a procedure for dimensionality analyses of response data from various test designs
Post on 10-Feb-2016
35 Views
Preview:
DESCRIPTION
TRANSCRIPT
A procedure for dimensionality analyses of response data from various
test designsJinming Zhang
William Stout
Introduction Dimension
Dimensional structure of the test (e.g., algebra and geometry)
statistical dimensional structure of response data
Incorporate both Judgments about test content and evidence from statistical analyses
Missing data: CAT & multistage testing Missing item pair measurement
DETECT index
Modified DETECT index
Example 3 Stage 1 booklet: 2 dim & Stage 2 booklet 1: 2
dim, Stage 2 booklet 2: 1 dim (additional dim), Stage 2 booklet 3: 1 dim (additional dim).
Bridge item (in Stage 1) E(1,2) and E(2,3), so E(1,3)
First-stage booklet should measure all of the constructs/contents the whole
test aim to measure, though it is unknown. classify examinees into different proficiency levels
Item 1 and 2 are measuring the same dimension
1. If E(1,2), E(2,3), and E(1,3) D(P*) is maximized in P* partition if item 1 and
2 are in the same cluster, other than in other P2. If only E(1,2) and E(2,3), and if item 2 is a bridge item
D(P*) is maximized in P* partition if item 1 and 2 are in the same cluster when item 2 is in the same cluster or not, other than in other P
Whether a test has an approximate simple structure or not
Discordance Resulted from
Not a approximate simple structure Inaccuracy of conditional covariance
estimation Given a partition of items,
What made Dd(P*) and PropD(P*) large Unidimensionality Violation of approximate simple
structure Inaccuracy of conditional covariance
estimation
polyDETECT Obtain the composite theta score:
unidimensinoal approximation & simple structure approach.
Use percentiles of composite theta scores as cut-off points in forming AHGs. Between 25 to 100 in each group
Cross-validation:
polyDETECT Evidence of multidimensionality
Condition 3 is hold or not? Each sizable cluster contains at least one
stage-1 item. Exist at least one sizable cluster that does not
contain any Stage-1 items, and all items in such cluster belong to the same booklet.
Exist at least one sizable cluster that does not contain any Stage-1 items, and all items in such cluster belong to at least two booklet.
Exist at least two sizable cluster that does not contain any Stage-1 items, items in each such cluster come from the same booklet but different clusters belong to different booklets.
Dealing with CAT data Estimates of item parameters were
obtained before conducting CAT. Why do dimensionality assessment on CAT data?
Sparse data set of CAT 100,000 responses are required at
least Item selection, item exposure
control, content balance to satisfy the condition 3
Simulation study M2PL Each booklet has 30 items Dimension: 1, 2, 3 Number of examinees: 750, 1500, 3000, 4000 Theta: MVN(0,sigma), correlation = 0.8 Cut-off points for low-, moderate-, and high-
scoring group: <10, 11~18, >18 About 37.82% unestimable item pairs Cross-validation Replication: 100 Composite theta: use unidimensional IRT model
Results
Real data analyses
Missing values are large (55%~71%)
Real data analyses
Weak dimensionality (M value) High PropD(P*) indicates a large amount of
spurious information to form partition P* Confirmatory analyses: D3 >D2
But DETECT tends to underestimate, so two-cluster partition solution may be preferred.
Real data analyses
High correlation indicates a weak degree of multidimensionality
Concluding remarks Moderate violation of approximate
simple structure is still hold
top related