IRT Flashcards
IRT’s desirable objectives (2)
(1) Administer SHORTER measures
(2) Compare scores across: DIFF measures of the SAME constructs in DISTINCT groups
Why is there a problem in administrating shorter measures according to CTT?
Problem bc relationship between LENGTH of test & RELIABILITY of test
-> Shorter test don’t have as high reliability as longer
Limitations of CTT (3)
(1) Adding/deleting items changes true score (because the true score is TEST-DEPENDENT, so comparison not possible across diff test forms)
(2) True score is interpretable ONLY in reference to NORM sample’s distribution of scores: SAMPLE-DEPENDENT
(3) Reliability of true score is function of the items used: All items of EQUALLY reliable, measure SAME RANGE of scores, reliability CONSTANT across scores
What’s the problem with CTT assumption that “Reliability of true score is function of the items used”?
In practice, some items are better than some others
Item Response Theory (IRT) Assumptions (4)
(1) True score defined on the LATENT trait dimension rather than observed score
(2) Knowing **PROPERTIES OF ITEM **a person endorses tell us the TRAIT LEVEL the person possesses
(3) Properties of an item do NOT change if we were to administer the item using different samples
(4) True score of the person does NOT change regardless of which sets of items we administer.
In IRT, we place both _______ and _______ on the same scale to be able to compare those two.
items characteristics; people characteristics
IRT is a family of mathematical models that describe the probability of a given response to an item as a function of _______________ and ____________. It models the _______________.
certain item characteristics; respondent true score; likelihood of you endorsing an item
IRT: What’s the chance you’re gonna answer YES to an item assessing HIGH attachment levels?
KNOWING what’s the level of attachment of an ITEM
+
Underlying level of INDIVIDUAL attachment
= likelihood of you saying yes.
Item Response Function
Representation of the probability of item endorsement across the range of true scores
=> models the likelihood of item endorsement across the entire range of underlying traits
IRT: TRUE SCORE =
PROB OF ENDORSING ITEMS WITH SPECIFIC CHARACTERISTICS given the trait level set.
Item Characteristic Curve (ICC)
Function that models the likelihood of endorsement => plot of the Item response function
Item Response Function
Probability that a person with a given ability level will answer CORRECTLY.
=> EQUATION that relates true score (theta) defined in latent dimension to the probability of endorsing an item.
=> DIFF CURVES FOR DIFF ITEMS!!!
Variables in Item Response Function
Y = Probability of item endorsement (“yes”) = HOW MUCH TRAIT LEVEL YOU POSSESS
X = Theta (latent trait) - e.g. entire range of math level
Theta is a CONTINUUM (from -infinity to +infinity)
Theta def + values
Entire range of latent trait.
=> CONTINUUM (from -infinity to +infinity)
=> Negative values = LOW levels
=> Positive values = HIGH levels
How does a typical ICC looks for items that are dichotomous (yes-no)?
S shape
Whare are item characteristics?
Item DIFFICULTY & Item DISCRIMINATION
What’s the “nature” of the ICC function?
MONOTONIC: Probability of item endorsement increases in theta.