design effect

USAGE SUMMARY

The phrase "design effect" is correct and usable in written English.
It is used to describe the power of a design to create an impact or effect. For example, "The sleek design of the new phone had a great design effect; everyone wanted one."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The design effect of this poll is 1.28.

The New York Times

The design effect of this poll is 1.1.

The New York Times

The design effect of this poll is 1.12.

The New York Times

The design effect of this poll is 1.08.

The New York Times

The design effect of this poll is 1.11.

The New York Times

Design effect indicates the efficiency of the design relative to a SRS.

New Zealand Journal of Forestry Science

As the extent of a design effect was unknown, simple random sample statistics were not used.

Research and Practice in Technology Enhanced Learning

Due to the multistage nature of the study, a design effect of 2 was considered.

Pastoralism

A design effect often relies on very subtle manipulations of scale.

Los Angeles Times

The design effect was accounted for in the final statistical analyses by multilevel modeling.

Plosone

The design effect is 1.07.

BMC Family Practice

Expert writing Tips

Best practice

In technical writing, use "design effect" precisely to refer to the measure of inefficiency in a study design compared to simple random sampling.

Common error

Avoid using "design effect" interchangeably with terms like standard error or confidence interval. "Design effect" specifically addresses the impact of complex sampling designs on variance, whereas other measures serve different analytical purposes.

Linguistic Context

The phrase "design effect" functions as a technical noun, primarily used within the fields of statistics and research methodology. As exemplified by Ludwig, it identifies a specific statistical concept related to the efficiency of study designs, particularly concerning sample sizes.

Expression frequency: Very common

Frequent in

Science

70%

News & Media

20%

Formal & Business

5%

Less common in

Academia

3%

Wiki

1%

Reference

1%

Ludwig's WRAP-UP

The phrase "design effect" is a technical term with a clear and consistent meaning in statistical analysis and research methodology. Ludwig AI identifies it as grammatically correct and frequently used, especially in scientific and news contexts. The "design effect" quantifies the impact of complex sampling strategies on statistical power, helping researchers adjust their analyses. While its usage is primarily formal and scientific, understanding it is crucial for accurate interpretation of research findings. Related phrases offer alternative ways to express similar concepts, focusing on the impact and implications of design choices. Avoiding confusion with other statistical measures and properly defining it in research contexts are essential for clear communication.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

impact of design

Focuses on the influence a design has.

influence of design

Highlights the persuasive power of a design.

design's effectiveness

Emphasizes how well a design achieves its goals.

consequences of design

Shifts focus to the results and outcomes of a design choice.

design's ramifications

Suggests a deeper and more far-reaching effect of a design.

design's implications

Explores the broader meaning and significance of a design.

intended design outcome

Specifically refers to the planned result of the design.

effect of the design choice

Focuses on the impact of a specific design decision.

the way design affects

Highlights the method through which the design has an effect.

result of the design

Emphasizes the final product or outcome of the design process.

FAQs

How is "design effect" used in statistical analysis?

In statistical analysis, the "design effect" is used to quantify the increase in the variance of a statistic due to the use of a complex sampling design compared to simple random sampling. It helps adjust sample sizes and standard errors to account for the clustering or stratification in the data.

What factors influence the "design effect" in a study?

The "design effect" is influenced by factors such as the degree of clustering within the sample, the intraclass correlation coefficient (ICC), and the sample size within each cluster. Higher clustering and ICC values typically lead to a larger design effect.

How do you calculate the "design effect"?

The "design effect" can be calculated using the formula: DE = 1 + (m - 1) * ICC, where m is the average cluster size and ICC is the intraclass correlation coefficient. Some researchers adjust the standard error = SE X√ design effect.

Why is it important to account for the "design effect" in research?

Accounting for the "design effect" is crucial because ignoring it can lead to underestimated standard errors and inflated Type I error rates. This can result in incorrect conclusions about the statistical significance of findings. Adjustments ensure more accurate and reliable results.