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How do you avoid introducing bias or error when inputing missing data?

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Missing data is a common challenge in statistics, especially when working with real-world datasets. It can affect the validity and reliability of your results, and introduce bias or error in your analysis. Inputting missing data is one way to deal with this problem, but it also comes with some risks and limitations. In this article, you will learn how to avoid introducing bias or error when inputting missing data, and what strategies you can use to handle different types of missing data.

本文章的要点总结

Use auxiliary information:

Leveraging related data can improve imputation. If age is missing, demographic details or income might provide clues for more accurate estimates, reducing the risk of bias.
Sensitivity analysis:

Testing imputations with varied methods and assumptions helps ensure robustness. It's a safety check against potential errors your 'best guess' data might introduce.

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1 What is inputting missing data?

Inputting missing data means replacing the missing values with some plausible estimates, based on the available information in the dataset or some assumptions about the data-generating process. The goal is to create a complete dataset that can be used for further analysis, without losing important information or distorting the underlying patterns. But, it can introduce bias or error if done incorrectly or without proper justification.

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Paul Bailey

Principal Economist at American Institutes for Research
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This talks about one of two possible concepts: single imputation or multiple imputation. Its not clear which. But, more importantly, impute has been changed to input, which makes it somewhat humorous. I’m guessing that’s not the intention.

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Tom Groleau

Director of the Business Analytics Center, Gatton College of Business and Economics
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If this is an "AI-powered" article, then AI isn't very powerful. As Paul points out, the correct term is imputing, not inputting. Maybe a human-powered editor should fix this.

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Peter Bednar

Senior Lecturer at University of Portsmouth
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wow a lot of confused use of vocabulary here. [1] What does 'real world dataset' actually mean? Seriously, what are the boundaries explicitly? [2] Missing data, what is that? missing in what way and from what? what is the definition of missing? In compared to what? What exactly is the definition of 'not missing'? Is there such a thing as completeness? [3] Validity and reliability of results? what exactly is relationship between data and validity of results? And how does missing data have anything to do with reliability? Is this not related to the data that are not missing? [4] Is there such a thing as non-biased assumptions? Really where? [5] How is error defined? Compared to what? [6] What is 'proper justification'? [7] you can avoid bias?

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2 Why does input introduce bias or error?

The inputting process introduces bias or error because it essentially creates artificial data that may not reflect the true values or the true distribution of the data. Depending on the method and the assumptions used, it can overestimate or underestimate the variability, skewness, kurtosis, correlations, or associations of the data. It can also mask the uncertainty or the missingness mechanism of the data, and affect the statistical inference or the generalizability of the results. Therefore, inputting data should be done with caution and transparency, and always checked for validity and sensitivity.

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Peter Bednar

Senior Lecturer at University of Portsmouth
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Verbal nonsense: What is the difference between artificial data and non artificial data? What is the definition of 'true values' and 'true distribution' and why would existing distribution be a good thing to follow? why would that be relevant? and so on.

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3 How to avoid bias or error when inputting missing data?

One way to avoid bias or error when inputting missing data is to choose an appropriate method that matches the type and the mechanism of the missing data. There are three main types of missing data: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). There are also different input methods, such as mean, median, mode, regression, nearest neighbor, multiple, or Bayesian. Each method has its own advantages and disadvantages, and some are more suitable for certain types of missing data than others.

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Paul Bailey

Principal Economist at American Institutes for Research
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This needs a more specific definition of MCAR, MAR, and NMAR because the full text bames are not entirely clear. An example of each would help.

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Peter Bednar

Senior Lecturer at University of Portsmouth
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Another collection of verbal nonsense. How is 'appropriateness' determined? By whom? in what context and so on? Then listing methods and talking about advantages and disadvantages without any referene to relevance or context. Or anything really, just verbal jelly.

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4 How to choose an appropriate input method?

To choose an appropriate input method, you need to understand the type and the mechanism of the missing data, and the characteristics and the objectives of your analysis. For example, if your data is MCAR, you can use simple methods like mean, median, or mode imputation, but if your data is MAR or MNAR, you may need more sophisticated methods like regression, nearest neighbor, multiple, or Bayesian. Consider the level of measurement, the distribution, the outliers, the sample size, the missing rate, and the potential impact of input on your analysis.

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Gene Hahn

Professor of Information and Decision Sciences and Author, "Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems"
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I question the statement "if your data is MCAR, you can use simple methods like mean, median, or mode imputation". This will populate your data with copies of the estimator and cause the variance to shrink, among other problems.

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Peter Bednar

Senior Lecturer at University of Portsmouth
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Type and mechanism of the missing data? Really. These articles are just collections of statements and propositions which are using jargong and are not in any meaningful way exploring the points described in the context of the headline of the actual article. To read this article is such a waste of time.

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5 How to check the validity and sensitivity of imputation?

Another way to avoid bias or error when inputting missing data is to check the validity and sensitivity of your input. You can do this by comparing the inputted dataset with the original dataset and evaluating the changes in the descriptive statistics, the distributions, the correlations, and the results of your analysis. You can also use different input methods, assumptions, or parameters, to see how they affect your results. You can also use methods like bootstrapping or cross-validation to estimate the uncertainty or variability of your data.

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Peter Bednar

Senior Lecturer at University of Portsmouth
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absolutely not, you cannot check validity of 'made up data' by comparing them with the original dataset. You have absolutely no idea if yothe data that you just made up have any what so ever relationship to any kind of reality. The point is that you have no idea whether or not the data that you do not have would have been examples of exceptions or not. and so on. This article really is nonsensical at best.

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6 How to report your input procedure and results?

The final way to avoid bias or error when inputing missing data is to report your input procedure and results clearly and transparently. You should describe the type and the mechanism of the missing data, the input method and the assumptions used, the validity and sensitivity checks performed, and the limitations and implications of your input. You should also provide the code or the software used and share the inputted dataset or the input model if possible. This will help your readers to understand, evaluate, and reproduce your analysis.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Fahad Mostafa, PhD

Statistician| Biomedical Data Scientist
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Avoid Imputation from Dependent Variables: Be cautious when imputing missing data based on other variables that may be influenced by the variable with missing data. Use Auxiliary Information: Incorporate auxiliary information related to the missing variable or other correlated variables. This information can improve the accuracy of imputation and reduce bias. For example, if age is missing, utilizing demographic characteristics or income as auxiliary variables may help impute missing values more accurately. Perform Sensitivity Analysis: Conduct sensitivity analyses to evaluate the robustness of the imputation results. This involves performing the analysis with different imputation methods or assumptions about missing data mechanisms

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How do you avoid introducing bias or error when inputing missing data?

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1 What is inputting missing data?

2 Why does input introduce bias or error?

3 How to avoid bias or error when inputting missing data?

4 How to choose an appropriate input method?

5 How to check the validity and sensitivity of imputation?

6 How to report your input procedure and results?

7 Here’s what else to consider

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How do you avoid introducing bias or error when inputing missing data?

1

2

3

4

5

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1 What is inputting missing data?

2 Why does input introduce bias or error?

3 How to avoid bias or error when inputting missing data?

4 How to choose an appropriate input method?

5 How to check the validity and sensitivity of imputation?

6 How to report your input procedure and results?

7 Here’s what else to consider

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