multiple imputation

USAGE SUMMARY

"multiple imputation" is correct and usable in written English.
You would use it when referring to the statistical technique of filling in missing data points in a dataset by drawing multiple values from a probability distribution. For example, you could say, "We used multiple imputation to fill in the missing responses on the survey."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

First, we used multiple imputation to impute stage.

British Journal of Cancer

These missing covariates were imputed with a multiple imputation model.

Critical Care

Multiple imputation by chained equations.

Health & Justice

Missing stage was handled by multiple imputation approach.

Cancer Epidemiology

We included all variables in the multiple imputation models.

The Journal of Chinese Sociology

Missing data were handled using multiple imputation by chained equation.

Annals of Intensive Care

We used multiple imputation to deal with missing data.

Intensive Care Medicine Experimental

Multiple imputation via chained equations is inherently a parametric method.

Large-scale Assessments in Education

Figure 16 Multiple imputation estimates after 30 imputations.

Large-scale Assessments in Education

For multiple imputation, this procedure is repeated m times.

Large-scale Assessments in Education

h This quantity figures prominently in multiple imputation.

Large-scale Assessments in Education

Expert writing Tips

Best practice

When using "multiple imputation", ensure that the imputation model includes all relevant variables to minimize bias and improve the accuracy of the imputed values.

Common error

A common mistake is applying "multiple imputation" without verifying that the missing data are Missing At Random (MAR). Always assess the potential for data being Missing Not At Random (MNAR), as "multiple imputation" under MAR assumptions may lead to biased results if MNAR is present.

Linguistic Context

The phrase "multiple imputation" functions as a noun phrase that identifies a specific statistical technique. It's used to describe the process of replacing missing data with multiple plausible values, creating multiple complete datasets for analysis. Ludwig AI confirms its usability in written English.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

In summary, "multiple imputation" is a well-established statistical technique used to address missing data in research. Ludwig AI confirms that the phrase is correct and appropriate for use in written English, particularly within scientific and academic contexts. As a noun phrase, it identifies a specific method for handling missing data, aiming to improve the accuracy and reliability of statistical analyses. The phrase is very common in scientific literature, with limited use in other areas. It's crucial to understand the assumptions behind "multiple imputation", such as Missing At Random (MAR), to avoid potential biases in results.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

chained equations imputation

This alternative refers to a specific type of multiple imputation that uses chained equations to impute missing values.

imputation of missing values

Focuses specifically on the act of imputing values to replace missing data points, without specifying the 'multiple' aspect.

missing data estimation

Emphasizes the statistical estimation aspect of replacing missing data, which is central to multiple imputation.

data imputation techniques

This alternative broadens the scope to include various methods used for imputing missing data, not limited to multiple imputation.

missing data handling methods

This is a more general term that encompasses all strategies for dealing with missing data, of which multiple imputation is one specific approach.

missing data replacement

This term is focused on the direct action of substituting values for missing entries.

Bayesian imputation

Specifies a particular statistical approach (Bayesian methods) to imputation, differentiating it from other imputation techniques.

predictive mean matching

This refers to another method used to perform imputation, and not necessarily multiple imputation.

hot deck imputation

Hot deck imputation is another method for handling missing data, distinct from multiple imputation.

single imputation methods

Contrasts with multiple imputation by emphasizing methods that generate only one set of imputed values, which is a different technique.

FAQs

How is "multiple imputation" used in statistical analysis?

"Multiple imputation" is used to handle missing data by creating multiple plausible values for the missing entries, generating several complete datasets. These datasets are then analyzed separately, and the results are combined to account for the uncertainty due to the missing data.

What are some advantages of using "multiple imputation" over other methods for handling missing data?

"Multiple imputation" offers advantages over methods like listwise deletion because it preserves sample size and statistical power. Compared to single imputation, it accounts for the uncertainty associated with the missing data, providing more accurate estimates and standard errors.

When is it appropriate to use "multiple imputation"?

"Multiple imputation" is appropriate when data are Missing At Random (MAR), meaning that the probability of missingness depends only on observed data and not on the missing values themselves. It is also suitable when the proportion of missing data is not too high.

Are there alternatives to "multiple imputation", and when should they be considered?

Alternatives include "single imputation", "complete case analysis", and "model-based methods". Single imputation is simpler but doesn't account for uncertainty. Complete case analysis can lead to bias if data are not Missing Completely At Random (MCAR). Model-based methods may be preferable when the missing data mechanism is complex.