What determines the Sample Size for Cross-Sectional Surveys?
January 6, 2021
Hiring Field Survey Enumerators: 5 Good Practices You Can Use
January 27, 2021
What determines the Sample Size for Cross-Sectional Surveys?
January 6, 2021
Hiring Field Survey Enumerators: 5 Good Practices You Can Use
January 27, 2021
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Questionnaire Design: Best Practices for High-Quality Measurements

After we finalize research questions and methods to answer those, the next step is data collection. Quantitative research uses structured and standardized questionnaires to collect data. These questionnaires ensure that all respondents are asked questions in the same manner and in the same sequence, and the answers are coded as a number. Even for a multiple-choice type of a question, each option has an underlaying numeric code. And open-ended questions which collect text data are often coded into a number during data processing stage. Once finalized these structured and standardized questionnaires are administered to hundreds of respondents as per the required sample size. Therefore, it becomes important to get the design and administration modality right. Otherwise, we are stuck with a large data set you cannot use or trust.

Today, technology makes us administered well-designed questionnaires with even better accuracy, validity, and speed than what is possible with paper-based surveys. Below, I highlight a few aspects you must consider in designing a questionnaire.

Questionnaire Design Best Practices

1.     Duration or Length

We researchers are greedy for more data, but more does not mean good! Nowadays researchers are moving towards lean data collection that forces them to ask how the data will be used, and question the need for extra information often collected but rarely used. We advise to keep the questionnaire length to <60 minutes in a rural setting and <30 minutes in cities.

2.     Language

Write a question the way you intend it to be asked without the need for repetition or explanation in the enumerator’s own words; that beats the purpose of a standardized questionnaire. Our tip – first think of the question in the local language and then translate into English, not vice versa.

3.     Questionnaire Revalidation

Some questions are just too important to get wrong. Ask such questions twice after some time gap and validate that the answers match.  If they don’t, the respondent will be a lot more attentive. But, please use this tip sparingly.

4.     Flow like Makkhan

Sorry for breaking into Hinglish but the questionnaire should run without hiccups and ask questions in a flow that is logical and ‘customized’ to previous answers. Administer a designed questionnaire to your non-research friend and if he yawns after 10 minutes, know you got it wrong. Fix section heading, section intro text, instructions for skipping-asking-customizing the question, question’s wording, and proper set of answers which can be entered quickly. 

Checklist: Questionnaire Design Best Practices
Checklist: Questionnaire Design Best Practices

5.     Pre-test and field tests

Always test the entire questionnaire for content and flow before proceeding with CAPI development. Rigorous research should ideally do a pre-test with target respondents, but at least trouble a few friends. A group meeting of researchers is a great way to dissect and finalize questionnaires.

6.     Update and Changes to the Questionnaire

A questionnaire is never final until a few days of the fieldwork is done. So you should be prepared for such changes but if they are major, then programming of your CAPI App and field trainings can be affected by these last-minute changes. Our rule of thumb is <5% questions and <10% ‘words’ are changed during field training and initial fieldwork.

To dive deeper into the topic, watch this space for the second part of this blog: ‘Tips for Developing a CAPI App that works like a charm’

Also read “Principles for Designing Good Questionnaires”

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As a Research Assistant, Alron provides support on projects, proposals, data analysis, ideation. He has over two years of experience in MIS, economic analyses, and policy research writing. He is passionate about migration-refugee crisis, decoding and mitigating disinformation mechanisms, and has written extensively on these topics, among others.

Alron holds a bachelor’s in economics from the University of Mumbai and is currently pursuing a Diploma in International Affairs.

As a Research Assistant, Alron provides support on projects, proposals, data analysis, ideation. He has over two years of experience in MIS, economic analyses, and policy research writing. He is passionate about migration-refugee crisis, decoding and mitigating disinformation mechanisms, and has written extensively on these topics, among others.

Alron holds a bachelor’s in economics from the University of Mumbai and is currently pursuing a Diploma in International Affairs.

Hrishikesh works as a Research Assistant at NEERMAN where he applies his skills to the development of impact assessment. Apart from this, he volunteers his time by working for various projects and activities associated with the Rotaract.

He holds a Master's degree in Social Work from the University of Mumbai.

Anshuman manages our survey research and data division. He leads a critical position that ensures researchers need for quality data are met on time and within budget.

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Rupam is working as a Senior Research Associate at NEERMAN. She has over 4 years of work experience in social/ development sector. Her area of interest is in domains like population and development, maternal and child nutrition, education, reproductive and child health, gender issues, transgender study etc. She holds her Master's degree in Population Studies from International Institute for Population Sciences (IIPS, Mumbai) and Bachelor’s from University of Delhi.

Anil has more than 25 years of experience in the area of data management and analysis. Previously he has worked with Nielsen India Pvt. Ltd. He is adept in facilitating operations for projects using CSPro (Version 7.6) Data Entry package and analysis of data using statistical package for social science (SPSS). He has proven ability in maintaining large databases and conducting field training of CAPI related aspects.

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      Pritesh is the backbone of the data processing and analytics division at NEERMAN and is responsible for data reporting, CAPI Design, STATE and ODK work in close collaboration with the Senior Data Manager. He also manages teams of assistants for data processing and QAQC. Pritesh holds a Bachelor’s Degree in Commerce from the University of Mumbai.

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