designing indicators
DESCRIPTION
Designing IndicatorsTRANSCRIPT
Survey Design & Data Collection
Lecture Overview
What should you measure?
What makes a good measure?
Measurement
Data Collection
Piloting
WHAT SHOULD YOU MEASURE?
What do we measure and where does it fit into the whole project?
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
INDICATORSWhat makes a good measure?
The Main Challenge in Measurement: Getting Accuracy and Precision
More accurate M
ore
pre
cis
e
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
M
ore
p
recis
e“Noisy”
“Precise”
Random error causes answer to bounce around
Measures of the same thing cluster together
Choices in Real Measurement Often Harder
More accurate M
ore
p
recis
e“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 M
ore
p
recis
e
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
MEASUREMENT Challenges of 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?
(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 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?
(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
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“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!
What is Hard to Measure?
(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
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
DATA COLLECTION
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
PILOTINGQuestionnaire is ready – so what’s next?
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