To determine if there are statistically significant differences in the mean decibel levels across the three intervals in the proposed DNP project,

QUESTION

A DNP project proposed differences in the mean score on noise at night as measured in decibels and recorded at minute intervals over a 24 hr period at three different intervals: 1-month pre noise campaign, 1 month after noise campaign and 2 months after noise campaign. Noise levels for all three 24 hr periods were normally distributed. What test of significance could be used to determine if there are statistically significant differences in the mean decibel level for each 24hr periodacross the three intervals?

The ACPES survey measures engagement in advance care planning on a scale between 4 and 16; ordinal data treated as interval. You found significance in a very small sample using the non-parametric Friedman test for dependent observations across three timeframes: baseline, 1 week after the educational workshop and 2 weeks afterwards. Now that you know there is significance, what test(s) can you use to pinpoint where those differences are?

ANSWER

To determine if there are statistically significant differences in the mean decibel levels across the three intervals in the proposed DNP project, a repeated measures analysis of variance (ANOVA) test can be used. Repeated measures ANOVA is appropriate when measuring the same variable multiple times in different conditions or intervals. In this case, the decibel levels are measured at three different intervals: 1-month pre-noise campaign, 1 month after the noise campaign, and 2 months after the noise campaign.

The repeated measures ANOVA compares the mean differences between the groups (intervals) while taking into account the within-subject correlation. It assesses whether there are significant differences in the mean decibel levels across the three intervals. If the null hypothesis is rejected, indicating a significant difference, post-hoc tests can be conducted to pinpoint where those differences occur.

Post-hoc tests help to identify specific pairwise comparisons between the intervals to determine which intervals significantly differ from each other. Commonly used post-hoc tests include Bonferroni, Tukey, or Scheffe tests. These tests adjust for multiple comparisons and provide more detailed information about the specific intervals that differ significantly.

In the case of the ACPES survey measuring engagement in advance care planning, the non-parametric Friedman test was used to establish significance among the three timeframes: baseline, 1 week after the educational workshop, and 2 weeks afterwards. Once significance is established, additional tests can be employed to pinpoint where those differences lie.

To determine the specific intervals with significant differences, pairwise post-hoc tests such as Wilcoxon signed-rank tests or Mann-Whitney U tests can be used. These tests compare pairs of timeframes and determine if there are significant differences in engagement in advance care planning between them.

By employing appropriate statistical tests such as repeated measures ANOVA and post-hoc tests, researchers can gain a deeper understanding of the significant differences in mean decibel levels or engagement in advance care planning across different time intervals. These tests help pinpoint the specific intervals where differences occur and contribute to a comprehensive analysis of the data.

In conclusion, statistical tests such as repeated measures ANOVA and post-hoc tests provide valuable insights into the significance of differences between intervals in the proposed DNP project and the ACPES survey. By applying these tests, researchers can identify and understand the specific intervals where statistically significant differences exist, contributing to the overall findings of the study and informing further analysis or interventions.