Psychology: Scientific Methodology

What are the 5 aspects of scientifc methodology?

1. preparation

2. pilot

3. data collection

4. data analysis

5. interpretation

Explain the preparation step of scientific methodology

• hypothesis-driven

  • form hypothesis and design empirical test

  • precise and testable

    • ex. if students attend lectures, then they will have better exam results

• null hypothesis —> no effect

  • a student’s exam result is not influenced by the amount of lectures this student has attended

• alternative hypothesis —> has an effect

  • if students attend lectures, then they will have better exam results

• across-subjects vs within-subjects design

  • Across-subjects: experimental vs control group

  • Within-subjects: one group, compare conditions

• dependent (y) vs independent (x) variable

  • independent: what the researcher is manipulating

  • dependent: what is being tested

  • ex. independent: how many lectures the student attends vs dependent: the exam score

Explain the pilot step of scientific methodology

• pilot study: initial run-through of an experiment in a small sample mostly to identify flaws in the procedure

  • floor effect: everyone scores low

  • ceiling effect: everyone score high

  • confusion: questions aren’t interpreted by participants

  • confounding variable: variable that affects the results other than the variable of interest

    • ex. age, sex, nationality, background

Define what an independent and dependent variable are in an experiment

• independent: what the researcher is manipulating

• dependent: what is being tested

Explain the data collection step of scientific methodology

• informed consent

  • participant is able to make an informed judgement about taking part in the study

Explain the data analysis step of scientific methodology **add in pics for each example

• distribution:

  • normal / gaussian distribution: probability distribution symmetric around the mean

• T-test:

  • Statistical data analysis applied to experimental data to test hypothesis (compare one group vs standard, e.g. zero, or compare two groups or two conditions)

  • one sample vs independent samples vs paired samples

• Analyses:

  • ANOVA-test: Statistical data analysis applied to experimental data to test hypothesis (compare more than two groups)

  • Correlation: relation between two variables (weak, strong, positive, negative)

    • ex. relation between “lecture attendance” & “exam result”

    • unexpected correlations:

      • same correlation coefficients but … different data —> strong positive vs left unexpected correlation graph

Explain the interpretation step of scientific methodology

• Significant result: low probability that result is due to chance

  • Reject null hypothesis

  • Accept alternative hypothesis

• Type I error → null hypothesis rejected, should have been accepted

• Type II error → null hypothesis accepted, should have been rejected

Correlation

ANOVA-test

T-test

distribution