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maccaman

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In summary, the F statistic is a measure of the variability of the data, and is good when the variances are likely to be equal. The Sig statistic indicates the probability that the variances are equal, and is high in your case indicating that the variances are most likely equal.

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maccaman

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- #2

xanthym

Science Advisor

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Statistical tests usually involve making various assumptions about the characteristics and parameters of the population being sampled. In your case, you are performing the "t-test" to test hypotheses about 2 sampled groups. One of the assumptions made by the standard "t-test" is that the 2 populations being sampled have EQUAL VARIANCES. The purpose of the Levenes Test is to test and verify that this equal variance assumption is reasonable.maccaman said:

The Levenes Test outputs 2 parameters in SPSS. The first is the F statistic value. The larger the F statistic number, the greater is the possibility the variances are different. Similarly, the smaller the F value, the greater is the probability that the variances are equal. In your case, F appears very small (which is good ).

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- #3

Umara00

- 1

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Im a lil confused here.. the thing is I am getting no F value in my SPSS output. Like the the F value area is blank. What do i do?

Levene's Test for Equality of Variance is a statistical test used to determine if the variances of two or more groups are equal. It is commonly used in the field of statistics to assess the assumption of equal variances in analysis of variance (ANOVA) and other statistical tests.

It is important to test for equality of variance because many statistical tests, such as ANOVA, assume that the variances of the groups being compared are equal. If this assumption is violated, it can lead to incorrect conclusions and inaccurate results. Therefore, it is necessary to check for equality of variance before performing these tests.

Levene's Test for Equality of Variance is performed by calculating the absolute deviations from the group means and then using these deviations to calculate a test statistic. This test statistic is then compared to a critical value from the F-distribution to determine if the variances are significantly different.

The assumptions of Levene's Test for Equality of Variance include: 1) the data is normally distributed within each group, 2) the data is independent, and 3) the variances are homogeneous (equal) across groups. Violation of these assumptions can lead to inaccurate results.

The possible outcomes of Levene's Test for Equality of Variance are: 1) if the test statistic is less than the critical value, the variances are considered equal, 2) if the test statistic is greater than the critical value, the variances are considered unequal, and 3) if the p-value is less than the chosen significance level, the null hypothesis of equal variances can be rejected.

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