Difference between revisions of "Questionnaire Design"

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* Outcomes that are not directly observable. Examples include: corruption, quality of care. Strategies to use include: [[Audit Studies]], [[Mystery Patients]].
* Outcomes that are not directly observable. Examples include: corruption, quality of care. Strategies to use include: [[Audit Studies]], [[Mystery Patients]].


Find information on measurement issues and methodological experiments in the following articles: [[Income & Expenditures]], [[Agricultural Production]], [[Subjective Well-being]], [[Risk Aversion]]
'''Note:''' It is always best to directly measure outcomes when possible. For example, consider the following two measures of literacy:
 
# "Can you read?" ''Answer choices'': yes, no
# "Can you please read me this sentence?" [Enumerators holds up showcard with a sentence written in the local language]. ''Answer choices:'' read sentence correctly, read sentence with some errors, unable to read sentence


== Back to Parent ==
== Back to Parent ==

Revision as of 18:44, 6 February 2017

This topic cover questionnaire design and measurement issues.


Read First

Do start with a careful review of existing survey instruments that cover similar topics. Don't reinvent the wheel -- working from a high-quality, previously-piloted survey instrument will save time and improve the quality of your final output.


Guidelines

Questionnaire design process

When designing a survey instrument from scratch, follow these steps:

  1. Review (or draft) a Theory of Change and Pre-Analysis Plan.
  2. Make a list of all intermediary and final outcomes of interest, as well as important covariates and sources of heterogeneity
  3. Prepare an outline of questionnaire modules, based on the above list. Get feedback from research team.
  4. For each module, prepare a list of specific indicators to measure. Get feedback from research team and implementing partners.
  5. Review existing questionnaires and compile databank of relevant questions for each module
  6. Draft questionnaire, noting source of each question (e.g. source: Uganda National Panel Survey (LSMS 2013-14), source: Uganda DHS 2011, source: Uganda Social Assistance Grants for Empowerment Programme 2013, Evaluation Follow-Up Survey [1], source: own design - extra attention required in pilot), and get feedback from research team and implementing partners
  7. Content-based Pilot
  8. Questionnaire Translation
  9. Questionnaire Programming

Designing a follow-up questionnaire is simpler. Try to keep as close to the baseline survey instrument as possible, to facilitate panel analysis. Better to add/subtract questions than to modify existing ones.

Key elements all questionnaires must have

Identification of target respondent

Unique ID

Informed Consent

The Institutional Review Board that granted approval for the study typically provides a template for requesting Informed Consent.

Basic Rules for Questionnaire Design

  • All questions should have pre-coded answer options. Answer options must be:
    • Clear, simple, and mutually exclusive
    • Exhaustive (tested and refined during the Survey Pilot)
    • Include 'other' (but if >5% of respondents choose 'other', answer choices were insufficiently exhaustive)
  • Include hints to the enumerator as necessary, typically coded to appear in italics (not part of the question read to the respondent)
    • For example, "For how many months did you work in the last 12 months? Enumerator: if less than 1 month, round up to 1

Measurement Issues

Keep in mind that even simple-seeming data points might not be simple to capture. Examples:

  • Age is not straightforward in contexts where people are innumerate, do not have birth certificates, or do not know their birth year.
  • Household size will depend entirely on how 'household member' is defined (only those currently living in the household? those who have lived more than 6 of the last 12 months in the household? what about domestic servants? students away at school who are economically dependent on the household? Household head who has migrated but sends remittances back to support the household?

Types of data that are hard to measure in a questionnaire include:

Note: It is always best to directly measure outcomes when possible. For example, consider the following two measures of literacy:

  1. "Can you read?" Answer choices: yes, no
  2. "Can you please read me this sentence?" [Enumerators holds up showcard with a sentence written in the local language]. Answer choices: read sentence correctly, read sentence with some errors, unable to read sentence

Back to Parent

This article is part of the topic Questionnaire Design


Additional Resources

Comprehensive resources on survey design

  • Development Impact Blog: Three New Papers Measuring Stuff that is Difficult to Measure [3]