RSCH FPX 7864 Assessment 4

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RSCH FPX 7864 Assessment 4  Data Analysis Plan

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The ANOVA for a single factor can be performed using the grades.jasp file from the database. It is a method of splitting diversity within the data into units that can be tested further (Kenton, 2021). It is used to compare two unrelated groups or more. Though one can distinguish the two groups, it still remains unclear what the difference between the two groups is. These are the files that are used: Quiz 3 and Sections. This part concerns classified and unclassified variables. Quiz 3, however, is performance-based and continuous.

The study question here is: What is the difference between the mean scores of students of groups on Quiz 3?

The null hypothesis states that differences between the mean scores of students in various groups on Quiz 3 are not significant.

The other alternative is that the mean scores of students in different groups on Quiz 3 are highly varied.

Testing Assumptions

Levene’s test determines if the means of two or more groups are equal (SPSS Tutorials, 2019). It is applied in inferential statistics. The test results will demonstrate that the null hypothesis is not true and that the variance among groups is equal. The p number of the test is 0.6 which is not 0.5 but more than it, so p >.05.Thus, the null theories can never be eliminated, and uniformity is achieved.

Results & Interpretation

All three students got the same score on Quiz 3. The table below shows what we can say.

This was achieved using the one-way ANOVA. Quiz 3 was grouped into three sets of scores. As can be seen from the table below, F (2, 102) =10.95, p <.001, which indicates that the null hypothesis, which states that there is no difference between the mean scores of three groups of students on Quiz 3, is rejected.

The HSD for Turkey (Frost, 2019) was used for the post hoc analysis. The difference between section 1 and section 2 is significant (p <.05) has been found. This is the reason the null theory fails. As no significant data were found between parts 3 and 1, null hypothesis could not be rejected (p >.05.). In parts 2 and 3, the null hypothesis is rejected as p is less than .05.

Statistical Conclusions

When a variable is qualitative and independent and the other is quantitative and dependent, the ANOVA test is used. The levels or groups of the independent variable. Two groups can be compared using one test.When many pairwise tests are conducted on the data, ANOVA sometimes wrongly rejects a true null hypothesis. The omnibus test helps to avoid an error of rejecting a hypothesis that is actually true (Ceri & Cicek, 2020). The omnibus test only has one flaw: it notes an issue but does not qualify it. The third and second quizzes were then compared using the One-way ANOVA which revealed that they are significantly different from each other.

Some things are not possible when you employ ANOVA to compare data from two or more groups. The p-value informs us on what the dissimilarity is between the two data sets. It is used to demonstrate the performance of the students on the post-hoc test for Quiz 3. From the given data, a p-value that is less than 0.5 suggests that the null hypothesis can be rejected. Hence, F is significant and mean of Quiz 3 is at least one pair homogeneous to the other two. Given p > .05, the null hypotheses in sections 1 and 3 should not be rejected.


The ANOVA test is concerned with the influence of the means of two or more groups on one or more factors. It also helps determine the similarity of the groups and keeps the type 1 error rate under control (if we have a false positive) in psychology (Ceri & Cicek, 2020). The comparison of two groups is done using the ANOVA test. For instance, it is used to know how mood, diet, and hormones impact mouse models of Alzheimer’s. The two-way ANOVA enables one to determine if medicine and age are related in any way. It helps identify which differences between the means are important. ANOVA can be used to see if the treatment makes the mental suffering even worse (Pok-Ja Cho & Ran, 2020).

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Ceri, V., & Cicek, I. (2020). Psychological well-being, depression and stress during COVID-19 pandemic in Turkey: A comparative study of healthcare professionals and non-healthcare professionals. Psychology, Health & Medicine, 26(1), 1–13.

Frost, J. (2019, January 14). Using Post Hoc Tests with ANOVA. Statistics by Jim.

Kenton, W. (2021, October 6). Analysis of Variance – ANOVA. Investopedia.

Pok-Ja Cho, Oh., & Ran, J. (2020). Changes in Fatigue, Psychological Distress, and Quality of Life After Chemotherapy in Women with Breast Cancer: A Prospective Study.

SPSS Tutorials. (2019). Levene’s Test – Quick Introduction.

How to Run Levene’s Test in SPSS?

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