Coping With Plenitude: A Computational Approach to Selecting the Right Algorithm

Ramina Sotoudeh, Paul DiMaggio

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which method will perform best on never-before-seen empirical data sets. We apply this strategy to a class of methods that group respondents to attitude surveys according to whether they share construals of a given domain. This allows us to identify the relative strengths and weaknesses of the methods we consider, including relational class analysis, correlational class analysis, and eight other such variants. Results support the “no free lunch” view that researchers should abandon the quest for one best algorithm in favor of matching algorithms to kinds of data for which each is most appropriate and provide direction on how to do so.

    Original languageEnglish (US)
    JournalSociological Methods and Research
    DOIs
    StateAccepted/In press - 2021

    Keywords

    • algorithm choice
    • attitude data
    • clustering
    • computational social science
    • culture
    • meta-features
    • relational class analysis

    ASJC Scopus subject areas

    • Social Sciences (miscellaneous)
    • Sociology and Political Science

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