Quantitative research methods Flashcards
What’s P hacking?
Changing the data so you get a significant value
What’s a proxy measure?
When you can’t directly measure it so you take lots of other values
What’s Harking?
Hypothesising after the results are known
What’s publication bias?
Not getting a significant difference is hard to get published
What’s a type 1 error?
If you find a significant difference where none should exist
Incorrect rejection of a true null hypothesis
Whats the Nuremberg code?
Informed consent is essential
Research should be based on prior animal work
Risks should be justified by benefits
Qualified scientists
Physical and mental suffering avoided
Research that could result in death or disabling shouldn’t be done
What’s a type 11 error?
When there is a significant difference but you fail to find it
What’s a cross sectional design?
Comparing different groups performances
What are longitudinal studies?
Comparing same groups performance at different time points
What’s observational research?
Correlation
Linear regression
Multiple regression
Useful for establishing relationships between variables, difficult to infer if its an actual cause and effect
How to establish cause and effect?
The dependent variable should vary only in changes to the independent variable
What does nominal mean?
Numbers used to distinguish amongst objects without quantitative value
What does ordinal mean?
Numbers used only to place objects in order
What does interval mean?
Scale on which equal intervals between objects represent equal differences (no true zero)
eg. celcius
What does ratio mean?
: Scale with a true zero point – ratios are meaningful. Ratio scale are often common physical ones of length, volume, time etc.
What’s Quasi experimental design?
Treatment group compared to control group
Static group comparison
No random assignment.
• Difficult to ensure baseline equivalence
True experimental gold standard approach?
Random assignment for treatment and control group
Effective research design?
Maximise systematic variance (driven by independent variables)
• Minimise error (random) variance
• Identify and control for confounding variables
How to Maximise systematic variance?
Proper manipulation of experimental conditions will ensure high variability of independent variables.
How to Minimise error (random) variance?
Reducing the part of the variability that is caused by measurement error.
What are nuisance variables?
Variables that produces underside variation in the dependent variable
Fixed by:
Conduct experiment in a controlled environment
Larger samples – randomly assign your subjects to different conditions
With small samples match your subjects on all
demographic variables across conditions
What are placebo and demand?
Some portion of effect due to the participant’s belief in the intervention
Participants want to please the experimenter.
Controlled by:
Good control conditions
• Keep the subjects ‘blind’
• Keep the purpose of the study hidden from the participant.
• If possible, disguise the independent variable.
• Sometimes this is difficult to balance with the ethics of participant recruitment and informed consent.
What’s central tendency?
Describes measures of the centre of a distribution:
Mean, median, mode
mean = average value (best one, as uses all values)
median = middle value
mode = most frequent recurring value
advantages and disadvantages of the mean average?
- Can be influenced by extreme values.
- Is affected by skewed distributions.
- Can only be used with interval or ratio data.
- Uses every value in the data set.
- Tends to be vey stable in different samples (enabling us to compare samples).