Handbook of Statistical Methods: Single Subject Design

Plural Publishing
Free sample

In the behavioral and clinical sciences, single-subject designs have increasingly become important tools for determining a treatment efficacy. Despite a large number of recommendations in recent years for more use of the designs, the majority of typical research methods textbooks still do not provide sufficient direction and information about single-subject designs. One of the main reasons is that data analysis of single-subject designs is still foreign to the vast majority of the investigators, practitioners, and students. 

With this book, the authors have developed a practical guide of the most commonly used approaches in analyzing and interpreting single-subject data. In doing so, they have arranged the methodologies used in a logical sequence using an array of research studies from the existing published literatures to illustrate specific applications. The handbook is also laid out for the readers in a highly lucid and straightforward manner, beginning with a brief discussion of each approach such as visual, inferential, and probabilistic model, the applications for which it is intended, and a step-by-step illustration of the test as used in an actual research study. 

Presented is a brief evaluation of the strengths and limitations of the test along with its suitability, or lack thereof, for particular scales of measurement. Also included are statistical applications of such computer programs as Minitab and SPSS for the analysis of statistical data. This new handbook provides the readers with a concise yet comprehensive approach to help them further understand the concepts as effectively and simply as possible. 

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Additional Information

Publisher
Plural Publishing
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Published on
Jun 2, 2008
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Pages
172
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ISBN
9781597568470
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Language
English
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Genres
Medical / Audiology & Speech Pathology
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Content Protection
This content is DRM protected.
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Available on Android devices
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Eligible for Family Library

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Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology.

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Brain and cognition in the omics era
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Illustrates the vital role NI is playing throughout the neurosciences.

Provides a sampling of NI tools and applications in neuroscience research, and lays out current organization structures that support NI.

Describes the lack of NI in neuropsychology, differentiates between NI systems for neuropsychology and conventional computerized assessment methods, and proposes criteria for neuropsychology-specific NI systems.

Describes NI applications and models currently in use in neuropsychology, and NI models for neuropsychology that are being pioneered in phenomics research.

Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology.

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A vital introduction to a profound technological practice, Neuroinformatics for Neuropsychology is important reading for clinical neuropsychologists, cognitive neuroscientists, behavioral neurologists, and speech-language pathologists. Researchers, clinicians, and graduate students interested in informatics for the brain-behavioral sciences will especially welcome this unique volume.

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Brain and cognition in the omics era
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The legacy of the endophenotype concept – its utility and limitationsVarious potential neurophenotypes of relevance to clinical neuroscience, including ResponseInhibition, Fear Conditioning and Extinction, Error Processing, Reward Dependence and Reward Deficiency, Face Perception, and Language Phenotypes
Dynamic (electrophysiological) and computational neurophenotypes
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The volume may be especially relevant to researchers and clinical practitioners in psychiatry and neuropsychology and to cognitive neuroscientists interested in the intersection of neuroscience with genomics, phenomics and other omics disciplines.

Eiki Satake
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With case studies included on a companion website, this text will help readers comprehend how the process of clinical research relates to the scientific method of problem solving. Readers will understand the importance of three key, interrelated tasks involved in a research study: description (why it was done), explanation (what was done and to whom), and contextualization (how the results relate to other bodies of knowledge).

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The two basic elements of statistical reasoning that constitute evidence-based practice: deductive inference (from effect to cause) and inductive inference (from cause to effect).Classical statistical methods-statistical terms/vocabulary, population parameters, and sampling methods-as well as descriptive statistical methods-measures, correlation, and regression.The fundamentals of statistical inference that include testing hypotheses using a z-test, t-test, ANOVA, and MANOVA.The concept of probability, through various concrete examples and a step-by-step approach, which is a fundamental part of the clinical decision-making process.Evidence-based probabilistic methods called Minimum Bayes Factor (MBF) for measuring the strength of clinical evidence more precisely and as an alternative to classical testing hypotheses methods.Rationales and procedures of other statistical methods frequently seen in clinical literature, like meta-analysis, nonparametric methods, categorical analyses, and single subject designs.

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