This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.

Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author's many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author's website.

This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition, the book focuses on the formulation of appropriate statistical models to study problems in business, economics, and the social sciences, as well as on how to interpret the results from statistical analyses. The book will be useful to students who are interested in rigorous applications of statistics to problems in business, economics and the social sciences, as well as students who have studied statistics in the past, but need a more solid grounding in statistical techniques to further their careers. Jacco Thijssen is professor of finance at the University of York, UK. He holds a PhD in mathematical economics from Tilburg University, Netherlands. His main research interests are in applications of optimal stopping theory, stochastic calculus, and game theory to problems in economics and finance. Professor Thijssen has earned several awards for his statistics teaching.

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For disciplines concerned with human well-being, such as medicine, psychology, and law, statistics must be used in accordance with standards for ethical practice. A Statistical Guide for the Ethically Perplexed illustrates the proper use of probabilistic and statistical reasoning in the behavioral, social, and biomedical sciences. Designed to be consulted when learning formal statistical techniques, the text describes common instances of both correct and false statistical and probabilistic reasoning. Lauded for their contributions to statistics, psychology, and psychometrics, the authors make statistical methods relevant to readers day-to-day lives by including real historical situations that demonstrate the role of statistics in reasoning and decision making. The historical vignettes encompass the English case of Sally Clark, breast cancer screening, risk and gambling, the Federal Rules of Evidence, "high-stakes" testing, regulatory issues in medicine, difficulties with observational studies, ethics in human experiments, health statistics, and much more. In addition to these topics, seven U.S. Supreme Court decisions reflect the influence of statistical and psychometric reasoning and interpretation/misinterpretation. Exploring the intersection of ethics and statistics, this comprehensive guide assists readers in becoming critical and ethical consumers and producers of statistical reasoning and analyses. It will help them reason correctly and use statistics in an ethical manner.

Statistical methodology is often conceived by social scientists in a technical manner; they use it for support rather than for illumination. This two-volume set attempts to provide some partial remedy to the problems that have led to this state of affairs. Both traditional issues, such as analysis of variance and the general linear model, as well as more novel methods like exploratory data analysis, are included. The editors aim to provide an updated survey on different aspects of empirical research and data analysis, facilitate the understanding of the internal logic underlying different methods, and provide novel and broader perspectives beyond what is usually covered in traditional curricula.

Computer simulation has become an important means for obtaining knowledge about nature. The practice of scientific simulation and the frequent use of uncertain simulation results in public policy raise a wide range of philosophical questions. Most prominently highlighted is the field of anthropogenic climate change are humans currently changing the climate? Referring to empirical results from science studies and political science, Simulating Nature: A Philosophical Study of Computer-Simulation Uncertainties and Their Role in Climate Science and Policy Advice, Second Edition addresses questions about the types of uncertainty associated with scientific simulation and about how these uncertainties can be communicated. The author, who participated in the United Nations Intergovernmental Panel on Climate Change (IPCC) plenaries in 2001 and 2007, discusses the assessment reports and workings of the IPCC. This second edition reflects the latest developments in climate change policy, including a thorough update and rewriting of sections that refer to the IPCC.

Designing a research project is possibly the most difficult task a dissertation writer faces. It is fraught with uncertainty: what is the best subject? What is the best method? For every answer found, there are often multiple subsequent questions, so it's easy to get lost in theoretical debates and buried under a mountain of literature. This book looks at literature review in the process of research design, and how to develop a research practice that will build skills in reading and writing about research literature-skills that remain valuable in both academic and professional careers. Literature review is approached as a process of engaging with the discourse of scholarly communities that will help graduate researchers refine, define, and express their own scholarly vision and voice. This orientation on research as an exploratory practice, rather than merely a series of predetermined steps in a systematic method, allows the researcher to deal with the uncertainties and changes that come with learning new ideas and new perspectives. The focus on the practical elements of research design makes this book an invaluable resource for graduate students writing dissertations. Practicing research allows room for experiment, error, and learning, ultimately helping graduate researchers use the literature effectively to build a solid scholarly foundation for their dissertation research project.

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

Primary research in education and social sciences is marked by a diversity of methods and perspectives. How can we accommodate and reflect such diversity at the level of synthesizing research? What are the critical methodological decisions in the process of a research synthesis, and how do these decisions open up certain possibilities, while closing down others? This book draws upon methodologically diverse literature on research synthesis methods and primary research methods to develop a framework for synthesizing research. It presents a Methodologically Inclusive Research Synthesis framework to facilitate critical and informed decision-making among the producers and users of research synthesis. Three guiding principles for a quality research synthesis are proposed: informed subjectivity and reflexivity, purposefully informed selective inclusivity, and audience-appropriate transparency. The book then provides a thorough discussion of how these principles might be enacted in the following six phases: -identifying an appropriate epistemological orientation -identifying an appropriate purpose -searching for relevant literature -evaluating, interpreting and distilling evidence from selected studies -constructing connected understandings -communicating with an audience. A wide range of techniques and perspectives from postpositivist, interpretive, participatory, critical and postmodern traditions are considered in the book, and Suri opens up new areas of debate by exploring numerous aspects of research syntheses from a methodologically inclusive perspective. The book will be valuable reading for researchers and postgraduates in education and social sciences.

Sponsored by the American Psychological Association, this is a representation of the proceedings of the National Conference on Graduate Education in Psychology. The proceedings begin with general introductory material, after which ten major issues are presented and discussed, answering such questions as: How can science and practice be combined? Should there be a core or individualized curriculum? What are the implications of the institutional and organizational setting? Are programs responsible for the marketability of their graduates? Major themes cutting across many presentations and recommendations include: the perceived unity or disunity of psychology as it is taught and as it organizationally exists; the acceptance and encouragement of diversity within a unified discipline; the quality of graduate education and its students; and recognition that graduate education involves people as well as curricula. Since the issues covered are of great concern to scientists, health service providers and educators alike, this book should have a significant impact on the field.

Advanced and Multivariate Statistical Methods, Seventh Edition provides conceptual and practical information regarding multivariate statistical techniques to students who do not necessarily need technical and/or mathematical expertise in these methods. This text has three main purposes. The first purpose is to facilitate conceptual understanding of multivariate statistical methods by limiting the technical nature of the discussion of those concepts and focusing on their practical applications. The second purpose is to provide students with the skills necessary to interpret research articles that have employed multivariate statistical techniques. Finally, the third purpose of AMSM is to prepare graduate students to apply multivariate statistical methods to the analysis of their own quantitative data or that of their institutions. New to the Seventh Edition All references to SPSS have been updated to Version 27.0 of the software. A brief discussion of practical significance has been added to Chapter 1. New data sets have now been incorporated into the book and are used extensively in the SPSS examples. All the SPSS data sets utilized in this edition are available for download via the companion website. Additional resources on this site include several video tutorials/walk-throughs of the SPSS procedures. These "how-to" videos run approximately 5-10 minutes in length. Advanced and Multivariate Statistical Methods was written for use by students taking a multivariate statistics course as part of a graduate degree program, for example in psychology, education, sociology, criminal justice, social work, mass communication, and nursing.

For those who teach students in psychology, education, and the social sciences, the Handbook of Demonstrations and Activities in the Teaching of Psychology, Second Edition provides practical applications and rich sources of ideas. Revised to include a wealth of new material (56% of the articles are new), these invaluable reference books contain the collective experience of teachers who have successfully dealt with students' difficulty in mastering important concepts about human behavior. Each volume features a table that lists the articles and identifies the primary and secondary courses in which readers can use each demonstration. Additionally, the subject index facilitates retrieval of articles according to topical headings, and the appendix notes the source as it originally appeared in Teaching of Psychology, the official journal of the Society for the Teaching of Psychology, Division Two of the American Psychological Association. Volume I consists of 97 articles about strategies for teaching introductory psychology, statistics, research methods, and the history of psychology classes. Divided into four sections (one for each specialty), the book suggests ways to stimulate interest, promote participation, grasp psychological terminology, and master necessary scientific skills.

Contains information for using R software with the examples in the textbook Sampling: Design and Analysis, 3rd edition by Sharon L. Lohr.

This is the first comprehensive survey of Descriptive Psychology. It provides a systematic account of the basic formulations and characteristic methodologies of this discipline which was developed by Peter G. Ossorio of the University of Colorado at Boulder. Dr. Ossorio defines Descriptive Psychology as "a set of systematically related concepts which is designed to provide formal access to all the facts and possible facts concerning persons and behavioroand therefore everything else as well."

A Step-by-Step Guide for Working with Violent Clients

Renowned family therapist Cloe Madanes presents a therapy of social action, a proven model of therapeutic intervention developed for professionals who work with violent men. At the very heart of this approach is the conviction that the offAnder is fully responsible for his actions. As evidence of this core belief, a therapy of social action requires the offAnder to acknowledge his violent actions, demonstrate authentic repentance, make amAnds to the victim, and find acceptable alternative behaviors.

What are the changes we see over the life-span? How can we explain them? And how do we account for individual differences? This volume continues to examine these questions and to report advances in empirical research within life-span development increasing its interdisciplinary nature. The relationships between individual development, social context, and historical change are salient issues discussed in this volume, as are nonnormative and atypical events contributing to life-span change.

The crux of the debate between proponents of behavioral psychology and cognitive psychology focuses on the issue of accessibility. Cognitivists believe that mental mechanisms and processes are accessible, and that their inner workings can be inferred from experimental observations of behavior. Behaviorists, on the contrary, believe that mental processes and mechanisms are inaccessible, and that nothing important about them can be inferred from even the most cleverly designed empirical studies. One argument that is repeatedly raised by cognitivists is that even though mental processes are not directly accessible, this should not be a barrier to unravelling the nature of the inner mental processes and mechanisms. Inference works for other sciences, such as physics, so why not psychology? If physics can work so successfully with their kind of inaccessibility to make enormous theoretical progress, then why not psychology? As with most previous psychological debates, there is no "killer argument" that can provide an unambiguous resolution. In its absence, author William Uttal explores the differing properties of physical and psychological time, space, and mathematics before coming to the conclusion that there are major discrepancies between the properties of the respective subject matters that make the analogy of comparable inaccessibilities a false one.

First Published in 1987. Routledge is an imprint of Taylor & Francis, an informa company.

The first of two volumes in the Advances in Child Development and Behavior series, Equity and Justice in Developmental Science: Theoretical and Methodological Issues focuses on conceptual issues, definitions, and critical concepts relevant to equity and justice for the developmental sciences. This volume covers critical methodological issues that serve to either challenge or advance our understanding of, and ability to promote, equity and justice in the developmental sciences. Both volumes bring together a growing body of developmental scholarship that addresses how issues relevant to equity and justice (or their opposites) affect development and developmental outcomes, as well as scholarship focused on mitigating the developmental consequences of inequity, inequality, and injustice for young people, families, and communities and ensuring that all young people have opportunities to develop and thrive.

This volume provides, for the first time, multidisciplinary perspectives on the problem of awareness of deficits following brain injury. Such deficits may involve perception, attention, memory, language, or motor functions, and they can seriously disrupt an individual's ability to function. However, some brain-damaged patients are entirely unaware of the existence or severity of their deficits, even when they are easily noticed by others. In addressing these topics, contributors cover the entire range of neuropsychological syndromes in which problems with awareness of deficit are observed: hemiplegia and hemianopia, amnesia, aphasia, traumatic head injury, dementia, and others. On the clinical side, leading researchers delineate the implications of awareness of deficits for rehabilitation and patient management, and the role of defense mechanisms such as denial. Theoretical discussions focus on the importance of awareness disturbances for better understanding such cognitive processes as attention, consciousness, and monitoring.

A greatly expanded and heavily revised second edition, this popular guide provides instructions and clear examples for running analyses of variance (ANOVA) and several other related statistical tests of significance with SPSS. No other guide offers the program statements required for the more advanced tests in analysis of variance. All of the programs in the book can be run using any version of SPSS, including versions 11 and 11.5. A table at the end of the preface indicates where each type of analysis (e.g., simple comparisons) can be found for each type of design (e.g., mixed two-factor design). Providing comprehensive coverage of the basic and advanced topics in ANOVA, this is the only book available that provides extensive coverage of SPSS syntax, including the commands and subcommands that tell SPSS what to do, as well as the pull-down menu point-and-click method (PAC). Detailed explanation of the syntax, including what is necessary, desired, and optional helps ensure that users can validate the analysis being performed. The book features the output of each design along with a complete explanation of the related printout. The new edition was reorganized to provide all analysis related to one design type in the same chapter. It now features expanded coverage of analysis of covariance (ANCOVA) and mixed designs, new chapters on designs with random factors, multivariate designs, syntax used in PAC, and all new examples of output with complete explanations. The new edition is accompanied by downloadable resources with all of the book's data sets, as well as exercises for each chapter. This book is ideal for readers familiar with the basic concepts of the ANOVA technique including both practicing researchers and data analysts, as well as advanced students learning analysis of variance.