Lecture 5

Question 1

Components in qualitative data analysis

Answer 1

• Data reduction
• Data display
• Data categorization (Conclusion drawing /verification)

Question 2

Data reduction

Answer 2

Refers to the process of selecting, focusing, simplifying, abstracting and transforming the data that appear in writing up field notes or transcription

Generates categories and identifies themes and patterns

Question 3

Data display

Answer 3

An organized, compressed assembly of information that permits drawing conclusions and taking action

can be in the form of a data matrix, figures, and so on

Question 4

Data categorization (conclusion drawing/ verification)

Answer 4

Involves distinguishing and grouping different categories of information.

The aim is to decompose information, aggregating them into categories that allow comparisons and distinctions.

Question 5

Data contextualization

Answer 5

A suggested by contemporary scholars additional stage in qualitative data analysis.

It involves assembling the collected information and the external contingencies and identifying links and connections.

The aim is to enlighten the likely relationships with events and contextual conditions.

Question 6

Trustworthiness of your analysis: Member validation

Answer 6

One particular method of note is to ask those being investigated to judge the analysis and interpretation themselves, by providing them with a summary of the analysis, and asking them tho critically comment upon the adequacy of the findings

Question 7

Trustworthiness of your analysis: Searching for negative cases and alternative explanations

Answer 7

Interpretation should not focus on identifying only cases to support the researchers’ ideas or explanations, but also identify and explain cases that contradict.

Question 8

Trustworthiness of your analysis: Triangulation

Answer 8

Combining the analysis with findings from different data sources is useful as a means to demonstrate trustworthiness in the analysis

Question 9

Trustworthiness of your analysis: The audit trail

Answer 9

To ensure quality all research should have an audit trail by which others are able to judge the process through which the research has been conducted, and the key decisions that have informed the research process

Question 10

Trustworthiness of your analysis: Reflexivity

Answer 10

Reflexivity means that researchers critically reflect on their own role within the data analysis process, and demonstrate an awareness of this, and how it may have influenced findings, to the reader

Question 11

Abstraction

Answer 11

Abstraction builds on categorization.

It includes both incorporating more concrete categories into fewer, more general ones, and recognizing that a unit of data is an empirical indicator of a more general construct of interest

Question 12

Comparison

Answer 12

Comparison explores differences and similarities across incidents within the data currently collected and provides guidelines for collecting additional data.

Constant comparative is to compare incidents in the data with other incidents appearing to belong to the same category, exploring their similarities and differences.

Question 13

Integration

Answer 13

Integration is to build theory based on data and departs by noting in the data that certain conditions, contexts, strategies, and outcomes tend to cluster together.

It requires the mapping of relationships between conceptual elements.

Question 14

Iteration

Answer 14

Iteration involves moving through data collection and analysis in such a way that preceding operations shape subsequent ones.

Moves back and forth between different stages

Question 15

Refutation

Answer 15

Refutation involves deliberately subjecting one’s emerging inferences - categories, constructs. propositions or conceptual framework - to empirical scrutiny

Use a negative case or negative incident to disconfirm the emerging analysis

Question 16

Dimensionalization

Answer 16

Involves identifying properties of categories and constructs

The researcher may explore a category’s attributes or characteristics along one or more dimensions

Question 17

Categorization

Answer 17

The process of classifying units of data

Involve naming or giving labels.

Question 18

Coding

Answer 18

Coding is analysis.

Coding is the organization of raw data into coceptual categories. Each code is effectively a category or ‘bin’ into which a piece of data is placed

Question 19

Codes should be

Answer 19

Valid; that is they should accurately reflect what is being researched

Mutually exclusive: in that codes should be distinct, with no overlap

Exhaustive: that is all relevant data should fit into a code

Question 20

Stages of coding

Answer 20

Open
axial
selective coding

Question 21

Open coding

Answer 21

The first level of coding, in open coding the researcher is identifying distinct concepts and themes for categorization.

Question 22

Axial coding

Answer 22

Using the codes developed in stage 1, the researcher rereads the qualitative data and searches for statements that may fit into any of the categories.

Further codes may also be developed in this stage.

In contrast to open coding, which focuses on identifying themes, axial coding further refines, aligns, and categorizes the themes.

Question 23

Selective coding

Answer 23

Once the first two stages of coding have been completed, the researchers should become more analytical, and look for constant comparison between patterns and explanation in the codes.

It enables the researcher to select and integrate categories of organized data from axial coding in cohesive and meaning-filled expressions.