Kellogg School of Management, Northwestern University, Evanston, IL, 60208, USA.
Psychometrika. 2018 Mar;83(1):255-271. doi: 10.1007/s11336-017-9571-z. Epub 2017 May 19.
We introduce multilevel multivariate meta-analysis methodology designed to account for the complexity of contemporary psychological research data. Our methodology directly models the observations from a set of studies in a manner that accounts for the variation and covariation induced by the facts that observations differ in their dependent measures and moderators and are nested within, for example, papers, studies, groups of subjects, and study conditions. Our methodology is motivated by data from papers and studies of the choice overload hypothesis. It more fully accounts for the complexity of choice overload data relative to two prior meta-analyses and thus provides richer insight. In particular, it shows that choice overload varies substantially as a function of the six dependent measures and four moderators examined in the domain and that there are potentially interesting and theoretically important interactions among them. It also shows that the various dependent measures have differing levels of variation and that levels up to and including the highest (i.e., the fifth, or paper, level) are necessary to capture the variation and covariation induced by the nesting structure. Our results have substantial implications for future studies of choice overload.
我们介绍了多层次多元荟萃分析方法，旨在解决当代心理学研究数据的复杂性。我们的方法直接对一组研究中的观测值进行建模，考虑到观测值在因变量和调节变量方面的差异，以及它们在例如论文、研究、受试者群体和研究条件等方面的嵌套关系所引起的变化和协变。我们的方法是受选择过载假说的论文和研究数据所启发。与之前的两项荟萃分析相比，它更充分地考虑了选择过载数据的复杂性，从而提供了更深入的见解。特别是，它表明选择过载在很大程度上取决于该领域中六个因变量和四个调节变量的函数关系，并且它们之间可能存在有趣且具有理论意义的相互作用。它还表明，各种因变量的变化程度不同，并且直到并包括最高（即第五个，即论文）水平都有必要捕捉嵌套结构引起的变化和协变。我们的结果对未来的选择过载研究具有重要意义。
