- 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

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.

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)

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.

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

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.

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.

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

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

"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.

* 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

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.

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.

"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.

Behavioral scientists - including those in psychology, infant and child development, education, animal behavior, marketing and usability studies - use many methods to measure behavior. Systematic observation is used to study relatively natural, spontaneous behavior as it unfolds sequentially in time. This book emphasizes digital means to record and code such behavior; while observational methods do not require them, they work better with them. Key topics include devising coding schemes, training observers and assessing reliability, as well as recording, representing and analyzing observational data. In clear and straightforward language, this book provides a thorough grounding in observational methods along with considerable practical advice. It describes standard conventions for sequential data and details how to perform sequential analysis with a computer program developed by the authors. The book is rich with examples of coding schemes and different approaches to sequential analysis, including both statistical and graphical means.

State-of-the art research synthesis methods for psychologists This fourth "Hotspots in Psychology" is devoted to systematic reviews and meta-analyses in research-active fields that have generated a considerable number of primary studies. The common denominator is the research synthesis nature of the contributions, and not a specific psychological topic or theme that all articles have to address. This issue explores methodological advances in research synthesis methods relevant for any subfield of psychology. The contributions include: the application of a network meta-analytic approach to analyze the effect of transcranial direct current stimulation on memory; analyzing the performance of a meta-analytic structural equation modeling approach when variables in primary studies have been artificially dichotomized; assessing quality-related aspects of systematic reviews with AMSTAR and AMSTAR2; as well as a graphical approach to depict study-level statistical power in the context of meta-analysis.STAR2; as well as a graphical approach to depict study-level statistical power in the context of meta-analysis.

-Offers a straightforward, detailed guide to how to ensure success of cross-border research projects and interventions, based on decades of experiences in many cultures, as well as meticulous research and documentation. -Accessibly written and illustrated with real examples and case studies of projects, and covers key topics including project management, cultural considerations, handling crises and threats, and successful implementation and dissemination. -Ideal for researchers and project managers - from large, global organisations to small NGOs, as well as university students and academics.

Interpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. Features of the ninth edition: * Covers a broad array of basic statistical concepts, including topics drawn from the New Statistics * Up-to-date journal excerpts reflecting contemporary styles in statistical reporting * Strong emphasis on data visualization * Ancillary materials include data sets with almost two hours of accompanying tutorial videos, which will help students and instructors apply lessons from the book to real-life scenarios About this book Each of the 63 exercises in the book contain three central components: 1) an introduction to a statistical concept, 2) a brief excerpt from a published research article that uses the statistical concept, and 3) a set of questions (with answers) that guides students into deeper learning about the concept. The questions on the journal excerpts promote learning by helping students * interpret information in tables and figures, * perform simple calculations to further their interpretations, * critique data-reporting techniques, and * evaluate procedures used to collect data. The questions in each exercise are divided into two parts: (1) Factual Questions and (2) Questions for Discussion. The Factual Questions require careful reading for details, while the discussion questions show that interpreting statistics is more than a mathematical exercise. These questions require students to apply good judgment as well as statistical reasoning in arriving at appropriate interpretations. Each exercise covers a limited number of topics, making it easy to coordinate the exercises with lectures or a traditional statistics textbook.

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

Richly illustrates the practice of programme theory driven evaluation with contemporary examples alongside the most recent advances in theory. Contains self-reflective chapters to allow the reader/novice evaluator to engage with and critique their own work. An excellent introduction to the basic concepts and approaches of theory-driven evaluation.

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.

Do you want to learn to read people's minds? In this student-friendly, practice-focussed textbook on EEG and biosignal analysis, you will learn how to: Implement your experiment in E-Prime or OpenSesame; Run your study in the psychophysiological laboratory; Analyse data in MATLAB by following simple steps. This textbook follows a unique approach by guiding you through a single EEG study, each part introducing the relevant core knowledge and commonly available software. Practical exercises help you master the skills to independently implement every aspect of an experiment, from setting up the lab to analysing the data. Suitable for developing both basic levels of skill for undergraduates as well as advancing towards a stronger command of analysis and understanding at postgraduate level. Michiel Spape is a Lecturer and Researcher in Psychology at the University of Helsinki.