Using Statistics in Social Research A Concise Approach

Using Statistics in Social Research A Concise Approach by Scott M. Lynch, published by Springer New York in 2013, is a comprehensive resource designed for upper-level undergraduate students. This edition, consisting of 229 pages, focuses on applied statistics within the social sciences. The book integrates statistical concepts into the research process, beginning with foundational philosophical issues such as deductive reasoning and hypothesis falsifiability, before progressing to data collection and measurement.
Readers will find a thorough exploration of probability theory and its significance in statistical reasoning and inference. The text covers essential statistical methods relevant to social science research, including z and t tests, confidence intervals, chi-square tests, and regression analysis. Concepts are illustrated with both real and simulated data, enhancing understanding. Additionally, the book provides guidance on effectively presenting statistics in various formats and offers suggestions for further reading and study, making it a valuable tool for those engaged in social science research.
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This book covers applied statistics for the social sciences with upper-level undergraduate students in mind. The chapters are based on lecture notes from an introductory statistics course the author has taught for a number of years. The book integrates statistics into the research process, with early chapters covering basic philosophical issues underpinning the process of scientific research. These include the concepts of deductive reasoning and the falsifiability of hypotheses, the development of a research question and hypotheses, and the process of data collection and measurement. Probability theory is then covered extensively with a focus on its role in laying the foundation for statistical reasoning and inference. After illustrating the Central Limit Theorem, later chapters address the key, basic statistical methods used in social science research, including various z and t tests and confidence intervals, nonparametric chi square tests, one-way analysis of variance, correlation, simple regression, and multiple regression, with a discussion of the key issues involved in thinking about causal processes. Concepts and topics are illustrated using both real and simulated data. The penultimate chapter presents rules and suggestions for the successful presentation of statistics in tabular and graphic formats, and the final chapter offers suggestions for subsequent reading and study.
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