Qualitative modes of data analysis provide ways of discerning, examining, comparing and contrasting, and interpreting meaningful patterns or themes.
The varieties of approaches – including ethnography, narrative analysis, discourse analysis, and textual analysis – correspond to different types of data, disciplinary traditions, objectives, and philosophical orientations.
What Is Qualitative Analysis?
We have few agreed-on canons for qualitative data analysis, in the sense of shared ground rules for drawing conclusions and verifying their sturdiness (Miles and Huberman, 1984).
Data analysis tends to be an ongoing and iterative (nonlinear) process in qualitative research.
The term we use to describe this process is interim analysis (i.e., the cyclical process of collecting and analyzing data during a single research study). Interim analysis continues until the process or topic the researcher is interested in is understood (or until you run out of resources!). Throughout the entire process of qualitative data analysis it is a good idea to engage in memoing (i.e., recording reflective notes about what you are learning from the data). The idea is to write memos to yourself when you have ideas and insights and to include those memos as additional data to be analyzed.
Throughout the course of qualitativeanalysis, the analyst should be asking and reasking the following questions:
What patterns and common themes emerge in responses dealing with specific items? How do these patterns (or lack thereof) help to illuminate the broader study question(s)?
Are there any deviations from these patterns? If yes, are there any factors that might explain these atypical responses?
What interesting stories emerge from the responses? How can these stories help to illuminate the broader study question(s)? Do any of these patterns or findings suggest that additional data may need to be collected? Do any of the study questions need to be revised?
Do the patterns that emerge corroborate the findings of any corresponding qualitative analyses that have been conducted? If not, what might explain these discrepancies?
Processes in Qualitative Analysis
Evaluators have identified a few basic commonalities in the process of making sense of qualitative data.
Miles and Huberman (1994) described the major phases of data analysis: data reduction, data display, and conclusion drawing and verification.
Data Entry and Storage
Qualitative researchers usually transcribe their data; that is, they type the text (from interviews, observational notes, memos, etc.) into word processing documents.
The mass of data has to be organized and somehow meaningfully reduced or reconfigured.
Data reduction refers to the process of selecting, focusing, simplifying, abstracting, and transforming the data that appear in written up field notes or transcriptions (Miles and Huberman, 1984).
It is here that you carefully read your transcribed data, line by line, and divide the data into meaningful analytical units (i.e., segmenting the data). When you locate meaningful segments, you code them.
Coding in data reduction
Coding is defined as marking the segments of data with symbols, descriptive words, or category names.
Again, whenever you find a meaningful segment of text in a transcript, you assign a code or category name to signify that particular segment. You continue this process until you have segmented all of your data and have completed the initial coding. During coding, you must keep a master list (i.e., a list of all the codes that are developed and used in the research study).
Then, the codes are reapplied to new segments of data each time an appropriate segment is encountered.
Qualitative research is very much an interpretative process! Qualitative research is more defensible when multiple coders are used and when high inter- and intra-coder reliability are obtained.
– Intercoder reliability refers to consistency among different coders. –
Intracoder reliability refers to consistency within a single coder.
Types of codes
You may decide to use a set of already existing codes with your data. These are called a priori codes.
A priori codes are codes that are developed before examining the current data.
Many qualitative researchers like to develop the codes as they code the data. These codes are called inductive codes.
Inductive codes are codes that are developed by the researcher by directly examining the data.
As you code your data, you may find that the same segment of data gets coded with more than one code. That’s fine, and it commonly occurs. These sets of codes are called cooccurring codes.
Data display is the second element or level in Miles and Huberman’s (1994) model of qualitative data analysis. Data display goes a step beyond data reduction to provide an organized, compressed assembly of information that permits conclusion drawing.
Spradley’s Universal Semantic
1. Strict inclusion X is a kind of Y
X is a place in Y; X is a part of Y
X is a result of Y; X is a cause of Y
X is a reason for doing Y
5. Location for action
X is a place for doing Y
X is used for Y
X is a way to do Y
X is a step (stage) in Y
X is an attribute (characteristic) of Y
– Diagraming is the process of making a sketch, drawing, or outline to show how something works or clarify the relationship between the parts of a whole.
– The use of diagrams are especially helpful for visually oriented learners.
– There are many types of diagrams that can be used in qualitative research.
– One type of diagram used in qualitative research that is similar to the diagrams used in causal modeling is called a network diagram.
– A network diagram is a diagram showing the direct links between variables or events over time.
Example of a diagram Reasons for late introduction of soft foods by young mothers
Conclusion Drawing and Verification
This activity is the third element of qualitative analysis.
Verification, integrally linked to conclusion drawing, entails revisiting the data as many times as necessary to cross-check or verify these emergent conclusions.
Validity means something different in this context than in quantitative evaluation. Here validity encompasses a much broader concern for whether the conclusions being drawn from the data are credible, defensible, warranted, and able to withstand alternative explanations.
Reporting the Data
Two ways of reporting qualitative data:
One way is summarising the major qualitative results in a separate section of the findings, with examples and quotations, following the objectives that guided the collection of this particular data.
Another possibility is to fully integrate different data sets in the chapter of findings, ordered according to the objectives of the entire study.
Further strategies for testing or confirming
qualitative findings to prove validity
1. Check for representativeness of data.
2. Check for bias due to observer bias or the influence of the researcher on the research situation.
3. Cross-check data with evidence from other, independent sources.
4. Compare and contrast data.
5. Use extreme (groups of) informants to the maximum.
6. Get feedback from your informants.