content validity
Evidence that the content of a test corresponds to the content of the construct it was designed to cover
Ecological validity
evidence that the results of a study, experiment or test can be applied, and allow inferences, to real-world conditions.
reliability
the ability of the measure to produce the same results under the same conditions.
test-retest reliabillity
The ability of a measure to produce consistent results when the same entities are tested at two different points in times.
Correlational research
observing what naturally goes on in the world without directly interfering with it.
Cross-sectional research
This term implies that data come from people at different age points with different people representing each age point
Experimental research
- One or more variable is systematically manipulated to see their effect (alone or in combination) on an outcome variable.
- Statements can be made about cause and effect
Systematic variation
differences in performance created by a specific experimental manipulation
unsystematic variation
Differences in performance created by unknown factors. (age, gender, IQ, Time of Day, Measurement error etc.)
Randomization
Minimizes unsystematic variation
Frequency distributions (AKA Histograms)
A graph plotting values of observations on the horizontal axis, with a bar showing how many times each value occurred in the data set.
The ‘Normal’ Distribution
- Bell shaped
- Symmetrical around the centre
Properties of frequency distributions
- Skew
- Kurtosis
Skew
- The symmetry of the distribution
- Positive skiew (scores bunched at low values with the tail pointing to high values)
- negative skew (scores bunched at high values with the tail pointing to low values)
Kurtosis
- the ‘heaviness of the tails
- leptokurtic = heavy tails
- Platykurtic = light tails
Deviance
- we can calculate the spread of scores by looking at how different each score is from the center of a distribution eg: the mean
Sum of squared errors (SS)
- indicates the total dispersion, or total deviance of scores from the mean
- it’s size is dependent on the number of scores in the data.
- More useful to work with the average dispersion, known as the variance
The sum of squares, variance, and standard deviation represent the same thing
- the ‘fit’ of the mean to the data
- the variability in the data
- how well the mean represents the observed data
- error
Population
- The collection of units (be they people, plankton, plants, cities, suicidal authors etc.) to which we want to generalize a set of findings or a statistical model.
Sample
a smaller (but hopefully representative) collection of units from a population used to determine truths about that population
calculating ‘error’
- a deviation is the difference between the mean and an actual data point
- deviations can be calculated by taking each score and subtracting the mean from it:
deviance=outcome(i)-model(i)
Sum of squared errors
- we could add the deviations to find out the total error
- deviations cancel out because some are positive and others negative
- therefore, we square each deviation
- if we add these squared deviations we get the Sum of Squared Errors (SS)
Mean squared error
Although the SS is a good measure of the accuracy of our model, it depends on the amount of data collected. To overcome this problem we use
The standard error
- SD tells us how well the mean represents the sample data
- but, if we want to estimate this parameter in the population, then we need to
