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E-grāmata: Exploratory and Descriptive Statistics

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Nervous about statistics? This guide offers a clear, straight to the point break down of exploratory and descriptive statistics and its potential. Anchored by lots of examples and exercises to enhance your learning, it offers gudience on how to:
  • Identify and access different types of variables and data
  • Select the best method for measuring your chosen variables and data
  • Use data viusalization techniques to tell stories with your data
  • Appropriately clean and manage your data

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.



Nervous about statistics? This guide offers a clear, straight to the point break down of exploratory and descriptive statistics and its potential. Anchored by lots of examples and exercises to enhance your learning, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
List of Figures, Tables and Boxes
ix
About the Authors xvii
1 Introducing Descriptive and Exploratory Statistics
1(22)
What Is This Book About?
2(1)
What's in Each
Chapter?
2(2)
New to Statistical Analysis? This Book Is for You!
4(1)
So, What Are Descriptive Statistics?
4(5)
What No Statistical Testing?
9(1)
Sounds Like Inferential Statistics Are More Important
9(1)
Types of Descriptive Statistics
10(5)
Categorical Data: Frequency Distributions
10(3)
Continuous or Interval-Level Data: Measures of Central Tendency
13(1)
Continuous or Interval-Level Data: Measures of Dispersion
13(2)
One Variable or Two?
15(1)
So, What Can I Do With Descriptive Statistics?
16(4)
Why Not Try Exploratory Data Analysis
20(3)
2 Finding Data to Describe
23(32)
Introduction
24(1)
Yours or Mine?
24(3)
What Is Primary Data?
24(2)
What Is Secondary Data?
26(1)
Know Thy Data
27(3)
What Do We Mean by Research Data?
27(1)
Administrative Data
28(2)
What's All This About `Open Data'?
30(2)
Show Me the Data!
32(5)
A Little Exploration First
32(1)
Data, Data, Everywhere
33(1)
Accessing and Downloading Data
34(1)
Are You Sure About This Data?
35(1)
Should I Go Large?
36(1)
Downloading Data
37(11)
The General Social Survey
37(5)
The National Survey of Sexual Attitudes and Lifestyles 2010-2012 (Natsal-3)
42(6)
Got Data: Let's Look Inside!
48(1)
Problem Buster!
49(1)
Shit In, Shit Out: And Other Key Principles of Data Management
49(1)
Some Basic Cleaning Tips
50(3)
Looking Ahead
53(2)
3 Measure Everything -- Learn Something -- Answer Nothing: An Exploration Into variables and Types of Measurement
55(26)
Introduction
56(1)
Measurement as a Taken for Granted
57(3)
Measuring the Social World
60(4)
Controversial and Contested Measurements
64(1)
Units of Measurement: Variables
64(8)
Classification - Gender: Men and Women?
65(1)
Counting Gender: How Many Men and Women?
66(4)
Measurement: Different Experiences of Men and Women?
70(1)
Explaining: Different Experiences of Men and Women?
71(1)
Levels of Measurement
72(2)
Categorical Variables
74(1)
Nominal
74(1)
Ordinal
74(1)
Interval Variables
74(3)
Ordinal
74(1)
Scale
75(2)
Looking Ahead
77(4)
4 I Am Not a Number, I Am a Categorical Variable
81(50)
Introduction
82(3)
Percentages: A Story of Parts and Wholes
85(2)
Categorical Data and Percentages
87(8)
Working With Valid Percent and Percent: Working With Missing Data
95(4)
Presenting Percentages, Don't Forget then
97(1)
What's This Cumulative Percent Column All About?
98(1)
To Merge or Not to Merge Responses
98(1)
Writing Up Results: Producing Descriptive Summaries
99(2)
Show, Compare or Present
100(1)
Rounding Up or Down?
101(1)
What to Report With Missing Data?
101(2)
Strengths of Using the Percent
103(2)
Working With Contingency Tables
105(2)
How to Guides for IBM SPSS and MS Excel
107(1)
How to Guide for IBM SPSS
107(8)
Carrying Out Univariate Analysis
108(1)
IBM SPSS Outputs
109(1)
Two's Company: Carrying Out Bivariate Analysis Using IBM SPSS
110(3)
Bivariate Analysis: Including Percentages
113(2)
How to Guide for MS Excel
115(12)
Using MS Word to Make Frequency Tables
117(7)
Two's Company in MS Excel
124(3)
Looking Ahead
127(4)
5 I Like Being Average, I Am an Interval Variable
131(28)
Introduction
132(1)
Measures of Central Tendency and Dispersion
132(1)
Averages in Everyday Life: Feedback Scores
133(1)
The Importance of Averages: Exploring Income and Poverty
134(2)
Rich Man, Poor Man, Beggar Man, Thief: The Problem When Measuring Average Incomes
136(1)
What's Wrong With Being Average: Income Inequalities and the Problem With Outliers
137(4)
Measures of Dispersion: Adding More Context to the Data
141(2)
Singing Stats!
143(2)
Standard Deviation: Horrible Name, but Really Useful
143(1)
Home on the Range? Using the Range Rule for a `Quick' Standard Deviation
144(1)
Average UK Income: But Which Average?
145(2)
Bivariate Analysis Using MCT
147(1)
When Averages Are Just Plain Silly
148(1)
I Am Above Average Actually!'
149(1)
Writing Up the Results: The End Is Nigh
149(2)
`How to' Calculate MCT and MoD Using IBM SPSS
151(4)
`How to' Calculate MCT and MoD Using MS Excel
155(1)
Looking Ahead
155(4)
6 Visualising Our Data
159(40)
Does a Picture Tell a Thousand Words?
160(1)
Does It Really Matter How I Present My Data?
160(4)
There's a Graph for That
164(25)
The One With the Slices
164(5)
The One With the Bars
169(5)
The One With the Lines
174(2)
The One With That Sounds Like Instagram
176(6)
The One With the Whiskers
182(3)
The One With All the Dots
185(4)
Spoilt for Choice!
189(1)
What Makes a Good Graph?
189(2)
Data Mapping
191(6)
Looking Ahead
197(2)
7 The Story Waiting to Be Told
199(22)
Introduction
200(1)
The Opposite Sex?
201(1)
The Gender Pay Gap
202(1)
Data Still Matters
203(2)
Measurement Still Matters
205(1)
`Let's Talk About Sex Baby'
205(2)
The Importance of Context When Exploring Data
207(2)
Beyond the Double Standard: Telling Stories of Sexual Difference
209(1)
Men Estimate -- Women Count: A Gender Difference to Recollecting
210(1)
Trigger Warning! Statistics in the Media
210(3)
The Transient Nature of the News Media
213(1)
Butter Is Good/Bad/Good/Bad/WTF for You!
213(3)
The Fine Line Between Mistakes and Misleading
216(1)
The Devil's in the Detail!
217(1)
Purveyors of Fake News!
217(4)
Glossary 221(6)
References 227(6)
Index 233
Professor Julie Scott Jones is a professor of sociology and the former Head of the Department of Sociology at Manchester Metropolitan University.  Julie has had a career long interest in social science research methods, editing seven books on the subject, including volumes on applied ethics.   She was the founder and original Director of the Manchester Metropolitan University Q-Step Centre, which received £1.15 million in funding from the Nuffield Foundation-ESRC-HEFCE.  Q-Step was an ambitious programme to change the training of quantitative methods and data literacy in social science students. She has co-authored several journal articles on the pedagogy of quantitative methods teaching, based on her current research in this field.  In 2022 her co-authored textbook Exploratory and Descriptive Statistics (2022) was published by SAGE.  Julie currently teaches quantitative data analysis and data management to final year undergraduate students.

Dr John E. Goldring is the Co-Director of the Q-Step Centre at Manchester Metropolitan University, one of 15 centres across the UK to receive funding to promote the development of quantitative methods teaching across the HE sector. Joining Manchester Metropolitan University in 2004, his initial research and teaching focus was on men, masculinity and health. He started teaching statistical analysis in 2012 where he developed a narrative approach to working with numbers based on a Freirean principles of raising critical consciousness and challenging social injustice. Teaching on research methods units at both undergraduate and postgraduate level, he has also successfully supervised a number of PhD students through to completion. In addition to co-authoring of a number of journal articles on pedagogic approaches to teaching statistics, he has written on ethnographies of mens health.