- Quantitative methods = research with numbers
- Qualitative methods = analysing language (text)
The research process
1. An observation that you want to understand
a. Define one or more variables that you would like to measure.
2. Generate a theory
a. You come up with possibilities (theory)
3. Generate predictions (hypothesis)
a. Prediction from a theory (hypothesis)
4. Collect data to test theory
5. Analyse these data
6. Link these data to your theory
- Falsification = the acts of disproving a hypothesis or theory
- To test hypothesis, we need to measure variables. Variables can change and might be
different between people/populations.
- Independent variable = is a value that does not depend on any other variables
o Can also be the predictor variable; the independent variable is the cause (predictor)
- Dependent variable = the value of this variable depends on the cause (independent
variable)
o Can also be outcome variable; the dependent variable is an effect (outcome)
Levels of measurement
- Variables can take on many different forms and levels of sophistication. The relationship
between what is being measured and the numbers that represent what is being measured is
known as the level of measurement.
- Categorical variable = is made up of categories. You can also be one category. It names
distinct entities.
o Binary variable = names just two distinct types of things, for example male or
female, alive or dead.
o Nominal variable = when two things are equivalent in some sense are given the
same name or number, but there are more than two possibilities.
Nominal data can only be used to consider frequencies.
o Ordinal variable = when categories are being ordered. Ordinal data tells us not only
that things have occurred, but also the order in which they occurred. But they do
not tell us anything about differences between values. If you order them 1,2,3 you
know 1 was better then 2, but you don’t know what the difference was, was it an
easy win etc.
- Continuous variable = one that gives us a score for each person and can take on a value on
the measurement scale that we are using.
o Interval variable = interval data are considerably more useful than ordinal data. We
must be sure that equal intervals on the scale represent equal differences in the
property being measured. For example when being rated on a scale from 1 to 5 you
need to be sure the difference between 1 and 2 is the same as between 2 and 3.
o Ratio variables = in addition to the measurement scale meeting the requirements of
an interval variable, the ratios of values along the scale should be meaningful. So, it
needs to have a true and meaningful zero point. For example a reaction time.
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