Whole-number and fraction calculations fit a two-factor structure significantly better than a one-factor model¶

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? A chi-square difference test showed the one-factor measurement model fit significantly worse than the two-factor model, Δχ2(1, N = 139) = 23.11, p = .000. → Namkung 2016

Evidence¶

Namkung 2016¶

Namkung, J. M., & Fuchs, L. S. (2016). Cognitive Predictors of Calculations and Number Line Estimation with Whole Numbers and Fractions among At-Risk Students. Journal of Educational Psychology, 108(2), 214-228. https://doi.org/10.1037/edu0000055

q3 · i? · associational · r2

Confirmatory factor analysis of the calculation outcome measures in 139 at-risk fourth graders: the two-factor model (whole-number and fraction calculation latent variables) fit excellently (χ2(4) = 3.23, p = .519; RMSEA = 0.000) and the quoted Satorra-Bentler adjusted difference test confirmed both dimensions were necessary.

"An adjusted chi-square difference tests (i.e., Δχ2) using the Satorra-Bentler scaling correction yielded a significantly worse fit of the one-factor measurement model, Δχ2(1, N = 139) = 23.11, p = .000."

Discussion¶

Related Claims¶

• Incoming calculation skill did not predict fraction calculations, suggesting whole-number calculation fluency may not automatically transfer to fractions — related
• Processing speed, attentive behavior, and incoming calculation skill significantly predict whole-number calculations in at-risk fourth graders, with the model accounting for 51% of variance — related
• Language and incoming calculation skill predict whole-number versus fraction calculations differentially, with significant path-equality tests — related
• Language, in addition to processing speed and attentive behavior, significantly predicts fraction calculations in at-risk fourth graders — related
• Tutor ratings do not entirely tap one underlying trait; one- and two-factor models fit unsatisfactorily — related