Applied Statistics In Social Sciences
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Applied Statistics Using R
If you want to learn to use R for data analysis but aren’t sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors’ own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.
Applied Statistics for the Social and Health Sciences
Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards. Reflecting the growing importance of "Big Data" in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science. Key features of the book include: interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts; thoroughly integrating the teaching of statistical theory with the teaching of data access, processing, and analysis in Stata; recognizing debates and critiques of the origins and uses of quantitative methods.
Applied Statistics
Applied Statistics: A Course for Social Sciences will introduce readers to the versatile statistical tools and techniques of estimation of parameters, testing of hypotheses (both in experimental and non-experimental set-ups), the use of multivariate models to answer many research questions in a single shot, data reduction techniques, etc. Besides emphasizing the need for computer support for data transformation, data condensation, data analysis, and storing the data for future use, the book also deals extensively with non-parametric tests for use in situations where assumptions of parametric tests are doubtful. Analyses of time series data and index numbers have also been included. Though primarily designed for analysis of economic data, they also have great relevance in the analysis of varieties of social and demographic data. The text is written in a straightforward style, keeping in view the mindset of the end users - social scientists. Numerous diagrams and tables have been integrated to illustrate important concepts and techniques of analysis. A glossary, statistical tables, and a comprehensive index have also been incorporated.