Using R for data analysis in social sciences : a research project-oriented approach / Quan Li.
Series: Oxford scholarship online: Publisher: New York, NY : Oxford University Press, 2019Description: 1 online resource : illustrations (black and white)Content type: text | still image Media type: computer Carrier type: online resourceISBN: 9780190656256 (ebook) :Subject(s): Social sciences -- Research -- Data processing | Social sciences -- Statistical methods
| R (Computer program language)Additional Physical Form: Print version : 9780190656218DDC classification: 330.2855133 LOC classification: H61.3 | .L52 2019Online resources: Oxford scholarship online Summary: Statistical analysis is common in the social sciences, and among the more popular programs is R. This text provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualise, and analyse data. The focus is on how to address substantive questions with data analysis and replicate published findings. The work adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualise data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyse data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares regression, and model assumption diagnostics.
| Item type | Current library | Class number | Copy number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
| ebook | House of Lords Library - Palace Online access | 1 | Available |
Previously issued in print: 2018.
Includes bibliographical references and index.
Statistical analysis is common in the social sciences, and among the more popular programs is R. This text provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualise, and analyse data. The focus is on how to address substantive questions with data analysis and replicate published findings. The work adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualise data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyse data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares regression, and model assumption diagnostics.
Specialized.
Description based on online resource; title from home page (viewed on February 4, 2019).