Data
====

1. ideo_interactions.data.R
  - N     : number of observations
  - party : party association? 1: Republican, 0: Democrat
  - score1: effect of election results
  - x     : pretreatment variable

2. ideo_overlap.data.R
  - N     : number of observations
  - party : party association? 1: Republican, 0: Democrat
  - score1: effect of election results
  - x     : pretreatment variable

3. ideo_reparam.data.R
  - N     : number of observations
  - party : party association? 1: Republican, 0: Democrat
  - score1: effect of election results
  - z1    : reparameterized term 1
  - z2	: reparameterized term 2

4. sesame.data.R
  - N          : number of observations
  - encouraged : encouraged to watch? 1: Yes, 0: No
  - watched    : watched Sesame Street? 1: Yes, 0: No
  - watched_hat: estimated casual effect of watching sesame street
  - y          : post test scores

5. sesame_one_pred_2b.data.R
  - N          : number of observations
  - encouraged : encouraged to watch? 1: Yes, 0: No
  - watched    : watched Sesame Street? 1: Yes, 0: No
  - watched_hat: estimated casual effect of watching sesame street
  - y          : post test scores

6. sesame_multi_preds_3b.data.R
  - N          : number of observations
  - encouraged : encouraged to watch? 1: Yes, 0: No
  - pretest    : pre test scores
  - setting    : setting category
  - site       : site category
  - watched    : watched Sesame Street? 1: Yes, 0: No
  - watched_hat: estimated casual effect of watching sesame street
  - y          : post test scores

Models
======

1. One predictor
  sesame_one_pred_2b.stan: lm(y ~ watched_hat)
  sesame_one_pred_a.stan : lm(watched ~ encouraged)
  sesame_one_pred_b.stan : lm(y ~ encouraged)

3. Multiple predictors without interaction
  ideo_reparam.stan        : lm(score1 ~ party + z1 + z2)
  ideo_two_pred.stan       : lm(score1 ~ party + x)
  sesame_mult_preds_3a.stan: lm(y ~ encouraged + pretest + factor(site) + setting)
  sesame_mult_preds_3b.stan: lm(y ~ watched_hat + pretest + factor(site) + setting)

3. Multiple predictors with interaction
  ideo_interactions.stan: lm(score1 ~ party + x + party:x)