The basis of psychological testing is...
What are test used for when it comes to psychology?
A test is a measurement device or technique used to quantify behavior or aid in the understanding or prediction of behavior.
Is a specific stimulus to which a person responds overtly
Scales and Raw scores
Scales relate raw scores on test items to some theoretical or empirical distribution.
What are test good for?
- Measure current behavior
- Predict future behavior
- Infer hidden behavior
Test can measure...
- States-temporal; change
Types of Test...
- Ability test-measure previous learning
- Aptitude test-measure potential for acquiring a particular skill
- Intelligence test-measures intelligence
Intelligence refers to...
- A person's general ability to...
- solve problems
- adapt to changing circumstances
- think abstractly
- profit from experience
Chinese civil service test
The American government established the American civil service commission
Developed mental test
Seguin Form Board
Developed to educate and evaluate the mentally handicapped.
A group given the test under standard conditions.
A sample that represents the group we are using comparisons.
How you perfomed compared to others who took the test.
- Could not find meaningful standardized responses
- Still widely used
- Full of Shit...lol
MMPI-Minnesota Multiphasic Personality Inverntory
uses empirical methods (factor analysis) to determine of a test response
What do statistics provide?
- Concise descriptions of lots of quantitative information.
- demographic breakdown
- prevalance of behavior
To Infer things...
- Deduce things you cant observe directly.
- Measure something in a small group of ppl.- the sample
- Infer the results apply to a larger group of ppl. - the population
using rules to assign numbers to objects
Scales have 3 properties...
Equal intervals-difference between any 2 points on the scale is the same
Absolute zero- it is possible to have none of the quality measured
Types of Scales
Nominal-not really scales, assigning random designator numbers to ppl or things
Ordinal-have moreness but not equal intervals or absolute zero. E.g. putting ppl in order shortest to tallest
Interval- have magniude and equal intervals but not necessarily absolute zero. E.g. temperature
Ratio- have magnitude, equal intervals and absolute zero
Displays how many times (frequency) each score (distribution) was obtained on a scale.
- x-axis=each score, from lowest to highest
- y-axis= # of times each score was obtained
Increased sample size; bell-shaped curve
- pos.=tails to the right
- neg.=tails to the left
IQ scores have a pos. skew
The distance between two consecutive measurements in your distribution.
Indicate the particular score below which a defined percentage of scores falls.
Divide the percentage scale into four groups
Divide the scale into tenths
Divide the scores into standard 9ths
this creates 3 groups= the lower, middle and high group
The middle 50% of scores (25%-75% percentiles)
The average score in a distribution
mean= (sum of all scores) / (# of scores)
The point at which 1/2 the scores are above and 1/2 are below
most common score
The variation of the scores around the mean
The average deviation around the mean
calculation: SD=Square root of the variance
Indicate how far from the mean a score is
universal expression of SD
- scores above the mean are + z-scores
- scores below the mean are - z-scores
between -3.0 and +3.0
McCall's T; T-Scores
The mean did not equal 0, as it is with z-scores it equaled 50
standard deviation did not equal 1, as it is with z-scores it equaled 10
Mathematical translation; transformations standardize the distribution.
- Average performance by standardization sample
- The standardization sample sets the norms
- Compare individuals to a normative group
- E.g. class exam scores
Compare an individual to a criterion on a specific skill, tasks, or knowledge
- A measure that can have multiple values
- E.g. weight, test score, etc.
As on changes, another changes
You can measure changes using...
- Multiple regression
A picture of the relationship between 2 variables
One variable increases on the horizontal, or x-axis
One variable increases on the vertical, or y-axis
The Regression Line
Help us make predictions between scores of two variables
Like a slope line on a graph
- steep= x may predict y
- flat= y may predict x
The difference between the actual score and the predicted score
the best fitting line minimizes the residual
correlation is like regression, except scores from each variable are standardized
Describes the magnitude and direction of relationship between 2 variables
Types of correlations
- Pos.- variables 1 and 2 go up or doen together
- Neg. - variables 1 and 2 move in opposite directions
- No correlation
The statistics used to measure correlation
Assume that there is no relationship between 2 variables
Pearson's r is used for...
2 countinuous variables
yes/no; true/false; pass/fail; etc...
All dichotomous variables are categorical
No Pearson's r for dichotomous variables
Between one continuous and one dichotomous variable
Standard error of estimate
- The standard deviation of the residuals
- Predicts fit of the regression line
- Smaller is better
Coefficient of determination
- The correlation coefficient squared (r2)
- Indicates how much of the variation in Y is due to X
Using the regression equation from one group of subject to predict performance in a different group of subjects
The amount of decrease in predictability from cross-validating
If the variability of a variable is extremely restricted, significant correlations may be difficult to find even if they are there
Creates factors: groups of related variables
Is all about error.
- Difference in true ability and measurement of ability.
- The inevitable inaccuracy of our measurements.
- How much our test do not reflect reality.
Psychological testing tries to...
- find the magnitude of error for each test
- try to minimize error for all tests
Tests that are relatively free from error are...
measure tangible things
- measure intangible things
- one that may over or under estimate the measurement
Test score theory
- Influences how we think about and calculate reliability
- assumes each person has a true score
- We do not report the true score
- we report the observed score
The distribution of random error is...
Methods that measure reliability
- test-retest method
- parallel forms method
- split-half method
- Administer the test at 2 different times
- compare each test-takers scores
- stable traits
- carry-over effects
Internal Consistency (IC)
when the variance is equal on different parts of the test
2 types of test to calculate IC
Dichotomous test & Likert scales
The reliability statistic
The variance of the true scores
The variance of the observed scores
Domain Sampling Model
- Uses a sample of items, not the entire domain of items
- pick several items from your original test
- give that small test
- record observed scores
- estimate true scores fro these observed scores
- More items = more reliability (generally)
Test that adapts to the test taker
Reliability of a Difference score (RDS)
- Subtract one score from another
- same test given at 2 points in time
- 2 sub-scores from one test
Make the comparison in Z-units
Problem with RDS
- The error of a difference score is inflated
- It absorbs error from both scores in the difference score.
Potential solution to RDS
Calculate RDS if you know... α of each test and r between these test
Even tests with high α and r may have low RDS
The similarity of rater 1 and rater 2's measurements
Agreement for Interrater reliability
- K = Pr (a)-Pr (e)
- 1-Pr (e)