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For social researchers who need to know what procedures to use under what circumstances in practical research projects, this book does not require an indepth understanding of statistical theory.
This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.
This innovative book provides a fresh take on quantitative data analysis within the social sciences. It presents variable-based and case-based approaches side-by-side encouraging you to learn a range of approaches and to understand which is the most appropriate for your research. Using two multidisciplinary non-experimental datasets throughout, the book demonstrates that data analysis is really an active dialogue between ideas and evidence. Each dataset is returned to throughout the chapters enabling you to see the role of the researcher in action; it also showcases the difference between each approach and the significance of researchers’ decisions that must be made as you move through your analysis. The book is divided into four clear sections: Data and their presentation Variable-based analyses Case-based analyses Comparing and combining approaches Clear, original and written for students this book should be compulsory reading for anyone looking to conduct non-experimental quantitative data analysis.
In Analysing Quantitative Survey Data, Jeremy Dawson introduces you to the key elements of analysing quantitative survey data using classical test theory, the measurement theory that underlies the techniques described in the book. The methodological assumptions, basic components and strengths and limitations of this analysis are explained and with the help of illustrative examples, you are guided through how to conduct the key procedures involved, including reliability analysis, exploratory and confirmatory factor analysis. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis.
This book is an accessible introduction to quantitative dataanalysis, concentrating on the key issues facing those new toresearch, such as how to decide which statistical procedure issuitable, and how to interpret the subsequent results. Each chapterincludes illustrative examples and a set of exercises that allowsreaders to test their understanding of the topic. The book, writtenfor graduate students in the social sciences, public health, andeducation, offers a practical approach to making sociological senseout of a body of quantitative data. The book also will be useful tomore experienced researchers who need a readily accessible handbookon quantitative methods. The author has posted stata files, updates and data sets athis websitehttp://tinyurl.com/Treiman-stata-files-data-sets.
In Analysing Quantitative Data, Charles A. Scherbaum and Kristen M. Shockley guide the reader through Understanding Quantitative Data Analysis, Basic Components of Quantitative Data Analysis, Conducting Quantitative Data Analysis, Examples of Quantitative Data Analysis and Conclusions. An appendix contains Excel Formulas. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods Series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis.
Quantitative data analysis is now a compulsory component of most degree courses in the social sciences and students are increasingly reliant on computers for the analysis of data. Quantitative Data Analysis with Minitab explains statistical tests for Minitab users using the same formulae free, non technical approach, as the very successful SPPS version. Students will learn a wide range of quantitative data analysis techniques and become familiar with how these techniques can be implemented through the latest version of Minitab. Techniques covered include univariate analysis (with frequency table, dispersion and histograms), bivariate (with contingency tables correlation, analysis of varience and non-parametric tests) and multivariate analysis (with multiple regression, path analysis, covarience and factor analysis). In addition the book covers issues such as sampling, statistical significance, conceptualisation and measurement and the selection of appropriate tests. Each chapter concludes with a set of exercises. Social science students will welcome this integrated, non mathematical introduction to quantitative data anlysis and the minitab package.
An accessible and user-friendly guide to quantitative data analysis in educational research, aimed at those with little or no prior knowledge of statistical methods.
For social researchers who need to know what procedures to use under what circumstances in practical research projects, this book does not require an indepth understanding of statistical theory.