The advent of complex and powerful computer-generated statistical models has greatly eroded the former prominence of social theory in data analysis, replacing it with an emphasis on statistical technique. To correct this trend, Carol S. Aneshensel presents a method for bringing data analysis and sta
Data Analysis for the Social Sciences: Integrating Theory and Practice
β Scribed by Douglas Bors
- Publisher
- SAGE Publications Ltd
- Year
- 2018
- Tongue
- English
- Leaves
- 664
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
β²This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.β² βRuth Horry, Psychology,Β Swansea UniversityΒ
β²This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.β²Β βBarbra Teater, Social Work,Β College of Staten Island, City University of New York
Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence.Β
With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability.
It shows you how to:
- Describe data with graphs, tables, and numbers
- Calculate probability and value distributions
- Test a priori and post hoc hypotheses
- Conduct Chi-squared tests and observational studies
- Structure ANOVA, ANCOVA, and factorial designs
Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book isΒ theΒ student-focused companion to support students through their statistics journeys. Β
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