We propose an abductive logic of scientific inference for quantitative research. The advent of computational sociology has exposed the limitations of a deductive logic of inquiry for quantitative researchers due to a lack of traditional sociological variables and an abundance of unfamiliar variables and data formats, complicating hypothesis testing. In response, some researchers have embraced inductive inference, but inductive analysis without theoretical guidance risks producing atheoretical findings. An abductive logic of inquiry rests on developing new theoretical insights based on surprising research results in light of existing theories. In computational sociology, such surprising findings can be cultivated by taking advantage of the analytical potential of scaled-up data and developing flexible analytical and visualization procedures. We illustrate these tactics with a surprising finding in a study of the labor supply decisions of New York City yellow cab drivers.

中文翻译：

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数字时代定量研究探究的溯因逻辑

我们为定量研究提出了科学推理的溯因逻辑。由于缺乏传统的社会学变量和大量不熟悉的变量和数据格式，使得假设检验复杂化，计算社会学的出现暴露了定量研究人员探究演绎逻辑的局限性。作为回应，一些研究人员接受了归纳推理，但没有理论指导的归纳分析有产生非理论发现的风险。探究的溯因逻辑依赖于基于现有理论的惊人研究结果开发新的理论见解。在计算社会学中，可以通过利用放大数据的分析潜力和开发灵活的分析和可视化程序来培养这种令人惊讶的发现。