This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Spencer, B. D. Articles by Foran, W. Search for Related Content Social Bookmarking                         What's this?

Sampling Probabilities for Aggregations, With Application to NELS:88 and Other Educational Longitudinal Surveys

Northwestern University

Wendene Foran

National Opinion Research Center

This article considers surveys where one can observe, after the sample is selected, that each member of the sample belongs to one or more aggregations. The population of aggregations is of interest, and we consider the probability that a given aggregation contains at least one sample member. An expression for the probability is derived as a function of population parameters, many of which can be known only if additional, costly data collection is undertaken. A variety of model-based estimators of those parameters is discussed, and their relative advantages and disadvantages are noted. The estimators are applied to a particular case, the National Educational Longitudinal Study of 1988 (NELS:88), in which the aggregations are tenth-grade schools. Evaluations of the estimators are presented. In NELS:88, a sample of eighth-grade schools and eighth-grade students in those schools was surveyed and 2 years later, when most of the students were in tenth grade, the students were resurveyed. If the tenth-grade school sample (i.e., the set of tenth-grade schools enrolling one or more sampled students) is a probabiliy sample, then we can make inferences to the population of tenth-grade schools. A requirement for a sample to be a probability sample is that the selection probabilities be nonzero for all units and be known for the selected units. This article discusses how those probabilities can be estimated from complete and incomplete data. Various estimators based on incomplete data are derived and empirically evaluated with data from a special test sample of schools and data from the Houston Independent School District.

Key Words: longitudinal surveys • probability sampling • sample surveys • weighting

Journal of Educational and Behavioral Statistics, Vol. 16, No. 1, 21-33 (1991)
DOI: 10.3102/10769986016001021


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?