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Rutten, Roel – Sociological Methods & Research, 2023
Uncertainty undermines causal claims; however, the nature of causal claims decides what counts as relevant uncertainty. Empirical robustness is imperative in regularity theories of causality. Regularity theory features strongly in QCA, making its case sensitivity a weakness. Following qualitative comparative analysis (QCA) founder Charles Ragin's…
Descriptors: Qualitative Research, Comparative Analysis, Causal Models, Ethics
Cody Ding – Educational Psychology Review, 2024
In the article "It's Just an Observation," Robinson and Wainer (Educational Psychology Review 35, Robinson, D., & Wainer, H. (2023). It's just an observation. Educational Psychology Review, 35(83), Published online: 14 August, 2023) lamented that educational psychology is moving toward the dark side of the quality continuum, with…
Descriptors: Journal Articles, Educational Psychology, Quality Assurance, Barriers
Gonzalez-Ocantos, Ezequiel; LaPorte, Jody – Sociological Methods & Research, 2021
Scholars who conduct process tracing often face the problem of missing data. The inability to document key steps in their causal chains makes it difficult to validate theoretical models. In this article, we conceptualize "missingness" as it relates to process tracing, describe different scenarios in which it is pervasive, and present…
Descriptors: Data, Research Problems, Qualitative Research, Causal Models
Cuartas, Jorge; McCoy, Dana Charles – International Journal of Behavioral Development, 2021
Mediation has played a critical role in developmental theory and research. Yet, developmentalists rarely discuss the methodological challenges of establishing causality in mediation analysis or potential strategies to improve the identification of causal mediation effects. In this article, we discuss the potential outcomes framework from…
Descriptors: Mediation Theory, Behavior Development, Influences, Inferences
Elwert, Felix; Pfeffer, Fabian T. – Sociological Methods & Research, 2022
Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…
Descriptors: Bias, Regression (Statistics), Evaluation Methods, Research
Cummiskey, Kevin; Adams, Bryan; Pleuss, James; Turner, Dusty; Clark, Nicholas; Watts, Krista – Journal of Statistics Education, 2020
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable…
Descriptors: Statistics, Teaching Methods, Inferences, Guidelines
Joyce, Kathryn E. – Educational Research and Evaluation, 2019
Within evidence-based education, results from randomised controlled trials (RCTs), and meta-analyses of them, are taken as reliable evidence for effectiveness -- they speak to "what works". Extending RCT results requires establishing that study samples and settings are representative of the intended target. Although widely recognised as…
Descriptors: Evidence Based Practice, Educational Research, Instructional Effectiveness, Randomized Controlled Trials
Mack, Michael R.; Hensen, Cory; Barbera, Jack – Journal of Chemical Education, 2019
Quasi-experiments are common in studies that estimate the effect of instructional interventions on student performance outcomes. In this type of research, the nature of the experimental design, the choice in assessment, the selection of comparison groups, and the statistical methods used to analyze the comparison data dictate the validity of…
Descriptors: Science Instruction, Comparative Analysis, Inferences, Validity
Motz, Benjamin A.; Carvalho, Paulo F.; de Leeuw, Joshua R.; Goldstone, Robert L. – Journal of Learning Analytics, 2018
To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real…
Descriptors: Causal Models, Statistical Inference, Inferences, Educational Experiments
Gopnik, Alison; Walker, Caren M. – American Journal of Play, 2013
Many researchers have long assumed imaginative play critical to the healthy cognitive, social, and emotional development of children, which has important implications for early-education policy and practice. But, the authors find, a careful review of the existing literature highlights a need for a better theory to clarify the nature of the…
Descriptors: Play, Child Development, Imagination, Logical Thinking
Weisberg, Deena S.; Gopnik, Alison – Cognitive Science, 2013
Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…
Descriptors: Causal Models, Bayesian Statistics, Young Children, Imagination
Kemp, Charles; Shafto, Patrick; Tenenbaum, Joshua B. – Cognitive Psychology, 2012
Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single…
Descriptors: Generalization, Logical Thinking, Inferences, Probability
Marcus, Sue M.; Stuart, Elizabeth A.; Wang, Pei; Shadish, William R.; Steiner, Peter M. – Psychological Methods, 2012
Although randomized studies have high internal validity, generalizability of the estimated causal effect from randomized clinical trials to real-world clinical or educational practice may be limited. We consider the implication of randomized assignment to treatment, as compared with choice of preferred treatment as it occurs in real-world…
Descriptors: Educational Practices, Program Effectiveness, Validity, Causal Models
Prescriptive Statements and Educational Practice: What Can Structural Equation Modeling (SEM) Offer?
Martin, Andrew J. – Educational Psychology Review, 2011
Longitudinal structural equation modeling (SEM) can be a basis for making prescriptive statements on educational practice and offers yields over "traditional" statistical techniques under the general linear model. The extent to which prescriptive statements can be made will rely on the appropriate accommodation of key elements of research design,…
Descriptors: Research Design, Structural Equation Models, Educational Practices, Inferences
Lund, Thorleif – Scandinavian Journal of Educational Research, 2010
The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…
Descriptors: Construct Validity, Validity, Statistical Inference, Inferences
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