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Cet ouvrage s'adresse à tous ceux qui s'intéressent au logiciel de statistiques SPSS: les analystes de marché, les économistes, les professionnels du domaine social, sans oublier les étudiants des cours de méthodologie et ceux qui peinent sur leur mémoire ou leur thèse. Le guide d'apprentissage s'adresse aux néophytes, les auteurs ayant fait le pari qu'il valait mieux ne supposer aucune connaissance en la matière. Il saura ainsi profiter à chacun. Introduction à l'analyse des données de sondage avec SPSS ne vise pas à se substituer au manuel de référence de SPSS -par ailleurs excessivement bien fait et complet-, mais souhaite plutôt accompagner l'exploration première de ce logiciel, aider à franchir le premier seuil, celui des manipulations de base. Le guide, conçu comme outil d'autoformation, a donc pour but de démystifier SPSS. Le lecteur trouvera des explications détaillées pour entrer ses données et produire les statistiques descriptives usuelles, notamment les distributions de fréquences et les tableaux croisés; il verra également comment illustrer les distributions par des graphiques. Grâce à ce livre, il vous suffira de quelques heures pour vous familiariser avec SPSS. Bonne pratique!

Social sciences --- Statistics --- Statistical methods --- Computer programs --- Analyse des données --- Social sciences - Statistical methods - Computer programs --- Statistics - Computer programs

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Statistics - Computer programs --- Database management - Computer programs --- Object-oriented methods (Computer science) --- Statistics --- Statistique --- Database management --- Économétrie. --- Logiciels. --- Computer programs. --- Econometrics --- Économétrie.

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Discovering Structural Equation Modeling Using Stata is devoted to Stata's sem command and all it can do. Learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. Each model covered is presented along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. The datasets used are downloadable, and you are encouraged to run the programs in a hands-on approach to learning. A particularly exciting feature of Stata is the SEM builder. This graphic interface for structural equation modeling allows you to draw publication-quality path diagrams and to fit the models without writing any programming code. When you fit a model with the SIM builder, Stata automatically generates the complete code that you can save for future use. Use of this unique tool is extensively covered in an appendix, and brief examples appear throughout the text. A miminal background in multiple regression is sufficient to benefit from this text. While it would be helpful to have some experience with Stata, it is not essential. Though the primary audience is those who are new to structural equation modeling, those who are already familiar with it will find this text useful for the Stata code it covers. Overall, the text is intended to be practical and will serve as a useful reference

Quantitative methods in social research --- Structural equation modeling --- Statistics --- Computer programs --- Stata --- Structural equation modeling. --- Computer programs. --- Stata. --- Modèles d'équations structurales. --- Statistics - Computer programs --- Modèles d'équations structurales.

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Programming --- Mathematical statistics --- Statistics --- Statistique --- Statistique mathématique --- Computer programs --- Logiciels --- 681.3*G3 --- 519.22 --- -Statistics --- -#TCPW V1.0 --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistical theory. Statistical models. Mathematical statistics in general --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistique mathématique --- #TCPW V1.0 --- Statistics - Computer programs --- Mathematical statistics - Computer programs

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Competing Risks and Multistate Models with R covers models that generalize the analysis of time to a single event (survival analysis) to analyzing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on non- and semiparametric methods.   This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis is placed on the interpretation of the results. A unique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. This simulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved.   This book is aimed at data analysts, with a background in standard survival analysis, who wish to understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods. The real data examples, R packages, and the entire R code used in the book are available online.   The authors are affiliated with the Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg and the Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany.  Jan Beyersmann is Senior Statistician and serves on the editorial board of Statistics in Medicine. Arthur Allignol is Statistician and has contributed several R packages on competing risks and multistate models.  Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg.  He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.

Statistical science --- Mathematical statistics --- statistisch onderzoek --- Statistics --- R (Computer program language) --- Computer programs --- R (Computer program language). --- Computer programs. --- Mathematical statistics. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Econometrics --- GNU-S (Computer program language) --- Domain-specific programming languages --- Statistics - Computer programs