Ssd for R: An R Package For Analyzing Single-Subject Data

Ssd for R: An R Package For Analyzing Single-Subject Data

Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R-the open-source statistical programming language, written by the book's authors, Charles Auerbach and Wendy Zeitlin.

SSD for R has numerous graphing and charting functions to conduct robust visual analysis. Besides the ability to create simple line graphs, additional features are available to add mean, median and standard deviation lines across phases to help better visualize change over time. This book also contains numerous tests of statistical significance, such as t-tests, chi-squares and the conservative dual criteria. Auerbach and Zeitlin guide readers through the analytical process based on the characteristics of their data. Several examples and illustrations are provided throughout to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation.

SSD for R is the only book of its kind to describe single-subject data analysis while providing free statistical software to do so. Additionally, the authors have an active website (http://ssdanalysis.com) with a growing number of instructional videos and a blog to build a community of researchers interested in single-subject designs.

Country	USA
Brand	Oxford University Press, USA
Manufacturer	Oxford University Press
Binding	Paperback
PartNumber	9780199343591
IsAdultProduct
Height	10
Length	7
Weight	0.67902376696
Width	0.38
ReleaseDate	2014-07-02
NumberOfItems	1

Ssd for R: An R Package For Analyzing Single-Subject Data

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