Principles & Methods of Statistical Analysis

Nonfiction, Social & Cultural Studies, Social Science, Statistics, Reference & Language, Reference, Research
Cover of the book Principles & Methods of Statistical Analysis by Jerome Frieman, Donald A. Saucier, Mr. Stuart S. Miller, SAGE Publications
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Author: Jerome Frieman, Donald A. Saucier, Mr. Stuart S. Miller ISBN: 9781483358604
Publisher: SAGE Publications Publication: January 20, 2017
Imprint: SAGE Publications, Inc Language: English
Author: Jerome Frieman, Donald A. Saucier, Mr. Stuart S. Miller
ISBN: 9781483358604
Publisher: SAGE Publications
Publication: January 20, 2017
Imprint: SAGE Publications, Inc
Language: English

This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying, stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the underlying theory behind statistical methods. By presenting examples and principles of statistics within the context of these timely issues, authors Jerome Frieman, Donald A. Saucier, and Stuart S. Miller show how the results of analyses can be used to answer research questions. New techniques for data analysis and a wide range of topics are covered, including how to deal with “messy data” and the importance of engaging in exploratory data analysis.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying, stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the underlying theory behind statistical methods. By presenting examples and principles of statistics within the context of these timely issues, authors Jerome Frieman, Donald A. Saucier, and Stuart S. Miller show how the results of analyses can be used to answer research questions. New techniques for data analysis and a wide range of topics are covered, including how to deal with “messy data” and the importance of engaging in exploratory data analysis.

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