An IRT–Multiple Indicators Multiple Causes (MIMIC) Approach as a Method of Examining Item Response Latency

Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modeling Technique: A Tutorial and an Annotated Example

Occupational stress and psychopathology in health professionals: an explorative study with the multiple indicators multiple causes (MIMIC) model approach.

Occupational stress is a multivariate process involving sources of pressure, psycho-physiological distress, locus of control, work dissatisfaction, depression, anxiety, mental health disorders, hopelessness, and suicide ideation. Healthcare professionals are known for higher rates of occupational-related distress (burnout and compassion fatigue) and higher rates of suicide. The purpose of this ...

A Multiple Indicators Multiple Causes (MIMIC) model of Behavioural and Psychological Symptoms in Dementia (BPSD).

INTRODUCTION Although there is evidence for distinct behavioural sub-phenotypes in Alzheimer's disease (AD), their inter-relationships and the effect of clinical variables on their expression have been little investigated. METHODS We have analysed a sample of 1850 probable AD patients from the UK and Greece with 10 item Neuropsychiatric Inventory (NPI) data. We applied a Multiple Indicators M...

Detecting Multidimensional Differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response Theory Likelihood Ratio Test, and Logistic Regression

Citation: Bulut O and Suh Y (2017) Detecting Multidimensional Differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response Theory Likelihood Ratio Test, and Logistic Regression. Front. Educ. 2:51. doi: 10.3389/feduc.2017.00051 detecting Multidimensional differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response theo...

Multiple indicators, multiple causes measurement error models.

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of t...