Chapter 11 Review Questions

The three-step procedure for coding qualitative data includes open coding, axial coding, and selective coding. With open coding, the researcher reviews the raw material with the goal of obtaining a general sense of its major themes. With axial coding, the researcher re-engages the data, this time with a more refined sense of purpose and a set of coding categories. During this stage, the analyst takes more detailed notes about the content found in the raw materials, categorizing specific phrases, events, or passages as belonging under the broad themes identified in stage one (tagging). During the third stage of qualitative analysis, the researcher mines the raw materials a final time, examining the data for additional and discrepant evidence. In this selective coding process the researcher makes sure that all the data are captured and described by key categories, and a core category emerges that tells the central story.

In order for social science research to be considered trustworthy, it should be authentic (correspond to what was observed), portable (able to be used to describe other cases), precise (others would reach similar conclusions would be reached under similar conditions), and impartial (findings based on evidence, not opinion). For qualitative researchers, that means that they strike to make their work credible, meaning that the results of the analysis fit with the reality being depicted; transferable, meaning that readers can apply the findings to understand other cases they are studying; dependable, meaning that the results have been produced based on precise methods; and confirmable, meaning that another individual could confirm the results.

On one hand, qualitative scholars are expected to provide sufficient support for their arguments. In most cases, this task means including lengthy excerpts from interview transcripts or direct quotations from source documents, both of which require a great deal of space. On the other hand, qualitative scholarship is expected to conform to the same tight word limits imposed by publishers, editors, and instructors. These expectations force analysts in the qualitative tradition to strike a delicate balance between presenting data and providing analysis. If researchers present too much direct evidence, they are criticized for simply transcribing the raw materials. The result is a collection of quotations rather than an academic analysis. On the other hand, if researchers focus too heavily on their own interpretation of the data, they are criticized for failing to provide enough evidence.

There are numerous ways for political scientists to bolster the authenticity, portability, precision, and impartiality of their research. Usually, researchers try to integrate as many techniques as possible to demonstrate the trustworthiness of their research. Some different techniques are triangulation, presenting detailed findings, using established techniques, reporting their methods, reporting discrepant evidence, publishing data, conducting member checks, and reporting biases.

The complementary strengths of qualitative and quantitative research have pushed many researchers to develop “hybrid” approaches as a “third way” of conducting social science research. Whether to compensate for the weaknesses of a single approach or to address a particularly complex topic, an increasing number of social scientists are adopting a broader perspective on research, combining qualitative and quantitative methods in a technique called mixed-methods research. For qualitative researchers, this approach may mean “quantizing” their analyses by buttressing their findings with reference to frequencies and other quantitative measures. By the same token, quantitative researchers may “qualitize” their data, contextualizing their findings with direct quotations from various documents or sources.