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Welcome to…

The 5th session of the Social Science & Conservation

Training Series WebEx

June 25, 2012

Quantitative Data Analysis in Social Science I & II

Session I: June 25Session II: July 25

Americas: 10 am HT | 1 pm PT | 2 pm MT | 3 pm CT | 4 pm ET

Asia-Pacific: 9 am Beijing | 10 am Palau | 11 am Brisbane

Supin Wongbusarakum Senior Social ScientistConservation Methods Central Science

Your hosts

Rebecca ShirerSocial Science Coda FellowConservation Scientist, Eastern New York Chapter

Today’s Flow

1. Important elements and

considerations before

data analysis begins

2. Best practices in data

management

3. Resources and Q&A

Learners’ Objectives

1. Understand how quantitative

data can be used

2. Know what is important to

assess a dataset

3. Apply best practices in

working with data

Questions

To ask the presenters a question, please send your question

– via the WebEx chat window to Supin, OR– email to swongbusarakum@tnc.org

If your question is directed to a specific speaker, please indicate Supin or Becky.

DataGroups of observations are called data, which may be qualitative or quantitative.

Ethical principles in obtaining data

• Respect -- “Free, prior and informed consent“ (FPIC)• No harm may be done to the participants –

anonymity and confidentiality• Justice--subjects should not be selected based on a

compromised or manipulated position

Oxfam Australia. 2010. Guide to Prior and Free Informed Consent. http://www.culturalsurvival.org/files/guidetofreepriorinformedconsent_0.pdf

National Institute of Health (NIH), Office of Human Subject Research. 1979. The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.

http://videocast.nih.gov/pdf/ohrp_belmont_report.pdf

What is data analysis?

“The goal is to transform data into information, and information into insight.” – Carly Fiorina

• Data analysis is the process of turning data into information

• Good analysis communicates something meaningful about the world

• What we measure and report is a statement about our beliefs and values

Considerations in Data Analysis

• Begin early--at the design of the study

• Prioritize learning and quality

• Verify and validate results when possible

• Share findings with those your collect data from.

• Do not manipulate the results to the end-users’ expectations

• Manage data

The quantitative method

• Numbers

• Data matrix

• Analyzed by

statistical methods

Strengths of Quantitative Research:• Precise, quantitative, numerical data• Testing hypothesis/confirming theories • Large population and numbers of variables possible• Generalizing finding, random samples with sufficient size• Facilitate systematic comparisons across groups, categories

and over time• Comparatively quick data collection• Less time consuming analysis (using statistical software) • May minimize personal bias • Explanation of (causal) relationships between social

phenomena.

Weaknesses of Quantitative Research:

• Only applicable for measurable (quantifiable) phenomena

• Confirmation bias• Simplifies and ”compresses” the complex reality, lack of detailed narrative

• Theories or categories might not reflect local constituencies’ understandings

• Too general for direct application to specific local situations, contexts, and individuals

“Data is a representation of real life” – Nathan Yau

Telling stories with data• Facts - TNC has protected 113,283,164 acres of land,

which is greater than the entire state of California

• Trends and patterns – The number of acres TNC has protected has increased by 13% since 2006

• Comparisons – TNC has protected more land in Meso- /South America than in the U.S. and Canada combined

• Relationships – Protected areas increase the value and desirability of surrounding lands (McConnell and Walls, 2005)

Data can take many forms

• The U.S. population (2010) is 310,232,863 people

• The U.S. is the third most populous country in the world

• The U.S. population growth rate is 0.963%

• Roughly 8 children are born in the U.S. every minute

Numbers or statements

Lists

Tables

Graphs

Pie

Bar

Line

Scatterplot

Maps

Infographics

Data can take many forms

Lists

Numbers or statements

Tables

Graphs

Bar

Line

Scatterplot

Maps

Infographics

The ten most populous countries:

1. China

2. India

3. United States

4. Indonesia

5. Brazil

6. Pakistan

7. Bangladesh

8. Nigeria

9. Russia

10. Japan

Photo Credit: Robert C. Hass

Data can take many forms

Tables

Lists

Numbers or statements

Graphs

Pie

Bar

Line

Scatterplot

Maps

Infographics

# Country 2010 Population

2050 Expected Population

% Change (2010-50)

1 China 1,330,141,295 1,303,723,332 -1.99%

2 India 1,173,108,018 1,656,553,632 41.21%

3 United States 310,232,863 439,010,253 41.51%

4 Indonesia 242,968,342 313,020,847 28.83%

5 Brazil 201,103,330 260,692,493 29.63%

6 Pakistan 184,404,791 276,428,758 49.90%

7 Bangladesh 156,118,464 233,587,279 49.62%

8 Nigeria 152,217,341 264,262,405 73.61%

9 Russia 139,390,205 109,187,353 -21.67%

10 Japan 126,804,433 93,673,826 -26.13%

Data can take many forms

Pie charts

Tables

Lists

Numbers or statements

Bar

Line

Scatterplot

Maps

Infographics

China19%

India17%

United

States5%

Rest of the World41%

2010 World Population

China India United StatesIndonesia Brazil PakistanBangladesh Nigeria RussiaJapan Rest of the World

Data can take many forms

Bar graphs

Pie charts

Tables

Lists

Numbers or statements

Line

Scatterplot

Maps

Infographics ChinaIndia

United St

ates

IndonesiaBra

zil

Pakistan

Bangladesh

NigeriaRussi

aJapan

Rest of t

he World

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

World Population

2010 2050

Data can take many forms

Line graphs

Bar graphs

Pie charts

Tables

Lists

Numbers or statements

Scatterplot

Maps

Infographics2000 2010 2050

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

1,400,000,000

1,600,000,000

1,800,000,000

China India United StatesIndonesia Brazil PakistanBangladesh Nigeria RussiaJapan

Data can take many forms

Scatterplots

Line graphs

Bar graphs

Pie charts

Tables

Lists

Numbers or statements

Maps

Infographics 0

5,000,000

10,000,000

15,000,000

20,000,0000

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

1,400,000,000

JapanRussiaNigeria

PakistanBrazilIndonesia

United States

India

China

Data can take many forms

Maps

Scatterplots

Line graphs

Bar graphs

Pie charts

Tables

Lists

Numbers or statements

Infographics

Data can take many forms

Infographics

Maps

Scatterplots

Line graphs

Bar graphs

Pie charts

Tables

Lists

Numbers or statements

Before you begin

What information do you need?

Why do you need it? How will it be used?

Who is the audience?

Get to know your data

What kind of data is it? Who collected it?

Why? How was it collected? What was the sampled

population? Sample size?

Is it clean or dirty?

Survey – 3 min

Data type limits your analysis options• Continuous: Can take any value on the numberline, with

meaningful scale and ratios (e.g. years worked at TNC)

Report mean and variance, t-tests, regression

• Categorical: Can only take a fixed set of possible values• Nominal – Categories cannot be ordered (e.g. favorite beverage)

• Binary data – has only two categories (e.g. Yes/No)• Ordinal - Categories have an order but do not describe degree of

difference (e.g. rating on a scale)

Report median, mode, percentages, contingency tables

Get to know your data

What kind of data is it? Who collected it?

Why? How was it collected? What was the sampled

population? Sample size?

Is it clean or dirty?

Sources of data

• Self• Other parts of TNC• Consultants• Partners• Government agencies• Research institutions• Internet

Get to know your data

What kind of data is it? Who collected it?

Why? How was it collected? What was the sampled

population? Sample size?

Is it clean or dirty?

Study designs• Experimental

• Randomized treatment/control

• Observational• Quasi-experimental• Pre-/Post-treatment

• Paired or un-paired• BACI

• Longitudinal• Following a cohort over time

Data collection methods

• Direct• Measurement• Survey• Interview• Observation

• Precision vs Accuracy• Know what tool or instrument was used

• Indirect• Calculation• Document analysis• Inference• Manipulation

Get to know your data

What kind of data is it? Who collected it?

Why? How was it collected? What was the sampled

population? Sample size?

Is it clean or dirty?

A note about statistics

• Descriptive statistics = analysis that helps describe or show the data in a meaningful way

• Inferential statistics = drawing generalizable conclusions from data subject to random variation

• Statistical significance = unlikely to have occurred by chance

Sample size

• Sample size = the number of sample units • How big a sample you need depends on the variance in

the population and the precision you need• Larger samples increase the chances that the estimated

value is within a certain range of the true value

  VariabilityPopulation 50% 40% 30% 20% 10%2,000 714 677 619 509 3223,000 811 764 690 556 3414,000 870 816 732 583 3505,000 909 850 760 601 3576,000 938 875 780 613 3617,000 959 892 795 622 3648,000 976 908 806 629 3679,000 989 920 815 635 36810,000 1000 929 823 639 370

Defining the population

Population = the group about which you want to make an inference

Examples: residents of a town, foresters, urban teens

Sample frame = the actual set of units the sample was drawn from

Example: randomly picking names from a phone book

Who might be missed?• Cell phone users• Unlisted numbers• Homeless

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

Population

Sample frame

O O O O O O O O O

Sampling methods• Census - Every unit counted• Random - Units randomly selected from the population

• Stratified • Clustered

• Non-random• Purposive• Convenience

Get to know your data

What kind of data is it? Who collected it?

Why? How was it collected? What was the sampled

population? Sample size?

Is it clean or dirty?

Data quality and sources of error• Sample bias

• Not every unit in the sample had an equal chance of being included

• Non-response error• Non-respondents may differ from respondents

• Measurement error• Problems with the measurement instrument

• Recording and transcription error• May occur during initial collection or subsequent transfer of data

• Calculation or coding error• A derived value is incorrectly generated

Data clean-up

Things to look for:• Missing or duplicate records

• Missing data• Values out of range• Varying format• Inconsistencies in text fields

• Data in the wrong field

Data management best practices• Unique records

• Every piece of data should be linked to a unique identifier• Example: Interviewee initials - John Adams and Jane Andrews

• Real zeros vs no response• Absence of data or data on absence?

• Backups• Copy paper forms, backup digital copies before making edits

• Metadata• Keep information on collection methods with the data

• Analysis log• You WILL forget what you did – write it down!

Tools and resources• Excel – Next session

Session II: July 25Americas:

10 am HT | 1 pm PT | 2 pm MT | 3 pm CT | 4 pm ET

Asia-Pacific: 9 am Beijing | 10 am Palau | 11 am Brisbane

• Relational data: Access, Oracle• Statistics software: JMP, R, SAS, SPSS, Minitab• Spatial data: ArcGIS, Google Earth

http://www.conservationgateway.org/subtopic/integrating-human-well-being-conservation

Questions and AnswersThank you!

Participant Survey• How many years have you worked at the Conservancy?

• Open-ended• How often do you work with data in your job?

• Never; Rarely; Sometimes; Often; Very Often• Rate on a scale of 1-5 (1=novice, 5=expert) how proficient

you are in using Excel?• What is your favorite work-time beverage?

• Coffee; Decaf coffee; tea; soda; water• How did you commute to work this morning?

• Open-ended