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関連する概念動画

Standard Error of the Mean01:13

Standard Error of the Mean

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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
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Calculating Standard Deviation01:08

Calculating Standard Deviation

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The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
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Standard Deviation01:10

Standard Deviation

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Behrens–Fisher Test00:57

Behrens–Fisher Test

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The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
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標準化された平均差は,結局のところそれほど標準的ではない.

Juyoung Jung1, Ariel M Aloe1

  • 1Educational Measurement and Statistics University of Iowa Iowa City Iowa USA.

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PubMed
まとめ
この要約は機械生成です。

この研究では,サンプルの変動性によるメタ解析の歪みに対処するために,調和された標準化された平均差 (HSMD) を導入します. HSMDは,より信頼性の高い効果サイズ推定と堅固なメタ分析的結論のための新しい感度分析を提供します.

キーワード:
変数係数データの調和エフェクトサイズメタ解析標準化された平均差

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科学分野:

  • バイオ統計学
  • 医学研究方法論

背景:

  • 標準化された平均差 (SMD) は,コーエンのdとヘッジスのgのように,メタ分析では一般的であるが,研究内の変動性に敏感である.
  • この敏感性は個々の効果の大きさの推定を歪め,全体的なメタ分析結果に影響を与える可能性があります.

研究 の 目的:

  • 調和した標準平均差 (HSMD) を新しい感度分析の枠組みとして導入する.
  • 研究内のサンプル変動によって引き起こされるメタ分析効果の歪みを評価し,対処する.
  • メタ分析合成の包括性を向上させる

主な方法:

  • 経験的基準を設定するために,変数係数 (CV) を使用して,研究内の相対的な変動性を調和させる.
  • SMDを一貫した変動性仮定に基づいて再計算する.
  • HSMDフレームワークをメタ分析データに適用し,研究特有の標準偏差の影響を評価する.

主要な成果:

  • 初期標準化変動によって,元の効果サイズとプールされた結果がどの程度影響を受けているかを示します.
  • メタ分析結果に対する研究内変動の影響を定量化する.
  • フレームワークは,報告された変動性メトリクスを欠く研究を組み込む能力を示します.

結論:

  • HSMDは,メタ解析における研究内変動に対する感受性を評価するための堅固な方法を提供します.
  • この新しい枠組みは,効果の大きさの推定とメタ分析の結論の信頼性を向上させます.
  • HSMDのアプローチは,多様な研究データを収納することによって,メタ分析合成を強化します.