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Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data.
- What Is “Missing Data” in A Questionnaire
- Types of Missing Data
- Effects and Implications of Missing Data
- How to Spot Survey Conclusions Where Data Was Missing
- How to Handle Missing Data
- Conclusion
The most common type of missing data in a questionnaire occurs when respondents skip one or more questions. Missing data could also indicate that respondents dropped out of a survey. This means they began answering a questionnaire and then stopped for whatever reason. Survey fatigue is the most common reason respondents stop answering questions. Wh...
Not all missing data is detrimental to your research; some type of missing data is ignorable while some aren’t. Missing data doesn’t become a major problem until it renders the study invalid. For example, if an important element of the survey is missing, the remaining responses may become invalid. These types of data must be closely monitored in a ...
Reduces Result Validity and Reliability
When important parts of a questionnaire’s data go missing, it becomes difficult to replicate the results. When results are inconsistent when the same survey is conducted with the same sample, it indicates an error in the survey. This means you can’t use the results to draw a meaningful conclusion or use it to recommend solutions for future studies.
Establishes Survey Bias
Missing data can sometimes be an indication that the survey is being carried out with bias, which means the results are unlikely to be valid. For example, you’re surveying teens getting braces in Ohio, but your sample size is limited to students from a single high school in Ohio. This survey’s data excludes a significant portion of the sample size, so drawing conclusions based on data from this high school is biased.
Reduce Elements of Data
When a large amount of data is missing from a survey on a specific subject, you may have to exclude it from the data results. This reduces elements of the data you gathered, and you’d have to reduce the factors you’re using as a basis for your conclusion.
Small Sample Size
When a large number of survey participants drop out of a survey, you’d have to exclude their entries from the data resulting in a smaller sample size. Small sample sizes can also be problematic for most surveys, as they can cause your data to become unreliable when scaled up to a larger sample size.
Limited Survey Elements
Surveys are meant to investigate as many factors as possible to obtain accurate results. But when there is missing data, researchers will most likely reduce the factors used to make conclusions from the survey. For example, a survey was conducted to determine the most preferred deodorant flavor, and the flavors tested were lemongrass, coconut, cinnamon, and cucumber. However, because a significant number of participants have not used lemongrass and cucumber deodorant, these flavors were remov...
Irrespective of how careful you are when handling data from a survey or a questionnaire, missing data is almost unavoidable. Here are some proven methods for handling missing data without compromising the validity of your research:
Missing data could comprise the validity of your data, making it difficult to reproduce. However, not all types of missing data are detrimental to the survey. In cases where you can’t ignore the missing data, you can simply remove it from the result. You could also use any of the other methods that allow you to replace missing data with calculated ...
Aug 26, 2020 · If threshold of null effect is not crossed, it might be valuable to then evaluate the change in effect estimate to assess whether the relative effect goes from an important to an unimportant effect. If the latter happens, then rate down the certainty for risk of bias associated with missing data.
Oct 10, 2014 · The term intention-to-treat holds no information about how missing outcomes were handled in the analysis, and participants with missing outcomes are typically omitted from the analysis. This results in a “complete case intention-to-treat analysis.”
- Rolf H.H. Groenwold, Karel G.M. Moons, Jan P. Vandenbroucke
- 2014
Jan 8, 2019 · In this study, we have explored the performance of six imputation methods representing fundamentally different ways to approach missing data, along with CCA.
- Marianne Riksheim Stavseth, Thomas Clausen, Jo Røislien, Jo Røislien
- 2019
Aug 5, 2022 · When there is an omitted variable in research it can lead to an incorrect conclusion about the influence of diverse variables on a particular result. Let’s consider an instance where a researcher tries to understand what influences unemployment.
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Sep 1, 2016 · An adequately powered study requires the accrual of a sufficient number of primary-outcome events, which can be achieved by recruiting more patients, enrolling patients at higher risk, prolonging...
