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37 Cards in this Set
 Front
 Back
Assumptions of Traditional Assessment

1. Behavior is a function of underlying mental constructs
2. Behavior and constructs inferred from behavior are fundamentally different 3. 5 problems of psychological measures 4. Behavior signs are independent of the situation 

5 problems of psychological measurement

1. No single approach universally accepted
2. Measurements based on limited sample 3. Measurement is subject to error 4. Measurement scales lack well defined units 5. Constructs are defined by 2 statistical relations relations 

Goals of Traditional Assessment

1. Assign construct based on variability in performance
2. Explain / predict behavior from construct value 3. Select treatments that are effective for individuals with similar construct values 

Scaling Person's Score

1. Compute mean as reference point
2. Compute deviation score x = X  u 3. Compute variance as index of total variability in set of scores 4. Compute z score as a standard scale z = (xu)/ o 5. Correlation: pxy = Sum or zxzy / N 6. Test scores as composite Scores 

Item discrimination Indices

1. Total score as criterion
2. Item difficulty 3. Distribution of Responses 4. D Coefficient 5. Item total correlation 6. Item reliability/validity index 

Generating initial item pool

1. Purpose
2. Identify behaviors represent construct 3. Table of Specifications 4. Initial item pool 

Vagonotic Measurement

phenomena defined into existence based on variation in a set of underlying observations


Mental constructs

psychological property/ attribute; innate, stable, influence behavior in dimension


Variance is the basis of

Correlation


Reliability

degree to which score difference can be attributed to systematic sources of variation


Systematic Variation

know influences; nonrandom fluctuations


True Score Theory

the average of the observed scores obtained over an infinite # of repeated testings with same test.


Realization of random variable

when you have a distribution of scores you can see


Expected value of random variable

mean of observed scores = true score


Reliability coefficient

Correlation between true and observed scores; estimate correlation on parallel tests


Standard error of measurement

discrepancy between examinees observed test score and true score; SD one person's scores over k # of parallel tests; substitue SD for the total test to estimate SEM for 1 person; confidence intervals


Generalizability Theory

1. Estimates of reliability based on how test is going to be used
2. Identify sources of measurement error (of concern) and conduct reliability study to assess effects simultaneously Partition garbage can error variance into different sources Based on ratio of variance estimates from diff sources; get variance by deviating scores from grand mean 

Facet
Single Facet design 
grouping variable in ANOVA
one way anova 

Factor Analysis

data reduction technique that use correlations among large amount of variables to create composite variables/factors
Looking for % of VAC ranks 

Matrix

table of scores with columns of variables and rows of persons


Factor

A linear combination of variables in matrix


Factor loadings

correlation with an item with a factor


Vector
Vector space Hyperspace 
Each factor column can be considered a vector
2 or more more than 2 dimensions of axis 

Decision 1

Define factors
Principal components; total variance common factors; covariance only communality estimates; ultr sq in diag on correlation matrix 

Decision 2

Extracting Successive Factors:
Eigenvalue = sum of squared factor loadings for each factor or column EIGENVALUE/ # of items = % VAC by a factor MINEGAN = 1 = atleast as much % VAC as an item Communality = proportion of an item's variance accounted for by the factors 

Decision 3

Rotation of initial factors to teminal factors
VARIMAX  orthogonal = 90 degrees QUARTIMIN  oblique = < 90 degrees 

VARIMAX
QUARTIMIN 
90
< 90 

Decision 4

Interpreting Factors
Minimum factor loading for inclusion Factor label  dimension represented 

Deviation Score

x = Xu


Variance

=


z score

z = xu/o


correlation

xy =


variance of composite

=


Cronbach's Alpha

= k


SEM

SEM = SD


Generalizability coefficient

persons


EIGENVALUE

sum of squared factor loadings for each column or factor
