Sample size and informetric model goodness-of-fit outcomes: a search engine log case study

  • Authors:

  • Isola Ajiferuke;Dietmar Wolfram;Felix Famoye

  • Affiliations:

  • Faculty of Information and Media Studies, University of Western Ontario, London, ON, Canada;School of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, WI, USA;Department of Mathematics, Central Michigan University, Mount Pleasant, MI, USA

  • Venue:
  • Journal of Information Science
  • Year:
  • 2006

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Abstract

The influence of sample size on informetric characteristics is examined to determine whether theoretical mathematical models can adequately fit large data sets. Two large data sets of queries submitted to the Excite search service were sampled for search characteristics (term frequencies, terms used per query, pages viewed per query, queries submitted per session) producing data sets of various sizes that were fitted to theoretical models to determine how the sample may influence a model's goodness-of-fit. Although theoretical models could adequately fit smaller data sets of up to 5000 observations in some cases, larger data sets could not be satisfactorily fitted using several goodness-of-fit techniques. Investigators must take into account that sample size does influence goodness-of-fit outcomes. The nature of the data and not the limitations of given goodness-of-fit tests results in significant outcomes. Such goodness-of-fit tests should be used for comparative purposes, rather than significance testing.