seda

The full SEDA dataset contains mean test scores on statewide testing data in
reading and math for every school district in the United States. See a
description of the data 
<a href="https://purl.stanford.edu/db586ns4974">here</a>. The data 
represented in this package represent a random sample of 10
cases in the full dataset. To access the full data, please visit the 
data archive in the above link.

datasets

A variety of methods are provided to estimate and visualize
distributional differences in terms of effect sizes. Particular emphasis
is upon evaluating differences between two or more distributions across
the entire scale, rather than at a single point (e.g., differences in
means). For example, Probability-Probability (PP) plots display the
difference between two or more distributions, matched by their empirical
CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing
for examinations of where on the scale distributional differences are
largest or smallest. The area under the PP curve (AUC) is an effect-size
metric, corresponding to the probability that a randomly selected
observation from the x-axis distribution will have a higher value
than a randomly selected observation from the y-axis distribution.
Binned effect size plots are also available, in which the distributions
are split into bins (set by the user) and separate effect sizes (Cohen's
d) are produced for each bin - again providing a means to evaluate the
consistency (or lack thereof) of the difference between two or more
distributions at different points on the scale. Evaluation of empirical
CDFs is also provided, with built-in arguments for providing annotations
to help evaluate distributional differences at specific points (e.g.,
semi-transparent shading). All function take a consistent argument
structure. Calculation of specific effect sizes is also possible. The
following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g,
(c) percentage above a cut, (d) transformed (normalized) percentage above
a cut, (e) area under the PP curve, and (f) the V statistic (see Ho,
2009; <doi:10.3102/1076998609332755>), which essentially transforms the
area under the curve to standard deviation units. By default, effect sizes
are calculated for all possible pairwise comparisons, but a reference
group (distribution) can be specified.

Daniel Anderson

esvis

Visualization and Estimation of Effect Sizes

seda function

A data frame with 32625 rows and 8 columns.<dl class="dl-horizontal">
 <dt>leaid</dt><dd>Integer. Local education authority identifier.</dd>
	 <dt>leaname</dt><dd>Character. Local education authority name.</dd>
 <dt>stateabb</dt><dd>Character. State abbreviation.</dd>
	 <dt>year</dt><dd>Integer. Year the data were collected.</dd>
	 <dt>grade</dt><dd>Integer. Grade level the data were collected.</dd>
 <dt>subject</dt><dd>Character. Whether the data were from reading or
			 mathematics.</dd>
 <dt>mean</dt><dd>Double. Mean test score for the LEA in the corresponding
				 subject/grade/year.</dd>
 <dt>se</dt><dd>Double. Standard error of the mean.</dd>
</dl>

Format

The full SEDA dataset contains mean test scores on statewide testing data in
reading and math for every school district in the United States. See a
description of the data 
<a href='https://purl.stanford.edu/db586ns4974'>here</a>. The data 
represented in this package represent a random sample of 10
cases in the full dataset. To access the full data, please visit the 
data archive in the above link.

Portion of the Stanford Educational Data Archive (SEDA). — seda

A data frame with 32625 rows and 8 columns.<dl class='dl-horizontal'>
 <dt>leaid</dt><dd>Integer. Local education authority identifier.</dd>
	 <dt>leaname</dt><dd>Character. Local education authority name.</dd>
 <dt>stateabb</dt><dd>Character. State abbreviation.</dd>
	 <dt>year</dt><dd>Integer. Year the data were collected.</dd>
	 <dt>grade</dt><dd>Integer. Grade level the data were collected.</dd>
 <dt>subject</dt><dd>Character. Whether the data were from reading or
			 mathematics.</dd>
 <dt>mean</dt><dd>Double. Mean test score for the LEA in the corresponding
				 subject/grade/year.</dd>
 <dt>se</dt><dd>Double. Standard error of the mean.</dd>
</dl>

seda: Portion of the Stanford Educational Data Archive (SEDA).

Description

Usage

Arguments

Format