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EPSY 546: LECTURE 1 SUMMARY. George Karabatsos. REVIEW. REVIEW. Test (& types of tests). REVIEW. Test (& types of tests) Item response scoring paradigms. REVIEW. Test (& types of tests) Item response scoring paradigms Data paradigm of test theory (typical). DATA PARADIGM.
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EPSY 546: LECTURE 1SUMMARY George Karabatsos
REVIEW • Test (& types of tests)
REVIEW • Test (& types of tests) • Item response scoring paradigms
REVIEW • Test (& types of tests) • Item response scoring paradigms • Data paradigm of test theory (typical)
REVIEW: Latent Trait • Latent Trait Re (unidimensional)
REVIEW: Latent Trait • Latent Trait Re (unidimensional) • Real Examples of Latent Traits
REVIEW: IRF • Item Response Function (IRF)
REVIEW: IRF • Item Response Function (IRF) • Represents different theories about latent traits.
REVIEW: IRF • Item Response Function (IRF) • Dichotomous response: Pj() = Pr[Xj = 1] = Pr[Correct Response to item j | ]
REVIEW: IRF • Item Response Function (IRF) • Polychotomous response: Pjk() = Pr[Xj> k | ] = Pr[Exceed category k of item j | ]
REVIEW: IRF • Item Response Function (IRF) • Dichotomous or Polychotomous response: Ej() = [Expected Rating for item j | ] 0 < Ej() < K
REVIEW: SCALES • The unweighted total score X+n stochastically orders the latent trait • (Hyunh, 1994; Grayson, 1988)
REVIEW: SCALES • 4 Scales of Measurement • Conjoint Measurement
REVIEW • Conjoint Measurement • Row Independence Axiom
REVIEW • Conjoint Measurement • Row Independence Axiom • Property: Ordinal Scaling and unidimensionality of (test score)
REVIEW • Conjoint Measurement • Row Independence Axiom • Property: Ordinal Scaling and unidimensionality of (test score) • IRF: Non-decreasing over
REVIEW • Conjoint Measurement • Row Independence Axiom • Property: Ordinal Scaling and unidimensionality of (test score) • IRF: Non-decreasing over • Models: MH, 2PL, 3PL, 4PL, True Score, Factor Analysis
REVIEW • Conjoint Measurement • Column Independence Axiom (adding)
REVIEW • Conjoint Measurement • Column Independence Axiom (adding) • Property: Ordinal Scaling and unidimensionality of both (test score)and item difficulty (item score)
REVIEW • Conjoint Measurement • Column Independence Axiom (adding) • Property: Ordinal Scaling and unidimensionality of both (test score)and item difficulty (item score) • IRF: Non-decreasing and non-intersecting over
REVIEW • Conjoint Measurement • Column Independence Axiom (adding) • Property: Ordinal Scaling and unidimensionality of both (test score)and item difficulty (item score) • IRF: Non-decreasing and non-intersecting over • Models: DM, ISOP
REVIEW • Conjoint Measurement • Thomsen Condition (adding)
REVIEW • Conjoint Measurement • Thomsen Condition (adding) • Property: Interval Scaling and unidimensionality of both (test score)and item difficulty (item score)
REVIEW • Conjoint Measurement • Thomsen Condition (adding) • Property: Interval Scaling and unidimensionality of both (test score)and item difficulty (item score) • IRF: Non-decreasing and parallel (non-intersecting) over
REVIEW • Conjoint Measurement • Thomsen Condition (adding) • Property: Interval Scaling and unidimensionality of both (test score)and item difficulty (item score) • IRF: Non-decreasing and parallel (non-intersecting) over • Models: Rasch Model, ADISOP
REVIEW • 5 Challenges of Latent Trait Measurement
REVIEW • 5 Challenges of Latent Trait Measurement • Test Theory attempts to address these challenges
REVIEW • Test Construction (10 Steps)
REVIEW • Test Construction (10 Steps) • Basic Statistics of Test Theory
REVIEW • Total Test Score (X+) variance = Sum[Item Variances] + Sum[Item Covariances]
EPSY 546: LECTURE 2TRUE SCORE TEST THEORY AND RELIABILITY George Karabatsos
TRUE SCORE MODEL • Theory: Test score is a random variable. X+nObserved Test Score of person n, Tn True Test Score (unknown) enRandom Error (unknown)
TRUE SCORE MODEL • The Observed person test scoreX+n is a random variable (according to some distribution) with mean Tn = E(X+n) and variance 2(X+n)= 2(en).
TRUE SCORE MODEL • The Observed person test scoreX+n is a random variable (according to some distribution) with mean Tn = E(X+n) and variance 2(X+n)= 2(en). • Random Error en = X+n– Tn is distributed with mean E(en) = E(X+n–Tn) = 0, and variance 2(en) = 2(X+n) .
TRUE SCORE MODEL • True Score: Tn true score of person n E (Xn) expected score of person n s Possible score s{0,1,…,s,…,S} pns Pr[Person n has test score s]
TRUE SCORE MODEL • 3 Assumptions: • Over the population of examinees, error has a mean of 0. E[e] = 0 • Over the population of examinees, true scores and error scores have 0 correlation. [T, e] = 0
TRUE SCORE MODEL • 3 Assumptions: • For a set of persons, the correlations of the error scores between two testings is zero. [e1, e2] = 0 • “Two testings”: when a set of persons take two separate tests, or complete two testing occasions with the same form. • The two sets of person scores are assumed to be randomly chosen from two independent distributions of possible observed scores.