Computes the callback shares and their confidence intervals. The analysis is restricted to the tests with discrimination cases.
stat_ecs(x, level = 0.95)A list with class "stat_ecs" containing 8 components : level, shares,
cp, wilson, student, t.student, t.pearson and t.fisher.
level: the level of the confidence intervals.
shares: a data frame containing the following variables.
disc: number of discrimination cases.
c10: number of tests with the 1st candidate preferred (2nd candidate discriminated against).
c01: number of tests with the 2nd candidate preferred (1st candidate discriminated against).
cdif: net discrimination c10-c01.
p_cand1: 1st candidate callback share (c10/disc).
p_cand2: 2nd candidate callback share (c01/disc).
p_cand_dif: p_cand1-1/2.
cp: a data frame containing the Clopper-Pearson confidence intervals,
from binom.test(), and the p-value of the Fisher test of independence between
the candidate type and the callback variable, from fisher.test().
inf_p_cand1: 1st candidate callback rate, lower bound.
sup_p_cand1: 1st candidate callback rate, upper bound.
inf_p_cand2: 2nd candidate callback rate, lower bound.
sup_p_cand2: 2nd candidate callback rate, upper bound.
wilson: a data frame containing the Wilson confidence intervals
and the p-value of the equality test of callback shares between the two
candidates, from prop.test().
inf_p_cand1: 1st candidate callback share, lower bound.
sup_p_cand1: 1st candidate callback share, upper bound.
inf_p_cand2: 2nd candidate callback share, lower bound.
sup_p_cand2: 2nd candidate callback share, upper bound.
inf_cand_dif: p_c10-p_c01, lower bound.
sup_cand_dif: p_c10-p_c01, upper bound.
student: a data frame containing the Student confidence intervals
and the p-value of the equality test of callback shares between the two
candidates.
inf_p_cand1: 1st candidate callback share, lower bound.
sup_p_cand1: 1st candidate callback share, upper bound.
inf_p_cand2: 2nd candidate callback share, lower bound.
sup_p_cand2: 2nd candidate callback share, upper bound.
inf_cand_dif: p_c10-p_c01, lower bound.
sup_cand_dif: p_c10-p_c01, upper bound.
t.fisher: a data frame containing the statistics of the Fisher test.
p_cand_dif: 1st candidate callback share - 1/2.
p_Fisher: the p-value of the Fisher test.
s_Fisher: the significance code of the Fisher test.
t.pearson: a data frame containing the statistics of the Pearson test.
p_cand_dif: 1st candidate callback share - 1/2.
Pearson: the value of Pearson's chi-squared test statistic.
p_Pearson: the p-value of the Pearson test.
s_Pearson: the significance code of the Pearson test.
t.student: A data frame containing the statistics of the Student test.
p_cand_dif: 1st candidate callback share - 1/2.
Student: the value of Student's test statistic.
p_Student: the p-value of the Student test.
s_Student: the significance code of the Student test.
a callback object.
a number, containing the level of the confidence intervals (0.95 by default).
Emmanuel Duguet
Clopper, C. J. & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413. doi:10.2307/2331986.
Wilson, E.B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22, 209–212. doi:10.2307/2276774.
data(labour1)
x <- callback(data=labour1,cluster="offer",candid="hist",callback="callback")
str(stat_ecs(x,level=0.9))
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