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Enhancing Survey Design: Best Practices for Effective Question Construction

This week's lecture focuses on improving survey design through practical examples of questionnaire items. We will discuss common pitfalls in survey questions, illustrated by examples of bad and better items, particularly in selecting options for describing living situations. Participants will analyze these items to identify flaws and propose solutions, exploring the differences between open-ended and closed-ended questions. The session aims to improve your ability to create effective surveys with clear, concise, and inclusive response options that enhance data collection.

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Enhancing Survey Design: Best Practices for Effective Question Construction

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  1. COMM 250 Agenda - Week 7 Housekeeping Today: RAT2 Return: C1 Lecture Continuing Surveys: Choices re: Scale items, layout Examples ITE 4

  2. In-Class Team Exercise # 4 - Part I First Do as Individuals, then produce a Team Version: Example of a BAD Item Which of the following describes your CURRENT living situation? 1) Married, no kids 5) Divorced 2) Married, 1-3 kids at home 6) Divorced, 1-3 kids at home 3) Married, 3 or more kids 7) Divorced, 3+ kids at home at home 8) Unmarried, but have kids 4) Unmarried ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- • What mistakes make this a bad item? • How would you fix this problem? Deliverable: a written answer to a & b ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

  3. Solution Example of a BETTER Item Which best describes your CURRENT living situation? 1) Married, no kids 5) Divorced, 1-3 kids at home 2) Married, 1-3 kids at home 6) Divorced, more than 3 kids at home 3) Married, more than 3 kids 7) Unmarried, no kids at home 8) Unmarried, 1-3 kids at home 4) Divorced, no kids 9) Other (Please specify: ______________ )

  4. Solution Example of a Better APPROACH What is your marital status? 1) Single 3) Divorced 2) Married 4) Widowed How many children do you have? ___ ___ How many CHILDREN currently live with you? ___ ___ How many other ADULTS currently live with you? ___ ___

  5. Scale Items Even / Odd Number of Values • Even - no midpoint - forces users to choose • Odd - has a midpoint - allows a “neutral” response • (I prefer Odd) Number of values 3-5-7-9 or 4-6-8-10 point scales: • 3-4 is simple but may not allow “discrimination” • 9-10 is usually overkill • 5-6-7 is usually best • (I prefer 7)

  6. Surveys The rest of Lecture 7 will be in the form of examples of questionnaire items on Overhead Transparencies - these will that highlight the lessons discussed in the following slides from Week 6.

  7. Open-ended vs. Closed Questions Open-ended Items (“Fill in the Blanks”) • Useful for “exploratory” data collection • ADV: Respondents (Rs) aren’t “led” by some list of available choices / opinions • DISADV: Requires much more work - to quantify, researcher must categorize and “code” responses Closed-ended Items (“Multiple Choice”) • Useful when all of the available responses are known • ADV: 1) Easier to quantify, and 2) Rs are reacting to the same stimulus materials (some list of choices) • DISADV: 1) Researcher may miss some important reasons/options

  8. Multiple Choice Items The Options (possible values) in MC Items should be: • Mutually Exclusive • Exhaustive • Consistent • Linear (follow in a logical order) • Clear and concise • Limited in number (so the researcher can make sense of them)

  9. Determinism & Free Will Determinism • Everything is determined in advance • If science knew all the rules, it could specify all outcomes (predict all events) • It seems to work with billiard balls – does it work for human behavior? • But does this imply that there is no free will? (We’ll return to this when we get to statistics.)

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