Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 0 |
| Since 2006 (last 20 years) | 5 |
Descriptor
| Models | 6 |
| Predictor Variables | 6 |
| Statistical Analysis | 3 |
| Computation | 2 |
| Data Analysis | 2 |
| Evaluation Methods | 2 |
| Academic Achievement | 1 |
| Adolescents | 1 |
| Bayesian Statistics | 1 |
| Behavior Problems | 1 |
| Class Size | 1 |
| More ▼ | |
Source
| Multivariate Behavioral… | 6 |
Author
Publication Type
| Journal Articles | 6 |
| Reports - Research | 3 |
| Reports - Evaluative | 2 |
| Reports - Descriptive | 1 |
Education Level
| Grade 8 | 1 |
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkRast, Philippe; Hofer, Scott M.; Sparks, Catharine – Multivariate Behavioral Research, 2012
A mixed effects location scale model was used to model and explain individual differences in within-person variability of negative and positive affect across 7 days (N=178) within a measurement burst design. The data come from undergraduate university students and are pooled from a study that was repeated at two consecutive years. Individual…
Descriptors: Individual Differences, Undergraduate Students, Psychological Patterns, Stress Variables
Shieh, Gwowen – Multivariate Behavioral Research, 2010
Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term.…
Descriptors: Multiple Regression Analysis, Misconceptions, Predictor Variables, Interaction
Kammeyer-Mueller, John; Steel, Piers D. G.; Rubenstein, Alex – Multivariate Behavioral Research, 2010
Common source bias has been the focus of much attention. To minimize the problem, researchers have sometimes been advised to take measurements of predictors from one observer and measurements of outcomes from another observer or to use separate occasions of measurement. We propose that these efforts to eliminate biases due to common source…
Descriptors: Statistical Bias, Predictor Variables, Measurement, Data Collection
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F. – Multivariate Behavioral Research, 2009
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
Descriptors: Class Size, Scaling, Predictor Variables, Models
Van Horn, M. Lee; Fagan, Abigail A.; Jaki, Thomas; Brown, Eric C.; Hawkins, J. David; Arthur, Michael W.; Abbott, Robert D.; Catalano, Richard F. – Multivariate Behavioral Research, 2008
There is evidence to suggest that the effects of behavioral interventions may be limited to specific types of individuals, but methods for evaluating such outcomes have not been fully developed. This study proposes the use of finite mixture models to evaluate whether interventions, and, specifically, group randomized trials, impact participants…
Descriptors: Intervention, Adolescents, Models, Behavior Problems
Moerbeek, Mirjam – Multivariate Behavioral Research, 2004
Multilevel analysis is an appropriate tool for the analysis of hierarchically structured data. There may, however, be reasons to ignore one of the levels of nesting in the data analysis. In this article a three level model with one predictor variable is used as a reference model and the top or intermediate level is ignored in the data analysis.…
Descriptors: Data Analysis, Predictor Variables, Computation, Statistical Analysis
