TY - JOUR
T1 - Problems in needs assessment data
T2 - Discrepancy analysis
AU - Lee, Yi Fang
AU - Altschuld, James W.
AU - White, Jeffry L.
N1 - Funding Information:
The Ohio Science and Engineering Alliance (OSEA) is a consortium of 15 institutions, funded by the National Science Foundation, with the goal of increasing the number of underrepresented minorities earning baccalaureate degrees and pursuing graduate study in STEM disciplines ( Ohio Science and Engineering Alliance, 2003 ). The alliance is part of a nationwide effort to rectify the low enrollment and graduation rates of underrepresented minorities ( National Science Foundation, 2004 ). Students are being lost to these fields ( National Science Foundation, 2006 ) with the attrition being more dramatic for minorities—African Americans, Hispanic, and Native Americans ( White, Altschuld, & Lee, 2006 ). OSEA started in 2003 and is one of 34 such alliances in the US. It facilitates a variety of statewide retention activities (statewide student research forum, exchange of students across institutions, faculty workshops, etc.).
PY - 2007/8
Y1 - 2007/8
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
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U2 - 10.1016/j.evalprogplan.2007.05.005
DO - 10.1016/j.evalprogplan.2007.05.005
M3 - Article
C2 - 17689331
AN - SCOPUS:34347225539
SN - 0149-7189
VL - 30
SP - 258
EP - 266
JO - Evaluation and Program Planning
JF - Evaluation and Program Planning
IS - 3
ER -
