"As statistical prediction becomes ubiquitous in many areas of psychology, a comprehensive guide to navigating these tools is needed, one that covers topics pertinent to those in psychology and the social sciences. Prediction Statistics for Psychological Assessment, by R. Karl Hanson, is the first book to teach students and practitioners the nuts and bolts of prediction statistics, while illustrating the utility of prediction and prediction tools in applied psychological practice. This valuable resource uses real-world examples, helpful explanations and practice exercises to support the use of prediction tools in psychological assessment. Actuarial risk assessment evaluators need to know how prediction tools work, how to evaluate them, and how to interpret their results in applied assessments. Written in a clear and accessible manner, this user-friendly book helps readers understand how to evaluate and interpret different kinds of prediction tools, appreciate the numeric information used in risk communication, and utilize prediction tools to inform evidence-based decision-making"--

This practical, conceptual introduction to statistical analysis by award-winning teacher Andrew N. Christopher uses published research with inherently interesting social sciences content to help students make clear connections between statistics and real life. Using a friendly, easy-to-understand presentation, Christopher walks students through the hand calculations of key statistical tools and provides step-by-step instructions on how to run the appropriate analyses for each type of statistic in SPSS and how to interpret the output. With the premise that a conceptual grasp of statistical techniques is critical for students to truly understand why they are doing what they are doing, the author avoids overly formulaic jargon and instead focuses on when and how to use statistical techniques appropriately.

This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.

Andrews argues for a pluralistic folk psychology that employs different kinds of practices and different kinds of cognitive tools (including personality trait attribution, stereotype activation, inductive reasoning about past behavior, and generalization from self) that are involved in our folk psychological practices.

Methods in Psychological Research introduces students to the rich world of research in psychology through student-friendly writing, compelling real-world examples, and frequent opportunities for practice. Using a relaxed yet supportive tone that eases student anxiety, the authors present a mixture of conceptual and practical discussions, and spark reader interest in research by covering meaningful topics that resonate with today’s students. In-text features like Conceptual Exercises, FYI sections, and FAQ sections with accompanying visual cues support learning throughout the research experience. The Fourth Edition equips students with the tools they need to understand research concepts, conduct their own experiments, and present their findings.

This volume features cutting-edge and impactful articles from across Springer's diverse journals publishing program. In this curated collection, our editorial team has brought together highly-cited and downloaded articles on the topic of Psychological Methods into one single resource. Moreover, this book enables readers to review a broad spectrum of quality research on a specialized topic, which we hope facilitates interdisciplinary and critical discussions of the topic at hand. As part of the Key Topics in Behavioral Sciences book series, this volume aims to serve as a quick reference for readers when writing or researching new topics or subject areas. Other topics in the series will include Psychological Research Methods, Health and Behavior, Industrial and Organizational Psychology, Sports Psychology, and Consumer Behavior. In the first section of the volume, articles focus on such topics as Artificial Intelligence, Cultural Learning, Human Evolution, Human-Computer Interaction, Referential Triangle, Social Interaction, Differences, Diversity, Evolution, Genetics, Populations, and Race. Next, the second section features research on Double Dialogicality, Generalization, Qualitative Research, Single Case, Data Analysis, Data Screening, Insufficient Effort Responding, Research Design, Research Methods, and Survey Research. Lastly in the final section of this collection, Data Snooping, Harking, Publication Bias, Simulation, Open Science, Philosophy Of Science, Questionable Research Practices, Research Ethics, Content-Analysis, Dictionary Analysis, Natural Language Processing, Structural Topic Modeling, Text Analysis, Thematic Analysis, and Topic Modeling are discussed.

`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology′ - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.