The Ultimate Guide To Effective Data Collection

The%20Ultimate%20Guide%20to%20Effective%20Data%20Collection

User Manual:

Open the PDF directly: View PDF .
Page Count: 44

Download
Open PDF In Browser	View PDF

The
Ultimate
Guide to

Effective
Data
Collection

The
Ultimate
Guide to

Effective
Data Collection
Chapters:
Introduction

Why Data Quality is Crucial

Chapter 1

Survey Design: Creating Your Research
Question, Outcomes and Indicators

Chapter 2

Data Collection Methods

Chapter 3

Qualitative vs. Quantitative Research

Chapter 4

Choosing Your Survey Questions

Chapter 5

Choosing the Right Survey Question Types

Chapter 6

Best Practices Around Writing Survey Questions

Chapter 7

The MECE Framework: Mutually Exclusive,
Collectively Exhaustive Questions

Chapter 8

Closed vs. Open-Ended Questions

Chapter 9

Sampling Your Population

The
Ultimate
Guide to

Effective
Data Collection
Bonus Content
1.

Designing a Great Survey

Piloting a Survey

Field Data Collection Plan

Census Survey

Introduction:
Why Data
Quality is
Crucial

Deming describes the importance that
organisations, funders, and people in
general give to data today. Data has become
fundamental in nearly every aspect of life.
Businesses and corporates use data to
make better decisions, increase profits,
grow revenues and improve efficiency.
Organizations such as hedge funds, stock
brokers and investment banks — where a split
second delay in decision making can lead to

“In God we trust.
All others must
bring data.”
- W. Edwards Deming

Statistician, professor, author,
lecturer and consultant

huge losses — have been stalwarts in using
data to make the smallest decisions.
In addition, the development and policy spaces
have seen the use of data to drive decision
making and increase impact. Non-profit and
government organisations are using data to
inform decisions, such as how much money to
invest in a particular project or how to improve
impact per dollar spent.

With the growing importance placed on data,
surveys have become an indispensable tool
for every organization, from billion-dollar tech
companies to rural nonprofits.
Creating a survey seems simple — just ask
a few simple questions, and you’ll get back
data to solve your every problem. However,
designing a survey correctly takes time and
knowledge. A poorly-designed survey will
lead to useless data, wasting your time and
money. As computer scientists say, “Garbage
in, garbage out.”

Case Study
In 1936, the Literary Digest polled 2.4 million
people on the upcoming U.S. presidential
election. After conducting one of the largest
and most expensive polls in history, the
Literary Digest predicted that Alfred Landon
would win the election 57% to 43% against
the incumbent Franklin D. Roosevelt. At the
same time, George Gallup polled around
50,000 people and predicted a win for
Roosevelt.
The actual results of the election were 62%
for Roosevelt against 38% for Landon. The
Literary Digest poll’s prediction had an error of
19%, the largest error in the history of major
public opinion polls in the U.S.
The explanation for this error — survey
design. Though Gallup surveyed only 2%
of the people that the Literary Digest did,
Gallup’s data was far more accurate because
he designed the survey and sampled the
population more effectively.

Designing a survey involves
several considerations:
• What is the purpose of your survey? What
data are you looking to collect?
• How can you best collect that data? What
sort of survey and research methodology
should you use?

• How should you write the questions in your
survey?
• Who should you survey?
This ebook is designed to take you through
these questions and help you design a survey
that will give you high-quality data. Chapter
1 will help you think through the purpose,
outcomes and indicators of your survey.
Chapters 2 and 3 will help you determine
what data collection method you should use,
as well as whether you need a qualitative or
quantitative survey. Chapters 4-8 address
writing the questions in your survey — what
you want to ask and best practices around
how to ask it. Lastly, Chapter 9 covers all
aspects of sampling your population —
sampling methods, best practices, and a
quick sample size formula.

Chapter 1:

Survey Design:
Creating Your
Research
Question,
Outcomes
and Indicators
The most important part of your survey is
determining your purpose – why are you
conducting this survey and what do you want
to learn?

Setting your research question, outcomes and
indicators clearly makes writing the rest of
your survey far simpler. Moreover, it ensures
that everyone in your organization is on the
same page about your survey.
This chapter will show how to build your
research question, outcomes and indicators
through an exploration of two case studies.

Determining Your Research
Question
Before you start collecting data, it is important
to figure out your research question. As part
of this process, you also should think broadly
about who you can survey.
To fully formulate your research question, you
should be able to answer three questions:

1. What is my research question?
2. Why am I collecting this information?
3. Who can I collect this data from?
Tool Tip
The people you are collecting data from are
called the “target population” or “sample”.

Case Study #1
You run an education NGO, which works on a
teacher training program across 1,500 schools
in Bihar and Uttar Pradesh. As part of your
program, your team trains teachers on how to
improve your students’ reading skills.

1. What is the research question behind
collecting data on the impact of the
programme?

How has my NGO improved the teaching skills
of the teachers we work with, and how has
this improved student reading skills?

2. Why am I collecting this information?
To measure the impact of my NGO’s
programme so I can compare it to my other
programmes and communicate its impact to
my funders.

3. Who can I collect data from?
I can collect data from the students and
teachers where the NGO works.

Case Study #2
Your NGO works with women’s SHGs from a
district in Jharkhand. Your model for change
is two fold – you directly impact the SHGs you
work with by helping them fundraise, which in
turn empowers women who participate in the
SHGs.

1. What is the research question behind
collecting data on the impact of the
programme?

How has my NGO contributed to increasing
the funding of the SHGs we work with?

2. Why am I collecting this information?
To measure the impact of my programme and
show the effect of our work.

3. Who can I collect data from?
I can collect data from the SHGs that my NGO
works with and the families of the members
of the SHGs. I can also collect data on SHG
funding from my program officers.
Tool Tip
The second question might seem
unnecessary. After all, it doesn’t directly go
into your research question. However, it is an
essential part of the survey design process.
If you cannot fully answer why you are
conducting your survey, you are not ready to
start your survey.

Once you can answer these three questions
for your own survey, you have figured out your
research question and target population.

Determining Your Outcomes
Once you have determined a research
question, you can create the set of outcomes
for your survey.
An outcome is something that you can track
to measure data on your research question.
Outcomes should be feasibly measurable.

Research Question
How has my NGO improved the teaching skills
of the teachers we work with, and how has
this improved student reading skills?

Outcomes
• Change in the funds raised by the SHGs
before and after they entered my programme
• Empowerment of women in the district

Determining Your Indicators
Once you have determined your set of
outcomes, you can create the indicators.
SMART indicators are metrics against which
you can measure your program’s progress or
effectiveness. SMART stands for:
Specific: Is it specific about who is changing
what and how?
Measurable: Can it be counted, observed,
analyzed, tested or otherwise quantified?
Agreeable: Is it agreeable and acceptable to
all stakeholders?
Relevant: Is it relevant to the intended outputs
and outcomes?
Timebound: Does it set a date or timeline for
achieving change?

Case Study #1

Change in teachers’ teaching skills
Change in students’ reading skills

You run an education NGO, which works on a
teacher training program across 1500 schools
in Bihar and Uttar Pradesh. As part of your
program, your team trains teachers on how to
improve your students’ reading skills.

Case Study #2

Research Question

Your NGO works with women’s SHGs from a
district in Jharkhand. Your model for change
is two fold – you directly impact the SHGs you
work with by helping them fundraise, which in
turn empowers women who participate in the
SHGs.

How has my NGO improved the teaching skills
of the teachers we work with, and how has
this improved student reading skills?

Outcomes

Research Question
How has my NGO contributed to increasing
the funding of the SHGs we work with?

Outcomes
• Change in teachers’ teaching skills
• Change in students’ reading skills

Indicators

Measuring change in teaching skills:

• Number of teachers that went through the
training program in a year
• Percentage of teachers that reported
improvement in skills over a year
Measuring change in reading skills:
• Number of students reached in a year
• Percentage of students that reported
improved reading skills over a year

Research Question
How has my NGO contributed to increasing
the funding of the SHGs we work with?

Outcomes
• Change in the funds raised by the SHGs
before and after they entered my programme
• Empowerment of the women in the district
Indicators

Indicators
• Number of SHGs that joined the program
over a year
• Funds raised for SHGs over a year
• Number of micro-loans given out by SHGs
over a year
• Percentage of SHG members that reported
more household decision-making power over
a year.
After creating your research question,
outcomes and indicators, you’ll know exactly
what data you need to collect. From there, it’s
much easier to design the rest of your survey.

Chapter 2:
Data
Collection
Methods

Once you know what data you want to
collect, it is important to figure out which data
collection method you will use. Each method
has its own advantages, disadvantages and
use cases.
Tool Tip
Any research is only as good as the data that
drives it, so choosing the right method of data
collection can make all the difference.

Observation
Seeing is believing, they say. Making direct
observations, when the situation allows for it,
is a very quick and effective way of collecting
data with minimal intrusion. Establishing the
right mechanism for making the observation is
all you need.

Advantages
• Non-responsive sample subjects are a nonissue when you are simply making direct
observations.
• This mode does not require a very extensive
and well-tailored training regime for the survey
workforce.
• It is not as time-consuming as the other
models that we will discuss below.

• Infrastructure requirement and preparation
time are minimal.

Disadvantages
• Heavy reliance on experts who must know
what to observe and how to interpret the
observations once the data collection is done.
• Possibility of missing out on the complete
picture due to the lack of direct interaction
with sample subjects.

Use Case

finer nuances, leaving the responses open to
interpretation. Interviews and Focus Group
Sessions, as we will see later, are instrumental
in overcoming this shortfall of questionnaires.
• Response rates can be quite low. Choosing
the right question types can help to optimize
response rates, but very little can be done to
encourage the respondents without directly
conversing with them.

Use Case

Making direct observations can be a
good way of collecting information about
mechanical, orderly tasks, like checking the
number of manual interventions required in
a day to keep an assembly line functioning
smoothly.

A survey can be carried out through directlyadministered questionnaires when the sample
subjects are relatively well-versed with the
ideas being discussed and comfortable
at making the right responses without
assistance. A survey about newspaper
reading habits, for example, would be perfect
for this mode.

Questionnaires

Interviews

Questionnaires, as we consider them here, are
stand-alone instruments for data collection
that are administered to the sample subjects
either through mail, phone or online. They
have long been one of the most popular data
collection methods.

Conducting interviews can help you overcome
most of the shortfalls of the previous two data
collection methods that we have discussed
here by allowing you to build a deeper
understanding of the thinking behind the
respondents’ answers.

Advantages

• Questionnaires give the researchers
an opportunity to carefully structure and
formulate the data collection plan with
precision.
• Respondents can take these questionnaires
at a convenient time and think about the
answers at their own pace.
• The reach is theoretically limitless. The
questionnaire can reach every corner of the
globe if the medium allows for it.

• Interviews help the researchers uncover rich,
deep insights and learn information that they
may have missed otherwise.
• The presence of an interviewer can give
the respondents additional comfort while
answering the questionnaire and ensure
correct interpretation of the questions.
• The physical presence of a persistent, welltrained interviewer can significantly improve
the response rate.

Disadvantages

• Questionnaires without human intervention
(as we have taken them here) can be quite
passive and can miss out on some of the

• Reaching out to all respondents to conduct
interviews is a massive, time-consuming
exercise that leads to a major increase in the

cost of conducting a survey.
• To ensure the effectiveness of the whole
exercise, the interviewers must be well-trained
in the necessary soft skills and the relevant
subject matter.

Use Case
Interviews are the most suitable method for
surveys that touch upon complex issues like
healthcare and family welfare. The presence
of an interviewer to help respondents interpret
and understand the questions can be critical
to the success of the survey.

Focus Group Discussions
Focus Group Discussions take the interactive
benefits of an interview to the next level by
bringing a carefully chosen group together for
a moderated discussion on the subject of the
survey.

Advantages
• The presence of several relevant people
together at the same time can encourage
them to engage in a healthy discussion and
may help researchers uncover information that
they may not have envisaged.
• It helps the researchers corroborate the facts
instantly; any inaccurate response will most
likely be countered by other members of the
focus group.
• It gives the researchers a chance to view
both sides of the coin and build a balanced
perspective on the matter.

Disadvantages
• Finding groups of people who are relevant
to the survey and persuading them to come
together for the session at the same time can
be a difficult task.
• The presence of excessively loud members
in the focus group can subdue the opinions of
those who are less vocal.
• The members of a focus group can often fall

prey to group-think if one of them turns out
to be remarkably persuasive and influential.
This will bury the diversity of opinion that may
have otherwise emerged. The moderator of a
Focus Group Discussion must be on guard to
prevent this from happening.

Use Case
Focus Group Discussions with the lecturers of
a university can be a good way of collecting
information on ways in which our education
system can be made more research-driven.

Tool Tip
Keeping these factors in mind will go a long
way toward helping you choose between
the four data collection methods. The recent
evolution of technology has given researchers
powerful tools and dramatically transformed
the ways that surveyors interface with survey
respondents.

Chapter 3:

Qualitative vs.
Quantitative
Research
Before you formulate your questionnaire, it is
important to consider what type of information
you’d like to collect — qualitative or
quantitative. Both qualitative and quantitative
research have their places in data collection.

Quantitative Research
Quantitative research (derived from the word
“quantity”) describes research that produces
countable or numerical results.

Examples of Quantitative Questions
How long does it take you to travel to work?
□
□
□
□

0-20 minutes
21-40 minutes
41-60 minutes
Over 1 hour

What forms of transportation do you use while
traveling to and from work? Please select all
that apply.
□
□
□
□
□
□

Personal car or taxi
Auto
Rickshaw
Bicycle
Metro
Other

Would you move to a new location just to
decrease your commute time?
□ Yes
□ No
□ Not applicable
Rate each form of transportation on a
scale of 1-5.
(1 is strongly dislike, 2 is dislike,
3 is neutral, 4 is like, 5 is strongly like)
□ Personal car or taxi
□ Auto
□ Rickshaw
□ Bicycle
□ Metro

Qualitative Research
Qualitative research describes research that
produces non-numerical results. It generally

investigates the “why” and “how” of your
research question.

Examples of Qualitative Questions
Do you like your commute to and from work?
Why?
How do you generally get to and from work?
Why is the metro your favorite form of
transportation?
Is there anything else you’d like to tell us
about your commute?

When to Use Qualitative and
Quantitative Research
Qualitative research is often used as
exploratory research. It is helpful to provide
insights into the problem you want to
research more, or it helps to identify ideas and
hypothesis for future quantitative research.
Qualitative research also is useful in learning
more about the “why” and “how” behind your
question.
Quantitative research is a great way to
generate numerical data, create usable
statistics, and generalize results or uncover
patterns from a larger population.
To figure out whether you should use
qualitative research, quantitative research, or a
mix of the two, look at your research question,
outcomes and indicators. (If you don’t have
these, go back to Chapter 1!)

Examples
Research question: Are the children in my
classrooms improving?
Quantitative data:
•
•
•

Children’s test scores over time
Children’s grades over time
Children’s scores on an evaluation created
for this research

Qualitative data:
•
•
•

Parents’ opinions on whether they think
their children are improving (and why)
Teachers’ thoughts on whether they think
their students are improving (and why)
Students’ feedback on whether they think
they are learning more (and why)

Research question: Is my women’s
empowerment program making participants
feel more independent?

Chapter 4:
Choosing
Your Survey
Questions

Quantitative data:
•

•
•

Ask participants to rank their
independence on a quantitative scale
before and after the program
Ask participants if they feel more
independent (Yes or No question)
Ask participants how likely they are to
engage in measures of independence
(i.e. standing up to their husband, taking
more control over household finances) on
a quantitative scale before and after the
program

Qualitative data:
•
•

•

Ask participants how they feel after
completing the program
Ask participants about whether they think
they are likely to engage in measures
of independence (i.e. standing up to
their husband, taking more control over
household finances) before and after the
program
Ask participants’ friends, husbands, and/
or children about the participants’ behavior
before and after the program

As the previous examples show, many
research questions can be answered using
both quantitative and qualitative research. To
decide which is right for you, think about your
research question, what questions you need
to answer, and the type of data that you are
hoping to collect.

Now that you are aware of the different
elements of a questionnaire, the next step is
to think about the various types of questions
that you would want to ask in a given
questionnaire. The below questions can help
you decide which questions you should ask.

1. What Kind of Information Do
You Need?
Different categories of information include:
personal background (name, religion, age,
caste, gender, etc.), education information,
health information, government schemes
subscription, etc.
For example, say that we are looking to
measure the change in students’ learning
outcomes. We could decide that we need
some personal details of the students (age
and gender) as well as learning levels of the
students, classroom activities of the teachers,
and some school-level information. We would
not need details on the religion or caste of the
students, personal details on the teachers, or
information about the students’ families.
Tool Tip
To arrive at the different sets of information,
put the outcome in the centre of a paper and
write all the things that can possibly impact
that outcome. Talk to your program officers
and field staff about it.

2. What Information Can Be
Easily Collected?
Personal information can be easily collected
but BMI, height, weight, etc. might be difficult
to collect. It is easy to ask someone their
weight, but the accuracy of this data is often
low. Measuring people’s weight with a scale is
far more accurate, but it is also more difficult.
Choose parameters that are useful and can be
collected effortlessly.
For example, say that you want to judge
a teacher’s classroom skills. You might be
tempted to capture a lot of information about
the classroom — you can probably sit in the
classroom and capture classroom activities
for an hour. Or you could simply do a 5-minute
observation to learn about what happens on a
typical day. You need to balance the effort in
collecting additional information and the value
of that information.
Tool Tip
To arrive at the final data points, think of
the following things: how difficult will it
be to collect that information, how would
respondents react to a particular question,
and how quickly can you collect a particular
piece of information?

3. What Information Is Actually
Useful for the Organization?
It is tempting to collect all information that
you can. But it is important to only collect
information that is useful for the organization.
For example, say that you want to judge a
teacher’s classroom skills through a 5-minute
observation. It would be easy to simultaneously
collect other information on the school or
students. However, don’t collect information
just because you can collect it! Only collect

information that will help you with your analysis.

4. Did You Include the 5
Key Questions (Introduction,
Identifiers, Consent, Open-Ended
Fields and Validations)?
Always have the following questions in your
questionnaire.
•

•
•

•

Introduction: the right introduction to the
survey can set the tone of the survey and
is often helpful in making the respondent
understand why the survey is crucial and
how it will help her/him in return.
Identifiers: name, age, father’s name and
location (for example).
Consent: most surveys in India require
organizations to seek the beneficiary’s
consent. It’s a good ethical practice.
Open-ended fields: ask for any information
that might not be captured by the specific
question types.
Validations: information like GPS and time
taken to validate whether the questionnaire
was filled correctly.

By leveraging smartphone-based tools
for data collection, you will be able to
automatically capture GPS location and the
average time taken for surveys. This will be
helpful in creating a check to ensure your field
surveyors are collecting accurate data from
the ground.
For instance, if you are collecting data from
households in a village, then possibly the
GPS coordinates of each survey response
should be a minimum of 15 metres apart.
Similarly, if the average time taken for a survey
is 20 minutes and one of your surveyors is
submitting responses in under 5 minutes, this
could indicate issues with his data quality and
validity.

Example
Say that you want to measure the

improvement in student learning outcomes at
a given school. Go through the four questions
above to create the most important questions
in your survey.
Question 1: What kind of information do
you need?
•
•
•

•

Reading level of the kids, to see
improvement in learning levels
Teacher effort aimed at reading, to see
improvement in teaching skills
Background information on teachers
and students, to track improvement and
changes
School information, to explain differences
in student learning outcomes in different
types of schools

Questions 2-3: What information can be
easily collected and is relevant to the
organization?
•

•

Reading level of the kids: use the ASER
battery and sample a few kids from a
classroom. Information on all the kids is
not necessary.
Teacher effort aimed at reading: observe
classrooms for 5-10 minutes while the
teachers are teaching reading skills.
Background information: for students, only
collect age, gender, class, and name; for
teachers, collect only experience, classes
and subjects taught.
School information: collect information on
number of students, teachers, teacherpupil ratio, as well as average fees. Any
other information that is irrelevant to the
analysis should not be collected.

Question 4: Did you include the 5 key
questions?
•
•

•

Add introduction
Add relevant identifiers for students
(if not already covered in background
information)
Add consent

•
•

Add an open-ended field for surveyor
comments
Add GPS and time stamp to validate the
information from the field

Chapter 5:

Choosing the
Right Survey
Question
Types
Choosing a question for your survey is not
enough. It is essential to choose the correct
question type. A good question asked in the
wrong way will not give you good data.
There are 8 main question types that you can
use for surveys. Below are descriptions of
each question type and when to use each.

Text
This is the most open-ended type of question.
One can type in anything. It is ideal to use this
type of question to collect a person’s name or
to collect qualitative information such as “Any
other feedback”.
Example: What is your name?

Dichotomous
(Yes/No, True/False)
Dichotomous questions seek a binary
response to a question.

Collect
Create more than 20 question types
and collect data from any android device
Collect is a mobile data collection tool to capture
data from the field, monitor progress of projects, and make
quick decisions based on real-time, accurate data.

TRY
COLLECT

Find out more at www.socialcops.com/collect

Example: Do you love to read?
A. Yes
B. No

Numerical
This is used to capture numbers. Numerical
questions should only be used to collect
specific numbers that cannot be confined into
certain ranges.
Example: How many times do you jog in a
month? (numbers only)

Multiple Choice Questions
This is the most frequently-used question
type. This can be single-select or multipleselect based on the need of the question.
Multiple choice questions (or MCQs) are highly
recommended, since they reduce the chances
of capturing the wrong information.
Example (single-select multiple choice): What
is your current education status?
•
•
•
•
•
•
•

Uneducated
Primary
Secondary
Senior Secondary
Graduate
Post-graduate
Doctorate

Example (multi-select multiple choice): Which
of the following subjects do you study?
•
•
•
•
•
•
•

Maths
Hindi
English
Science
Social Science
Environmental Studies
Other

When creating options in an MCQ, it is
very important that all options are mutually

exclusive but collectively exhaustive. (See
Chapter 7 for more information.)

Tabular/Roster
These are used to capture the same sets
of information about multiple entities. For
example, personal background about a
household can be captured using a table.

Name

Relationship Age Education
with head

2
3
4
5

Scale
This question type is generally used to record
preferences, opinions and ratings.
For example, you can use scale for capturing
information on how good a particular class
was. This question could use a 5-point
scale question: Bad, Fair, Good, Very Good,
Excellent.
Example: How was the food?
Poor, Average, Good, Excellent

Media Questions
Sometimes you want to capture information
like pictures, audio, or drawings. If you are
using mobile-based technology, you can
use media questions to verify captured
information.
Example: Take a photo of the anganwadi you
visited.

Maps and Timestamps

Q1: Do you love to study?

If you are using mobile-based technology, it is
also possible to capture geo-coordinates and
time-stamps to enrich your data and make it
more verifiable.

Q2: If yes, why do you love to study?

Example: Take the geo-location of the
anganwadi you visited.

Chapter 6:

Best Practices
Around
Writing Survey
Questions
Chapters 4 and 5 talked about choosing the
correct questions and question types for your
survey. Here are a few additional tips to help
you frame questions correctly.

1. One Question at a Time
Keep the questions simple, crisp, and to
the point. Make sure that you are not asking
multiple questions in one question – it might
make the answer complex and confuse the
respondent.

A. Yes
B. No

A. To maximize knowledge
B. To learn new things
C. To score good grades
D. Other

Remember, the “length” of your questionnaire
is not determined by the number of questions,
but by the time taken to answer them.
The second format of asking the same
question breaks down the questions while
simultaneously reducing the time taken to
answer them. People who answer “No” in Q1
will not be asked the next question – reducing
the time taken by the surveyor in explaining
Q2. Breaking down the two questions also
allowed us to turn Q2 into a closed-ended
question, which reduced the time taken in
answering Q2.

2. Beware of Subjective
Questions
Use text/subjective questions only when there
is no other suitable type of question you can
use. Most subjective questions can easily be
written as MCQs. The problem with subjective
questions is that, if you let people input
answers, the same thing can be said in many
different ways.
Take the case of a village name: Gandipet.
People can have many different ways
of writing the same thing: Gandipetta,
Gandipetu, Gandipettu, etc. Hence, it is
always better to list out all possible options.

Q1: Do you love to study? Why?

For questions like state/district/block/village
name, list all possible options in the form of a
list. For questions related to age, give a list of
suitable ranges.

3. Ask Objective Questions

For example, see what is better:

Do not include the answer in your question, as
it will introduce surveying bias.

C. Shared answers in a test
D. Had lunch from another student’s tiffin box

For example, rather than asking “Do you
think India is on a downward trajectory?”, you
should ask “What trajectory is India on? A.
Downward B. Upward C. Other”.

While asking sensitive questions, it also
helps to use the right words. In the previous
example, “copying” and “cheating” were not
used because they will make the question
negative. Using more neutral words like
“sharing” makes people more likely to answer
honestly.

4. Avoid Negative Questions
One of the best ways to be objective is by
avoiding negative questions. Most positive
questions are more direct than negative
questions.
For example, rather than asking “What are
the reasons India is not growing?”, ask “What
are the factors affecting India’s growth?” This
reduces bias and makes your question more
objective.

5. Be Careful while Asking
Sensitive Questions
Don’t ask any critical or sensitive information
directly. People are often unwilling to share
sensitive information with a third party.
For example, asking a question like “What
is your income?” as a numerical question
might result in dishonest answers by
survey respondents. Asking this question
as a multiple choice question by bucketing
responses into different income brackets
might result in more accurate responses.
For any sensitive questions, keep it under
cover. For example, if you were to ask
students whether they have cheated in an
exam, it might be better to ask the question in
a multiple-choice question that does not focus
on cheating:
I have done the following with my school
friends:
A. Played cricket/soccer after school
B. Been punished for coming to class late
because I was playing

6. Don’t Ask for Too Much Detail
It is important to have the right amount of
detail. Don’t dig deeper than needed, and do
take only superficial information.
For example, if you want to know about the
sources of energy at home, don’t ask about
all the appliances used. Only ask whether
specific energy sources (electricity, cowdung,
gas, etc.) are being used or not. Make sure to
cover all the major sources of energy.

Chapter 7:
The MECE
Framework:
Mutually
Exclusive,
Collectively
Exhaustive
Questions

Once you are clear about your research
question and the type of data you will
collect, the next step is to put together
a questionnaire that can help you collect
that data. Before putting together the
questionnaire, it is important to understand
the MECE framework.

MECE Framework
“MECE” stands for “Mutually Exclusive and
Collectively Exhaustive”.
When designing your questionnaire, it is
important to ensure that all the different
questions and sections are mutually exclusive
and collectively exhaustive. “Mutually
exclusive” means that no two questions
should be repeated. “Collectively exhaustive”
means that questions should be chosen in a
way that captures all the required information.
It is important to note that the MECE
framework applies to both questions and
answer choices.

Examples
Consider the following two questions, and
figure out whether they are MECE.

Q1: What is the educational status of
all the members of the household?
Q2: Name the highest educated
member here.
Answer: This is not MECE because we can
capture the information needed in Q2 in Q1
itself. We don’t need another question. Thus it
is not mutually exclusive.
Consider the following answer choice, and
figure out whether it is MECE.

Question: How many children
do you have?
A. 1
B. 2
C. 3
D. 4 or more

Answer: This is not MECE. This answer choice
covers all the positive values, but it doesn’t
give an option for 0. Thus it isn’t collectively
exhaustive.

Exercise
See whether the answer choices in the
following questions are MECE or not:

Q1: What is your religion?
A. Hindu
B. Muslim
C. Christian
D. Sikh

Answer: No, these answers are not MECE.
We haven’t included Jainism, Buddhism,
and several other religions. The choices
are mutually exclusive but not collectively
exhaustive. (See the Tool Tip below.)

Q2: Which bracket does your age lie?
A. 0-10
B. 11-20
C. 21-43
D. 44-80

Answer: No, these choices are mutually
exclusive, but they are not collectively
exhaustive. They don’t cover the option for
ages greater than 80

Q3. Which category do you fall in?
A. General
B. OBC
C. SC
D. ST

Answer: Yes, these answers are MECE. The
answer choices are mutually exclusive (no
overlap) as well as collectively exhaustive
(covers all possible options).
Tool Tip
An easy way to ensure that a multiple choice
question is collectively exhaustive is to add
the option “Other”. If the enumerator chooses
“Other”, you can ask the question “If other,
please specify”.

Chapter 8:
Closed vs.
Open-Ended
Questions

Every question on a survey will be either an
closed or open-ended question. This means
that closed and open-ended questions are
at the core of your survey design. It is crucial
to know the difference between closed and
open-ended questions and when to use each.

The Difference Between Closed
and Open-Ended Questions
Closed-ended questions have a defined,
closed set of responses. This means that
respondents only have a limited number
of options for their answer to the question.
Closed-ended questions come in a multitude
of forms, but they are often in the form of
multiple choice (single or multi-select), scale
or dichotomous questions.

Here are a few examples of
closed-ended questions:

How old are you?
• 0-10 years old
• 11-20 years old
• 21-30 years old
• 31-40 years old
• 41-50 years old
• Over 50 years old

Do you feel better today
than yesterday?
• Yes
• No
• I feel the same

Are you pregnant?
• Yes
• No

How do you feel about this
health program?
• Strongly dislike
• Dislike
• Neutral
• Like
• Strongly like
In contrast, open-ended questions do not
have a defined set of responses. This means
that the set of possible responses is infinite,
so respondents can provide any answer they
like. Open-ended questions are generally in
the form of narrative or text questions.
Here are the previous examples, reworded as
open-ended questions:

How old are you?
How do you feel today
compared to yesterday?

Is it possible that you might
be pregnant?
What do you think about this
health program?
These are open-ended questions because
the answers to these questions are not predetermined, like they were previously. For
example, in the first question, the respondent
is not limited to 6 age brackets; they can
answer with any number, or even ages like “8
years and 3 months”.

When to Use Open-Ended
Questions
In general, open-ended questions are useful
for qualitative research, learning more
information about a topic, and surveys with
small sample sizes. There are four main cases
when open-ended questions should be used.

1. Preliminary Research
It is often helpful to conduct preliminary
research to learn more about your problem
before conducting your final survey. Openended questions are a key component of
preliminary research, since you generally
won’t know the answers that you’ll receive.
Instead, you are looking to gain information
that you likely don’t know.
For example, imagine that you want to
improve your website. Before you can write
an effective survey, it would be useful to get
people’s general thoughts on your website.
This will help you write a more targeted final
survey.
Your preliminary research could use openended questions like:
• What do you think about this website?
• What are your favorite parts of the website?
• What are your least favorite parts of the
website?

• What could be improved on the website?
Using the answers to these questions would
help to write the final survey. For example, if
many people said during preliminary research
that they don’t like the colors on the website,
you could include a section on your final
survey where respondents rank different color
palettes.
In addition, you can use preliminary research
to improve the closed-ended questions in your
final survey. Most surveys use closed-ended
questions, but writing closed-ended questions
requires knowing the possible set of answers
to your questions. Often, you don’t know
this before you start your survey. Preliminary
research with open-ended questions is helpful
to learning the set of answers for future
closed-ended questions.
For example, imagine that you want to learn
more about why people are not attending your
meetings. It would be easy to analyse the
results of a closed-ended question like:
Why did you miss the last meeting?
• It was too early for me to attend
• It was too late for me to attend
• It was too far from my house
• Other
However, preliminary research would be a
great way to learn the full set of possible
answers. For example, you could use an
open-ended question (e.g. “Why did you miss
the last meeting?”) to get more information
on why people miss meetings. Then, once
you understand the most common reasons,
you can write a much better closed-ended
question in your final survey.

2. Expert Interviews
Experts usually know more about a subject
than you will, so it is useful to use open-ended
questions to get as much information as

possible. Limiting experts to a pre-determined
set of responses with closed-ended questions
will be less productive than giving them the
freedom to demonstrate their knowledge and
talk at length.

3. Surveys with a Small Sample Size
For a large number of respondents, it can
be difficult to read and analyse the answers
to open-ended questions. Open-ended
questions can often lead to responses of
several sentences or paragraphs. Comparing
these answers across dozens or hundreds of
respondents is extremely difficult and timeconsuming.
However, this becomes much easier if you’re
conducting a survey with a small sample size
(e.g. under 20 respondents). For small sample
sizes, open-ended questions are a great way
to solicit more detailed information in a way
that is still analyzable.

4. The End of Any Survey
The end of a survey is the perfect place to
include an open-ended question. No matter
how well designed a survey is, it can never
account for all possible opinions and data.
Including an open-ended question at the end
of a survey — such as “Is there anything else
you’d like to tell me?” or “Is there anything
that I’ve missed?” — will allow respondents to
share extra information, opinions, or concerns.
Giving respondents the freedom to include
additional information or comments is also a
good way to show respect for the time and
effort they took in completing your survey.

When to use Closed-Ended
Questions
Closed-ended questions should be used for
easier analysis and reporting of the data you
are collecting.
For example, imagine that you are polling

1,000 people about their internet usage. If you
ask the open-ended question “Tell me about
your internet usage?”, you will end up with
1000 unique responses that cannot easily be
analysed or reported. Instead, if you use a
closed-ended questions like the one below,
you will be able to better understand and
report the results.
On average, how many hours do you use the
internet per week?
• 0-5 hours
• 6-10 hours
• 11-15 hours
• 16-20 hours
• Greater than 20 hours
With a closed-ended question, you can easily
analyse the data and report a clear result like
“63% of respondents use the internet less
than 5 hours per week”.
Tool Tip
In general, qualitative research will use openended questions and quantitative research will
use closed-ended questions. See Chapter 4
for more details on qualitative and quantitative
research.

Chapter 9:

Sampling Your
Population
Ideally, a survey should gather data on every
single person in the target population. For
example, a survey about learning outcomes

at a small school could track the test scores
of every student. Collecting data on everyone
in the target population is the best case
scenario, since it ensures that everybody
who matters to the survey is represented
accurately.
However, this is only possible if the population
is small enough and the researchers have
sufficient resources to reach out to everyone.
This often is not the case, so researchers
have to identify a subset of the population to
survey.
How you choose this subset of the target
population is crucial to the quality of your
data. The group must be carefully identified
and representative of the larger population,
else your data will not be useful for drawing
inferences.
If done right, survey sampling can save
time and money while allowing you to draw
interferences about a large group of people.

3 Things to Keep in Mind While
Choosing a Sample Population
1. Consistency
It is important that researchers understand
the population on a case-by-case basis and
test the sample for consistency before going
ahead with the survey. This is especially
critical for surveys that track changes across
time and space. If your sample is consistent,
you can be confident that any change in
the data reflects real change across the
population, rather than change across atypical
individuals in the population.

2. Diversity
Ensuring diversity of the sample is a tall order,
as reaching some portions of the population
and convincing them to participate in the
survey can be difficult. However, for a sample
to truly represent the population, the sample

must be as diverse as the population itself and
sensitive to local differences.

3. Transparency
There are several constraints that dictate
the size and structure of the population. It
is imperative that researchers discuss these
limitations and maintain transparency about
the procedures followed while selecting the
sample, so that the results of the survey are
seen with the right perspective.

Choosing Your
Sampling Technique
Probability Sampling
For probability sampling techniques, each
person in the population has a defined,
non-zero probability of being included in
the sample. Probability sampling provides
the most valid or credible results because it
reflects the characteristics of the population
from which they are selected. There are
three probability sampling methods: random
sampling, systematic sampling and stratified
sampling.

Random Sampling
When: There is a very large population and
it is difficult to identify every member of the
population.
How: The entire process of sampling is
done in a single step, where each subject is
selected independently of the other people in
the sample.
Pros: In this technique, each member of the
population has an equal chance of being
selected for the sample.
Cons: When there is a very large population,
it is often difficult to identify every member
of the population so the pool of subjects can
become biased. For example, dialing numbers

from a phone book may not be entirely
random since the numbers would correspond
to a localized region.

Sunday customers. They can choose every
10th customer entering the supermarket and
conduct the study on this sample.

Use case: Want to study and understand
the rice consumption pattern across rural
India? While it might not be possible to cover
every household, you could draw meaningful
insights by building your sample from
randomly-selected districts or villages.

Stratified Sampling

Systematic Sampling
When: Your given population is logically
homogenous. This means that they all share
a characteristic that is important to the
survey. For example, suppose a supermarket
wants to study the buying habits of their
Sunday customers. The customers who enter
the supermarket on Sunday are a logically
homogeneous population since they share 2
key qualities: “customers of the supermarket”,
and “visited the supermarket on Sunday”.
How: Arrange the elements of the population
in some order and select terms at regular
intervals from the list.
Pros: Systematic sampling is far simpler
than random sampling, and it ensures that
the population will be evenly sampled. In
random sampling, there is a chance that the
sample might include a clustered selection
of subjects. This can be avoided through
systematic sampling.
Cons: The possible weakness is an inherent
periodicity of the list (i.e. if the people you
are surveying are already ordered in a certain
non-random way). This can be avoided
by randomizing the list of your population
entities, as you would randomize a deck of
cards for instance, before you proceed with
systematic sampling.
Use Case: Continuing with the earlier
example, the supermarket can use systematic
sampling to study the buying habits of their

When: You can divide your population into
characteristics of importance for the research.
How: A stratified sample, in essence,
tries to recreate the statistical features of
the population on a smaller scale. Before
sampling, the population is divided into
characteristics of importance for the
research. For example, by gender, social
class, education level, religion, etc. Then the
population is randomly sampled within each
category or stratum. If 38% of the population
is college-educated, then 38% of the sample
is randomly selected from the collegeeducated subset of the population.
Pros: This method attempts to overcome the
shortcomings of random sampling by splitting
the population into various distinct segments
and selecting entities from each of them. This
ensures that every category of the population
is represented in the sample. Stratified
sampling is often used when one or more
of the sections in the population have a low
incidence relative to the other sections.
Cons: Stratified sampling is the most complex
method of sampling. It lays down criteria
that may be difficult to fulfill. This can place a
heavy strain on available resources.
Use Case: If 38% of the population is collegeeducated and 72% of the population have not
been to college, then 38% of the sample is
randomly selected from the college-educated
subset of the population and 72% of the
sample is randomly selected from the rest of
the population. Maintaining the ratios while
selecting a randomized sample is key to
stratified sampling.

Non-Probability Sampling
For non-probability sampling, the sample is
constructed with no probability structure. The
selection is not randomized, so the resulting
sample is not fully representative of the target
population. There are three non-probability
sampling methods: convenience sampling,
snowball sampling and quota sampling.

Convenience Sampling
When: During preliminary research efforts.
How: As the name suggests, the elements of
such a sample are picked only on the basis of
convenience in terms of availability, reach and
accessibility.
Pros: The sample is created quickly without
adding any additional burden on the available
resources.
Cons: The likelihood of this approach leading
to a sample that is truly representative of the
population is very poor.
Use Case: This method is often used during
preliminary research efforts to get a gross
estimate of the results, without incurring the
cost or time required to select a random
sample. For example, interviewing 10 of
your friends over the phone would count as
convenience sampling.

Snowball Sampling
When: When you can rely on your initial
respondents to refer you to the next
respondents.
How: Just as the snowball rolls and gathers
mass, the sample constructed in this way
will grow in size as you conduct the survey.
At the end of the survey, you ask your initial
respondents to refer you to other people to
survey.

Pros: Though the costs associated with this
method are significantly lower, you will still
end up with a sample that is very relevant to
your study.
Cons: You restrict yourself to only a small,
homogenous section of the population.
Use Case: Snowball sampling can be useful
when you need the sample to reflect certain
features that are difficult to find. For example,
to conduct a survey of people who go jogging
in a certain park in the mornings, snowball
sampling would be a quick, accurate way to
create the sample. You can find someone who
jogs in the park in the morning, then ask them
to refer you to their friends who also jog in that
park in the morning.

Quota Sampling
When: When you can characterize the
population based on certain desired features.
How: Quota sampling is the non-probability
equivalent of stratified sampling. It starts with
characterizing the population based on certain
desired features and assigns a quota to each
subset of the population.
Pros: This process can be extended to cover
several characteristics and varying degrees of
complexity.
Cons: Though the method is superior to
convenience and snowball sampling, it does
not offer the statistical insights of any of the
probability methods.
Use Case: If a survey requires a sample of
fifty men and fifty women, a quota sample will
survey respondents until the right number of
each type has been surveyed. Unlike stratified
sampling, the sample isn’t necessarily randomized.

Tool Tip
Probability methods are clearly more accurate
but the costs can be prohibitive. For the initial
stages of a study, non-probability methods
might be sufficient to give you a sense of what
you’re dealing with. For detailed insights and
results that you can rely on, move on to the
more sophisticated probabalistic methods
as the study gathers pace and takes a more
concrete structure.

Minimizing Sampling Error
There is one easy way to minimize sampling
error – increase the sample population size.
The more respondents you have, the more
accurate your survey will be. However, it
isn’t always possible to increase the sample
population because of financial restrictions.
Avoiding three common errors will help to
minimize sampling error without increasing
your sample size.

1. Avoid Population
Specification Errors
A population specification error occurs when
a critical segment of the population is not
included in the sample. This is the result of a
knowledge problem or gap. The results of a
survey with a population specification error
will shed some light into your issue, but they
cannot provide the full picture.
For example, imagine that you want to learn
more about household decision making, so
you survey men about the decisions in their
household. This would be correct if only
men make household decisions, but often
women and children also have influence over
decisions. By only surveying men, you will
miss out on part of the picture.
An easy way to avoid population specification
errors is to learn how similar surveys sampled

their target population. By checking on other
surveys, you can be sure that you are not
forgetting a critical segment of your sample
population.

2. Avoid Sample Frame Error
Sample frame error occurs when a survey
samples the wrong segment of the total
population, usually because the surveyor has
missed a new trend or change in their target
population.
For example, imagine that you want to learn
how attendees feel about your program, and
you use the attendance sheets from two
weeks ago to create a target population.
However, unknown to you, a new group of
people started attending your program one
week ago. The results of your survey will be
misleading, since they do not include one of
the key segments of your target population.
An easy way to avoid sample frame error is
to take plenty of time to study your target
population. Be sure that nothing has changed
about it recently, and be sure that you have
accounted for all types of people in your
sample.

3. Avoid Non-Response Error
It is normal for some targeted respondents
to not respond to a survey. However, this can
become a problem if the non-respondents
generally hold a view that is different from the
respondents. As a result, the final data will
be skewed toward the opinions of those who
responded.
For example, imagine that you want to survey
housewives about their free time, and you
do this by calling them on the phone during
the day. The women who do not respond are
the ones who have less free time (since they
don’t have enough time to pick up the phone).
Meanwhile, the women who respond are the
ones who have more free time. The results

of the data will report higher free time among
housewives than is actually true.
An easy way to avoid non-response error is to
conduct follow-up surveys or contact those
who did respond through alternative means.
In addition, keeping the questionnaire short
will help encourage people to respond the first
time.

Calculating Sample Size
Determining the size of your sample
population is one of the most difficult
decisions to make in your survey. A larger
sample can yield more accurate results — but
the more responses you collect, the more
expensive it gets.

Statistical Variables

95%, then you find that 68% of women take
iron pills. You can be 95% confident that the
true number of women who take iron pills is
between 66% and 70%.
The confidence level corresponds to a given
z-score:
Confidence level

Z-score

90%

1.645

95%

1.96

99%

2.576

Sample Size Formula for a
Population of Unknown Size

To calculate the best sample size for your
needs, you need to make 2 decisions about
how accurate you want your data to be.

Often, a population is too large to easily
measure. When this is the case, you can use
a sample size formula that does not account
for the size of the large population that you are
surveying.

1. Margin of error
(also known as confidence interval):

The sample size formula when you don’t know
the size of your large population is

No sample will be perfect, so you need to
decide how much error you are willing to
allow. The margin of error determines how
much variance you want to allow in your data.
For example, if you set a margin of error of 5%
and find that 68% of women take iron pills,
then that means that the real percentage of
women who take iron pills is between 63%
(68-5=63) and 73% (68+5=73).
2. Confidence level:
How confident do you want to be that the
actual number falls within your margin of
error? The most common confidence intervals
are 90%, 95%, and 99% confident.
For example, imagine that you set a margin
of error of 2% and a confidence level of

where n is the sample size you should use,
m is the margin of error, and z is the z-score.
(Note that if you have a margin of error of 5%,
m = 0.05)
It is usually safe to use a margin of error of
5% and a confidence level of 95%. If you
plug these numbers into the formula, you get
a sample size of 384. This means that 384 is
a safe sample size for a large population of
unknown size.

Sample Size Formula for a
Population of Known Size
You can calculate a more accurate sample

size if you know the size of the population that
you are surveying.
For example, if you are studying the learning
outcomes for a school with 200 students,
your population size is 200. If you are studying
women in Gujarat, the population size is the
total number of women in Gujarat.
This does not have to be exact. Even an
estimate of the population size will result in
a better sample size than using the formula
above.

m is the margin of error, and z is the z-score.
(Note that if you have a margin of error of 5%,
m = 0.05, z=1.96)
It is usually safe to use a margin of error of 5%
and a confidence level of 95%. The sample
size formula with those figures (margin of error
of 5% and confidence level of 95%) is

The sample size formula when you know the
size of your population is

where n is the sample size you should use,
and p is the size of the population
being surveyed.

where n is the sample size you should use,
p is the size of the population being surveyed,

Tool Tip
Don’t know which sample size formula to use?
The last formula (for a sample of known size
with margin of error of 5% and confidence
interval of 95%) is your safest bet.

References
1. Introduction - Case Study
Details on polling during the 1936 U.S. Presidential Election from
Dennis DeTurck, “Case Study 1: The 1936 Literary Digest Poll”, University of Pennsylvania
(https://www.math.upenn.edu/~deturck/m170/wk4/lecture/case1.html)
2. Chapter 9 - “Calculating sample size”
Sample size formulas derived from Glenn D. Israel, “Determining Sample Size”,
Fact Sheet PEOD-6, University of Florida, 1992
(http://sociology.soc.uoc.gr/socmedia/papageo/metaptyxiakoi/sample_size/samplesize1.pdf)

Bonus
Content
1. Designing a
Great Survey
Surveys are the bedrock of data-driven research. The quality and reliability of your data
and, by extension, your entire project hinges on the effectiveness of your survey. Get
it wrong and you’ll be left wading through
the murky marshes of meaningless data that
make little or no sense, with an immense
investment of time and effort wasted. Going
ahead with an undercooked survey also reflects poorly on your organization. The people
who have been through your earlier survey will
be less than accommodating when you reach
out to them next time even if you return with a
new and improved version.
It is important then to get your survey right
the first time, and doing that requires a concerted and conscious focus on thought and
planning. Don’t let this be a daunting proposition though; think of it as an opportunity.
Good data means good results, and if your
survey has been designed well, half your work
is done right there. It’s easy, really. Let’s walk
through the eight steps to designing an ideal
survey. For ease of understanding, we’ll consider these steps with respect to an impact
assessment for a self-help group.

1. Set your goals
Defining the purpose of your survey in clear,
unambiguous terms is absolutely vital. It sets
the direction for everything you do. Coming
back to the drawing board and reminding
yourself of the purpose of the survey can be
a good way to get back on track when the
team feels like it’s stuck in a rut and is unable
to draw inspiration to continue driving forward. The goals of the survey also dictate all
the other aspects of survey design to a large
extent.

Example

An example of a clear, constructive goal could
be “assess the impact of microfinance on
people living in a certain district”. As we move
through the rest of the steps, we’ll see how
this goal lends itself to every aspect of the
survey.

2. Narrow down on your target population
In order to collect data that is relevant to the
purpose of your study, it is important that you
reach out to the right people. Identifying the
right sample for your survey is another critical aspect of survey and bears heavily on the
structure and mode of survey that you choose
to employ. The nature and language of the
questions used while formulating the questionnaire also depend heavily on the target
population.

Example

Selection of the target population follows
largely from the goal that you have selected
for your survey. The target in the case of the
example we’ve taken here would be the working population of the district between 18 and
60 years of age.

3. Structure the survey
Dividing a questionnaire into categories results
in an intuitive structure that is easy for participants to navigate. The researcher can also
improve the survey experience further by providing additional explanation at the beginning
of each section. This gives the respondent an
idea of what to expect. Categorizing the questions while designing the survey, even before
you get down to writing the questions, helps
maintain the focus on the different research
objectives and ensures a balanced output.

Example

Dividing the questionnaire, for instance, into
sections on Particulars (personal background
information) and Impact Assessment (Social,
Educational and Cultural) will lend a logical
flow to the survey that makes it easier to
grasp. This will also make it easier for you to
assign categories to the data that you collect
and simplify the analysis process.

4. Select the mode of your survey
The mode that you employ to administer the
survey depends on the sample type and size.
Use the mediums that are the most effective
in reaching your target population. The time
span of the research is also an important
factor that impacts this decision. Leveraging
technology to maximize the extent and depth
of your reach might come in very handy in
such situations. SocialCops has had great
success in using mobile application and lowcost smartphones to collect data at the grassroots level.

Example

Selecting the wrong mode for your survey
can cripple your research. Launching a webbased survey that needs people to visit a
website in order to answer the questionnaire,
for instance, will be nearly useless while trying
to reach people in villages that have limited or
no internet access. The mode of the survey
should use tools and infrastructure that can
easily reach your target population and account for the comfort level that your respondents have towards the technology that you
employ.

5. Choose the right question type
Using the right tool for the right job is essential in any endeavor. Questions are the tools of
your survey and picking the wrong question
type can be as awkward as using a screwdriver to knit a pullover. Throw in a good mix
of closed-ended questions – dichotomous
(yes/no), multiple choice, and ordinal scale
(rank, preference) – after considering the purpose that each question type will serve. Top it
up with open-ended questions where necessary. Read more about how to optimize your
survey quality by choosing the right question
types.

6. Formulate the questions
Words mean different things to different
people and taking care of some of the finer
nuances involved in formulating an effective
question can go a long way. Don’t leave any
scope for ambiguity. Be clear about what
you need and get your questions proofread
by somebody who is not familiar with your
study before sending out the survey. Brevity
is essential. Respondents are more likely to
respond positively to questions that are concise and able to hold their attention. Avoid
unnecessary jargon; the language should be
as simple and generic as possible. And finally,
the answer choices must be well defined.

Example

If you ask respondents to rank their level of
satisfaction on a scale from 1 to 5 but fail to
explain whether 1 is very satisfied or 5 is very
satisfied, their responses will be of little value.

7. Introduce the survey
Introduction is often considered to be the
most critical part of the survey, as a majority
of the respondents make up their mind about
whether they would like to answer the survey
after going through the introduction. The introduction, thus, needs to make a good, strong
first impression. It sets the tone for the rest of
the survey and lays down the context in a simple, understandable way. Begin with a statement thanking the respondents for their time
and explain the subject of the study along with
a confidentiality statement to address privacy
concerns. Mention the expected time required
to complete the survey and display the percentage completed as the respondent moves
ahead.

8. Take the field
Once the survey is ready, execute your plans
through a robust collection mechanism. If the
survey is being conducted in person, make
sure the people who administer the survey
completely understand the purpose of the
survey and are comfortable conversing with
the population they’re supposed to interact
with. Don’t forget to train them on the use
of open-ended and unstructured questions
and brief them about the technicalities of the
survey. Before you head out to launch your
survey in the field or online, test the survey
with a small control group to see if everything
functions the way you expect it to.
Eight simple steps and you’re all set to go! If
you have designed your survey well and executed your plans to perfection, good, clean
data filled with tremendous potential for gathering insights will start surging in.

2. Piloting a
Survey
Collecting primary data at any scale is
challenging. Though data quality can be
difficult to measure, it is crucial to ensure that
you are not wasting time on poor quality data.
Creating a good survey is one of the best
ways to ensure data quality. A bad survey
will only lead to bad data, and thus bad
inferences.
How can you ensure that your survey will
collect relevant, accurate, useful data?
Pilot your survey. In a pilot, you can test all
aspects of your survey — question flow, order,
language, etc. — before you use the survey
to collect real data. Piloting helps you identify
and fix issues that would have led to poor
quality data. A pilot is like putting your survey
through a simulator to understand what is
right and wrong.
Piloting can be a time and energy-intensive
process. However, it can also be fun, since
piloting leads to unparalleled levels of
learning!

The goal of piloting
Piloting should help you answer the following
questions:
• Am I catering to my audience?
• Will my data collectors be able to
seamlessly collect data using this survey?
• Does my survey format cater to my needs?
Is it capturing too much or too little?
• Is my survey collecting the data in the
correct format?
In addition, piloting can help you understand
implementation hassles. By testing out your
survey in advance, you can predict any
problems that might arise during the actual
roll-out. For example, a pilot can help you

learn how surveyors should be traveling from
place to place, whether you should inform
respondents in advance, what your surveyors
should carry with them, and more.
Piloting can be full of sleepless nights and
stressful days! Here are 10 tenets of piloting to
help the process go smoother. We developed
these tenets from our experiences rolling out
all kinds of survey — large and small, long and
short, quantitative and qualitative.

1. Ensure that you have done enough
secondary research
You don’t have to learn everything about
your survey in the field. A pilot will be more
successful if you first check how others have
conducted similar surveys. A good best
practice is to read at least 10 similar surveys.
These can be found in research papers,
company websites, and ebooks. Learning
from existing data collection materials will give
you a great head start.

2. Take feedback from your
organization
Present your draft survey to lots of people
for feedback. Some of the basic issues or
changes can be identified by people within
your organization. You can also speak to
people who have done similar exercises
before or reach out to a few experts in other
offices.

3. Choose a representative sample
for piloting
During your pilot, you should survey a sample
of your final audience. Don’t choose this
sample randomly. Choose each person in your
pilot deliberately.
Make sure to consider the following factors
for each of the participants in your pilot:
• Age
• Gender

•
•
•
•

Education status
Income status
Caste (in the Indian context)
Geography

You can also read this resource to learn more
about how to build a rigorous sample.

4. Pilot the survey in the correct
medium
Ensure that you pilot the survey in the
medium it will roll out in. Do not do any of the
following:
Pilot a paper survey if you will actually collect
data on a mobile app.
Pilot an English survey if you will actually
collect data with a Hindi survey.
Pilot a small survey of 10 questions if you
will collect data with a long survey of 100
questions.

5. Integrate the pilot with your
training
Usually, organizations conduct surveyor
training before data collection starts. Use the
pilot as part of this training. The pilot will give
the surveyors a chance to test out the survey
in the field, ask questions about the survey,
and figure out if they have any issues.

6. Question the survey questions
The most important part of the format is to
understand what questions are you asking
and what information you are getting.
One of the problems to look for during your
pilot is compound questions — a question
that asks two or more questions at the same
time. For example, “How many children do
you have and which one is the youngest?” is
a compound question. It actually is asking two
separate questions: “How many children do
you have?” and “Name the child which is the
youngest.” Be sure that each question on the
survey only asks for one piece of information.
In addition, the pilot will show which phrases

in your survey are widely understood and
which ones are vague terms or jargon. Be
sure to eliminate any vague words or jargon
from your survey, no matter how obvious they
might seem to you.

7. Examine the data being collected
Don’t just focus on the questions themselves;
be sure to also look at the data that is being
collected. Sometimes, issues in the collected
data can show problems in your survey. Use
your data to make a data-driven decision on
what’s working and what’s not working.

8. Pilot all aspects of the survey
Pilot the question flow, the order of the
questions, question types, and even the help
text to ensure that the survey is clear to both
your surveyors and respondents. To pilot
question flow and order, you should observe
how comfortable surveyors and respondents
are as they go through the survey. Check
whether the surveyors or respondents get
confused or give the same information twice.
If this happens, it might be helpful to change
the order of the questions or add help text.

9. Collect feedback from everyone
Since piloting is an iterative process, it
is important to ensure that you include
viewpoints from all stakeholders. Get
feedback from supervisors, surveyors,
observers, respondents, and any other
people in the pilot, and make sure everyone
contributes during feedback sessions. In
addition, you can observe different people to
compile your own feedback.
Consider and balance all these viewpoints
carefully. It is important to make sure that you
don’t get biased by any single opinion.

10. Never re-pilot the same version of
a survey
At the end of each day, incorporate all the

feedback into your survey. Then you can
use this updated survey for the next day of
piloting. By immediately updating your survey,
you won’t waste time on the same feedback.

11. Be thoughtful about what
changes to make
You don’t need to include every piloting
insight. Sometimes, you will get bad feedback
if a surveyor misunderstands something or the
sample respondents are not chosen correctly.
It is important to remain critical and make sure
you are only incorporating the right feedback
— something that will drastically improve the
survey for everyone (rather than just one or
two surveyors) and will not make the survey
more difficult. A good practice is to make sure
that a significant number of people (more than
60%) agree to the survey changes.

A typical pilot day
Morning session: Make sure that your
surveyors know the purpose of the survey
and what you are testing before they collect
data. For example, tell them to think about
which questions to keep and which to remove,
which questions should be asked first, how
the questions can be worded better, whether
help text should be added, and how the
respondents reacted, how interesting the
survey was, etc. If you tell your auditors
what you are looking for, you will get better
responses during the evening session.
Day monitoring: While the data collectors
are collecting data, you shouldn’t sit back
and relax. Shadow them to understand and
observe the entire exercise. Don’t correct
them when they go wrong; just record the
mistake.Be sure to follow diverse people
and watch several surveys end to end (if the
survey is a reasonable length).
Evening session: Once everyone is back from
the field, gather everyone’s viewpoints on the
survey. It’s ideal to go over the entire survey,
question by question.

Night session: Assemble the day’s feedback,
analyze what feedback to include, and update
the survey accordingly.
That’s how you pilot like a boss!
Happy data collection.

3. Field Data
Collection
Plan
Once you have created a great survey, you
need to deploy it in the field. Field data
collection is a complex process that often
requires lots of time, money, and people.
Deploying your survey in the field effectively
will not only help save these resources, but it
will also make your data more reliable.
This plan covers the 5 steps required to
deploy a survey in the field. (The process of
deploying a survey in the field is known as a
field plan.) Though this content was written
with mobile-based data collection in mind, all
types of surveys and organizations can benefit
from the steps and best practices below.

Step 1: Identify your resources
The first step of creating a field plan is to
estimate the resources you have for your
project. The main resources to consider are
budget and time.
Budget and time will determine how many
responses you should collect, how many
surveyors you should recruit, and how much
you should spend on resources like devices,
documents, projectors, refreshments, training
logistics, salaries, and more.
Your Scope of Work (SOW) will help determine
the budget and time available for your project.
The SOW is a formal document that describes
the work activities, deliverable, timelines and
milestones, pricing, quality requirements,
governance terms and conditions, etc. The
SOW should also outline all the parties
involved in the project, the budget, and the
timelines.

Step 2: Recruit your field staff
Your field staff are crucial — they are the
eyes and ears on field, as well as the people
collecting every piece of data. That is why it is
vital to find reliable and trustworthy field staff.
Before recruiting field staff, it is important
to consider their responsibilities.
What should they know in advance? How will
they be trained? How will they collect data?
Who will be responsible for monitoring the
data collection process? Once you answer
these questions, you will be one step closer to
hiring a great team of field staff.
Next you must create a fixed hierarchy.
Creating a fixed hierarchy ensures that a clear
reporting and management system is in place,
project timelines are being met, and field staff
are supported at every step of data collection.
The most common hierarchy uses three types
of people: Field Managers, Monitors, and
Surveyors.
Field managers lead the entire team working
in the field. They supervise and oversee tasks
of field employees, run training programs, and
ensure that everyone works as effectively as
possible.
Monitors directly manage and support the
surveyors. It is usually best to recruit 1
monitor for every 10 surveyors.
Surveyors collect data from the field by
directly interacting with the sample population
and recording survey responses.
The last step is to recruit field staff at all
levels.
When recruiting field staff, consider the
following four factors:
1. Education qualifications
2. Ability to handle technology
3. Previous experience
4. Willingness to learn new things

Step 3: Create a plan
An implementation plan is the complete plan
for training and data collection. Having a plan
in place ensures that everyone is on the same
page, which makes data collection go more
smoothly.
First, estimate the number of days required
for training field staff.
Ideally you should not train more than 50-60
people in one room at one time. Based on
how complex and long your survey is, you
could do a one-day training, two-day training,
or trainings spread out over multiple days. By
multiplying the duration of training (in days)
and number of batches (with each batch
50-60 staff members), you can calculate the
number of total days needed for training.
Tip: If you are short on time, conduct parallel
trainings of multiple batches.
Second, outline the training.
Your training will have two main components:
1. Understanding the technology
2. Understanding the questionnaire
Assess how complex each of these
components will be, then accordingly estimate
what materials and people will be required for
each training.
Third, estimate the number of days for data
collection.
You can do this by calculating the following:
1. Total number of responses required (also
known as the sample size)
2. Total number of surveyors
3. Average time taken to fill each survey
Once you know these three numbers, you can
calculate the number of surveys each surveyor
can complete in one day. It is equal to the
length of the day (most field surveys run for 8
hours per day) divided by the average time per
survey.

Multiply the number of surveyors by the
number of surveys per surveyor per day, then
divide the sample size by this number. Add the
days added for re-surveying and you will have
the total number of days for data collection.
The number of surveyors and total number of
days of data collection are a trade off. Hiring
more surveyors will decrease the time needed
for data collection. However, training time and
total field staff will need to increase, which
requires more resources. In contrast, hiring
fewer surveyors will increase the time needed
for data collection.
Fourth, create a daily schedule for your
field staff.
Here is an example schedule:
• 10 am: Morning session
• 11 am: Survey starts
• 2 pm: Lunch time
• 3 pm: Survey restarts
• 6 pm: Evening Session
It is a good practice to include morning and
evening sessions as a part of your team’s
daily schedule. Morning sessions allow for
a quick check-in with the team on how they
are feeling, their goals for the day, and the
progress of their project. Evening sessions
allow for feedback and review. A good
practice is to ask the team how their day
went, if they faced any challenges, and if
they have any feedback for the questionnaire
or data collection app during the evening
session.

How to calculate your sample size
Determining the size of your sample
population is one of the most difficult
decisions to make in your survey. A larger
sample can yield more accurate results — but
the more responses you collect, the more
expensive it gets.
The best way to calculate a sample size is
to use the size of the population you are
surveying. For example, if you are studying

the learning outcomes for a school with 200
students, your population size is 200. If you
are studying women in Gujarat, the population
size is the total number of women in Gujarat.
This does not have to be exact.

Step 4: Train field staff
After creating an implementation plan, you
need to prepare for and conduct training for
your staff.
Important preparations include the following:
• Phones/tablets should be procured and
given to the field staff.
• Make field staff aware of the fact that
training will happen on tablets.
• Create a plan for setting up the training
material.
• Field staff should be aware of the purpose
of the training.
The best way to conduct training for mobilebased data collection is to divide the training
into three parts: technical training, survey
training, and data collection training.
Technical training focuses on learning the
mobile-based data collection technology.
Before conducting the technical training, it is
helpful to go through the data collection app
yourself. Be sure to try the full functionalities
of the app – make a questionnaire from
scratch and fill out an entire questionnaire to
understand how the application functions.
Survey training should happen only once
your field staff is comfortable with the device
and the app. Then the next step is to train the
staff about the survey itself. Survey training
should focus on explaining the logic behind
the survey questionnaire and how to ask the
different types of questions on the survey.
Make sure you cover the following:
1. If you have multiple questionnaires,
explain the purpose and types of different
questionnaires. Suppose you have two
questionnaires – baseline and monitoring.
Tell field staff why baseline data is important

and how it can help make the program better.
Then tell them what monitoring is and how it
will be helpful.
2. Explain survey details (such as the average
time taken, flow of the questions, potential
pitfalls, etc.) to the field staff so that they can
best collect data.
3. Run the field staff through each question to
explain how they should be asking it. Some
questions might be tricky, especially if the
question is difficult to understand for the
respondent.
Survey training is also a good time to get
feedback on the questionnaires, so be sure to
ask field staff if there are better ways of asking
each question.
Data collection training covers the best
practices of data collection. This helps to
improve the quality of data collected by
surveyors.
Best practices include the following:
1. Do not prompt answers to respondents.
Only state the possible options.
2. Do not repeat questions beyond a certain
point.
3. Let the respondent take his/her own time.
4. Follow the sequence of the questions. Don’t
skip questions based on your judgment.
5. Read out all the options correctly to the
respondents.
6. While moving from one section of the
questionnaire to another, give the respondent
a heads-up on the upcoming questions.
7. Data collection needs to be unbiased
(recorded without personal bias), time-bound
(recorded in estimated time), and as per the
format of survey.
In addition, explaining the entire project to
surveyors can help encourage them to collect
good data. Some points to cover include:
• The purpose of the project.
• Surveyors’ role in the larger picture of
informing program’s decisions.
• The fact that the organization trusts

surveyors with the important task of data
collection.
• The project uses mobile-based tools to help
make data collection easier, quicker, and more
reliable for surveyors.

Story from the field
Pramila was a volunteer with Swades
Foundation and was helping with the data
collection process. She dropped out of school
after clearing grade 7. When she entered the
training workshop, she was very apprehensive
of using a tablet to collect data in different
forms — typing up text, capturing audios and
videos, and mapping households.
After four hours of training, Pramila was able
to not only use the tablet well, but she was
also able to use Collect (our data collection
app) well.
During this training, there were many Pramilas
in the room who learned to use the app in just
four hours.
This happened because the training was
well-planned, interactive, inspiring and wellexecuted. You can also inspire your field staff
and kickstart data collection by planning your
trainings well and communicating project
rationale and learning outcomes for both the
project and the team. In addition, sharing
success stories like that of Pramila can help
field surveyors feel confident in their ability to
contribute to the project.

Step 5: Monitor your team
The final step is monitoring the team while
they collect data.
Observe surveyors on the ground. While
your surveyors are piloting or collecting data,
observe them to learn common mistakes,
doubts, and best practices. You can share
these learnings in the morning or evening
sessions so everyone can benefit from them.

Monitor the data that is being collected.
Regularly monitor the data that is coming in to
check for any inconsistencies or problems.
Take feedback from your surveyors. Take
feedback on their experience, if they are
facing any challenges, or if they require any
additional resources.
Give feedback to surveyors. Give constructive
criticism to surveyors as you observe them on
the ground. Don’t be mean, be specific, and
be sure to explain the rationale behind your
feedback and how each person can improve.

4. Census
Survey

problems because he spoke to a woman.
This caused problems since men were not
supposed to speak to women in that village.
It is always a good idea to have a genderbalanced team of surveyors and monitors.
This will help to navigate most social norms.

3. Villages — you can’t see me
SocialCops recently worked with Tata Trusts
as the data intelligence partner to conduct two
census surveys in Chandrapur (Maharashtra)
and Noamundi (Jharkhand). Chandrapur and
Noamundi are some of the most backward
areas in their states. The areas have difficult
terrain with little or no access to phone
connectivity and electricity.
The large-scale census surveys collected data
for over 60,000 households within 2 months
using tablets. The data was then used to
help in micro-planning activities in the area.
SocialCops’ challenge was to train local
people to collect this data while overcoming
operational challenges that arose throughout
the survey.

We used Census village lists to decide which
villages that we should survey. However, we
were sometimes left searching for villages that
didn’t exist.
Though it is standard practice to trust Census
records, try asking local coordinators for a list
of villages.

4. Piloting — a never-ending story

These are some of our major
learnings from these two
surveys.

Each geography is unique with its own set
of demographic, geographic, social, and
economic problems. It’s important to tweak
each survey for its specific geography. Even
after consulting experts and doing two rounds
of piloting, surveyors were still calling our
team for changes in the survey.
With Collect, we can edit surveys in the field
and sync those changes on all other devices.
This helped us tweak our surveys on the go.

1. Internet — that thing of tomorrow

5. Training — a leaky, indirect system

When surveying in tribal areas of Maharashtra
and Jharkhand, we often couldn’t find cellular
connection, let alone internet, for hours.
Sometimes we had to travel up to 8 hours to
reach areas with cell or internet connectivity.
We used our Collect app to collect data
offline throughout Chandrapur and Noamundi.
Collected data was synced to our servers at
the end of the week.

Any large-scale census survey usually involves
a train-the-trainers model. This means that
we train people who then train the surveyors,
rather than us training the surveyors directly.
This indirect method can lead to knowledge
leaks as information is transferred from person
to person.
Closely monitor surveyors’ training, since that
training can make or break a survey. A good
training can reduce survey bias, while a bad
training leads to incorrect data.

2. Gender neutrality — the magic
wand that eases social norms
It’s interesting how we had to anticipate social
dynamics since they could intervene with our
work. In one village, our field manager caused

6. Google Play Services — the jugaad
method
It can be difficult to get enough bandwidth to

download a phone app while working in rural
India. However, all the tablets used in our
census surveys needed Google Play Services
(used for updating the phone’s location,
scanning barcodes, and more), which required
46 MBs to download. How could we do this
for 350 tablets?
Instead of trying to directly download the file
to the tablets, we used our own jugaad. We
first downloaded Google Play Services to
our computer, then we transferred the file to
all 350 tablets over USB. If we later found a
tablet without Google Play Services, we used
ShareIt to share Google Play Services over
Bluetooth.
Even basic functions like downloading apps
might not work in certain areas. Be ready to
do some extra work.

7. Survey tablets — collateral in
disguise
If you think the fact that you are paying
surveyors gives you leverage, think again! Our
surveyors didn’t let us collect our tablets until
their checks were processed — something we
didn’t factor in as we planned our timelines.
Make sure to schedule a time for payment and
pay accordingly. Keeping these operational
processes running smoothly will increase
people’s confidence in your organization.
Make sure to increase your timelines by 25%
for any unexpected issues.

8. Final — there’s no such thing
During our large-scale census surveys, issues
constantly come up from the field — feature
requests on the application, optimization
requests, etc. These are worth considering,
since they help the survey application become
more robust and optimized for people’s needs.
With frequent updates to our APK, our survey
team had to follow a rigorous schedule:
• 7 – 8 am: Travel to the villages
• 8 – 10 am: Distribute tablets so the survey
can start on schedule

• 10 am – 6 pm: Monitor surveyors and
collected data
• 6 – 9 pm: Collect all tablets from the field
so that the daily surveying schedule is not
disturbed
• 9 – 10 pm: Travel to the district headquarters
• 10 pm – 1 am: Install updated APKs on 350
tablets
• 1 – 7 am: Rotate charging tablets and sleep
Make sure that your field team is dedicated
and hard working. They might have to spend
sleepless nights to run the survey smoothly.

Written & Designed By:
Christine Garcia
Content Manager

Gaurav Jha

Content Creator, Data for Impact

Richa Verma
Resident Entrepreneur

Sahaj Talwar
Graphic Designer

Collect
Create Mobile Surveys and Collect
Data From Any Android Device
Collect is a smartphone-based data collection tool to capture
data from the field, monitor progress of projects, and make
quick decisions based on real-time, accurate data.

TRY
COLLECT

Find out more at www.socialcops.com/collect

“SocialCops is taking big data in a direction
that very few companies have been able to do:
providing data and insights that can help solve real
problems for most of the planet.”
- Pankaj

Jain,

Venture Partner at 500 Startups

About SocialCops
Our world’s most important decisions are
crippled by an astonishing lack of data. No
map in the world can tell women the safest
route home or tell the police the best route to
patrol. No survey tracks parameters like
teacher attendance or school infrastructure in
realtime. National level healthcare decisions
affecting millions are made based on a
sample survey of 100 people.
SocialCops is a data intelligence company
that empowers organizations to make tough
decisions and solve the world’s most critical
problems.

Our platform brings the entire decisionmaking process — collecting primary data,
accessing external data, linking internal data,
cleaning and transforming data, and
visualizing data — to one place. This makes it
faster, more efficient, and easier to make an
important decision through data.
Our goal is to take the big data revolution to
where it matters the most – to use in
decisions that affect human health, well being,
safety and livelihoods.

Partners

We work with over 150 partners from 7 different countries to confront the world's most critical
problems through data intelligence. Our partners include the Government of India, United
Nations, Bill & Melinda Gates Foundation, Tata Trusts, Oxfam India, Unilever, and BASF.

Recognition
One of India's fastest growing startups, SocialCops was featured on Fortune India 40 Under 40
and Forbes Asia 30 Under 30 two years in a row. Our work has won us accolades globally –
including recognition from Nascomm as one of India's top 10 startups, the United Nations World
Youth Summit Award, Global Social Entrepreneurship Competition, IBM/IEEE Smart Planet
Challenge and grants from Microsoft, IBM and Salesforce.

Thank You For Reading
What Did You Think?
Give us feedback and help us improve.
(It takes less than a minute.)

CLICK HERE

Source Exif Data:

File Type                       : PDF
File Type Extension             : pdf
MIME Type                       : application/pdf
PDF Version                     : 1.7
Linearized                      : No
Create Date                     : 2017:06:12 18:41:47+05:30
Creator                         : Adobe InDesign CC 2015 (Macintosh)
Modify Date                     : 2017:12:13 12:34:05+05:30
Has XFA                         : No
Language                        : en-US
XMP Toolkit                     : Adobe XMP Core 5.6-c015 84.159810, 2016/09/10-02:41:30
Metadata Date                   : 2017:12:13 12:34:05+05:30
Creator Tool                    : Adobe InDesign CC 2015 (Macintosh)
Instance ID                     : uuid:e6fd9014-9b76-e54d-acc0-5979a44eaf52
Original Document ID            : xmp.did:7a5967a5-f3a6-49d0-8037-a7d355fb3d71
Document ID                     : xmp.id:268aadbe-3b2a-4d0e-b3f7-a8e0d5398998
Rendition Class                 : proof:pdf
Derived From Instance ID        : xmp.iid:6b7f2ed9-9c7b-4a37-a5f4-aae9ec21607e
Derived From Document ID        : xmp.did:e92c0c1a-1b17-4beb-b8bd-f95e7e109948
Derived From Original Document ID: xmp.did:7a5967a5-f3a6-49d0-8037-a7d355fb3d71
Derived From Rendition Class    : default
History Action                  : converted
History Parameters              : from application/x-indesign to application/pdf
History Software Agent          : Adobe InDesign CC 2015 (Macintosh)
History Changed                 : /
History When                    : 2017:06:12 18:41:47+05:30
Format                          : application/pdf
Producer                        : Adobe PDF Library 15.0
Trapped                         : False
Page Count                      : 44

EXIF Metadata provided by EXIF.tools

The Ultimate Guide To Effective Data Collection

The%20Ultimate%20Guide%20to%20Effective%20Data%20Collection

The%20Ultimate%20Guide%20to%20Effective%20Data%20Collection

The%20Ultimate%20Guide%20to%20Effective%20Data%20Collection

Navigation menu

Versions of this User Manual:

Views

Navigation