Comparing Effective Size: Base function for bootstrapping

A row-resampling statistic for two columns of lineup choices.

Usage

compare_eff_sizes.boot(linedf, d)

Arguments

linedf

A dataframe of lineup data. Must consist of 2 columns, each containing data for 2 independent lineups

d

Indices for bootstrap sample. Argument used by boot function to select samples for bootstrapping

Details

This statistic applies the same row indices to both columns. Using it with an ordinary row bootstrap therefore resamples paired observations. For independent lineup samples use effsize_compare, which resamples each sample independently. Bootstrap intervals remain approximations; inclusion or exclusion of zero does not establish calibration in small or nearly uniform samples.

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.

Examples

# Two independent lineups of 6 members, 50 mock witnesses each
set.seed(1)
linedf <- data.frame(lineup1 = sample(1:6, 50, replace = TRUE),
                     lineup2 = sample(1:6, 50, replace = TRUE))
# Difference in effective size for the observed data
compare_eff_sizes.boot(linedf, seq_len(nrow(linedf)))
#> [1] -0.07855048
# Resample the independent lineups separately
effsize_compare(linedf, R = 100)
#> 
#> The two Effective sizes are  5.681818   5.760369
#> If the interval includes 0, ns at p = .05
#> Confidence intervals of difference [95%]
#> Normal Theory -1.086 0.941
#> Bootstrap: percentile (R = 100) -1.355 0.949
#> Bootstrap: bias-corrected (R = 100) -1.281 0.964