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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
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
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
Shi, Dingjing; Tong, Xin – Sociological Methods & Research, 2022
This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…
Descriptors: Bayesian Statistics, Monte Carlo Methods, Computer Software, Causal Models
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
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
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
Lei, Wu; Qing, Fang; Zhou, Jin – International Journal of Distance Education Technologies, 2016
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…
Descriptors: Causal Models, Attribution Theory, Correlation, Evaluation Methods
Moore, D. G.; George, R. – Journal of Intellectual Disability Research, 2011
Across many academic disciplines visualisation and notation systems are used for modelling data and developing theory, but in child development visual models are not widely used; yet researchers and students of developmental difficulties may benefit from a visualisation and notation system which can clearly map developmental outcomes and…
Descriptors: Child Development, Visualization, Models, Developmental Stages
Econometric Methods for Causal Evaluation of Education Policies and Practices: A Non-Technical Guide
Schlotter, Martin; Schwerdt, Guido; Woessmann, Ludger – Education Economics, 2011
Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition…
Descriptors: Educational Policy, Policy Analysis, Research Methodology, Economics
Schochet, Peter Z.; Puma, Mike; Deke, John – National Center for Education Evaluation and Regional Assistance, 2014
This report summarizes the complex research literature on quantitative methods for assessing how impacts of educational interventions on instructional practices and student learning differ across students, educators, and schools. It also provides technical guidance about the use and interpretation of these methods. The research topics addressed…
Descriptors: Statistical Analysis, Evaluation Methods, Educational Research, Intervention
Bui, Hong; Baruch, Yehuda – Learning Organization, 2010
Purpose: The purpose of this paper is to offer a theoretical contribution to explicate the various factors and aspects that influence Senge's five disciplines and their outcomes. Design/methodology/approach: The paper develops a conceptual framework for the analysis of antecedents and outcomes of Senge's five disciplines, and offers moderators to…
Descriptors: Causal Models, Organizational Development, Learning, Workplace Learning
Nettles, Stephen M.; Petscher, Yaacov – Journal of Personnel Evaluation in Education, 2007
Measurement of principal implementation behaviors has proved difficult to researchers in educational leadership due to a lack of consensus on the operational definitions of leadership constructs. The Principal Implementation Questionnaire (PIQ) was developed and validated with the intention of providing clarity in the assessment of principal…
Descriptors: Reading Programs, Instructional Leadership, Hypothesis Testing, Causal Models
White, Peter A. – Psychological Review, 2006
It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This…
Descriptors: Psychological Evaluation, Evaluation Methods, Influences, Attribution Theory
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