Journal ArticleDOI
A special latin square for the use of each subject “as his own control”
TLDR
When the purpose of the experiment is to compare treatments, the Sequences × Positions Latin Square has been employed to control unwanted effects attributable to individuals, position, and sequence; if subject interactions are present, square uniqueness may be used as the error term and the bias in the test of treatments will be conservative.Abstract:
When the purpose of the experiment is to compare treatments, the Sequences × Positions Latin Square has been employed to control unwanted effects attributable to individuals, position, and sequence. This particular Latin Square has been subjected to criticism on the grounds there is confounding due to structure, random variables, and subject interactions. Special Latin Square, a subclass of the Sequences × Positions Latin Square, is basically ap ×p factorial design in blocks of sizep. The two factors are treatments (T) and positions (P). Sequence is one component of theTP interaction, and square uniqueness is the sum of the remaining components. This completely replicated factorial design has no structural or random variable confounding; if subject interactions are present, square uniqueness may be used as the error term and the bias in the test of treatments will be conservative.read more
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Single case experimental designs
TL;DR: A large variety of experimental strategies have been carried out by behavioral researchers as discussed by the authors, including group-comparison designs (cf. Kazdin, 1980) and single-case experimental designs (Barlow & Hersen, 1984; Hersen & Barlow, 1976).
Journal ArticleDOI
Alternating treatments design: one strategy for comparing the effects of two treatments in a single subject.
David H. Barlow,Steven C. Hayes +1 more
TL;DR: Methods of minimizing multiple treatment interference as well as methods of studying these effects are outlined, and appropriate uses of Alternating Treatments Designs are described and discussed in the context of recent examples.
Journal ArticleDOI
Single Subject Designs A Perspective on the Controversy Over Employing Statistical Inference and Implications for Research and Training in Behavior Modification
TL;DR: In this paper, the authors surveyed four major behavior modification journals from their inception through 1974 for their use of inferential statistical tests in empirical research studies and found that Parametric analysis-of-variance and various nonparametric tests were generally the most commonly employed statistics within the journals sampled.
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The simultaneous-treatment design
TL;DR: The simultaneous treatment design as discussed by the authors provides a means of comparing two or more different treatments with an individual subject, where different treatments are implemented in the same phase but are balanced with respect to different conditions of administration (e.g., treatment agents, time periods, and situations).
Journal ArticleDOI
A Simultaneous Comparison of Three Methods for Language Training with an Autistic Child: An Experimental Single Case Analysis.
TL;DR: In a single-case, simultaneous-treatment design, three methods for experimental language acquisition in one autistic child were compared using a Latin square design and trend-line analysis, showing a total communication approach to be significantly superior to sign-based and verbalization approaches.
References
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Statistical Principles in Experimental Design
TL;DR: In this article, the authors introduce the principles of estimation and inference: means and variance, means and variations, and means and variance of estimators and inferors, and the analysis of factorial experiments having repeated measures on the same element.
Journal ArticleDOI
Statistical Principles in Experimental Design
TL;DR: This chapter discusses design and analysis of single-Factor Experiments: Completely Randomized Design and Factorial Experiments in which Some of the Interactions are Confounded.
Book
Introduction to the Theory of Statistics
TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.