Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 7 |
Descriptor
Nonparametric Statistics | 7 |
Statistical Analysis | 7 |
Bayesian Statistics | 2 |
Causal Models | 2 |
Computation | 2 |
Employment | 2 |
Goodness of Fit | 2 |
Simulation | 2 |
Statistical Inference | 2 |
Children | 1 |
Depression (Psychology) | 1 |
More ▼ |
Source
Journal of Educational and… | 7 |
Author
Berger, Moritz | 1 |
Browne, Michael W. | 1 |
González, Jorge | 1 |
Guanglei Hong | 1 |
Heather D. Hill | 1 |
Jo, Booil | 1 |
Jonah Deutsch | 1 |
Keller, Bryan | 1 |
Liang, Longjuan | 1 |
Mealli, Fabrizia | 1 |
Pacini, Barbara | 1 |
More ▼ |
Publication Type
Journal Articles | 7 |
Reports - Research | 6 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
California | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Varas, Inés M.; González, Jorge; Quintana, Fernando A. – Journal of Educational and Behavioral Statistics, 2020
Equating is a family of statistical models and methods used to adjust scores on different test forms so that they can be comparable and used interchangeably. Equated scores are obtained estimating the equating transformation function, which maps the scores on the scale of one test form into their equivalents on the scale of other one. All the…
Descriptors: Bayesian Statistics, Nonparametric Statistics, Equated Scores, Statistical Analysis
Keller, Bryan – Journal of Educational and Behavioral Statistics, 2020
Widespread availability of rich educational databases facilitates the use of conditioning strategies to estimate causal effects with nonexperimental data. With dozens, hundreds, or more potential predictors, variable selection can be useful for practical reasons related to communicating results and for statistical reasons related to improving the…
Descriptors: Nonparametric Statistics, Computation, Testing, Causal Models
Mealli, Fabrizia; Pacini, Barbara; Stanghellini, Elena – Journal of Educational and Behavioral Statistics, 2016
Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded…
Descriptors: Graphs, Nonparametric Statistics, Causal Models, Statistical Analysis
Berger, Moritz; Tutz, Gerhard – Journal of Educational and Behavioral Statistics, 2016
Detection of differential item functioning (DIF) by use of the logistic modeling approach has a long tradition. One big advantage of the approach is that it can be used to investigate nonuniform (NUDIF) as well as uniform DIF (UDIF). The classical approach allows one to detect DIF by distinguishing between multiple groups. We propose an…
Descriptors: Test Bias, Regression (Statistics), Nonparametric Statistics, Statistical Analysis
Liang, Longjuan; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2015
If standard two-parameter item response functions are employed in the analysis of a test with some newly constructed items, it can be expected that, for some items, the item response function (IRF) will not fit the data well. This lack of fit can also occur when standard IRFs are fitted to personality or psychopathology items. When investigating…
Descriptors: Item Response Theory, Statistical Analysis, Goodness of Fit, Bayesian Statistics
Guanglei Hong; Jonah Deutsch; Heather D. Hill – Journal of Educational and Behavioral Statistics, 2015
Conventional methods for mediation analysis generate biased results when the mediator--outcome relationship depends on the treatment condition. This article shows how the ratio-of-mediator-probability weighting (RMPW) method can be used to decompose total effects into natural direct and indirect effects in the presence of treatment-by-mediator…
Descriptors: Weighted Scores, Probability, Statistical Analysis, Interaction
Jo, Booil; Vinokur, Amiram D. – Journal of Educational and Behavioral Statistics, 2011
When identification of causal effects relies on untestable assumptions regarding nonidentified parameters, sensitivity of causal effect estimates is often questioned. For proper interpretation of causal effect estimates in this situation, deriving bounds on causal parameters or exploring the sensitivity of estimates to scientifically plausible…
Descriptors: Statistical Analysis, Statistical Inference, Nonparametric Statistics, Intervention