Function for comparing effective size of two independent lineups (Tredoux, 1998)
Value
Effective size, significance level, and confidence intervals (95 normal theory, percentile & bias-corrected)
Details
This function is a master function, calling other functions it needs, and reporting results in some detail Each lineup is resampled independently; row alignment has no sampling meaning. Confidence intervals are bootstrap approximations and can be unreliable for small or nearly uniform empirical choice distributions. Normal and BCa intervals can under-cover in finite concentrated samples; percentile intervals can be conservative near uniformity. None provides a distribution-free level-.05 test of equal population effective sizes.
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, R = 100)
#>
#> The two Effective sizes are 5.847953 5.411255
#> If the interval includes 0, ns at p = .05
#> Confidence intervals of difference [95%]
#> Normal Theory -0.284 1.236
#> Bootstrap: percentile (R = 100) -0.336 1.367
#> Bootstrap: bias-corrected (R = 100) -0.195 1.533