Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology

Evaluation of public programming currently tends toward plans that are set in advance of any sampling and adhered to throughout. Because increments in the knowledge profile during the course of an evaluation might beckon adjustment of the working procedure, fixed evaluation methodology may be cost-i...

Full description

Bibliographic Details
Main Author: Minkoff, Alan S.
Format: Working Paper
Language:en_US
Published: Massachusetts Institute of Technology, Operations Research Center 2004
Online Access:http://hdl.handle.net/1721.1/5172
_version_ 1811069299117260800
author Minkoff, Alan S.
author_facet Minkoff, Alan S.
author_sort Minkoff, Alan S.
collection MIT
description Evaluation of public programming currently tends toward plans that are set in advance of any sampling and adhered to throughout. Because increments in the knowledge profile during the course of an evaluation might beckon adjustment of the working procedure, fixed evaluation methodology may be cost-inefficient. It is desired to develop a methodology that is adaptive to changes in the knowledge profile. This might be most easily accomplished by borrowing ideas from some of the disciplines in which relevant problems occur. The most promising fields for this task include classical and Bayesian statistics, reliability theory, and dynamic programming. This paper reviews the techniques in classical statistics that seem most apt for handling the problem of adaptive changes in an evaluation to updated knowledge profiles, and considers the paths along which future research ought to be conducted.
first_indexed 2024-09-23T08:08:53Z
format Working Paper
id mit-1721.1/5172
institution Massachusetts Institute of Technology
language en_US
last_indexed 2024-09-23T08:08:53Z
publishDate 2004
publisher Massachusetts Institute of Technology, Operations Research Center
record_format dspace
spelling mit-1721.1/51722019-04-09T16:55:52Z Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology Minkoff, Alan S. Evaluation of public programming currently tends toward plans that are set in advance of any sampling and adhered to throughout. Because increments in the knowledge profile during the course of an evaluation might beckon adjustment of the working procedure, fixed evaluation methodology may be cost-inefficient. It is desired to develop a methodology that is adaptive to changes in the knowledge profile. This might be most easily accomplished by borrowing ideas from some of the disciplines in which relevant problems occur. The most promising fields for this task include classical and Bayesian statistics, reliability theory, and dynamic programming. This paper reviews the techniques in classical statistics that seem most apt for handling the problem of adaptive changes in an evaluation to updated knowledge profiles, and considers the paths along which future research ought to be conducted. 2004-05-28T19:26:23Z 2004-05-28T19:26:23Z 1981-02 Working Paper http://hdl.handle.net/1721.1/5172 en_US Operations Research Center Working Paper;OR 110-81 1744 bytes 2156379 bytes application/pdf application/pdf Massachusetts Institute of Technology, Operations Research Center
spellingShingle Minkoff, Alan S.
Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title_full Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title_fullStr Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title_full_unstemmed Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title_short Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology
title_sort preliminary survey of classical statistical techniques for incorporation into adaptive evaluation methodology
url http://hdl.handle.net/1721.1/5172
work_keys_str_mv AT minkoffalans preliminarysurveyofclassicalstatisticaltechniquesforincorporationintoadaptiveevaluationmethodology