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 ...
Author: Hong Jiao
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.
Factor analysis is a technique that is widely used in psychometrics. ... Factor analytic computer programs will give an estimate of how many such factors there
may be in a set of data, and of how these factors relate to the items or subtests.
Author: John Rust
Today psychometrics plays an increasingly important role in all our lives as testing and assessment occurs from preschool until retirement. This book introduces the reader to the subject in all its aspects, ranging from its early history, school examinations, how to construct your own test, controversies about IQ and recent developments in testing on the internet. In Part one of Modern Psychometrics, Rust and Golombok outline the history of the field and discuss central theoretical issues such as IQ, personality and integrity testing and the impact of computer technology and the internet. In Part two a practical step-by-step guide to the development of a psychometric test is provided. This will enable anyone wishing to develop their own test to plan, design, construct and validate it to a professional standard. This third edition has been extensively updated and expanded to take into account recent developments in the field, making it the ideal companion for those studying for the British Psychological Society’s Certificates of Competence in Testing. Modern Psychometrics combines an up to date scientific approach to the subject with a full consideration of the political and ethical issues involved in the large scale implementation of psychometrics testing in today’s highly networked society, particularly in terms of issues of diversity and internationalism. It will be useful to students and practictioners at all levels who are interested in psychometrics.
28 A Dichotomization Method for Boolean Analysis of Quantifiable Co-
Occurrence Data Peter Theuns' ABSTRACT Boolean analysis is a partial order
generalization of scalogram analysis. It enables one to build a family of
implication schemes ...
Author: Gerhard H. Fischer
Publisher: Springer Science & Business Media
Contributions to Mathematical Psychology, Psycho§ metrics and Methodology presents the most esteemed research findings of the 22nd European Mathematical Psychology Group meeting in Vienna, Austria, September 1991. The selection of work appearing in this volume contains not only contributions to mathematical psychology in the narrow sense, but also work in psychometrics and methodology, with the common element of all contributions being their attempt to deal with scientific problems in psychology with rigorous mathematics reasoning. The book contains 28 chapters divided into five parts: Perception, Learning, and Cognition; Choice and Reaction Time; Social Systems; Measurement and Psychometrics; and Methodology. It is of interest to all mathematical psychologists, educational psychologists, and graduate students in these areas.
... Precedents for the Dichotomy Empirical Evidence for the Dichotomy Previous
Studies A Reanalysis of Whitely ' s 1977 Data A New Sorting Study A
Comparison of GRE Candidates With Different Majors Factor Analytic Evidence
Earlier Work ...
Author: Isaac I. Bejar
The major objective of the investigation presented in this book is to assess the validity of analogies, a component of the GRE General Test, from a perspective other than the prediction of grade-point average. The book examines a very practical problem in test construction: the apparent inability of item writers to regularly and predictably construct verbal items in general, and analogy items in particular, that are both difficult and sufficiently discriminating. The book demonstrates that the incorporation of results from the cognitive laboratory into the test development process is a natural step and should be attempted. The book focuses on analogical reasoning because it is a pervasive human-thought process.
data mining, see data-adaptive research data munging, see data preparation data organization, 5,58 data partitioning, ... interval estimate item analysis, psychometrics, 115 K Kappa, see classification, predictive accuracy key-value
store, see ...
Author: Thomas W. Miller
Publisher: FT Press
To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
data organization, 5, 66 data partitioning, 6 data preparation, 280 missing data,
280 data science, 1–12, 277,278 data ... programming interaction effect, 287
interval estimate, see statistic, interval estimate item analysis, psychometrics, 143
Author: Thomas W. Miller
Publisher: FT Press
Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Emerging Research and Opportunities Hai-Jew, Shalin. coding, linguistic analysis, psychometrics, stylometry, network analysis, and others, as applied to
open-ended questions from online surveys (and combined with human close
Author: Hai-Jew, Shalin
Publisher: IGI Global
Online survey research suites offer a vast array of capabilities, supporting the presentation of virtually every type of digital data text, imagery, audio, video, and multimedia forms. With some researcher sophistication, these online survey research suites can enable a wide range of quantitative, qualitative, and mixed methods research. Online Survey Design and Data Analytics: Emerging Research and Opportunities is a critical scholarly resource that explores the utilization of online platforms for setting up surveys to achieve a specific result, eliciting data in in-depth ways and applying creative analytics methods to online survey data. Highlighting topics such as coding, education-based analysis, and online Delphi studies, this publication is ideal for researchers, professionals, academicians, data analysts, IT consultants, and students.
Psychometric Testing offers an in-depth examination of the strengths and limitations psychometric testing, with coverage of diverse methods of test development and application.
Author: Barry Cripps
Publisher: John Wiley & Sons
This book offers an examination of the strengths and limitations psychometric testing, with coverage of diverse methods of test development and application. It explores a variety of topics related to the field, including test construction, use and applications in human resources and training, assessment and verification of training courses, and consulting and includes applications for clinical psychology, performance psychology, and sport and exercise psychology across a range of professions (research, teaching, coaching, consulting, and advising).
Antonak , R. F. Psychometric analysis of the Attitude toward Disabled Persons
Scale , Form 0 . Rehabilitation Counseling Bulletin , 1980 ... Cohen , J. Multiple
regression as a general data - analytic system . Psychological Bulletin , 1968 , 70
Author: Psychometric Society. European MeetingPublish On: 1993
The Nature of Data One cannot draw valid conclusions except from premises .
The logical form of any premise or conclusion is that of a proposition ( see Copi ,
1954 ) . Thus to be able to draw conclusions in any data analysis , one must first ...
Poor agreement may also be a consequence of the availability to the staff of
additional data such as self - ratings and biographical information . However ,
whatever the reason for these differences , if we want to assume that ratings
Prom the ...
The advantage of investigating psychometric bias at the item type level was to
restrict use of a biased item type. The study also ... There was no significant
evidence of psychometric bias. ... Chapter 5 addresses questions of data analysis
data preparation, 241 missing data, 241 data science, 237,239 data visualization
bar chart, 15, 206 bubble chart, 208 ... 317 Internet Services Provider, 317
interview, 267, 268 intranet, 317 IRC, 317 IT, 317 item analysis, psychometrics,
Author: Thomas W. Miller
Publisher: FT Press
Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.
DATA ANALYSIS IN THE SOCIAL SCIENCES 1 . 0 Introduction This chapter is a
short introduction to some of the current topics in psychometrics and data analysis in the social sciences in general . Its purpose is to justify to some extent
His scholarly interests are in statistics and psychometrics. His recent research
interests have ... Her research interests lie at the intersection of statistical models
for categorical data analysis and psychometrics. Her major line of research deals
Author: Roger E Millsap
`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology' - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.
Data Analysis COUPON FOR A FREE SPECIMEN COPY OF COMPUTATIONAL
STATISTICS & DATA ANALYSIS Thm ... U.S.A. (Econometrics); P.M. Gentler,
U.S.A (Psychometrics); T.J. Boardman, U.S.A. (Statistical Computing); M. Brown,
... Attitudes , Decision making , Transfer functions , Statistical analysis , Psychometrics , Analysis of variance . Identifiers : * Satiation ... Major data
processing comро ents of the model are identified and ' real - time input
processing values ' are ...
In social and behavioral research at least a psychometric and a data - analytic
level can be distinguished and it might be useful to distinguish other aspects (
such as data collection ) as well . The mutual influence is not only between the ...
Thus the elements in the vector are not functionally independent , in contrast to
the case of measured data . The point of introducing the notion of category data is
that it provides for a further extension of linear factor analysis to cover ...