removed missing data

USAGE SUMMARY

The phrase "removed missing data" is not correct in standard written English.
It may be intended to describe the action of eliminating data that is absent or incomplete, but the wording is confusing. Example: "In the data cleaning process, we removed missing data to ensure the accuracy of our analysis."

⚠ May contain grammatical issues

Science

Human-verified examples from authoritative sources

Exact Expressions

2 human-written examples

However, even if these two items are removed, missing data remains higher for ABC than for the other scales (6.0% vs. 0.9 1.3%).

BMC Geriatrics

We undertook a preliminary analysis on 2 datasets: 1 with all the 13 markers (missing data present) and 1 where the 5 nuclear markers were removed (missing data of 10%).

BMC Biology

Human-verified similar examples from authoritative sources

Similar Expressions

58 human-written examples

Pairwise deletion removes missing data only for variables that are being used in a particular statistical computation.

International Journal of STEM Education

The majority of studies we reviewed also used listwise deletion to remove missing data and create a matched dataset.

International Journal of STEM Education

Data filtering processes have been performed, with the objective to remove missing data due to faulty instrument prior to boxplot representation.

International Journal of Energy and Environmental Engineering

In our models that adjusted for the propensity score or for other covariates we created dummy variables for missing race, obesity status, and income to minimize the number of observations that needed to be removed for missing data.

Plosone

Initially, 490 individuals were identified, but 44 were removed for missing data.

Diabetes Care

Thus, the proposed algorithm is a velocity-based algorithm that automatically determines direction-, task-, eye- and individual-specific thresholds to identify fixations and saccades from the POR, and removes outliers, missing data, blinks and other anomalies in the eye-movement recording.

Behavior Research Methods

Casewise deletion removed observations with missing data.

BMC Health Services Research

dOne patient was removed because of missing data.

Breast Cancer Research

The simplest method removed genes with missing data.

G3: Genes, Genomes, Genetics

Expert writing Tips

Best practice

When discussing data analysis, use precise language. Instead of saying "removed missing data", specify what was removed, such as "removed rows with missing values" or "removed variables with incomplete data".

Common error

Avoid using the vague phrase "removed missing data". Be specific about what was removed. For example, specify whether you removed entire rows, specific columns, or individual data points.

Linguistic Context

The phrase "removed missing data" functions as a description of a data processing step, where data points or records containing incomplete information are eliminated from a dataset. While seemingly straightforward, Ludwig AI identifies this phrasing as not correct in standard written English. Consider rephrasing for better clarity and precision.

Expression frequency: Rare

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Encyclopedias

0%

Ludwig's WRAP-UP

In summary, while the phrase "removed missing data" is used to describe the action of eliminating incomplete information from a dataset, Ludwig AI flags it as not correct in standard written English. It is advisable to use more precise and grammatically correct alternatives such as "eliminated missing data" or "excluded data with missing values". The term appears most frequently in scientific contexts, where clarity and precision are crucial. Remember to always specify what exactly you are removing from your data.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

eliminated missing data

Replaces "removed" with "eliminated", which is a more formal and precise term for data processing.

deleted incomplete data

Uses "deleted" instead of "removed" and "incomplete" instead of "missing", offering a slight variation in terminology.

excluded data with missing values

Rephrases the action to focus on excluding data based on the presence of missing values.

omitted observations with missing information

Substitutes "omitted" for "removed" and uses "observations" and "information" to provide a broader, more descriptive alternative.

discarded data points with missing entries

Employs "discarded" and specifies "data points" and "missing entries" for clarity.

filtered out missing data

Uses the phrasal verb "filtered out" to describe the removal process.

rectified data with missing elements

Focuses on fixing data, and also mentions that there are missing elements.

purged records with missing fields

Uses a more forceful term, "purged", suggesting a thorough removal of records with missing data.

handled instances of missing data by removal

Describes the handling of missing data using a lengthier, more formal structure, specifying that removal was the method used.

addressed the missing data issue through data point removal

Rephrases the statement to highlight addressing the issue of missing data by using a more descriptive and elaborate structure.

FAQs

What is a more appropriate way to say "removed missing data"?

Instead of "removed missing data", consider using more precise phrases such as "eliminated missing data", "deleted incomplete data", or "excluded data with missing values" to clearly convey the action taken.

Why is "removed missing data" considered grammatically awkward?

The phrase "removed missing data" is awkward because "missing data" functions as a noun, and "removed" needs a more direct object. More precise phrasing clarifies what specific data entries or observations were eliminated.

In data analysis, is it better to remove or impute missing data?

Whether to remove or impute missing data depends on the context. Removing data (as in "excluded data with missing values") is simpler, but imputation (filling in missing values) may preserve more of your dataset and avoid bias. Consult statistical best practices for your field.

What are the potential biases introduced by removing missing data?

Removing missing data can introduce bias if the missingness is not random. If the missing data is related to the variable itself or another variable in the dataset, removing those observations could skew your results. Always consider why the data is missing before deciding to remove it.