Skip to main content

Scott Ketter's "Methods can matter: Where web surveys produce different results than phone interviews"

Pew recently revisited the question of how survey modality can influence survey responses. In particular, this survey used both web and telephone based surveys to ask participants about their attitudes towards politicians, perceptions of discrimination, and their satisfaction with life.

As summarized in the article, the big differences are:

"1) People expressed more negative views of politicians in Web surveys than in phone surveys." 




"2) People who took phone surveys were more likely than those who took Web surveys to say that certain groups of people – such as gays and lesbians, Hispanics, and blacks – faced “a lot” of discrimination." 


"3) People were more likely to say they are happy with their family and social life when asked by a person over the phone than when answering questions on the Web."  



The social psychologist in me likes this as an example of the Social Desirability Bias. When speaking directly to another human being, we report greater life satisfaction, we are more critical of politicians, and more sympathetic towards members of minority groups.

The statistician in me thinks this is a good example for discussing sources of error in research. Even a completely conscientious research using valid, reliable measures may have their data effected based on how it is collected. It might be interesting to asks students to generate lists of research topics (say, market research about cereal preference versus opinions about abortion) and whether students think you could get "true" answers via telephone or web surveys. What is a "true" answer, how could we evaluate or measure this? How could we come up with an implicit or behavioral measure of something like satisfaction with family life, then test which survey modality is most congruent with an implicit or behavioral measure? What do students think would happen if you used face-to-face interviews or paper and pencil surveys in a classroom of people completing surveys?

Additionally, you can't call yourself a proper stats geek unless you follow Pew Research Center on either Twitter (@pewresearch) or on Facebook . So many good examples of interesting data!

Popular posts from this blog

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr.  Molly Metz maintains a curated list of hilarious "How you doing?" scales.  2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.  So, I decided to create a post about three ways to use these scales in your stats classes:  1) Teaching the basics of variables. 2) Nominal vs. ordinal scales.  3) Daily check-in with your students.  1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created  a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/