I am interested in knowing the advantages they offer over each other, and their limitations.
I am a novice in this field and would really appreciate the answers expressed in lucid language.
Let me offer an explanation in the context of design of experiments between a fixed effects model and a random effects model. A factor is said to be fixed effects if its levels are chosen specifically. If so statistical inference will apply to the specific levels. This is a deterministic model. No probability distribution is involved. On the other hand a factor is said to be random effects if its levels are randomly chosen from a population of such levels. In this case statistical inference apply to the population of levels and not to the levels selected. This is a stochastic model. Probability distributions are involved. A stochastic model has a wider scope of interpretation and application than a deterministic one. However the specificity in a deterministic model can be an advantage. The generality in a stochastic model can also be an advantage. It all depends on the aim and vision of the researcher.
Dear Farid Benyahia,
Thank you very much for suggesting an interesting paper.
Dear Ette Etuk,
Thank you very much for your explanation. It proved beneficial.
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