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qualitative data
 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

quantitative data
test scores, reaction time, heart rate, brainwave measurements

lures/distractors
wrong answers on a multiple choice test

item analysis
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

Two kinds of test theory
 classical test theory (older)
 IRT (item response theory) newer replacing classical test theory

item discrimination
 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

itemtest/item whole correlation
the correltation between a particular item and the scores on the entire test

what does it mean when the testitem correlation is high
 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

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

measure of test coherence
whetherthe items all test the same thing

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

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

difficulty
the percent correct for an item

reliability
the itemwhole score correlation

process of test development
 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

multivariate research
 gathering many variables
 testing and survey research

multivariate statistical analysis
taking complex data and reducing it to simpler forms

qualitative (multivariate methods)
 categorical or classification data
 ex. gender, race, poltical affiliation

quantitative (mulivariate methods)
test scores, reaction time, heart rate, brainwave measurements

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

properties of a correlation matrix
 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

simplifying the correlation matrix
 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/False:
Factor analysis and PCA are similar techniques
true

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

