Browsing by Author "Holzmeister F"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Examining the replicability of online experiments selected by a decision market.
    (Nature Research, 2024-11-19) Holzmeister F; Johannesson M; Camerer CF; Chen Y; Ho T-H; Hoogeveen S; Huber J; Imai N; Imai T; Jin L; Kirchler M; Ly A; Mandl B; Manfredi D; Nave G; Nosek BA; Pfeiffer T; Sarafoglou A; Schwaiger R; Wagenmakers E-J; Waldén V; Dreber A
    Here we test the feasibility of using decision markets to select studies for replication and provide evidence about the replicability of online experiments. Social scientists (n = 162) traded on the outcome of close replications of 41 systematically selected MTurk social science experiments published in PNAS 2015-2018, knowing that the 12 studies with the lowest and the 12 with the highest final market prices would be selected for replication, along with 2 randomly selected studies. The replication rate, based on the statistical significance indicator, was 83% for the top-12 and 33% for the bottom-12 group. Overall, 54% of the studies were successfully replicated, with replication effect size estimates averaging 45% of the original effect size estimates. The replication rate varied between 54% and 62% for alternative replication indicators. The observed replicability of MTurk experiments is comparable to that of previous systematic replication projects involving laboratory experiments.
  • Thumbnail Image
    Item
    Forecasting the publication and citation outcomes of COVID-19 preprints
    (The Royal Society, 2022-09) Gordon M; Bishop M; Chen Y; Dreber A; Goldfedder B; Holzmeister F; Johannesson M; Liu Y; Tran L; Twardy C; Wang J; Pfeiffer T
    Many publications on COVID-19 were released on preprint servers such as medRxiv and bioRxiv. It is unknown how reliable these preprints are, and which ones will eventually be published in scientific journals. In this study, we use crowdsourced human forecasts to predict publication outcomes and future citation counts for a sample of 400 preprints with high Altmetric score. Most of these preprints were published within 1 year of upload on a preprint server (70%), with a considerable fraction (45%) appearing in a high-impact journal with a journal impact factor of at least 10. On average, the preprints received 162 citations within the first year. We found that forecasters can predict if preprints will be published after 1 year and if the publishing journal has high impact. Forecasts are also informative with respect to Google Scholar citations within 1 year of upload on a preprint server. For both types of assessment, we found statistically significant positive correlations between forecasts and observed outcomes. While the forecasts can help to provide a preliminary assessment of preprints at a faster pace than traditional peer-review, it remains to be investigated if such an assessment is suited to identify methodological problems in preprints.
  • Thumbnail Image
    Item
    On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions
    (Elsevier Inc, 2023-11) Schaerer M; Plessis CD; Nguyen MHB; van Aert RCM; Tiokhin L; Lakens D; Clemente EG; Pfeiffer T; Dreber A; Johannesson M; Clark CJ; Uhlmann EL; Abraham AT; Adamus M; Akinci C; Alberti F; Alsharawy AM; Alzahawi S; Anseel F; Arndt F; Balkan B; Baskin E; Bearden CE; Benotsch EG; Bernritter S; Black SR; Bleidorn W; Boysen AP; Brienza JP; Brown M; Brown SEV; Brown JW; Buckley J; Buttliere B; Byrd N; Cígler H; Capitan T; Cherubini P; Chong SY; Ciftci EE; Conrad CD; Conway P; Costa E; Cox JA; Cox DJ; Cruz F; Dawson IGJ; Demiral EE; Derrick JL; Doshi S; Dunleavy DJ; Durham JD; Elbaek CT; Ellis DA; Ert E; Espinoza MP; Füllbrunn SC; Fath S; Furrer R; Fiala L; Fillon AA; Forsgren M; Fytraki AT; Galarza FB; Gandhi L; Garrison SM; Geraldes D; Ghasemi O; Gjoneska B; Gothilander J; Grühn D; Grieder M; Hafenbrädl S; Halkias G; Hancock R; Hantula DA; Harton HC; Hoffmann CP; Holzmeister F; Hoŕak F; Hosch A-K; Imada H; Ioannidis K; Jaeger B; Janas M; Janik B; Pratap KC R; Keel PK; Keeley JW; Keller L; Kenrick DT; Kiely KM; Knutsson M; Kovacheva A; Kovera MB; Krivoshchekov V; Krumrei-Mancuso EJ; Kulibert D; Lacko D; Lemay EP
    A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.
  • Thumbnail Image
    Item
    Predicting the replicability of social and behavioural science claims in COVID-19 preprints
    (Springer Nature Limited, 2024-12-20) Marcoci A; Wilkinson DP; Vercammen A; Wintle BC; Abatayo AL; Baskin E; Berkman H; Buchanan EM; Capitán S; Capitán T; Chan G; Cheng KJG; Coupé T; Dryhurst S; Duan J; Edlund JE; Errington TM; Fedor A; Fidler F; Field JG; Fox N; Fraser H; Freeman ALJ; Hanea A; Holzmeister F; Hong S; Huggins R; Huntington-Klein N; Johannesson M; Jones AM; Kapoor H; Kerr J; Kline Struhl M; Kołczyńska M; Liu Y; Loomas Z; Luis B; Méndez E; Miske O; Mody F; Nast C; Nosek BA; Simon Parsons E; Pfeiffer T; Reed WR; Roozenbeek J; Schlyfestone AR; Schneider CR; Soh A; Song Z; Tagat A; Tutor M; Tyner AH; Urbanska K; van der Linden S
    Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise ('beginners') updated their estimates and confidence in their judgements significantly more than groups with greater task expertise ('experienced'). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners' average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P < 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of 'fast science' under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.