Making Sense Of Factor Analysis

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Making Sense of Factor Analysis

Author : Marjorie A. Pett
Publisher : SAGE
ISBN 13 : 0761919503
Total Pages : 369 pages
Book Rating : 4.7/5 (619 download)

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Book Synopsis Making Sense of Factor Analysis by : Marjorie A. Pett

Download or read book Making Sense of Factor Analysis written by Marjorie A. Pett and published by SAGE. This book was released on 2003-03-21 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many health care practitioners and researchers are aware of the need to employ factor analysis in order to develop more sensitive instruments for data collection. Unfortunately, factor analysis is not a unidimensional approach that is easily understood by even the most experienced of researchers. Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research presents a straightforward explanation of the complex statistical procedures involved in factor analysis. Authors Marjorie A. Pett, Nancy M. Lackey, and John J. Sullivan provide a step-by-step approach to analyzing data using statistical computer packages like SPSS and SAS. Emphasizing the interrelationship between factor analysis and test construction, the authors examine numerous practical and theoretical decisions that must be made to efficiently run and accurately interpret the outcomes of these sophisticated computer programs. This accessible volume will help both novice and experienced health care professionals to Increase their knowledge of the use of factor analysis in health care research Understand journal articles that report the use of factor analysis in test construction and instrument development Create new data collection instruments Examine the reliability and structure of existing health care instruments Interpret and report computer-generated output from a factor analysis run Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research offers a practical method for developing tests, validating instruments, and reporting outcomes through the use of factor analysis. To facilitate learning, the authors provide concrete testing examples, three appendices of additional information, and a glossary of key terms. Ideal for graduate level nursing students, this book is also an invaluable resource for health care researchers.

Making Sense of Multivariate Data Analysis

Author : John Spicer
Publisher : SAGE
ISBN 13 : 9781412904018
Total Pages : 256 pages
Book Rating : 4.9/5 (4 download)

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Book Synopsis Making Sense of Multivariate Data Analysis by : John Spicer

Download or read book Making Sense of Multivariate Data Analysis written by John Spicer and published by SAGE. This book was released on 2005 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: A short introduction to the subject, this text is aimed at students & practitioners in the behavioural & social sciences. It offers a conceptual overview of the foundations of MDA & of a range of specific techniques including multiple regression, logistic regression & log-linear analysis.

Factor Analysis and Related Methods

Author : Roderick P. McDonald
Publisher : Psychology Press
ISBN 13 : 1317768760
Total Pages : 280 pages
Book Rating : 4.3/5 (177 download)

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Book Synopsis Factor Analysis and Related Methods by : Roderick P. McDonald

Download or read book Factor Analysis and Related Methods written by Roderick P. McDonald and published by Psychology Press. This book was released on 2014-01-14 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor Analysis is a genetic term for a somewhat vaguely delimited set of techniques for data processing, mainly applicable to the social and biological sciences. These techniques have been developed for the analysis of mutual relationships among a number of measurements made on a number of measurable entities. In the broad sense, factor analysis comprises a number of statistical models which yield testable hypotheses -- hypotheses that may confirm or disconfirm in terms of the usual statistical procedures for making tests of significance. It also comprises a number of simplifying procedures for the approximate description of data, which do not in any sense constitute disconfirmable hypotheses, except in the loose sense that they supply approximations to the data. In literature, the two types of analysis have often been confused. This book clarifies the concepts of factor analysis for students or professionals in the social sciences who wish to know the technique, rather than the mathematics, of factor theory. Mathematical concepts are described to have an intuitive meaning for the non-mathematical reader. An account of the elements of matrix algebra, in the appendix, and the (mathematical) notes following each chapter will help the reader who wishes to receive a more advanced treatment of the subject. Factor Analysis and Related Methods should prove a useful text for graduate and advanced undergraduate students in economics, the behavioral sciences, and education. Researchers and practitioners in those fields will also find this book a handy reference.

Exploratory Factor Analysis

Author : Diana Mindrila
Publisher :
ISBN 13 : 9781536124866
Total Pages : 0 pages
Book Rating : 4.1/5 (248 download)

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Book Synopsis Exploratory Factor Analysis by : Diana Mindrila

Download or read book Exploratory Factor Analysis written by Diana Mindrila and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In education, researchers often work with complex data sets that include a multitude of variables. One question that often arises in such contexts is whether the structure of associations that underlies the data is accounted for by a latent construct. Exploratory factor analysis is a multivariate correlational procedure that helps researchers overcome such challenges. It helps reduce large data sets into main components or identify distinct constructs that account for the pattern of correlations among observed variables. These unobservable constructs are referred to as common factors, latent variables, or internal attributes, and they exert linear influences on more than one observed variable. Although exploratory factor analysis is widely used, many applied educational researchers and practitioners are not yet familiar with this procedure and are intimidated by the technical terminology. This book provides a conceptual description of this method and includes a collection of applied research studies that illustrates the application of exploratory factor analysis in school improvement research. The first chapter provides a theoretical overview of exploratory factor analysis. It explains the purposes for which this procedure can be used, the related terminology, the distinction between key concepts, the steps that must be taken, and the criteria for making the decisions. This information can serve as a starting point for researchers who need a brief, conceptual introduction to this topic. The following chapters present a series of research studies in which exploratory factor analysis was employed either by itself or in conjunction with other statistical procedures. The studies presented in this book address a variety of research problems in the field of school improvement. They specify how the factor analytic procedure was applied, and explain the theoretical contributions and the practical applications of the factor analytic results. In most studies, results from factor analysis were used for subsequent statistical procedures, thus helping researchers address more complex research questions and enriching the results.

A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling

Author : Larry Hatcher
Publisher : SAS Institute
ISBN 13 : 1612903878
Total Pages : 444 pages
Book Rating : 4.6/5 (129 download)

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Book Synopsis A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling by : Larry Hatcher

Download or read book A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling written by Larry Hatcher and published by SAS Institute. This book was released on 2013-03-01 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all userseven those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Exploratory Factor Analysis

Author : Leandre R. Fabrigar
Publisher : Oxford University Press
ISBN 13 : 0199734178
Total Pages : 170 pages
Book Rating : 4.1/5 (997 download)

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Book Synopsis Exploratory Factor Analysis by : Leandre R. Fabrigar

Download or read book Exploratory Factor Analysis written by Leandre R. Fabrigar and published by Oxford University Press. This book was released on 2012-01-12 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a non-mathematical introduction to the theory and application of Exploratory Factor Analysis. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the use of principal components analysis versus common factor analysis, and procedures for determining the appropriate number of factors.

Best Practices in Exploratory Factor Analysis

Author : Jason W. Osborne
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781500594343
Total Pages : 0 pages
Book Rating : 4.5/5 (943 download)

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Book Synopsis Best Practices in Exploratory Factor Analysis by : Jason W. Osborne

Download or read book Best Practices in Exploratory Factor Analysis written by Jason W. Osborne and published by Createspace Independent Publishing Platform. This book was released on 2014-07-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Best Practices in Exploratory Factor Analysis (EFA) is a practitioner-oriented look at this popular and often-misunderstood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data.Each chapter reviews important concepts, uses real-world data to provide authentic examples of analyses, and provides guidance for interpreting the results of these analysis. Not only does this book clarify often-confusing issues like various extraction techniques, what rotation is really rotating, and how to use parallel analysis and MAP criteria to decide how many factors you have, but it also introduces replication statistics and bootstrap analysis so that you can better understand how precisely your data are helping you estimate population parameters. Bootstrap analysis also informs readers of your work as to the likelihood of replication, which can give you more credibility. At the end of each chapter, the author has recommendations as to how to enhance your mastery of the material, including access to the data sets used in the chapter through his web site. Other resources include syntax and macros for easily incorporating these progressive aspects of exploratory factor analysis into your practice. The web site will also include enrichment activities, answer keys to select exercises, and other resources. The fourth "best practices" book by the author, Best Practices in Exploratory Factor Analysis continues the tradition of clearly-written, accessible guides for those just learning quantitative methods or for those who have been researching for decades.NEW in August 2014! Chapters on factor scores, higher-order factor analysis, and reliability. Chapters: 1 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS 2 EXTRACTION AND ROTATION 3 SAMPLE SIZE MATTERS 4 REPLICATION STATISTICS IN EFA 5 BOOTSTRAP APPLICATIONS IN EFA 6 DATA CLEANING AND EFA 7 ARE FACTOR SCORES A GOOD IDEA? 8 HIGHER ORDER FACTORS 9 AFTER THE EFA: INTERNAL CONSISTENCY 10 SUMMARY AND CONCLUSIONS

Confirmatory Factor Analysis for Applied Research, Second Edition

Author : Timothy A. Brown
Publisher : Guilford Publications
ISBN 13 : 146251779X
Total Pages : 482 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Confirmatory Factor Analysis for Applied Research, Second Edition by : Timothy A. Brown

Download or read book Confirmatory Factor Analysis for Applied Research, Second Edition written by Timothy A. Brown and published by Guilford Publications. This book was released on 2015-01-07 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA) for its emphasis on practical and conceptual aspects rather than mathematics or formulas. Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities ...

Assessing Measurement Invariance for Applied Research

Author : Craig S. Wells
Publisher : Cambridge University Press
ISBN 13 : 1108485227
Total Pages : 417 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Assessing Measurement Invariance for Applied Research by : Craig S. Wells

Download or read book Assessing Measurement Invariance for Applied Research written by Craig S. Wells and published by Cambridge University Press. This book was released on 2021-06-03 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This user-friendly guide illustrates how to assess measurement invariance using computer programs, statistical methods, and real data.

Principal Components Analysis

Author : George H. Dunteman
Publisher : SAGE
ISBN 13 : 9780803931046
Total Pages : 98 pages
Book Rating : 4.9/5 (31 download)

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Book Synopsis Principal Components Analysis by : George H. Dunteman

Download or read book Principal Components Analysis written by George H. Dunteman and published by SAGE. This book was released on 1989-05 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.

Latent Variable Models

Author : John C. Loehlin
Publisher : Psychology Press
ISBN 13 : 1135614334
Total Pages : 498 pages
Book Rating : 4.1/5 (356 download)

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Book Synopsis Latent Variable Models by : John C. Loehlin

Download or read book Latent Variable Models written by John C. Loehlin and published by Psychology Press. This book was released on 2004-05-20 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: *a data CD that features the correlation and covariance matrices used in the exercises; *new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and *reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.

Best Practices in Quantitative Methods

Author : Jason W. Osborne
Publisher : SAGE
ISBN 13 : 1412940656
Total Pages : 609 pages
Book Rating : 4.4/5 (129 download)

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Book Synopsis Best Practices in Quantitative Methods by : Jason W. Osborne

Download or read book Best Practices in Quantitative Methods written by Jason W. Osborne and published by SAGE. This book was released on 2008 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Learning Statistics with R

Author : Daniel Navarro
Publisher : Lulu.com
ISBN 13 : 1326189727
Total Pages : 617 pages
Book Rating : 4.3/5 (261 download)

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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

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Nursing Research

Author : Denise F. Polit
Publisher : Lippincott Williams & Wilkins
ISBN 13 :
Total Pages : 678 pages
Book Rating : 4.:/5 (43 download)

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Book Synopsis Nursing Research by : Denise F. Polit

Download or read book Nursing Research written by Denise F. Polit and published by Lippincott Williams & Wilkins. This book was released on 1983 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Sixth Edition of this classic text maintains its place as the "Gold Standard" of nursing research. Nationally and internationally known, respected and used, the text provides readers with the skills they need to design and implement a research investigation and critically evaluate published research reports. Now completely revised and updated to reflect the latest trends in quantitative and qualitative research, this essential guide offers a focused, "how-to" approach. New in this edition: expanded discussion of qualitative approaches; demonstration of qualitative and quantitative approaches working together; charts and tables offer description of qualitative approaches; stronger emphasis on the "hands-on, how-to" methodology; more in-depth examination of reasearch difference; research more powerful research utilization.

Computational Genomics with R

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Handbook of Applied Multivariate Statistics and Mathematical Modeling

Author : Howard E.A. Tinsley
Publisher : Academic Press
ISBN 13 : 9780080533568
Total Pages : 721 pages
Book Rating : 4.5/5 (335 download)

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Book Synopsis Handbook of Applied Multivariate Statistics and Mathematical Modeling by : Howard E.A. Tinsley

Download or read book Handbook of Applied Multivariate Statistics and Mathematical Modeling written by Howard E.A. Tinsley and published by Academic Press. This book was released on 2000-05-22 with total page 721 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.