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| data analytics in psychology: Big Data in Psychological Research Sang Eun Woo, Louis Tay, Robert W. Proctor, 2020 Big Data in Psychological Research provides an overview of big data theory, research design and analysis, collection methods, applications, ethical concerns, best practices, and future research directions for psychologists. |
| data analytics in psychology: Introducing Research and Data in Psychology Ann Searle, 1999 This book shows how research design and data analysis are attainable and useful skills. It introduces both experimental and non-experimental methods of research and the analysis of data using both descriptive and inferential statistics. |
| data analytics in psychology: Big Data at Work Scott Tonidandel, Eden B. King, Jose M. Cortina, 2015-11-06 The amount of data in our world has been exploding, and analyzing large data sets—so called big data—will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics. |
| data analytics in psychology: Advanced R Hadley Wickham, 2015-09-15 An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does. |
| data analytics in psychology: Behavioral Research Data Analysis with R Yuelin Li, Jonathan Baron, 2011-12-02 This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research. The authors emphasize practical data analytic skills so that they can be quickly incorporated into readers’ own research. |
| data analytics in psychology: Data Analysis for Psychology George L. Dunbar, 1998 Data analysis is an essential psychological aid to understanding human behaviour in terms of its causes. By observing and measuring certain behavioural patterns, psychologists can obtain data necessary to support - or refute - a given hypothesis. It is the business of data analysis to cut through the jungle of numbers to the underlying psychological structure. 'Data Analysis for Psychology' provides a clear and straightforward explanation of the most important data analysis techniques for psychologists. Using concrete examples to illustrate the analysis of data, concepts are explored in plain language and without unnecessary technical detail. At the heart of the book is the idea of a statistical model and the process of accounting for structure, or pattern, in data. Explaining familiar techniques like analysis of variance, the book goes further to examine exploratory data analysis, log-linear analysis, analysis of co-variance and other advanced methods. With the emphasis throughout on clarity, the text is written for undergraduate psychology students undertaking independent research. |
| data analytics in psychology: Handbook of Language Analysis in Psychology Morteza Dehghani, Ryan L. Boyd, 2022-03-02 Recent years have seen an explosion of interest in the use of computerized text analysis methods to address basic psychological questions. This comprehensive handbook brings together leading language analysis scholars to present foundational concepts and methods for investigating human thought, feeling, and behavior using language. Contributors work toward integrating psychological science and theory with natural language processing (NLP) and machine learning. Ethical issues in working with natural language data sets are discussed in depth. The volume showcases NLP-driven techniques and applications in areas including interpersonal relationships, personality, morality, deception, social biases, political psychology, psychopathology, and public health. |
| data analytics in psychology: Research Methods and Data Analysis for Psychology Stuart Wilson, Rory MacLean, 2011-01-01 Psychology is a fascinating subject that can inspire students; the opportunity to conduct individual research can be immensely rewarding. However, the prospect of getting to grips with designing research and analysing data can be daunting. This book has been written to show students that research methods and data analysis can be interesting and to help students understand why the subject is important. Tailor-made for students coming to research methods and data analysis for the first time, and with a wealth of captivating examples and an engaging writing style, this text is an essential tool for all undergraduate psychology students. |
| data analytics in psychology: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data analytics in psychology: Secondary Data Analysis Kali H. Trzesniewski, M. Brent Donnellan, Richard Eric Lucas, 2011 This wide-ranging yet practical book shows how the analysis of secondary data can provide unique opportunities for advancing psychological science. --Book Jacket. |
| data analytics in psychology: Interpreting and Using Statistics in Psychological Research Andrew N. Christopher, 2016-08-30 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. |
| data analytics in psychology: Introduction to Research Methods and Data Analysis in Psychology Darren Langdridge, Gareth Hagger-Johnson, 2013 This is a comprehensive introduction to research methods and data analysis. The book assumes no previous knowledge of research methods or psychology and provides an accessible and jargon-free way into this frequently difficult topic area. |
| data analytics in psychology: Categorical Data Analysis for the Behavioral and Social Sciences Razia Azen, Cindy M. Walker, 2021-05-26 Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a Look Ahead section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book’s accessible approach. |
| data analytics in psychology: Introducing HR Analytics with Machine Learning Christopher M. Rosett, Austin Hagerty, 2021-06-14 This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for today’s organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today’s data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR. This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy. |
| data analytics in psychology: Data Analysis Edward L. Wike, 2018-02-06 This book is intended for psychology majors and graduate students who are conducting experiments for the first time and are faced with the task of making sense out of their data. This much needed how-to-do-it text illustrates the application of statistical methods to the data from small samples. It also serves as a handbook, with twenty-two tables presented at the end of the text that will allow the student to carry out virtually every computation necessary in analyzing his data. Almost all of the examples and illustrations are drawn from actual experiments so that the student can see how professional scientists examine their data. The book also shows students the kinds of data that are encountered in psychological research, the kinds of questions investigators seek to answer, and how these questions are approached. The author asserts that statistics is not an abstract discipline but a tool in research. However, the book also imparts a philosophy of data analysis and its meaning, a concern for questions of the function of data analysis and the interpretations that legitimately can be drawn from data. In brief, Data Analysis asks: What kinds of data are met in psychological research? What can we do with these data? What can we conclude as a result of this doing? The book will be invaluable for students who, even though they may have taken a previous statistics course, are still unsure of what statistical techniques should be used in interpreting their data. |
| data analytics in psychology: Atlas of Moral Psychology Kurt Gray, Jesse Graham, 2018-01-23 This comprehensive and cutting-edge volume maps out the terrain of moral psychology, a dynamic and evolving area of research. In 57 concise chapters, leading authorities and up-and-coming scholars explore fundamental issues and current controversies. The volume systematically reviews the empirical evidence base and presents influential theories of moral judgment and behavior. It is organized around the key questions that must be addressed for a complete understanding of the moral mind. |
| data analytics in psychology: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| data analytics in psychology: Data Analytics for the Social Sciences G. David Garson, 2021-11-30 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. |
| data analytics in psychology: Predictive Analytics of Psychological Disorders in Healthcare Mamta Mittal, Lalit Mohan Goyal, 2022-05-20 This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses. |
| data analytics in psychology: Talking About Statistics Brian S. Everitt, Dale F. Hay, 1992-10-06 Adopting a case-study approach, the authors use an example drawn from child psychiatry and follow it through to demonstrate the statistical methods available to tackle a range of research questions and data. Describes how collaborations between statisticians and behavioral scientists can benefit the design and analysis of a practical research project. Includes the latest analytical techniques. |
| data analytics in psychology: Strategy Beyond the Hockey Stick Chris Bradley, Martin Hirt, Sven Smit, 2018-02-06 Beat the odds with a bold strategy from McKinsey & Company Every once in a while, a genuinely fresh approach to business strategy appears —legendary business professor Richard Rumelt, UCLA McKinsey & Company's newest, most definitive, and most irreverent book on strategy—which thousands of executives are already using—is a must-read for all C-suite executives looking to create winning corporate strategies. Strategy Beyond the Hockey Stick is spearheading an empirical revolution in the field of strategy. Based on an extensive analysis of the key factors that drove the long-term performance of thousands of global companies, the book offers a ground-breaking formula that enables you to objectively assess your strategy's real odds of future success. This book is fundamental. The principles laid out here, with compelling data, are a great way around the social pitfalls in strategy development. —Frans Van Houten, CEO, Royal Philips N.V. The authors have discovered that over a 10-year period, just 1 in 12 companies manage to jump from the middle tier of corporate performance—where 60% of companies reside, making very little economic profit—to the top quintile where 90% of global economic profit is made. This movement does not happen by magic—it depends on your company's current position, the trends it faces, and the big moves you make to give it the strongest chance of vaulting over the competition. This is not another strategy framework. Rather, Strategy Beyond the Hockey Stick shows, through empirical analysis and the experiences of dozens of companies that have successfully made multiple big moves, that to dramatically improve performance, you have to overcome incrementalism and corporate inertia. A different kind of book—I couldn't put it down. Inspiring new insights on the facts of what it takes to move a company's performance, combined with practical advice on how to deal with real-life dynamics in management teams. —Jane Fraser, CEO, Citigroup Latin America |
| data analytics in psychology: Psychology of Intelligence Analysis Richards J Heuer, 2020-03-05 In this seminal work, published by the C.I.A. itself, produced by Intelligence veteran Richards Heuer discusses three pivotal points. First, human minds are ill-equipped (poorly wired) to cope effectively with both inherent and induced uncertainty. Second, increased knowledge of our inherent biases tends to be of little assistance to the analyst. And lastly, tools and techniques that apply higher levels of critical thinking can substantially improve analysis on complex problems. |
| data analytics in psychology: Statistics for Psychology Using R Vivek M. Belhekar, 2016-10-31 A unique textbook introducing and demonstrating the use of R in psychology. Statistics for Psychology Using R comprehensively covers standard statistical methods along with advanced topics such as multivariate techniques, factor analysis, and multiple regression widely used in the field of psychology and other social sciences. Its innovative structure and pedagogical approach coupled with numerous worked-out examples and self-assessment tests make it a user-friendly and easy-to-understand companion for students and scholars with limited background in statistics. The standout feature of this textbook is that it demonstrates the application of R—a free, flexible, and dynamically changing software for statistical computing and data analysis, which is becoming increasingly popular across social and behavioral sciences. |
| data analytics in psychology: Foucault, Psychology and the Analytics of Power D. Hook, 2007-08-16 This book introduces and applies Foucault's key concepts and procedures, specifically for a psychology readership. Drawing on recently published Collège de France lectures, it is useful to those concerned with Foucault's engagement with the 'psy-disciplines' and those interested in the practical application of Foucault's critical research methods. |
| data analytics in psychology: Validation of Score Meaning for the Next Generation of Assessments Kadriye Ercikan, James W. Pellegrino, 2017-03-27 Despite developments in research and practice on using examinee response process data in assessment design, the use of such data in test validation is rare. Validation of Score Meaning in the Next Generation of Assessments Using Response Processes highlights the importance of validity evidence based on response processes and provides guidance to measurement researchers and practitioners in creating and using such evidence as a regular part of the assessment validation process. Response processes refer to approaches and behaviors of examinees when they interpret assessment situations and formulate and generate solutions as revealed through verbalizations, eye movements, response times, or computer clicks. Such response process data can provide information about the extent to which items and tasks engage examinees in the intended ways. With contributions from the top researchers in the field of assessment, this volume includes chapters that focus on methodological issues and on applications across multiple contexts of assessment interpretation and use. In Part I of this book, contributors discuss the framing of validity as an evidence-based argument for the interpretation of the meaning of test scores, the specifics of different methods of response process data collection and analysis, and the use of response process data relative to issues of validation as highlighted in the joint standards on testing. In Part II, chapter authors offer examples that illustrate the use of response process data in assessment validation. These cases are provided specifically to address issues related to the analysis and interpretation of performance on assessments of complex cognition, assessments designed to inform classroom learning and instruction, and assessments intended for students with varying cultural and linguistic backgrounds. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. |
| data analytics in psychology: Neural Data Science Erik Lee Nylen, Pascal Wallisch, 2017-02-24 A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. - Includes discussions of both MATLAB and Python in parallel - Introduces the canonical data analysis cascade, standardizing the data analysis flow - Presents tactics that strategically, tactically, and algorithmically help improve the organization of code |
| data analytics in psychology: Statistical Power Analysis for the Behavioral Sciences Jacob Cohen, 2013-05-13 Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of qualifying dependent variables and; * expanded power and sample size tables for multiple regression/correlation. |
| data analytics in psychology: MarketPsych Richard L. Peterson, Frank F. Murtha, 2010-07-30 An investor's guide to understanding the most elusive (yet most important) aspect of successful investing - yourself. Why is it that the investing performance of so many smart people reliably and predictably falls short? The answer is not that they know too little about the markets. In fact, they know too little about themselves. Combining the latest findings from the academic fields of behavioral finance and experimental psychology with the down-and-dirty real-world wisdom of successful investors, Drs. Richard Peterson and Frank Murtha guide both new and experienced investors through the psychological learning process necessary to achieve their financial goals. In an easy and entertaining style that masks the book’s scientific rigor, the authors make complex scientific insights readily understandable and actionable, shattering a number of investing myths along the way. You will gain understanding of your true investing motivations, learn to avoid the unseen forces that subvert your performance, and build your investor identity - the foundation for long-lasting investing success. Replete with humorous games, insightful self-assessments, entertaining exercises, and concrete planning tools, this book goes beyond mere education. MarketPsych: How to Manage Fear and Build Your Investor Identity functions as a psychological outfitter for your unique investing journey, providing the tools, training and equipment to help you navigate the right paths, stay on them, and see your journey through to success. |
| data analytics in psychology: Understanding Criminological Research John Martyn Chamberlain, 2012-11-09 Criminological research lies at the heart of criminological theory, influences social policy development, as well as informs criminal justice practice. The ability to collect, analyse and present empirical data is a core skill every student of criminology must learn. Written as an engaging step-by-step guide and illustrated by detailed case studies, this book guides the reader in how to analyse criminological data. Key features of the book include: o Guidance on how to identify a research topic, designing a research study, accounting for the role of the researcher and writing up and presenting research findings. o A thorough account of the development of qualitative and quantitative research methodologies and data analysis within the field of criminology. o Relevant and up-to-date case studies, drawn from internationally published criminological research sources. o Clear and accessible chapter content supported by helpful introductions, concise summaries, self-study questions and suggestions for further reading. Understanding Criminological Research: A Guide to Data Analysis in invaluable reading for both undergraduate and postgraduate students in criminology and criminal justice. |
| data analytics in psychology: Data Analysis with Machine Learning for Psychologists Chandril Ghosh, 2022-10-17 The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market. While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science. Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions. |
| data analytics in psychology: SPSS for Psychologists Nicola Brace, 2005 |
| data analytics in psychology: Big Data in Psychological Research Sang Eun Woo, Louis Tay (Psychologist), Robert W. Proctor, 2020 Technological advances have led to an abundance of widely available data on every aspect of life today. Psychologists today have more information than ever before on human cognition, emotion, attitudes, and behavior. Big Data in Psychological Research addresses the opportunities and challenges that this data presents to psychological researchers. This edited collection provides an overview of theoretical approaches to the utility and purpose of big data, approaches to research design and analysis, collection methods, applications, limitations, best practice recommendations, and key issues related to privacy, security, and ethical concerns that are essential to understand for anyone working with big data. The book also discusses potential future research directions aimed at improving the quality and interpretation of big data projects, as well as the training and evaluation of psychological science teams that conduct research using big data-- |
| data analytics in psychology: Theory-Based Data Analysis for the Social Sciences Carol S. Aneshensel, 2013 This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of third variables to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions. |
| data analytics in psychology: Thinking, Fast and Slow Daniel Kahneman, 2011-10-25 *Major New York Times Bestseller *More than 2.6 million copies sold *One of The New York Times Book Review's ten best books of the year *Selected by The Wall Street Journal as one of the best nonfiction books of the year *Presidential Medal of Freedom Recipient *Daniel Kahneman's work with Amos Tversky is the subject of Michael Lewis's best-selling The Undoing Project: A Friendship That Changed Our Minds In his mega bestseller, Thinking, Fast and Slow, Daniel Kahneman, world-famous psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive biases on everything from playing the stock market to planning our next vacation—each of these can be understood only by knowing how the two systems shape our judgments and decisions. Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Topping bestseller lists for almost ten years, Thinking, Fast and Slow is a contemporary classic, an essential book that has changed the lives of millions of readers. |
| data analytics in psychology: The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis Todd D. Little, 2013-02-01 Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences. |
| data analytics in psychology: Data Analysis Charles M. Judd, Gary H. McClelland, Carey S. Ryan, 2017 Noted for its model-comparison approach and unified framework based on the general linear model (GLM), this classic text provides readers with a greater understanding of a variety of statistical procedures including analysis of variance (ANOVA) and regression. |
| data analytics in psychology: Introduction to Meta-Analysis Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein, 2011-08-24 This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Introduction to Meta-Analysis: Outlines the role of meta-analysis in the research process Shows how to compute effects sizes and treatment effects Explains the fixed-effect and random-effects models for synthesizing data Demonstrates how to assess and interpret variation in effect size across studies Clarifies concepts using text and figures, followed by formulas and examples Explains how to avoid common mistakes in meta-analysis Discusses controversies in meta-analysis Features a web site with additional material and exercises A superb combination of lucid prose and informative graphics, written by four of the world’s leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and Rothstein provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students, who used pre-publication versions of some of the chapters, raved about the clarity of the explanations and examples. David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics. The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice. Jesse A. Berlin, ScD Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University |
| data analytics in psychology: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| data analytics in psychology: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| data analytics in psychology: Princeton Review AP Psychology Premium Prep, 2022 The Princeton Review, 2021-08-03 Make sure you’re studying with the most up-to-date prep materials! Look for the newest edition of this title, The Princeton Review AP Psychology Premium Prep, 2023 (ISBN: 9780593450871, on-sale August 2022). Publisher's Note: Products purchased from third-party sellers are not guaranteed by the publisher for quality or authenticity, and may not include access to online tests or materials included with the original product. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
