NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1052522
Record Type: Journal
Publication Date: 2015-Mar
Pages: 25
Abstractor: As Provided
Reference Count: 38
ISSN: ISSN-1098-2140
Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship between Two Variables and an Outcome
Dong, Nianbo
American Journal of Evaluation, v36 n1 p42-66 Mar 2015
Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children's reading achievement vary by child care quality.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Early Childhood Environment Rating Scale; Early Childhood Longitudinal Survey