Sentence examples for account for missing responses from inspiring English sources

The phrase "account for missing responses" is correct and usable in written English.
It can be used when discussing the need to explain or consider the absence of certain responses in a survey, study, or data collection.
Example: "In our analysis, we must account for missing responses to ensure the accuracy of our findings."
Alternatives: "consider absent responses" or "explain the lack of responses."

Exact(1)

To account for missing responses in SHLC we used the sample weights provided in the survey.

Similar(59)

¥Trust score transformed and weighted for missing responses.

No imputation was performed for missing responses.

A further 979 (4%) of women were excluded for missing responses.

After accounting for non-responders and missing responses on questionnaire items, data were available from 294 out of 330 and 229 out of 287 men, respectively (only those who responded at time-point 3 were sent a follow-up questionnaire, and during one week men were not posted questionnaires due to an administrative error, thus reducing the denominator to 287).

Cultural differences that might make this a more sensitive question in Spain might account for the higher percentage of missing responses on this question in Spain.

All reported estimates are weighted by sampling and non-response weights to account for missing data owing to non-response at later sweeps (Hansen, 2008; Plewis, 2007).

17 30 All analyses took the clustered stratified study design into account by using the survey commands in Stata version 10. 31 All reported estimates are weighted by sampling and non-response weights to account for missing data because of non-response at later sweeps.

Appropriate multiple imputation procedures will be employed to account for missing data for treatment response.

Population prevalence estimates for symptomatic radiographic foot OA overall and for each joint were calculated using multiple imputation and weighted logistic regression modelling to account for missing data and non-response.

Strengths of our study include the population size, the primary care setting and the combination of multiple imputation and weighted logistic regression modelling to account for missing data and non-response.

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