Confidence Intervals for Proportion

Description

Function to compute ci high for each foil in a lineup

Usage

allfoil_cihigh(linetabprops, sumlineup)

Arguments

References

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

Bias for each lineup member

Description

Function to compute bias for each lineup member (assuming foil is suspect, from Malpass, 1981)

Usage

allfoilbias(lineup_table, target_pos, k)

Arguments

References

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

Examples

lineup_vec <- round(runif(100, 1, 6))
lineup_table <- table(lineup_vec)
x <- allfoilbias(lineup_table, 5, 6)

Lineup proportion for all lineup members

Description

Computes lineup proportion for each member in a lineup

Usage

allprop(lineup_vec, k)

Arguments

Value

Returns a vector containing lineup proportion for each lineup member

References

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

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
x <- allprop(lineup_vec, k = 6)

Chi-squared estimate of homogeneity of diagnosticity ratio

Description

Function for getting chi-squared value for homogeneity of diagnosticity ratios

Usage

chi_diag(df)

Arguments

Details

• To compute linedf, use the diag_param helper function.

• To compute df, apply ln_diag_ratio, var_lnd & d_weights functions to linedf, then bind results into one dataframe (see Examples)

• The order in which the estimates are bound together (i.e., their position in the dataframe) is important, and should always be as follows: row 1: var, row 2: lnd, row 3: wi.

Value

Chi squared estimate of homogeneity of diagnosticity ratios for k independent lineups

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)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Get ln(d), variance of ln(d) & d weights:
ratio <- ln_diag_ratio(linedf)
var <- var_lnd(linedf)
wi <- d_weights(linedf)

#Bind estimates into one df of 3 rows & x observations
#(see Details above)
df <- t(cbind(ratio, var, wi))

#Call:
chi_diag(df)

Comparing Effective Size: Base function for bootstrapping

Description

A base function for bootstrapping a dataframe of choices for 2 independent lineups

Usage

compare_eff_sizes.boot(linedf, d)

Arguments

Details

The approach here is to compute the effective size of each lineup separately, and to take the difference between them. This is then bootstrapped, and if the bootstrap does not contain 0, we conclude the effective size estimates are different at p = alpha

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

Mean diagnosticity ratio for k lineup pairs

Description

Function for computing pooled estimator from a set of k diagnosticity ratios

Usage

d_bar(df)

Arguments

Details

. The order in which the estimates are bound together (i.e., their position in the dataframe) is important, and should always be as follows:

• row 1: var

• row 2: lnd

• row 3: wi

Value

Mean diagnosticity ratio for k independent lineups

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)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Compute ln(d):
ratio <- ln_diag_ratio(linedf)

#Compute variance of ln(d):
var <- var_lnd(linedf)

#Compute weights for pooled estimator:
wi <- d_weights(linedf)

#Bind ln(d), variance of ln(d) and weights into one df (of 3 rows & x observations)
#(see Details above):
df <- t(cbind(ratio, var, wi))

#Call:
d_bar(df)

Diagnosticity ratio weights

Description

Function to compute weights of each diagnosticity ratio for k lineup pairs

Usage

d_weights(linedf)

Arguments

Details

In order to obtain a pooled estimate of a set of diagnosticity ratios, we use a weight for each ratio that is equal to the inverse of its variance.

To get linedf, use the diag_param helper function

diag_param returns a dataframe containing the following:

• 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

Value

A dataframe with one column containing weights for each pair of lineups for which diagnosticity ratio is being calculated.

References

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

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)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Call:
wi <- d_weights(linedf)

Helper function

Description

Checks that number of lineup choices contained in a vector is accurate

Usage

datacheck1(lineup_vec, k)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the dataframe due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate.

Helper function

Description

Checks that number of lineup choices contained in a vector is accurate

Usage

datacheck2(lineup_pres, lineup_abs, k1, k2)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the dataframe due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate

Helper function

Description

Checks that number of lineup choices contained in a data table is accurate

Usage

datacheck3(lineup_table, k)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the data due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate.

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

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)

Diagnosticty Ratio (Tredoux, 1998)

Description

Computes Wells's adjusted diagnosticity ratio for one lineup pair (see: Tredoux, 1998)

Usage

diag_ratio_T(lineup_pres, lineup_abs, pos_pres, pos_abs, k1, k2)

Arguments

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

#Data:
lineup_pres <- round(runif(100, 1, 6))
lineup_abs <- round(runif(70, 1, 5))
pos_pres <- 3
pos_abs <- 5

#Call:
diag_ratio_T(lineup_pres, lineup_abs, pos_pres, pos_abs, 6, 5)
diag_ratio_T(lineup_pres, lineup_abs, 3, 5, 6, 5)

Diagnosticity Ratio (Wells & Lindsay, 1980; Wells & Turtle, 1986)

Description

Computes Wells' diagnosticity ratio for one lineup pair

Usage

diag_ratio_W(lineup_pres, lineup_abs, pos_pres, pos_abs, k1, k2)

Arguments

References

Wells, G. L., & Lindsay, R. C. L. (1980).On estimating the diagnosticity of eyewitness nonidentifications.Psychological Bulletin, 88, 776-784.

Wells, G. L., & Turtle, J. W. (1986). Eyewitness identification: The importance of lineup models. Psychological Bulletin, 99, 320-329.

Examples

#Data:
lineup_pres <- round(runif(100, 1, 6))
lineup_abs <- round(runif(70, 1, 5))
pos_pres <- 3
pos_abs <- 5

#Call:
diag_ratio_W(lineup_pres, lineup_abs, pos_pres, pos_abs, 6, 5)
diag_ratio_W(lineup_pres, lineup_abs, 3, 5, 6, 5)

Effective Size per Foils

Description

Function for computing effective size (Malpass, 1981) counting foils who fall within the CI for chance guessing

Usage

eff_size_per_foils(lineup_vec, target_pos, k, conf = 0.95)

Arguments

References

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

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

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- c(1, 2, 3, 4, 5, 6)

#Call:
eff_size_per_foils(lineup_vec, target_pos, 6)
eff_size_per_foils(lineup_vec, target_pos, conf = 0.95, 6)

Master Function: Comparing Effective Size

Description

Function for comparing effective size of two independent lineups (Tredoux, 1998)

Usage

effsize_compare(linedf)

Arguments

Details

This function is a master function, calling other functions it needs, and reporting results in some detail

Value

Effective size, significance level, and confidence intervals (95 normal theory, percentile & bias-corrected)

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:

lineup_vec1 <- round(runif(100, 1, 6))
lineup_vec2 <- round(runif(100, 1, 6))
linedf <- as.data.frame(cbind(lineup_vec1, lineup_vec2))

#Call:
x <- effsize_compare(linedf)

Effective Size (Tredoux, 1998)

Description

Function for generating Effective Size (Tredoux, 1998)

Usage

esize_T(lineup_table)

Arguments

Details

Reduces the size of a lineup from a (corrected) nominal starting value by the degree to which members are, in sum, chosen below the level of chance expectation.

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

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_table <- table(lineup_vec)

#Call:
e <- esize_T(lineup_table)

Bootstrapped Effective Size (Tredoux, 1998)

Description

Base function for generating boostrapped Effective Size (Tredoux, 1998)

Usage

esize_T_boot(lineup_table, d)

Arguments

Details

Reduces the size of a lineup from a (corrected) nominal starting value by the degree to which members are, in sum, chosen below the level of chance expectation.

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Effective Size with Confidence Intervals from Normal Theory (Tredoux, 1998)

Description

Function for generating Effective Size (Tredoux, 1998) with CIs from normal theory

Usage

esize_T_ci_n(lineup_table, alpha)

Arguments

Details

Reduces the size of a lineup from a (corrected) nominal starting value by the degree to which members are, in sum, chosen below the level of chance expectation

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

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_table <- table(lineup_vec)

#Call:
e_ci <- esize_T_ci_n(lineup_table, .95)

Effective Size

Description

Function for computing Effective Size

Usage

esize_m(lineup_table, k, both = FALSE)

Arguments

Details

Reduces the size of a lineup from a (corrected) nominal starting value by the degree to which members are, in sum, chosen below the level of chance expectation.

Value

Malpass's original & adjusted estimates of effective size

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

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
esize_m(lineup_vec, 6, both = TRUE)
esize_m(lineup_vec, 6)

Bootstrapped Effective Size

Description

Base function for computing bootstrapped effective size

Usage

esize_m_boot(lineup_vec, d, k)

Arguments

Details

Function to call when bootstrap resampling using boot function (in package 'boot')

Value

If printarg=FALSE, provides only Malpass's priginal calculation of effective size

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Get boot object:
bootobject <- boot::boot(lineup_vec, esize_m_boot, k = 6, R=1000)
bootobject

#To get confidence intervals:
cis <- boot::boot.ci(bootobject, conf = 0.95, type = "all")

Compute similarity of faces in a lineup; experimental function

Description

Function to compute the degree to which each face in a set of faces loads onto a common factor computed from the faces.

Usage

face_sim()

Details

• The faces need to be standardized for inter-pupil distance, and for pupil location prior to running the function

• The user will be asked to choose a set of faces through a dialog box. These should be jpeg files.

• There is no argument to the function; it gets what it needs from the dialog box. The function prints the lineup array to the viewer pane in R, and reports the loading of each face on the first common factor in a factor analysis (using fa in package psych)

References

Tredoux, C. (2002). A direct measure of facial similarity and its relation to human similarity perceptions. Journal of Experimental Psychology: Applied, 8(3), 180-193. doi:10.1037/1076-898x.8.3.180

Functional Size

Description

This function computes functional size (N/D), where N = number of mock witnesses, & D = number of mock witnesses choosing the target

Usage

func_size(lineup_vec, target_pos)

Arguments

Details

An estimation of the number of plausible lineup members in a given lineup.

Value

Functional size of lineup

References

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

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- 3

#Call:
x <- func_size(lineup_vec, target_pos)
x <- func_size(lineup_vec, 3)

Bootstrapped Functional Size

Description

This function is a base function for the bootstrapping that ensues to compute bootstrapped confidence intervals for the estimate of functional size

Usage

func_size.boot(lineup_vec, d = d, target_pos)

Arguments

Details

Function is called bootstrap resampling using boot function (in package 'boot') This function is never called by the user - it is called in the functional size master function

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Functional Size with Bootstrapped Confidence Intervals

Description

This function is a master function, calling other functions it needs, and reporting results in some detail

Usage

func_size_report(lineup_vec, target_pos, k, R)

Arguments

Details

Function depends on functions from package 'boot'

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- 3

#Call:
x <- func_size_report(lineup_vec, target_pos, 6)
x <- func_size_report(lineup_vec, 3, 6)

Percentile of Bootstrapped Lineup Proportion

Description

Function for computing arbitrary percentile of bootstrapped lineup proportion

Usage

gen_boot_propci(lineuprops, perc = 0.05)

Arguments

Details

Can be used to calculate confidence intervals at desired level of alpha

Value

Arbitrary percentile of bootstrapped lineup proportion

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- 3
k <- 6

#Bootstrap data:
lineup_boot_df <- gen_boot_samples(lineup_vec, 1000)

#Compute proportion for bootstrap samples:
lineuprops <- gen_lineup_prop(lineup_boot_df, target_pos, 6)

#Get boot CIs:
prop_bootci_lower <- gen_boot_propci(lineuprops, perc = .025)
prop_bootci_upper <- gen_boot_propci(lineuprops, perc = .975)

Descriptive statistics for bootstrapped lineup proportion

Description

Function for computing mean. med and se of boot proportion

Usage

gen_boot_propmean_se(lineuprops)

Arguments

Value

Mean, median, standard deviation, standard error & 95 CIs of lineup proportion across a bootstrapped dataframe

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- 3

#Bootstrap data:
lineup_boot_df <- gen_boot_samples(lineup_vec, 1000)

#Compute proportion for bootstrap samples:
lineuprops <- gen_lineup_prop(lineup_boot_df, target_pos = 3, k = 6)

#Call:
gen_boot_propmean_se(lineuprops)

#OR:

lineuprops <- boot::boot(lineup_vec, lineup_prop_boot, target_pos = 3, R = 1000)
gen_boot_propmean_se(lineuprops$t)

Bootstrap resampling

Description

Function for generating bootstrapped samples from 1 vector of lineup data

Usage

gen_boot_samples(lineup_vec, bootno)

Arguments

Value

A dataframe of bootstrapped lineup data

Examples

#Data:
lineup_vec <- round(runif(100,1,6))
bootno <- 1000

#Call:
bootdf <- gen_boot_samples(lineup_vec, bootno)

Bootstrapped resampling

Description

Function for generating bootstrapped samples for of k vectors of lineup choices

Usage

gen_boot_samples_list(lineup_list, bootno)

Arguments

Value

A list of bootstrapped lineup data Length of list = no. of boostrap sample draws

Examples

#Data:
A <-  round(runif(100,1,6))
B <-  round(runif(70,1,5))
C <-  round(runif(20,1,4))
linelist <- list(A, B, C)
rm(A, B, C)

bootno <- 1000

#Call:
bootdata <- gen_boot_samples_list(linelist, bootno)

Effective Size (across a dataframe)

Description

Function for computing Effective Size (Tredoux, 1998) on lineups contained as columns in a df, usually from a bootstrapped sample

Usage

gen_esize_m(lineup_boot_df, k)

Arguments

Details

This function computes effective size for k lineups simultaneously.

Value

A vector of effective size calculations for each lineup in bootstrapped df

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

#Data:
lineup_vec <- round(runif(100,1,6))
bootdf <- gen_boot_samples(lineup_vec, 1000)

#Call:
esize_vec <- gen_esize_m(bootdf, 6)

Bootstrapped Confidence Intervals for Effective Size

Description

Function for computing Effective Size (Malpass, 1981, as adjusted by Tredoux, 1998) with CIs from bootstrap df of lineups

Usage

gen_esize_m_ci(lineupsizes, perc = 0.05)

Arguments

Value

Confidence intervals for effective size

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
k <- 6

#Use gen_boot_samples to get bootstrapped data:
bootdata <- gen_boot_samples(lineup_vec, 1000)

#Compute effective size over df of bootstrapped data:
lineupsizes <- gen_esize_m(bootdata, 6)

#Call:
gen_esize_m_ci(lineupsizes)
gen_esize_m_ci(lineupsizes, perc = .025)
gen_esize_m_ci(lineupsizes, perc = .975)

Lineup proportion over dataframe

Description

Function for computing lineup proportion over a dataframe

Usage

gen_lineup_prop(lineup_boot_df, target_pos, k)

Arguments

Value

A vector of bootstrapped proportions, indicating the frequency with which a target was identified in a lineup Length of vector = number of bootstrap sample draws

Examples

#Data
lineup_vec <- round(runif(100, 1, 6))
target_pos <- 3

#Get bootstrapped data:
lineup_boot_df <- gen_boot_samples(lineup_vec, 1000)

#Call:
lineuprops <- gen_lineup_prop(lineup_boot_df, 3, 6)

Lineup vector

Description

A function that takes a table of lineup choices and generates a vector of raw lineup data

Usage

gen_linevec(lineup_table, k)

Arguments

Value

Returns a vector of lineup choices

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_table <- table(lineup_vec)

#Call:
lineup_vec <- gen_linevec(lineup_table, 3)

Master function: Homogeneity of diagnosticity ratio

Description

This function provides assesses the homogeneity of the diagnosticity ratio of k lineup pairs.

Usage

homog_diag(lineup_pres_list, lineup_abs_list, pos_list, k)

Arguments

Details

Master function for assessing homogeneity of diagnosticity ratio for k independent lineups.

Value

Computes diagnosticity ratio with chi-squared estimate and significance level 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)

#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:
homog_diag(lineup_pres_list, lineup_abs_list, pos_list, k)

Homogeneity of diagnosticity ratio with bootstrapped CIs

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

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)

I Component of Effective Size(Tredoux, 1998)

Description

Function for generating I component of effective size

Usage

i_esize_T(lineup_table)

Arguments

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

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_table <- table(lineup_vec)

#Call:
i <- i_esize_T(lineup_table)

line73

Description

A dataframe of lineup choices for 1 trial; real data from a Doob & Kirshenbaum (1973) article. This article was the first to discuss lineup size 1 column x 42 observations

42 mock witnesses Nominal size is 6 Target position is 4

Usage

data(line73)

Format

An object of class data.frame with 42 rows and 1 columns.

References

Doob, A. N., & Kirshenbaum, H. M. (1973). Bias in police lineups-Partial remembering. Journal of Police Science and Administration, 1, 287-293.

Confidence intervals for lineup proportion

Description

Computes bootstrapped confidence intervals for lineup proportion

Usage

lineup_boot_allprop(lineup_vec, k, conf = 0.95)

Arguments

Details

Function that computes bootstrapped lineup proportion using 1000 bootstrap draws Calls 'boot function in 'boot' package

Value

Returns a vector of bias corrected confidence intervals for lineup proportion for each member in a lineup

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
lineuprops_ci <- lineup_boot_allprop(lineup_vec, k= 6)
lineuprops_ci <- lineup_boot_allprop(lineup_vec, k= 6, conf = 0.975)

Bootstrapped lineup proportion

Description

Base function for computing bootstrapped lineup proportion for a lineup member

Usage

lineup_prop_boot(lineup_vec, d, target_pos)

Arguments

Details

Function to call when bootstrap resampling using boot function

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

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.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

lineup_vec <- round(runif(100, 1, 6))

bootobject <- boot::boot(lineup_vec, lineup_prop_boot, target_pos = 3, R = 1000)
cis <- boot::boot.ci(bootobject, conf = 0.95, type = "all")

Lineup proportion

Description

Computes the proportion of mock witnesses identifying a particular lineup member

Usage

lineup_prop_tab(lineup_table, target_pos)

Arguments

Value

Returns a proportion indicating the frequency with which a lineup member was selected

References

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

#Data:
lineup_vec <- round(runif(100, 1))
lineup_table <- table(lineup_vec)

#Call:
lineup_prop_tab(lineup_table, 3)
lineup_prop_tab(table(lineup_vec), 2)

Lineup proportion

Description

Computes the proportion of mock witnesses identifying a particular lineup member

Usage

lineup_prop_vec(lineup_vec, target_pos, k)

Arguments

Value

Returns a proportion indicating the frequency with which a target was identified in a lineup

References

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

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
lineup_prop_vec(lineup_vec, 3, 6)

Ln of Diagnosticity Ratio

Description

Computes ln of diagnosticity ratio: ln(d)

Usage

ln_diag_ratio(linedf)

Arguments

Details

To get linedf, use the diag_param helper function

diag_param returns a dataframe containing the following:

• 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)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Call:
lnd <- ln_diag_ratio(linedf)

Compute and plot ROC curve for lineup accuracy ~ confidence

Description

Function to compute and plot an ROC curve for data from an eyewitness experiment, where accuracy is recorded for target present and target absent lineups

Usage

make_roc(df_confacc)

Arguments

Details

The approach is outlined in several papers by Mickes, Wixted, Gronlund, Clark, and others (see References)

This function is a user level function. It chains the two roc functions together. The user must pass a dataframe, with one column indicating confidence, and another accuracy, and these must be named as such.

The approach is outlined in several papers by Mickes, Wixted, Gronlund, Clark, and others (see references)

Value

An ROC object of package pROC

References

Gronlund, S. D., Wixted, J. T., & Mickes, L. (2014). Evaluating eyewitness identification procedures using receiver operating characteristic analysis. Current Directions in Psychological Science, 23(1), 3-10.

Examples

#Data:
data(mickwick)

#Call:
make_roc(mickwick)

Helper functions: Compute and plot ROC curve for lineup accuracy ~ confidence

Description

Function to compute and plot an ROC curve for data from an eyewitness experiment, where accuracy is recorded for target present and target absent lineups

Usage

make_rocdata(df_confacc)

Arguments

Value

An ROC object of package pROC

Helper functions

Description

Several helper functions that compute bootcis from proportions

Usage

makevec_prop(prop, n)

Arguments

Confidence & Accuracy data (Mickes & Wixted)

Description

Data capturing confidence and accuracy for a lineup trial

Usage

data(mickwick)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 2 columns.

Details

mickwick

mockdata

Description

A dataframe containing real lineup data.

3 columns x 94 observations.

The first column simply provides the participant number, whilst the second and third contain data for each participant's choice (i.e., the position of the lineup member that they identified) and a confidence score for each choice.

In the field of eyewitness testimony, it is common for participants to rate how confident they are that they have accurately identified the perpetrator. Here, confidence is recorded as a percentage.

This lineup has the following parameters:

• 94 mock witness choices

• Target position is 7

• Nominal size is 9

Usage

data(mockdata)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 94 rows and 3 columns.

nortje2012

Description

A data file containing lineup data for 3 independent lineups. 133 rows x 3 columns. Each lineup trial consisted of 133 mock witnesses & 8 lineup members.

• : Target position for lineup 1 is 3

• : Target position for lineup 2 is 6

• : Target position for lineup 3 is 2

Usage

data(nortje2012)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 133 rows and 3 columns.

Rep index

Description

Helper function that repeats a number k times

Usage

rep_index(index, num)

Arguments

Details

Can be used to create vectors of lineup data

Examples

rep_index(1, 10)

Rotate vector

Description

Helper function to rotate vector

Usage

rot_vector(lineup_vec)

Arguments

Helper function

Description

Helper function

Usage

show_lineup(file_list)

Arguments

Variance of diagnosticity ratio (Tredoux)

Description

Computes the variance of the diagnosticity ratio for a lineup pair

Usage

var_diag_ratio(lineup_pres, lineup_abs, pos_pres, pos_abs, k1, k2)

Arguments

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

#Data:
lineup_pres <- round(runif(100, 1, 6))
lineup_abs <- round(runif(100, 1, 6))
pos_pres <- 3
pos_abs <- 4

#Call:
var_d <- var_diag_ratio(lineup_pres, lineup_abs, pos_pres, pos_abs, 6, 6)
var_d <- var_diag_ratio(lineup_pres, lineup_abs, 3, 4, 6, 6)

Variance of ln of diagnosticity ratio

Description

Function to compute variance of ln(d) for k lineup pairs

Usage

var_lnd(linedf)

Arguments

Details

To get linedf, use the diag_param helper function

diag_param returns a dataframe containing the following:

• 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

Value

A dataframe containing ln of the variance of the diagnosticity ratio for each lineup.

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)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)
#Call:
var <- var_lnd(linedf)