Standardisation Flashcards
Define psychometrics and psychological testing
It’s the science of psychological measurement; psychological tests are a measuring device or procedure designed to measure psychology-related variables
List 5 different types of psychological tests and provide examples
- Mental ability (IQ tests, memory, vocabulary, spatial ability);
- Achievement (educational, course assessments, competence, experimental performance measures);
- Personality-type (no correct answer; assessing traits);
- Interests and attitudes (vocational, social psych questionnaires);
- Neuropsychological tests (memory, psychomotor coordination, abstract thinking, IQ, personality, etc)
What are the four key assumptions behind psychological tests?
- People differ on traits
- These traits are measurable
- These traits are relatively stable over time
- These traits relate to actual behaviour
What are the advantages of standardisation?
It helps us interpret what the scores mean relative to the appropriate sample of people; raw scores alone are uninformative.
Explain what norm and normative sample are
Normative sample is a standardised score relative to a sample of people (reference sample/standardised sample); the norm is the data they yield
What issues might we need to consider when recruiting a standardised sample?
A bigger sample is more likely to lead to an accurate and stable representation of the population (but
stability doesn’t guarantee representativeness); may need equal ratio of males to females, same age, socio-economic and educational distribution; same geographic origins of the population
Define stratified cluster sampling
Deliberately recruiting representatives to get particular ratios of subgroups (as opposed to random
sampling)
If stratified cluster sampling fails or is not possible, what’s another option?
Weighting (e.g. If male to female ratio is uneven, count each female as 1.5 people, and each male as .67 of a person)
Why is the normal curve (aka the Laplace-gauss curve) important in psychology?
If we can assume something has a normal curve, then knowing only the mean and standard deviation can tell us how someone’s score compares with everyone else
When does a distribution come closer to a normal curve?
When a sample is larger, and with a wider range of things measured
When a sample is normally distributed, what is the mean equal to?
Both median and mode (so 50% of people are above/below the mean)
In a normal distribution, what percentage of scores are +/-1 SD around the mean?;
What about +/-2 SDs?
68%;
95%
What’s the general definition of cognitive impairment and what’s notable about the properties of this
definition?
An IQ of below 2 SDs below the mean (less than 70); this means “mentally retarded” is always in comparison with the rest of the population (it’s not absolute)
Why is it convenient for our distributions to be normal?
We can do more powerful (parametric) statistical tests, and makes scales more comparable
What are the different strategies we can use to make a skewed distribution normal?
Do a non-linear transformation to make it more normal (e.g. take the square root or logarithm); or redesign the measure if possible (e.g. change wording of items)
Does performing a non-linear transformation change the rank order of scores?
No, it just stretches some bits of the scale more than others
Why do we use standard scores?
Makes the interpretation simpler; anchors the mean and SD of the scale; can potentially compare performance across different scales
What are the means and SD of:
z score?;
T score?;
IQ score?
Z score: mean = 0, SD = 1;
T score: mean = 50, SD = 10;
IQ score: mean = 100, SD = 15
How do we covert a raw score into a z score?
z = X - Xbar / SD
How do we covert a z score into a T score?
T = z(10) + 50
How do we covert a z score into an IQ score?
IQ = z(15) + 100
Does calculating a T score change the shape of the distribution?
No, it is a linear transformation (also avoids negative numbers)
A person scores 70 on a test, where the mean is 85, and SD is 20. You have created a standardised
scale where the mean is 100 and the SD is 50. How would you find their score according to your
scale?
z = (70 - 85)/20 = -0.75;
-0.75(50) + 100 = 62.5
Give 3 examples of linear transforms, and 3 of non-linear transforms?
Linear: z, T and IQ tests;
Non-linear: logarithms, square roots and percentile ranks
