multilevel modeling using r chapman hall crc statistics in the social and behavioral sciences

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Neue Trends In Den Sozialwissenschaften

Author : Sebastian Jäckle
ISBN : 9783658171896
Genre : Social Science
File Size : 84. 24 MB
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Dieses Buch stellt einige wichtige und zukunftsträchtige neuere Methoden in den Sozialwissenschaften vor. Ziel des Buches ist, einerseits deren Grundlogik zu klären und andererseits zu zeigen, inwiefern sie den klassischen Methodenkatalog sinnvoll ergänzen können. Dazu wird das Spektrum an mit diesen Techniken bearbeitbaren Fragestellungen aufgezeigt, Beispielarbeiten diskutiert, nötige Voraussetzungen z.B. in Bezug auf die Datenqualität angesprochen, und damit insgesamt das Potential dieser Verfahren veranschaulicht. Zudem gibt jeder Beitrag praktische Tipps für die Umsetzung eigener Forschungsarbeiten und anhand kommentierter Literaturempfehlungen Ansatzpunkte für die intensivere Beschäftigung mit den Methoden. Daneben wird (sofern angebracht) kurz diskutiert welche Softwarepakete sich für die Anwendung eignen.

Multilevel Modeling Using R

Author : W. Holmes Finch
ISBN : 9781466515864
Genre : Mathematics
File Size : 45. 65 MB
Format : PDF
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A powerful tool for analyzing nested designs in a variety of fields, multilevel/hierarchical modeling allows researchers to account for data collected at multiple levels. Multilevel Modeling Using R provides you with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book. Through the R code and detailed explanations provided, this book gives you the tools to launch your own investigations in multilevel modeling and gain insight into your research.

Bayesian Models For Astrophysical Data

Author : Joseph M. Hilbe
ISBN : 9781108210744
Genre : Mathematics
File Size : 27. 63 MB
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This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

Statistical Rethinking

Author : Richard McElreath
ISBN : 9781315362618
Genre : Mathematics
File Size : 32. 14 MB
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Handbuch Der Sozialwissenschaftlichen Datenanalyse

Author : Christof Wolf
ISBN : 9783531920382
Genre : Political Science
File Size : 86. 96 MB
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Das Handbuch der sozialwissenschaftlichen Datenanalyse bietet in über 40 Kapiteln eine umfassende Darstellung multivariater Analyseverfahren. Schwerpunkte des Handbuchs bilden Grundlagen der Datenanalyse, regressionsanalytische Verfahren für Quer- und Längsschnittsdaten sowie Skalierungsverfahren. Behandelt werden u. a. OLS-, logistische und robuste Regression, Strukturgleichungsmodelle, Mehrebenen-, Panel-, Ereignisdaten- und Zeitreihenanalyse, MDS und Rasch-Modelle. Darüber hinaus werden viele neuere Verfahren dargestellt, etwa multiple Imputation, Bootstrappen, Analyse latenter Klassen und propensity score matching. Jedes Kapitel beginnt mit einer allgemein verständlichen Einführung. Es folgt eine Darstellung der mathematisch-statistischen Grundlagen. Anschließend wird jedes Verfahren anhand eines sozialwissenschaftlichen Beispiels vorgestellt. Die Beiträge enden mit Hinweisen auf typische Anwendungsfehler und einer kommentierten Literaturempfehlung.

Using Geostatistical Models To Study Neighborhood Effects

Author : Steven James Pierce
ISBN : MSU:31293030635928
Genre : Community psychology
File Size : 76. 84 MB
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Applied Survey Data Analysis

Author : Steven G. Heeringa
ISBN : 1420080679
Genre : Mathematics
File Size : 24. 19 MB
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Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches. Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

Ordered Regression Models

Author : Andrew S. Fullerton
ISBN : 9781466569744
Genre : Mathematics
File Size : 41. 47 MB
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Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. Web Resource More detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.

Strukturgleichungsmodellierung

Author : Rolf Weiber
ISBN : 9783642350122
Genre : Business & Economics
File Size : 80. 58 MB
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Alle Analysen werden an einem durchgehenden Fallbeispiel konkret und transparent durchgeführt Strukturgleichungsmodelle sind in allen Wissenschaftsdisziplinen von größter Bedeutung, da sie das Standardinstrument zur empirischen Prüfung von Hypothesensystemen darstellen. Dabei stehen oftmals die Beziehungen zwischen hypothetischen Konstrukten (latenten Variablen) im Fokus des Interesses. Das Buch zeichnet den gesamten Prozess der Strukturgleichungsmodellierung von der Konzeptualisierung theoretischer Konstrukte über die Spezifikation von Messmodellen, die Reliabilitäts- und Validitätsprüfung mittels konfirmatorischer Faktorenanalyse bis hin zur Prüfung von kausalen Wirkhypothesen mittels Kovarianzstrukturanalyse sowie PLS nach. Alle Arbeitsschritte werden an einem durchgehenden Fallbeispiel unter Verwendung von SPSS, AMOS und SmartPLS veranschaulicht und jeweils konkrete Anwendungsempfehlungen gegeben. Ebenso werden weiterführende Aspekte wie die Mehrgruppenkausalanalyse, die Spezifikation reflektiver und formativer Messansätze sowie MIMIC-Modelle einsteigergerecht behandelt.

Latent Markov Models For Longitudinal Data

Author : Francesco Bartolucci
ISBN : 9781466583719
Genre : Mathematics
File Size : 86. 28 MB
Format : PDF, Kindle
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Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

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