Scientific LogAnalyzer: A Web-based tool for analyses of server log files in psychological research

  • Ulf-Dietrich ReipsEmail author
  • Stefan Stieger


Scientific LogAnalyzer is a platform-independent interactive Web service for the analysis of log files. Scientific LogAnalyzer offers several features not available in other log file analysis tools—for example, organizational criteria and computational algorithms suited to aid behavioral and social scientists. Scientific LogAnalyzer is highly flexible on the input side (unlimited types of log file formats), while strictly keeping a scientific output format. Features include (1) free definition of log file format, (2) searching and marking dependent on any combination of strings (necessary for identifying conditions in experiment data), (3) computation of response times, (4) detection of multiple sessions, (5) speedy analysis of large log files, (6) output in HTML and/or tab-delimited form, suitable for import into statistics software, and (7) a module for analyzing and visualizing drop-out. Several methodological features specifically needed in the analysis of data collected in Internet-based experiments have been implemented in the Web-based tool and are described in this article. A regression analysis with data from 44 log file analyses shows that the size of the log file and the domain name lookup are the two main factors determining the duration of an analysis. It is less than a minute for a standard experimental study with a 2 × 2 design, a dozen Web pages, and 48 participants (ca. 800 lines, including data from drop-outs). The current version of Scientific LogAnalyzer is freely available for small log files. Its Web address is


Emotional Infidelity Multiple Submission Time Variable Output Search Argument Internet Science 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. American Psychological Association (2001). Retaining raw data (§7.10). InPublication manual of the American Psychological Association (5th ed., p. 342). Washington, DC: Author.Google Scholar
  2. Berendt, B. (2002). Using site semantics to analyze, visualize, and support navigation.Data Mining & Knowledge Discovery,6,37–59.CrossRefGoogle Scholar
  3. Berendt, B., &Brenstein, E. (2001). Visualizing individual differences in Web navigation: STRATDYN, a tool for analyzing navigation patterns.Behavior Research Methods, Instruments, & Computers,33,243–257.CrossRefGoogle Scholar
  4. Birnbaum, M. H. (2004). Human research and data collection via the Internet.Annual Review of Psychology,55,803–832.PubMedCrossRefGoogle Scholar
  5. Eichstaedt, J. (2001). An inaccurate-timing filter for reaction time measurement by JAVA applets implementing Internet-based experiments.Behavior Research Methods, Instruments, & Computers,33,179–186.CrossRefGoogle Scholar
  6. Frick, A., Bächtiger, M. T., &Reips, U.-D. (2001). Financial incentives, personal information, and drop out in online studies. In U.-D. Reips & M. Bosnjak (Eds.),Dimensions of Internet science (pp. 209–219). Lengerich: Pabst.Google Scholar
  7. Reips, U.-D. (2000a). Das psychologische Experimentieren im Internet (2. überarbeitete Auflage) [Psychological experimenting on the Internet (Rev. ed.)]. In B. Batinic (Ed.),Internet für Psychologen (pp. 319–343). Göttingen: Hogrefe.Google Scholar
  8. Reips, U.-D. (2000b). The Web experiment method: Advantages, disadvantages, and solutions. In M. H. Birnbaum (Ed.),Psychological experiments on the Internet (pp. 89–114). San Diego: Academic Press.CrossRefGoogle Scholar
  9. Reips, U.-D. (2001). The Web Experimental Psychology Lab: Five years of data collection on the Internet.Behavior Research Methods, Instruments, & Computers,33,201–211.CrossRefGoogle Scholar
  10. Reips, U.-D. (2002a). Internet-based psychological experimenting: Five dos and five don’ts.Social Science Computer Review,20,241–249.Google Scholar
  11. Reips, U.-D. (2002b). Standards for Internet-based experimenting.Experimental Psychology,49, 243–256.PubMedGoogle Scholar
  12. Reips, U.-D. (2002c). Theory and techniques of Web experimenting. In B. Batinic, U.-D. Reips, & M. Bosnjak (Eds.),Online social sciences (pp. 229–259). Seattle: Hogrefe & Huber.Google Scholar
  13. Reips, U.-D. (2003). Psychologische Forschung zum und im Internet [Psychological research on and in the Internet].Psychologie in Österreich,22, 19–25.Google Scholar
  14. Reips, U.-D., &Neuhaus, C. (2002). WEXTOR: A Web-based tool for generating and visualizing experimental designs and procedures.Behavior Research Methods, Instruments, & Computers,34,234–240.CrossRefGoogle Scholar
  15. Richter, T., Naumann, J., &Noller, S. (2003). LOGPAT: A semiautomatic way to analyze hypertext navigation behavior.Swiss Journal of Psychology,62, 113–120.CrossRefGoogle Scholar
  16. Schmidt, W. C. (2001). Presentation accuracy of Web animation methods.Behavior Research Methods, Instruments, & Computers,33,187–200.CrossRefGoogle Scholar
  17. Schwarz, S., &Reips, U.-D. (2001). CGI versus JavaScript: A Web experiment on the reversed hindsight bias. In U.-D. Reips & M. Bosnjak (Eds.),Dimensions of Internet science (pp. 75–90). Lengerich: Pabst.Google Scholar
  18. Voracek, M., Stieger, S., &Gindl, A. (2001). Online replication of evolutionary psychology evidence: Sex differences in sexual jealousy in imagined scenarios of mate’s sexual versus emotional infidelity. In U.-D. Reips & M. Bosnjak (Eds.),Dimensions of Internet science (pp. 91–112). Lengerich: Pabst.Google Scholar

Copyright information

© Psychonomic Society, Inc. 2004

Authors and Affiliations

  1. 1.Sozial- und WirtschaftspsychologieUniversität ZürichZurichSwitzerland
  2. 2.Medical University of ViennaViennaAustria

Personalised recommendations