Conduct Web experiments using PHP, Part 2
By Paul Meagher2005-03-18
Categorical data analysis
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In the first article in this series, you developed PHP-based code that did the following:
• 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).
Tutorial Pages:
» Categorical data analysis
» 2x2 contingency tables
» Sampling model
» Discrete probability distributions
» Binomial sampling model
» Poisson sampling model
» Envisioning your results
» Eliciting your prior distribution
» Model fitting with chi-square
» Null effects model
» Independence model
» Prior model
» DOE explorer
» Explorer output
» Conclusions
» Resources
First published by IBM developerWorks
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