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.

* 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.

* The first book of its kind to offer a training framework for neuropsychologists who use psychometrists in their practice * An essential resource for psychologists/neuropsychologists who employ technicians and those involved in training graduate students who are just learning to administer cognitive tests. * Includes guidance on methods to prepare the psychometrist for testing with unique populations and responding to atypical behaviors * Includes coverage on promoting board certification of your psychometrist, and the process and procedures required to be successful

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.

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.

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.

- 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

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.

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.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks.

Integrates the act of creating a measure with the act of using the measure for a specific purpose Emphasis on concrete examples drawn from simulated or empirical data. Engaging and accessible writing style. Provides a deep conceptual treatment of measurement in the context of education and psychology Inter-disciplinary appeal

Integrates the act of creating a measure with the act of using the measure for a specific purpose Emphasis on concrete examples drawn from simulated or empirical data. Engaging and accessible writing style. Provides a deep conceptual treatment of measurement in the context of education and psychology Inter-disciplinary appeal

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.

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.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.

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.

There isn't a book currently on the market which focuses on multiple hypotheses testing. - Can be used on a range of course, including social & behavioral sciences, biological sciences, as well as professional researchers. Includes various examples of the multiple hypotheses method in practice in a variety of fields, including: sport and crime.