Session Ready
Exercise

Analyzing and interpreting the school data

What predicts a student's increase in math skills (mathgain)? Does student's sex, teacher's preparation, mathprep, or teacher's knowledge, mathknow? Also, students learn within classrooms, classid, and schools, schoolid.

With this model, coefficients have 3 outcomes:

  • If it is positive and significantly different from zero, the variable predicts an increase in students' math gain.
  • If it is negative and significantly different from zero, the variable predicts a decrease in students' math gain.
  • If it is not significantly different than zero, the variable likely does not predict a change.

The DataCamp course, Foundations of Inference, covers models interpretation in greater detail.

Instructions 1/2
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  • Build a lmer() predicting mathgain with sex, mathprep, and mathknow as fixed-effects and classid and schoolid as random-effects using the data.frame studentData. Make sure you use the variables in this order to pass DataCamp's correctness test.
  • Look at the summary() outputs of the model.
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