standardized effect size

USAGE SUMMARY

The phrase "standardized effect size" is correct and usable in written English.
It is typically used in statistical analysis and research to quantify the size of an effect or difference in a standardized way, allowing for comparison across studies. Example: "The study reported a standardized effect size of 0.5, indicating a moderate effect of the intervention on the participants' outcomes."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The standardized effect size (SES) and standardized response mean (SRM) were used to calculate the responsiveness of additional items and the total score for the rating items.

Health and Quality of Life Outcomes

The Begg and Mazumdar rank correlation test reports the rank correlation (Kendall's tau) between the standardized effect size and the standard errors of these effects.

Breast Cancer Research

That paper compared MID to standardized effect size (ES) and standard error of measurement (SEM) as options to consider when seeking to evaluate change over time.

Health and Quality of Life Outcomes

Cohen's standardized effect size and the standard error of measurement (SEM) represent alternative strategies that can be employed to compare change over time.

Health and Quality of Life Outcomes

In the pilot study, the improvement was 2.4 with a standard deviation of 4.1 providing a Cohen's standardized effect size of 0.58.

BMC Geriatrics

Expressed relative to the standard deviation, the difference found in the adjusted model corresponds with a standardized effect size in DNA methylation of −0.13 SDS.

Clinical Epigenetics

Contact with a treatment user caused a modest increase in stigma (standardized effect size = 0.15, p = 0.03).

Social Science & Medicine

A key assumption underlying the principle that power increases with sample size is that the standardized effect size is fixed over time.

Journal of Psychiatric Research

Including all information in one meta-analytical synthesis yields a standardized effect size estimate of Cohen's h = 0.12, documenting a small but reliable effect of PTP interventions.

Transportation Research Part A: Policy and Practice

Mixed-effects meta-regression analyses found no significant associations between the TOTPAR – SPID difference in standardized effect size and trial design characteristics.

The Journal of Pain

Standardized effect size scores for MPD using presence-absence data showed significant clustering of bee communities at five of six farms and two of the five hill prairies.

Agriculture, Ecosystems & Environment

Expert writing Tips

Best practice

When reporting a "standardized effect size", always specify the type of standardization used (e.g. Cohen's d, Hedge's g) and include confidence intervals to provide a measure of uncertainty.

Common error

A statistically significant result (low p-value) doesn't automatically mean the effect is meaningful. Always consider the "standardized effect size" to understand the real-world relevance of your findings.

Linguistic Context

The phrase "standardized effect size" functions as a noun phrase in statistical analysis. It is a technical term used to quantify the magnitude of an effect relative to the variability in the data. Ludwig highlights its usage in scientific and academic contexts.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

Formal & Business

0%

News & Media

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "standardized effect size" is a common and grammatically correct term primarily used in scientific and academic fields. Ludwig's analysis indicates it functions as a noun phrase to quantify the magnitude of an effect relative to the variability in data. It serves the purpose of providing a scale-free measure for comparing results across studies. As Ludwig AI highlights, it is essential to specify the type of standardization used (e.g. Cohen's d, Hedge's g) and consider the practical importance of results alongside statistical significance.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

effect size measure

Focuses on the measurement aspect of effect size without explicitly mentioning standardization.

cohen's d

Refers to a specific type of standardized effect size, useful when referring to this particular measure.

standardized mean difference

Highlights that the effect size is calculated based on the difference between means, standardized by a measure of variability.

hedge's g

Similar to Cohen's d, but uses a correction for small sample sizes.

estimate of effect magnitude

Emphasizes the estimation of the size of an effect rather than its standardized nature.

magnitude of the effect

Highlights the practical significance of the research findings.

quantified effect

Indicates that the effect has been measured and expressed numerically.

statistical effect

A broad term that refers to the impact or influence of one variable on another in a statistical context.

practical significance

Focuses on the real-world importance or relevance of the observed effect.

treatment effect

Highlights the results of a certain treatment in order to quantify it.

FAQs

How is a "standardized effect size" calculated?

A "standardized effect size" is calculated by dividing the difference between the means of two groups by their pooled standard deviation. This provides a measure of the magnitude of the difference that is independent of the original units of measurement.

What does a "standardized effect size" tell you?

A "standardized effect size" tells you the magnitude of the difference between two groups in terms of standard deviation units. This allows you to compare effects across different studies, even if they use different measurement scales.

What are some common types of "standardized effect size"?

Some common types of "standardized effect size" include Cohen's d, Hedges' g, and eta-squared. Cohen's d is used for comparing two group means, while eta-squared is used for measuring the proportion of variance explained by a factor in ANOVA.

How do I interpret a "standardized effect size" value?

Generally, a "standardized effect size" of 0.2 is considered small, 0.5 is medium, and 0.8 or greater is large. However, the interpretation can depend on the field of study. A small effect may still be meaningful in certain contexts. You can also consider "practical significance".