omitted missing data

USAGE SUMMARY

The phrase "omitted missing data" is correct and usable in written English.
It can be used in contexts related to data analysis, research, or statistics when referring to data that has been intentionally left out or is absent. Example: "In our study, we found that the omitted missing data significantly affected the overall results."

✓ Grammatically correct

Science

Human-verified similar examples from authoritative sources

Similar Expressions

60 human-written examples

Table omits missing data.

BMC Public Health

The intent-to-treat (ITT) principle consisting of available case analysis and full ITT analysis will be applied for statistical analysis, omitting missing data.

BMJ Open

Discusses extensions to discrete response, panel data, and time series models, as well as issues such as omitted variables, missing data, sample selection, randomized and quasi-experiments, and instrumental variables.

Harvard University

"No opinion" was omitted, and missing data for each question were not included.

BMC Medical Education

Of the 147 questionnaires eight parents notified that their child was no longer hypersensitive to food, and another five questionnaires were omitted for missing data.

Health and Quality of Life Outcomes

The "less-inclusive" method omitted codons with any missing data, omitted mtDNA SRX022291 (which contained more missing data than any other mtDNA) and calculated divergence using the mutational path that minimized D N between codons with multiple substitutions.

G3: Genes, Genomes, Genetics

Eight percent of observations (not subjects) were omitted because of missing data.

European Journal of Human Genetics

Probe-set level data identified as array outliers by dChip were omitted and considered missing data in subsequent analyses.

Arthritis Research and Therapy

Of this number, 371 adolescents were omitted due to missing data on the instruments relevant for the current study.

BMC Endocrine Disorders

For serum samples, all patients were included, except for analysis of progression-free survival where 18 patients were omitted due to missing data.

Blood Cancer Journal

A total of 428 practices were omitted due to missing data at the beginning or end of the time period, indicating that these practices were established or closed in this time period.

BMC Family Practice

Expert writing Tips

Best practice

When reporting data analysis, clarify the reasons for omitting missing data and its potential impact on results. Transparency ensures credibility and allows readers to assess the validity of conclusions.

Common error

Avoid omitting missing data without explanation. Always describe the criteria used for data omission and any potential biases introduced by this decision.

Linguistic Context

The phrase "omitted missing data" functions as a descriptor in research and statistical contexts. It specifies that certain data points, which were originally absent, have been intentionally left out from analysis. Ludwig AI indicates this usage aligns with standard English grammar.

Expression frequency: Missing

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, the phrase "omitted missing data" refers to the practice of excluding data points that are absent from a dataset. While grammatically correct, according to Ludwig AI, its usage is infrequent and generally confined to scientific and academic contexts. When using this phrase, it's crucial to provide clear justification for the data omission to maintain transparency and validity in your research. Consider exploring alternatives like "excluded missing data" or "removed missing data" for nuanced variations. Ludwig AI indicates that while acceptable, ensuring clarity and justification are paramount when discussing the handling of missing data.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

excluded missing data

Replaces "omitted" with "excluded", emphasizing the act of actively keeping data out.

removed missing data

Uses "removed" to indicate a clear action of taking the data away.

left out missing data

Employs a more informal phrasing using "left out" instead of "omitted".

disregarded missing data

Substitutes "omitted" with "disregarded", suggesting the data was not considered important.

ignored missing data

Uses "ignored" to show that the data was consciously overlooked.

missing data not included

Rephrases to emphasize that the missing data was not part of the analysis.

missing data was excluded

Passive voice construction highlighting the exclusion of missing data.

missing data was removed

Passive voice emphasizing the removal of the data.

missing data not considered

Highlights that the missing data played no part in any calculations or conclusions.

missing data disregarded

Highlights that missing data was considered but rejected.

FAQs

How to explain the omission of missing data in a research paper?

Clearly state the amount of missing data, the reasons for its absence, and the method used to handle it (e.g., complete case analysis, which effectively omits the data). Also, discuss potential biases introduced by omitting this data.

What are alternatives to omitting missing data in statistical analysis?

Instead of omitting, consider techniques like imputation (replacing missing values with estimated ones), or using statistical models that can handle missing data directly, such as mixed-effects models or full information maximum likelihood (FIML).

Is it acceptable to omit missing data?

Omitting missing data is acceptable if the amount is small and it is missing completely at random (MCAR). However, if data is missing not at random (MNAR) or missing at random (MAR), omitting it can introduce bias. Consider alternative methods in these cases.

What does it mean when a study mentions that "missing data were omitted"?

It means that any data points that were absent or unrecorded for certain variables were excluded from the analysis. Researchers often do this to simplify calculations or because they cannot reliably estimate the missing values. This approach can affect the study's conclusions if the missingness is not random.