handling missing data

USAGE SUMMARY

The phrase "handling missing data" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or research when discussing methods for dealing with incomplete datasets. Example: "In our study, we focused on handling missing data to ensure the accuracy of our results."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

58 human-written examples

Studying risk-adjusted outcomes in health care relies on statistical approaches to handling missing data.

Journal of the American College of Surgeons

Will the method of handling missing data lead to different conclusions?

BMC Medical Research Methodology

Imputation is one of the most commonly used approaches to handling missing data.

BMC Medical Research Methodology

This review also provided recommendations for avoiding and handling missing data.

BMC Medical Research Methodology

Other important future directions include handling missing data and variable rates across the sequence.

Bioinformatics

Along with multiple imputation approaches, FIML is recommended as one of the best approaches to handling missing data [ 23, 24].

Health and Quality of Life Outcomes

63 65 HLM provides flexibility in handling missing data 65 even when data are missing at random (MAR).

BMJ Open

In line with current recommendations, our approach to handling missing data has been described in the study protocol [ 4].

BMC Psychiatry

Careful considerations should thus be made on handling missing data.

Clinical Epidemiology

The protocol contained instructions for handling missing data.

BMJ Open

Human-verified similar examples from authoritative sources

Similar Expressions

1 human-written examples

In addition to improved annotation accuracy, the experimental results demonstrate the success of the method in handling missing-data scenarios.

Bioinformatics

Expert writing Tips

Best practice

When "handling missing data", clearly document the methods used (e.g., multiple imputation, complete case analysis) in your research report to ensure transparency and reproducibility.

Common error

Avoid blindly applying missing data techniques without verifying their underlying assumptions (e.g., Missing At Random). If assumptions are violated, results may be biased.

Linguistic Context

The phrase "handling missing data" functions as a gerund phrase, acting as a noun. It describes the activity or process of managing incomplete information, a common challenge in data analysis. Ludwig AI validates its widespread use.

Expression frequency: Common

Frequent in

Science

90%

Academia

7%

Formal & Business

3%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "handling missing data" is a gerund phrase commonly used in scientific and academic writing to describe the process of dealing with incomplete datasets. Ludwig AI confirms that the phrase is correct and frequently appears in contexts related to statistics, research, and data analysis. Key strategies include multiple imputation and complete case analysis, and careful documentation of these methods is crucial for research transparency. Remember to consider the assumptions underlying any missing data technique to avoid biased results.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

managing incomplete data

This alternative replaces "handling" with "managing" and "missing" with "incomplete", emphasizing the act of overseeing and controlling data that isn't fully present.

addressing data gaps

This alternative uses "addressing" instead of "handling", focusing on the act of confronting and resolving issues related to gaps in the data.

dealing with missing information

This option uses "dealing with" instead of "handling" and "information" instead of "data", providing a more general approach to the concept.

treating absent data

This alternative uses "treating" to convey the action of processing or managing absent data in a specific way, such as through statistical methods.

accounting for data loss

This focuses on the aspect of data loss and how it's incorporated or explained within an analysis or study.

compensating for missing values

This alternative highlights the action of making up for or balancing the lack of specific data points with other methods.

rectifying incomplete datasets

This suggests correcting or improving datasets where information is lacking, often through techniques like imputation.

processing data deficiencies

This focuses on handling inadequacies in the information available, typically in a scientific or technical context.

remedying data omissions

This alternative conveys correcting or repairing instances where data has been left out or excluded.

navigating data voids

This suggests finding a path or way forward despite significant gaps or absences in the data, highlighting a more exploratory or creative approach.

FAQs

What are some common techniques for "handling missing data"?

Common techniques include complete case analysis, single imputation, multiple imputation, and full information maximum likelihood (FIML). The choice depends on the amount and pattern of missingness.

How does multiple imputation help in "handling missing data"?

Multiple imputation creates several plausible datasets by filling in missing values, reflecting the uncertainty about the true values. This approach generally provides more accurate results than single imputation or complete case analysis.

What does it mean if data is 'missing at random' when "handling missing data"?

Data is 'missing at random' (MAR) if the probability of missingness depends only on observed data, not on the unobserved values themselves. This assumption is crucial for many imputation methods to be valid.

What are some alternatives to "handling missing data"?

You can use alternatives like "managing incomplete data", "addressing data gaps", or "dealing with missing information", depending on the specific context.