collier last test

• categorical or classification data
• ex. gender, race, where you live, political affiliation
• much test data are qualitative
• ex. responses to a given test item can be right or wrong or a,b,c, or d

test scores, reaction time, heart rate, brain-wave measurements

wrong answers on a multiple choice test

if a test has only two answers (the right and wrong answer) then indicating which answer was selected is basically the same as indicating whether the correct answer was chosen

• classical test theory (older)
• IRT (item response theory) newer replacing classical test theory

• the ability of the item to tell the difference between different people
• an item that everyone got right does not discriminate
• an item that everyone got wrong does not discriminate
• items that are too easy or too difficult have low discriminations

the correltation between a particular item and the scores on the entire test

• it means that ppl who got the item right tend to have high test scores, and ppl who got it wrong tend to have low test scores.
• the item discriminates between ppl who knew the material and ppl who didnt
• the test item correlation is used as an index of item discrimination

• tells how much they all tend to intercorrelate.
• can also be called measure of test coherence

whetherthe items all test the same thing

called Cronbach's Alpha and is the most standard measure of test reliability

the version of this formula that applies totests where there are right and wrong answers

the percent correct for an item

the item-whole score correlation

• 1. make up a bunch of test items and create a test
• 2. give the test to alot of ppl
• 3. get the correlations among the items
• 4. throw out items that correlatepoorly with the reamining items
• 5. if there are too few items left, creae sme new ones and start over
• 6. continue this until the test has enough items tha all have high correlations w/ each other

• gathering many variables
• testing and survey research

taking complex data and reducing it to simpler forms

• categorical or classification data
• ex. gender, race, poltical affiliation

test scores, reaction time, heart rate, brain-wave measurements

• a method for construct validation
• gives us a method to Xray inside the vlack box of abstract construct that we are interested in

• it contains the correlations or every item with every other item
• it is square
• the diagonal elements represent the correlation of each item with itsel, and so these are always 1
• it is symmetrical

• technique for packaging together variables into super variables
• do this by looking for items that correlate highly with each other, and packaging them together
• if looking at one trait all items should intercorrelate, and should get one super variable that can be name after the trait

true

both factor analysis and principal components analysis boil down the correlation matrix to a smaller number of super variables