L2 Flashcards
Making Causal Claims Measuring Relationships Detecting Signals
When can we make causal claims?
When we use an experimental design (manipulate the IV and measure DV)
What is a correlation design?
Relationships among variables that are observed and measured
What is operationalisation?
Strictly defining variables so they can be measured
Why do we need to be careful when thinking about correlation vs causation?
Sometimes there are other variables which are related to both correlated variables that we are unaware of that is causing the difference
What is the third variable problem?
A confound in correlational studies: where another ‘lurking’ or unobserved variable can explain the relationship between the observed variable.
Wine being correlated with weight loss is an example of?
The third variable problem
- E.g. wine is usually drank while socialising and being social is more likely to make you want to lose weight (the third variable)*
- Knowing there is a relationship between two variables does not mean there is causation*
What do you need to do with your study to infer causation?
Have a randomised controlled experiment
- Where experimenters control the independent variables, randomly assign participants and materials and help manage other sources of variation.*
- Allows us to say X increases Y, X decreases Y, X results in Y etc.*
What are the two methods we can use to compute correlations/measure relationships?
Pearsons correlation coefficient (r)
Spearmans rank correlation coefficient (rs)
Describe the features of Pearsons correlation coefficient
Describe the features of Spearman’s rank correlation coefficient
What does a monotonic relationship mean?
as x goes up, y goes up
does not go up and down
Which correlation analysis is best when the data is assumed to be linear?
Pearson’s
How can we measure if X predicts Y? (rather than a causation or correlation)
Simple Linear Regression
Describe the features of a simple linear regression (R2)
When should we use multiple regression?
When there is more than one X variable
Describe the features of a multiple regression (R2)
Detecting Signals
What is Signal Detection Theory (hypothesis testing)?
How many ways of being right and how many ways of being wrong are there in signal detection theory?
2 x 2
How is signal detection theory similar to null hypothesis testing?
There are two ways of being right and two ways of being wrong
Describe where the decisions are made on a graph using signal detection theory
Getting better evidence and reducing chance factors reduces the degree of ___ in signal detection theory
Overlap (chance 1 and 2 errors)
Is psychology good at operationalising and having low type 2 and type 1 error rates?
No, our experiments are typically ‘fuzzy’ in terms of clarity
What is a liberal response bias?
When experimenters tend to err in favour of saying a relationship exists as opposed to saying a relationship doesn’t exist (i.e. higher type 1 error rate, lower type 2 error rate)
What is a conservative response bias?
Where you lean towards accepting the null hypothesis instead of rejecting the null
Baggage screeners at an airport are an example of
Signal Detection Theory with a liberal response bias
They have a large ‘noise’ as they correctly reject a lot of bags but they have adjusted the decision making criteria
What does having a noise curve that is highly variable mean (high variance of the curves)?
How variable our curves are (how different the things are that make up the curve)
