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Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse – Grantee Submission, 2022
Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units…
Descriptors: Causal Models, Statistical Inference, Computation, Evaluation Methods
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Quiroz, Waldo; Rubilar, Cristian Merino – Chemistry Education Research and Practice, 2015
This study develops a tool to identify errors in the presentation of natural laws based on the epistemology and ontology of the Scientific Realism of Mario Bunge. The tool is able to identify errors of different types: (1) epistemological, in which the law is incorrectly presented as data correlation instead of as a pattern of causality; (2)…
Descriptors: Chemistry, Scientific Concepts, Scientific Principles, Error Patterns
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Onwuegbuzie, Anthony J.; Daniel, Larry G. – 1999
The purpose of this paper is to provide an in-depth critical analysis of the use and misuse of correlation coefficients. Various analytical and interpretational misconceptions are reviewed, beginning with the egregious assumption that correlational statistics may be useful in inferring causality. Additional misconceptions, stemming from…
Descriptors: Causal Models, Correlation, Effect Size, Error of Measurement
Thompson, Bruce – 1994
The present paper suggests that multivariate methods ought to be used more frequently in behavioral research and explores the potential consequences of failing to use multivariate methods when these methods are appropriate. The paper explores in detail two reasons why multivariate methods are usually vital. The first is that they limit the…
Descriptors: Analysis of Covariance, Behavioral Science Research, Causal Models, Correlation