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TASK 1 (CORTINA (CRONBAHCS COEFFICIENT ALPHA IS (is a more general version…

- - - - Errors associated with time relevant  test-retest or multiple administrations of parallel tests
      - Errors associated with use of different items  internal consistency estimates (e.g. Cronbach’s alpha) or single administration of parallel tests
  - - - o Kuder-Richardson applies only to dichotomous items, whereas alpha applies to any set of items regardless of response scale
      - o Definition of equivalence: there is very little variance specific to individual items
  - - - Conclusion: alpha says only how correlated halves are, nothing about if the items measure what they are intended to measure
      - Useful for estimating reliability when item-specific variance is a unidimensional test of interest
        
        o Test has large alpha  large portion of variance in test is attributable to general and group factors
        
        o Very little item specific variance (uniqueness gets assessed by alpha)
- - - - Consistency factors - stable traits of individual, true scores
      - Inconsistency factors - characteristics of individual that have nothing to do with the attribute being measured, but still affect test scores - error
        
        unsystemtic measurement errors
        
        item selection (not equaly fair to all persons)
        
        test administration (changing environemnt etc)
        
        test scoring (u score test differently for people - for example issue for the projective test when you change the scoring)
        
        systematic measurement errors
        
        when test measures something else, that is supposed to (problm of vlaidity and test development)
    - - Mean ME is = 0
      - True scores and error scores are uncorrlated (if they were, then ME would be more systematic than unsystematic)
      - measurement errors (ME) are random
      - MEs are not correlated with errors in other tests (becasue they would be systematic - would relate to some other construct/factor)
    - - Temporal
        
        Test-retest reliability
        
        Alternate forms reliability
      - Internal Consistency
        
        Split-half reliability
        
        o SPEARMAN-BROWN FORMULA r(SB) = 2r(hh) / [1 + r(hh)] whereby r(SB) is the estimated reliability of the full test, and r(hh) the half-test reliability
        
        Coefficient alpha (Cronbach's alpha) which is just all the possible split-half combinations
        
        r(α) = coefficient alpha, N = number of items, σ²(j) = variance of 1 item, σ² = total variance of scores
        
        Kuder-Richardson Formula 20 (KR20) = internal ocnsistency for dichotomous tests
        
        KR20 = (N/(N-1)) * (1-(SUM pq)/total variance)
        
        Interscorer reliability = sample of tests is independently scored by 2+ examiners and scores for pairs of examiners are then correlated (nice supplement to other reliabilities)
        
        WHICH RELIABILITY TO CHOOSE
        
        Tests designed to be administered in same individual more than once = test-retest
        
        Tests purport to possess factorial purity  coefficient alpha
        
        Factorially complex tests (e.g. general intelligence measures) = coefficient alpha not appropriate (internal consistency measure not enough)
        
        Tests where items are carefully ordered according to difficulty level = split-half
        
        Tests with subjectivity scoring = interscorer reliability
  - - - Rasch model / 1-parameter model: p(Φ) = 1 / (1 + e^(-( Φ –b)), whereby, p (Φ) = probability of a respondent with trait level Φ correctly responding to an item with difficulty b, Φ = amount of trait one possesses, b= item difficult
      - 2-parameter model: adds item discrimination index to equation
      - 3-parameter model: adds a guessing parameters
  - - - Standard error of difference: comparison between abilities in subject  SEdifference can help determine if difference between scores is significant
      - SEdiff = √ ((SEM1)² + (SEM2)²)
        
        Assumed that the 2 scores are on the same scale
        
        Example: person scores IQverbal = 112 and IQperformance = 105  is 7 points difference significant?  compute SEdiff = 4.74  not significant