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Evidence-Based Survey Design: The Use of Sliders

Thursday, October 24, 2019
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Put yourself in this scenario: Next week, you will deliver a workshop to a group of professionals. After the workshop, you want to send a feedback survey to the participants, which they can complete online. You are currently developing that survey with questions such as:

  • How relevant was the content of the workshop to your job role?
  • How effective was the practice session in helping you learn the content?
  • How likely are you to use the skills you learned from this workshop without assistance?
  • How likely are you to recommend this workshop to your co-workers?

These survey questions should be closed-ended questions, and your online survey software offers several design options for rating scales.

For the type of response options, you may choose:

  • verbal descriptor scales
  • numerical rating scales.

For the level of data precision, you may choose:

  • discrete rating scales
  • continuous rating scales.

With these design options in mind, let’s say you narrowed your choices to three different rating scales (A through C below).

A. Four-Point Verbal Descriptor Scale

  • Not at all
  • A little bit
  • Quite a bit
  • Definitely

B. 11-Point Numerical Rating Scale
Not at all - 1 - 2 - 3 - 4 - 5 - 6 - 7 -8 - 9 - 10 - Definitely

C. Slider With a Range From Zero to 100 (measuring data up to the hundredth place after the decimal point; for example, 32.93 or 65.28)

Chyung_Slider.png
The first two scales (A and B) are discrete rating scales. They provide a discrete number of options from which to choose. The number of options in typical discrete rating scales may vary from two to 11. The third one (C) is a continuous rating scale. The slider shown allows for a much more granular level of data to be recorded.

Now, you’re not sure which rating scale you should use and why. Your co-worker is recommending you use the slider because it is a new technology and makes your survey look cool. Is the “coolness factor” really going to inspire more engagement? How can you find trustworthy advice about the pros and cons of using sliders?
We took a deep dive into this question, reviewing dozens of studies. We discovered that some researchers reported a few benefits when using continuous rating scales.

  • The data obtained from continuous rating scales may show higher inter-rater reliability compared with using a discrete rating scale with a limited number of options to choose from (Wall et al. 2017).
  • Continuous rating scales may be less prone to the ceiling effect than discrete rating scales with fewer options to choose from (Voutilainen et al. 2015). A ceiling effect is referred to the phenomenon where respondents select the top options, which results in little data variance.

Despite the benefits of using continuous rating scales, some researchers warn of technical drawbacks when using sliders:

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  • It may take longer for respondents to manipulate drag-and-drop sliders and complete survey items compared with the amount of time spent clicking radio buttons typically provided on discrete rating scales (Cook et al. 2001; Couper et al. 2006).
  • Sliders (drag-and-drop) produce more incomplete data (for example, respondents did not move the slider bar to indicate their own opinion) compared with clickable radio buttons (Funke 2016).
  • Sliders used on mobile phones have resulted in more nonresponses than sliders used on tablets or desktop computers (Toepoel and Funke 2018).

Also, be aware that not all sliders produce continuous data. Despite appearances, some sliders may be designed to make the marker snap between a limited number of grid lines. The data collection from a slider showing a range from zero to 100 may actually be as coarse as an 11-point scale if the marker placed between units of 10 rounds up or down. For example, while one slider as a continuous rating scale may record a value of 16.37, another may snap the marker placed at the same position up to 20, making it a discrete rating scale.

So, research shows that sliders aren’t always cool to use or the most appropriate option for your task. You may want to avoid using sliders if your respondents are likely to complete your survey with a cell phone. Even when respondents are completing your survey on a desktop computer, you may have reasons to be concerned about the potential for nonresponses; for example, sliders may be more frustrating to use if respondents are tired, uninterested, or not technically savvy.

Point-and-click options consistently cause fewer frustrations for users than drag-and-drop options in the research we reviewed. Especially for respondents who have visual impairment or limited manual dexterity, sliders are not an appropriate choice. In other situations where you can ensure or expect that respondents would take time to carefully complete your survey, the use of sliders may help produce quality data with reduced ceiling effects and improved inter-reliability of data.

This post is one in a series of articles that I present to help other practitioners make evidence-based decisions when designing surveys. For more information about using continuous rating scales, please see this article published by my research team at Boise State University’s Organizational Performance and Workplace Learning department.

References

Antoun, C., M.P. Couper, and F.G. Conrad. 2017. “Effects of Mobile Versus PC Web on Survey Response Quality.” Public Opinion Quarterly 81:280–306. https://doi.org/10.1093/poq/nfw088.

Carey, J.L., T.J. Ganley, M.D. Milewski, J.D. Polousky, K.G. Shea, E.J. Wall, and A. Zbojniewicz. 2017. “The Reliability of Assessing Radiographic Healing of Osteochondritis Dissecans of the Knee.” The American Journal of Sports Medicine 45(6): 1370–1375. https://doi.org/10.1177/0363546517698933.

Conrad, F.G., M.P. Couper, E. Singer, and R. Tourangeau. 2006. “Evaluating the Effectiveness of Visual Analog Scales: A Web Experiment.” Social Science Computer Review 24(2): 227–245. https://doi.org/10.1177/0894439305281503.

Cook, C., F. Heath, R.L. Thompson, and B. Thompson. 2001. “Score Reliability in Web- or Internet-Based Surveys: Unnumbered Graphic Rating Scales Versus Likert-Type Scales.” Educational and Psychological Measurement 61(4): 697–706. https://doi.org/10.1177/00131640121971356.

Funke, F. 2016. “A Web Experiment Showing Negative Effects of Slider Scales Compared to Visual Analogue Scales and Radio Button Scales.” Social Science Computer Review 34(2): 244–254. https://doi.org/10.1177/0894439315575477.

Funke, F., and V. Toepoel. 2018. “Sliders, Visual Analogue Scales, or Buttons: Influence of Formats and Scales in Mobile and Desktop Surveys.” Mathematical Population Studies 25(2): 112–122. https://doi.org/10.1080/08898480.2018.1439245.

Kvist, T., T. Pitkäaho, K. Vehviläinen-Julkunen, and A. Voutilainen. 2015. “How to Ask About Patient Satisfaction? The Visual Analogue Scale Is Less Vulnerable to Confounding Factors and Ceiling Effect Than a Symmetric Likert Scale.” Journal of Advanced Nursing 72(4): 946–957. https://doi.org/10.1111/jan.12875.

About the Author

Yonnie Chyung, EdD, is a professor and associate chair of the Organizational Performance and Workplace Learning department at Boise State University. She teaches graduate courses on program evaluation, quantitative research, and survey design. She is the author of 10-Step Evaluation for Training and Performance Improvement (Sage 2019) and Foundations of Instructional and Performance Technology (HRD Press 2008). Yonnie provides consulting to organizations to perform statistical analysis on their organizational data and conduct program evaluations, often involving students in her research and consulting projects. Recently, she has been developing evidence-based survey design principles for training and performance improvement practitioners.

About the Author

Ieva Swanson, MS, has been a workplace learning and development specialist since 2007. Her work in the classroom and in digital media is solidly rooted in human performance improvement, evidence-based practice, and a sense of humor. She has served on chapter boards for the Association for Talent Development (ATD St. Louis) and the International Society for Performance Improvement (ISPI Bay Area/Boise State). She is a member of the eLearning Guild, and a volunteer contributor to Designers for Learning. She has indulged a continuing interest in research and a connection with Boise State University’s Organizational Performance and Workplace Learning Department by participating in Dr. Yonnie Chyung’s Workplace-Oriented Research Central (WORC) Lab, focusing on evidence-based survey design.

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