A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators Multiple Causes

A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators Multiple Causes

BACKGROUND Measurement equivalence is an essential prerequisite for making valid comparisons in mental health questionnaires across groups. In most methods used for assessing measurement equivalence, which is known as Differential Item Functioning (DIF), latent variables are assumed to be continuous. OBJECTIVE To compare a new method called Latent Class Regression (LCR) designed for discrete ...

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...

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

Multiple Output Regression with Latent Noise

In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal. Therefore, (1) explaining away the structured noise in multiple-output regression is of paramount importance. Additionally, (2) assumptions about the correlation structure of the regre...

A multiple indicators and multiple causes model for valuation

Value assessment (valuation) of certain real properties is commonly required for a variety of purposes. In this article an approach to define an estimator of the value of a real property, with good statistical properties is proposed. This estimator reaches a consensus between the various available methods. The estimator proposed uses all available information optimally, including the informatio...