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Summary Methodology 3 (VU)
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Summary of all the lectures from the 3rd year Psychology course Methodology 3.
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Methodology 3TENTAMEN/EXAM: 27 March 2020
INDEX
1. Introduction & Null hypothesis 2
2. T-tests, degrees of freedom 6
3. Power, effect size, non-parametric tests 8
4. ANOVA 1 11
5. ANOVA 2 14
6. Confidence intervals, correlation and regression 18
7. Multiple Regression Analysis (MRA) 20
This summary includes (almost) everything from the lectures and the chapters from the book.
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, 2
Introduction, null hypothesis
Greer & Mulhern, Ch. 6, 9
Example drunk driving and driving performance. Different questions to ask:
- What is the incidence of drunk-driving? Descriptive research
- Is there a relationship between alcohol dose and driving performance? Relational research
- Does alcohol dose influence driving performance? Experimental research
Is there a relationship between alcohol dose and driving performance? Relational research
- Participants: 30 car drivers
- Two measurements per participants:
o Permillage alcohol (breathalyzer test)
o Deviation from the ideal track (# steering wheel reversals)
- Present in scatter plot
o Each data point = 1 participant, two measurements
o When we generally look at the data, we see an increasing line à
regression line
o Can calculate correlation coefficient (r): strength of linear relationship
- Limitation of this design: we can never find out whether it is a causal relationship
Does alcohol dose influence driving performance? Experimental research
- Hypothesis: Alcohol reduces driving performance
o Hypothesis implies/specifies a causal relationship. To test it, we must perform an experiment.
o The researcher..
§ Manipulates independent variable(s)
§ Controls other variables (keeping constant; randomization; counterbalancing)
§ Measures effect on dependent variable(s)
o If correctly carried out, an experiment has high internal validity
§ Means that we can substantiate our causal claim: we can attribute an effect that we see on
the dependent variable to the independent variable
§ No confounds
- Choosing variables/measurements
o Choice of independent variable: alcohol dose, e.g. with two levels: 0 or 1 permillage
o Choice of dependent variable: deviation from ideal track
o For those variables, we need operational definitions: what do we mean when we talk about
someone’s driving performance?
§ In our study, straightforward for independent variable: permillage
§ Less straightforward for dependent variable
• Representativeness for the construct driving performance
• Also has to be sensitive to the effect of alcohol dose that you use in the experiment
- Drawing the sample
o Population of interest = e.g. Dutch citizens with driver’s license
o How to draw sample
§ Random (or stratified) sample
§ Convenience sample
o Here we draw a convenience sample. Why? We are interested in general relationship between the
variables and not in absolute population parameters
- Assigning to conditions
o Between subjects design (independent groups design)
§ Independent groups of participants for each condition
§ Random assignment to the groups
, 3
o Within subjects design
§ Each participant contributes to each condition à so no group differences! Because everyone
is in both conditions
§ Random assignment to order of conditions (A-B; B-A)
- Results
o Shown in scatter plot: each point corresponds to one participant
§ The two dots that are not filled in with the line in between the points are
the means of each group
o Not very convenient, we are not interested in individual data but in the mean: is
it on average the case that there is a relationship?
§ Individual data may lead to clutter, especially in complex designs.
o Take out individual data points: show only means
§ This now shows the effect caused by alcohol dose on deviation
§ Disadvantages: loss of information on variability within groups…
• Why is this relevant?
o Enter statistics
§ Descriptive statistics
• E.g. we found a correlation of .65 in the sample – we found an effect of alcohol dose in
the sample
§ We are actually more interested in inferential statistics
• Is there a positive correlation in the population? Or was the r = .65 a coincidence
• Is there actually an effect of alcohol dose on deviation in the population, or was the
observed effect a coincidence?
Inferential statistics
- E.g. if the means were based on only 2 observations, the 0 permillage group would be better than the 1
pmg group (left graph). However, if we add one observation that is a bit extreme, we find that possibly
the 0 pmg group is worse than the 1 pmg group (right graph).
- So we need additional observations… how many???
- What can we say about the population on the basis of our sample data?
o Inconvenient truth = we can never be sure that an
observed effect in the sample also exists in the
population. Our finding can always be just a
coincidence!!!
o What we do to tackle this = express our uncertainty in
terms of probabilities
o Which probabilities?
§ Attempt 1: calculating the probability of the
observed data – however, we cannot do this, because
for this we would need to know the population
parameters!
• Formulating a hypothesis about a population and test it:
§ Attempt 2: Possible hypotheses about effect sizes in populations
• E.g.: H: r (alcohol dose, steering error) = .80
• Then collect data and calculate the probability of the data given that H applies: p(data|H)
• But we can only do this if there is any motivation for e.g. saying r = .80
• Disadvantages
o We often have no clue about effect sizes
o We often just want to know if there is an effect at all; effect size is of later concern
§ Attempt 3: Proof by contradiction.
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