SSD for R (eBook, PDF)
An R Package for Analyzing Single-Subject Data
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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 robust open-source statistical programming language, written by the book's authors. SSD for R has the mo...
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 robust open-source statistical programming language, written by the book's authors. SSD for R has the most comprehensive functionality specifically designed for the analysis of single-subject research data currently available. 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. Graphs can be annotated with text. SSD for R also contains a wide variety of functions to conduct statistical analyses that have traditionally been conducted with single-subject data. These include numerous descriptive statistics and effect size functions as well as tests of statistical significance, such as t-tests, chi-squares and the conservative dual criteria. Finally, SSD for R has the capability of analyzing group-level data. The authors step readers through the analytical process based on the characteristics of their data. Numerous 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. This 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 with a growing number of instructional videos and a blog to build a community of researchers interested in single-subject designs.
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