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Problems in needs assessment data: discrepancy analysis.

Yi-Fang Lee¹, James W Altschuld, Jeffry L White

¹National Chi Nan University 1, University Rd., Puli Nantou 545, Taiwan. ivanalee@ncnu.edu.tw

Evaluation and Program Planning

|August 11, 2007

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Needs assessment (NA) identifies gaps between desired and current states. This study addresses challenges in NA discrepancy analysis, particularly missing data, to improve evaluations for STEM programs serving minority students.

Area of Science:

Educational research
Program evaluation
STEM education

Background:

Needs assessment (NA) typically relies on analyzing discrepancies between desired and present conditions.
Existing research lacks comprehensive examination of subtle issues within discrepancy analysis, such as handling missing data.
These analytical challenges can impact the validity of needs assessments, particularly in diverse student populations.

Purpose of the Study:

To identify and discuss subtle problems in discrepancy analysis within needs assessment.
To propose solutions for challenges encountered in needs assessment, specifically concerning missing data.
To enhance the rigor and reliability of needs assessments in academic settings, particularly for underrepresented groups in STEM.

Main Methods:

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The study reviews existing needs assessment methodologies and identifies common analytical challenges.

It specifically examines the impact of missing data on discrepancy score calculations.

Case examples from a needs assessment of minority students in university STEM programs are used for illustration.

Main Results:

Missing data in either the desired or present state significantly complicates discrepancy analysis.
Varied sample sizes (item n's) due to missing data can lead to unreliable discrepancy scores.
The study highlights the need for robust methods to handle incomplete datasets in needs assessments.

Conclusions:

Addressing missing data is crucial for accurate needs assessment and program evaluation.
The findings offer practical guidance for needs assessors and evaluators working with complex datasets.
Improved methods for discrepancy analysis can lead to more effective interventions for minority students in STEM.