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Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and advanced r

1 Descriptive Statistical Techniques -- 2 Environmetric Methods of Nonstationary Time-Series Analysis: Univaraute Methods -- 3 Regression and Correlation -- 4 Factor and Correlation Analysis of Multivariate Environmental Data -- 5 Errors and Detection Limits -- 6 Visual Representation of Data Including Graphical Exploratory Data Analysis -- 7 Quality Assurance for Environmental Assessment Activities.

"Panel Data Analysis using EViews provides graduate students, researchers, and statisticians with step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. The author explores a variety of panel data models, along with the authors own empirical findings, demonstrating the advantages and limitations of each model. The text also examines various alternative models based on panel data, as well as the best and worst ANCOVA models"--

"Panel Data Analysis using EViews provides graduate students, researchers, and statisticians with step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. The author explores a variety of panel data models, along with the authors own empirical findings, demonstrating the advantages and limitations of each model. The text also examines various alternative models based on panel data, as well as the best and worst ANCOVA models"--

Part I: Survival measurements and concepts -- 1. Survival data -- 2. Measures of mortality and morbidity, ratios, proportions, and means -- 3. Survival distributions -- Part II: Mortality experiences and life tables -- 4. Life tables: fundamentals and construction -- 5. Complete mortality data, estimation of survival function -- 6. Incomplete mortality data: follow-up studies -- 7. Fitting parametric survival distributions -- 8. Comparison of mortality experiences -- 9. Theory of competing causes: probabilistic approach -- 10. Multiple decrement life tables -- 1. Single decrement life tables associated with multiple decrement life tables: their interpretation and meaning -- 12. Estimation and testing hypotheses in competing risk analysis -- Part IV: Some more advanced topics -- 13. Concomitant variables in lifetime distributions models -- 14. Age of onset distributions -- 15. Models of aging and chronic diseases.

In der Entscheidungsphase für ein CAQDAS-Programm ("Computer Assisted Qualitative Data Analysis Software") im Rahmen meines Forschungsprojektes über die Bedeutung von Kultur und Religion für die Menschenrechte von Frauen in Malaysia habe ich mir zum einen Gedanken über die Dominanz der gegenstandsbezogenen Theoriebildung (Grounded Theory) und zum anderen über die Dominanz des Kodierens als Methode gemacht. Im ersten Teil des Aufsatzes diskutiere ich, inwieweit meine Forschung auf den Prinzipien und Praktiken der Grounded Theory (GT) aufbaut bzw. aufgrund meines hypothesengeleiteten Rahmenkonzepts und der Auswahlkriterien für Untersuchungsteilnehmer(innen) davon abweicht. Ich zeige, in welcher Weise die Anwendung der GT zur Verbesserung des qualitativen Forschungsprozesses im Rahmen meiner Arbeit beigetragen hat. Dabei ist ein Ziel, den paradigmatischen Status der GT im Zusammenhang mit CAQDAS und die damit verbundene Homogenisierung zu entmystifizieren. Im zweiten Teil des Aufsatzes wird die Problematik des Kodierens als Analysemethode behandelt, und es wird gezeigt, wie diese mittels Reflexivität und "Agency" (Selbstbestimmung) des Forschers/der Forscherin bewältigt werden kann. Hierbei stehen eine pragmatische Herangehensweise und die Anwendung von Kodes als Mittel zum Zweck – und nicht als eigentliches Ziel – im Vordergrund. Abschließend wird die Frage erörtert, ob CAQDAS die Güte der qualitativen Datenanalyse und die Transparenz der Methode erhöht, (z.B. durch die Verwendung eines Audittrails) und somit zu einer nachvollziehbaren, innovativen und effektiven Forschung beiträgt.

In this book, basic statistical knowledge is conveyed in an understandable and application-oriented manner. The readers should be enabled to carry out their own empirical evaluations and to understand or critically reflect on existing analyses. The third edition is extended by a detailed chapter on the logic of significance tests. A replication syntax for the statistical program Stata is provided online as supplementary material. The Contents On the task of empirical social research and quantitative data analysis in the sociological research process - On the data situation: own data collections or secondary analyses? - First steps of data analysis: preparation, data description and bivariate correlations - Significance testing - Linear regression - On the logic of data analysis: Which evaluation strategy fits best to my research question? - Logistic regressions - An outlook on advanced statistical analysis methods The target group · Students and teachers of sociology and other social sciences · Practitioners in the field of empirical market, opinion, and social research The Authors Dr. Florian G. Hartmann is a Senior Scientist at the Paris Lodron University of Salzburg (Department of Psychology). Dr. Johannes Kopp is Professor of Sociology at the University of Trier. Dr. Daniel Lois holds the professorship for Social Science Methodology at the Universität der Bundeswehr München. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.