This book will be written primarily for graduate students, advanced undergraduates, and professionals in the fields of school psychology, special education, and other areas of education, as well as the health professions. We see the book as being a viable textbook for courses in research design, applied statistics, applied behavioral analysis, and practicum, among others. We would not assume of the readers any prior knowledge about single subjects designs, nor any prior statistical experience. We will provide an introductory chapter devoted to basic statistical concepts, including measures of central tendency (e.g., mean, median, mode), measures of variation (e.g., variance, standard deviation, range, inter-quartile range), correlation, frequency distributions, and effect sizes. In addition, given that the book will rely heavily on R software, the introductory chapter will also devote attention to the basics of using the software for organizing data, conducting basic statistical analyses, and for graphics. The R commands used to carry out these analyses will be largely automated so that users will only need to define the range for their data, and then enter it into the R spreadsheet. We envision these tools being available on the book website, with instructions for using them available in the book itself. We envision the book as being useful either as a primary text for a course in educational research designs, school psychology practicum, applied behavioral analysis, special education, or applied statistics. We also anticipate that individuals working in schools, school districts, mental health facilities, hospitals, applied behavioral analysis clinics, and evaluation organizations, as well as faculty members needing a practical resource for single subject design research, will all serve as a market for the book. In short, the readership would include graduate students, faculty members, teachers, psychologists, social workers, counselors, medical professionals, applied behavioral analysis professionals, program evaluators, and others whose work focuses on monitoring changes in individuals, particularly as the result of specific treatment conditions. We believe that this book could be marketed through professional organizations such as the American Educational Research Association (AERA), the National Association of School Psychologists, the National Association of Special Education Teachers, the Association for Professional Behavior Analysis, the American Psychological Association (APA), the Association for Psychological Science, and the American Evaluation Association. Within AERA, the following special interest groups would have particular interest in this book: Action Research, Classroom Observation, Disability Studies in Education, Mixed Methods Research, Qualitative Research, and Special Education Research. The book could also be marketed to state departments of education and their special education and school psychology divisions. Currently, many state departments of education require documentation for Response to Intervention (RtI) and Multi-Tiered Systems of Support (MTSS) procedures for individual students. The method taught in this proposed book would allow educators and student support personnel to document the effectiveness of interventions systematically and accurately.

The book is designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SAS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. The authors do not assume any more than basic familiarity with these issues, and devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. This book will be useful either as a primary text for a course on applied measurement where SAS is the main platform for instruction, or as a supplement to a more theoretical text. The readership will include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. This book aims to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter covers an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics are briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that is presented subsequently. Readers are then presented with examples illustrating a particular concept (e.g., reliability). These examples include a discussion of the particular analysis, along with the SAS code necessary to conduct them. The resulting output is then discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up is also included in the text. This mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work.

The book will be designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SPSS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. We will not assume any more than basic familiarity with these issues, and will devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. We envision the book as being useful either as a primary text for a course on applied measurement where SPSS is the main platform for instruction, or as a supplement to a more theoretical text. We also anticipate that readers working in government agencies responsible for testing and measurement issues at the local, state and national levels, and private testing, survey and market research companies, as well as faculty members needing a practical resource for psychometric practice will serve as a market for the book. In short, the readership would include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. Readers will then be presented with examples illustrating a particular concept (e.g., reliability). These examples will include a discussion of the particular analysis, along with the SPSS code necessary to conduct them. The resulting output will then be discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up will also be included in the text. It is hoped that this mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work. To our knowledge, no book outlining psychometric practice using commonly available software such as SPSS currently exists. Given that many practitioners in academia, government and private industry use SPSS for statistical analyses of testing data, we believe that our book will fill an important niche in the market. It will contain very practical information regarding how to conduct a wide variety of psychometric analyses, along with tips on interpretation of results and the appropriate format for reporting these results. We believe that it will prove useful to individuals in educational measurement, psychometrics, and survey and market research. Our text will add to the literature by providing users with a single reference containing the major ideas in applied psychometrics with instructions and examples for conducting the analyses in SPSS. In addition, we will provide original macros for estimating a variety of statistics and conducting analyses common in educational and psychological measurement.

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text's boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets. Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data. -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models. -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data. -www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work. The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis. The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data. A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters. Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book's practical approach. The book provides sufficient conceptual background information to serve as a standalone text. Familiarity with basic statistical concepts is assumed but basic knowledge of R is not.

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

El objetivo del libro es proporcionar a los lectores las herramientas necesarias para evaluar la calidad psicome trica de las medidas educativas y psicolo gicas, asi como de encuestas y cuestionarios. Cada capi tulo aborda un tema relativo a la pra ctica psicome trica y de la medida, con e nfasis en la aplicacio n. Los temas sera n tratados brevemente desde una perspectiva teo rica/te cnica con el fin de proporcionar al lector los antecedentes necesarios para utilizar e interpretar correctamente los ana lisis estadi sticos que se presentara n con posteriormente. Este libro esta dirigido a investigadores, profesionales y estudiantes de posgrado (Ma ster y Doctorado) que buscan una gui a para realizar ana lisis psicome tricos de diferentes instrumentos de evaluacio n. Asumimos un nivel ba sico de conocimientos estadi sticos, pero estos conceptos se ira n repasando a lo largo de los diferentes capi tulos. Nos imaginamos que este texto (a) esperara pacientemente en algunos despachos implorando ser entregado, como recurso, a un estudiante, (b) tendra un lugar fijo en las mesas de los despachos donde continuamente, y debido a su uso diario, sera el primero del monto n, (c) estara en las mochilas de los estudiantes de postgrado, y los acompan ara felizmente desde casa al trabajo o a las clases y vuelta, (d) aparecera orgullosamente como un texto de referencia en los programas y guia s de estudio, y finalmente (e) como ocasional posavasos mientras se reflexiona profundamente acerca de co mo resolver los problemas de medida. Esperamos que a trave s de estos usos, sobre todo el u ltimo, aportar algu n conocimiento y ayuda que permita a los lectores aplicar adecuadamente las te cnicas y los conceptos abordados en este manual. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. The anticipated audience for this book includes researchers, practitioners, and graduate students searching for a guide to perform common psychometric analyses on various assessments, as discussed in many psychometric texts. We envision that this text will (a) patiently wait on some office shelves begging to be handed to a student as a resource, (b) have a permanent home on desks where it continually rises to the top of the stacks for daily use of the applied researcher, (c) be happily carried in bags to and from work and class by the graduate student learning techniques, (d) be listed proudly as a reference text on syllabi, and finally (e) as an occasional drink coaster while deep thoughts are pondered about how to solve measurement problems. We hope that through such uses, particularly the latter, that we have provided some insight and assistance to the user in appropriately applying the techniques and concepts discussed.

Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action. Key Features: Description of regularization methods in a user friendly and easy to read manner Inclusion of regularization-based approaches for a variety of statistical analyses commonly used in the social sciences, including both univariate and multivariate models Fully developed extended examples using multiple software packages, including R, SAS, and SPSS Website containing all datasets and software scripts used in the examples Inclusion of both frequentist and Bayesian regularization approaches Application exercises for each chapter that instructors could use in class, and independent researchers could use to practice what they have learned from the book

This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text's boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets. Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data. -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models. -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data. -www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work. The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis. The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data. A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters. Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book's practical approach. The book provides sufficient conceptual background information to serve as a standalone text. Familiarity with basic statistical concepts is assumed but basic knowledge of R is not.

This book will be written primarily for graduate students, advanced undergraduates, and professionals in the fields of school psychology, special education, and other areas of education, as well as the health professions. We see the book as being a viable textbook for courses in research design, applied statistics, applied behavioral analysis, and practicum, among others. We would not assume of the readers any prior knowledge about single subjects designs, nor any prior statistical experience. We will provide an introductory chapter devoted to basic statistical concepts, including measures of central tendency (e.g., mean, median, mode), measures of variation (e.g., variance, standard deviation, range, inter-quartile range), correlation, frequency distributions, and effect sizes. In addition, given that the book will rely heavily on R software, the introductory chapter will also devote attention to the basics of using the software for organizing data, conducting basic statistical analyses, and for graphics. The R commands used to carry out these analyses will be largely automated so that users will only need to define the range for their data, and then enter it into the R spreadsheet. We envision these tools being available on the book website, with instructions for using them available in the book itself. We envision the book as being useful either as a primary text for a course in educational research designs, school psychology practicum, applied behavioral analysis, special education, or applied statistics. We also anticipate that individuals working in schools, school districts, mental health facilities, hospitals, applied behavioral analysis clinics, and evaluation organizations, as well as faculty members needing a practical resource for single subject design research, will all serve as a market for the book. In short, the readership would include graduate students, faculty members, teachers, psychologists, social workers, counselors, medical professionals, applied behavioral analysis professionals, program evaluators, and others whose work focuses on monitoring changes in individuals, particularly as the result of specific treatment conditions. We believe that this book could be marketed through professional organizations such as the American Educational Research Association (AERA), the National Association of School Psychologists, the National Association of Special Education Teachers, the Association for Professional Behavior Analysis, the American Psychological Association (APA), the Association for Psychological Science, and the American Evaluation Association. Within AERA, the following special interest groups would have particular interest in this book: Action Research, Classroom Observation, Disability Studies in Education, Mixed Methods Research, Qualitative Research, and Special Education Research. The book could also be marketed to state departments of education and their special education and school psychology divisions. Currently, many state departments of education require documentation for Response to Intervention (RtI) and Multi-Tiered Systems of Support (MTSS) procedures for individual students. The method taught in this proposed book would allow educators and student support personnel to document the effectiveness of interventions systematically and accurately.

The book will be designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SPSS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. We will not assume any more than basic familiarity with these issues, and will devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. We envision the book as being useful either as a primary text for a course on applied measurement where SPSS is the main platform for instruction, or as a supplement to a more theoretical text. We also anticipate that readers working in government agencies responsible for testing and measurement issues at the local, state and national levels, and private testing, survey and market research companies, as well as faculty members needing a practical resource for psychometric practice will serve as a market for the book. In short, the readership would include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. Readers will then be presented with examples illustrating a particular concept (e.g., reliability). These examples will include a discussion of the particular analysis, along with the SPSS code necessary to conduct them. The resulting output will then be discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up will also be included in the text. It is hoped that this mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work. To our knowledge, no book outlining psychometric practice using commonly available software such as SPSS currently exists. Given that many practitioners in academia, government and private industry use SPSS for statistical analyses of testing data, we believe that our book will fill an important niche in the market. It will contain very practical information regarding how to conduct a wide variety of psychometric analyses, along with tips on interpretation of results and the appropriate format for reporting these results. We believe that it will prove useful to individuals in educational measurement, psychometrics, and survey and market research. Our text will add to the literature by providing users with a single reference containing the major ideas in applied psychometrics with instructions and examples for conducting the analyses in SPSS. In addition, we will provide original macros for estimating a variety of statistics and conducting analyses common in educational and psychological measurement.

El objetivo del libro es proporcionar a los lectores las herramientas necesarias para evaluar la calidad psicome trica de las medidas educativas y psicolo gicas, asi como de encuestas y cuestionarios. Cada capi tulo aborda un tema relativo a la pra ctica psicome trica y de la medida, con e nfasis en la aplicacio n. Los temas sera n tratados brevemente desde una perspectiva teo rica/te cnica con el fin de proporcionar al lector los antecedentes necesarios para utilizar e interpretar correctamente los ana lisis estadi sticos que se presentara n con posteriormente. Este libro esta dirigido a investigadores, profesionales y estudiantes de posgrado (Ma ster y Doctorado) que buscan una gui a para realizar ana lisis psicome tricos de diferentes instrumentos de evaluacio n. Asumimos un nivel ba sico de conocimientos estadi sticos, pero estos conceptos se ira n repasando a lo largo de los diferentes capi tulos. Nos imaginamos que este texto (a) esperara pacientemente en algunos despachos implorando ser entregado, como recurso, a un estudiante, (b) tendra un lugar fijo en las mesas de los despachos donde continuamente, y debido a su uso diario, sera el primero del monto n, (c) estara en las mochilas de los estudiantes de postgrado, y los acompan ara felizmente desde casa al trabajo o a las clases y vuelta, (d) aparecera orgullosamente como un texto de referencia en los programas y guia s de estudio, y finalmente (e) como ocasional posavasos mientras se reflexiona profundamente acerca de co mo resolver los problemas de medida. Esperamos que a trave s de estos usos, sobre todo el u ltimo, aportar algu n conocimiento y ayuda que permita a los lectores aplicar adecuadamente las te cnicas y los conceptos abordados en este manual. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. The anticipated audience for this book includes researchers, practitioners, and graduate students searching for a guide to perform common psychometric analyses on various assessments, as discussed in many psychometric texts. We envision that this text will (a) patiently wait on some office shelves begging to be handed to a student as a resource, (b) have a permanent home on desks where it continually rises to the top of the stacks for daily use of the applied researcher, (c) be happily carried in bags to and from work and class by the graduate student learning techniques, (d) be listed proudly as a reference text on syllabi, and finally (e) as an occasional drink coaster while deep thoughts are pondered about how to solve measurement problems. We hope that through such uses, particularly the latter, that we have provided some insight and assistance to the user in appropriately applying the techniques and concepts discussed.

The book is designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SAS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. The authors do not assume any more than basic familiarity with these issues, and devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. This book will be useful either as a primary text for a course on applied measurement where SAS is the main platform for instruction, or as a supplement to a more theoretical text. The readership will include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. This book aims to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter covers an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics are briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that is presented subsequently. Readers are then presented with examples illustrating a particular concept (e.g., reliability). These examples include a discussion of the particular analysis, along with the SAS code necessary to conduct them. The resulting output is then discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up is also included in the text. This mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work.

A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. Exploratory Factor Analysis by W. Holmes Finch provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data. It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.