Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Causal Models | 6 |
Inferences | 6 |
Social Science Research | 6 |
Evaluation Methods | 3 |
Research Methodology | 3 |
Educational Research | 2 |
Test Bias | 2 |
Abstract Reasoning | 1 |
Access to Education | 1 |
Attribution Theory | 1 |
Bias | 1 |
More ▼ |
Source
Sociological Methods &… | 2 |
Evaluation and Research in… | 1 |
Journal of Educational and… | 1 |
Review of Higher Education | 1 |
Studies in Second Language… | 1 |
Author
Briggs, Derek C. | 1 |
Elwert, Felix | 1 |
Gorard, Stephen | 1 |
Lazar, Nicole A. | 1 |
Pfeffer, Fabian T. | 1 |
Riegg, Stephanie K. | 1 |
Thiem, Alrik | 1 |
Publication Type
Journal Articles | 6 |
Reports - Descriptive | 4 |
Reports - Evaluative | 2 |
Education Level
Higher Education | 1 |
Audience
Researchers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
SAT (College Admission Test) | 1 |
What Works Clearinghouse Rating
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
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
Riegg, Stephanie K. – Review of Higher Education, 2008
This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…
Descriptors: Research Methodology, Causal Models, Inferences, Test Bias
Lazar, Nicole A. – Studies in Second Language Acquisition, 2004
The study of SLA, as is true for much social science research, aims broadly at answering questions of causality--for instance, "Is one learning context more likely than another to promote gains in second language learning?" Context-of-learning research in the study of SLA, however, often involves observational, rather than experimental,…
Descriptors: Social Science Research, Causal Models, Second Language Learning, Social Sciences
Briggs, Derek C. – Journal of Educational and Behavioral Statistics, 2004
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the…
Descriptors: Social Science Research, Statistical Inference, Causal Models, Test Bias
Gorard, Stephen – Evaluation and Research in Education, 2002
This paper contains a consideration of the nature and role of warrants for research conclusions in educational research. The paper argues the need for an explicit warrant in the form of a logical and persuasive link between the evidence produced and the conclusions drawn (with appropriate qualifications and caveats). It describes social scientific…
Descriptors: Educational Research, Persuasive Discourse, Validity, Logical Thinking