Sentence examples for mean square statistic from inspiring English sources

Two fit statistics were calculated for each person and each item to evaluate the degree of fit of the model to the data: (i) infit mean square statistic focuses on unexpected responses near a person or item measure, and (ii) outfit mean square statistic places more emphasis on unexpected responses far from a person or item measure, being more sensitive to outliers.

We use the "outfit" mean square statistic T j (x|θ γ) (Johnson et al. [1999]) to diagnose possible misfit of any particular item, T j ( x | θ, γ ) = ∑ i = 1 N x ij − E ij N W ij, (11).

Infit infit mean square statistic, Oufit outfit mean square statistic Note.

δ average item challenge, SE standard error of the item challenge, Infit infit mean square statistic, Oufit outfit mean square statistic aThe original item 9 was reversed in these analyses Note.

Category fit was evaluated by the infit and outfit mean square statistic computed for each rating category.

We assessed item fit by using the infit mean square statistic; values from 0.5-1.5 are considered useful for measurement, and values greater than 2.0 degrade measurement [ 11].

MNSQ = Mean-square statistic with expectation 1. 0 = never a problem. 1 = almost never a problem. 2 = sometimes a problem.

MNSQ = Mean-square statistic with expectation 1. Table 8 shows average measures, infit and outfit MNSQ, and step measures on the four subscales.

The infit statistic is an information-weighted mean square fit statistic, which reduces the inordinate impact of outlier responses [ 31].

For instance, the number of misfitting items identified by the t-statistic was four times greater than those identified by the mean square fit statistic (23 and 5, respectively).

δ = level of severity (higher values indicate higher severity); SE = standard error; MNSQ = mean square fit statistic.