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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.

Policy analysis is the cornerstone of evidence-based policy making.1 It identifies the problems, informs programme design, supports the monitoring of policy implementation and is needed to evaluate programme impacts (Scott 2005). Rigorous and credible policy evidence is necessary to ensure the transparency and accountability of policy decisions, to secure political and public support and, hence, the allocation of financial resources. Sound policy analysis helps design effective and efficient programmes, thereby maximizing programme impact.

Now days, social network analysis has a great theoretical and practical significance. It is an interdisciplinary subject which spreads over various areas such as, social psychology, anthropology, economics, demography, communication studies, geography, biology, history, sociolinguistics, information science, organizational studies, political science, development studies, and computer science. The popular online social networks like Twitter, Facebook, etc hold big amounts of data about the people and their likes, dislikes, behaviors, relations etc. Graph based mining tools are required to analyze these huge social networks. A number of such analysis and visualization tools are available which have their own functionalities and characteristics. Choosing an appropriate tool for a particular task is difficult to decide. This paper puts an effort to do a survey on the different data collection mechanisms, relational data measurements, network data representations together with the different social network dataset formats and social network analysis and visualization tools.