homog_diag_boot: Homogeneity of diagnosticity ratio with bootstrapped CIs

In r4lineups: Statistical Inference on Lineup Fairness

Description

Function for computing bootstrapped estimates of homogeneity of diagnosticity ratio

Usage

homog_diag_boot(lineup_pres_list, lineup_abs_list, k, R = 100)

Arguments

lineup_pres_list	A list containing k vectors of lineup choices for k lineups, in which the target was present
lineup_abs_list	A list containing k vectors of lineup choices for k lineups, in which the target was absent
k	Number of members in lineup. Must be specified by user (scalar).
R	Number of bootstrap replications. Defaults to R = 100.

Details

Computes bootstrapped diagnosticity ratio with chi-squared estimate, significance level and confidence intervals for k lineup pairs

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Target present data:
A <-  round(runif(100,1,6))
B <-  round(runif(70,1,5))
C <-  round(runif(20,1,4))
lineup_pres_list <- list(A, B, C)
rm(A, B, C)

#Target absent data:
A <-  round(runif(100,1,6))
B <-  round(runif(70,1,5))
C <-  round(runif(20,1,4))
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

r4lineups documentation built on May 2, 2019, 7:10 a.m.