Department of Psychology, University of Illinois at Urbana-Champaign.
Department of Information Systems and Decision Sciences, California State University, Fullerton.
Psychol Methods. 2019 Apr;24(2):135-152. doi: 10.1037/met0000196. Epub 2018 Oct 25.
Statistical analyses of data often add some additional constraints to a theory and leave out others, so as to convert the theory into a testable hypothesis. In the case of binary data, such as yes/no responses, or such as the presence/absence of a symptom or a behavior, theories often actually predict that certain response probabilities change monotonically in a specific direction and/or that certain response probabilities are bounded from above or below in specific ways. A regression analysis is not really true to such a theory in that it may leave out parsimonious constraints and in that extraneous assumptions like linearity or log-linearity, or even the assumption of a functional relationship, are dictated by the method rather than the theory. That mismatch may well bias the results of empirical analysis and jeopardize attempts at meaningful replication of psychological research. This tutorial shows how contemporary order-constrained methods can shed more light on such questions, using far weaker auxiliary assumptions, while also formulating more detailed, nuanced, and concise hypotheses, and allowing for quantitative model selection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
统计分析数据通常会给理论增加一些额外的约束，而排除其他的约束，从而将理论转化为可检验的假设。在二进制数据的情况下，例如是/否的回答，或者是症状或行为的存在/不存在，理论通常实际上预测某些反应概率会沿着特定的方向单调变化，或者某些反应概率以上或以下的方式受到特定的限制。回归分析并不真正符合这样的理论，因为它可能会忽略简约的约束，而且像线性或对数线性，甚至是功能关系的假设，都是由方法而不是理论决定的。这种不匹配很可能会影响实证分析的结果，并危及对心理研究有意义的复制的尝试。本教程展示了如何使用更弱的辅助假设，通过当代的有序约束方法，来更深入地研究这些问题，同时还可以制定更详细、细致和简洁的假设，并允许进行定量模型选择。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
