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Data Analysis With Machine Learning For Psychologists
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Book Synopsis Data Analysis with Machine Learning for Psychologists by : Chandril Ghosh
Download or read book Data Analysis with Machine Learning for Psychologists written by Chandril Ghosh and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. .
Book Synopsis Data Analysis with Machine Learning for Psychologists by : Chandril Ghosh
Download or read book Data Analysis with Machine Learning for Psychologists written by Chandril Ghosh and published by Springer Nature. This book was released on 2022-10-17 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Big Data at Work by : Scott Tonidandel
Download or read book Big Data at Work written by Scott Tonidandel and published by Routledge. This book was released on 2015-11-06 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Introducing HR Analytics with Machine Learning by : Christopher M. Rosett
Download or read book Introducing HR Analytics with Machine Learning written by Christopher M. Rosett and published by Springer Nature. This book was released on 2021-06-14 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Big Data in Psychology by : Mike W. L. Cheung
Download or read book Big Data in Psychology written by Mike W. L. Cheung and published by . This book was released on 2019-03-11 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis An Introduction to Artificial Psychology by : Hojjatollah Farahani
Download or read book An Introduction to Artificial Psychology written by Hojjatollah Farahani and published by Springer Nature. This book was released on 2023-05-18 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software.
Book Synopsis Explainable and Interpretable Models in Computer Vision and Machine Learning by : Hugo Jair Escalante
Download or read book Explainable and Interpretable Models in Computer Vision and Machine Learning written by Hugo Jair Escalante and published by Springer. This book was released on 2018-11-29 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Book Synopsis Big Data in Psychological Research by : Sang Eun Woo
Download or read book Big Data in Psychological Research written by Sang Eun Woo and published by American Psychological Association (APA). This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Categories and Concepts by : Iven van Mechelen
Download or read book Categories and Concepts written by Iven van Mechelen and published by . This book was released on 1993 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: A book aimed at advanced undergraduates and graduates in cognitive science and artificial intelligence, linguistics, applied mathematics and data analysis.
Book Synopsis Computational Social Psychology by : Robin R. Vallacher
Download or read book Computational Social Psychology written by Robin R. Vallacher and published by Routledge. This book was released on 2017-05-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Parsing Psychology: Statistical and Computational Methods using Physiological, Behavioral, Social, and Cognitive Data by : Pietro Cipresso
Download or read book Parsing Psychology: Statistical and Computational Methods using Physiological, Behavioral, Social, and Cognitive Data written by Pietro Cipresso and published by Frontiers Media SA. This book was released on 2020-02-14 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Book Synopsis Handbook of Language Analysis in Psychology by : Morteza Dehghani
Download or read book Handbook of Language Analysis in Psychology written by Morteza Dehghani and published by Guilford Publications. This book was released on 2022-03-02 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Recognizing Human Needs Using Machine Learning-Powered Psychology-Based Framework by : Rajwa Abdullah Alharthi
Download or read book Recognizing Human Needs Using Machine Learning-Powered Psychology-Based Framework written by Rajwa Abdullah Alharthi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognizing the basic needs of individuals can provide the root explanations for their feelings and help predict their behavior, as explained by the Human Needs Theories (HNT). Comprehension in regards to citizen needs in the context of a smart city improves situational awareness and could guide authorities in decision making processes, facilitating effective planning for the future. Accordingly, we aim to shed light on public reaction towards political, religious or terrorism events in regard to psychological needs by adopting the new form of sensing which employs the idea of utilizing citizens, themselves, as "soft sensors", in the hopes of creating a powerful tool for deciphering the temperament of a population. In this thesis, we propose a psychological need recognition framework which consists of several modules, including data collection, data preprocessing, feature extraction, and contextualization. The proposed framework utilizes a theoretical-based multi-layered reference model that is based on research in the field of motivational psychology. We use the constructed layers of the proposed reference model to identify an individual's basic psychological needs, measure their need satisfaction level, and assess their social surroundings in various aspects of life. We introduce a psychological needs dataset, which is a collection of social media content annotated by psychologists based on each layer of the reference model. Several techniques are employed to encourage high-quality annotations. We design and develop need classification and regression models, with each corresponding to one of the predefined concepts that form the theoretical layered reference model; namely: 1) Need Content Recognition (NCR) model, 2) Need Type Identification (NTI) model, 3) Need Satisfaction Level Measurement (NSM) model, 4) Social Context Evaluation (SCE) model, and 5) Life Aspect Identification (LAI) model. For a more comprehensive and deeper analysis, the Frustrated Need Intensity Estimator (FNIE) model and the Satisfied Need Intensity Estimator (SNIE) model are developed to determine the intensity score of the satisfaction level. Over the process of developing the classification models, we conduct many experiments to explore and compare the effect of different preprocessing steps and data preparation techniques. We evaluate the predictive powers of various textual, psychological, semantic, lexicon-based and twitter-specific features. In order to provide benchmark results, we compare and evaluate the performance of diverse machine learning algorithms for recognizing psychological needs. Our results confirm the effectiveness of the developed psychological needs models. The proposed framework is engaged to recognize individuals' needs, measure their satisfaction level and evaluate their surrounding environment in response to two critical events: the Florida shooting event, which occurred on February 14th, 2018, along with the related March for Our Lives event which followed on March 24th, 2018, and the New Zealand terror attacks which occurred on March 15th, 2019.
Book Synopsis Data Analytics and Psychometrics by : Hong Jiao
Download or read book Data Analytics and Psychometrics written by Hong Jiao and published by IAP. This book was released on 2018-12-01 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large-scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student’s learning and guide teacher’s instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.
Book Synopsis Learning Statistics with R by : Daniel Navarro
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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
Book Synopsis Artificial Intelligence in Behavioral and Mental Health Care by : David D. Luxton
Download or read book Artificial Intelligence in Behavioral and Mental Health Care written by David D. Luxton and published by Academic Press. This book was released on 2015-09-10 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings
Book Synopsis Using Machine Learning to Detect Emotions and Predict Human Psychology by : Rai, Mritunjay
Download or read book Using Machine Learning to Detect Emotions and Predict Human Psychology written by Rai, Mritunjay and published by IGI Global. This book was released on 2024-02-26 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.
