8. Anticipate analysis—design the evaluation
data collection to facilitate analysis.
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- Design the evaluation to meet deadlines. Qualitative
analysis is labor intensive and time-consuming. Leave sufficient time
to do rigorous analysis. Where collaborative or participatory approaches
have been used, provide time for genuine collaboration in the analysis.
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- Stay focused on the primary evaluation questions and issues.
The open-ended nature of qualitative inquiry provides lots of opportunities
to get sidetracked. While it is important to explore unanticipated
outcomes, side effects, and unexpected consequences, do so in relation
to primary issues related to program processes, outcomes, and impacts.
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- Know what criteria will be used by primary intended users
to judge the quality of the findings.
- Traditional research criteria, e.g., rigor, validity, reliability,
generalizability, triangulation of data types and sources
- Evaluation standards: utility, feasibility, propriety, accuracy
- Nontraditional criteria: trustworthiness, diversity of perspectives,
clarity of voice, credibility of the inquirer to primary users
of the findings
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- Be prepared for the creativity, ambiguities, and challenges
of analysis. Qualitative inquiry generates a great volume
of data—lengthy descriptions of observations and detailed transcripts
from interviews.
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- Protect the data. Being out in the field, in the
world where programs are taking place, provides lots of opportunities
to misplace or lose data. This can threaten promises of confidentiality
as well as undermine the credibility of the evaluation. Fieldwork
requires being well organized to label the voluminous data obtained
(so as to know when, where, and from whom it was gathered), keep it
organized, and maintain it securely.
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