Practical Research Planning And Design 11th Edition By Pearson – Test Bank

Chapter 11

ANALYZING QUALITATIVE DATA

1. Qualitative data analysis is a process of
a. deduction.
b. induction.
c. generalizability.
d. statistical analysis.

2. Esme is interested in the personal experiences of first-year medical students and plans an ethnographic study, in which she’ll collect observational field notes, extensive interview data, and diaries from the participants. For her analysis, she plans to begin by developing a start list. In other words, Esme will:
a. make a list of categories and themes derived directly from the research problem.
b. briefly review the data in search of topics or themes that pop out right away.
c. divide the data into general but meaningful chunks before beginning more detailed analysis.
d. apply her initial coding scheme to a subset of her data.

3. Kavanir has developed an initial coding scheme for his qualitative data. Ideally, his next step in his analysis should be to:
a. develop a more detailed set of subcodes that will capture the nuances of the data.
b. apply it to a subset of the data and then re-evaluate the codes.
c. collect his data.
d. compute interrater reliability.

4. Both Marianne and Bill examined the same set of qualitative data collected from couples in a study designed to understand relationship stress. Interestingly, the themes that emerged from the data were not consistent across the two researchers. Of the following, which is the most likely reason for the discrepancy?
a. The data were not valid.
b. The coding scheme was not reliable.
c. Qualitative analysis is too subjective.
d. The patterns in the data were too subtle.

5. Morisha is a policy researcher who studies parents’ interest in charter schools. As part of her work she conducted extended interviews with parents who wanted to enter their children into charter schools for high school. While conducting her interviews, she noticed one family whose reasons were so unlike the others that she wondered whether she should even include the data in her sample. Your best advice to her would be to:
a. exclude the data because the family is such an extreme outlier; maybe they weren’t really honest.
b. review the interview questions to see if they’re biased in some way.
c. collect more data because this family probably isn’t unique.
d. keep the data, and make sure her analysis plan accounts for a broad range of responses.

6. The process of qualitative data analysis is sometimes described as a spiral, which involves four iterative steps that gradually move the process forward. The final step in the qualitative data analysis spiral is usually:
a. quantifying certain noteworthy characteristics or events.
b. developing a new, coherent theory to account for the findings.
c. synthesizing and interpreting the data.
d. writing a paper for possible publication.

7. In a grounded theory study, the focus of data analysis is to:
a. identify a new theory arising from the data.
b. test a theory grounded in previous research.
c. use open categories to identify theoretical properties rather than themes.
d. follow a structured and systematic analytical plan based on previous theories.

8. A key component of data analysis in a content analysis study is to:
a. engage in axial coding around a core category.
b. speculate about possible cause-and-effect relationships.
c. systematically identify exceptions, contradictions, and outliers.
d. identify frequencies and other summary statistics for the main coding categories.

Multiple-Choice Questions

1. b
2. a
3. b
4. b
5. d
6. c
7. a
8. d

Essay Questions

9. Qualitative researchers need to do their best to maintain “rigorous subjectivity” — acknowledging their own biases and making efforts to reduce and/or balance them. Some strategies for enhancing trustworthiness include:
• keeping detailed notes of procedures and decisions
• being careful to distinguish between observation and interpretation in notes and in analysis
• collecting different types of data (e.g., interview, observation) and comparing them during analysis (i.e., triangulation)
• having more than one person code some or all the data and retraining if reliability is low
• considering various possible explanations for outliers and apparent contradictions