The following discussion of steps that can be followed to code a text or set of texts during conceptual analysis use campaign speeches made by Bill Clinton during the 1992 presidential campaign as an example. To read about each step, click on the items in the list below:
4. Decide on how you will distinguish among concepts.
6. Decide what to do with "irrelevant" information.
7. Code the texts.
8. Analyze your results.
Step One: Decide the Level of Analysis
First, the researcher must decide upon the level of analysis. With the health care speeches, to continue the example, the researcher must decide whether to code for a single word, such as "inexpensive," or for sets of words or phrases, such as "coverage for everyone."
Step Two: Decide How Many Concepts to Code For
Step Three: Decide Whether to Code for Existence or Frequency of a Concept
After a certain number and set of concepts are chosen for coding , the researcher must answer a key question: is he/she going to code for existence or frequency? This is important, because it changes the coding process. When coding for existence, "inexpensive" would only be counted once, no matter how many times it appeared. This would be a very basic coding process and would give the researcher a very limited perspective of the text. However, the number of times "inexpensive" appears in a text might be more indicative of importance. Knowing that "inexpensive" appeared 50 times, for example, compared to 15 appearances of "coverage for everyone," might lead a researcher to interpret that Clinton is trying to sell his health care plan based more on economic benefits, not comprehensive coverage. Knowing that "inexpensive" appeared, but not that it appeared 50 times, would not allow the researcher to make this interpretation, regardless of whether it is valid or not.
Step Four: Decide on How You Will Distinguish Among Concepts
The researcher must next decide on the level of generalization, i.e. whether concepts are to be coded exactly as they appear, or if they can be recorded as the same even when they appear in different forms. For example, "expensive" might also appear as "expensiveness." The research needs to determine if the two words mean radically different things to him/her, or if they are similar enough that they can be coded as being the same thing, i.e. "expensive words." In line with this, is the need to determine the level of implication one is going to allow. This entails more than subtle differences in tense or spelling, as with "expensive" and "expensiveness." Determining the level of implication would allow the researcher to code not only for the word "expensive," but also for words that imply "expensive." This could perhaps include technical words, jargon, or political euphemism, such as "economically challenging," that the researcher decides does not merit a separate category, but is better represented under the category "expensive," due to its implicit meaning of "expensive."
Step Five: Develop Rules for Coding Your Texts
After taking the generalization of concepts into consideration, a researcher will want to create translation rules that will allow him/her to streamline and organize the coding process so that he/she is coding for exactly what he/she wants to code for. Developing a set of rules helps the researcher insure that he/she is coding things consistently throughout the text, in the same way every time. If a researcher coded "economically challenging" as a separate category from "expensive" in one paragraph, then coded it under the umbrella of "expensive" when it occurred in the next paragraph, his/her data would be invalid. The interpretations drawn from that data will subsequently be invalid as well. Translation rules protect against this and give the coding process a crucial level of consistency and coherence.
Step Six: Decide What To Do with "Irrelevant" Information
The next choice a researcher must make involves irrelevant information. The researcher must decide whether irrelevant information should be ignored (as Weber, 1990, suggests), or used to reexamine and/or alter the coding scheme. In the case of this example, words like "and" and "the," as they appear by themselves, would be ignored. They add nothing to the quantification of words like "inexpensive" and "expensive" and can be disregarded without impacting the outcome of the coding.
Step Seven: Code the Texts
Once these choices about irrelevant information are made, the next step is to code the text. This is done either by hand, i.e. reading through the text and manually writing down concept occurrences, or through the use of various computer programs. Coding with a computer is one of contemporary conceptual analysis' greatest assets. By inputting one's categories, content analysis programs can easily automate the coding process and examine huge amounts of data, and a wider range of texts, quickly and efficiently. But automation is very dependent on the researcher's preparation and category construction. When coding is done manually, a researcher can recognize errors far more easily. A computer is only a tool and can only code based on the information it is given. This problem is most apparent when coding for implicit information, where category preparation is essential for accurate coding.
Step Eight: Analyze Your Results
Once the coding is done, the researcher examines the data and attempts to draw whatever conclusions and generalizations are possible. Of course, before these can be drawn, the researcher must decide what to do with the information in the text that is not coded. One's options include either deleting or skipping over unwanted material, or viewing all information as relevant and important and using it to reexamine, reassess and perhaps even alter one's coding scheme. Furthermore, given that the conceptual analyst is dealing only with quantitative data, the levels of interpretation and generalizability are very limited. The researcher can only extrapolate as far as the data will allow. But it is possible to see trends, for example, that are indicative of much larger ideas. Using the example from step three, if the concept "inexpensive" appears 50 times, compared to 15 appearances of "coverage for everyone," then the researcher can pretty safely extrapolate that there does appear to be a greater emphasis on the economics of the health care plan, as opposed to its universal coverage for all Americans. It must be kept in mind that conceptual analysis, while extremely useful and effective for providing this type of information when done right, is limited by its focus and the quantitative nature of its examination. To more fully explore the relationships that exist between these concepts, one must turn to relational analysis.
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