Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Evaluation Review
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Muthen, B.
Right arrow Articles by Jöreskog, K. G.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Muthen, B.
Right arrow Articles by Jöreskog, K. G.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Selectivity Problems in Quasi-Experimental Studies

Bengt Muthen

University of California, Los Angeles

Karl G. Jöreskog

University of Uppsala, Sweden

Selectivity problems can occur whenever one tries to estimate population parameters from a nonrandom sample. The sample may be nonrandom because only individuals with certain characteristics are selected into the sample (sample selection), or because individuals participate voluntarily in the sample (self-selection). Selective samples can also occur because individuals fall out of the sample for various reasons, despite an initial random sample (attrition). In such situations it is important to model the selection process as realistically as possible. Selectivity problems are discussed in terms of a general model that is estimated by the maximum likelihood method. Both single-group and multiple- group analyses are considered. The multiple-group case is related to the problem of evaluation of treatment effects in nonequivalent control group designs. The general model and the estimation procedure is illustrated by means of a simulation study. An extension of the general model to latent variable models is discussed.

Evaluation Review, Vol. 7, No. 2, 139-174 (1983)
DOI: 10.1177/0193841X8300700201
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Party PoliticsHome page
S. Hug
Studying the Electoral Success of New Political Parties: A Methodological Note
Party Politics, April 1, 2000; 6(2): 187 - 197.
[Abstract] [PDF]

Home page
 JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
D. Kaplan
The Impact of BIB Spiraling-Induced Missing Data Patterns on Goodness-of-Fit Tests in Factor Analysis
Journal of Educational and Behavioral Statistics, January 1, 1995; 20(1): 69 - 82.
[Abstract] [PDF]

Home page
 JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
B. Muthen and J. Lehman
Multiple Group IRT Modeling: Applications to Item Bias Analysis
Journal of Educational and Behavioral Statistics, January 1, 1985; 10(2): 133 - 142.
[Abstract] [PDF]

Home page
 Applied Psychological MeasurementHome page
R. L. Linn and C. N. Hastings
Group Differentiated Prediction
Applied Psychological Measurement, April 1, 1984; 8(2): 165 - 172.
[Abstract]