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*Furnishes a thorough introduction and detailed information about
the linear regression model, including how to understand and
interpret its results, test assumptions, and adapt the model when
assumptions are not satisfied. *Uses numerous graphs in R to
illustrate the model's results, assumptions, and other features.
*Does not assume a background in calculus or linear algebra;
rather, an introductory statistics course and familiarity with
elementary algebra are sufficient. *Provides many examples using
real world datasets relevant to various academic disciplines.
*Fully integrates the R software environment in its numerous
examples.
*Furnishes a thorough introduction and detailed information about
the linear regression model, including how to understand and
interpret its results, test assumptions, and adapt the model when
assumptions are not satisfied. *Uses numerous graphs in R to
illustrate the model's results, assumptions, and other features.
*Does not assume a background in calculus or linear algebra;
rather, an introductory statistics course and familiarity with
elementary algebra are sufficient. *Provides many examples using
real world datasets relevant to various academic disciplines.
*Fully integrates the R software environment in its numerous
examples.
The Church of Jesus Christ of Latter-day Saints, more commonly
known as the Mormon Church, is quickly becoming a global religion
with more than 12 million members worldwide. In Japan, the number
of official members has more than doubled since 1980. Yet this
impressive growth has not been accompanied by research on Japanese
Mormons. What attracts Japanese people, most of whom have little
experience with Christianity, to an American faith? How are their
lives as Japanese people affected by the Mormon Church? Based on
research in a small congregation in northern Japan and in-depth
interviews with foreign missionaries, Japanese Saints is the first
book to provide an in-depth, qualitative examination of what it is
like to be a Japanese Mormon. Hoffmann pays particular attention to
how members joined the LDS Church, how it has affected
relationships with family and friends, and what membership in the
Church entails.
The world is saturated with data. We are regularly presented with
data in words, tables, and graphics. Students from many academic
fields are now expected to be educated about data in one form or
another. Yet the typical sequence of courses-introductory
statistics and research methods-does not provide sufficient
information about how to focus in on a research question, how to
access data and work with datasets, or how to present data to
various audiences. Principles of Data Management and Presentation
addresses this gap. Assuming only that students have some
familiarity with basic statistics and research methods, it provides
a comprehensive set of principles for understanding and using data
as part of a research project, including: * how to narrow a
research topic to a specific research question * how to access and
organize data that are useful for answering a research question *
how to use software such as Stata, SPSS, and SAS to manage data *
how to present data so that they convey a clear and effective
message A companion website includes material to enhance the
learning experience-specifically statistical software code and the
datasets used in the examples, in text format as well as Stata,
SPSS, and SAS formats. Visit www.ucpress.edu/go/datamanagement,
Downloads tab.
The world is saturated with data. We are regularly presented with
data in words, tables, and graphics. Students from many academic
fields are now expected to be educated about data in one form or
another. Yet the typical sequence of courses-introductory
statistics and research methods-does not provide sufficient
information about how to focus in on a research question, how to
access data and work with datasets, or how to present data to
various audiences. Principles of Data Management and Presentation
addresses this gap. Assuming only that students have some
familiarity with basic statistics and research methods, it provides
a comprehensive set of principles for understanding and using data
as part of a research project, including: * how to narrow a
research topic to a specific research question * how to access and
organize data that are useful for answering a research question *
how to use software such as Stata, SPSS, and SAS to manage data *
how to present data so that they convey a clear and effective
message A companion website includes material to enhance the
learning experience-specifically statistical software code and the
datasets used in the examples, in text format as well as Stata,
SPSS, and SAS formats. Visit www.ucpress.edu/go/datamanagement,
Downloads tab.
Social science and behavioral science students and researchers are
often confronted with data that are categorical, count a
phenomenon, or have been collected over time. Sociologists
examining the likelihood of interracial marriage, political
scientists studying voting behavior, criminologists counting the
number of offenses people commit, health scientists studying the
number of suicides across neighborhoods, and psychologists modeling
mental health treatment success are all interested in outcomes that
are not continuous. Instead, they must measure and analyze these
events and phenomena in a discrete manner. This book provides an
introduction and overview of several statistical models designed
for these types of outcomes - all presented with the assumption
that the reader has only a good working knowledge of elementary
algebra and has taken introductory statistics and linear regression
analysis. Numerous examples from the social sciences demonstrate
the practical applications of these models. The chapters address
logistic and probit models, including those designed for ordinal
and nominal variables, regular and zero-inflated Poisson and
negative binomial models, event history models, models for
longitudinal data, multilevel models, and data reduction techniques
such as principal components and factor analysis. Each chapter
discusses how to utilize the models and test their assumptions with
the statistical software Stata, and also includes exercise sets so
readers can practice using these techniques. Appendices show how to
estimate the models in SAS, SPSS, and R; provide a review of
regression assumptions using simulations; and discuss missing data.
A companion website includes downloadable versions of all the data
sets used in the book.
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