| data science 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 science 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 science 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 science and psychology: Big Data in Cognitive Science Michael N. Jones, 2016-11-03 The primary goal of this volume is to present cutting-edge examples of mining large and naturalistic datasets to discover important principles of cognition and to evaluate theories in a way that would not be possible without such scale. It explores techniques that have been underexploited by cognitive psychologists and explains how big data from numerous sources can inform researchers with different research interests and shed further light on how brain, cognition and behavior are interconnected. The book fills a major gap in the literature and has the potential to rapidly advance knowledge throughout the field. It is essential reading for any cognitive psychology researcher. |
| data science 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 science 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 science and psychology: Introduction to Research Methods and Data Analysis in Psychology Darren Langdridge, Gareth Hagger-Johnson, 2013-04-29 This third edition of Introduction to Research Methods and Data Analysis in Psychology provides you with a unique, balanced blend of quantitative and qualitative research methods. Highly practical in nature, the book guides you, step-by-step, through the research process and is underpinned by SPSS screenshots, diagrams and examples throughout. |
| data science and psychology: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science 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 science and psychology: Analysing Qualitative Data in Psychology Evanthia Lyons, Adrian Coyle, 2007-10-25 Analysing Qualitative Data in Psychology equips students and researchers in psychology and the social sciences to carry out qualitative data analysis, focusing on four major methods (grounded theory, interpretative phenomenological analysis, discourse analysis and narrative analysis). Assuming no prior knowledge of qualitative research, chapters on the nature, assumptions and practicalities of each method are written by acknowledged experts. To help students and researchers make informed methodological choices about their own research the book addresses data collection and the writing up of research using each method, while providing a sustained comparison of the four methods, backed up with authoritative analyses using the different methods. |
| data science 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 science and psychology: The Psychology of Technology Sandra Matz, 2022-01-11 The rapid advancements in technology, and our increasing interaction with it, have key implications for the field of psychology. The Psychology of Technology brings together research from different subdisciplines across psychology to address the ways in which technology and Big Data are changing how psychological research is conducted. It also examines how technology allows us to better understand human psychology. This text showcases cutting-edge research at the intersection of psychology and technology to provide an outlook into the future of psychological research in a tech-enabled world. The growing capabilities and reach of technology show no signs of abating, so it is critically important that psychology understand it and harness it effectively and ethically. Chapters offer fascinating and novel insights about the human condition using digital technologies as a window into human psychology, highlight the opportunities and challenges people face interacting with digital tech, and address the consequences of technology for individuals and societies. The intricacies of human-machine interaction, analyses of digital footprints, and big data approaches are investigated in detail. |
| data science 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 science 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 science and psychology: The Psychology of Science and the Origins of the Scientific Mind Gregory J. Feist, 2008-10-01 In this book, Gregory Feist reviews and consolidates the scattered literatures on the psychology of science, then calls for the establishment of the field as a unique discipline. He offers the most comprehensive perspective yet on how science came to be possible in our species and on the important role of psychological forces in an individual’s development of scientific interest, talent, and creativity. Without a psychological perspective, Feist argues, we cannot fully understand the development of scientific thinking or scientific genius. The author explores the major subdisciplines within psychology as well as allied areas, including biological neuroscience and developmental, cognitive, personality, and social psychology, to show how each sheds light on how scientific thinking, interest, and talent arise. He assesses which elements of scientific thinking have their origin in evolved mental mechanisms and considers how humans may have developed the highly sophisticated scientific fields we know today. In his fascinating and authoritative book, Feist deals thoughtfully with the mysteries of the human mind and convincingly argues that the creation of the psychology of science as a distinct discipline is essential to deeper understanding of human thought processes. |
| data science and psychology: Science as Psychology Lisa M. Osbeck, Nancy J. Nersessian, Kareen R. Malone, Wendy C. Newstetter, 2010-11-22 Science as Psychology reveals the complexity and richness of rationality by demonstrating how social relationships, emotion, culture, and identity are implicated in the problem-solving practices of laboratory scientists. In this study, the authors gather and analyze interview and observational data from innovation-focused laboratories in the engineering sciences to show how the complex practices of laboratory research scientists provide rich psychological insights, and how a better understanding of science practice facilitates understanding of human beings more generally. The study focuses not on dismantling the rational core of scientific practice, but on illustrating how social, personal, and cognitive processes are intricately woven together in scientific thinking. The book is thus a contribution to science studies, the psychology of science, and general psychology. |
| data science and psychology: Statistics and Data Analysis for the Behavioral Sciences Dana Dunn, 2001 Dana Dunn combines the quantitative aspects of statistics with written explanations of what the results of statistical tests mean in a way that students will understand. He incorporates APA style in examples and an appendix to expose students to the expected style of prose. For students with math anxiety or who just need a refresher on basic mathematical functions, he has included an appendix so that faculty are not forced to spend class time reviewing these basic concepts. The book includes a student friendly system of pedagogy to ensure student success. Where possible, Dunn has included examples and projects for students to conduct research on their own lives to draw personalized meaning from the world of statistics. |
| data science and psychology: The Psychology of Science Abraham H. Maslow, 1969 |
| data science and psychology: Data Science Qurban A Memon, Shakeel Ahmed Khoja, 2019-09-26 The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science. |
| data science and psychology: Advanced Research Methods for Applied Psychology Paula Brough, 2018-08-14 This is the first comprehensive guide to the range of research methods available to applied psychologists. Ideally suited to students and researchers alike, and covering both quantitative and qualitative techniques, the book takes readers on a journey from research design to final reporting. The book is divided into four sections, with chapters written by leading international researchers working in a range of applied settings: Getting Started Data Collection Data Analysis Research Dissemination With coverage of sampling and ethical issues, and chapters on everything from experimental and quasi-experimental designs to longitudinal data collection and focus groups, the book provides a concise overview not only of the options available for applied research, but also of how to make sense of the data produced. It includes chapters on organizational interventions and the use of digital technologies, and concludes with chapters on how to publish your research, whether it’s a thesis, journal article or organisational report. This is a must-have book for anyone conducting psychological research in an applied setting. |
| data science and psychology: Computational Social Psychology Robin R. Vallacher, Stephen J. Read, Andrzej Nowak, 2017-05-25 Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology. |
| data science and psychology: The Psychology of Attention Harold Pashler, 1999-07-26 In the past two decades, attention has been one of the most investigated areas of research in perception and cognition. However, the literature on the field contains a bewildering array of findings, and empirical progress has not been matched by consensus on major theoretical issues. The Psychology of Attention presents a systematic review of the main lines of research on attention; the topics range from perception of threshold stimuli to memory storage and decision making. The book develops empirical generalizations about the major issues and suggests possible underlying theoretical principles. Pashler argues that widely assumed notions of processing resources and automaticity are of limited value in understanding human information processing. He proposes a central bottleneck for decision making and memory retrieval, and describes evidence that distinguishes this limitation from perceptual limitations and limited-capacity short-term memory. |
| data science and psychology: Psychology Statistics For Dummies Donncha Hanna, Martin Dempster, 2013-01-29 The introduction to statistics that psychology students can't afford to be without Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides you with the step-by-step instructions necessary for carrying out data analysis. Psychology Statistics For Dummies: Serves as an easily accessible supplement to doorstop-sized psychology textbooks Provides psychology students with psychology-specific statistics instruction Includes clear explanations and instruction on performing statistical analysis Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students |
| data science 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 science and psychology: Text Mining with R Julia Silge, David Robinson, 2017-06-12 Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling. |
| data science and psychology: Understand, Manage, and Prevent Algorithmic Bias Tobias Baer, 2019-06-07 Are algorithms friend or foe? The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias. What You'll Learn Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias Who This Book is For Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias |
| data science and psychology: Psychology for Sustainability Britain A. Scott, Elise L. Amel, Susan M. Koger, Christie M. Manning, 2015-07-24 Psychology for Sustainability, 4th Edition -- known as Psychology of Environmental Problems: Psychology for Sustainability in its previous edition -- applies psychological theory and research to so-called environmental problems, which actually result from human behavior that degrades natural systems. This upbeat, user-friendly edition represents a dramatic reorganization and includes a substantial amount of new content that will be useful to students and faculty in a variety of disciplines—and to people outside of academia, as well. The literature reviewed throughout the text is up-to-date, and reflects the burgeoning efforts of many in the behavioral sciences who are working to create a more sustainable society. The 4th Edition is organized in four sections. The first section provides a foundation by familiarizing readers with the current ecological crisis and its historical origins, and by offering a vision for a sustainable future.The next five chapters present psychological research methods, theory, and findings pertinent to understanding, and changing, unsustainable behavior. The third section addresses the reciprocal relationship between planetary and human wellbeing and the final chapter encourages readers to take what they have learned and apply it to move behavior in a sustainable direction. The book concludes with a variety of theoretically and empirically grounded ideas for how to face this challenging task with positivity, wisdom, and enthusiasm. This textbook may be used as a primary or secondary textbook in a wide range of courses on Ecological Psychology, Environmental Science, Sustainability Sciences, Environmental Education, and Social Marketing. It also provides a valuable resource for professional audiences of policymakers, legislators, and those working on sustainable communities. |
| data science and psychology: Thinking Clearly with Data Ethan Bueno de Mesquita, Anthony Fowler, 2021-11-16 An engaging introduction to data science that emphasizes critical thinking over statistical techniques An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity Uses real-world examples and data from a wide variety of subjects Includes practice questions and data exercises |
| data science and psychology: Research Methods in Occupational Health Psychology Robert R. Sinclair, Mo Wang, Lois E. Tetrick, 2012-11-12 Research Methods in Occupational Health Psychology: Measurement, Design, and Data Analysis provides a state-of-the-art review of current issues and best practices in the science of Occupational Health Psychology. Occupational Health Psychology (OHP) is a multidisciplinary and rapidly growing area of research and it is difficult or impossible for researchers to keep up with developments in all of the fields where scholars conduct OHP science. This book will help OHP scholars improve their own research by translating recent innovations in methodology into sets of concrete recommendations that will help scholars improve their own research as well as their training of future researchers. |
| data science and psychology: Handbook of Computational Social Science, Volume 2 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. |
| data science and psychology: The Cambridge Handbook of Technology and Employee Behavior Richard N. Landers, 2019-02-14 Experts from across all industrial-organizational (IO) psychology describe how increasingly rapid technological change has affected the field. In each chapter, authors describe how this has altered the meaning of IO research within a particular subdomain and what steps must be taken to avoid IO research from becoming obsolete. This Handbook presents a forward-looking review of IO psychology's understanding of both workplace technology and how technology is used in IO research methods. Using interdisciplinary perspectives to further this understanding and serving as a focal text from which this research will grow, it tackles three main questions facing the field. First, how has technology affected IO psychological theory and practice to date? Second, given the current trends in both research and practice, could IO psychological theories be rendered obsolete? Third, what are the highest priorities for both research and practice to ensure IO psychology remains appropriately engaged with technology moving forward? |
| data science and psychology: Essentials of Interpretative Phenomenological Analysis Jonathan A. Smith, Isabella E. Nizza, 2021-08-31 The brief, practical texts in the Essentials of Qualitative Methods series introduce social science and psychology researchers to key approaches to to qualitative methods, offering exciting opportunities to gather in-depth qualitative data and to develop rich and useful findings. Essentials of Interpretative Phenomenological Analysis is a step-by-step guide to a research method that investigates how people make sense of their lived experience in the context of their personal and social worlds. It is especially well-suited to exploring experiences perceived as highly significant, such as major life and relationship changes, health challenges, and other emotion-laden events. IPA studies highlight convergence and divergence across participants, showing both the experiential themes that the participants share and the unique way each theme is manifested for the individual. About the Essentials of Qualitative Methods book series: Even for experienced researchers, selecting and correctly applying the right method can be challenging. In this groundbreaking series, leading experts in qualitative methods provide clear, crisp, and comprehensive descriptions of their approach, including its methodological integrity, and its benefits and limitations. Each book includes numerous examples to enable readers to quickly and thoroughly grasp how to leverage these valuable methods. |
| data science and psychology: Big Data in Psychology Mike W. L. Cheung, Suzanne Jak, 2019-03-11 Big data is becoming more prevalent in psychology and the behavioral sciences, and so are the methodological and statistical issues that arise from its use. Psychologists need to be equipped to deal with these. Big data can be generated in experimental studies where, for example, participants' physiological and psychological responses are tracked over time or where human brain imaging is employed. Observational data from websites such as Facebook, Twitter, and Google is also of increasing interest to psychologists. These sometimes huge data sets, which are often too large for standard computers and can also contain multiple types of data, bring with them challenging questions about data quality and the generalizability of the results as well as which statistical tools are suitable for analyzing them.The contributions in this volume explore these challenges, looking at the potential of applying machine learning techniques to big data in psychology as well as the split/analyze/meta-analyze (SAM) approach, which allows big data to be split up into smaller datasets so they can be analyzed with conventional multivariate techniques on standard computers. The issues of replicability, prediction accuracy, and combining types of data are also investigated. |
| data science and psychology: Online Statistics Education David M Lane, 2014-12-02 Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational experience. This part I features the book Front Matter, Chapters 1-10, and the full Glossary. Chapters Include:: I. Introduction, II. Graphing Distributions, III. Summarizing Distributions, IV. Describing Bivariate Data, V. Probability, VI. Research Design, VII. Normal Distributions, VIII. Advanced Graphs, IX. Sampling Distributions, and X. Estimation. Online Statistics Education: A Multimedia Course of Study (http: //onlinestatbook.com/). Project Leader: David M. Lane, Rice University. |
| data science 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 science and psychology: Data Science, Classification, and Related Methods Chikio Hayashi, Keiji Yajima, Hans H. Bock, 2014-01-15 |
| data science and psychology: Guide to Teaching Data Science Orit Hazzan, Koby Mike, 2023-03-20 Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. |
| data science and psychology: Encyclopedia of Behavioral Medicine Marc D. Gellman, J. Rick Turner, |
| data science 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 science and psychology: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-21 Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. How to lead in data science shares unique leadership techniques from high-performance data teams. It's filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You'll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you'll build practical skills to grow and improve your team, your company's data culture, and yourself. |
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