case1302: Pygmalion Effect

Description Usage Format Source References Examples

Description

One company of soldiers in each of 10 platoons was assigned to a Pygmalion treatment group, with remaining companies in the platoon assigned to a control group. Leaders of the Pygmalion platoons were told their soldiers had done particularly well on a battery of tests which were, in fact, non-existent. In this randomised block experiment, platoons are experimental units, companies are blocks, and average Practical Specialty test score for soldiers in a platoon is the response. The researchers wished to see if the platoon response was affected by the artificially-induced expectations of the platoon leader.

Usage

1

Format

A data frame with 29 observations on the following 3 variables.

Company

a factor indicating company identification, with levels "C1", "C2", ..., "C10"

Treat

a factor indicating treatment with two levels, "Pygmalion" and "Control"

Score

average score on practical specialty test of all soldiers in the platoon

Source

Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed), Duxbury.

References

Eden, D. (1990). Pygmalion Without Interpersonal Contrast Effects: Whole Groups Gain from Raising Manager Expectations, Journal of Applied Psychology 75(4): 395–398.

Examples

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str(case1302)

# two-way model with interactions
fitfull <- aov(Score ~ Company*Treat, case1302)
# No problems are indicated by residual plot
plot(fitfull)
# Interaction terms are not statistically significant
anova(fitfull)  
# Additive model, with "treatment contrast" for treatment:
fitadditive <- aov(Score ~ Company + Treat, case1302)
# Interpret treatment effect as coefficient of Treat
anova(fitadditive)  

Example output

'data.frame':	29 obs. of  3 variables:
 $ Company: Factor w/ 10 levels "C1","C2","C3",..: 1 1 1 2 2 2 3 3 4 4 ...
 $ Treat  : Factor w/ 2 levels "Pygmalion","Control": 1 2 2 1 2 2 1 2 1 2 ...
 $ Score  : num  80 63.2 69.2 83.9 63.1 ...
Warning messages:
1: not plotting observations with leverage one:
  1, 4, 7, 8, 9, 12, 15, 18, 21, 24, 27 
2: not plotting observations with leverage one:
  1, 4, 7, 8, 9, 12, 15, 18, 21, 24, 27 
Analysis of Variance Table

Response: Score
              Df Sum Sq Mean Sq F value  Pr(>F)  
Company        9 670.98   74.55  1.4367 0.29902  
Treat          1 338.88  338.88  6.5304 0.03092 *
Company:Treat  9 311.46   34.61  0.6669 0.72212  
Residuals      9 467.04   51.89                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table

Response: Score
          Df Sum Sq Mean Sq F value  Pr(>F)  
Company    9 670.98   74.55  1.7238 0.15556  
Treat      1 338.88  338.88  7.8354 0.01186 *
Residuals 18 778.50   43.25                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sleuth2 documentation built on Jan. 24, 2019, 9:04 a.m.