ssm_cohort

Sample data downloaded from the California Community College's Chancellor's Office Student Success Metrics dashboard.

datasets

Implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index.
California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Research/Files/PercentagePointGapMethod2017.ashx>.
California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans. <https://www.cccco.edu/-/media/CCCCO-Website/Files/DII/guidelines-for-measuring-disproportionate-impact-in-equity-plans-tfa-ada.pdf>.

Vinh Nguyen

DisImpact

Calculates Disproportionate Impact When Binary Success Data are
Disaggregated by Subgroups

ssm_cohort function

A data frame with summarized data:<dl>
 <dt>value</dt>
<dd>Success count (numerator).</dd> <dt>denom</dt>
<dd>Group size (denominator).</dd> <dt>categoryLabel</dt>
<dd>Metric or outcome.</dd> <dt>academicYear</dt>
<dd>Academic year for given data.</dd> <dt>disagg1</dt>
<dd>Different levels of disaggregation.</dd> <dt>subgroup1</dt>
<dd>Groups corresponding to each disaggregation in <code>disagg1</code>.</dd> <dt>disagg2</dt>
<dd>Second level of disaggregation: 'None' or 'Gender'.</dd> <dt>subgroup2</dt>
<dd>Groups corresponding to each disaggregation in <code>disagg2</code>.</dd> <dt>cohort</dt>
<dd>Not actually a cohort, but the time-window for the outcome in <code>categoryLabel</code>.</dd> <dt>localeName</dt>
<dd>College name.</dd> <dt>metricID</dt>
<dd>ID for current metric.</dd> <dt>title</dt>
<dd>Title of visualization.</dd> <dt>categoryID</dt>
<dd>ID for <code>categoryLabel</code>.</dd> <dt>perc</dt>
<dd><code>value / denom</code>.</dd> <dt>dataType</dt>
<dd>All are 'Percent'.</dd> <dt>missingFlag</dt>
<dd>1 if missing.</dd> <dt>ferpaFlag</dt>
<dd>1 if FERPA-suppressed.</dd> <dt>X20</dt>
<dd>Ignore.</dd> <dt>description</dt>
<dd>Ignore.</dd> <dt>source</dt>
<dd>Ignore.</dd>
</dl>

Format

Long summarized disaggregated data set — ssm_cohort

A data frame with summarized data:<dl>
 <dt>value</dt>
<dd>Success count (numerator).</dd>

 <dt>denom</dt>
<dd>Group size (denominator).</dd>

 <dt>categoryLabel</dt>
<dd>Metric or outcome.</dd>

 <dt>academicYear</dt>
<dd>Academic year for given data.</dd>

 <dt>disagg1</dt>
<dd>Different levels of disaggregation.</dd>

 <dt>subgroup1</dt>
<dd>Groups corresponding to each disaggregation in <code>disagg1</code>.</dd>

 <dt>disagg2</dt>
<dd>Second level of disaggregation: 'None' or 'Gender'.</dd>

 <dt>subgroup2</dt>
<dd>Groups corresponding to each disaggregation in <code>disagg2</code>.</dd>

 <dt>cohort</dt>
<dd>Not actually a cohort, but the time-window for the outcome in <code>categoryLabel</code>.</dd>

 <dt>localeName</dt>
<dd>College name.</dd>

 <dt>metricID</dt>
<dd>ID for current metric.</dd>

 <dt>title</dt>
<dd>Title of visualization.</dd>

 <dt>categoryID</dt>
<dd>ID for <code>categoryLabel</code>.</dd>

 <dt>perc</dt>
<dd><code>value / denom</code>.</dd>

 <dt>dataType</dt>
<dd>All are 'Percent'.</dd>

 <dt>missingFlag</dt>
<dd>1 if missing.</dd>

 <dt>ferpaFlag</dt>
<dd>1 if FERPA-suppressed.</dd>

 <dt>X20</dt>
<dd>Ignore.</dd>

 <dt>description</dt>
<dd>Ignore.</dd>

 <dt>source</dt>
<dd>Ignore.</dd>


</dl>

ssm_cohort: Long summarized disaggregated data set

Description

Usage

Arguments

Format

Examples