Random effects vs. fixed effects in twin studies I am conducting a study on identical twins, such that the data can be considered as paired observations. I want to run a co-twin control analysis on the data to study the relationship between the independent variable Entrepreneurship (binary variable, 1 being entrepreneur) and the dependent variable Stress (ordinal variable ranging from 1 to 3, with 1 being least stress). 
I have followed the work of Hu et al. (1998) (see paper here) who apply the random-effects ordinal logistic model.  


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*Hu et al. (1998). Modelling ordinal responses from co-twin control studies, Statistics in Medicine, 17, 957Ð970.


My question is, why do they use a random-effects model? Why not fixed effects? What are the reasons for using a random-effects model instead of a fixed-effects model? I'm more familiar with fixed effects models, and can't seem to understand their justification of using random effects.
 A: Their specific reasons, from the linked paper, include:

Use of the random-effects approach in modelling co-twin control data
  is intuitively appealing. We can think of the responses from each
  member of an MZ twin pair as repeated measures for one pair. The
  random-effects model assumes that the logit varies from one pair to
  the next by $\nu_i$. This assumption is reasonable because each pair of
  twins has its own unique shared genetic and environmental background.
  Thus, this variability reflects natural heterogeneity due to
  unmeasured genetic and environmental factors among all the twin pairs.
  This heterogeneity is represented by a Gausssian probability
  distribution, that is, the random pair effect, $\nu_i$, which is assumed
  normally distributed in the population.

and

One important feature of the random-effects approach is that it can
  readily accommodate missing data in the response variables.

and

In random-effects models, the effects of the individual-level
  confounding variables also involve both within-pair and between-pair
  comparisons. Thus, they may differ from the effects estimated through
  the conditional likelihood approach. In addition, we can estimate the
  effects of pair-level variables (for example, age) with use of the
  random-effects approach, but not with use of the conditional
  likelihood approach, though we can estimate the interactions between
  pair-level variables and the exposure using both approaches.

Do you have particular disagreements with these or are they unclear? Or are you looking for general principles that are not particularly tied to this study at all?
In terms of generally why you'd use a random effect, that's pretty complex and unfortunately different fields define "fixed effect" and "random effect" differently. There are also different names given to mixed-effects modeling, including "hierarchical" modeling. (Not to mention a lot of mixed-effects modeling now-a-days is Bayesian rather than Frequentist.)
In this particular study, they were looking at intra- and inter-twin-pair differences, so mixed-effects models naturally come to mind. As I understand it, a fixed-effect model will either pool completely across all observations (if you leave out a group variable), or will not pool at all across observations (if you include a group variable). A mixed-effects model allows for limited pooling, driven by the data. ("Borrowing strength" is the term.) Not sure how much borrowing strength applies in a paired study like this, though.
Last, since they are treating twins as repeated measurements, their errors will be correlated, which violates the independence assumption of a fixed-effects model.
