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Homogeneity of diagnosticity ratio with bootstrapped CIs

Source: R/homog_diag_boot.R
homog_diag_boot.Rd

Function for computing bootstrapped estimates of homogeneity of diagnosticity ratio

Usage

homog_diag_boot(
  lineup_pres_list,
  lineup_abs_list,
  k,
  R = 100,
  pos_list = NULL,
  seed = NULL,
  conf.level = 0.95
)

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.

pos_list

Suspect positions for each lineup pair, in the same format as diag_param(). This is required; earlier releases attempted to infer suspect positions from observed choices, which cannot be done validly.

seed

Optional integer seed for reproducible resampling.

conf.level

Confidence level for the percentile intervals and the per-pair intervals, and the complement of the level at which heterogeneity is declared. Defaults to 0.95.

Value

Invisibly returns a list containing the observed mean diagnosticity, chi-square statistic with degrees of freedom and asymptotic p-value, percentile intervals, a heterogeneous flag, a parameters data frame of per-pair ratios and intervals, all attempted bootstrap draws (failed rows retained as NA), n_successful, n_failed, and R. Intervals are conditional on successful draws when failures occur.

Details

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

The interval on the pooled ratio and the chi-square answer different questions, and can disagree. See homog_diag for the distinction; as there, a note is printed whenever the k ratios are heterogeneous and the pooled estimate should not be reported on its own.

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 <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)

#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

pos_list <- c(3, 2, 1)
k <- c(6, 5, 4)
homog_diag_boot(lineup_pres_list, lineup_abs_list, k, R = 20,
               pos_list = pos_list, seed = 1)
#> Diagnosticity ratio for each lineup pair:
#>  pair     d       95% CI
#>     1 1.000 [0.54, 1.84]
#>     2 1.000 [0.52, 1.94]
#>     3 1.000 [0.34, 2.93]
#> 
#> Mean diagnosticity ratio is 1 
#> Confidence intervals (percentile) 0.757 1.395 
#> Chi-squared estimate is 0 
#> Confidence intervals (percentile): 0.333 4.967 
#> Sig (asymptotic, df = 2):  1 
#> 
#> Note: no evidence of heterogeneity (p >= 0.05), so the ratios are
#> consistent with a common value and the pooled mean is a reasonable summary.
#>