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Xu Qin; Lijuan Wang – Grantee Submission, 2023
Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and…
Descriptors: Causal Models, Mediation Theory, Computer Software, Statistical Analysis
Peng Ding; Luke W. Miratrix – Grantee Submission, 2019
For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have…
Descriptors: Causal Models, Statistical Inference, Randomized Controlled Trials, Bayesian Statistics
Vuorre, Matti; Bolger, Niall – Grantee Submission, 2018
Statistical mediation allows researchers to investigate potential causal effects of experimental manipulations through intervening variables. It is a powerful tool for assessing the presence and strength of postulated causal mechanisms. Although mediation is used in certain areas of psychology, it is rarely applied in cognitive psychology and…
Descriptors: Statistical Analysis, Hierarchical Linear Modeling, Cognitive Psychology, Neurosciences
Peng Ding; Fan Li – Grantee Submission, 2018
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential…
Descriptors: Attribution Theory, Causal Models, Statistical Inference, Research Problems
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McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…
Descriptors: Probability, Age Differences, Children, Intervention
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Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction