We studied publication bias in the social sciences by analyzing a known population of conducted studies--221 in total--in which there is a full accounting of what is published and unpublished. We leveraged Time-sharing Experiments in the Social Sciences (TESS), a National Science Foundation-sponsored program in which researchers propose survey-based experiments to be run on representative samples of American adults. Because TESS proposals undergo rigorous peer review, the studies in the sample all exceed a substantial quality threshold. Strong results are 40 percentage points more likely to be published than are null results and 60 percentage points more likely to be written up. We provide direct evidence of publication bias and identify the stage of research production at which publication bias occurs: Authors do not write up and submit null findings.

Many scholars have raised concerns about the credibility of empirical findings in psychology, arguing that the proportion of false positives reported in the published literature dramatically exceeds the rate implied by standard significance levels. A major contributor of false positives is the practice of reporting a subset of the potentially relevant statistical analyses pertaining to a research project. This study is the first to provide direct evidence of selective underreporting in psychology experiments. To overcome the problem that the complete experimental design and full set of measured variables are not accessible for most published research, we identify a population of published psychology experiments from a competitive grant program for which questionnaires and data are made publicly available because of an institutional rule. We find that about 40% of studies fail to fully report all experimental conditions and about 70% of studies do not report all outcome variables included in the questionnaire. Reported effect sizes are about twice as large as unreported effect sizes and are about 3 times more likely to be statistically significant.

Weighting techniques are employed to generalize results from survey experiments to populations of theoretical and substantive interest. Although weighting is often viewed as a second-order methodological issue, these adjustment methods invoke untestable assumptions about the nature of sample selection and potential heterogeneity in the treatment effect. Therefore, although weighting is a useful technique in estimating population quantities, it can introduce bias and also be used as a researcher degree of freedom. We review survey experiments published in three major journals from 2000–2015 and find that there are no standard operating procedures for weighting survey experiments. We argue that all survey experiments should report the sample average treatment effect (SATE). Researchers seeking to generalize to a broader population can weight to estimate the population average treatment effect (PATE), but should discuss the construction and application of weights in a detailed and transparent manner given the possibility that weighting can introduce bias.

The accuracy of published findings is compromised when researchers fail to report and adjust for multiple testing. Preregistration of studies and the requirement of preanalysis plans for publication are two proposed solutions to combat this problem. Some have raised concerns that such changes in research practice may hinder inductive learning. However, without knowing the extent of underreporting, it is difficult to assess the costs and benefits of institutional reforms. This paper examines published survey experiments conducted as part of the Time-sharing Experiments in the Social Sciences program, where the questionnaires are made publicly available, allowing us to compare planned design features against what is reported in published research. We find that: (1) 30% of papers report fewer experimental conditions in the published paper than in the questionnaire; (2) roughly 60% of papers report fewer outcome variables than what are listed in the questionnaire; and (3) about 80% of papers fail to report all experimental conditions and outcomes. These findings suggest that published statistical tests understate the probability of type I errors.

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.