This function calculates the parameters needed to calculate the diagnosticity ratio for several lineup pairs.
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
- pos_list
Suspect positions for each lineup pair. Supply a numeric vector with one position per pair when TP and TA suspect positions match, or a list whose elements contain either one shared position or two positions in TP, TA order.
- k
A vector indexing number of members in each lineup pair. Must be specified by user (scalar).
Value
Returns a dataframe containing:
n11: Number of mock witnesses who identified the suspect in the target present condition
n21: Number of mock witnesses who did not identify the suspect in the target present condition
n12: Number of mock witnesses who identified the suspect in the target absent condition
n22: Number of mock witnesses who did not identify the suspect in the target absent condition
Details
Lineup pairs consist of one lineup in which the target was present (TP) and one lineup in which the target was absent (TA).
Each lineup pair must occupy corresponding positions in the TA and TP lists.
Example:
For a lineup pair A that consists of (1)TP lineup and (2)TA lineup: A(1) is the first vector in the TP list A(2) is the first vector in the TA list
The order in which nominal size for each lineup pair is listed must also correspond with the positions of each respective lineup in the lineup lists (i.e., if lineup 1 has k = 6, then the first element of vector 'k' = 6)
Data must be in a list format. This allows the function to compare lineups in which the number of choices and number of lineup members differs.
TP and TA vectors may have different sample sizes; they are counted independently and are never compared element by element.
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)
# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)
#Nominal size:
k <- c(6, 5, 4)
#Call:
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)