Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

The Student Survival Guide for Research Methods in Psychology is designed to support students enrolled in undergraduate or graduate level research methods courses by providing them with the tools they need to succeed. It goes beyond course material to help students engage more fully with research methods content. This survival guide presents clear step-by-step instructions that will help students hone the basic skills to succeed and thrive in their research methods classes and to navigate common pitfalls. The book covers core practical skills, like formatting and writing at an APA standard, understanding research literature (particularly academic journals), using SPSS, and broader skills like how to communicate with your professor, time management, and teamwork skills. It is a highly effective primer text for all psychology students undertaking research methods courses and will also be particularly helpful for students who are currently undertaking these modules and don't feel fully prepared for them.

Positive Ageing and Learning from Centenarians evaluates the mechanisms of positive ageing in a uniquely interdisciplinary way to explore the question of how we age and how some people age successfully. Drawing together the findings of recognised longevity researchers from around the world, the book applies an integrated vision to educational and social aspects of human ageing. It examines research into centenarians, and considers most of the disciplines related to longevity and healthy aging and aspects such as education, psychology, philosophy, anthropology, demography, sociology, economics as well as those related to nutrition and biological factors of longevity. The book examines how the results of these scientific investigations could improve the well-being of the oldest olds in the future, especially in the context of ageing societies. It provides an answer to the question of what we can learn from centenarians and what lessons we can from their lifestyle, which can contribute to live longer, better and happier. Based on cutting-edge research, the book will be highly relevant reading for researchers, academics and students in the field of ageing and longevity, mental health research, health science, gerontology and psychology.

Detection Theory: A User's Guide is an introduction to one of the most important tools for the analysis of data where choices must be made and performance is not perfect. In these cases, detection theory can transform judgments about subjective experiences, such as perceptions and memories, into quantitative data ready for analysis and modeling. For beginners, the first three chapters introduce measuring detection and discrimination, evaluating decision criteria, and the utility of receiver operating characteristics. Later chapters cover more advanced research paradigms, including: complete tools for application, including flowcharts, tables, and software; student-friendly language; complete coverage of content area, including both one-dimensional and multidimensional models; integrated treatment of threshold and nonparametric approaches; an organized, tutorial level introduction to multidimensional detection theory; and popular discrimination paradigms presented as applications of multidimensional detection theory. This modern summary of signal detection theory is both a self-contained reference work for users and a readable text for graduate students and researchers learning the material either in courses or on their own.

Offers a coherent view on all methodological options to reduce mode effects Reviews inference procedures designed to minimize the effect of mode effects Discusses several different sources of mode-specific error Provides a roadmap for practitioners who design and analyse mixed-mode surveys Summarises the literature on mixed-mode surveys from questionnaire design to analysis

Requires minimal prerequisites Explained in basic terms Illustrated with binary datasets and real life examples Covers primary concepts and methods Accessible to undergraduates Suitable for a heterogeneous audience

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. *Uses numerous graphs in R to illustrate the model's results, assumptions, and other features. *Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient. *Provides many examples using real world datasets relevant to various academic disciplines. *Fully integrates the R software environment in its numerous examples.

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. *Uses numerous graphs in R to illustrate the model's results, assumptions, and other features. *Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient. *Provides many examples using real world datasets relevant to various academic disciplines. *Fully integrates the R software environment in its numerous examples.

The Student Survival Guide for Research Methods in Psychology is designed to support students enrolled in undergraduate or graduate level research methods courses by providing them with the tools they need to succeed. It goes beyond course material to help students engage more fully with research methods content. This survival guide presents clear step-by-step instructions that will help students hone the basic skills to succeed and thrive in their research methods classes and to navigate common pitfalls. The book covers core practical skills, like formatting and writing at an APA standard, understanding research literature (particularly academic journals), using SPSS, and broader skills like how to communicate with your professor, time management, and teamwork skills. It is a highly effective primer text for all psychology students undertaking research methods courses and will also be particularly helpful for students who are currently undertaking these modules and don't feel fully prepared for them.

A Journey into Open Science and Research Transparency in Psychology introduces the open science movement from psychology through a narrative that integrates song lyrics, national parks, and concerns about diversity, social justice, and sustainability. Along the way, readers receive practical guidance on how to plan and share their research, matching the ideals of scientific transparency. This book considers all the fundamental topics related to the open science movement, including: (a) causes of and responses to the Replication Crisis, (b) crowdsourcing and meta-science research, (c) preregistration, (d) statistical approaches, (e) questionable research practices, (f) research and publication ethics, (g) connections to career topics, (h) finding open science resources, (i) how open science initiatives promote diverse, just, and sustainable outcomes, and (j) the path moving forward. Each topic is introduced using terminology and language aimed at intermediate-level college students who have completed research methods courses. But the book invites all readers to reconsider their research approach and join the Scientific Revolution 2.0. Each chapter describes the associated content and includes exercises intended to help readers plan, conduct, and share their research. This short book is intended as a supplemental text for research methods courses or just a fun and informative exploration of the fundamental topics associated with the Replication Crisis in psychology and the resulting movement to increase scientific transparency in methods.

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with small-area spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented, followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges. Robert Haining is Emeritus Professor in Human Geography, University of Cambridge, England. He is the author of Spatial Data Analysis in the Social and Environmental Sciences (1990) and Spatial Data Analysis: Theory and Practice (2003). He is a Fellow of the RGS-IBG and of the Academy of Social Sciences. Guangquan Li is Senior Lecturer in Statistics in the Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle, England. His research includes the development and application of Bayesian methods in the social and health sciences. He is a Fellow of the Royal Statistical Society.

1. Phil Cushman is a highly esteemed scholar in the academy, and has been a prolific author. The present project of featuring the work of his students is an innovative approach to dissemination of scholarship on hermeneutics. 2. Interest in hermeneutic approaches is on the rise, but little has been written on applying these approaches to a dissertation. This book will fill an important and niche. 3. Dissertations cover a range of challenging and topical issues, such as end of life care, motherhood, trauma and racism.

1. Phil Cushman is a highly esteemed scholar in the academy, and has been a prolific author. The present project of featuring the work of his students is an innovative approach to dissemination of scholarship on hermeneutics. 2. Interest in hermeneutic approaches is on the rise, but little has been written on applying these approaches to a dissertation. This book will fill an important and niche. 3. Dissertations cover a range of challenging and topical issues, such as end of life care, motherhood, trauma and racism.

The Generic Qualitative Approach to a Dissertation in the Social Sciences: A Step by Step Guide is a practical guide for the graduate students and faculty planning and executing a generic qualitative dissertation in the social sciences. Generic qualitative research is a methodology that seeks to understand human experience by taking a qualitative stance and using qualitative procedures. Based on Sandra Kostere and Kim Kostere's experiences of serving on dissertation committees, this book aims to demystify both the nuances and the procedures of qualitative research, with the aim of empowering students to conduct meaningful dissertation research and present findings that are rigorous, credible, and trustworthy. It examines the fundamental principles and assumptions underlying the generic qualitative method, then covers each stage of the research process including creation of research questions, interviews, and then offers three ways of analyzing the data gathered and presenting the results. With examples of the generic qualitative method in practice to show students how to conduct their research confidently, and chapters designed to walk the researcher through each step of the dissertation process, this book is specifically tailored for the accessible generic method, and will be useful for graduate students and faculty developing dissertations in Psychology, Education, Nursing and the social sciences.

1. This book is applicable to courses across the social and behavioral science on a wide range of quantitative methods courses. 2. The book is based on SPSS for EFA - one of the most popular statistics software packages used in behavioral sciences. 3. Clear step-by-step guidance combined with screen shots to show how to apply EFA to real data.

Spatializing Social Media charts the theoretical and methodological challenges in analyzing and visualizing social media data mapped to geographic areas. It introduces the reader to concepts, theories, and methods that sit at the crossroads between spatial and social network analysis to unpack the conceptual differences between online and face-to-face social networks and the nonlinear effects triggered by social activity that overlaps online and offline. The book is divided into four sections, with the first accounting for the differences between space (the geometrical arrangements that structure and enable forms of interaction) and place (the mechanisms through which social meanings are attached to physical locations). The second section covers the rationale of social network analysis and the ontological differences, stating that relationships, more than individual and independent attributes, are key to understanding of social behavior. The third section covers a range of case studies that successfully mapped social media activity to geographically situated areas and considers the inflection of homophilous dependencies across online and offline social networks. The fourth and last section of the book explores a range of networks and discusses methods for and approaches to plotting a social network graph onto a map, including the purpose-built R package Spatial Social Media. The book takes a non-mathematical approach to social networks and spatial statistics suitable for postgraduate students in sociology, psychology and the social sciences.

Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches. The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.

A Sound Basis for the Theory of Statistical Inference Measuring Statistical Evidence Using Relative Belief provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to measure statistical evidence allows you to answer the basic question of when a statistical analysis is correct. The book attempts to establish a gold standard for how a statistical analysis should proceed. It first introduces basic features of the overall approach, such as the roles of subjectivity, objectivity, infinity, and utility in statistical analyses. It next discusses the meaning of probability and the various positions taken on probability. The author then focuses on the definition of statistical evidence and how it should be measured. He presents a method for measuring statistical evidence and develops a theory of inference based on this method. He also discusses how statisticians should choose the ingredients for a statistical problem and how these choices are to be checked for their relevance in an application.

Clearly organised around a single question--is love possible?--Joanne Brown's book provides conceptualizations of love and of its possibility from sociological, philosophical and psychoanalytic viewpoints. Material from biographical, narrative interviews are presented in order to look at how people from two age groups conceptualise love and view its realisation or possibility in their own lives. The book argues for the importance of a psychosocial understanding of love and provides a critical discussion of the philosophy and methods of psychosocial studies.

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.

At a time when there is increasing need to offer psychotherapeutic approaches that accommodate clients' religious and spiritual beliefs, and acknowledge the potential for healing and growth offered by religious frameworks, this book explores psychology from an Islamic paradigm and demonstrates how Islamic understandings of human nature, the self, and the soul can inform an Islamic psychotherapy. Drawing on a qualitative, grounded theory analysis of interviews with Islamic scholars and clinicians, this unique volume distils complex religious concepts to reconcile Islamic theology with contemporary notions of psychology. Chapters offer nuanced explanations of relevant Islamic tradition and theological sources, consider how this relates to Western notions of psychotherapy and common misconceptions, and draw uniquely on first-hand data to develop a new theory of Islamic psychology. This, in turn, informs an innovative and empirically driven model of practice that translates Islamic understandings of human psychology into a clinical framework for Islamic psychotherapy. An outstanding scholarly contribution to the modern and emerging discipline of Islamic psychology, this book makes a pioneering contribution to the integration of the Islamic sciences and clinical mental health practice. It will be a key resource for scholars, researchers, and practicing clinicians with an interest in Islamic psychology and Muslim mental health, as well as religion, spirituality and psychology more broadly.

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 volume introduces the methodological value of hermeneutic dialogue in the field of theoretical and philosophical psychology. It reflects on the works of Frank Richardson, who has made, and continues to make, seminal contributions to the field, as well as having influenced the work of many of the practitioners engaged in this field today. Each chapter explores a major topic of hermeneutic dialogue and is authored by a scholar whose work has been directly impacted by Richardson's life and research. The chapters illuminate a variety of issues in psychology, such as instrumentalism, individualism, relationality, social ontology, the wisdom of limits, neoliberalism, and the idea that theory is a form of praxis. All contributions in this volume illustrate aspects of theory as practice coming to expression in reflection on theoretical and philosophical psychology and trace some of the implications for psychology, political philosophy, social justice, community, human dignity, and transcendence. This book will be a valuable resource for students and scholars of theoretical and philosophical psychology, philosophy of the mind, and personality theories.

Understanding Young Onset Dementia provides a state-of-the-art overview of approaches to care and evaluation for people with young onset dementia. It reviews the challenges in providing care and services, outlines new innovations in treatment and explores the impact of the condition to offer guidance about best practice in care. Written by world-leading researchers and experts in the field, this book gives key evidence for best practice and focuses on lived experience of those with young onset dementia. It has a broad focus looking at aspects of care beyond diagnosis and gives a comprehensive summary of the current qualitative and quantitative research in the field of young onset dementia. This international collaboration fills a much-needed gap in the academic market and is vital to guide learning and deliver future innovations. This book will be of great interest for academics, scholars and post graduate students in the field of mental health and dementia research. It will also appeal to neurologists, psychiatrist, geriatricians and psychologists looking to update their knowledge or already working in the field.

This book offers different theoretical approaches about what clinical research is. Clinical Research in Psychoanalysis is a unique contribution to the attempts to bridge the gap between clinicians and researchers and to create a culture of a more rigorous and systematic inquiry. It provides an innovative experience because for the first time different methods and perspectives were used to analyse one same clinical material. This was done by analysts from different working parties of the International Psychoanalytical Association (IPA), from a range of different schools of psychoanalytic thought. This allows the reader to have a vision of the different methods that are currently being used by some working parties of the IPA and to learn about the strengths of each one for certain situations and types of research. This book revaluates clinical research, intending to make links between the analysts working through the working parties and the different ways of thinking in clinical research. By covering key topics, such as how working parties can facilitate different types of research; the place of metaphor in psychoanalytic research and practice; and the future for psychoanalytic research, this text is a fruitful dialogue between different theoretical conceptions and between clinicians and researchers, that will expand our perspectives on the evidence we find in clinical material and will broaden our views on the patient. This book offers a unique and invaluable experience to psychologists and psychoanalysts who are trying to improve their clinical practice and bring research evidence into their psychoanalytic practice. It is an invaluable contribution to psychoanalytic training of candidates, teachers, and students.