| data analysis in single subject studies generally includes: Research Methods in Education Joseph Check, Russell K. Schutt, 2011-10-27 Research Methods in Education introduces research methods as an integrated set of techniques for investigating questions about the educational world. This lively, innovative text helps students connect technique and substance, appreciate the value of both qualitative and quantitative methodologies, and make ethical research decisions. It weaves actual research stories into the presentation of research topics, and it emphasizes validity, authenticity, and practical significance as overarching research goals. The text is divided into three sections: Foundations of Research (5 chapters), Research Design and Data Collection (7 chapters), and Analyzing and Reporting Data (3 chapters). This tripartite conceptual framework honors traditional quantitative approaches while reflecting the growing popularity of qualitative studies, mixed method designs, and school-based techniques. This approach provides a comprehensive, conceptually unified, and well-written introduction to the exciting but complex field of educational research. |
| data analysis in single subject studies generally includes: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| data analysis in single subject studies generally includes: Single Case Experimental Designs David H. Barlow, Michel Hersen, 1984 |
| data analysis in single subject studies generally includes: Single-Case Research Methods for the Behavioral and Health Sciences David L. Morgan, Robin K. Morgan, 2008-07-29 This text ntroduces readers to the history, epistemology, and strategies of single-case research design. The authors offer concrete information on how to observe, measure, and interpret change in relevant outcome variables and how to design strategies that promote causal inferences. Key Features Includes case vignettes on specific single-case designs Describes clinical and applied case studies Draws on multiple examples of single-case designs from published journals across a wide range of disciplines Covers recent developments in applied research, including meta-analysis and the distinction between statistical and clinical significance Provides pedagogical tools to help readers master the material, including a glossary, interim summaries, end-of-chapter review questions, and activities that encourage active processing of material. Intended Audience This text is intended for students and practitioners in a variety of disciplines—including psychology, nursing, physical therapy, and occupational therapy—who are increasingly called upon to document the effectiveness of interventions. |
| data analysis in single subject studies generally includes: Small Clinical Trials Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Small-Number-Participant Clinical Research Trials, 2001-01-01 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. |
| data analysis in single subject studies generally includes: Single-subject Experimental Research Susan B. Neuman, Sandra McCormick, 1995 Intended as a guide for both novice and experienced researchers, this collection of articles focuses on single-subject experimental research used for investigations in literacy. The premise of the collection is that this single-subject approach allows researchers--both those from outside the classroom and teacher-researchers--to discover how an instructional intervention affects the student actually in need of assistance. The articles and their authors are: (1) What Is Single-Subject Experimental Research? (Sandra McCormick); (2) Reversal Designs (David B. Yaden, Jr.); (3) Multiple-Baseline Designs (James Kucera and Saul Axelrod); (4) Alternating-Treatments Designs (Susan B. Neuman); (5) Statistical Analysis Procedures for Single-Subject Designs (Michael L. Kamil); (6)Combining Single-Subject Designs with Qualitative Research (Tanja L. Bisesi and Taffy E. Raphael); (7) Teachers Using Single-Subject Designs in the Classroom (Judith A. Braithwaite); and (8) Important Issues Related to Single-Subject Experimental Research (Annemarie Sullivan Palincsar and Andrea DeBruin Parecki). Three appendixes conclude the collection: Appendix A--Conventions for Displaying Data on Line Graphs (Sandra McCormick); Appendix B--Comparison of Research Methodologies Commonly Used in Literacy Investigations (Sandra McCormick); and Appendix C--Single-Subject Experimental Research Studies with Literacy-Related Themes (Susan B. Neuman). (NKA) |
| data analysis in single subject studies generally includes: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases.--Publisher's description. |
| data analysis in single subject studies generally includes: Analyzing Single System Design Data William Nugent, 2009-12-11 Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time. They also allow researchers to gather data where it might be difficult to conduct a study involving treatment and control groups; in a school setting, or a community mental health agency, for example, random assignment may be impossible, whereas individual student or client progress across time can be more easily monitored. This pocket guide reviews a wide range of techniques for analyzing single system design data, including visual analysis methods, graphical methods, and statistical methods. From basic visual observation to complex ARIMA statistical models for use with interrupted time series designs, numerous data analysis methods are described and illustrated in this unique and handy book. The author frankly describes limitations and strengths of the data analysis methods so that readers can select an appropriate method and use the results responsibly in order to improve practice and client well-being. This accessible yet in-depth introduction will serve as a highly practical resource for doctoral students and researchers alike. |
| data analysis in single subject studies generally includes: The Prevention and Treatment of Missing Data in Clinical Trials National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Handling Missing Data in Clinical Trials, 2010-12-21 Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data. |
| data analysis in single subject studies generally includes: Single-Case Research Design and Analysis (Psychology Revivals) Thomas R. Kratochwill, Joel R. Levin, 2015-04-10 Originally published in 1992, the editors of this volume fulfill three main goals: to take stock of progress in the development of data-analysis procedures for single-subject research; to clearly explain errors of application and consider them within the context of new theoretical and empirical information of the time; and to closely examine new developments in the analysis of data from single-subject or small n experiments. To meet these goals, this book provides examples of applicable single-subject research data analysis. It presents a wide variety of topics and perspectives and hopes that readers will select the data-analysis strategies that best reflect their methodological approaches, statistical sophistication, and philosophical beliefs. These strategies include visual analysis, nonparametric tests, time-series experiments, applications of statistical procedures for multiple behaviors, applications of meta-analysis in single-subject research, and discussions of issues related to the application and misapplication of selected techniques. |
| data analysis in single subject studies generally includes: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov) |
| data analysis in single subject studies generally includes: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| data analysis in single subject studies generally includes: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| data analysis in single subject studies generally includes: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| data analysis in single subject studies generally includes: Longitudinal Data Analysis Garrett Fitzmaurice, Marie Davidian, Geert Verbeke, Geert Molenberghs, 2008-08-11 Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory |
| data analysis in single subject studies generally includes: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| data analysis in single subject studies generally includes: Finding What Works in Health Care Institute of Medicine, Board on Health Care Services, Committee on Standards for Systematic Reviews of Comparative Effectiveness Research, 2011-07-20 Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research. |
| data analysis in single subject studies generally includes: Single-case Research Designs Alan E. Kazdin, 2011 Kazdin's text is a notable contrast to the quantitative methodology approach that pervades the biological and social sciences. The methodology in Single-Case Reasearch Designs focuses on a widely applicable methodology for evaluating interventions, such as treatment, or psychotherapy, using applied behavior anlaysis. However, this revision aims to encompass a broader range of research areas that utilize single-case designs. The text will convey the pertinence of this research methodology to disciplines ranging from psychology and medicine to business and industry. The first edition of this book, which was published in 1982, still sells a steady amount of copies today. The fact that professors continue to use the first edition of this book more than twenty years after it was published is a testament to the quality of information, organization, and narrative throughout the text. The possibility of a revision has professors excited that they can expose their students toa well-written, clear, and updated text that will reflect the current status of single-case research. |
| data analysis in single subject studies generally includes: Science and Social Work Stuart Kirk, William J. Reid, 2002-01-02 Science and Social Work is a critical appraisal of the strategies and methods that have been used to develop knowledge for social work practice. It identifies the major ways in which social workers have drawn upon scientific knowledge and techniques, placing each one in historical perspective by explaining the nature of the problems it was designed to solve and the philosophical, political, and practical questions it raised. Kirk and Reid offer a balanced appraisal of the promises, accomplishments, and limits of such approaches, demonstrating how the fruits of scientific research can aid clinical practice with individuals, families and groups. |
| data analysis in single subject studies generally includes: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| data analysis in single subject studies generally includes: Qualitative Data Analysis Ian Dey, 2003-09-02 Qualitative Data Analysis shows that learning how to analyse qualitative data by computer can be fun. Written in a stimulating style, with examples drawn mainly from every day life and contemporary humour, it should appeal to a wide audience. |
| data analysis in single subject studies generally includes: How to Design and Evaluate Research in Education Jack R. Fraenkel, Norman E. Wallen, 2005-04 How to Design and Evaluate Research in Education provides a comprehensive introduction to educational research. Step-by-step analysis of real research studies provides students with practical examples of how to prepare their work and read that of others. End-of-chapter problem sheets, comprehensive coverage of data analysis, and information on how to prepare research proposals and reports make it appropriate both for courses that focus on doing research and for those that stress how to read and understand research. |
| data analysis in single subject studies generally includes: The Blackwell Guide to Research Methods in Bilingualism and Multilingualism Li Wei, Melissa G. Moyer, 2017-11-27 As globalization has increased awareness of the extent of language contact and linguistic diversity, questions concerning bilingualism and multilingualism have taken on an increasing importance from both practical and scholarly points of view. Written by leading experts and practitioners in the field, The Blackwell Guide to Research Methods in Bilingualism and Multilingualism: Highlights the interdisciplinary nature of research on bilingualism and multilingualism and offers a practical guide to the procedures and tools for collecting and analyzing data Specifically addresses methodological issues, discussing research topics, core concepts and approaches, and the methods and techniques available Links theory to method, and to data, and answers a real need for a know-how volume on bilingualism and multilingualism that deals with its methodology in a systematic and coherent way |
| data analysis in single subject studies generally includes: SSD for R Charles Auerbach, Wendy Zeitlin, 2021-10-22 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. SSD for R serves as a guide for those desiring to conduct single-subject data analysis and introduces 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 that was written by the book's authors. This second edition of SSD for R is the most comprehensive guide to the numerous graphing and charting functions for conducting robust visual analysis including line graphs and more complex standard deviation lines. 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. In addition to presentations and assignments, this new edition contains more examples and illustrations 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. For more instructional videos, blogs, and a growing community of researchers interested in single-subject designs, visit the authors' website: http://ssdanalysis.com. |
| data analysis in single subject studies generally includes: Individual Participant Data Meta-Analysis Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart, 2021-06-08 Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points. Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout. |
| data analysis in single subject studies generally includes: Single Case Research Methodology Jennifer R. Ledford, David L. Gast, 2014-03-26 In this anticipated new edition of Single Case Research Methodology, David L. Gast and Jennifer R. Ledford detail why and how to apply standard principles of single case research methodology to one’s own research or professional project. Using numerous and varied examples, they demonstrate how single case research can be used for research in behavioral and school psychology, special education, speech and communication sciences, language and literacy, occupational therapy, and social work. This thoroughly updated new edition features two entirely new chapters on measurement systems and controversial issues in single subject research, in addition to sample data sheets, graphic displays, and detailed guidelines for conducting visual analysis of graphic data. This book will be an important resource to student researchers, practitioners, and university faculty who are interested in answering applied research questions and objectively evaluating educational and clinical practices. |
| data analysis in single subject studies generally includes: The Coding Manual for Qualitative Researchers Johnny Saldana, 2009-02-19 The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example. |
| data analysis in single subject studies generally includes: Methods of Literacy Research Michael L. Kamil, Peter B. Mosenthal, P. David Pearson, Rebecca Barr, 2001-07 This volume packages the reading reseach methodology chapters from the HANDBOOK OF READING RESEARCH, VOLUME III. Intended as a text for upper undergraduate and graduate level reading research methods courses and as a resource for scholars in the field. |
| data analysis in single subject studies generally includes: Handbook of Neuroimaging Data Analysis Hernando Ombao, Martin Lindquist, Wesley Thompson, John Aston, 2016-11-18 This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science. |
| data analysis in single subject studies generally includes: Statistics Applied to Clinical Studies Ton J. Cleophas, Aeilko H. Zwinderman, 2012-02-09 Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis. |
| data analysis in single subject studies generally includes: The Analysis of Covariance and Alternatives Bradley Huitema, 2011-10-24 A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering. |
| data analysis in single subject studies generally includes: Decision Making, Affect, and Learning Mauricio R. Delgado, Elizabeth A. Phelps, Trevor W. Robbins, 2011-03-24 Focuses on decision making and emotional processing, investigating the psychological and neural systems underlying decision making, and the relationship with reward, affect, and learning. Considers neurodevelopmental and clinical aspects and looks at the applied aspects for other disciplines, including neuroeconomics. |
| data analysis in single subject studies generally includes: Experimental and Quasi-Experimental Designs for Research Donald T. Campbell, Julian C. Stanley, 2015-09-03 We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control. |
| data analysis in single subject studies generally includes: Single Subject Research Methodology in Behavioral Sciences Jennifer R. Ledford, David L. Gast, 2009-10-16 This book is written for student researchers, practitioners, and university faculty who are interested in answering applied research questions and objectively evaluating educational and clinical practices. The basic tenet of single-subject research methodology is that the individual is of primary importance and that each individual study participant serves as his or her own control. It is because of this focus on the individual that clinicians and educators have been using single-subject research designs for over 40 years to answer applied research questions. Although the methodology has its roots in behavioral psychology and applied behavioral analysis, it has been used by applied researchers across a variety of disciplines such as special education, speech and communication sciences, language and literacy, therapeutic recreation, occupational therapy, and social work. Key features include the following: Applied Focus – More than any other text, this one focuses on the nuts and bolts of how to use single-subject research in constructing one’s research project or in evaluating one’s professional practice. Numerous and Varied Examples – The book abounds in examples from special education and other disciplines and populations within the applied research literature. Reader Friendly – The text is written in a reader friendly style, features sample data sheets and graphic displays, and includes detailed guidelines for conducting visual analysis of graphic data. Expertise – The author’s long and distinguished career in teaching single-subject research is augmented in this book by contributions from other experts in the field. |
| data analysis in single subject studies generally includes: The Individual Subject and Scientific Psychology Jaan Valsiner, 2013-06-29 |
| data analysis in single subject studies generally includes: Handbook Of Clinical And Experimental Neuropsychology Gianfranco Denes, Luigi Pizzamiglio, 2020-03-06 The domain of neuroscience has had one of the most explosive growths in recent decades: within this development there has been a remarkable and renewed interest in the study of the relations between behaviour and the central nervous system. Part of this new attention is connected with the contribution of new technologies (PET, fMRI) permitting more precise mapping of neural structures responsible for cognitive functions and the development of new theoretical models of mental activities. The diffusion of new pathologies (for example the pattern of cognitive impairment associated with AIDS) has further enlarged the field of clinical neuropsychology. Finally there has been an expanding clinical interest in the understanding and management of age-related cognitive changes. This volume is the translated and updated version of the second edition of Manuale di Neuropsicologia (Zanichelli, 1996), by the same authors, and it reflects the current status of the art. It is intended to blend clinical and theoretical aspects of neuropsychology. The first part discusses the instrumental and clinical methods of investigation in neuropsychology, together with their development. A long section is dedicated to the language and memory disorders. The impairment of non-verbal cognitve functions, such as the disorders of space orientation, of of visuo-perceptive abilities, and of the emotions and attention, are extensively discussed. The pattern of degenerative dementias is thorougly described, as e is thoroughly described, as well as a number of new topics, such as a neuropsychological approach to consciousness. Finally, perspectives for treatment of some cognitive disorders are outlined. |
| data analysis in single subject studies generally includes: Practice-Based Research R. Trent Codd, III, 2018-05-01 Practice-Based Research shows mental-health practitioners how to establish viable and productive research programs in routine clinical settings. Chapters written by experts in practice-based research use real-world examples to help clinicians work through some of the most common barriers to research output in these settings, including lack of access to institutional review boards, lack of organizational support, and limited access to financial resources. Specialized chapters also provide information on research methods and step-by-step suggestions tailored to a variety of practice settings. This is an essential volume for clinicians interested in establishing successful, long-lasting practice-based research programs. |
| data analysis in single subject studies generally includes: Specialty Competencies in School Psychology Ph.D. Rosemary Flanagan, Ph.D. Jeffrey A. Miller, 2010-08-26 Although School Psychology first became recognized as a specialty in professional psychology by CRSPPP in 1998, this area of psychology can be traced back to the late 19th century, where it can be thought to have developed alongside Clinical Psychology due to the types of cases seen in Lightner Witmer's Psychological Clinic that opened in 1896. Over the years, this psychology specialty has become to encompass the science and practice of psychology with regard to a wide range of learners, including children, youth, and families, as it impacts the schooling or educational process. In this volume, Drs. Flanagan and Miller provide a comprehensive overview of the foundational and functional competencies related to the specialty of school psychology. As the U.S. attempts to reclaim its stature as a leader in education, school psychologists are likely to play a crucial role across multiple tasks and levels. As such, the reader interested in school psychology will find this volume very educational. Series in Specialty Competencies in Professional Psychology Series Editors Arthur M. Nezu and Christine Maguth Nezu As the field of psychology continues to grow and new specialty areas emerge and achieve recognition, it has become increasingly important to define the standards of professional specialty practice. Developed and conceived in response to this need for practical guidelines, this series presents methods, strategies, and techniques for conducting day-to-day practice in any given psychology specialty. The topical volumes address best practices across the functional and foundational competencies that characterize the various psychology specialties, including clinical psychology, cognitive and behavioral psychology, school psychology, geropsychology, forensic psychology, clinical neuropsychology, couples and family psychology, and more. Functional competencies include common practice activities like assessment and intervention, while foundational competencies represent core knowledge areas such as ethical and legal issues, cultural diversity, and professional identification. In addition to describing these competencies, each volume provides a definition, description, and development timeline of a particular specialty, including its essential and characteristic pattern of activities, as well as its distinctive and unique features. Written by recognized experts in their respective fields, volumes are comprehensive, up-to-date, and accessible. These volumes offer invaluable guidance to not only practicing mental health professionals, but those training for specialty practice as well. |
| data analysis in single subject studies generally includes: Rehabilitation Research - E-Book Catherine H. Balthazar, Ann M. Vendrely, 2021-07-03 - UPDATED! Revised evidence-based content throughout provides students and rehabilitation practitioners with the most current information. - UPDATED! Coverage of the latest research methods and references ensures content is current and applicable for today's PT, OT, and SLP students. - NEW! Analysis and Interpretation of Data from Single Subject Designs chapter. - NEW! Content on evaluating the quality of online and open-access journals. |
| data analysis in single subject studies generally includes: Case Research in Public Management David E McNabb, 2014-12-18 Highly readable and non-technical, this handbook is designed to help students and non-profit managers gain a working knowledge of the principles and practices of conducting qualitative case study research in public organizations. This book is a motherload of practical and comprehensive guidance to planning, conducting, analyzing, and reporting case research project findings. McNabb begins with a detailed rationale for the use of the case research approach in public administration, non-profit organizations, and political science. Then it provides step-by-step instructions on how to conduct single-case, multicase, and meta-analysis research, with guidelines on organizing and writing the case report. Case Research in Public Management also includes many examples of case studies in a wide range of important topics in public administration, including performance management, sustainable government, technology management, security issues, emergency and disaster management, social and health services, infrastructure, public transportation, and transforming the work of government. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
