Home

  1. Introduction
  2. Graphing Distributions
  3. Summarizing Distributions
  4. Describing Bivariate Data
  5. Probability
  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions
  10. Estimation
  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power
  14. Regression
  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size

  20. Case Studies
    1. Contents
      Standard
    2. Angry Moods
      Standard
    3. Flatulence
      Standard
    4. Physicians Reactions
      Standard
    5. Teacher Ratings
      Standard
    6. Diet and Health
      Standard
    7. Smiles and Leniency
      Standard
    8. Animal Research
      Standard
    9. ADHD Treatment
      Standard
    10. Weapons and Aggression
      Standard
    11. SAT and College GPA
      Standard
    12. Stereograms
      Standard
    13. Driving
      Standard
    14. Stroop Interference
      Standard  
    15. TV Violence
      Standard
    16. Obesity and Bias
      Standard
    17. Shaking and Stirring Martinis
      Standard
    18. Adolescent Lifestyle Choices
      Standard
    19. Chocolate and Body Weight
      Standard
    20. Bedroom TV and Hispanic Children
      Standard
    21. Weight and Sleep Apnea
      Standard
    22. Misusing SEM
      Standard
    23. School Gardens and Vegetable Consumption
      Standard
    24. TV and Hypertension
      Standard
    25. Dietary Supplements
      Standard
    26. Young People and Binge Drinking
      Standard
    27. Sugar Consumption in the US Diet
      Standard
    28. Nutrition Information Sources and Older Adults
      Standard
    29. Mind Set Exercise and the Placebo Effect
      Standard
    30. Predicting Present and Future Affect
      Standard
    31. Exercise and Memory
      Standard
    32. Parental Recognition of Child Obesity
      Standard
    33. Educational Attainment and Racial, Ethnic, and Gender Disparity
      Standard

  21. Calculators
  22. Glossary
  Stroop Interference



Research conducted by: Statistics Class

Case study prepared by: David Lane

Overview
Naming the ink color of color words can be difficult. For example, if asked to name the color of the word "blue" is difficult because the answer (red) conflicts with the word "blue." This interference is called "Stroop Interference" after the researcher who first discovered the phenomenon.

This case study is a classroom demonstration. Students in an introductory statistics class were each given three tasks. In the "words" task, students read the names of 60 color words written in black ink; in the "color" task, students named the colors of 60 rectangles; in the "interference" task, students named the ink color of 60 conflicting color words. The times to read the stimuli were recorded. There were 31 female and 16 male students.


Questions to Answer
Is naming conflicting color names faster or slower than naming color rectangles? Which is faster, naming color rectangles or reading color names? Are there gender differences?

Design Issues
This was not a well-controlled experiment since it was just a classroom demonstration. The order in which the students performed the tasks may not have been counterbalanced or randomized.

Descriptions of Variables
Variable
Description
Gender 1 for female, 2 for male
Words Time in seconds to read 60 color words
Colors Time in seconds to name 60 color rectangles
Interfer Time in seconds to name colors of conflicting words


References

Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 28, 643-662.


Links
Exercises
  1. Compute the mean for words.
  2. Compute the mean and standard deviation for "colors."
  3. Create parallel box plots for males and females for "colors."
  4. Create back-to-back stem and leaf plots for "colors" as a function of gender (You may have to do this by hand).
  5. Create a stem and leaf plot for "interference."
  6. Create a scatterplot showing "color" on the Y-axis and "words" on the X-axis.
  7. Compute the correlation between "color" and "words."
  8. Compute the correlation between "color" and "words" using only the 23 fastest color-namers.
  9. Do a t test comparing males and females on "color."
  10. Compute the 95% confidence interval for "interference."
  11. Do a t-test of the difference between "colors" and "interference."
  12. Do a 2 x 3 ANOVA with gender as the between subject variable and task (Colors, Words, Interference) as within-subject variables.