Quantitative Social Science
eBook - PDF

Quantitative Social Science

An Introduction

Kosuke Imai

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  1. 432 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Quantitative Social Science

An Introduction

Kosuke Imai

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About This Book

An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields

Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science.

Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.

Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors.

  • Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science
  • Provides hands-on instruction using R programming, not paper-and-pencil statistics
  • Includes more than forty data sets from actual research for students to test their skills on
  • Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
  • Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises
  • Offers a solid foundation for further study
  • Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

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1.3
Introduction
to
R
15
Table
1.2.
World
Population
Estimates.
Year
World
population
(thousands)
1950
2,525,779
1960
3,026,003
1970
3,691,173
1980
4,449,049
1990
5,320,817
2000
6,127,700
2010
6,916,183
Source:
United
Nations,
Department
of
Economic
and
Social
Affairs,
Popu-
lation
Division
(2013).
World
Population
Prospects:
The
2012
Revision,
DVD
Edition
.
We
also
note
that
the
c()
function
can
be
used
to
combine
multiple
vectors.
pop.first
<-
c
(2525779,
3026003,
3691173)
pop.second
<-
c
(4449049,
5320817,
6127700,
6916183)
pop.all
<-
c
(pop.first,
pop.second)
pop.all
##
[1]
2525779
3026003
3691173
4449049
5320817
6127700
6916183
To
access
specific
elements
of
a
vector,
we
use
square
brackets
[
]
.
This
is
called
indexing
.
Multiple
elements
can
be
extracted
via
a
vector
of
indices
within
square
brackets.
Also
within
square
brackets
the
dash,
-
,
removes
the
corresponding
element
from
a
vector.
Note
that
none
of
these
operations
change
the
original
vector.
world.pop[2]
##
[1]
3026003
world.pop[
c
(2,
4)]
##
[1]
3026003
4449049
world.pop[
c
(4,
2)]
##
[1]
4449049
3026003
world.pop[-3]
##
[1]
2525779
3026003
4449049
5320817
6127700
6916183
Since
each
element
of
this
vector
is
a
numeric
value,
we
can
apply
arithmetic
operations
to
it.
The
operations
will
be
repeated
for
each
element
of
the
vector.
Let’s

Table of contents