. Author manuscript; available in PMC: 2016 Mar 1.

Published in final edited form as: Psychol Methods. 2013 Dec 23;20(1):117–141. doi: 10.1037/a0034523

Table 5.

Predicting MMOD Convergence from Simulation Parameters

Raw Coefficient

Odds Ratio

Parameter

Estimate

S.E.

R_{Nagel}^{2}

Estimate

Lower CI

Upper CI

Intercept

4.14

(2.57)

63.08

50.98

78.05

Occasions₄

−0.68

(0.07)

.001

0.50

0.44

0.58

Occasions₅

−0.90

(0.07)

0.41

0.36

0.47

Items₂₀

1.82

(0.06)

.056

6.20

5.51

6.97

Factors₂

−1.89

(0.09)

.120

0.15

0.13

0.18

Factors₃

−3.43

(0.09)

0.03

0.04

Loadings_.6

2.88

(0.07)

.267

17.75

15.46

20.38

Loadings_.8

5.49

(0.16)

242.61

177.30

331.99

Auto_.8

−3.02

(0.07)

.141

0.05

0.04

0.06

n₅₀₀

1.18

(0.06)

.057

3.25

2.88

3.68

n₁₀₀₀

2.23

(0.07)

9.31

8.04

10.78

Note. Logistic regression of convergence (1=converged, 0=failure) on six simulation parameters. Simulation parameters treated as factors or dummy-coded variables, with 3 occasions, 10 items, 1 factor, loadings of 0.4, autoregression of 0.4 and a sample size of 250 treated as the baseline condition. R² indicates variance accounted for by each simulation parameter. Auto=Factor Autocorrelation, Load=Factor Loadings, n=Sample Size. $R_{Nagel}^{2} = partial Nagelkerke’s pseudo - R^{2}$ , calculated as $Δ R_{Nagel}^{2}$ when each predictor was removed from the model. $R_{Nagel}^{2} = 5.84$ for full model. C-index (area under ROC curve) = 0.94.