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Sequential unordered logit applied to college selection with imperfect information
Author(s) -
Elliott Donald,
Hollenhorst Jerry
Publication year - 1981
Publication title -
behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 0005-7940
DOI - 10.1002/bs.3830260407
Subject(s) - logit , mixed logit , selection (genetic algorithm) , logistic regression , odds , computer science , imperfect , econometrics , set (abstract data type) , ordered logit , discrete choice , operations research , economics , mathematics , machine learning , linguistics , philosophy , programming language
The authors examine the implications of logit‐based choice models for the input‐transducer and output‐transducer functions of an organization (in their application, a university). Logit models, defined as causal statistical models in ‐which the dependent variable is the odds that a particular event occurs, can be used to predict outcomes when deciders must choose among discrete alternatives (as in selecting a college or university). Unlike previous logit studies, the role of information flows between the organization and organisms outside the organization (potential students) is considered. It is shown that: (1) Static conditional logit models may yield inaccurate selection probabilities if, in reality, selection is made by recursive elimination of alternatives with new information added after each elimination; (2) a sequential unordered logit model can be developed which permits adjustment of the choice set during the information collection process; (3) this model can be used to evaluate the effectiveness of marketing policies. The model is applied to college selection and recruitment of college‐bound high school seniors in southern Illinois.