This dissertation proposed and undertook the meta-analysis of the anchoring effect, which utilizes a numeric suggestion to guide subsequent judgments of a target question. Anchoring is thought to operate through a selective accessibility function, such that one who receives an anchor then is primed to access specific information consistent with the number, which in turn pulls a final judgment closer to the anchoring point (Strack & Mussweiler, 1997). This phenomenon is of particular interest in the field of law, where anchors are frequently used to guide sentencing decisions and influence damage awards. Anchoring is frequently cited as a robust and strong effect, but the extent of this potency is unclear. Additionally, a number of moderators (chiefly, expertise and anchor extremity) have been hypothesized, but the true impact of these factors is obscured.Thus, a meta-analysis was conducted, using Schmidt and Hunter's (2014) variance-centric approach. A total of eighty-four effect sizes (Pearson's r) were calculated from the literature, and corrections for artifacts were made where possible. The resulting mean weighted effect size among all included anchoring studies was r = .401, with a corrected correlation estimate of .558. Initial results indicated greater variance than would be expected from sampling error alone, so a number of moderators were evaluated. Expertise was found to be an aggravating moderator; rather than mitigating the effect, having domain-relevant knowledge exacerbated it. Extremity of the anchor was found to slightly weaken the impact. The studies involving a law context (k = 34) were evaluated in a separate analysis, resulting in a mean weighted effect size of r = .360. Additional moderator analyses were conducted, with expertise following a similar pattern as the wider collection of studies. The meaningfulness of the anchor proved to significantly strengthen the effect.The implications of these results were discussed, specifically as they may apply in the courtroom. Future research directions and optimized practices are discussed. The limitations of this meta-analytic approach are acknowledged, and some reflect back upon the optimal practices for this line of research.
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