差异性项目 功能 效果 尺寸 用于有效性 信息 使用
W Holmes Finch1, Maria Dolores Hidalgo Montesinos2, Brian F French3
1Ball State University, Muncie, IN, USA.
Educational and psychological measurement
|November 25, 2024
概括
效果大小有助于量化差异物品功能 (DIF) 大小. 在模拟研究中,日志概率比率和Mantle-Haenszel日志概率比率差异准确地确定了哪个评估具有更多的DIF.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 统计分析 统计分析
背景情况:
- 对差异性项目功能 (DIF) 的统计显著性测试缺乏大小解释.
- 效应大小对于理解检测到的DIF的实际意义至关重要.
- 对于DIF分析,存在各种效果大小测量和解释准则.
研究的目的:
- 为了比较DIF效应大小的表现,在量化和比较DIF的两个评估中进行量化和比较.
- 评估效果大小是否准确地捕捉了总体DIF,并识别了DIF较少的评估.
- 在各种模拟条件下识别可靠的DIF效应大小指标.
主要方法:
- 进行了一项模拟研究,操纵影响效果大小和DIF检测的因素.
- 对比了不同DIF效应大小指标的性能.
- 效果大小应用于真实数据集,以实践示例.
主要成果:
- 固定效应的日志概率比率 (Ln) 和曼特尔-汉泽尔日志概率比率的方差 (Mantel-Haenszel log odds ratio) 显示出高准确性.
- 这些措施有效地确定了哪项评估显示出更高的DIF金额.
- 几种效果大小在各种模拟场景中显示出可靠的性能.
结论:
- 建议使用日志概率比率和Mantle-Haenszel日志概率比率差异来量化DIF大小,并比较评估之间的DIF水平.
- 这些效应大小在DIF分析中提供了超出统计意义的宝贵见解.
- 进一步的研究应该集中在效应大小上,以提高对DIF大小的理解.
相关概念视频
01:15One-Way ANOVA: Equal Sample Sizes
3.2K
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...
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...
3.2K
01:15One-Way ANOVA: Unequal Sample Sizes
5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K
01:14Identifying Statistically Significant Differences: The F-Test
1.6K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
1.6K
01:19Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K
01:19Testing a Claim about Standard Deviation
2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
01:09Comparing Experimental Results: Student's t-Test
1.5K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
1.5K