Update cleaner to read parent races.
Update uploader to set races for parents.
Add race graphs to analyze page. Show both measure and scale level graphs
Update demographics table with lanugage options
Create a lanugage table to hold the new languages
Update the demographic loader to input languages into the database
Update the cleaner to read the language column
Update the parent table to hold a reference to a language
Update the data uploader script to read the language from the csv and update the language information for any parent items that already exist (or create database entries if none already exist)
update the analyze interface to add controls for selecting ‘parents by group’ and a dropdown for ‘parent by language’
Update the analyze controller to read the parent-by-group parameter
Create a graph for the parent-by-group view
Bubble up averages for language calculations.
Make sure n-size only counts responses for a given measure.
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
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.
