Designing Indicators

Survey Design & Data Collection

Lecture Overview
 What should you measure?
 What makes a good measure?
 Measurement
 Data Collection
 Piloting

What do we measure and where does it fit into the whole project?
WHAT SHOULD YOU MEASURE?

What Should You Measure?
 Follow the Theory of Change
• Characteristics: Who are the people the program works with, and
what is their environment?
Sub-groups, covariates, predictors of compliance
• Channels: How does the program work, or fail to work?
• Outcomes: What is the purpose of the program?
• Assumptions: What should have happened in order for the
program to succeed?
 List all indicators you intend to measure
• Use participatory approach to develop indicators (existing
instruments, experts, beneficiaries, stakeholders)
• Assess based on feasibility, time, cost and importance

Methods of Data Collection
 Administrative data
 Surveys- household/individual
 logs/diaries
 Qualitative – eg. focus groups,
RRA
 Games and choice problems
 Observation
 Health/Education tests and
measures

What makes a good measure?
INDICATORS

The Main Challenge in Measurement: Getting Accuracy
and Precision
More accurate 
 More precise

Terms “Biased” and “Unbiased” Used to Describe
Accuracy
More accurate 
“Biased” “Unbiased
” On average, we get
the wrong answer
On average, we get
the right answer

Terms “Noisy” and “Precise” Used to Describe Precision
 More precise
“Noisy”
Random error
causes answer to
bounce around
“Precise”
Measures of the
same thing cluster
together

Choices in Real Measurement Often Harder
More accurate 
 More precise
“Noisy” but
“Unbiased”
“Precise” but
“Biased”
Random error
causes answer to
bounce around
Measures of the
same thing cluster
together

The Main Challenge in Measurement: Getting Accuracy and
Precision
More accurate 
 More precise

Accuracy
 In theory:
• How well does the indicator map to the outcome? (e.g.
intelligence  IQ tests)
 In practice: Are you getting unbiased answers?
• Social desirability bias (response bias)
• Anchoring bias (Strack and Mussweiler, 1997)
Did Mahatma Gandhi die before or after age 9?
Did Mahatma Gandhi die before or after age 140?
• Framing effect
Given that violence against women is a problem, should we impose
nighttime curfews?

Precision and Random Error
 In theory: The measure is consistent, precise, but not necessarily valid
 In practice:
• Length, fatigue
• “How much did you spend on broccoli yesterday?” (as a measure of
annual broccoli spending)
• Ambiguous wording (definitions, relationships, recall period, units of
question)
Eg. Definition of terms – ‘household’, ‘income’
• Recall period/units of question
• Type of answer -Open/Closed
• Choice of options for closed questions
 Likert (i.e. Strongly disagree, disagree, neither agree nor disagree, . . .)
Rankings
• Surveyor training/quality

Challenges of Measurement
MEASUREMENT

The Basics
 Data that should be easy?
• E.g. Age, # of rooms in house, # in HH
 What is the survey question identifying?
• E.g. Are HH members people who are related to the household
head? People who eat in the household? People who sleep in
the household?
 Pre-test questions in local languages

The Basics: Units of Observation
Choosing Modules: Units of Observation
Often this is simple: For example, sex and age are clearly attributes of
individuals. Roofing material is attribute of the dwelling.
Not always obvious: To collect information on credit, one could ask
household’s
 All current outstanding loans.
 All loans taken and repaid in the last one year.
 All “borrowing events” (all the times a household tried to borrow,
whether successfully or not).
Choice is determined by expected analytical use and reliability of
information

The Basics: Deciding Who to Ask
 “Target respondent”: should be most informed person for each
module. Respondents for each module can vary.
 For example: to measure use of Teaching Learning Materials,
should we survey the headmaster? Teachers? SMC? Parents?
Students?
 Choice of modules decides target respondent, and target
respondent shapes the design of questions.

What is hard to measure in a survey?
(1) Things people do not know very well
(2) Things people do not want to talk about
(3) Abstract concepts
(4) Things that are not (always) directly observable
(5) Things that are best directly observed

How much tea did you consume last month?
A. <2 liters
B. 2-5 liters
C. 6-10 liters
D. >11 liters

1. Things people do not know very well
 What: Anything to estimate, particularly across time. Prone to
recall error and poor estimation
• Examples: distance to health center, profit, consumption,
income, plot size
 Strategies:
• Consistency checks – How much did you spend in the last
week on x? How much did you spend in the last 4 weeks on x?
• Multiple measurements of same indicator – How many minutes
does it take to walk to the health center? How many kilometers
away is the health center?

How many cups of tea did you consume yesterday?
A. 0
B. 1-3
C. 4-6
D. >6

What is Hard to Measure?

How frequently do you yell at your partner?
A. Daily
B. Several times per week
C. Once per week
D. Once per month
E. Never

2. Things people don’t want to talk about
 What: Anything socially “risky” or something painful
• Examples: sexual activity, alcohol and drug use, domestic
violence, conduct during wartime, mental health
 Strategies:
• Don’t start with the hard stuff!
• Consider asking questions in third person
• Always ensure comfort and privacy of respondent
• Think of innovative techniques – vignettes, list randomization

How frequently does your partner yell at you?
A. Daily
B. Several times per week
C. Once per week
D. Once per month
E. Never

What is Hard to Measure?
27

“I feel more empowered now than last year”
A. Strongly disagree
B. Disagree
C. Neither agree nor disagree
D. Agree
E. Strongly agree

3. Abstract concepts
 What: Potentially the most challenging and interesting type of
difficult-to-measure indicators
• Examples: empowerment, bargaining power, social cohesion, risk
aversion
 Strategies:
• Three key steps when measuring “abstract concepts”
• Define what you mean by your abstract concept
• Choose the outcome that you want to serve as the measurement
of your concept
• Design a good question to measure that outcome
 Often choice between choosing a self-reported measure and a
behavioral measure – both can add value!

4. Things that aren’t Directly Observable
 What: You may want to measure outcomes that you can’t ask
directly about or directly observe
• Examples: corruption, fraud, discrimination
 Strategies:
• Audit studies, e.g. CVs and racial discrimination
• Multiple sources of data, e.g. inputs of funds vs. outputs received by
recipients, pollution reports by different parties
• Don’t worry – there have already been lots of clever people before you
– so do literature reviews!

5. Things that are Best Directly Observed
 What: Behavioral preferences, anything that is more believable
when done than said
 Strategies:
• Develop detailed protocols
• Ensure data collection of behavioral measures done under the same
circumstances for all individuals

Use of Data
 Reporting
• On Inputs and Outputs (Achievement of physical and financial targets)
 Monitoring
• Of Processes and Implementation (Doing things right)
 Evaluation
• Of Outcomes and Impact (Doing the right thing)
 Management and Decision Making
• Using relevant and timely information for decision making (reporting and
monitoring for mid term correction; evaluation for planning and scale up)
ALL OF THE ABOVE DEPEND ON THE AVAILABILITY OF RELIABLE,
ACCURATE AND TIMELY DATA

Problems in Data Collection
 Data reliability (will we get the same data, when collected
again?)
 Data validity (Are we measuring what we say we are
measuring?)
 Data integrity (Is the data free of
manipulation?)
 Data accuracy/precision (Is the data measuring the “indicator”
accurately?)
 Data timeliness (Are we getting the data in
time?)
 Data security/confidentiality (Loss of data / loss of
privacy)

Reliability of Data Collection
 The process of collecting “good” data requires a lot of efforts and
thought
 Need to make sure that the data collected is precise and accurate.
 avoid false or misleading conclusions
 The survey process:
• Design of questionnaire  Survey printed on paper/electronic 
filled in by enumerator interviewing the respondent  data entry
 electronic dataset
 Where can this go wrong?

Reliability of Survey Data
 Start with a pilot
 Paper vs. electronic survey
 Surveyors and supervision
 Following up the respondents
 Problems with respondents
 Neutrality

Questionnaire is ready – so what’s next?
PILOTING

Importance of Piloting
 Finding the best way to procure required information
• choice of respondent
• type and wording of questions
• order of sections
 Piloting and fine-tuning different response options and components
 Understanding of time taken, respondent fatigue, and other
constraints

Steps in Piloting
ALWAYS allow time for piloting and back-and-forth between team on
the field and the researchers
Two phases of piloting
Phase 1: Early stages of questionnaire development
 Understand the purpose of the questionnaire
 test and develop new questions
 adapt questions to context
 build options and skips
 Re-work, share and re-test
 Build familiarity, adapt local terms, get a sense of time

Steps in Piloting
Phase 2: Field testing just before surveying
 Final touches to translation
 questions and instructions
 Keep it as close to final survey as possible.

Things to Look for During the Pilot
 Comprehension of questions
 Ordering of questions - priming
 Variation in responses
 Missing answers
 More questions for clarifications? Cut questions? consistency checks?
 Is the choice of respondent appropriate?
 Respondent fatigue or discomfort
 Need to add or correct filters? Need to add clear surveyor instructions?
 Is the format (phone or paper) user-friendly? Does it need to be improved?

Discuss Potentially Difficult Questions with the Respondent
Example 1: Simplify/clarify questions
 Do you use Student Evaluation Sheets in your school?
• Yes
• No
• Don’t know/Not sure
• No response
 They might not know it by this name (show them a sample)
 You may need to break it up into several questions to get at what you want
• Do you have them?
• Have you been trained on how to use them?
• Do you use them?

Discuss Potentially Difficult Questions with the Respondent
Example 2 : Ordering questions and priming
 Yesterday, how much time did you spend cooking, cleaning,
playing with your child, teaching/doing homework with your
child?
 Do you think its important for mothers to play with children?
 Do you think mothers or fathers should be more responsible
for a child’s education?
If Questions 2 and 3 had come before 1, there could’ve been a
possible
bias, order and wording of questions is important

Importance of Language and Translation
 The local language is probably not English, which makes things
tricky as to the wording of certain questions
• But people may be familiar with “official” words in English
rather than the local language
 Translate
• Ensures that every surveyor knows the exact wording of the
questions, instead of having to translate on the fly
 Back-translate
• Helps clarify when local-language words are used that don’t
have the same meaning as the original English

Documentation and Feedback
 Notes – time, difficulties, required or suggested changes
 Meetings to share inputs
 Draft document
 Keep different versions of the questionnaire

Designing Indicators

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