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Handbook Of Univariate And Multivariate Data Analysis With Ibm Spss Second Edition

Author: Robert Ho
Publisher: CRC Press
ISBN: 1439890218
Size: 13.28 MB
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Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book’s CRC Press web page.

Himalayan Quality Of Life

Author: Benjamin L. Saitluanga
Publisher: Springer
ISBN: 3319537806
Size: 54.53 MB
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The book is a study of intra-urban inequality in quality of life (QOL) in Aizawl city. The main objectives of the study include analysis of processes and patterns of social differentiation along the three-dimensional space of Aizawl city as well as analysis of spatial inequality in QOL at the lowest administrative structure of the city. An investigation into spatial pattern of residential differentiation was done at both horizontal and vertical spaces. Spatial variation in well-being of residents of Aizawl city and the quality of their immediate environment was also studied by taking both objective and subjective indicators. The study employed a number of descriptive, inferential and multivariate statistical techniques including correlation, factor analysis, principal component analysis, cluster analysis and spatial autocorrelation methods like Moran’s I and Local Indicators of Spatial Association (LISA). Mapping techniques and graphical methods like Choropleth map, histogram and line graph were also used. With the help of factor analysis, the social space of Aizawl city was found to be differentiated along socio-economic status, family status, household size status, workers status and ethnic status. The most important factor determining residential differentiation was socio-economic status. Choropleth map of factor scores reveals that the inner city localities were dominated by high socio-economic class while poorer people dominated the peripheries. Non-local ethnic minorities were few but concentrated in some adjoining peripheral localities as well as in inner city localities which have been inhabited by their ancestors since the colonial period. Vertical pattern of residential differentiation was also analyzed by taking income variable as a proxy of socio-economic status. Multi-storey buildings in Aizawl city were co-inhabited by both richer people and poorer people. The richer people were found at the top floors while the poorer people occupied the basement floors. Normally, the owners of the buildings were found at the top floors while the basement floors were dominated by the renters. Spatial variation in QOL was measured with the help of principal component analysis as a weighting technique by taking variables pertaining to both objective and subjective QOL dimensions. The values of composite QOL index showed that the central localities have scored better than their peripheral counterparts. Correlation analysis of the relationship between objective indicators and subjective indicators provided a low positive value indicating the absence of relationship between the two dimensions of quality of life. Spatial autocorrelation analysis was also performed to see the pattern of clustering of spatially weighted QOL variables across Local Councils. With the help of Global Moran’s I, spatial clusters and spatial outliers were observed for objective dimension of QOL within the study area. The value of Moran’s I was found to be insignificant for subjective QOL dimension indicating the absence of significant pattern of clustering. The study also identified 7 social areas of Aizawl city on the basis of factor scores and composite scores of QOL variables calculated for all Local Councils. The identification of clusters was taken out with the help of hierarchical clustering method of cluster analysis. These clusters were labeled appropriate names and their characteristics were described in detail. The thesis concluded with recommendation of designating these social areas as ‘social development planning zones’ for obtaining inclusive development.

R In A Nutshell

Author: Joseph Adler
Publisher: O'Reilly Germany
ISBN: 3897216507
Size: 60.50 MB
Format: PDF
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Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Statistik Workshop F R Programmierer

Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Size: 46.96 MB
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Spss 16

Author: Achim Bühl
Publisher: Pearson Deutschland GmbH
ISBN: 9783827373328
Size: 72.67 MB
Format: PDF
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Die Standardeinführung für SPSS ist auf der Basis zahlreicher neuer Datensätze für die Version 16 vollständig überarbeitet und erweitert worden. Ausgehend von Problemstellungen aus der Praxis wird gezeigt, wie Sie mit SPSS arbeiten können. Die Beispiele basieren meist auf Fallstudien und sind vor allem dem sozialwissenschaftlichen und dem psychologisch-medizinischen Bereich entnommen. Der Autor beschreibt ausführlich den kompletten statistischen Inhalt der Module Base, Regression Models und Advanced Models. In der 11. Auflage des Werks nimmt erstmals auch die Korrespondenzanalyse einen breiten Raum ein; ein Verfahren, das immer häufiger eingesetzt wird und Zusammenhänge von Variablen optisch als Punkte eines geometrischen Raums aufbereitet.

An Introduction To Multilevel Modeling Techniques

Author: Ronald H. Heck
Publisher: Routledge
ISBN: 1317598504
Size: 11.82 MB
Format: PDF, Kindle
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Univariate and multivariate multilevel models are used to understand how to design studies and analyze data in this comprehensive text distinguished by its variety of applications from the educational, behavioral, and social sciences. Basic and advanced models are developed from the multilevel regression (MLM) and latent variable (SEM) traditions within one unified analytic framework for investigating hierarchical data. The authors provide examples using each modeling approach and also explore situations where alternative approaches may be more appropriate, given the research goals. Numerous examples and exercises allow readers to test their understanding of the techniques presented. Changes to the new edition include: -The use of Mplus 7.2 for running the analyses including the input and data files at www.routledge.com/9781848725522. -Expanded discussion of MLM and SEM model-building that outlines the steps taken in the process, the relevant Mplus syntax, and tips on how to evaluate the models. -Expanded pedagogical program now with chapter objectives, boldfaced key terms, a glossary, and more tables and graphs to help students better understand key concepts and techniques. -Numerous, varied examples developed throughout which make this book appropriate for use in education, psychology, business, sociology, and the health sciences. -Expanded coverage of missing data problems in MLM using ML estimation and multiple imputation to provide currently-accepted solutions (Ch. 10). -New chapter on three-level univariate and multilevel multivariate MLM models provides greater options for investigating more complex theoretical relationships(Ch.4). -New chapter on MLM and SEM models with categorical outcomes facilitates the specification of multilevel models with observed and latent outcomes (Ch.8). -New chapter on multilevel and longitudinal mixture models provides readers with options for identifying emergent groups in hierarchical data (Ch.9). -New chapter on the utilization of sample weights, power analysis, and missing data provides guidance on technical issues of increasing concern for research publication (Ch.10). Ideal as a text for graduate courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this book’s practical approach also appeals to researchers. Recommended prerequisites are introductory univariate and multivariate statistics.

Applied Multivariate Statistics For The Social Sciences

Author: Keenan A. Pituch
Publisher: Routledge
ISBN: 1317805917
Size: 33.53 MB
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Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. Features new to this edition include: NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises). Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.

Sarstedt Sch Tz Ibm Spss Syntax

Author: Marko Sarstedt
Publisher: Vahlen
ISBN: 3800643626
Size: 30.66 MB
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Der souveräne Umgang mit der SPSS Syntax bietet einen unschätzbaren Vorteil für die tägliche Arbeit von Anwendern, die mit der Analyse von Daten zu tun haben. Das Buch ist eine integrierte Einführung in die Steuersprache von IBM SPSS Statistics für Studenten, Forscher und Praktiker. Es behandelt neben den notwendigen Grundlagen die Themengebiete Datenaufbereitung, Datentrans-formation und -modifikation. Weitere Themengebiete umfassen die Makro- und Matrixsprache, die in der 2. Auflage deutlich erweitert worden sind. Die Neuauflage wurde von Grund auf neu bearbeitet und um zahlreiche typische Anwendungsbeispiele ergänzt, die anhand realer Daten u.?a. des J.?D. Power and Associates Customer Satisfaction Index veranschaulicht werden. Die zugehörigen Datensätze sind als kostenloses Zusatzmaterial im Internet erhältlich.