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Deciphering the Data: A Comparison of Computer and Traditional Coding in Content Analysis
Unformatted Document Text:  10 Lexis-Nexis database of newspaper content to find the relevant articles from the Austin American-Statesman. This computerized search failed to find six of the 107 articles. Those articles contained 25 of the 1170 total paragraphs included in the data set. Using key words and dates, a specialized search was made for the missing articles. Apparently they were not included in the online versions of the newspaper for those days. Since most computer program content analysis projects use an online database to analyze the text, the team chose not to include those articles in the data set. Codebook. While the traditional codebook is the key document, almost an instruction manual for human coders, the codebook is only a jumping-off point for computer-assisted content analysis. The codebook provides a list of possible answers to the research question. Then the researchers must come up with the explicit directions on how the computer can find and retrieve that information. The first step was to cut down the codebook to a manageable level. The codebook for the traditional coders included 90 potential attributes and three different candidates. This would mean the possibility of 270 different answers to the question with each assertion. Since the research question deals with the image of the candidates, the list of attributes was reduced to roughly 25 separate codes for each candidate focusing on the areas of personal qualifications, character, and some biographical information. Some of the attributes included leadership, experience, ethnicity/race, personal wealth, and occupation. But these steps were just the beginning for creating a workable codebook for computer assisted content analysis. A process must still be created to allow the computer to find those attributes.

Authors: Conway, Mike.
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10
Lexis-Nexis database of newspaper content to find the relevant articles from the Austin
American-Statesman. This computerized search failed to find six of the 107 articles.
Those articles contained 25 of the 1170 total paragraphs included in the data set. Using
key words and dates, a specialized search was made for the missing articles. Apparently
they were not included in the online versions of the newspaper for those days. Since
most computer program content analysis projects use an online database to analyze the
text, the team chose not to include those articles in the data set.
Codebook. While the traditional codebook is the key document, almost an
instruction manual for human coders, the codebook is only a jumping-off point for
computer-assisted content analysis. The codebook provides a list of possible answers to
the research question. Then the researchers must come up with the explicit directions on
how the computer can find and retrieve that information.
The first step was to cut down the codebook to a manageable level. The
codebook for the traditional coders included 90 potential attributes and three different
candidates. This would mean the possibility of 270 different answers to the question with
each assertion. Since the research question deals with the image of the candidates, the list
of attributes was reduced to roughly 25 separate codes for each candidate focusing on the
areas of personal qualifications, character, and some biographical information. Some of
the attributes included leadership, experience, ethnicity/race, personal wealth, and
occupation. But these steps were just the beginning for creating a workable codebook for
computer assisted content analysis. A process must still be created to allow the computer
to find those attributes.


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