| data analytics and 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 and 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 and psychology: Data Analysis for Psychology George L. Dunbar, 1998 This book is an accessible and straightforward introduction to data analysis. Unlike many statistics texts, it does not go into great mathematical or statistical detail. Instead, the emphasis throughout is on clarity, and ideas are conveyed verbally rather than via algebra. Nevertheless, thebook covers material sophisticated enough to allow psychology students to undertake research at any stage of their degree, presenting it in the context of practical activities and issues in psychological research. |
| data analytics and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 analytics and 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 and psychology: Scientific American: Presenting Psychology Deborah Licht, Misty Hull, Coco Ballantyne, 2021-10-27 Written by two teachers and a science journalist, Presenting Psychology introduces the basics to psychology through magazine-style profiles and video interviews of real people, whose stories provide compelling contexts for the field’s key ideas. |
| data analytics and 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 and psychology: Data Analysis for Experimental Design Richard Gonzalez, 2009-01-01 This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless exceptions to the rule that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by advanced undergraduates preparing to do senior theses. Useful pedagogical features include: Discussions of the assumptions that underlie each statistical test Sequential, step-by-step presentations of statistical procedures End-of-chapter questions and exercises Accessible writing style with scenarios and examples This book is intended for graduate students in psychology and education, practicing researchers seeking a readable refresher on analysis of experimental designs, and advanced undergraduates preparing senior theses. It serves as a text for graduate level experimental design, data analysis, and experimental methods courses taught in departments of psychology and education. It is also useful as a supplemental text for advanced undergraduate honors courses. |
| data analytics and psychology: SPSS for Psychologists Nicola Brace, 2005 |
| data analytics and psychology: The History of Statistics Stephen M. Stigler, 1990-03-01 This magnificent book is the first comprehensive history of statistics from its beginnings around 1700 to its emergence as a distinct and mature discipline around 1900. Stephen M. Stigler shows how statistics arose from the interplay of mathematical concepts and the needs of several applied sciences including astronomy, geodesy, experimental psychology, genetics, and sociology. He addresses many intriguing questions: How did scientists learn to combine measurements made under different conditions? And how were they led to use probability theory to measure the accuracy of the result? Why were statistical methods used successfully in astronomy long before they began to play a significant role in the social sciences? How could the introduction of least squares predate the discovery of regression by more than eighty years? On what grounds can the major works of men such as Bernoulli, De Moivre, Bayes, Quetelet, and Lexis be considered partial failures, while those of Laplace, Galton, Edgeworth, Pearson, and Yule are counted as successes? How did Galton’s probability machine (the quincunx) provide him with the key to the major advance of the last half of the nineteenth century? Stigler’s emphasis is upon how, when, and where the methods of probability theory were developed for measuring uncertainty in experimental and observational science, for reducing uncertainty, and as a conceptual framework for quantitative studies in the social sciences. He describes with care the scientific context in which the different methods evolved and identifies the problems (conceptual or mathematical) that retarded the growth of mathematical statistics and the conceptual developments that permitted major breakthroughs. Statisticians, historians of science, and social and behavioral scientists will gain from this book a deeper understanding of the use of statistical methods and a better grasp of the promise and limitations of such techniques. The product of ten years of research, The History of Statistics will appeal to all who are interested in the humanistic study of science. |
| data analytics and psychology: Real Data Analysis Shlomo S. Sawilowsky, 2007-01-01 The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide (d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series. |
| data analytics and psychology: Cognitive Science, Computational Intelligence, and Data Analytics Vikas Khare, Sanjeet Kumar Dwivedi, Monica Bhatia, 2024-06-06 Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. - Demystifies the theory of data analytics using a step-by-step approach - Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code - Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools - Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications |
| data analytics and 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 and psychology: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| data analytics and psychology: Responsible Conduct of Research Adil E. Shamoo, David B. Resnik, 2009-02-12 Recent scandals and controversies, such as data fabrication in federally funded science, data manipulation and distortion in private industry, and human embryonic stem cell research, illustrate the importance of ethics in science. Responsible Conduct of Research, now in a completely updated second edition, provides an introduction to the social, ethical, and legal issues facing scientists today. |
| data analytics and psychology: Statistics in Psychology Using R and SPSS Dieter Rasch, Klaus Kubinger, Takuya Yanagida, 2011-12-12 Statistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance. In other words, no analysis applies if the minimum size is not determined in order to fulfil certain precision requirements. The book looks at the process of empirical research into the following seven stages: Formulation of the problem Stipulation of the precision requirements Selecting the statistical model for the planning and analysis The (optimal) design of the experiment or survey Performing the experiment or the survey Statistical analysis of the observed results Interpretation of the results. |
| data analytics and psychology: Experimental Psychology Prof. Dr. Bilal Semih Bozdemir, Experimental psychology is a branch of psychology that employs scientific methods to study and understand psychological phenomena. The principal objective of experimental psychology is to investigate the underlying cognitive processes, emotions, behaviors, and social interactions through empirical observation and controlled experimentation. This chapter aims to provide an overview of the foundational principles that underpin experimental psychology, its objectives, and the significance of its methodologies in the broader context of psychological research. |
| data analytics and psychology: Data Science with R for Psychologists and Healthcare Professionals Christian Ryan, 2021-12-23 This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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Advancing Resilience in Low Income Housing Using Climate …
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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 …
George Mason University 2024-2025 Academic Year Tuition
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Structure of a Data Analysis Report - Carnegie Mellon …
The data analysis report isn’t quite like a research paper or term paper in a class, nor like aresearch article in a journal. It is meant, primarily, to start an organized conversation between …
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Sports intelligence, an advanced approach that utilizes data analytics, psychology, and technology, has emerged as a crucial factor in optimizing athletic performance in today's …
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predictive analytics promises to translate data into superior and actionable knowledge. Data pioneers collect and combine information on individuals and their activities (e.g., purchasing or …
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Artificial Intelligence in Education: AIEd for Personalised …
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Human Resource Analytics: Implications for Strategy …
HR analytics is a relatively new term and reflects a multidisciplinary trend toward using data to inform decision making, as reflected in the rapid growth of data science. With that said, HR …
Applications of machine learning to behavioral sciences: focus …
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Educational Data Mining and Learning Analytics - DRAFT
seen as a sister community to learning analytics. Educational Data Mining first emerged in a workshop series starting in 2005, which became an annual confer-ence in 2008 and spawned …
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Applications of machine learning to behavioral sciences: focus …
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George Mason University 2023-2024 Academic Year Tuition
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Marketinganalytics:Thebridgebetweencustomer ...
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PSYCHOLOGY 295 Data Analysis in Psychological Research
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Data-Directed Education: The Future of AI in Education - NSF
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Using Data to Improve Educational Outcomes
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Applications of machine learning to behavioral sciences: focus …
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A Practical Guide to Big Data Research in Psychology Big data is about the real world, captured in an array of formats, from medical records to texts of speeches to photographs. The massive …
Visualizing Data to Support Judgement, Inference, and …
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Network analysis: a brief overview and tutorial - Taylor
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Understanding Pakikipagkapwa Through Analytics
Understanding Pakikipagkapwa Through Analytics Exploring Filipino Relational Communication in Online Spaces Vladimer Kobayashi, Ph.D.,1 Maria Margarita Lavides, Ph.D.,2 Cristabel …
The Bachelor of Science (BS) in Data Analytics program is …
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data to show that the null hypothesis is not true, based on the likelihood of selecting a sample mean from a population (the likelihood is the criterion). The likelihood or level of significance is …
Psychological AI: Designing Algorithms Informed by Human …
once hailed as the great success of big-data analytics, analyzed the correlations of some 50 million search terms with indicators of flu and tested 450 million dif-ferent algorithms to find the …
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Examining the two groups data reveals that the mean of the quiet group is higher than the noise group, thus the common sense notion that noise might impair test performance appears to be …
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thematic electives, data analytics, and field-based dissertations. The faculty associated with this programme are trained in economics, political science, sociology, anthropology, philosophy, …
