quantitative data

USAGE SUMMARY

The phrase "quantitative data" is correct and usable in written English.
You can use it to refer to numerical data or information that can be measured and quantified. For example, "We collected a wide range of quantitative data from the survey respondents."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Quantitative data are means ± s.e.m.m

Nature

Very little quantitative data.

Cornell University

Quantitative data are shown in e.

Nature

Quantitative data are shown in i.

Nature

Quantitative data were depicted in g.

Nature

The collection of quantitative data.

University of California, Berkeley

Quantitative data are depicted in b.

Nature

Quantitative data are depicted in e.

Nature

Quantitative data are shown in m.

Nature

Quantitative data are shown in t.

Nature

It included qualitative as well as quantitative data.

The Guardian

Expert writing Tips

Best practice

When presenting "quantitative data", always specify the units of measurement and the methods used to collect and analyze the data for clarity and reproducibility.

Common error

Avoid interpreting "quantitative data" as direct proof of causation. Ensure that any causal claims are supported by additional evidence and rigorous statistical analysis, not just correlation.

Linguistic Context

The phrase "quantitative data" functions primarily as a noun phrase. It typically acts as the subject or object of a sentence, denoting a specific type of information that is numerical and measurable. Ludwig AI indicates that the phrase is grammatically correct and widely used.

Expression frequency: Very common

Frequent in

Science

70%

News & Media

15%

Academia

10%

Less common in

Formal & Business

3%

Encyclopedias

1%

Reference

1%

Ludwig's WRAP-UP

In summary, "quantitative data" is a noun phrase denoting numerical and measurable information, frequently used in scientific, academic, and news contexts. As highlighted by Ludwig AI, the phrase is grammatically correct and widely accepted. When using "quantitative data", it's crucial to specify measurement units and analytical methods and avoid misinterpreting correlations as causations. Alternative phrases like "numerical data" or "measurable data" offer similar meanings. Remember, using "quantitative data" effectively involves understanding its nuances and applying best practices for accurate communication and analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Numerical data

Replaces "quantitative" with a direct synonym referring to numbers.

Measurable data

Focuses on the aspect of being able to measure the data.

Statistical data

Emphasizes the statistical nature of the data.

Empirical data

Highlights the data's origin from observation or experiment.

Quantifiable results

Shifts the focus to the results that can be quantified.

Structured data

Highlights the organization of the data in a defined format.

Hard data

Implies the data is factual and reliable.

Computational data

Emphasizes that the data is suitable for computational analysis.

Metric data

Highlights the data's role in measurement and evaluation.

Countable data

Focuses on the ability to count the individual elements within the data set.

FAQs

How is "quantitative data" used in research?

"Quantitative data" is used to measure and quantify relationships between variables, test hypotheses, and make statistical inferences in a study.

What's the difference between "quantitative data" and "qualitative data"?

"Quantitative data" involves numerical measurements, while "qualitative data" involves descriptive characteristics or qualities that cannot be easily measured numerically.

Which are examples of "quantitative data"?

Examples of "quantitative data" include measurements like height, weight, temperature, test scores, and survey results presented as numerical values.

How do you analyze "quantitative data"?

"Quantitative data" is analyzed using statistical methods such as t-tests, ANOVA, regression analysis, and descriptive statistics like mean, median, and standard deviation.