Basic Qualitative Analysis for Extension Evaluation

Questions to consider before beginning: What is your theoretical framework/logic model/conceptual framework, and how does it inform what you see during

analysis? What do you, as the analyst, believe about the program you are evaluating, and how might it inform what you see during

analysis?

Inductive Text Analysis Use data to discover concepts, themes, or models Evaluator as interpreter; highly involved Codes emerge from data

Inductive Analysis: 6 Steps1. Collect and organize raw data.

a. Begin data analysis immediately, regardless of number of collection pointsb. Make transcription decisions (who, when, how)c. Begin audit trail (documentary record of choices/changes/task allocation during data collection and analysis; product

of data collection)d. Begin research journal (initial reactions about data as it is collected and analyzed; part of body of data)e. Participant key/aliases/anonymity

2. Read through all data. 3. Create and apply codes. Repeat.

a. Codes are words or phrases that provide a description of an important piece of datab. Track codesc. Higher-level codes often related to programming/evaluation aimsd. Lower-level codes often more related to actual words or phrases in the texte. Not all text will be coded.f. Continue comparing new codes to previous codes: Has anything changed? Any new insights? Track changes.

4. Refine codes to reduce overlapa. Once coding is complete, read through and reduce any redundancy, as needed.

5. Create categories.a. Categories are key aspects of data that have emerged as important/informative based on codes.

6. Narrative analysis.a. Write about your findings, which is another level of analysis. Use “I” words to place yourself in your work, explain

your process, and tell your story as the interpreter of dataDeductive Text Analysis

Data is analyzed according to prior assumptions/pre-established framework Evaluator role: compare and contrast to see if data fits within pre-established framework Codes are decided upon before data collection

Deductive Analysis: 5 Steps1. Develop data categories.2. Clearly define data categories.3. Read through all data and apply categories.4. Count: quantitative outcome.5. Narrative and visual analysis.

Don’t Forget…. Ethics: informed consent, anonymity and confidentiality, additional consent if wanting to use large chunks of data from

participants in reports, newsletters, Web sites, etc. Credibility: be transparent in your work, track all that you’ve done; keep a research journal; triangulate (reach consensus if

working in group; try to use multiple methods of data collection, i.e., pre/post survey from interview/focus group participants); be reflexive, and include a reflexive paragraph in your narrative analysis

Adapted from:Scott, Brigitte. (2014). “Who’s Afraid of Qualitative Analysis?” Presented at the National Extension for Extension Program & Staff Development Professionals. San Antonio, Texas: Dec. 9–11.

E-mail: brigit2@vt.eduTwitter: @4ed_eval