Analysis of Variance (ANOVA)

DNP-prepared nurses are always required to have the skills to evaluate and compare
various aspects of patient care between their departments or care facilities and other departments,
hospitals, or clinics. For example, they should have the ability to compare the effects of patient
wait times between their practice and the wait times are allied practices. One recommended
technique for appropriate analysis of data collected from this exercise is ANOVA (analysis of
variance). According to Gray & Grove (2020), analysis of variance is a statistical approach that
researchers can use to compare two or more conditions or categories to determine existing
differences on a particular continuous dependent variable. This inferential technique is often
endorsed because of its low error margin when examining differences between three or more
conditions/groups. This discussion will summarize week Five’s “ANOVA Exercises SPSS”
output document.

Summary

The week 5 ANOVA Exercises SPSS Output document compares the “overall
satisfaction, material wellbeing” of three conditions or variables: no housing problem (N=367),
one housing problem (N=264), two or more housing problems (N=304). The total sample
population (N) is 935. There are four tables, including descriptives, a test of homogeneity of
variances, ANOVA, and multiple comparisons. Table one titled “descriptives” shows the mean,
standard deviation, standard error, maximum and minimum values, and 95 percent CI
(confidence interval) for mean, including lower and upper bound, for the three variables. For
example, the mean, standard deviation, and standard error for “No Housing Problem” (N=367)
are 12.71, 2.353, and 0.123, respectively. The upper bound and lower bound values are 12.47

ANALYSIS OF VARIANCE 3
and 12.95, respectively. For “One Housing Problem (N=264),” the mean, standard deviation,
standard error, lower bound, and upper bound are 11.97, 2.588, 0.159, 11.66, and 12.28,
respectively. For “Two or More Housing Problems (N=304),” the values are 10.57, 2.594, 0.149,
10.28, and 10.86, respectively. For the “Total” (N=935), the values stand at 11.80, 2.658, 0.087,
11.63. and 11.97, respectively. The maximum and minimum values for all the variables are 16
and 4, respectively. The term “95% CI” refers to the range of values that a researcher can be 95
percent self-assured or confident that carries the population’s “true mean” (Landau & Everitt,
2017).
Table two, “Test of Homogeneity of Variances,” shows Levene’s test results. Levene’s
test examines if the three samples contain equal variances – what is cumulatively known as the
“homogeneity of variances.” The null hypothesis at the 0.122 significance level can only be
rejected if the Levene test statistic value is higher than the critical value. Also, the variances are
statistically significant if p ≤ 0.05 and statistically insignificant when p > 0.05 (Verma &Abdel-
Salam, 2019). Therefore, the variances are statistically insignificant because Levene’s p-value
(0.122) is above 0.05. The null hypothesis stands because there lacks enough evidence to reject
it.
The same principle applies to the rest of the tables: a comparison is statistically
significant if p ≤ 0.05 and statistically insignificant when p > 0.05. For example, table three
(ANOVA) shows that the relationship between the three variables (No Housing Problem, One
Housing Problem, and Two or More Housing Problems” is statistically significant because the p-
value = 0.000. This value is less than 0.05. The final table, “Multiple Comparisons,” indicates
that all three comparisons are statistically significant because the p values are less than 0.05. For
example, the p values for “No Housing Problem vs. One Housing Problem” and “No Housing

ANALYSIS OF VARIANCE 4
Problem vs. Two or More Housing Problems” are 0.001 and 0.000, respectively. All the other p-
values (0.001, 0.000, 0.000, and 0.000) fall below the 0.05 value, meaning they are all
statistically significant.

ANALYSIS OF VARIANCE 5

References

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research:
Appraisal, synthesis, and generation of evidence (9 th ed.). Elsevier.
Landau, S., & Everitt, B. S. (2017). A handbook of statistical analyses using SPSS. CRC Press
LLC.
Verma, J. P., & Abdel-Salam, G. (2019). Testing statistical assumptions in research. John Wiley
& Sons.