program success depends on students' ability to demonstrate successful development of core competencies. Student evaluations must therefore be aligned with learning objectives and overall program goals. Graduate level educational programs typically incorporate…
Descriptors: Student Evaluation, Evaluation Methods, Statistics Education, Alignment (Education)
Wing, Coady; Bello-Gomez, Ricardo A. – American Journal of Evaluation, 2018
Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…
Descriptors: Regression (Statistics), Research Design, Validity, Evaluation Methods
Keele, Luke – American Journal of Evaluation, 2015
In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…
Descriptors: Mediation Theory, Causal Models, Inferences, Path Analysis
Chatterjee, Atanu – European Journal of Physics Education, 2013
Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…
Descriptors: Physics, Scientific Concepts, Influences, Causal Models
Pearl, Judea – Cognitive Science, 2013
Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the "possible worlds" account of counterfactuals, this "structural" model enjoys the advantages of representational economy,…
Descriptors: Causal Models, Cognitive Science, Sentences, Inferences
Sloman, Steven A. – Cognitive Science, 2013
Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation…
Descriptors: Causal Models, Cognitive Psychology, Cognitive Science, Cognitive Processes
Gopnik, Alison; Wellman, Henry M. – Psychological Bulletin, 2012
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
Descriptors: Causal Models, Theory of Mind, Probability, Cognitive Development
Previous Page | Next Page »
Pages: 1 | 2