Skip to main content
. Author manuscript; available in PMC: 2018 Aug 1.
Published in final edited form as: Cortex. 2017 May 25;93:79–91. doi: 10.1016/j.cortex.2017.05.008

Table 4.

Model comparisons

Model Comparison A
Model logLik deviance Chisq df p-value
Detection Latency ~ 1 + (Error Type | Subject) −1051.3 2102.6
Detection Latency ~ Detection Type + (Error Type | Subject) −1008.5 2017.0 85.65 1 <0.001*
Detection Latency ~ Detection Type + Error Type+(Error Type | Subject) −1003.4 2006.8 10.20 1 <0.01*
Detection Latency ~ Detection Type*Error Type + (Error Type | Subject) −990.7 1981.4 25.35 1 <0.001*
Model Comparison B
Model logLik deviance Chisq df p-value
Detection Latency ~ 1 + (Error Type | Subject) −737.5 1475.0
Detection Latency ~ Phonological Overlap + (Error Type | Subject) −718.1 1436.3 38.75 1 <.001*
Detection Latency ~ Phonological Overlap + Repair Accuracy + (Error Type | Subject) −713.6 1427.2 9.03 1 <.01*
Detection Latency ~ Phonological Overlap + Repair Accuracy + Error Type + (Error Type | Subject) −710.4 1420.7 6.53 1 .01*
Detection Latency ~ Phonological Overlap + Repair Accuracy*Error Type + (Error Type | Subject) −703.7 1407.5 13.22 1 <.001*

Note. The base model includes the intercept and random effects represented as (Error Type | Subject). The subsequent comparison models show the individually added fixed effects in bold, with “*” representing the complete set of main effects and interactions. Improvements in model fit were evaluated using the change in the deviance statistic, which is distributed as chi-squared with degrees of freedom equal to the number of parameters added.