Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Bayesian Statistics | 3 |
Computer Software | 3 |
Hierarchical Linear Modeling | 3 |
Regression (Statistics) | 3 |
Statistical Analysis | 2 |
Behavioral Science Research | 1 |
Computation | 1 |
Data | 1 |
Data Analysis | 1 |
Inferences | 1 |
Maximum Likelihood Statistics | 1 |
More ▼ |
Author
Andrew Gelman | 1 |
Craig K. Enders | 1 |
Daniel Lee | 1 |
Enders, Craig K. | 1 |
Jiqiang Guo | 1 |
Keller, Brian T. | 1 |
Levy, Roy | 1 |
Publication Type
Reports - Research | 2 |
Journal Articles | 1 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
Enders, Craig K.; Keller, Brian T.; Levy, Roy – Grantee Submission, 2018
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…
Descriptors: Hierarchical Linear Modeling, Behavioral Science Research, Computer Software, Bayesian Statistics
Andrew Gelman; Daniel Lee; Jiqiang Guo – Journal of Educational and Behavioral Statistics, 2015
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Descriptors: Programming Languages, Bayesian Statistics, Inferences, Monte Carlo Methods