management of missing data

USAGE SUMMARY

The phrase "management of missing data" is correct and usable in written English.
It can be used in contexts related to data analysis, research, or statistics where handling incomplete data is necessary. Example: "The management of missing data is crucial for ensuring the accuracy of our research findings."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

8 human-written examples

Analysis issues (eg, procedure for management of missing data).

BMJ Open

Management of missing data followed recommended methods [ 72].

BMC Complementary and Alternative Medicine

Multiple imputation will be employed for the management of missing data.

BMC Psychiatry

18, 23 The use of unrepresentative samples and the management of missing data were also problematic, regardless of whether a retrospective or prospective design was used.

Clinical interventions in aging

Data mining of the FAERS requires complex data processing to obtain the final dataset; in particular, an ad hoc drug mapping, duplicate detection and removal as well as management of missing data.

Drug Safety

IRT has shown advantages such as the management of missing data [ 5], the possibility to obtain an interval measure for the latent trait, the comparison of latent traits levels independently of the instrument, the management of possible floor and ceiling effects [ 6, 7].

BMC Medical Research Methodology

Human-verified similar examples from authoritative sources

Similar Expressions

52 human-written examples

Items in the perception of unit management scale had the highest proportion of missing data together with item 22: "Fatigue impairs my performance during emergency situations (eg, emergency resuscitation, seizure)" (7.2%).

Clinical Epidemiology

Items in the 'Conflict Resolution and Management' domain also demonstrated a high proportion of missing data despite the extended observation period in the field test.

BMC Medical Education

We conclude that measuring/reporting the amount of missing data is mandatory when data collection and data management procedures for benchmarking are being developed.

Critical Care

Seven steps to minimizing the amount of missing data are defined as documentation, training, monitoring reports, patient contact, data entry and management, pilot studies, and communication.

Biological Psychiatry

The rate of missing data was 2.4%.

Rice

Expert writing Tips

Best practice

When reporting research results, clearly specify the methods used for the "management of missing data" to ensure transparency and reproducibility.

Common error

Failing to analyze the patterns of missing data (e.g. Missing Completely At Random, Missing At Random, Missing Not At Random) can lead to biased results. Always investigate the nature of missingness before applying any "management of missing data" techniques.

Linguistic Context

The phrase "management of missing data" functions as a noun phrase, typically acting as the subject or object of a sentence. It describes the overall process of handling incomplete data, as illustrated in the Ludwig examples.

Expression frequency: Uncommon

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, "management of missing data" is a noun phrase that refers to the structured handling of incomplete information, crucial in maintaining the validity of data analyses. According to Ludwig, it is grammatically correct and commonly used in scientific contexts. Alternative phrases include "handling missing data" and "treatment of missing data". When using this phrase, remember to specify the methods used for handling missing data and analyze the patterns of missingness to avoid biased results. This practice ensures transparency and rigor in research.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

handling missing data

Focuses on the action of dealing with missing data, similar to managing it.

addressing missing data

Implies a more direct approach to resolving issues caused by missing data.

treatment of missing data

Emphasizes the specific methods used to process missing data.

dealing with incomplete data

Uses 'incomplete data' as a synonym for 'missing data'.

missing data protocols

Refers to the established procedures for handling missing data.

strategies for missing data

Highlights the planning and methods used to manage missing data.

missing data imputation

Focuses specifically on the process of filling in missing values.

techniques for handling missing values

Highlights a technical way of managing missing data.

approaches to missing data

Focuses on theoretical and methodological frameworks.

missing data analysis

Highlights the analytical aspect of managing the missing data.

FAQs

What does "management of missing data" mean?

The phrase "management of missing data" refers to the processes and techniques used to handle incomplete or absent values in a dataset, ensuring that analyses and conclusions drawn from the data are as accurate and reliable as possible.

Why is the "management of missing data" important in research?

Proper "management of missing data" is crucial because missing values can introduce bias, reduce statistical power, and affect the validity of research findings. Addressing missing data appropriately helps maintain the integrity of the results.

What are some common strategies for the "management of missing data"?

Common strategies include deletion methods (removing cases with missing data), imputation techniques (replacing missing values with estimated ones), and model-based approaches that account for missing data directly. Specific methods like "multiple imputation" are also widely used.

What's the difference between "management of missing data" and simply ignoring missing values?

Ignoring missing values can lead to biased and unreliable results. The "management of missing data" involves employing specific techniques to account for these missing values, aiming to minimize bias and improve the accuracy of data analysis. Ignoring them is generally not a sound statistical practice.