Conduct Web experiments using PHP, Part 2
By Paul Meagher2005-03-18
Categorical data analysis
• Generated a 1-, 2- or 3-factor experimental design with a randomized order of presentation
• Assigned a Web offer version (factor-level combination) to each new and returning Web site visitor
• Logged whether a Web site visitor responded to the particular Web offer version they were assigned (along with the number of exposures and the time it took to elicit their response)
My purpose in this article is to analyze the resulting data. (You will absorb the reasoning in this article more easily if you have read or are familiar with concepts discussed in the prerequisite articles suggested in Resources.)
Categorical data analysis
Categorical data analysis, or CDA, is concerned with the simulation and analysis of data measured using a categorical scale of measurement. CDA is relevant to your goals because the WebOffer data table consists of two categorical explanatory variables (an image factor and a text factor) and the main categorical response variable (joined). To develop models for how these variables might be related, you will find CDA concepts and techniques useful.
Table 1 displays the subset of the WebOffer columns that I will focus on in this article.
Table 1. WebOffer columns to be analyzed
| image | text | joined |
| person | long | NULL |
| person | short | y |
| product | short | NULL |
| ... | ||
Observe that none of the columns contains numeric data, so the numeric operations you can perform on this data given your measurement scale are limited to counting the number of times that particular factor-level combinations were present when a response occurred (such as, computing joint frequency counts).
First published by IBM developerWorks
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