Share
Explore BrainMass

Analyze the data with the parametric one-way ANOVA,

This is what the professor wanted from my university per verbatem:

1. READ Chapter 20 in the Howell textbook.
2. If necessary, review Chapter 16.
3. Using appndixd.xxx, GPA as the DV, and Dropout as the IV (grouping variable)?
a.You may need to convert Dropout to numerical data,
b.Analyze the data with the parametric one-way ANOVA,
c.Analyze the data with Kruskal-Wallis one-way ANOVA.
4. Send the output of your analysis to Dr. Lisa Cree by e-mail. IN THE BODY OF YOUR E-MAIL MESSAGE, interpret the results of your analysis. Compare your results.

here is what others have com eup with usin SPSS.

The parametric statistical analysis incorporates an assumption that the studied variables are of a normal distribution. When the data regarding Grade 9 GPA and the high school dropout rate (0=finishes high school /1= dropout of high school) of the sample is evaluated with the parametric one-way ANOVA the F statistic equals 3.612. This was calculated by the ratio of the between group variability (variability of GPA between high school graduates and dropouts) and within group/error variability (variability of GPA within the graduate group and dropout group). This F statistic of 3.612 has a p value of 0.061. Typically the Null hypothesis would be rejected if the p<0.05. Thus with this analysis the p value =0.061 is not <0.05 and thus there is no significant difference between the grade 9 GPA of high school graduates and high school dropouts.

The nonparametric statistical analysis is utilized when the assumption of normalized data is not maintained and/or the dependent variable is measured on a nominal or ordinate scale. When analyzing the same data; grade 9 GPA to high school dropout rate, with a nonparametric analysis such as the Kruskal-Wallis one-way ANOVA a different result is determined. The Kruskal-Wallis one-way ANOVA ranks the GPA scores from 1 to 88(n=88). The mean rank is then calculated for each group and then compared for statistical difference between the groups. With this data the mean rank for high school graduates is 46.55 and the mean rank for high school dropouts is 28.50. The Chi-square calculation using the mean ranks is calculated to be 4.454. This is interpreted as a statically significant difference between the mean ranks of the groups at a p value=0.035 which is <0.05 and the Null hypothesis can be rejected. This in turn can be interpreted as there is a significant difference between the grade 9 GPA scores of high school graduates and grade 9 GPA scores of high school dropouts.

Therefore with this set of data there is a significant difference whether it is analyzed with parametric analysis or nonparametric analysis. Thus when conducting any study, careful consideration to whether the assumptions of the parametric analysis are met.

hope this helps.

Attachments

$2.19