variable label

USAGE SUMMARY

The phrase "variable label" is correct and usable in written English.
It is typically used in the context of data analysis or programming to refer to a descriptive name assigned to a variable for clarity. Example: "In our dataset, the variable label for age is 'respondent_age', which helps us understand what the data represents."

✓ Grammatically correct

Academia

Science

Wiki

Human-verified examples from authoritative sources

Exact Expressions

9 human-written examples

The codebook should provide information about each variable, including variable label and value label (see more below).

Yale University

Table 1 Labels of variables used in Eq. (2) Variable Label DC Diameter of core DT,i Inner diameter of tube αC Thermal expansion coefficient of core material αT Thermal expansion coefficient of tube material Tcur,Startrt cure temperature of adhesive Tenv Environmental temperature.

Applied Adhesion Science

For details on the specific variables used, including the variable label and a description, see Supplemental Material, File S1.

Environmental Health Perspectives

In another case (MICS3 for Belize 2006), terms in the variable label were separated with the character '\', which was not included in the normalisation statements; but this was fixed before the second iteration.

BMC Medical Informatics and Decision Making

In the first dataset, both examples of key terms would identify variable 'im4cd' (note that the variable label contains terms in French, which will be identified through the thesaurus).

BMC Medical Informatics and Decision Making

In the second dataset, only the first key term will be found, because the term 'vaccination' is absent from the variable label 'dpt3 day' (note that 'vaccination' is a redundant term).

BMC Medical Informatics and Decision Making

Human-verified similar examples from authoritative sources

Similar Expressions

51 human-written examples

Variable labels and value labels should clearly describe the information or question recorded in that variable (see more in "Codebook" below).

Yale University

For example, during data preparation and cleaning, the identity of experimental conditions or the variable labels can be masked so that the output is not interpretable in terms of the research hypothesis.

Nature

It handles missing data, value and variable labels and all of the other details that are necessary to move as much information as is possible from one file format to another.

Columbia University

The variable labels V[0], V[1], etc. are the names assigned to input data.

Journal of Petroleum Exploration and Production Technology

Shaded variable labels in Figure 5 indicated that immune indices that were related in the previous analysis [5] were not related when the PCA was performed on each species separately.

Plosone

Expert writing Tips

Best practice

When documenting datasets, provide clear and concise "variable labels" to ensure users understand the meaning of each variable.

Common error

Avoid using abbreviations or jargon in "variable labels" without providing a clear definition elsewhere. Inconsistency makes datasets difficult to interpret.

Linguistic Context

The phrase "variable label" functions primarily as a noun phrase. According to Ludwig AI, it serves to identify and describe a specific element within a dataset or a program, providing context and meaning to the underlying data. It's used to explain the purpose of a variable.

Expression frequency: Uncommon

Frequent in

Science

40%

Academia

35%

Wiki

25%

Less common in

News & Media

0%

Formal & Business

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "variable label" is a noun phrase primarily used in academic, scientific, and wiki contexts to describe a variable's descriptive name in datasets or programs. Ludwig AI confirms its grammatical correctness and suitability for providing clarity in technical documentation. Alternatives include "data field descriptor", "variable description", and "feature label". When using this phrase, ensure clear and consistent labeling to prevent misinterpretation. Consistent with the "aiResponseStatus", it effectively communicates the purpose and meaning of variables in data-driven environments.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

variable description

Simplifies the phrase by using a more general term 'description'.

feature label

Highlights the 'feature' aspect, which can be common in machine learning or data analysis.

data field descriptor

This alternative emphasizes the descriptive aspect of the label within a data context.

field name explanation

This phrase focuses on explaining the meaning of a field within a dataset.

data category name

Focuses on the naming aspect of data categories.

attribute identifier

This term is more generic, applicable in object-oriented programming or database design.

information field title

Highlights the 'title' aspect of an information field.

column heading definition

Specifically for spreadsheet or database contexts, this highlights the definition of a column header.

data point annotation

This option describes an annotation provided for a data point.

data element tag

More technical, emphasizing the tagging or identification of a data element.

FAQs

How do I use "variable label" in a sentence?

You can use "variable label" to describe the descriptive name assigned to a variable in a dataset, like in the sentence: "The "variable label" for 'age' is 'respondent_age', providing clarity about the data's meaning."

What's the difference between "variable label" and "variable name"?

While both identify a variable, "variable label" is often a more descriptive and human-readable name, whereas "variable name" might be a shorter, more technical identifier used in code or databases.

What can I say instead of "variable label"?

You can use alternatives such as "data field descriptor", "variable description", or "feature label" depending on the context.

Why is providing a clear "variable label" important?

A clear "variable label" ensures that anyone working with the data understands what each variable represents, reducing ambiguity and improving data analysis accuracy.