case0601: Discrimination Against the Handicapped

In Sleuth3: Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

Description

Study explores how physical handicaps affect people's perception of employment qualifications. Researchers prepared 5 videotaped job interviews using actors with a script designed to reflect an interview with an applicant of average qualifications. The 5 tapes differed only in that the applicant appeared with a different handicap in each one. Seventy undergraduate students were randomly assigned to view the tapes and rate the qualification of the applicant on a 0-10 point scale.

Usage

case0601

Format

A data frame with 70 observations on the following 2 variables.

Score

is the score each student gave to the applicant

Handicap

is a factor variable with 5 levels—"None", "Amputee", "Crutches", "Hearing" and "Wheelchair"

Source

Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

References

Cesare, S.J., Tannenbaum, R.J. and Dalessio, A. (1990). Interviewers' Decisions Related to Applicant Handicap Type and Rater Empathy, Human Performance 3(3): 157–171.

Examples

str(case0601) 
attach(case0601) 

## EXPLORATION
myHandicap  <- factor(Handicap,  
  levels=c("None","Amputee","Crutches","Hearing","Wheelchair"))  
boxplot(Score ~ myHandicap, 
  ylab= "Qualification Score Assigned by Student to Interviewee",  
  xlab= "Treatment Group--Handicap Portrayed (14 Students in each Group)", 
  main= "Handicap Discrimination Experiment on 70 Undergraduate Students") 
myAov  <- aov(Score ~ myHandicap) 
plot(myAov, which=1) # Plot residuals versus estimated means 
summary(myAov) 

## COMPARE MEAN QUALIFICATION SCORE OF EVERY HANDICAP GROUP TO "NONE"  
if(require(multcomp)){     # Use the multcomp library
  myDunnett  <- glht(myAov, linfct = mcp(myHandicap = "Dunnett"))  
  summary(myDunnett) 
  confint(myDunnett,level=.95) 
  opar <- par(no.readonly=TRUE)  # Save current graphics parameter settings
  par(mar=c(4.1,8.1,4.1,1.1)) # Change margins 
  plot(myDunnett, 
    xlab="Difference in Mean Qualification Score (and Dunnet-adjusted CIs)") 
  par(opar)  # Restore original graphics parameter settings
} 

## COMPARE EVERY MEAN TO EVERY OTHER MEAN
if(require(multcomp)){   # Use the multcomp library
  myTukey   <- glht(myAov, linfct = mcp(myHandicap = "Tukey"))  
  summary(myTukey) 
}

## TEST THE CONTRAST OF DISPLAY 6.4
myAov2        <- aov(Score ~ myHandicap - 1)    
myContrast    <- rbind(c(0, -1/2, 1/2, -1/2, 1/2)) 
if(require(multcomp)){   # Use the multcomp library
  myComparison  <- glht(myAov2, linfct=myContrast)
  summary(myComparison, test=adjusted("none"))  
  confint(myComparison)  
}  


# BOXPLOTS FOR PRESENTATION   
boxplot(Score ~ myHandicap, 
  ylab= "Qualification Score Assigned by Student to Video Job Applicant",  
  xlab="Handicap Portrayed by Job Applicant in Video (14 Students in each Group)",  
  main= "Handicap Discrimination Experiment on 70 Undergraduate Students", 
  col="green", boxlwd=2, medlwd=2, whisklty=1, whisklwd=2, staplewex=.2,  
	staplelwd=2, outlwd=2, outpch=21,  outbg="green", outcex=1.5) 
	
detach(case0601) 

Example output

'data.frame':	70 obs. of  2 variables:
 $ Score   : num  1.9 2.5 3 3.6 4.1 4.2 4.9 5.1 5.4 5.9 ...
 $ Handicap: Factor w/ 5 levels "Amputee","Crutches",..: 4 4 4 4 4 4 4 4 4 4 ...
            Df Sum Sq Mean Sq F value Pr(>F)  
myHandicap   4  30.52   7.630   2.862 0.0301 *
Residuals   65 173.32   2.666                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Loading required package: multcomp
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS

Attaching package: 'TH.data'

The following object is masked from 'package:MASS':

    geyser


	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = Score ~ myHandicap)

Linear Hypotheses:
                           Estimate Std. Error t value Pr(>|t|)  
Amputee - None == 0         -0.4714     0.6172  -0.764   0.9400  
Crutches - None == 0         1.0214     0.6172   1.655   0.4686  
Hearing - None == 0         -0.8500     0.6172  -1.377   0.6442  
Wheelchair - None == 0       0.4429     0.6172   0.718   0.9517  
Crutches - Amputee == 0      1.4929     0.6172   2.419   0.1232  
Hearing - Amputee == 0      -0.3786     0.6172  -0.613   0.9725  
Wheelchair - Amputee == 0    0.9143     0.6172   1.481   0.5781  
Hearing - Crutches == 0     -1.8714     0.6172  -3.032   0.0278 *
Wheelchair - Crutches == 0  -0.5786     0.6172  -0.937   0.8812  
Wheelchair - Hearing == 0    1.2929     0.6172   2.095   0.2349  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)


	 Simultaneous Confidence Intervals

Fit: aov(formula = Score ~ myHandicap - 1)

Quantile = 1.9971
95% family-wise confidence level
 

Linear Hypotheses:
       Estimate lwr    upr   
1 == 0 1.3929   0.5213 2.2644

Sleuth3 documentation built on May 2, 2019, 6:41 a.m.