DelemereDounavi

Data from a multiple baseline design conducted by Delemere &amp; Dounavi (2018).
The variables are as follows:<ul>
<li><code>intervention</code>. Type of intervention received: bedtime fading or positive routines.</li>
<li><code>case</code>. Participant identifier.</li>
<li><code>session</code>. Measurement occasion.</li>
<li><code>condition</code>. Factor identifying the phase of the design (baseline or treatment).</li>
<li><code>outcome</code>. Total sleep onset latency in minutes across nights.</li>
</ul>

datasets

Provides a set of tools for estimating hierarchical linear
models and effect sizes based on data from single-case designs.
Functions are provided for calculating standardized mean difference effect sizes that
are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>;
Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>;
Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>;
and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>.
Includes an interactive web interface.

James Pustejovsky

scdhlm

Estimating Hierarchical Linear Models for Single-Case Designs

Man Chen

Bethany Hamilton

DelemereDounavi function

A data frame with 187 rows and 5 variables

DelemereDounavi: Delemere & Dounavi (2018)

Description

Arguments

Format