
October 2017
Handling missing data: Multiple imputation for the applied researcher
A 4-hour workshop taught by Ryan S. Wells, Ph.D. You may also be interested in our increasing web survey response rates workshop. Register Overview Item-missing data is a serious concern for any quantitative researcher. Survey participants regularly skip questions, and administrative data is often incomplete. But what do to about this issue is not always straightforward. Researchers often analyze only complete cases (listwise deletion) or they impute missing information using means or regression analysis. These approaches can lead to biased estimates and artificially small standard…
Find out more »May 2019
Handling missing data: Multiple imputation for the applied researcher
A 4-hour workshop taught by Ryan S. Wells, Ph.D. You may also be interested in our increasing web survey response rates workshop. Register Overview Item-missing data is a serious concern for any quantitative researcher. Survey participants regularly skip questions, and administrative data is often incomplete. But what do to about this issue is not always straightforward. Researchers often analyze only complete cases (listwise deletion) or they impute missing information using means or regression analysis. These approaches can lead to biased estimates and artificially small standard…
Find out more »May 2020
Handling missing data: Multiple imputation for the applied researcher
A 4-hour workshop taught by Ryan S. Wells, Ph.D. You may also be interested in our increasing web survey response rates workshop. Register Overview Item-missing data is a serious concern for any quantitative researcher. Survey participants regularly skip questions, and administrative data is often incomplete. But what do to about this issue is not always straightforward. Researchers often analyze only complete cases (listwise deletion) or they impute missing information using means or regression analysis. These approaches can lead to biased estimates and artificially small standard…
Find out more »October 2021
Handling missing data: Multiple imputation for the applied researcher
A 4-hour workshop taught by Ryan S. Wells, Ph.D. You may also be interested in our increasing web survey response rates workshop. Register Overview Item-missing data is a serious concern for any quantitative researcher. Survey participants regularly skip questions, and administrative data is often incomplete. But what do to about this issue is not always straightforward. Researchers often analyze only complete cases (listwise deletion) or they impute missing information using means or regression analysis. These approaches can lead to biased estimates and artificially small standard…
Find out more »