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Quantitative Social Science

Name: Quantitative Social Science
Author: Kosuke Imai

An Introduction

Kosuke Imai

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432 pages
English
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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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Information

Publisher

Princeton University Press

Year

2017

ISBN

9781400885251

Topic

Social Sciences

Subtopic

Social Science Research & Methodology

Index

Social Sciences

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