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Biomedical subjects

Stefan Stieger

Publications and source records attributed to Stefan Stieger.

2 recordsLinked to original sources

Using instant messaging for Internet-based interviews.

One method of data collection that has rarely been applied online is the one-on-one interview. Because of its widespread use, the Internet-based service instant messaging (IM) seems to be suitable to conduct scientific online interviews. A unique benefit of IM is the existence of public address books. These can be used both as a sampling frame and as a cross-reference to validate respondents' demographic data. The feasibility of IM interviews was examined in a WWW survey as well as in actual IM interviews that were combined with an experimental manipulation of the request for participation. On the basis of self-reports, respondent behavior, and data in the address books, the studies have demonstrated that the risk of receiving false data in IM interviews is small. Not only is the quality of the obtainable data satisfying but the contact rate, response rate, and retention rate as well. Moreover, the experimental test demonstrated that the response rate is influenced by the information provided in the chat request. On the basis of the study results, recommendations are given as to when and how IM interviews should be used as a data collection method.

Adolescent↗

Scientific LogAnalyzer: a web-based tool for analyses of server log files in psychological research.

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 x 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 http://genpsylab-logcrunsh.unizh.ch/.

Data Interpretation, Statistical↗