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In survey methodology, the design effect (generally denoted as or ) is a measure of the expected impact of a sampling design on the variance of an estimator for some parameter. It is calculated as the ratio of the variance of an estimator based on a sample from an (often) complex sampling design, to the variance of an alternative estimator based on a simple random sample (SRS) of the same number of elements. The Deff (be it estimated, or known a-priori) can be used to adjust the variance of an estimator in cases where the sample is not drawn using simple random sampling. It may also be useful in sample size calculations and for quantifying the representativeness of a sample. The term "design effect" was coined by Leslie Kish in 1965.

Attributes	Values
rdfs:label	تأثير تصميم (ar) Design effect (en)
rdfs:comment	تأثير التصميم (أو تقديرات تباين الوحدة) (Design effect) هو تعديل مستخدم في بعض أنواع الدراسات ، مثل تلك التي تستخدم أخذ العينات العنقودية أو التجربة العشوائية الخاضعة للرقابة العنقودية ، للسماح بهيكل التصميم، يضخم التعديل تباين تقديرات المتغيرات ، وبالتالي أخطاءها المعيارية ، وهو أمر ضروري للسماح بالارتباطات بين مجموعات الملاحظات. وهو مشابه لعامل تضخم التباين ويستخدم في حسابات حجم العينة. تم تقديم المصطلح من قبل ليزلي كيش في عام 1965. (ar) In survey methodology, the design effect (generally denoted as or ) is a measure of the expected impact of a sampling design on the variance of an estimator for some parameter. It is calculated as the ratio of the variance of an estimator based on a sample from an (often) complex sampling design, to the variance of an alternative estimator based on a simple random sample (SRS) of the same number of elements. The Deff (be it estimated, or known a-priori) can be used to adjust the variance of an estimator in cases where the sample is not drawn using simple random sampling. It may also be useful in sample size calculations and for quantifying the representativeness of a sample. The term "design effect" was coined by Leslie Kish in 1965. (en)
dcterms:subject	Medical statistics Design of experiments
Wikipage page ID	26286630 (xsd:integer)
Wikipage revision ID	1117749264 (xsd:integer)
Link from a Wikipage to another Wikipage	Ronald Fisher Multinomial distribution Meta-analysis Bernoulli distribution Bessel's correction Variance Descriptive statistics Design effect Intraclass correlation Inverse-variance weighting Inverse probability weighting Standard deviation Sample size determination Point estimation Confidence interval Correlation Estimator Composer Quota sampling Cluster randomised controlled trial Empirical distribution function Convex combination Optimal design Arithmetic mean Medical statistics Bernoulli sampling Leslie Kish Simple linear regression Simple random sampling Cluster analysis Cluster sampling Horvitz–Thompson estimator Propensity score matching Statistical parameter Design of experiments Weighted arithmetic mean Disjoint sets Line fitting Sampling error Poisson sampling Sampling design Expected value No free lunch theorem Parameter Sampling frame Random variable Block matrix Index of dispersion Effective sample size Weighted mean Missing data Beauty Behind the Madness Independence (probability theory) Independent and identically distributed random variables Raking Real number Subsampling Simple random sample Scaling (geometry) Standard error Variance inflation factor Survey methodology Imputation (statistics) Systematic sampling Poisson binomial distribution Ratio estimator Uncorrelatedness (probability theory) Accidental sampling Stratified sampling Ratio distribution Intra-class correlation Sample size Weighted average
Link from a Wikipage to an external page	https://surveyinsights.org/%3Fp=4919
sameAs	Design effect Design effect Design effect Design effect Design effect
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title	[Proof] (en)
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has abstract	تأثير التصميم (أو تقديرات تباين الوحدة) (Design effect) هو تعديل مستخدم في بعض أنواع الدراسات ، مثل تلك التي تستخدم أخذ العينات العنقودية أو التجربة العشوائية الخاضعة للرقابة العنقودية ، للسماح بهيكل التصميم، يضخم التعديل تباين تقديرات المتغيرات ، وبالتالي أخطاءها المعيارية ، وهو أمر ضروري للسماح بالارتباطات بين مجموعات الملاحظات. وهو مشابه لعامل تضخم التباين ويستخدم في حسابات حجم العينة. تم تقديم المصطلح من قبل ليزلي كيش في عام 1965. (ar) In survey methodology, the design effect (generally denoted as or ) is a measure of the expected impact of a sampling design on the variance of an estimator for some parameter. It is calculated as the ratio of the variance of an estimator based on a sample from an (often) complex sampling design, to the variance of an alternative estimator based on a simple random sample (SRS) of the same number of elements. The Deff (be it estimated, or known a-priori) can be used to adjust the variance of an estimator in cases where the sample is not drawn using simple random sampling. It may also be useful in sample size calculations and for quantifying the representativeness of a sample. The term "design effect" was coined by Leslie Kish in 1965. The design effect is a positive real number that indicates an inflation, or deflation in the variance of an estimator for some parameter, that is due to the study not using SRS (with , when the variances are identical). Some potential complex sampling that could introduce Deff that is different than 1 include: cluster sampling (such as when there is correlation between observations), stratified sampling, cluster randomized controlled trial, disproportional (unequal probability) sample, non-coverage, non-response, statistical adjustments of the data, etc.. Deff can be used in sample size calculations, quantifying the representative of a sample (to a target population), as well as for adjusting (often inflating) the variance of some estimator (in cases when we can calculate that estimator's variance assuming SRS). The term "Design effect" was coined by Leslie Kish in 1965. Ever since, many calculations (and estimators) have been proposed, in the literature, for describing the effect of known sampling design on the increase/decrease in the variance of estimators of interest. In general, the design effect varies between statistics of interests, such as the total or ratio mean; it also matters if the design (e.g.: selection probabilities) are correlated with the outcome of interest. And lastly, it is influenced by the distribution of the outcome itself. All of these should be considered when estimating and using design effect in practice. (en)

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