Publication Date
In 2025 | 2 |
Since 2024 | 9 |
Since 2021 (last 5 years) | 13 |
Since 2016 (last 10 years) | 16 |
Since 2006 (last 20 years) | 28 |
Descriptor
Source
Author
Publication Type
Journal Articles | 33 |
Reports - Research | 20 |
Reports - Descriptive | 6 |
Reports - Evaluative | 6 |
Information Analyses | 2 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Grade 9 | 1 |
High Schools | 1 |
Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Environment… | 1 |
Early Childhood Longitudinal… | 1 |
Wechsler Intelligence Scale… | 1 |
What Works Clearinghouse Rating
Carlos Cinelli; Andrew Forney; Judea Pearl – Sociological Methods & Research, 2024
Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is…
Descriptors: Regression (Statistics), Robustness (Statistics), Error of Measurement, Testing Problems
Julian Schuessler; Peter Selb – Sociological Methods & Research, 2025
Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of…
Descriptors: Data Collection, Graphs, Error of Measurement, Statistical Bias
Qian Zhang; Qi Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In the article, we focused on the issues of measurement error and omitted confounders while conducting mediation analysis under experimental studies. Depending on informativeness of the confounders between the mediator (M) and outcome (Y), we described two approaches. When researchers are confident that primary confounders are included (e.g.,…
Descriptors: Error of Measurement, Research and Development, Mediation Theory, Causal Models
Jeroen D. Mulder; Kim Luijken; Bas B. L. Penning de Vries; Ellen L. Hamaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of…
Descriptors: Structural Equation Models, Time on Task, Time Management, Causal Models
Myoung-jae Lee; Goeun Lee; Jin-young Choi – Sociological Methods & Research, 2025
A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…
Descriptors: Probability, Least Squares Statistics, Regression (Statistics), Causal Models
Adam N. Glynn; Miguel R. Rueda; Julian Schuessler – Sociological Methods & Research, 2024
Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and…
Descriptors: Causal Models, Statistical Inference, Error of Measurement, Least Squares Statistics
Xiaohui Luo; Yueqin Hu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Causal Models
Yuejin Zhou; Wenwu Wang; Tao Hu; Tiejun Tong; Zhonghua Liu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Vignettes
Qinyun Lin; Amy K. Nuttall; Qian Zhang; Kenneth A. Frank – Grantee Submission, 2023
Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved…
Descriptors: Causal Models, Mediation Theory, Error of Measurement, Statistical Inference
Ke-Hai Yuan; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models…
Descriptors: Structural Equation Models, Path Analysis, Weighted Scores, Comparative Testing
Arel-Bundock, Vincent – Sociological Methods & Research, 2022
Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer…
Descriptors: Comparative Analysis, Qualitative Research, Attribution Theory, Computer Simulation
Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Scott, Paul Wesley – Practical Assessment, Research & Evaluation, 2019
Two approaches to causal inference in the presence of non-random assignment are presented: The Propensity Score approach which pseudo-randomizes by balancing groups on observed propensity to be in treatment, and the Endogenous Treatment Effects approach which utilizes systems of equations to explicitly model selection into treatment. The three…
Descriptors: Causal Models, Statistical Inference, Probability, Scores
Ellison, George T. H. – Journal of Statistics and Data Science Education, 2021
Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…
Descriptors: Statistics Education, Medical Education, Undergraduate Students, Graphs
Dorie, Vincent; Harada, Masataka; Carnegie, Nicole Bohme; Hill, Jennifer – Grantee Submission, 2016
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis…
Descriptors: Bayesian Statistics, Mathematical Models, Causal Models, Statistical Bias