Because of the large numbers of possible participants recruited on the Internet within a short period of time, data analysis can often follow briefly after the recruitment process. In the case of the replication of the cup experiment, I collected 162 data sets within 8 hours (Reips, 2003). Log files contain information in a format of one line per accessed piece of material. However, for a useful statistical analysis, most often a "one row per participant" format is needed. A Web-based service to do this transformation is Scientific LogAnalyzer. Several methodological features specifically needed for the analyses of data collected using Web-based assessment methods were implemented in Scientific LogAnalyzer (e.g., the detection and handling of multiple sessions, computation of response times, and a module for analyzing and visualizing dropout). Figure 6.6 shows an example of the dropout tree generated by Scientific LogAnalyzer.
Each node can be expanded or collapsed, and absolute and relative frequencies of choices of paths are calculated and displayed. After a speedy analysis of even large log files (Reips & Stieger, 2004), Scientific LogAnalyzer creates output in HTML or a tab-delimited form suited for import into statistics software. A more detailed example of a log file analysis is available from Scientific LogAnalyzer's online help.
This section presented a description of how to create, conduct, and analyze data from a Web-based study with those tools my colleagues and I developed in our group. Of course there are alternative approaches. (For the design of simple, one-page Web surveys, use SurveyWiz; Birnbaum, 2000.) FactorWiz, also by Birnbaum (2000), is a tool for one-page within-subjects factorial experiments. Yule and Cooper (2003) recently published Express, a program for large-scale simulations also used for Internet-based experimenting. Web-based
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