|
Now, you can
| ||
|
Add/share/save | ||
| ||
|
| ||
| ||
|
Choosing parametric test
Explanations > Social Research > Analysis > Choosing a parametric test
Choosing the test | Discussion | See also
Choosing the test
Use the table below to choose the test. See below for further details.
|
How many separate samples? |
|||||||||||||||
|
1 |
|
||||||||||||||
|
2 |
|
||||||||||||||
| >2 |
|
||||||||||||||
Discussion
Parametric tests assume an underlying Normal (bell-shaped) distribution, which is often forced through means of samples (see the Central limit theorem).
Test statistic
The test statistic in all tests is calculated as:
systematic variation / random variation
= (measured difference between sample means) / (mean difference expected by chance)
= (variability between treatments) / (variability within treatments)
Principles
The common principles of measurement are:
- A sample (a set of scores) is measured for each population or treatment condition
- For each sample, the mean and a spread figure (sum of squares, variance or standard deviation) is calculated.
- The difference between sample means is calculated. This is the numerator of the test statistic and indicates systematic (predicted) difference between treatment conditions.
- The variation within each sample indicates unsystematic (random, unpredictable) variation. This is the denominator of the test statistic.
Design types
Single sample designs take data from a single sample to test a hypothesis about a single population.
Independent-measures designs take separate samples from each population or treatment.
Related-samples designs, including repeated-measures and matched-subjects designs.
Repeated measures designs have only one sample, with each subject being measured in all treatment conditions.
In matched-subject designs, each person in one sample is matched with a subject in each of the other samples.
See also
|
|