diag_param {r4lineups}

Parameters for diagnosticity ratio

Description

This function calculates the parameters needed to calculate the diagnosticity ratio for several lineup pairs.

Usage

diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

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

A list containing k numeric vectors indexing lineup member positions for each lineup pair

k

A vector indexing number of members in each lineup pair. Must be specified by user (scalar).

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 TP 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.

• The following warning will appear if vectors comprising lineup lists are of different lengths: longer object length is not a multiple of shorter object length. This does not affect the accuracy of the function and can be ignored.

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

• n13: Number of mock witnesses who did not identify the suspect in the target absent condition

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)

#Pos list
lineup1_pos <- c(1, 2, 3, 4, 5, 6)
lineup2_pos <- c(1, 2, 3, 4, 5)
lineup3_pos <- c(1, 2, 3, 4)
pos_list <- list(lineup1_pos, lineup2_pos, lineup3_pos)
rm(lineup1_pos, lineup2_pos, lineup3_pos)

#Nominal size:
k <- c(6, 5, 4)

#Call:
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)