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This introductory textbook takes a building-block approach that
emphasizes the application and interpretation of statistics in
research in crime and justice. This text is meant for both students
and professionals who want to gain a basic understanding of common
statistical methods used in criminology and criminal justice before
advancing to more complex statistical analyses in future volumes.
This book emphasizes comprehension and interpretation. As the
statistical methods discussed become more complex and demanding to
compute, it integrates statistical software. It provides readers
with an accessible understanding of popular statistical programs
used to examine real-life crime and justice problems (including
SPSS, Stata, and R). In addition, the book includes supplemental
resources such as a glossary of key terms, practice questions, and
sample data. Basic Statistics in Criminology and Criminal Justice
aims to give students and researchers a core understanding of
statistical concepts and methods that will leave them with the
confidence and tools to tackle the statistical problems in their
own research work.
This book provides hands-on guidance for researchers and
practitioners in criminal justice and criminology to perform
statistical analyses and data visualization in the free and
open-source software R. It offers a step-by-step guide for
beginners to become familiar with the RStudio platform and
tidyverse set of packages. This volume will help users master the
fundamentals of the R programming language, providing tutorials in
each chapter that lay out research questions and hypotheses
centering around a real criminal justice dataset, such as data from
the National Survey on Drug Use and Health, National Crime
Victimization Survey, Youth Risk Behavior Surveillance System, The
Monitoring the Future Study, and The National Youth Survey. Users
will also learn how to manipulate common sources of agency data,
such as calls-for-service (CFS) data. The end of each chapter
includes exercises that reinforce the R tutorial examples, designed
to help master the software as well as to provide practice on
statistical concepts, data analysis, and interpretation of results.
The text can be used as a stand-alone guide to learning R or it can
be used as a companion guide to an introductory statistics
textbook, such as Basic Statistics in Criminal Justice (2020).
This book provides the student, researcher or practitioner with the
tools to understand many of the most commonly used advanced
statistical analysis tools in criminology and criminal justice, and
also to apply them to research problems. The volume is structured
around two main topics, giving the user flexibility to find what
they need quickly. The first is "the general linear model" which is
the main analytic approach used to understand what influences
outcomes in crime and justice. It presents a series of approaches
from OLS multivariate regression, through logistic regression and
multi-nomial regression, hierarchical regression, to count
regression. The volume also examines alternative methods for
estimating unbiased outcomes that are becoming more common in
criminology and criminal justice, including analyses of randomized
experiments and propensity score matching. It also examines the
problem of statistical power, and how it can be used to better
design studies. Finally, it discusses meta analysis, which is used
to summarize studies; and geographic statistical analysis, which
allows us to take into account the ways in which geographies may
influence our statistical conclusions.
This book provides the student, researcher or practitioner with the
tools to understand many of the most commonly used advanced
statistical analysis tools in criminology and criminal justice, and
also to apply them to research problems. The volume is structured
around two main topics, giving the user flexibility to find what
they need quickly. The first is "the general linear model" which is
the main analytic approach used to understand what influences
outcomes in crime and justice. It presents a series of approaches
from OLS multivariate regression, through logistic regression and
multi-nomial regression, hierarchical regression, to count
regression. The volume also examines alternative methods for
estimating unbiased outcomes that are becoming more common in
criminology and criminal justice, including analyses of randomized
experiments and propensity score matching. It also examines the
problem of statistical power, and how it can be used to better
design studies. Finally, it discusses meta analysis, which is used
to summarize studies; and geographic statistical analysis, which
allows us to take into account the ways in which geographies may
influence our statistical conclusions.
This book provides hands-on guidance for researchers and
practitioners in criminal justice and criminology to perform
statistical analyses and data visualization in the free and
open-source software R. It offers a step-by-step guide for
beginners to become familiar with the RStudio platform and
tidyverse set of packages. This volume will help users master the
fundamentals of the R programming language, providing tutorials in
each chapter that lay out research questions and hypotheses
centering around a real criminal justice dataset, such as data from
the National Survey on Drug Use and Health, National Crime
Victimization Survey, Youth Risk Behavior Surveillance System, The
Monitoring the Future Study, and The National Youth Survey. Users
will also learn how to manipulate common sources of agency data,
such as calls-for-service (CFS) data. The end of each chapter
includes exercises that reinforce the R tutorial examples, designed
to help master the software as well as to provide practice on
statistical concepts, data analysis, and interpretation of results.
The text can be used as a stand-alone guide to learning R or it can
be used as a companion guide to an introductory statistics
textbook, such as Basic Statistics in Criminal Justice (2020).
This introductory textbook takes a building-block approach that
emphasizes the application and interpretation of statistics in
research in crime and justice. This text is meant for both students
and professionals who want to gain a basic understanding of common
statistical methods used in criminology and criminal justice before
advancing to more complex statistical analyses in future volumes.
This book emphasizes comprehension and interpretation. As the
statistical methods discussed become more complex and demanding to
compute, it integrates statistical software. It provides readers
with an accessible understanding of popular statistical programs
used to examine real-life crime and justice problems (including
SPSS, Stata, and R). In addition, the book includes supplemental
resources such as a glossary of key terms, practice questions, and
sample data. Basic Statistics in Criminology and Criminal Justice
aims to give students and researchers a core understanding of
statistical concepts and methods that will leave them with the
confidence and tools to tackle the statistical problems in their
own research work.
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