reading

Pre- and post-treatment reading comprehension test scores for 66 students randomly assigned to one of three methods for teaching reading comprehension.

datasets

Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.

Joseph J Lee

randomizationInference

Flexible Randomization-Based Inference

reading function

A data set of 66 students with 6 variables:<dl class="dl-horizontal">
 <dt><code>Subject</code></dt><dd>student ID numbers</dd>
 <dt><code>Block</code></dt><dd>block designations</dd>
 <dt><code>Group</code></dt><dd>treatment group assignments</dd>
 <dt><code>Pre1</code></dt><dd>pre-treatment reading comprehension test scores</dd>
 <dt><code>Post1</code></dt><dd>post-treatment reading comprehension test scores</dd>
 <dt><code>Diff1</code></dt><dd>changes in test score (improvement if positive)</dd>
 </dl>

Format

Reading Data — reading

A data set of 66 students with 6 variables:<dl class='dl-horizontal'>
 <dt><code>Subject</code></dt><dd>student ID numbers</dd>
 <dt><code>Block</code></dt><dd>block designations</dd>
 <dt><code>Group</code></dt><dd>treatment group assignments</dd>
 <dt><code>Pre1</code></dt><dd>pre-treatment reading comprehension test scores</dd>
 <dt><code>Post1</code></dt><dd>post-treatment reading comprehension test scores</dd>
 <dt><code>Diff1</code></dt><dd>changes in test score (improvement if positive)</dd>
 </dl>

reading: Reading Data

Description

Usage

Arguments

Format

Examples