Cases and Stories of Transformative Action Research builds on its companion book, Principles and Methods of Transformative Action Research, by describing and analyzing dozens of examples of successful action research efforts pursued in the past five decades by students and faculty of the Western Institute for Social Research. Some projects are large-scale, and some are modest interventions in the everyday lives of those participating. Some are formal organizational efforts; others are the results of individual or small group initiatives. Included are chapters on community needs assessments and innovative grassroots approaches to program evaluation; the challenges of improving our decision-making during the crisis of the COVID-19 pandemic; strategies of intellectual activism in addressing the growing problem of workplace bullying; action research to preserve and share the history of the Omaha tribe; and plans for an innovative school-based project based on collaborative action-and-inquiry between students and Artificial Intelligence. In addition, there are a number of detailed stories about the use of transformative action research in such areas as somatic and trauma counseling, ethnic studies, health disparities, gender differences, grassroots popular education, and the improvement of statewide steps for preventing child abuse, among many others. This book can serve as an undergraduate or graduate social sciences text on research methods. It is also a guidebook for action-oriented research by academics, professionals, and lay people alike.

This book has been prepared to help psychiatrists expand their knowledge of statistical methods and fills the gaps in their applications as well as introduces data analysis software. The book emphasizes the classification of fundamental statistical methods in psychiatry research that are precise and simple. Professionals in the field of mental health and allied subjects without any mathematical background can easily understand all the relevant statistical methods and carry out the analysis and interpret the results in their respective fields without consulting a statistician. The sequence of the chapters, the sections within the chapters, the subsections within the sections, and the points within the subsections have all been arranged to help professionals in classification refine their knowledge in statistical methods and fill the gaps, if any. Emphasizing simplicity, the fundamental statistical methods are demonstrated by means of arithmetical examples that may be reworked with pencil and paper in a matter of minutes. The results of the rework have to be checked by using SPSS, and in this way professionals are introduced to this psychiatrist-friendly data analysis software. Topics covered include: * An overview of psychiatry research * The organization and collection of data * Descriptive statistics * The basis of statistical inference * Tests of significance * Correlational data analysis * Multivariate data analysis * Meta-analysis * Reporting the results * Statistical software The language of the book is very simple and covers all aspects of statistical methods starting from organization and collection of data to descriptive statistics, statistical inference, multivariate analysis, and meta-analysis. Two chapters on computer applications deal with the most popular data analysis software: SPSS. The book will be very valuable to professionals and post-graduate students in psychiatry and allied fields, such as psychiatric social work, clinical psychology, psychiatric nursing, and mental health education and administration.

This volume, Statistical Methods in Psychiatry Research and SPSS, now going into its second edition, has been helping psychiatrists expand their knowledge of statistical methods and fills the gaps in their applications as well as introduces data analysis software. It addresses the statistical needs of physicians and presents a simplified approach. The book emphasizes the classification of fundamental statistical methods in psychiatry research that are precise and simple. Professionals in the field of mental health and allied subjects without any mathematical background will easily understand all the relevant statistical methods and carry out the analysis and interpret the results in their respective field without consulting any statistician. This new volume has over 100 pages of new material, including several new appendixes. The sequence of the chapters, the sections within the chapters, the subsections within the sections, and the points within the subsections have all been arranged to help professionals in classification refine their knowledge in statistical methods and fills the gaps.

This book introduces the latest meta-analytical methods and discusses their applications in the field of psychiatry. A comprehensive list of methods used in meta-analysis has been described in simple language and demonstrated with real-time examples. This informative volume explains the importance of meta-analysis and describes how it differs from narrative and systematic reviews. It also relates the historical development of meta-analysis and explains methods used for locating and selecting the required studies in a given domain. Suitable software is examined in detail as well.

This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.

First published in 2004. Routledge is an imprint of Taylor & Francis, an informa company.

This edited volume recognizes that resilience, and the most effective means of harnessing it, differ across individuals, contexts and time. Presenting chapters written by a range of scholars and clinicians, the book highlights effective evidence-based approaches to nurturing resilience, before, during and after a traumatic experience or event. By identifying distinct therapeutic tools which can be used effectively to meet the particular needs and limitations associated with different age groups, clients and types of experience, the volume addresses specific challenges and benefits of nurturing resilience and informs best practice as well as self-care. Approaches explored in the volume include the use of group activities to teach resilience to children, the role of sense-making for victims of sex trafficking, and the ways in which identity and spirituality can be used to help young and older adults in the face of pain and bereavement. Chapters also draw on the lived experiences of those who have engaged in a personal or guided journey towards finding new meaning and achieving posttraumatic growth following experiences of trauma. The rich variety of approaches offered here will be of interest to clinicians, counsellors, scholars and researchers involved in the practice and study of building resilience, as well as trauma studies, psychology and mental health more broadly. The personal and practice-based real-life stories in this volume will also resonate with individuals, family and community members facing adversity.

This book introduces a new data analysis technique that addresses long standing criticisms of the current standard statistics. Observation Oriented Modelling presents the mathematics and techniques underlying the new method, discussing causality, modelling, and logical hypothesis testing. Examples of how to approach and interpret data using OOM are presented throughout the book, including analysis of several classic studies in psychology. These analyses are conducted using comprehensive software for the Windows operating system that has been written to accompany the book and will be provided free to book buyers on an accompanying website.

The software has a user-friendly interface, similar to SPSS and SAS, which are the two most commonly used software analysis packages, and the analysis options are flexible enough to replace numerous traditional techniques such as t-tests, ANOVA, correlation, multiple regression, mediation analysis, chi-square tests, factor analysis, and inter-rater reliability. The output and graphs generated by the software are also easy to interpret, and all effect sizes are presented in a common metric; namely, the number of observations correctly classified by the algorithm. The software is designed so that undergraduate students in psychology will have no difficulty learning how to use the software and interpreting the results of the analyses.

* Describes the problems that statistics are meant to answer, why popularly used statistics often fail to fully answer the question, and how OOM overcomes these obstacles * Chapters include examples of statistical analysis using OOM * Software for OOM comes free with the book * Accompanying websiteinclude svideo instruction on OOM use "

In 1968, Stanley Kubrick completed and released his magnum opus motion picture 2001: A Space Odyssey; a time that was also tremendously important in the formation of the psychoanalytic theory of Jacques Lacan. Bringing these figures together, Bristow offers a study that goes beyond, as the film did. He extends Lacan's late topological insights, delves into conceptualisations of desire, in G. W. F. Hegel, Alexandre Kojeve, and Lacan himself, and deals with the major themes of cuts (filmic and psychoanalytic); space; silence; surreality; and 'das Ding', in relation to the movie's enigmatic monolith. This book is a tour de force of psychoanalytic theory and space odyssey that will appeal to academics and practitioners of psychoanalysis and film studies, as well as to any fan of Kubrick's work.

This book reconstructs the rise and fall of Wilhelm Wundt's fortunes, focusing for the first time on the role of Richard Avenarius as catalyst for the so-called "positivist repudiation of Wundt." Krauss specifically looks at the progressive disavowal of Wundtian ideas in the world of scientific psychology, and especially by his former pupils. This book provides important historical context and a critical discussion of the current state of research, in addition to a detailed consideration of Wundt's and Avenarius' systems of thought, as well as on their personal relationship. The author outlines the reception of Avenarius' conceptions among Wundt's pupils, such as Kulpe, Munsterberg and Titchener, and among other psychologists of the time, such as Ward, James and Ebbinghaus. Finally, this book presents Wundt's two-fold attempt to respond to the new trend through a criticism of the "materialistic" psychology, and a reformulation of his own ideas.

This volume explores the abiding intellectual inertia in scientific psychology in relation to the discipline's engagement with problematic beliefs and assumptions underlying mainstream research practices, despite repeated critical analyses which reveal the weaknesses, and in some cases complete inappropriateness, of these methods. Such paradigmatic inertia is especially troublesome for a scholarly discipline claiming status as a science. The book offers penetrating analyses of many (albeit not all) of the most important areas where mainstream practices require either compelling justifications for their continuation or adjustments - possibly including abandonment - toward more apposite alternatives. Specific areas of concern addressed in this book include the systemic misinterpretation of statistical knowledge; the prevalence of a conception of measurement at odds with yet purporting to mimic the natural sciences; the continuing widespread reliance on null hypothesis testing; and the continuing resistance within psychology to the explicit incorporation of qualitative methods into its methodological toolbox. Broader level chapters examine mainstream psychology's systemic disregard for critical analysis of its tenets, and the epistemic and ethical problems this has created. This is a vital and engaging resource for researchers across psychology, and those in the wider behavioural and social sciences who have an interest in, or who use, psychological research methods.

Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem gives a number of perspectives from top methodologists and applied researchers on the best ways to attempt to answer Age-Period-Cohort related questions about society. Age-Period-Cohort (APC) analysis is a fundamental topic for any quantitative social scientist studying individuals over time. At the same time, it is also one of the most misunderstood and underestimated topics in quantitative methods. As such, this book is key reference material for researchers wanting to know how to deal with APC issues appropriately in their statistical modelling. It deals with the identification problem caused by the co-linearity of the three variables, considers why some currently used methods are problematic and suggests ideas for what applied researchers interested in APC analysis should do. Whilst the perspectives are varied, the book provides a unified view of the subject in a reader-friendly way that will be accessible to social scientists with a moderate level of quantitative understanding, across the social and health sciences.

This book explores sexual crime and intellectual functioning. Drawing on expertise from clinical practice and applied research, the volume begins with an exploration of the theoretical and historical background to the interest in links between sexual offending and intellectual functioning. The authors then move on to discuss assessment of intellectual functioning in prison, interventions for low intellectual functioning, autistic spectrum and personality disorder. This book offers a rare insight into the phenomenon of high IQ and sexual offending, a much neglected aspect of the sexual crime literature, and includes novel research that unpacks this link. It further offers an extraordinary insight into the experiences of a person of superior IQ in the criminal justice system for a sexual offence. The book is relevant not only to psychologists, criminologists, social workers and students, but also to practitioners, researchers and the general public with an interest in learning about sexual offending and intellectual functioning.

In Systemic Constellations: Theory, Practice, and Applications, Damian Janus examines systemic constellations, a breakthrough method of psychotherapy, coaching, and consulting developed by Bert Hellinger. Janus examines numerous case studies and addresses the potential of Hellinger's approach for improving clients' mental health.

* Provides an up to date reference point for ethnographic research conducted into healthcare research * Embodies an outline of major methodological approaches to health and well-being ethnography * Includes illustrative case-studies of ethnographical research within the healthcare setting. * Offers a holistic view of ethnography, taking a multi-disciplinary approach

Praise for previous editions: "... a classic with a long history." - Statistical Papers "The fact that the first edition of this book was published in 1971 ... [is] testimony to the book's success over a long period." - ISI Short Book Reviews "... one of the best books available for a theory course on nonparametric statistics. ... very well written and organized ... recommended for teachers and graduate students." - Biometrics "... There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition." - Technometrics "... Useful to students and research workers ... a good textbook for a beginning graduate-level course in nonparametric statistics." - Journal of the American Statistical Association Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new references Nonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.

This accessible guide offers a concise introduction to the science behind worry in children, summarising research from across psychology to explore the role of worry in a range of circumstances, from everyday worries to those that can seriously impact children's lives. Wilson draws on theories from clinical, developmental and cognitive psychology to explain how children's worry is influenced by both developmental and systemic factors, examining the processes involved in pathological worry in a range of childhood anxiety disorders. Covering topics including different definitions of worry, the influence of children's development on worry, Generalised Anxiety Disorder (GAD) in children, and the role parents play in children's worry, this book offers a new model of worry in children with important implications for prevention and intervention strategies. Understanding Children's Worry is valuable reading for students in clinical, educational and developmental psychology, and professionals in child mental health.

This book provides an overview of the innovative, arts-based research method of body mapping and offers a snapshot of the field. The review of body mapping projects by Boydell et al. confirms the potential research and therapeutic benefits associated with body mapping. The book describes a series of body mapping research projects that focus on populations marginalised by disability, mental health status, and other vulnerable identities. Chapters focus on summarising the current state of the art and its application with marginalised groups; analytic strategies for body mapping; highlighting body mapping as a creation and a dissemination process; emerging body mapping techniques including web-based, virtual reality, and wearable technology applications; and measuring the impact of body maps on planning, practice, and behaviour. Contributors and editors include interdisciplinary experts from the fields of psychology, sociology, anthropology, and beyond. Offering innovative ways of engaging in body mapping research, which result in real-world impact, this book is an essential resource for postgraduate students and researchers.

Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which both the generalization loss and the marginal likelihood are estimated even if the posterior distribution cannot be approximated by any normal distribution. Features Explains Bayesian inference not subjectively but objectively. Provides a mathematical framework for conventional Bayesian theorems. Introduces and proves new theorems. Cross validation and information criteria of Bayesian statistics are studied from the mathematical point of view. Illustrates applications to several statistical problems, for example, model selection, hyperparameter optimization, and hypothesis tests. This book provides basic introductions for students, researchers, and users of Bayesian statistics, as well as applied mathematicians. Author Sumio Watanabe is a professor of Department of Mathematical and Computing Science at Tokyo Institute of Technology. He studies the relationship between algebraic geometry and mathematical statistics.

In this book, Hackett introduces the traditional usage of the mapping sentence within quantitative research, reviews its philosophical underpinnings, and proposes the "declarative mapping sentence" as an instrument and approach to qualitative scholarship. With a helpful glossary and a range of illustrative tables, Hackett takes the reader through a straightforward introduction to mapping sentences and their construction, before discussing declarative mapping sentences and possible future research directions. This innovative direction for social research provides a flexible structure for research domain, and it allows qualitative research results to be uniformly sorted. Declarative Mapping Sentences in Qualitative Research will be essential reading for researchers, academics, and postgraduate students in the fields of qualitative psychology and psychological methods, as well as philosophical psychology and social science research methods.

In this book the author's theoretical framework builds on linguistic and psychological research, arguing that similar image-schematic notions should be grouped together into interconnected family hierarchies, with complexity increasing with regard to the addition of spatial and conceptual primitives. She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention. The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.

With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The second edition of Analyzing Spatial Models of Choice and Judgment demonstrates how to estimate and interpret spatial models with a variety of methods using the open-source programming language R. Requiring only basic knowledge of R, the book enables social science researchers to apply the methods to their own data. Also suitable for experienced methodologists, it presents the latest methods for modeling the distances between points. The authors explain the basic theory behind empirical spatial models, then illustrate the estimation technique behind implementing each method, exploring the advantages and limitations while providing visualizations to understand the results. This second edition updates and expands the methods and software discussed in the first edition, including new coverage of methods for ordinal data and anchoring vignettes in surveys, as well as an entire chapter dedicated to Bayesian methods. The second edition is made easier to use by the inclusion of an R package, which provides all data and functions used in the book. David A. Armstrong II is Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University. His research interests include measurement, Democracy and state repressive action. Ryan Bakker is Reader in Comparative Politics at the University of Essex. His research interests include applied Bayesian modeling, measurement, Western European politics, and EU politics. Royce Carroll is Professor in Comparative Politics at the University of Essex. His research focuses on measurement of ideology and the comparative politics of legislatures and political parties. Christopher Hare is Assistant Professor in Political Science at the University of California, Davis. His research focuses on ideology and voting behavior in US politics, political polarization, and measurement. Keith T. Poole is Philip H. Alston Jr. Distinguished Professor of Political Science at the University of Georgia. His research interests include methodology, US political-economic history, economic growth and entrepreneurship. Howard Rosenthal is Professor of Politics at NYU and Roger Williams Straus Professor of Social Sciences, Emeritus, at Princeton. Rosenthal's research focuses on political economy, American politics and methodology.

Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.

Scientometrics for the Humanities and Social Sciences is the first ever book on scientometrics that deals with the historical development of both quantitative and qualitative data analysis in scientometric studies. It focuses on its applicability in new and emerging areas of inquiry. This important book presents the inherent potential for data mining and analysis of qualitative data in scientometrics. The author provides select cases of scientometric studies in the humanities and social sciences, explaining their research objectives, sources of data and methodologies. It illustrates how data can be gathered not only from prominent online databases and repositories, but also from journals that are not stored in these databases. With the support of specific examples, the book shows how data on demographic variables can be collected to supplement scientometric data. The book deals with a research methodology which has an increasing applicability not only to the study of science, but also to the study of the disciplines in the humanities and social sciences.

Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem gives a number of perspectives from top methodologists and applied researchers on the best ways to attempt to answer Age-Period-Cohort related questions about society. Age-Period-Cohort (APC) analysis is a fundamental topic for any quantitative social scientist studying individuals over time. At the same time, it is also one of the most misunderstood and underestimated topics in quantitative methods. As such, this book is key reference material for researchers wanting to know how to deal with APC issues appropriately in their statistical modelling. It deals with the identification problem caused by the co-linearity of the three variables, considers why some currently used methods are problematic and suggests ideas for what applied researchers interested in APC analysis should do. Whilst the perspectives are varied, the book provides a unified view of the subject in a reader-friendly way that will be accessible to social scientists with a moderate level of quantitative understanding, across the social and health sciences.