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Statistical Strategies For Small Sample Research

Author: Rick H. Hoyle
Publisher: SAGE
ISBN: 9780761908869
Size: 52.75 MB
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This book provides encouragement and strategies for researchers who routinely address research questions using data from small samples. Chapters cover such topics as: using multiple imputation software with small sets; computing and combining effect sizes; bootstrap hypothesis testing; application of latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.

Small Clinical Trials

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 9780309171144
Size: 22.45 MB
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Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.

Sample Size Determination In Quantitative Social Work Research

Author: Patrick Dattalo
Publisher: Oxford University Press
ISBN: 0190295546
Size: 62.74 MB
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A researchers decision about the sample to draw in a study may have an enormous impact on the results, and it rests on numerous statistical and practical considerations that can be difficult to juggle. Computer programs help, but no single software package exists that allows researchers to determine sample size across all statistical procedures. This pocket guide shows social work students, educators, and researchers how to prevent some of the mistakes that would result from a wrong sample size decision by describing and critiquing four main approaches to determining sample size. In concise, example-rich chapters, Dattalo covers sample-size determination using power analysis, confidence intervals, computer-intensive strategies, and ethical or cost considerations, as well as techniques for advanced and emerging statistical strategies such as structural equation modeling, multilevel analysis, repeated measures MANOVA and repeated measures ANOVA. He also offers strategies for mitigating pressures to increase sample size when doing so may not be feasible. Whether as an introduction to the process for students or as a refresher for experienced researchers, this practical guide is a perfect overview of a crucial but often overlooked step in empirical social work research.

Data Analytic Techniques For Dynamical Systems

Author: Steven M Boker
Publisher: Psychology Press
ISBN: 1135611548
Size: 63.54 MB
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Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes. The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use: time series models from a discrete time perspective stochastic differential equations in continuous time estimating continuous time differential equation models multilevel models of differential equations to estimate within-person dynamics and the corresponding population means new SEM models for dynamical systems data Data Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.

Methodological And Biostatistical Foundations Of Clinical Neuropsychology And Medical And Health Disciplines

Author: Domenic V Cicchetti
Publisher: CRC Press
ISBN: 9789026519642
Size: 42.98 MB
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The goal of the second edition is to introduce the advance undergraduate or graduate student and more seasoned research scientists in any of the allied health sciences to a wide array of methodological and biostatistical issues, as they occur in the context of both published and ongoing research. Some sixty-four articles published between 1992 and 2002 have been selected from the Journal of Clinical and Experimental Neuropsychology, The Clinical Neuropsychologist, and Child Neuropsychology and reproduced in this volume. While building upon a working knowledge and understanding of the basic univariate data analytic techniques and the research designs to which they apply, the approach to the more complex multivariate techniques is presented primarily at a conceptual and essentially non-mathematical level. While the issue of the complexity of some of the more recent and standard approaches to data analytic strategies, and their important role to specific research designs is important to convey, there remains an even more fundamental issue of whether the results of correctly applied data analytic strategies make any practical or clinical sense, above and beyond their having reached levels of "statistical significance". These critical issues are addressed throughout various commentaries that the editors make at appropriate points in the text. The volume will appeal to advanced undergraduate and graduate students as well as clinical neuropsychologists and research scientists in any of the allied health disciplines.

Data Analysis With Small Samples And Non Normal Data

Author: Carl F. Siebert
Publisher: Oxford University Press
ISBN: 0199391491
Size: 78.17 MB
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In social sciences, education, and public health research, researchers often conduct small pilot studies (or may have planned for a larger sample but lost too many cases due to attrition or missingness), leaving them with a smaller sample than they expected and thus less power for their statistical analyses. Similarly, researchers may find that their data are not normally distributed -- especially in clinical samples -- or that the data may not meet other assumptions required for parametric analyses. In these situations, nonparametric analytic strategies can be especially useful, though they are likely unfamiliar. A clearly written reference book, Data Analysis with Small Samples and Non-Normal Data offers step-by-step instructions for each analytic technique in these situations. Researchers can easily find what they need, matching their situation to the case-based scenarios that illustrate the many uses of nonparametric strategies. Unlike most statistics books, this text is written in straightforward language (thereby making it accessible for nonstatisticians) while providing useful information for those already familiar with nonparametric tests. Screenshots of the software and output allow readers to follow along with each step of an analysis. Assumptions for each of the tests, typical situations in which to use each test, and descriptions of how to explain the findings in both statistical and everyday language are all included for each nonparametric strategy. Additionally, a useful companion website provides SPSS syntax for each test, along with the data set used for the scenarios in the book. Researchers can use the data set, following the steps in the book, to practice each technique before using it with their own data. Ultimately, the many helpful features of this book make it an ideal long-term reference for researchers to keep in their personal libraries.

Handbook Of Structural Equation Modeling

Author: Rick H. Hoyle
Publisher: Guilford Publications
ISBN: 1462516793
Size: 70.34 MB
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The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.

How To Lie With Statistics

Author: Darrell Huff
Publisher: W. W. Norton & Company
ISBN: 9780393070873
Size: 26.30 MB
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Over Half a Million Copies Sold--an Honest-to-Goodness Bestseller Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform.

How Many Subjects

Author: Helena Chmura Kraemer
Publisher: SAGE Publications
ISBN: 1483319555
Size: 54.95 MB
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With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition of How Many Subjects? by Helena Chmura Kraemer and Christine Blasey introduces a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide range of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can then easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons demonstrate important principles of design, measurement, and analysis that are rarely discussed in courses or textbooks, making this book a valuable instructional resource as well as a must-have guide for frequent reference.

Handbook Of Applied Multivariate Statistics And Mathematical Modeling

Author: Howard E.A. Tinsley
Publisher: Academic Press
ISBN: 9780080533568
Size: 18.92 MB
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Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.