ch4 Flashcards
Descriptive statistics
methods of summarising the data in an informative way
•Measures of central tendency
•Mean, median, mode
Measures of dispersion
Range, standard deviation, variance, interquartile range
Inferential statistics
Methods to draw conclusion (or to make inferences) •Mean difference test •Chi-square test •Analysis of Variance (ANOVA) •Regression analysis •Logit analysis
Measurement scale: Ratio
Meaningful differences and ratios between values due to a natural zero point
Measurement scale: Interval
Meaningful differences between values, but no natural zero point
Measurement scale: Ordinal
Ranked or ordered
Measurement scale: Nominal
Categories/groups, no logical order
Choosing between descriptive statistics
Scale Measure of central tendency dispersion
Nominal Mode -
Ordinal Median (interquartile range)
Interval Mean Standard deviation, variance
Ratio Mean Standard deviation, variance
Choosing between inferential statistics
IV Scale DV Scale Statistical technique
No/Or No/Or Chi-square test
No/Or In/Ra T-test, ANOVA
In/Ra No/Or Logit analysis
In/Ra In/Ra Regression analysis
When there are multiple IVs in a study, with different measurement scales: highest scale determines the statistical technique
T-test or ANOVA?
T-test: compares two means (two levels of an IV)
ANOVA: can compare more than two levels
Choosing inferential statistics: rating scales
- Likert scale
- Semantic differentials
! Treated as interval scales
Population
entire group of people, firms, events or things of interest for which you would like to make inferences
Sample
A subset of the population of interest
Sampling frame
A physical representation/ a list/ a database where you see the elements of the target population
Low representativeness
=Properties of the population are over- or underrepresented in the sample
=high sampling error
Destructive sampling
e.g. destroying lightbulbs to test their lifespan
The sampling process (4 steps)
- Define the population
- Determine the sampling frame
- Determine the sampling design
(Probability sampling | Non-probability sampling) - Determine the sample size
Coverage error
Sampling frame does not equal to population
Under-coverage
True population members are excluded
Miss-coverage
Non-population members are included
Solutions to coverage errors
- If small, recognise but ignore
* If large, redefine the population in terms of the sampling frame