Search results
Results From The WOW.Com Content Network
This halves reliability estimate is then stepped up to the full test length using the Spearman–Brown prediction formula. There are several ways of splitting a test to estimate reliability. For example, a 40-item vocabulary test could be split into two subtests, the first one made up of items 1 through 20 and the second made up of items 21 ...
KR-21 is a simplified version of KR-20, which can be used when the difficulty of all items on the test are known to be equal. Like KR-20, KR-21 was first set forth as the twenty-first formula discussed in Kuder and Richardson's 1937 paper. The formula for KR-21 is as such:
Until the development of tau-equivalent reliability, split-half reliability using the Spearman-Brown formula was the only way to obtain inter-item reliability. [4] [5] After splitting the whole item into arbitrary halves, the correlation between the split-halves can be converted into reliability by applying the Spearman-Brown formula.
The alternative hypothesis depends on which version of the test is used, but is usually stationarity or trend-stationarity. It is an augmented version of the Dickey–Fuller test for a larger and more complicated set of time series models. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number.
Bartlett's test is used to test the null hypothesis, H 0 that all k population variances are equal against the alternative that at least two are different. If there are k samples with sizes and sample variances then Bartlett's test statistic is
Student's t-test is a statistical test used to test whether the difference between the response of two groups is statistically significant or not. It is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis.
In statistics, the Sobel test is a method of testing the significance of a mediation effect. The test is based on the work of Michael E. Sobel, [1] [2] and is an application of the delta method.
The one-sample version serves a purpose similar to that of the one-sample Student's t-test. [2] For two matched samples, it is a paired difference test like the paired Student's t -test (also known as the " t -test for matched pairs" or " t -test for dependent samples").