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Statistical Approaches To Measurement Invariance

Author: Roger E. Millsap
Publisher: Routledge
ISBN: 1136761128
Size: 14.96 MB
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This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states. Measurement models that underlie psychometric practice are described, including their strengths and limitations. Practical strategies and examples for dealing with bias detection are provided throughout.

Measurement Invariance

Author: Rens Van De Schoot
Publisher: Frontiers Media SA
ISBN: 288919650X
Size: 29.25 MB
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Multi-item surveys are frequently used to study scores on latent factors, like human values, attitudes and behavior. Such studies often include a comparison, between specific groups of individuals, either at one or multiple points in time. If such latent factor means are to be meaningfully compared, the measurement structures including the latent factor and their survey items should be stable across groups and/or over time, that is ‘invariant’. Recent developments in statistics have provided new analytical tools for assessing measurement invariance (MI). The aim of this special issue is to provide a forum for a discussion of MI, covering some crucial ‘themes’: (1) ways to assess and deal with measurement non-invariance; (2) Bayesian and IRT methods employing the concept of approximate measurement invariance; and (3) new or adjusted approaches for testing MI to fit increasingly complex statistical models and specific characteristics of survey data. The special issue started with a kick-off meeting where all potential contributors shared ideas on potential papers. This expert workshop was organized at Utrecht University in The Netherlands and was funded by the Netherlands Organization for Scientific Research (NWO-VENI-451-11-008). After the kick-off meeting the authors submitted their papers, all of which were reviewed by experts in the field. The papers in the eBook are listed in alphabetical order, but in the editorial the papers are introduced thematically. Although it is impossible to cover all areas of relevant research in the field of MI, papers in this eBook provide insight on important aspects of measurement invariance. We hope that the discussions included in this special issue will stimulate further research on MI and facilitate further discussions to support the understanding of the role of MI in multi-item surveys.

Effektivit T Des Schulsystems Beim Abbau Sozialer Ungleichheit

Author: Agnes Simona Stancel-Piatak
Publisher: Waxmann Verlag
ISBN: 3830984804
Size: 26.88 MB
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Mit dieser Arbeit legt Frau Dr. Agnes Stancel-Pi?tak eine Untersuchung im Überschneidungsbereich von der Schuleffektivitätsforschung und der Forschung zur Perpetuierung sozialer Ungleichheit im Schulsystem vor. Sie nimmt individuelle und schulische Bedingungen sozialer Reproduktion am Übergang von der Grundschule in die Sekundarstufe in den Blick und zeigt, wie diese beiden bisher in der Regel getrennt betrachteten Konzepte analytisch gemeinsam verankert werden können. Hierzu entwirft sie eine Rahmenkonzeption, anhand derer sich die verschiedenen Effekte in einem Mehrebenen-Strukturgleichungsmodell simultan modellieren und damit auch in ihren Relationen bestimmen und interpretieren lassen. Der Autorin ist es damit gelungen, einen wichtigen Beitrag zur differenzierten Analyse der Perpetuierung von Bildungsungleichheiten vorzustellen. Neben der inhaltlichen Auseinandersetzung mit dem Thema ergänzt sie in ihrer Arbeit auch Überlegungen zur Modellierung von Kausalitätsannahmen und der Generalisierbarkeit von Befunden aus Sekundäranalysen von großangelegten Schulleistungsuntersuchungen, um so methodologische Grenzen und Möglichkeiten solcher Analysen zu beleuchten. Zusammenfassend kann festgestellt werden, dass die Arbeit einen vielschichtigen Einblick in Untersuchungen über Bedingungen, Perspektiven und auch Grenzen gesellschaftlicher Bemühungen, soziale Ungleichheiten zu reduzieren, bietet. Prof. Dr. Knut Schwippert Im Anschluss an eine mehrjährige Forschungstätigkeit an der Universität Hamburg arbeitet Frau Dr. Agnes Stancel-Pi?tak gegenwärtig als stellvertretende Leiterin der Forschungsabteilung (Research and Analysis Unit) am Data Processing and Research Center (DPC) der International Association for the Evaluation of Educational Achievement (IEA). Als Konsortialmitglied ist sie involviert in die theoretische Fundierung, Entwicklung und Analyse im Rahmen von internationalen großangelegten Untersuchungen (Teaching and Learning International Survey 2018; TALIS Starting Strong Survey 2018). Ihre Forschungstätigkeit umfasst neben den inhaltlichen Analysen zur Schuleffektivität sowie zur Reproduktion sozialer Ungleichheit im Schulsystem ebenso forschungsmethodische Schwerpunkte im Bereich von Datenanalyse großangelegter Untersuchungen, unter anderem anhand von Mehrebenen-Strukturgleichungsmodellen, Invarianzanalysen und Analysen im Rahmen der Item-Response-Theorie.

Latent Variable Modeling With R

Author: W. Holmes Finch
Publisher: Routledge
ISBN: 1317970764
Size: 72.60 MB
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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.

Longitudinal Structural Equation Modeling

Author: Jason T. Newsom
Publisher: Routledge
ISBN: 1317975359
Size: 21.10 MB
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This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling options are available for which hypotheses. The author demonstrates how SEM is related to other longitudinal data techniques throughout. By exploring connections between models, readers gain a better understanding of when to choose one analysis over another. The book explores basic models to sophisticated ones including the statistical and conceptual underpinnings that are the building blocks of the analyses. Accessibly written, research examples from the behavioral and social sciences and results interpretations are provided throughout. The emphasis is on concepts and practical guidance for applied research rather than on mathematical proofs. New terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues. Each chapter also includes examples of each model type, comment sections that provide practical guidance, model extensions, and recommended readings. Highlights include: Covers the major SEM approaches to longitudinal analysis in one resource. Explores connections between longitudinal SEM models to enhance integration. Numerous examples that help readers match research questions to appropriate analyses and interpret results. Reviews practical issues related to model specification and estimation to reinforce connections. Analyzes continuous and discrete (binary and ordinal) variables throughout for breadth not found in other sources. Reviews key SEM concepts for those who need a refresher (Ch. 1). Emphasizes how to apply and interpret each model through realistic data examples. Provides the book’s data sets at www.longitudinalsem.com along with the Mplus and R-lavaan syntax used to generate the results. Introduces the LISREL notation system used throughout (Appendix A). The chapters can be read out of order but it is best to read chapters 1 – 4 first because most of the later chapters refer back to them. The book opens with a review of latent variables and analysis of binary and ordinal variables. Chapter 2 applies this information to assessing longitudinal measurement invariance. SEM tests of dependent means and proportions over time points are explored in Chapter 3, and stability and change, difference scores, and lagged regression are covered in Chapter 4. The remaining chapters are each devoted to one major type of longitudinal SEM -- repeated measures analysis models, full cross-lagged panel models and simplex models, modeling stability with state-trait models, linear and nonlinear growth curve models, latent difference score models, latent transition analysis, time series analysis, survival analysis, and attrition. Missing data is discussed in the context of many of the preceding models in Chapter 13. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this text also appeals to researchers in these fields. Intended for those without an extensive math background, prerequisites include familiarity with basic SEM. Matrix algebra is avoided in all but a few places.

Statistik In Der Psychologie

Author: Klaus D. Kubinger
Publisher: Hogrefe Verlag
ISBN: 3840923565
Size: 63.89 MB
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Dieses Lehrbuch vermittelt die für das Psychologiestudium wichtigen Methoden der wissenschaftlichen Disziplin „Statistik“. Nach einer Einführung in die gängigen Messmethoden werden die Grundlagen der Beschreibenden Statistik erläutert. Weitere Kapitel behandeln die Voraussetzungen und Methoden der Schließenden Statistik, wie Wahrscheinlichkeitstheorie und Zufallsziehungen, und erörtern Methoden zur Schätzung von grundlegenden Parametern wie Erwartungswert und Varianz. Ausführlich werden die Verfahren der Varianz-, Regressions- und Korrelationsanalyse sowie die einfache und multiple Regression dargestellt. Abschließend gehen die Autoren auf Grundlagen der Modellbildung und theoriebildende Verfahren wie die Cluster- und die Faktorenanalyse ein. Zur Illustration der Inhalte werden viele Beispiele aus der psychologischen Forschung verwendet, wobei immer auch das Vorgehen mit den Statistikprogrammen R und SPSS erläutert wird. Ein breiter Raum wird der Untersuchungsplanung gewidmet, die für empirische Forschungsarbeiten im Master- und Promotionsstudium unverzichtbar ist. Das Buch vermittelt somit nicht nur das notwendige Verständnis für die Inhalte der Statistik, sondern stellt für fortgeschrittene Studierende auch ein geeignetes Nachschlagewerk dar.

Handbook Of Health Survey Methods

Author: Timothy P. Johnson
Publisher: John Wiley & Sons
ISBN: 1118594746
Size: 25.45 MB
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A comprehensive guidebook to the current methodologiesand practices used in health surveys A unique and self-contained resource, Handbook of HealthSurvey Methods presents techniques necessary for confrontingchallenges that are specific to health survey research. Thehandbook guides readers through the development of sample designs,data collection procedures, and analytic methods for studies aimedat gathering health information on general and targetedpopulations. The book is organized into five well-defined sections: Design andSampling Issues, Measurement Issues, Field Issues, Health Surveysof Special Populations, and Data Management and Analysis.Maintaining an easy-to-follow format, each chapter begins with anintroduction, followed by an overview of the main concepts,theories, and applications associated with each topic. Finally,each chapter provides connections to relevant online resources foradditional study and reference. The Handbook of Health SurveyMethods features: 29 methodological chapters written by highly qualified expertsin academia, research, and industry A treatment of the best statistical practices and specificmethodologies for collecting data from special populations such assexual minorities, persons with disabilities, patients, andpractitioners Discussions on issues specific to health research includingdeveloping physical health and mental health measures, collectinginformation on sensitive topics, sampling for clinical trials,collecting biospecimens, working with proxy respondents, andlinking health data to administrative and other external datasources Numerous real-world examples from the latest research in thefields of public health, biomedicine, and health psychology Handbook of Health Survey Methods is an ideal reference foracademics, researchers, and practitioners who apply survey methodsand analyze data in the fields of biomedicine, public health,epidemiology, and biostatistics. The handbook is also a usefulsupplement for upper-undergraduate and graduate-level courses onsurvey methodology.

Oxford Handbook Of Methods In Positive Psychology

Author: Anthony D. Ong
Publisher: Oxford University Press
ISBN: 9780199775095
Size: 29.61 MB
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In the short time since the publication of the Handbook of Positive Psychology esearch results on the psychology of human strengths have proliferated. However, no major volume has documented the methods and theory used to achieve these results. Oxford Handbook of Methods in Positive Psychology fills this need, providing a broad overview of diverse contemporary methods in positive psychology. With contributions from both leading scholars and promising young investigators, the handbook serves to illuminate and, at times, challenge traditional approaches. Incorporating multiple levels of analysis, from biology to culture, the contributors present state-of-the art techniques, including those for estimating variability and change at the level of the individual, identifying reliability of measurements within and across individuals, and separating individual differences in growth from aspects of phenomena that exhibit shorter-term variability over time. The volume covers such topics as wisdom, health, hope, resilience, religion, relationships, emotions, well-being, character strengths, and laughter. It enhances our understanding of the balance between human deficits and strengths and demonstrates their connections to other problems. Oxford Handbook of Methods in Positive Psychology will be the essential reference for methods in positive psychology.

Statistical Methods For Validation Of Assessment Scale Data In Counseling And Related Fields

Author: Dimiter M. Dimitrov
Publisher: John Wiley & Sons
ISBN: 1119019281
Size: 63.24 MB
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“Dr. Dimitrov has constructed a masterpiece—a classic resource that should adorn the shelf of every counseling researcher and graduate student serious about the construction and validation of high quality research instruments. —Bradley T. Erford, PhD Loyola University Maryland Past President, American Counseling Association “This book offers a comprehensive treatment of the statistical models and methods needed to properly examine the psychometric properties of assessment scale data. It is certain to become a definitive reference for both novice and experienced researchers alike.” —George A. Marcoulides, PhD University of California, Riverside This instructive book presents statistical methods and procedures for the validation of assessment scale data used in counseling, psychology, education, and related fields. In Part I, measurement scales, reliability, and the unified construct-based model of validity are discussed, along with key steps in instrument development. Part II describes factor analyses in construct validation, including exploratory factor analysis, confirmatory factor analysis, and models of multitrait-multimethod data analysis. Traditional and Rasch-based analyses of binary and rating scales are examined in Part III. Dr. Dimitrov offers students, researchers, and clinicians step-by-step guidance on contemporary methodological principles, statistical methods, and psychometric procedures that are useful in the development or validation of assessment scale data. Numerous examples, tables, and figures provided throughout the text illustrate the underlying principles of measurement in a clear and concise manner for practical application. *Requests for digital versions from the ACA can be found on wiley.com. *To request print copies, please visit the ACA website here. *Reproduction requests for material from books published by ACA should be directed to [email protected]

Hidden Figures Unerkannte Heldinnen

Author: Margot Lee Shetterly
Publisher: HarperCollins
ISBN: 3959676433
Size: 69.12 MB
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1943 stellt das Langley Memorial Aeronautical Laboratory der NACA,die später zur NASA wird, erstmalig afroamerikanische Frauen ein. "Menschliche Rechner" - unter ihnen Dorothy Vaughan, die 1953 Vorgesetzte der brillanten afroamerikanischen Mathematikerin Katherine Johnson wird. Trotz Diskriminierung und Vorurteilen, treiben sie die Forschungen der NASA voran und Katherine Johnsons Berechnungen werden maßgeblich für den Erfolg der Apollo-Missionen. Dies ist ihre Geschichte. "Mit dieser unglaublich mitreißenden und vielschichtigen Erzählung zeigt Shetterly ihr Können. Die Geschichte begeistert in allen Aspekten." Booklist