The third edition of Handbook of Marriage and the Family describes, analyzes, synthesizes, and critiques the current research and theory about family relationships, family structural variations, and the role of families in society. This updated Handbook provides the most comprehensive state-of-the art assessment of the existing knowledge of family life, with particular attention to variations due to gender, socioeconomic, race, ethnic, cultural, and life-style diversity. The Handbook also aims to provide the best synthesis of our existing scholarship on families that will be a primary source for scholars and professionals but also serve as the primary graduate text for graduate courses on family relationships and the roles of families in society. In addition, the involvement of chapter authors from a variety of fields including family psychology, family sociology, child development, family studies, public health, and family therapy, gives the Handbook a multidisciplinary and interdisciplinary framework.

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)

This volume establishes the conceptual foundation for sustained investigation into tool development in neuroscience. Neuroscience relies on diverse and sophisticated experimental tools, and its ultimate explanatory target-our brains and hence the organ driving our behaviors-catapults the investigation of these research tools into a philosophical spotlight. The chapters in this volume integrate the currently scattered work on tool development in neuroscience into the broader philosophy of science community. They also present an accessible compendium for neuroscientists interested in the broader theoretical dimensions of their experimental practices. The chapters are divided into five thematic sections. Section 1 discusses the development of revolutionary research tools across neuroscience's history and argues to various conclusions concerning the relationship between new research tools and theory progress in neuroscience. Section 2 shows how a focus on research tools and their development in neuroscience transforms some traditional epistemological issues and questions about knowledge production in philosophy of science. Section 3 speaks to the most general questions about the way we characterize the nature of the portion of the world that this science addresses. Section 4 discusses hybrid research tools that integrate laboratory and computational methods in exciting new ways. Finally, Section 5 extends research on tool development to the related science of genetics. The Tools of Neuroscience Experiment will be of interest to philosophers and philosophically minded scientists working at the intersection of philosophy and neuroscience.

Body Image in Eating Disorders explores issues relating to the prevention, clinical diagnosis, and psychological treatment of distortions of body image in eating disorders. It presents a multifactorial model of indicators for diagnosis and treatment, considering psychological, sociocultural, and family indicators. Based on original empirical research with women and girls suffering from eating disorders, the book draws attention to limitations and dilemmas related to psychological diagnosis and treatment of people with eating disorders including anorexia readiness syndrome, bulimia, and bigorexia. The book proposes an integrative psychodynamic approach to the diagnosis and treatment of body image disorders and presents case studies illustrating examples of application of integration of psychodynamic therapy and psychodrama in psychological treatment of young people suffering from eating disorders. It considers risk factors including abnormal body image for the development of eating disorders and argues that psychological diagnosis of the body image is an important factor in determining the right direction of psychological treatment for people with eating disorders. Drawing on theoretical foundations and evidence-based clinical practice, the book will be of great interest to researchers, academics, and students in the fields of clinical and applied psychology, mental health, and specialists in eating disorders. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Writing allows people to convey information to others who are remote in time and space, vastly increasing the range over which people can cooperate and the amount they can learn. Mastering the writing system of one's language is crucial for success in a modern society. This book examines how children learn to write words. It provides a theoretical framework that integrates findings from a wide range of age groups-from children who are producing their first scribbles to experienced spellers who are writing complex words. To set the stage for these discussions, early chapters of the book consider the nature of writing systems and the nature of learning itself. The following chapters review various aspects of orthographic development, including the learning of symbol shapes and punctuation. Each chapter reviews research with learners of a variety of languages and writing systems, revealing underlying similarities. Discussions of how orthography is and should be taught are incorporated into each chapter, making the book of interest to educators as well as to psychologists, cognitive scientists, and linguists. This book is unique in the range of topics and languages that it covers and the degree to which it integrates linguistic insights about the nature of writing systems with discussions of how people learn to use these systems. It is written in a scholarly yet accessible manner, making it suited for a wide audience.

This edited book presents international perspectives on the role of mental health problems in understanding and managing the risk of violent extremism. The chapters included in this book address two themes. First, they describe the research findings on the nature and prevalence of the range of mental health problems (psychosis, personality disorder, post-traumatic stress disorder, anxiety and depression, autism spectrum disorders) in young people and adults who have in the past, committed acts of violence motivated at least in part by extremist ideologies, or who have attempted or threatened such acts, or who for other reasons are thought to be at risk of doing so. Second, the chapters examine what is known about the relationship - or the functional link - between mental health problems and violent extremism. The focus of this book is on clinical practice and understanding the nature of the challenge faced by practitioners and their response to it. It will therefore be of interest to mental health practitioners, service managers and commissioners, and policy makers with a remit to understand and mitigate risk of radicalisation and violent extremism. The chapters in this book were originally published in The Journal of Forensic Psychiatry & Psychology.

* It offers an original answer to this question: evaluation spreads because we want to be evaluated. * Developing a critical reflection from a psychoanalytic perspective, it argues that workers are not mere victims of evaluation systems but are complicit in them. * Benedicte Vidaillet focuses on the aspects of our subjectivity that come into play in evaluation at work -our expectations, desires, need for recognition, our conceptions of ourselves at work, as well as our relationship with others such as colleagues, managers or clients - to explore how evaluation affects us, where it gets its evocative power, and what it stirs within us to make us want it, despite its detrimental effects in its currently practiced form. * Chapters draw on real-life examples, case studies from a variety of organizations, and observations from clinical practice, to provide insight into the many mechanisms that have enabled evaluation to spread unimpeded through our subjective complicity in the process, revealing how they came to seem so innocuous. * This book will be of interest to scholars studying the topic of evaluation at work from a critical perspective as well as professionals who use evaluation systems or are under the pressure of evaluation in all sectors and organizations. * By exposing the psychological mechanisms that evaluation uses to appeal to us, it gives each of us the tools we need to break free of its grasp.

This volume is a compilation of articles that shed light on psychopathology, how the one struggling with it experiences its implications, and how it affects everyday life. For one to be categorized as exhibiting positive mental health, an individual should not experience psychopathology, and additionally exhibit high levels of emotional well-being as well as high levels of psychological and social functioning. The dual-factor model of mental health suggests that enhancing positive mental health and alleviating psychopathology do not automatically go together and are not opposite of one another. There is accumulating evidence that psychopathology and positive mental health function along two different continua that are only moderately interrelated. However, to know what wellbeing is, understand good mental health, and enhance adaptive functioning, we need to explore and understand psychopathology, and how it affects us. The volume is divided into three conceptual sections: The Experience of Psychopathology, which is devoted to describing what it is and how it is experienced; The Effect of Psychopathology on Everyday Life, describes various effects that psychopathology has on the daily life of the sufferer; Coherence, Resilience and Recovery, which focuses on dealing with it, coping with the symptoms, and developing resilience. The chapters in this book were originally published in The Journal of Psychology.

Interviewer Effects from a Total Survey Error Perspective presents a comprehensive collection of state-of-the-art research on interviewer-administered survey data collection. Interviewers play an essential role in the collection of the high-quality survey data used to learn about our society and improve the human condition. Although many surveys are conducted using self-administered modes, interviewer-administered modes continue to be optimal for surveys that require high levels of participation, include difficult-to-survey populations, and collect biophysical data. Survey interviewing is complex, multifaceted, and challenging. Interviewers are responsible for locating sampled units, contacting sampled individuals and convincing them to cooperate, asking questions on a variety of topics, collecting other kinds of data, and providing data about respondents and the interview environment. Careful attention to the methodology that underlies survey interviewing is essential for interviewer-administered data collections to succeed. In 2019, survey methodologists, survey practitioners, and survey operations specialists participated in an international workshop at the University of Nebraska-Lincoln to identify best practices for surveys employing interviewers and outline an agenda for future methodological research. This book features 23 chapters on survey interviewing by these worldwide leaders in the theory and practice of survey interviewing. Chapters include: The legacy of Dr. Charles F. Cannell's groundbreaking research on training survey interviewers and the theory of survey interviewing Best practices for training survey interviewers Interviewer management and monitoring during data collection The complex effects of interviewers on survey nonresponse Collecting survey measures and survey paradata in different modes Designing studies to estimate and evaluate interviewer effects Best practices for analyzing interviewer effects Key gaps in the research literature, including an agenda for future methodological research Chapter appendices available to download from https://digitalcommons.unl.edu/sociw/ Written for managers of survey interviewers, survey methodologists, and students interested in the survey data collection process, this unique reference uses the Total Survey Error framework to examine optimal approaches to survey interviewing, presenting state-of-the-art methodological research on all stages of the survey process involving interviewers. Acknowledging the important history of survey interviewing while looking to the future, this one-of-a-kind reference provides researchers and practitioners with a roadmap for maximizing data quality in interviewer-administered surveys.

Recognizing Frantz Fanon's remarkable legacy to applied mental health and therapeutic practices which decolonize, humanize, and empower marginalized populations, this text serves as a timely call for research, education, and clinical work to establish and further develop Fanonian approaches and practices. As the first collection to focus on contemporary clinical applications of Fanon's research and practice, this volume adopts a transnational lens through which to capture the global reach of Fanon's work. Contributors from Africa, Australia, Europe, and North America offer nuanced insight into historical and theoretical methods, clinical case studies, and community-based innovations to place Fanon's research and practice in context. Organized into four key areas, including the Historical Significance of Fanon's Clinical Work; Theory and Fanonian Praxis; Psychotherapeutic and Community Applications; and Action Research, each section of the book reflects an impressive diversity of practices around the world, and considers the role of political and socioeconomic context, structures of gender oppression, racial identities, and their intersection within those practices. A unique manifesto to the ground-breaking and immensely relevant work of Frantz Fanon, this book will be of great interest to graduate and post graduate students, researchers, academics and professionals in counselling psychology, mental health research, and psychotherapy.

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.

"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.

"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.

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

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

Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph structure. Valued graph allows one to incorporate the connections or links among the population units in addition. The links may provide effectively access to the part of population that is the primary target, which is the case for many unconventional sampling methods, such as indirect, network, line-intercept or adaptive cluster sampling. Or, one may be interested in the structure of the connections, in terms of the corresponding graph properties or parameters, such as when various breadth- or depth-first non-exhaustive search algorithms are applied to obtain compressed views of large often dynamic graphs. Graph sampling provides a statistical approach to study real graphs from either of these perspectives. It is based on exploring the variation over all possible sample graphs (or subgraphs) which can be taken from the given population graph, by means of the relevant known sampling probabilities. The resulting design-based inference is valid whatever the unknown properties of the given real graphs. One-of-a-kind treatise of multidisciplinary topics relevant to statistics, mathematics and data science. Probabilistic treatment of breadth-first and depth-first non-exhaustive search algorithms in graphs. Presenting cutting-edge theory and methods based on latest research. Pathfinding for future research on sampling from real graphs. Graph Sampling can primarily be used as a resource for researchers working with sampling or graph problems, and as the basis of an advanced course for post-graduate students in statistics, mathematics and data science.

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.

This volume examines behavioral genetic research on temperament and personality from a number of perspectives. It takes a developmental perspective on a number of issues across the lifespan, focusing on personality and temperament. The first section focuses on the development of temperament and personality. Typically this has involved exploring genetic and environmental contributions to phenotypic stability and instability, but more recently there has been research that examines the etiology of intra-individual change/growth trajectories. The second section examines genetic and environmental contributions to the association between temperament and personality and other behaviors. The third and fourth sections discuss genotype-environment correlations and interactions, and introduces the reader to molecular genetics research on temperament and personality. Chapter 11 will discuss the significance of this type of research and Chapter 12 will provide an example of specific line of research exploring genes associated with temperament.

In these uncertain times, how much can you trust health news? Is the research behind breaking headlines reliable? This book is an indispensable resource for students and general readers, helping them evaluate and think critically about health information. "People Who Drink Coffee Live Longer." "Students Learn Better When Listening to Classical Music." "Scientists Discover the Gene That Causes Obesity." We are constantly bombarded with reports of "groundbreaking" health findings that use attention-grabbing headlines and seem to be backed by credible science. Yet many of these studies and the news articles that discuss them fall prey to a variety of problems that can produce misleading and inaccurate results. Some of these may be easy to notice-like a research study on the benefits of red meat funded by the beef industry, or a study with a sample size of only 10 people-but others are much harder to spot. Skewed Studies: Exploring the Limits and Flaws of Health and Psychology Research examines the most pervasive problems plaguing health research and reporting today, using clear, accessible language and employing real-world examples to illustrate key concepts. Beyond simply outlining issues, it provides readers with the knowledge and skills to evaluate research studies and news reports for themselves, improving their health literacy and critical thinking skills. Brings together and thoroughly explores the many ways in which health research and reporting can be flawed and problematic Improves readers' critical thinking skills and gives them practical tools to better evaluate the health information they come across Explains scientific and statistical concepts in clear, easy-to-understand language Includes a curated and annotated directory of resources for readers seeking additional information

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.

* Aims to revive the field study method and demonstrate the importance of studying the behaviour of subjects in real-life, rather than laboratory conditions while complying with the current methodological and ethical standards * Examines the advantages and limitations of the field study method, whilst offering practical guidance on how it can be used in experiments now and in the future * Suitable for graduate and undergraduate students taking courses in methodology, and researchers looking to use field study methods in their research

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.