Failures to replicate a key result of the selective accessibility theory of anchoring

Numerical anchoring effects describe the assimilative effect of a previously presented number on subsequent numerical estimates. Such effects are robust and consequential. A number of different accounts have been proposed to explain these effects. What is currently unclear is under which situations different mechanisms play more or less critical roles. An extant test from the literature is proposed as a ‘signature test’ for the operation of selective accessibility mechanisms. Four experiments were conducted to ascertain the evidence for selective accessibility with this test, tests that subsequently failed. A fifth experiment employed a different methodology, and again failed to show evidence for selective accessibility. Subsequent discussion suggests that the robustness of anchoring effects is remarkable, but the theoretical basis for some previous tests of the selective accessibility account of anchoring is shaky, and we advise against its use in this capacity

The open anchoring quest dataset:anchored estimates from 96 studies on anchoring effects

People’s estimates are biased toward previously considered numbers (anchoring). We have aggregated all available data from anchoring studies that included at least two anchors into one large dataset. Data were standardized to comprise one estimate per row, coded according to a wide range of variables, and are available for download and analyses online (https://metaanalyses.shinyapps.io/OpAQ/). Because the dataset includes both original and meta-data it allows for fine-grained analyses (e.g., correlations of estimates for different tasks) but also for meta-analyses (e.g., effect sizes for anchoring effects)

The open anchoring quest dataset: Anchored estimates from 96 Studies on anchoring effects

People’s estimates are biased toward previously considered numbers (anchoring). We have aggregated all available data from anchoring studies that included at least two anchors into one large dataset. Data were standardized to comprise one estimate per row, coded according to a wide range of variables, and are available for download and analyses online (https://metaanalyses.shinyapps.io/OpAQ/). Because the dataset includes both original and meta-data it allows for fine-grained analyses (e.g., correlations of estimates for different tasks) but also for meta-analyses (e.g., effect sizes for anchoring effects)