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Research Methodology - Analysis Of Variance

Classification of models - Analysis Of Variance

   Posted On :  26.05.2018 10:51 pm

Linear models for the sample data may broadly be classified into three types as follows:

Classification of models
 
 
Linear models for the sample data may broadly be classified into three types as follows:
 
1. Random effect model
 
2. Fixed effect model
 
3. Mixed effect model
 
In any variance components model, the error component has always random effects, since it occurs purely in a random manner. All other components may be either mixed or random.
 

Random effect model

  
A model in which each of the factors has random effect (including error effect) is called a random effect model or simply a random model.

Fixed effect model

 
A model in which each of the factors has fixed effects, buy only the error effect is random is called a fixed effect model or simply a fixed model.
 

Mixed effect model

 

A model in which some of the factors have fixed effects and some others have random effects is called a mixed effect model or simply a mixed model.
 
In what follows, we shall restrict ourselves to a fixed effect model. In a fixed effect model, the main objective is to estimate the effects and find the measure of variability among each of the factors and finally to
find the variability among the error effects.
 
The ANOVA technique is mainly based on the linear model which depends on the types of data used in the linear model. There are several types of data in ANOVA, depending on the number of sources of variation namely,

One-way classified data,
 
Two-way classified data, …
 
m-way classified data.
 

One-way classified data

 
When the set of observations is distributed over different levels of a single factor, then it gives one-way classified data.

 

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