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.

社会学家越来越多地面临着在代表相同任务的合理方法的竞争算法中的选择，而在它们之间进行选择时几乎没有指导。我们开发了一种策略，该策略使用模拟数据来确定不同方法表现良好的条件，并应用从模拟中学到的知识来预测哪种方法在前所未有的经验数据集上表现最佳。我们将此策略应用于一类方法，这些方法根据受访者是否共享给定领域的解释将受访者分组进行态度调查。这使我们能够确定我们考虑的方法的相对优势和劣势，包括关系类分析、相关类分析和其他八个此类变体。