The primary goal of the case study and the other contextualizing strategies is an idiographic explanation that focuses on an in-depth understanding of this particular case. Such an understanding might enhance one’s comprehension of other cases and situations, but the primary focus is description, not generalization. The advantage of profiles, narratives, and case studies is the rich and detailed descriptions they provide of people’s lives, experiences, and circumstances. One disadvantage of these contextualizing strategies is that they depend heavily on the subjective and intuitive judgments of the researchers closest to the data. Another researcher producing a case study or profile of the same person or situation might come up with quite a different story. Another disadvantage is the limited ability to generalize beyond the individual case.
Displaying Data
A data display is on organized presentation of data that enables researchers and their audiences to draw some conclusions from the data and to move on to the next stage of the research (Strauss, 1987; Miles and Huberman, 1994). In any data analysis, the researcher must display the data so that a convincing argument is made that supports the conclusions reached in the research. In science, we don’t take the researcher’s word in regard to conclusions; we need to be convinced by a display of the data. In quantitative data analysis, an important part of the display of data is the numbers, in the form of contingency tables, charts, or descriptive and inferential statistics (see Chapter 13 and 14). Some narrative is required clearly explain their interpretation, but the core of the argument is the display of the numbers. In qualitative data analysis. The core of the argument is not numbers; although there may be some numbers to show, different kinds of data displays are used. Creating data displays is very much a part of data analysis; creating the displays assists the researcher in identifying and clarifying the concepts and categories that are emerging from the coding and other strategies being used on the data.
The primary goal of the case study and the other contextualizing strategies is an idiographic explanation that focuses on an in-depth understanding of this particular case. Such an understanding might enhance one’s comprehension of other cases and situations, but the primary focus is description, not generalization. The advantage of profiles, narratives, and case studies is the rich and detailed descriptions they provide of people’s lives, experiences, and circumstances. One disadvantage of these contextualizing strategies is that they depend heavily on the subjective and intuitive judgments of the researchers closest to the data. Another researcher producing a case study or profile of the same person or situation might come up with quite a different story. Another disadvantage is the limited ability to generalize beyond the individual case.
Displaying Data
A data display is on organized presentation of data that enables researchers and their audiences to draw some conclusions from the data and to move on to the next stage of the research (Strauss, 1987; Miles and Huberman, 1994). In any data analysis, the researcher must display the data so that a convincing argument is made that supports the conclusions reached in the research. In science, we don’t take the researcher’s word in regard to conclusions; we need to be convinced by a display of the data. In quantitative data analysis, an important part of the display of data is the numbers, in the form of contingency tables, charts, or descriptive and inferential statistics (see Chapter 13 and 14). Some narrative is required clearly explain their interpretation, but the core of the argument is the display of the numbers. In qualitative data analysis. The core of the argument is not numbers; although there may be some numbers to show, different kinds of data displays are used. Creating data displays is very much a part of data analysis; creating the displays assists the researcher in identifying and clarifying the concepts and categories that are emerging from the coding and other strategies being used on the data.
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