Qualitative Analysis Involves:

Data analysis requires:

Qualitative data analysis is primarily an inductive process of comparison in which the categories and patterns emerge from the data from specific questions that the researcher asks about the data. The researcher codes the data into categories, and then identifies (sorts) similarities and distinctions between categories to discover patterns or relationships among the categories. Synthesis or analysis is the key to identify patterns. Types of analysis are called strategies rather than procedures.

Qualitative analysis is no less rigorous than statistical procedures, nor is it data reduction. The qualitative researcher does not force data into the researcher's presuppositions, but instead immerses him or herself in the data to let the data "speak." Qualitative researchers are expected to monitor and report their analytical techniques, processes, and reasons for decisions.

There are five main approaches to analysis, each with subcategories of variations to the approaches. Based on the research problem, the researcher selects an approach.

1. Descriptive Narration of stories and events focused on groups & their activities that change over time.

Descriptive Narrations Contain at Least 4 Elements:

1. people
2. incidents
3. participants' language
4. participants' meaning

2. Topology (of shared experiences) classifies different types of experiences with the same phenomenon.

3. Theme Analysis describes recurring themes found in the data such as visual qualities, behaviorial characteristics, discourse topics, or participants' expressed concerns.

4. Grounded Theory - theory building analyses that proposes a theory as an explanation that is grounded in the data.

5. Concept Analysis describes each subcomponent and its relationship to other subcomponents of the concept

constructs - a complex abstraction that is not directly observable (meaning is assigned based on prior theory) ex: "public perception" or "art"

concept - the perspectives of participants (grounded in data) ex: "museum" or "art"

1. DESCRIPTION (visual & verbal) & Reflection (observer's comments, interpretations, questions)

2. SUMMARY OF__________________
Do immediately after field visit, or as soon as possible.
Summarize what you learned about your topic. What were the important details?

INTERIM ANALYSIS - done at intervals during data collection (usually after 3-5 field visits)

(a) to make decisions in data collection
(b) to identify emerging topics & recurring pattern

To do an interim analysis:

1. Read all of the data to get a sense of the whole (Write down ideas about the data as you read it.)
2. Divide into relevant chunks. Read again, scanning for topics. List topics in the margins. At this stage focus on the range of topics, rather than meaning of topics, that emerge from the data within the focus of the key research question(s).
3. Look at topics that reoccur (such as activities, feelings, folk sayings). Look for clusters or regularities.
4. Code all material that belongs to one topic (category) and organize into (1) major topics; (2) unique topics; (3) left over topics.
5. Refocus inquiry based on findings relevant to research question.

3. CODE data

4. SORT data

5. INTERPRET data using metaphors and analogies to illuminate subtle and abstract meanings.

6. CORROBORATE interpretations, check that interpretations are GROUNDED in data; and compare interpretations to PRIOR RESEARCH

a. Compare interpreted data with literature. Explore different ways to look at the data applying different concepts, models, or theories.

b. Try out emerging ideas and themes on participants:

- to clarify meaning conveyed by participants

- to refine one's understanding of these meanings