Problems in needs assessment data: Discrepancy analysis

Yi Fang Lee; James W. Altschuld; Jeffry L. White

doi:10.1016/j.evalprogplan.2007.05.005

Problems in needs assessment data: Discrepancy analysis

Yi Fang Lee, James W. Altschuld, Jeffry L. White

Department of Industrial Education

Research output: Contribution to journal › Article

14 Citations (Scopus)

Abstract

Needs assessment (NA) is generally based on the discrepancy between two conditions-the desired and present states. To date, there has not been an extensive research regarding a number of subtle problems in discrepancy analysis. One such example is missing data for one or both the two states. This leads to highly varied item n's for calculating discrepancy scores. Concerns like this arose in a NA study of minority students in science, technology, engineering, and mathematics programs in universities. A number of problems observed in this context are discussed as well as possible solutions for them. The results should be valuable to needs assessors and evaluators responsible for assessing needs.

Original language	English
Pages (from-to)	258-266
Number of pages	9
Journal	Evaluation and Program Planning
Volume	30
Issue number	3
DOIs	https://doi.org/10.1016/j.evalprogplan.2007.05.005
Publication status	Published - 2007 Aug 1

Fingerprint

Keywords

Discrepancy analysis
Discrepancy-based needs
Needs assessment

ASJC Scopus subject areas

Business and International Management
Social Psychology
Geography, Planning and Development
Strategy and Management
Public Health, Environmental and Occupational Health

Cite this

Lee, Y. F., Altschuld, J. W., & White, J. L. (2007). Problems in needs assessment data: Discrepancy analysis. Evaluation and Program Planning, 30(3), 258-266. https://doi.org/10.1016/j.evalprogplan.2007.05.005

@article{ed23033edbd141c08871c535dde92d90,

title = "Problems in needs assessment data: Discrepancy analysis",

abstract = "Needs assessment (NA) is generally based on the discrepancy between two conditions-the desired and present states. To date, there has not been an extensive research regarding a number of subtle problems in discrepancy analysis. One such example is missing data for one or both the two states. This leads to highly varied item n's for calculating discrepancy scores. Concerns like this arose in a NA study of minority students in science, technology, engineering, and mathematics programs in universities. A number of problems observed in this context are discussed as well as possible solutions for them. The results should be valuable to needs assessors and evaluators responsible for assessing needs.",

keywords = "Discrepancy analysis, Discrepancy-based needs, Needs assessment",

author = "Lee, {Yi Fang} and Altschuld, {James W.} and White, {Jeffry L.}",

year = "2007",

month = aug

day = "1",

doi = "10.1016/j.evalprogplan.2007.05.005",

language = "English",

volume = "30",

pages = "258--266",

journal = "Evaluation and Program Planning",

issn = "0149-7189",

publisher = "Elsevier Limited",

number = "3",

}

TY - JOUR

T1 - Problems in needs assessment data

T2 - Discrepancy analysis

AU - Lee, Yi Fang

AU - Altschuld, James W.

AU - White, Jeffry L.

PY - 2007/8/1

Y1 - 2007/8/1

N2 - Needs assessment (NA) is generally based on the discrepancy between two conditions-the desired and present states. To date, there has not been an extensive research regarding a number of subtle problems in discrepancy analysis. One such example is missing data for one or both the two states. This leads to highly varied item n's for calculating discrepancy scores. Concerns like this arose in a NA study of minority students in science, technology, engineering, and mathematics programs in universities. A number of problems observed in this context are discussed as well as possible solutions for them. The results should be valuable to needs assessors and evaluators responsible for assessing needs.

AB - Needs assessment (NA) is generally based on the discrepancy between two conditions-the desired and present states. To date, there has not been an extensive research regarding a number of subtle problems in discrepancy analysis. One such example is missing data for one or both the two states. This leads to highly varied item n's for calculating discrepancy scores. Concerns like this arose in a NA study of minority students in science, technology, engineering, and mathematics programs in universities. A number of problems observed in this context are discussed as well as possible solutions for them. The results should be valuable to needs assessors and evaluators responsible for assessing needs.

KW - Discrepancy analysis

KW - Discrepancy-based needs

KW - Needs assessment

UR - http://www.scopus.com/inward/record.url?scp=34347225539&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34347225539&partnerID=8YFLogxK

U2 - 10.1016/j.evalprogplan.2007.05.005

DO - 10.1016/j.evalprogplan.2007.05.005

M3 - Article

C2 - 17689331

AN - SCOPUS:34347225539

VL - 30

SP - 258

EP - 266

JO - Evaluation and Program Planning

JF - Evaluation and Program Planning

SN - 0149-7189

IS - 3

ER -

Access to Document

10.1016/j.evalprogplan.2007.05.005