Lineup Bias Research Articles

Estimation of eyewitness error rates in fair and biased lineups.

The risk of mistaken identification for innocent suspects in lineups can be estimated by correcting the overall error rate by the number of people in the lineup. We compared this nominal size correction to a new effective size correction, which adjusts the error rate for the number of plausible lineup members. We hypothesized that (a) increasing lineup bias would increase misidentifications of a designated innocent suspect; (b) with the effective size correction, increasing lineup bias would also increase the estimate of innocent-suspect misidentifications; and (c) with the nominal size correction, lineup bias would have no effect on the estimate of innocent-suspect misidentifications. In a reanalysis of previous literature, we obtained 10 data sets from Open Science Framework. In three new experiments (Ns = 686, 405, and 1,531, respectively), participants observed a staged crime and completed a fair or biased lineup. In the reanalysis of previous literature, less than four of six lineup members were identified frequently enough to be classified as plausible, M = 3.78, 95% confidence interval [CI: 2.20, 5.36]. In the new experiments, increasing lineup bias increased mistaken identifications of a designated innocent suspect, odds ratio (OR) = 5.50, 95% CI [2.77, 10.95] and also increased the effective size-corrected estimate of innocent-suspect misidentifications, OR = 3.04, 95% CI [2.13, 4.33]. With the nominal size correction, lineup bias had no effect on the estimate of innocent-suspect misidentifications, OR = 0.84, 95% CI [0.60, 1.18]. Most lineups include a combination of plausible and implausible lineup members. Contrary to the nominal size correction, which ignores implausible lineup members, the effective size correction is sensitive to implausible lineup members and accounts for lineup bias when estimating the risk of innocent suspect misidentifications. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluating lineup fairness: Variations across methods and measures.

Triers of fact sometimes consider lineup fairness when determining the suggestiveness of an identification procedure. Likewise, researchers often consider lineup fairness when comparing results across studies. Despite their importance, lineup fairness measures have received scant empirical attention and researchers inconsistently conduct and report mock-witness tasks and lineup fairness measures. We conducted a large-scale, online experiment (N = 1,010) to examine how lineup fairness measures varied with mock-witness task methodologies as well as to explore the validity and reliability of the measures. In comparison to descriptions compiled from multiple witnesses, when individual descriptions were presented in the mock-witness task, lineup fairness measures indicated a higher number of plausible lineup members but more bias toward the suspect. Target-absent lineups were consistently estimated to be fairer than target-present lineups-which is problematic because it suggests that lineups containing innocent suspects are less likely to be challenged in court than lineups containing guilty suspects. Correlations within lineup size measures and within some lineup bias measures indicated convergent validity and the correlations across the lineup size and lineup bias measures demonstrated discriminant validity. The reliability of lineup fairness measures across different descriptions was low and reliability across different sets of mock witnesses was moderate to high, depending on the measure. Researchers reporting lineup fairness measures should specify the type of description presented, the amount of detail in the description, and whether the mock witnesses viewed target-present and/or -absent lineups. (PsycINFO Database Record

Can the Elimination Lineup Procedure Overcome Lineup Bias: Comparison of Procedures

Sequential and elimination lineup procedures were compared to examine the effect of clothing bias (i.e., innocent suspect is the only lineup member wearing similar clothing to that worn by the criminal at the time of the crime) on correct rejection rates. After witnessing a staged, videotaped theft, participants (N = 67) were presented with a sequential lineup procedure versus an elimination lineup procedure in which the lineup contained clothing bias or not. The elimination lineup procedure produced a significantly higher correct rejection rate than the sequential lineup procedure. The elimination procedure was able to neutralize the negative effect of clothing bias on correct rejection rates.

The Decomposition and Recomposition of Eyewitness Identifications: Eyewitness Reliability, Guessing and Lineup Bias

Traditional measures used by eyewitness researchers often paint an incomplete picture of the complex process that is an eyewitness identification. We seek to answer the following questions: 1) What proportion of suspect identifications are simply guesses, and what is the source of these guesses? Are they rooted in blind luck or do they stem from a poorly constructed (biased) array, in which the suspect (guilty or innocent) may be the only lineup member who resembles the perpetrator and is selected by someone who has a weak memory for the perpetrator’s appearance and merely selects the best match to their memory. 2) How biased is an array? While there are procedures which attempt to estimate lineup bias, they are often expensive (in participants and/or time), difficult to reproduce (live arrays), may be indirect (such as using mock witnesses to select “suspects” merely on the basis of suspect descriptions) and/or may not reflect the performance of the array in its natural circumstances as presented to actual witnesses. 3) What proportion of correct identifications are reliable ones? In particular, in a given set of eyewitness parameters, what proportion of eyewitness are able to identify the perpetrator when present and also correctly reject the perpetrator when absent? We describe a procedure for taking eyewitness summary data as input, and decomposing those numbers into the proportion of reliable eyewitnesses (witnesses who are accurate in both perpetrator-present and perpetrator-absent arrays), cautious eyewitnesses who never make a selection, and eyewitnesses who can be induced by variations in identification procedures to make guess. From these terms we calculate what percentage of hits in a given set of lineup parameters can be attributed to reliable eyewitnesses, how many are caused by random guessing, and how many result from guessing due to bias in presentations of arrays.

Getting the Picture: Airtime and Lineup Bias on Canadian Networks during the 2006 Federal Election

This research article addresses the issue of media bias as it played out on Canada’s three major television networks during coverage of the 2006 federal election. The data suggest that in spite of critics’ concerns that networks exhibit political bias, this was not evident. However, a more subtle and systemic bias was apparent. Front-runners (i.e., parties that polls indicated would do well) received substantially more coverage than other parties. Conversely, parties that were perceived as being less successful received less coverage than political front-runners. In addition, reports about front-runners were placed higher in the lineup. These empirical findings should be of interest to critics on both the right and left of the political spectrum who are concerned about the gatekeeping and agenda-setting functions of the media.

Validity of the mockwitness paradigm: testing the assumptions.

Mockwitness identifications are used to provide a quantitative measure of lineup fairness. Some theoretical and practical assumptions of this paradigm have not been studied in terms of mockwitnesses' decision processes and procedural variation (e.g., instructions, lineup presentation method), and the current experiment was conducted to empirically evaluate these assumptions. Four hundred and eighty mockwitnesses were given physical information about a culprit, received 1 of 4 variations of lineup instructions, and were asked to identify the culprit from either a fair or unfair sequential lineup containing 1 of 2 targets. Lineup bias estimates varied as a result of lineup fairness and the target presented. Mockwitnesses generally reported that the target's physical description was their main source of identifying information. Our findings support the use of mockwitness identifications as a useful technique for sequential lineup evaluation, but only for mockwitnesses who selected only 1 lineup member. Recommendations for the use of this evaluation procedure are discussed.

Statistical considerations when determining measures of lineup size and lineup bias

Much eyewitness research has centred on the development and evaluation of measures of lineup properties. These measures have proved useful to researchers and to expert witnesses who testify on eyewitness testimony. However, the inferential statistical properties of the measures themselves are rarely taken into account, despite the fact that most of them are derived from the mock witness task, which relies on an implicit probability model. This is a failing, and it is argued that estimates of lineup properties reported without inferential considerations may be misleading. Methods are suggested here for reasoning inferentially about lineup measures, and for planning studies to ensure adequate statistical power. Suggestions made by eyewitness researchers regarding the practical interpretation of lineup measures are critically evaluated, as are recommendations for new lineup measures, as suggested by Lindsay et al. (this issue). Copyright © 1999 John Wiley & Sons, Ltd.

Measuring the goodness of lineups: parameter estimation, question effects, and limits to the mock witness paradigm

Lineups and photospreads can be biased against a criminal suspect and there is a need to measure this bias. The mock witness method has been accepted by eyewitness scientists since the 1970s as the paradigm for generating the data on which various metrics of bias are based. We note the reasons that structural lineup bias can lead to mistaken identification and we discuss the statistical metrics that are proposed to measure the degree of bias. Using mock witness data, we show that estimates of bias can vary as a function of the question asked of the mock witnesses. In addition, we note various lineup biases that are not measured with the mock witness paradigm, such as procedural biases and problems with propitious heterogeneity. Copyright © 1999 John Wiley & Sons, Ltd.

Measures of lineup fairness: do they postdict identification accuracy?

Various measures have been proposed to test the fairness of lineups (Doob & Kirshenbaum, 1973; Malpass, 1981; Wells, Leippe and Ostrom, 1979). In three experiments, we examined the relationship of identification accuracy to measures of lineup bias (proportion of mock witness choices, defendant bias, functional size) and lineup size (effective size, number of acceptable lineup members). Lineup bias measures successfully postdict false positive choices, but only for some lineup procedures. Lineup size measures rarely postdict false positive decisions. Lineup fairness measures do not postdict false identifications from sequential lineups. Implications for measuring and reporting lineup fairness in court are discussed. Copyright © 1999 John Wiley & Sons, Ltd.

Standards for Evaluating the Fairness of Photograph Lineups

In theory a lineup is fair, from the perspective of the suspect, to the extent that the suspect is not distinctive in appearance from the other lineup members (foils) and all of the foils are reasonably similar in general appearance to the suspect. Lineup fairness can be divided into two dimensions-size and bias. Several proposed methods for empirically evaluating lineup fairness were assessed in terms of their sensitivity to absolute standards of lineup fairness and their discriminability, the extent that they differentiated between lineups designed to be fair and those constructed via less rigorous selection procedures. One of three lineup size measures and both measures of lineup bias showed significant discriminability. The sensitivity of the measures depended on the criteria arbitrarily chosen or, when using statistical significance, on sample size. Race of mock witness had some impact on evaluations of Black lineups but not on evaluations of White lineups. Results indicated that a sample size of 18 ...