instead of hard coded values. Produce a clean csv and a csv with all
the removed values and columns with reason for removal. Add script for
running cleaning for each project
Seed db with survey_item information to keep track of which survey_item is on the short form survey. REcalculate response rate depending on whether school to regular survey or short form survey.
Correct score for short form schools. Finishes #181284202
average those groupings and the way up the framework. Likert scores for
a survey_item are averaged. Then all the survey_items in a scale are
averaged. Then student scales in a measure are averaged. And teacher
scales in a measure are averaged. Then the average of those two
calculations becomes the score for a measure. Then the measures in a
subcategory are averaged.
