NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 20250
Since 20240
Since 2021 (last 5 years)0
Since 2016 (last 10 years)0
Since 2006 (last 20 years)2
Descriptor
Probability3
Computation2
Sample Size2
Comparative Analysis1
Computer Software1
Correlation1
Diseases1
Evaluation Methods1
Factor Analysis1
Form Classes (Languages)1
Intervals1
Mathematics Education1
Measurement1
Models1
Multiple Regression Analysis1
Observation1
Outcomes of Treatment1
Regression (Statistics)1
Research Design1
Scores1
Simulation1
Statistical Analysis1
More ▼
Source
Multivariate Behavioral…3
Author
Algina, James1
Amemiya, Yasuo1
Austin, Peter C.1
Guo, Jia1
Olejnik, Stephen1
Wall, Melanie M.1
Publication Type
Journal Articles3
Reports - Descriptive3
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing all 3 results Save | Export
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
Peer reviewed Peer reviewed
Direct linkDirect link
Austin, Peter C. – Multivariate Behavioral Research, 2011
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing…
Descriptors: Probability, Scores, Statistical Analysis, Computation
Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor with Continuous and Dichotomous Observed Variables
Peer reviewed Peer reviewed
Direct linkDirect link
Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo – Multivariate Behavioral Research, 2012
Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…
Descriptors: Sample Size, Simulation, Form Classes (Languages), Diseases
Sample Size Tables for Correlation Analysis with Applications in Partial Correlation and Multiple Regression Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Algina, James; Olejnik, Stephen – Multivariate Behavioral Research, 2003
Tables for selecting sample size in correlation studies are presented. Some of the tables allow selection of sample size so that r (or r[squared], depending on the statistic the researcher plans to interpret) will be within a target interval around the population parameter with probability 0.95. The intervals are [plus or minus] 0.05, [plus or…
Descriptors: Probability, Intervals, Sample Size, Multiple Regression Analysis