Study Notes on Internal Validity, Confounds, and Statistical Tests

Internal Validity and Confounds

  • Internal Validity:
    • Definition: The extent to which we can be confident that changes in the independent variable (IV) caused changes in the dependent variable (DV).
  • Confounds:
    • Definition: A factor that covaries with the independent variable.
    • Explanation of Covarying: “Covaries with the independent variable” means that different values of the confound are perfectly matched with different levels of the IV.
    • Example:
    • Toothpaste A is always used with a red toothbrush, while Toothpaste B is always used with a blue toothbrush.
    • Implications of Confounds:
    • They provide possible alternative explanations for the effects on the DV, indicating other potential causes apart from the IV.

Statistics Review: Testing the Significance of Group Differences

Statistical Tests for Two Samples

  • Independent-Groups t-test:
    • Definition: A statistical test where subjects in the groups are independent of each other.
  • Related-Samples/Repeated-Measures t-test:
    • Definition: A statistical test where the same subjects are used in all conditions, or related pairs of subjects are analyzed.
  • Common Requirements for t-tests:
    • The dependent variable (DV) must be a numerical variable (quantity or amount of something) measured for each subject.
    • A mean value of the DV can be computed for each group.

Statistical Tests for Three or More Samples

  • One-Way Independent-Groups ANOVA:
    • Definition: A statistical test used with three or more levels of one independent variable, where subjects in the groups are independent of each other.
  • One-Way Repeated-Measures ANOVA:
    • Definition: A statistical test with three or more levels of one independent variable, where the same subjects perform in all conditions.
  • Common Requirements for ANOVAs:
    • The dependent variable (DV) must be a numerical variable (quantity or amount of something) measured for each subject.
    • A mean value of the DV can be computed for each group.

Example Research Questions

1. Impact of Music on Sorting Mail

  • Research Question: Does the presence of music affect how quickly postal workers sort mail?
  • Independent-Groups t-test Design:
    • IV: Presence or absence of music (1) music, (2) no music.
    • DV: Time taken to sort 100 pieces of mail.
    • Method: One group of postal workers sorts mail with music, another in silence. Measure sorting time per group.
  • Repeated-Measures t-test Design:
    • Method: Same group of postal workers sorts mail first with music, then in silence. Measure sorting time in both conditions.

2. Impact of TEMPO on Sorting Mail

  • Research Question: Does the TEMPO of music affect how quickly postal workers sort mail?
    • Rationale: Exploring different tempos with uncertainty on which might influence sorting efficiency.
  • Independent-Groups ANOVA Design:
    • IV: Tempo of music (1) 60 beats per minute (BPM), (2) 120 BPM, (3) 180 BPM.
    • DV: Time taken to sort 100 pieces of mail (or how many pieces sorted in 5 minutes).
    • Method: Separate groups of postal workers sort mail at each specified BPM. Measure sorting time for each group.
  • Repeated-Measures ANOVA Design:
    • Method: Same group of postal workers sorts mail at each tempo (60 BPM, then 120 BPM, and finally 180 BPM). Measure sorting time per condition.