Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology [Registered Report Stage 1 Protocol]
2020
Abstract
Although variation in effect sizes and predicted values among
studies of similar phenomena is inevitable, there is evidence that such
variation may far exceed what might be produced by sampling error. This
evidence comes from a growing meta-research agenda that seeks to describe and
explain variation in reliability of scientific results. One possible
explanation for variation among results is differences among researchers in the
decisions they make regarding statistical analyses. The best evidence for this
comes from a recent social science study that asked 29 different research teams
to answer the same question independently by analyzing the same data set. Although
many of the effect sizes were similar, some differed substantially from the
average. We plan to implement an analogous study in ecology and evolutionary
biology, a field in which there has been no empirical exploration of the
variation in effect sizes or model predictions of dependent variables generated
by analytical decisions of different researchers. We have obtained two unpublished
data sets, one from evolutionary ecology and one from conservation ecology, and
we will recruit as many independent scientists as possible to conduct analyses
of these data to answer prespecified research questions. We will also recruit
peer reviewers to rate the analyses based on their methodological descriptions
so that we have multiple ratings of each analysis. Next we will quantify the
variability in choices of independent variables among analyses and, using
meta-analytic techniques, describe and quantify the degree of variability among
effect sizes and predicted values for each of the data sets. Finally, we will
quantify the extent to which deviation of individual effect sizes and predicted
values from the meta-analytic mean for that data set is explained by peer
review ratings and by the ‘uniqueness’ of the set of variables chosen for the
analysis by each team.
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