Taylor-Russell tables (Taylor & Russell, 1939) are designed to estimate the percentage of future employees who will be successful on the job if a particular selection method (eg. test, assessment center, interview) is used. I have already described the kibit russell taylor-tool (in German).

Now I will show how the number of recruited candidates is calculated precisely with R. I use the manipulate package. Therefore, the code will only run in the RSudio IDE. The big advantage is that one can easily observe the influences of

1. the base rate of potentially suitable persons in the non-selected group of applicants as well as
2. the validity of the selection process (by moving the slider).

This little program is excellent for teaching.

The calculations are based on an article of Richard A. Mclellan in the journal “Personnel Decisions International” (1999): Theoretical Expectancies: Replacing Classic Utility Tables with Flexible, Accurate Computing Procedures.

And this is the result. Have fun trying.

F1 <- function(P) {
  SPLIT <- 0.42
  A0 <- 2.50662823884
  A1 <- -18.61500062529
  A2 <- 41.391199773534
  A3 <- -25.44106049637
  B1 <- -8.4735109309
  B2 <- 23.08336743743
  B3 <- -21.06224101826
  B4 <- 3.13082909833
  C0 <- -2.78718931138
  C1 <- -2.29796479134
  C2 <- 4.85014127135
  C3 <- 2.32121276858
  D1 <- 3.54388924762
  D2 <- 1.63706781897 
  Q <- P - 0.5
  if (abs(Q) <= SPLIT) {
    R <- Q*Q
    PPN <- Q * (((A3 * R + A2) * R + A1) * R + A0) / ((((B4 * R + B3) * R + B2) * R + B1)*R +1.0)
    return(PPN)
  }
  R <- P
  if (Q > 0) {R =1.0-P}
  if (R <= 0) {
    print("You have entered a value that is not permitted. The result is false.")
    return(0)
  }
  R <- sqrt(-log(R))
  PPN <- (((C3 * R + C2) * R + C1) * R + C0) / ((D2 * R + D1) * R + 1.0)
  if (Q < 0) {PPN =-PPN}
  return(PPN)
}


F2 <- function(X) {
  P1A <- 242.667955230532
  P1B <- 21.97926616182942
  P1C <- 6.996383488661914
  P1D <- -3.5609843701815E-02
  Q1A <- 215.058875869861
  Q1B <- 91.1649054045149
  Q1C <- 15.0827976304078
  Q1D <- 1.0
  P2A <- 300.459261020162
  P2B <- 451.918953711873
  P2C <- 339.320816734344
  P2D <- 152.98928504694
  P2E <- 43.1622272220567
  P2F <- 7.21175825088309
  P2G <- .564195517478994
  P2H <- -1.36864857382717E-07
  Q2A <- 300.459260956983
  Q2B <- 790.950925327898
  Q2C <- 931.35409485061
  Q2D <- 638.980264465631
  Q2E <- 277.585444743988
  Q2F <- 77.0001529352295
  Q2G <- 12.7827273196294
  Q2H <- 1.0
  P3A <- -2.99610707703542E-03
  P3B <- -4.94730910623251E-02
  P3C <- -.226956593539687
  P3D <- -.278661308609648
  P3E <- -2.23192459734185E-02
  Q3A <- 1.06209230528468E-02
  Q3B <- .19130892610783
  Q3C <- 1.05167510706793
  Q3D <- 1.98733201817135
  Q3E <- 1.0
  SQRT2 <- 1.4142135623731
  SQRTPI <- 1.77245385090552
  
  Y <- X/SQRT2
  if (Y < 0) {
    Y <- -Y
    SN <- -1.0
  } else {
    SN <- 1.0
  }
  Y2 <- Y * Y
  if (Y < 0.46875) {
    R1 <- ((P1D * Y2 + P1C) * Y2 + P1B) * Y2 + P1A
    R2 <- ((Q1D * Y2 + Q1C) * Y2 + Q1B) * Y2 + Q1A
    ERFVAL <- Y * R1 / R2
    if (SN == 1) LOAREA <- 0.5 + 0.5 * ERFVAL
    else LOAREA <- 0.5 - 0.5 * ERFVAL
  } else {
    if (Y < 4.0) {
      R1 <- ((((((P2H * Y + P2G) * Y + P2F) * Y + P2E) * Y + P2D) * Y + P2C) * Y + P2B) * Y + P2A
      R2 <- ((((((Q2H * Y + Q2G) * Y + Q2F) * Y + Q2E) * Y + Q2D) * Y + Q2C) * Y + Q2B) * Y + Q2A
      ERFCVAL <- exp(-Y2) * R1 / R2
    } else {
      Z <- Y2 * Y2
      R1 <- (((P3E * Z + P3D) * Z + P3C) * Z + P3B) * Z + P3A
      R2 <- (((Q3E * Z + Q3D) * Z + Q3C) * Z + Q3B) * Z + Q3A
      ERFCVAL <- (exp(-Y2) / Y) * (1.0 / SQRTPI + R1 / (R2 * Y2))
    }
    if (SN == 1) LOAREA <- 1.0 - 0.5 * ERFCVAL
    else LOAREA <- 0.5 * ERFCVAL
  }
  UPAREA <- 1.0 - LOAREA
  return(UPAREA)
}


F3 <- function(H1, HK, R) {
  X <- c(0.04691008, 0.23076534, 0.5, 0.76923466, 0.95308992)
  W <- c(0.018854042, 0.038088059, 0.0452707394, 0.038088059, 0.018854042)
  H2 <- HK
  H12 <- (H1*H1 + H2*H2)/2.0
  BV <- 0
  if (abs(R) >= 0.7) {
    R2 <- 1.0-R*R
    R3 <- sqrt(R2)
    if (R < 0) H2 <- -H2
    H3 <- H1*H2
    H7 <- exp(-H3 / 2.0)
    if (R2 != 0) {
      H6 <- abs(H1 - H2)
      H5 <- H6 * H6 / 2.0
      H6 <- H6 / R3
      AA <- 0.5 - (H3 / 8.0)
      AB <- 3.0 - (2.0 * AA * H5)
      BV <- 0.13298076 * H6 * AB * F2(H6) - exp(-H5 / R2) * (AB + AA * R2) * 0.053051647
      for (i in 1:5) {
        R1 <- R3 * X[i]
        RR <- R1 * R1
        R2 <- sqrt( 1.0- RR)
        BV <- BV - W[i] * exp(-H5 / RR) * (exp(-H3 / (1.0 + R2)) / R2 / H7 - 1.0 - AA	* RR)
      }
    }
    if (R > 0 & H1 > H2) {
      BV <- BV * R3 * H7 + F2(H1)
      return(BV)
    }
    if (R > 0 & H1 <= H2) {
      BV <- BV * R3 * H7 + F2(H2)
      return(BV)
    }
    if (R < 0 & (F2(H1) - F2(H2)) < 0) {
      BV <- 0 - BV * R3 * H7
      return(BV)
    }
    if (R < 0 & (F2(H1) - F2(H2)) >= 0) {
      BV <- (F2(H1) - F2(H2)) - BV * R3 * H7
      return(BV)
    }
  }
  H3 <- H1 * H2
  for (i in 1:5)
  {
    R1 <- R * X[i]
    RR2 <- 1.0 - R1 * R1
    BV <- BV + W[i] * exp((R1 * H3 - H12) / RR2) / sqrt(RR2)
  }
  BV <- F2(H1) * F2(H2) + R * BV
  return(BV)
}


true_positives <- function(N, ToSelect,BaseRate, Validity) {round(F3(F1(1.0-ToSelect/N), F1(1.0-BaseRate), Validity)/(ToSelect/N)*ToSelect,1)}
false_positives <- function(N, ToSelect,BaseRate, Validity) {round(ToSelect-F3(F1(1.0-ToSelect/N), F1(1.0-BaseRate), Validity)/(ToSelect/N)*ToSelect,1)}
false_negatives <- function(N, ToSelect,BaseRate, Validity) {round(N*BaseRate - F3(F1(1.0-ToSelect/N), F1(1.0-BaseRate), Validity)/(ToSelect/N)*ToSelect,1)}
true_negatives <- function(N, ToSelect,BaseRate, Validity) {N - true_positives(N, ToSelect,BaseRate, Validity) - false_positives(N, ToSelect,BaseRate, Validity) - false_negatives(N, ToSelect,BaseRate, Validity)}


library(manipulate)

manipulate(
  barplot(
    matrix(c(true_positives(Applicants, StaffRequirement, BaseRate, Validity),
             false_positives(Applicants, StaffRequirement, BaseRate, Validity),
             true_negatives(Applicants, StaffRequirement, BaseRate, Validity),
             false_negatives(Applicants, StaffRequirement, BaseRate, Validity)),
             nrow = 2, ncol=2, byrow=FALSE,
           dimnames = list(c("rightly", "wrongly"), c("recruited", "rejected"))),
           legend.text=TRUE, main="Reflect your personnel selection!"),
  Applicants=slider(1,100, step=1, initial = 50),
  StaffRequirement=slider(1,100, step=1, initial = 10),
  BaseRate=slider(0,1, step=.01, initial = .25),
  Validity=slider(0,1, step=.01, initial = .37))