ISBN-13: 9781138289017 / Angielski / Miękka / 2024 / 180 str.
ISBN-13: 9781138289017 / Angielski / Miękka / 2024 / 180 str.
This approachable and persuasive text uses real-world examples to teach Criminal Justice students how to use data with precision and effectiveness. Not intended for researchers, Data Analysis in Crime and Justice is an essential resource for students looking to make an impact through informed, intelligent police work. Withrow puts data analysis techniques into context for the future criminal justice practitioner. Data Analysis in Crime and Justice clearly explains and illustrates the main concepts in statistics and qualitative research as they are used by police or corrections officers. Unlike other statistics textbooks for undergraduates, Withrow speaks plainly and engagingly to criminal justice students, the majority of whom will work in the field rather than pursuing criminological research. Professionals in law enforcement and other sectors of the justice system need to discriminate between valid and invalid uses of data, and between conclusive evidence and political and media hype. The pedagogical approach of Data Analysis in Crime and Justice is equivalent to Withrow’s best-selling Research Methods in Crime and Justice: Demonstrates the relevance of analytical skills in criminal justice practice through ‘real-world’ criminal justice examples of analytical projects Uses stories to illustrate how researchers (scholars and practitioners) conduct analytical projects Focuses on practical (i.e. applied) analytical skills and not on overly technical jargon and statistical theory Presents statistics and other analytical strategies as tools to achieve a particular end (i.e,. answer a research question) rather than teaching statistics (as a mathematics course) Ideal as a companion volume to the author’s Research Methods in Crime and Justice or other RM texts, or as a stand-alone textbook for an undergraduate course introducing statistics and data analysis, Data Analysis in Crime and Justice fills a gap in the existing literature.