Complex Survey Data Analysis with SAS (R) is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT (R) procedures. The book offers comprehensive coverage of the most essential topics, including: Drawing random samples Descriptive statistics for continuous and categorical variables Fitting and interpreting linear and logistic regression models Survival analysis Domain estimation Replication variance estimation methods Weight adjustment and imputation methods for handling missing data The easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author's website: http://mason.gmu.edu/~tlewis18/. While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation. Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.

People often try to figure out why they acted the way they did or why others close to them acted in a certain way. The thoughts we have about why things happened are known as attributions. People have these thoughts about communication behavior, and they communicate the thoughts that they have. This book brings together scholars from a variety of disciplines whose work focuses on the interplay of attribution processes and communication behavior in close relationships.

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.

"A book perfect for this moment" -Katherine M. O'Regan, Former Assistant Secretary, US Department of Housing and Urban Development More than fifty years after the passage of the Fair Housing Act, American cities remain divided along the very same lines that this landmark legislation explicitly outlawed. Keeping Races in Their Places tells the story of these lines-who drew them, why they drew them, where they drew them, and how they continue to circumscribe residents' opportunities to this very day. Weaving together sophisticated statistical analyses of more than a century's worth of data with an engaging, accessible narrative that brings the numbers to life, Keeping Races in Their Places exposes the entrenched effects of redlining on American communities. This one-of-a-kind contribution to the real estate and urban economics literature applies the author's original geographic information systems analyses to historical maps to reveal redlining's causal role in shaping today's cities. Spanning the era from the Great Migration to the Great Recession, Keeping Races in Their Places uncovers the roots of the Black-white wealth gap, the subprime lending crisis, and today's lack of affordable housing in maps created by banks nearly a century ago. Most of all, it offers hope that with the latest scholarly tools we can pinpoint how things went wrong-and what we must do to make them right.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

It is universally accepted that sensitive and responsive caregiving leads to positive cognitive and socio-emotional outcomes for children. While several intervention approaches exist, this text brings together the rationale and current evidence base for one such approach-the Mediational Intervention for Sensitizing Caregivers (MISC). MISC integrates aspects of socio-emotional health and cognitive development as well as being less culturally intrusive than existing approaches. It is a strengths-based program complementing existing practices and cultures. Editors bring together in one volume the theory and research from the last decade supporting the MISC approach. Chapters focus on a range of topics, such as training the trainer, maternal depression and MISC, applying MISC to families reunited after migration-related separation and more. The book also focuses on several country-specific cases, such as applying MISC to HIV/AIDS-affected children in South Africa or in early childhood care settings in Israel. This book is essential reading for those working in early educational or clinical settings tasked with developing policy to ensure optimal child developmental outcomes. The book is applicable to professionals from a wide variety of disciplines including clinical, counselling, educational, psychology, psychiatry, paediatrics, nursing, social work and public health.

- The first practical introduction to second-order and growth mixture models using Mplus 8.4 -Introduces simple and complex models through incremental steps with increasing complexity -Each model is presented with figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results, to maximize understanding. - Second-order and growth mixture modeling is increasingly being used in various disciplines to analyze changes in individual attributes such as personal behaviors and relationships over time

- The first practical introduction to second-order and growth mixture models using Mplus 8.4 -Introduces simple and complex models through incremental steps with increasing complexity -Each model is presented with figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results, to maximize understanding. - Second-order and growth mixture modeling is increasingly being used in various disciplines to analyze changes in individual attributes such as personal behaviors and relationships over time

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

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

"...how a man rallies to life's challenges and weathers its storms tells everything of who he is and all that he is likely to become." —St. Augustine

It has long been understood that how a person adjusts to life stresses is a major component of his or her ability to lead a fulfilling life. Yet it wasn't until the 1960s that coping became a discrete topic of psychological inquiry. Since then, coping has risen to a position of prominence in the modern psychological discourse—especially within the personality, cognitive, and behavioral spheres—and, within the past decade alone, many important discoveries have been made about its mechanisms and functioning, and its role in ongoing psychological and physical health and well-being.

A book whose time has come at last, the Handbook of Coping is the first professional reference devoted exclusively to the psychology of coping. Reporting the observations and insights of nearly sixty leading authorities in stress and coping from a wide range of affiliations and schools of thought, it brings readers the state of the art in coping theory, research, assessment, and applications. In orchestrating the book, the editors have scrupulously avoided imposing any particular slant or point of view, other than the need to foster greater eclecticism and cooperation between researchers and clinicians concerned with the phenomenon of coping.

The Handbook of Coping is divided into five overlapping parts, the first of which serves to lay the conceptual foundations of all that follows. It traces the history of coping from its origins in psychoanalytic theories of unconscious defense mechanisms, and provides an exhaustive review of the latest conceptualizations, models, and constructs. The following section provides an in-depth exploration of current research methodology, measurement, and assessment tools. Part Three explores key facets of coping in a broad range of specific domains, including everyday hassles, chronic disease, cataclysmic events, and many others. The penultimate section focuses on individual differences. Among important topics covered here are coping styles and dispositions; the role of family, social support, and education; and coping behaviors across the life span. The final section, Part Five, is devoted to current applications. Clinical parameters are defined and a number of specific interventions are described, as are proven techniques for helping clients to improve their coping skills.

A comprehensive guide to contemporary coping theory, research, and applications, the Handbook of Coping is an indispensable resource for practitioners, researchers, students, and educators in psychology, the health sciences, and epidemiology.

Of related interest ...

EGO DEFENSES: Theory and Measurement —Edited by Hope R. Conte and Robert Plutchik

This book explores the nature and manifestations of defense mechanisms and traces ego defense theory and research from Freud's initial conceptualization through recent work in object-relations theory and other psychoanalytically oriented approaches. It provides clinical guidelines for diagnosing, assessing, and dealing with defenses, reviews empirical research techniques, and indicates their value in development and in psychotherapy. This volume should be of value to theoreticians, clinicians, and researchers interested in finding appropriate tools for measurement of defense mechanisms. 1994

SOCIAL SUPPORT: An Interactional View —Edited by Barbara R. Sarason, Irwin G. Sarason, and Gregory R. Pierce

The study of social support and its relationship to personality, health, and adjustment is one of the fastest growing areas of research and application in psychology. This book contains integrative surveys of clinical and field studies, experimental investigations, and life-span explorations. It approaches social support as an important facet of interpersonal relationships and shows its undesirable, as well as its positive, features. 1990 (0-471-60624-3) 528 pp.

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

With an emphasis on social science applications, Event History Analysis with R, Second Edition, presents an introduction to survival and event history analysis using real-life examples. Since publication of the first edition, focus in the field has gradually shifted towards the analysis of large and complex datasets. This has led to new ways of tabulating and analysing tabulated data with the same precision and power as that of an analysis of the full data set. Tabulation also makes it possible to share sensitive data with others without violating integrity. The new edition extends on the content of the first by both improving on already given methods and introducing new methods. There are two new chapters, Explanatory Variables and Regression, and Register- Based Survival Data Models. The book has been restructured to improve the flow, and there are significant updates to the computing in the supporting R package. Features * Introduction to survival and event history analysis and how to solve problems with incomplete data using Cox regression. * Parametric proportional hazards models, including the Weibull, Exponential, Extreme Value, and Gompertz distributions. * Parametric accelerated failure time models with the Lognormal, Loglogistic, Gompertz, Exponential, Extreme Value, and Weibull distributions. * Proportional hazards models for occurrence/exposure data, useful with tabular and register based data, often with a huge amount of observed events. * Special treatments of external communal covariates, selections from the Lexis diagram, and creating period as well as cohort statistics. * "Weird bootstrap" sampling suitable for Cox regression with small to medium-sized data sets. * Supported by an R package (https://CRAN.R-project.org/package=eha), including code and data for most examples in the book. * A dedicated home page for the book at http://ehar.se/r/ehar2 This substantial update to this popular book remains an excellent resource for researchers and practitioners of applied event history analysis and survival analysis. It can be used as a text for a course for graduate students or for self-study.

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.

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.

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

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.

Outcome Research and the Future of Psychoanalysis explores the connection between outcome studies and important and complex questions of clinical practices, research methodologies, epistemology, and sociological considerations. Presenting the ideas and voices of leading experts in clinical and extra-clinical research in psychoanalysis, the book provides an overview of the state of the art of outcome research, its results and implications. Furthermore, its contributions discuss the basic premises and ideas of outcome research and in which way the contemporary Zeitgeist might shape the future of psychoanalysis. Divided into three parts, the book begins by discussing the scientific basis of psychoanalysis and advances in psychoanalytic thinking as well as the state of the art of psychoanalytic outcome research, critically analyzing so-called evidence-based therapies. Part II of the book contains exemplary research projects that are discussed from a clinical perspective, illustrating the dialogue between researchers and clinicians. Lastly, in Part III, several psychoanalysts review the importance of critical thinking and research in psychoanalytical education. Thought-provoking and expertly written and researched, this book is a useful resource for academics, researchers and postgraduate students in the fields of mental health, psychotherapy, and psychoanalysis.

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.

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

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.

*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.