Comparing a single case to a control group – Applying linear mixed effects models to repeated measures data

Cortex ◽  
2015 ◽  
Vol 71 ◽  
pp. 148-159 ◽  
Author(s):  
Stefan Huber ◽  
Elise Klein ◽  
Korbinian Moeller ◽  
Klaus Willmes
Keyword(s):  
Repeated Measures ◽  
Single Case ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Control Group ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Repeated Measures Data

The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

2021 ◽  
pp. 001316442199489
Author(s):  
Luyao Peng ◽  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Aggregate Level ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Best Linear Unbiased

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

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