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
to survey table. We no longer check or keep track of the survey type.
Instead we look in the database to see if a survey item has at least 10
responses. If it does, that survey item was presented to the respondent
and we count it, and all responses when calculating the response rate.
Remove response rate timestamp from caching logic because we no longer
add the response rate to the database. All response rates are calculated
on the fly
Update three_b_two scraper to use teacher only numbers
swap over to using https://profiles.doe.mass.edu/statereport/gradesubjectstaffing.aspx as the source of staffing information
rate model. Create a loader to refresh response rates for all
subcategories.
Use precalculated response rates in views
Wrap more elements in page caching
Calculate a response rate for a subcategory if one does not already
exist
Implement large speed improvements to score calculations. Add page caching to all pages. Small speed improvements to response rate by filtering out
survey items without responses with `none?` `method vs count == 0`.
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.
rebuilt