handling of missing data

USAGE SUMMARY

The phrase "handling of missing data" is correct and usable in written English.
You can use it when referring to how data that has gone missing should be addressed. For example, "In this study, we will discuss the various methods of handling of missing data."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

58 human-written examples

To recommend methodological standards in the prevention and handling of missing data for primary patient-centered outcomes research (PCOR).

Journal of Clinical Epidemiology

We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-fitting/under-fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data.

Stanford University

Assess (i) the quality of reporting and handling of missing data (MD) in palliative care trials, (ii) whether there are differences in the reporting of criteria specified by the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement compared with those not specified, and (iii) the association of the reporting of MD with journal impact factor and CONSORT endorsement status.

Journal of Clinical Epidemiology

Handling of missing data and statistical analysis were conducted by using Stata 12.0.

The Journal of Chinese Sociology

The proper handling of missing data is a complicated endeavor [10].

EURASIP Journal on Advances in Signal Processing

This was largely due to the significant fraction of missing data, necessitating a principled approach to the handling of missing data in statistical estimation.

Complex Adaptive Systems Modeling

In addition, meta-epidemiological research in orthodontics has indicated that inadequate randomization procedures, blinding and handling of missing data are pervasive within clinical trials [7].

Progress in Orthodontics

Without minimizing the need to also improve FAM-MDR's handling of missing data (whether at the genotype or phenotype level), not being able to account for the full complexity of a pedigree is certainly a drawback of PGMDR.

Plosone

Listwise deletion was employed in the handling of missing data.

BMC International Health and Human Rights

This model allows analyzing data from repeated measurements as well as proper handling of missing data.

BMC Pediatrics

The handling of missing data is another important guideline for data processing.

BMC Public Health

Expert writing Tips

Best practice

Clearly document your method for "handling of missing data" in research reports to ensure transparency and reproducibility. Specify whether you used imputation, deletion, or other techniques.

Common error

Failing to acknowledge or address missing data can introduce bias and lead to inaccurate conclusions. Always assess the extent and pattern of missingness before selecting a "handling of missing data" approach.

Linguistic Context

The phrase "handling of missing data" functions as a noun phrase, often serving as the object of a verb or preposition. Ludwig AI confirms that is a correct and usable term in written English. Its role is typically to describe the procedures or techniques used to address incomplete datasets.

Expression frequency: Very common

Frequent in

Science

98%

Formal & Business

1%

News & Media

1%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "handling of missing data" is a grammatically correct and frequently used term, especially in scientific and academic contexts, as Ludwig AI confirms. It refers to the methods and strategies used to manage incomplete information in datasets. Best practices include documenting your methods, assessing the extent of missingness, and carefully considering potential biases. Common errors involve ignoring the impact of missing data or using inappropriate imputation techniques. When using this phrase, ensure clarity and transparency in your reporting to maintain the credibility of your analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

treatment of missing data

Replaces "handling" with "treatment", emphasizing the action taken to address the missing data.

management of missing data

Substitutes "handling" with "management", focusing on the organizational aspect of dealing with missing data.

addressing missing data

Uses the verb form "addressing" to highlight the act of dealing with the issue of missing data.

dealing with missing data

Employs "dealing with" to convey a more general approach to handling the problem of missing data.

missing data procedures

Focuses on the established steps or methods used to manage missing data.

approaches to missing data

Highlights different strategies or methodologies applied to missing data.

techniques for missing data

Emphasizes the specific methods and tools used in managing missing data.

missing data imputation

Specifically refers to the process of filling in missing data with estimated values.

missing data analysis

Focuses on the statistical examination and interpretation of patterns in missing data.

strategies for incomplete data

Uses "incomplete data" as a synonym for missing data, broadening the scope slightly.

FAQs

What are common methods for "handling of missing data"?

Common methods include deletion (removing rows with missing values), imputation (replacing missing values with estimated values), and model-based approaches. The choice depends on the amount and pattern of missing data, as well as the goals of the analysis.

When is it appropriate to use imputation for "handling of missing data"?

Imputation is appropriate when data is missing at random and the proportion of missing data is not too high. Methods like multiple imputation can provide more accurate estimates compared to single imputation methods. However, it's essential to understand and justify your assumptions when using imputation.

What is the 'last observation carried forward' method in the context of "handling of missing data"?

The last observation carried forward (LOCF) is a simple imputation method where the last observed value for a subject is used to replace subsequent missing values. While easy to implement, LOCF can introduce bias and is generally not recommended unless strong assumptions can be justified. More robust techniques are "multiple imputation".

What are the potential biases associated with different methods of "handling of missing data"?

Deletion methods can lead to bias if missing data is not completely random. Simple imputation methods can underestimate variance and distort relationships. More sophisticated methods like "multiple imputation" can reduce bias but rely on assumptions that should be carefully evaluated. Always perform sensitivity analyses to assess the robustness of results to different missing data assumptions.