Chi-square difference tests show significant loading invariance constraints for Kansas Reading but all constraints significant for Michigan Mathematics¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2013 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? For Kansas MAP Reading tests, chi-square increases are significant for testing L invariance but not significant for testing LV invariance; for Michigan Mathematics tests, all chi-square increases are significant for both L and LV invariance. → Shudong Wang 2013

Evidence¶

Shudong Wang 2013¶

Shudong Wang, Marty McCall, Hong Jiao, & Gregg Harris. (2013). Construct Validity and Measurement Invariance of Computerized Adaptive Testing: Application to Measures of Academic Progress (MAP) Using Confirmatory Factor Analysis. Journal of Educational and Developmental Psychology, 3(1). https://doi.org/10.5539/jedp.v3n1p88

q2 · i? · associational · r2

Nested-model chi-square difference tests in the MGCFA analyses of Kansas MAP Reading and Michigan MAP Mathematics tests across grades. The article notes the chi-square test may be misleading with large samples and recommends other GOF differences as adjuncts.

"For the Kansas MAP Reading Tests (see Table 8), χ2 increases ( Δχ2) are significant for testing L invariance at different terms, but not significant for testing LV invariance. The rest of the states’ results show a similar pattern. For Michigan Mathematics Tests, all χ2 increases are significant for both L and LV invariance."

Discussion¶

Related Claims¶

• The authors conclude that MAP reading and mathematics CAT constructs are invariant across five time points in ten states (except Indiana mathematics), though their mathematics tables show strong-invariance CFI drops above 0.01 in most states — related
• MAP tests show metric invariance across grades for all studied tests except Michigan Mathematics tests — related
• A one-factor unidimensional model is the most reasonable fit for MAP computerized adaptive Reading and Mathematics tests across grades, terms, and ten states, though Michigan Mathematics misses the RMSEA criterion — related
• Establishing longitudinal factorial invariance is neither sufficient nor necessary for examining developmental change — related
• MLGM model fit failed for the Indiana mathematics test, the sole exception to invariance support — related