Random digit dialing and internet panel data collection methods in two Canadian provinces: Comparing costs, data missingness, straightlining, and sociodemographic characteristics of sample, and responses from a survey on nutrition policy support and causes of chronic disease.

Background: There is little consensus on what public health survey administration methods are better (data generation and cost-wise) for collecting data on knowledge, attitudes, and beliefs (KAB). We compare random digit dialing (RDD) and internet panel sampling methods for gathering KAB data on chronic disease etiology and nutrition policy. Design and methods: We collected survey data from residents of Alberta and Manitoba in 2017, using population-based samples generated through: RDD and an internet panel. We calculated response rate and cost for each mode. To compare missing data and straightlining, we used linear regression. We used Chi-squared tests to compare sociodemographic characteristics between the two modes and to the 2016 Canadian Census data. KAB responses were also compared between modes using Chi-squared tests. Results: The internet panel was less expensive and had more missing data than the RDD. Straightlining was comparable across modes. Both modes tended to oversample specific population groups (e.g. older adults); while undersampling others (e.g. Indigenous people) compared to the Canadian Census. RDD had more females and older respondents than the internet panel respondents. Internet panel respondents were less supportive of nutrition policy options, and agreed more with individual responsibility and blame for obesity, compared to RDD respondents. Conclusions: Both modes present advantages and disadvantages. Differences in sociodemographics and KAB responses between modes indicate sampling methods for public health surveys may be considered in survey design and administration. Researchers should discuss their findings vis-a-vis the specific limitations of each method they employed and adopt strategies to mitigate them.