Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Causal Models | 4 |
Comparative Analysis | 4 |
Social Science Research | 4 |
Monte Carlo Methods | 2 |
Attribution Theory | 1 |
Case Studies | 1 |
Criticism | 1 |
Data Analysis | 1 |
Diversity | 1 |
Inferences | 1 |
Markov Processes | 1 |
More ▼ |
Publication Type
Journal Articles | 4 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Pula, Besnik – International Journal of Social Research Methodology, 2021
Comparative and case study researchers have responded to critiques of their methods by developing formal procedures to validate theoretical claims through set theoretical logics of causal conditions. This 'logico-formalist turn' has involved the stricter application of the schemas of set theory and the philosophy of logic to raise validation…
Descriptors: Comparative Analysis, Case Studies, Realism, Social Science Research
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
Leszczensky, Lars; Wolbring, Tobias – Sociological Methods & Research, 2022
Does "X" affect "Y"? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the…
Descriptors: Attribution Theory, Causal Models, Comparative Analysis, Statistical Bias

Draper, David – Journal of Educational and Behavioral Statistics, 1995
The use of hierarchical models in social science research is discussed, with emphasis on causal inference and consideration of the limitations of hierarchical models. The increased use of Gibbs sampling and other Markov-chain Monte Carlo methods in the application of hierarchical models is recommended. (SLD)
Descriptors: Causal Models, Comparative Analysis, Markov Processes, Maximum Likelihood Statistics