Search

A perennial issue of survey research is that some participants do not answer all questions. Interactive follow-up requests are a novel approach to this problem. However, research on their effectiveness is scarce. I present the most comprehensive study yet on the effects of interactive requests on item non-responses. Theoretically, I outline different pathways whereby follow-up requests may effectively increase response rates and improve data quality: reminding, motivating, instructing, monitoring, and sanctioning. To test my hypothesis that interactive requests increase item response rates, I conducted an online survey experiment in 2021 on diverse samples of around 3,100 respondents in ten countries worldwide. I find that follow-up requests generally increase response rates, although effects vary by country. Depending on the question and survey design, interactive requests reduce item non-responses by up to 47 per cent across countries, while not adversely affecting data quality. I thus recommend response requests to increase survey data efficiency.

Objective

To investigate item non-response in a postal food-frequency questionnaire (FFQ), and to assess the effect of substituting/imputing missing values on dietary intake levels in the Norwegian Women and Cancer study (NOWAC). We have adapted and probably for the first time applied k nearest neighbours (KNN) imputation to FFQ data.

Design

Data from a recent reproducibility study were used. The FFQ was mailed twice (test–retest) about 3 months apart to the same subjects. Missing responses in the test FFQ were imputed using the null value (frequencies = null, amount = smallest), the sample mode, the sample median, KNN, and retest values.

Setting

A methodological substudy of NOWAC, a national population-based cohort.

Subjects

A random sample of 2000 women aged 46–75 years was drawn from the cohort in 2002 (response 75%). The imputation methods were compared for 1430 women who completed at least 50% of the test FFQ.

Results

We imputed 16% missing values in the overall test data matrix. Compared to null value imputation, the largest differences in estimated dietary intake were seen for KNN, and for food items with a high proportion of missing. Imputation with retest values increased total energy intake, indicating that not all missing values are caused by respondents failing to specify no consumption, and that null value imputation may lead to underestimation and misclassification.

Conclusion

Missing values in FFQs present a methodological challenge. We encourage the application and evaluation of newer imputation methods, including KNN, which may reduce imputation errors and give more accurate intake estimates.

Search Results

Refine search

Refine search

Actions for selected content:

2 results

The Effects of Interactive Requests on the Quantity and Quality of Survey Responses: An International Methodological Experiment

Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation: effects on dietary intake in the Norwegian Women and Cancer study (NOWAC)

Search Results

Refine search

Refine search

Actions for selected content:

Save Search

2 results

The Effects of Interactive Requests on the Quantity and Quality of Survey Responses: An International Methodological Experiment

Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation: effects on dietary intake in the Norwegian Women and Cancer study (NOWAC)