On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms

Piet Daas (Corresponding author), Wolter Hassink, Bart Klijs

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Abstract

A statistical classification model was developed to identify online platform organizations based on the texts on their website. The model was subsequently used to identify all (potential) platform organizations with a website included in the Dutch Business Register. The empirical outcomes of the statistical model were plausible in terms of the words and the bimodal distribution of fitted probabilities, but the results indicated an overestimation of the number of platform organizations. Next, the external validity of the outcomes was investigated through a survey of the organizations that were identified as a platform organization by the statistical classification model. The response by the organizations to the survey confirmed a substantial number of type-I errors. Furthermore, it revealed a positive association between the fitted probability of the text-based classification model and the organization’s response to the survey question on being an online platform organization. The survey results indicated that the text-based classification model can be used to obtain a subpopulation of potential platform organizations from the entire population of businesses with a website. This subpopulation may form a good starting point to study platform organizations in more detail.
Original languageEnglish
Pages (from-to)190-211
Number of pages21
JournalJournal of Official Statistics
Volume40
Issue number1
DOIs
Publication statusPublished - 1 Mar 2024

Keywords

  • extrernal validation
  • type I error
  • machine learning
  • web pages

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