Assignment Prompt & Instructions
– 2 years ago
**files (4)**
– ## WK5DISDATA.docx
– ## NURS_8211_WK5_ComparisonofMeansMorethn2.pptx
– NURS_8211_WK5_Kruskal-WallisOutput.docx
– NURS_8211_WK5_RRT_ANOVA.docx
**## WK5DISDATA.docx**
Self-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal Wallis
Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered within the assigned week. These Discussions are designed to give you the opportunity to collaborate with your peers and faculty, test your knowledge, ask questions, practice research analysis, and assist your peers.
You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373726)
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Laerd Statistics: Sign up for a one-month plan using this link
[https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.](https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599)
·
[One Way ANOVA in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php)
https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php
·
[Kruskal-Wallis Test in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php)
https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 14, “Analysis of Variance: Two Groups Too Many?” (pp. 269–273, 279)
·
**Document:** [Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876?wrap=1) [Download Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876/download?download_frd=1)
·
**Document:** [Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878?wrap=1) [Download Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878/download?download_frd=1)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Niedz, B. (2024).**
**_Comparison of means, part II: ANOVA and Krustkal Wallis_ [Video]. Walden University Canvas. https://waldenu.instructure.com**
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Document:** [**Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)**](https://waldenu.instructure.com/courses/157739/files/12465889?wrap=1)
[Required Resources for Topic: ANOVA](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Turgut, M., & Yıldız, H. (2023).
[Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional studyLinks to an external site.](https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=174028410&site=eds-live&scope=site&custid=s6527200).
_BMC Palliative Care, 22_(1), 195.
**To prepare:**
· Read and view the Learning Resources in Doherty and Skalsky, and Dang et al. (2021) in Required Readings.
· View the video on comparison of means.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t_ test and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t_ test).
· Were these the correct tests to be used in these analyses? Explain why.
For this Self-Study Discussion, you may post throughout Week 5. You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to any or all of the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t-test_ and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t-test_).
· Were these the correct tests to be used in these analyses? Explain why.
Our interactive discussion addresses the following learning objectives:
· Differentiate between ANOVA and Kruskal-Wallis tests of significance
· Summarize statistical findings for ANOVA and for Kruskal-Wallis
[Previous](https://waldenu.instructure.com/courses/157739/modules/items/6373730)
[Next](https://waldenu.instructure.com/courses/157739/modules/items/6373743)
**## NURS_8211_WK5_ComparisonofMeansMorethn2.pptx**
NURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Objectives
Differentiate between ANOVA and Kruskal-Wallis tests of significance
Summarize statistical findings for ANOVA and for Kruskal-Wallis tests
Comparison of Means
Last week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U test
This week we challenge that by adding additional comparisons
Once you have three groups to compare, you actually have 3 possible comparisons
Practice-Focused question
Is there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?
One-Way anova
Uses the F statistic
Provides a p value that encompasses all possible combinations, but does not indicate by itself where those statistically significant findings occur.
Allows for “post hoc” multiple comparisons that do provide that focus
Has multiple assumptions associated with it. Most notably: data must be “normally distributed”
Sample size is large enough and passes the four tests of normality. Observations are all independent (that is, in this sample there were no patients who had both cardiac and respiratory components……..RRTs were classified as one or the other of the three groups.
5
Anova Measurement
Rapid Response Team (RRT) analysis with 75 RRTs
Activating nurse completed a survey with seven questions indicating their level of satisfaction with the RRT (1= very dissatisfied, 5=very satisfied)
Scores were summed yielding a total score on all seven questions and averaged across the entire sample
The highest possible score (very satisfied) was 35 and the lowest possible score was 7.
RRTs were described as cardiac, respiratory, neurology or other.
There were 42 cardiac, 12 respiratory and 21 neurology RRTs in the sample. There were no RRTs described in the other category.
6
ANOVA Results
Cardiac M: 30.0238 SD 6.42210
Respiratory M: 31.00 SD 3.30289
Neurology M: 13.9524 SD 12.92082
| | | | | | |
| — | — | — | — | — | — |
| ANOVA | | | | | |
| RRTEvaluation | | | | | |
| | Sum of Squares | df | Mean Square | F | Sig. |
| Between Groups | 4020.391 | 2 | 2010.196 | 28.104 | <.001 |
| Within Groups | 5149.929 | 72 | 71.527 | | |
| Total | 9170.320 | 74 | | | |
Now there is a lot of information in these results, most of which you do not see mentioned in a published study that used ANOVA. Sometimes, you see the F statistic (28.104). In Salkind & Frey, you can learn how to actually do an ANOVA in excel, which is for your information. In this course we are most concerned about understanding the basic elements of the test, and how to interpret.
So, the F statistic is accompanied by the all-important p value in the table above, and as you can see, it is indicating a statistically significant result. With a p value of <.001 we can be confident that the differences are not due to chance, and more likely to some factor. But the ANOVA level of significance does NOT tell us WHERE those differences are. Is it between cardiac and respiratory, or cardiac and neurology, or between respiratory and neurology? Three groups, three possible comparisons.
Now a one-way ANOVA test is used when there is more than one comparison (that would be two groups, and an independent samples t test). As we have three groups, we also have three comparisons. So, a post hoc test helps to fathom out just where those comparisons are located.
7
Post hoc: scheffe
| | | | | | | |
| — | — | — | — | — | — | — |
| Multiple Comparisons | | | | | | |
| Dependent Variable: RRTEvaluation | | | | | | |
| Scheffe | | | | | | |
| (I) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | (J) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| | | | | | Lower Bound | Upper Bound |
| cardiac | respiratory | -.97619 | 2.76832 | .940 | -7.8958 | 5.9434 |
| | neurology | 16.07143* | 2.26032 | <.001 | 10.4216 | 21.7212 |
| respiratory | cardiac | .97619 | 2.76832 | .940 | -5.9434 | 7.8958 |
| | neurology | 17.04762* | 3.06049 | <.001 | 9.3977 | 24.6975 |
| neurology | cardiac | -16.07143* | 2.26032 | <.001 | -21.7212 | -10.4216 |
| | respiratory | -17.04762* | 3.06049 | <.001 | -24.6975 | -9.3977 |
| *. The mean difference is significant at the 0.05 level. | | | | | | |
As you can see, the Scheffe post hoc test, computes the difference in the means, and then calculates the significance level. (Behind the scenes in the statistical software, there are actually several independent samples t tests that make these 2-group comparisons.)
Note the lack of statistical significance between cardiac and respiratory. That is likely because the difference was so small, less than a point. You would need a huge sample, far greater than 75 cases to be able to see this statistically. But notice that the difference between cardiac and neurology is much bigger and the p value is also statistically significant (p<.001). Finally, notice that the difference between respiratory and neurlogy is also statistically significant (also with a p value of < .001).
8
RRT project small sample
N=25
Data did not meet the normality assumption
A nonparametric test is needed
Kruskal-Wallis
Measurement mechanisms: 7-item survey completed by activating nurses
Three types of RRTs: cardiac (n=14), respiratory (n=3), neurology (n=7), other (n=1) for a total of (N=25)
Much smaller sample, but practice-focused question was the same. Is there a difference by RRT type in activating nurse satisfaction?
9
Differences in means
Cardiac: M=30.0714 SD 5.71724
Respiratory: M=29.6667 SD=3.05505
Neurology: M=28.2857 SD=10.33948
Is there a difference in the means? WOW they are very close……only fractional differences.
The small sample indicates that the data are likely to violate the assumptions of the ANOVA test. So let’s, use the nonparametric equivalent, the Kruksal-Wallis test.
10
| | | | |
| — | — | — | — |
| Ranks | | | |
| | Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | N | Mean Rank |
| RRTEvalSum | cardiac | 14 | 12.86 |
| | respiratory | 3 | 9.67 |
| | neurology | 7 | 13.50 |
| | all other | 1 | 21.50 |
| | Total | 25 | |
| | |
| — | — |
| Test Statisticsa,b | |
| | RRTEvalSum |
| Kruskal-Wallis H | 2.059 |
| df | 3 |
| Asymp. Sig. | .560 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | |
The statistical software discounts the one case in the “all other” category and compares cardiac, respiratory, and neurology. The H statistic used in the statistical software was 2.059 with 3 degrees of freedom and an asymtotic significance level of .560. As this result is far greater than the accepted .05 level of significance, there is no point in going any farther. The difference in the means is not statistically significant.
Degrees of freedom has to do with (essentially) an estimate of the sample size. In this case, all four groups are counted yielding 3 degrees of freedom (1-#of groups).
Salkind and Frey 5th edition has a good explanation of effect size (the magnitude of the difference) on pp243-246.
Read about the word asymptotic on page 183.
Now, because the sample size is so very small, the student took pains to explain that with a small between the groups, she would have needed a huge sample, in fact over 250 in order to see whether this small difference was statistically significant or not.
Thus, this very small dataset exhibits a very likely case of a type II error, when significance was not found, but it may, in fact, be there.
11
Turgut & Yildez (2023)Table 3
N=200 PICU nurses
Professional grief and posttraumatic growth measured against a number of characteristics in nurses.
ANOVA used to determine the impact of duration of work in the unit against professional grief
There will be differences between the way professional grief is experienced based on tenure (duration of work in the PICU unit).
Professional grief & duration of work in the unit
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores.
Nurses who had education on the terminal period and grief had increased score on TRIG
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores, which means their level of professional grief was higher. They used an ANOVA because there are three categories of answers. They must have used a post hoc to localize the differences as this is explained in the narrative of the paper.
Nurses who had education on the terminal period and grief had increased score on TRIG. Which means that their level of grief was lower. They used a t test because there were only two categories: did they receive this specialized type of education yes or no?
13
Key Points
When there are more than 2 groups, you need a test that can accommodate multiple comparisons at once
Both the one-way ANOVA and the Kruskal-Wallis tests have requirements, called assumptions.
The ANOVA has post hoc tests that can pinpoint where the differences are, exactly.
When the Kruskal-Wallis test is not significant, no other analysis is necessary
Small samples can potentially lead to
14
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**NURS_8211_WK5_Kruskal-WallisOutput.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk5-kruskal-wallisoutput-docx)
**NURS_8211_WK5_RRT_ANOVA.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk5-rrt-anova-docx)
**NURS_8211_WK5_RRT_ANOVA.docx**
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**## WK5DISDATA.docx**
Self-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal Wallis
Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered within the assigned week. These Discussions are designed to give you the opportunity to collaborate with your peers and faculty, test your knowledge, ask questions, practice research analysis, and assist your peers.
You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373726)
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Laerd Statistics: Sign up for a one-month plan using this link
[https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.](https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599)
·
[One Way ANOVA in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php)
https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php
·
[Kruskal-Wallis Test in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php)
https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 14, “Analysis of Variance: Two Groups Too Many?” (pp. 269–273, 279)
·
**Document:** [Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876?wrap=1) [Download Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876/download?download_frd=1)
·
**Document:** [Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878?wrap=1) [Download Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878/download?download_frd=1)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Niedz, B. (2024).**
**_Comparison of means, part II: ANOVA and Krustkal Wallis_ [Video]. Walden University Canvas. https://waldenu.instructure.com**
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Document:** [**Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)**](https://waldenu.instructure.com/courses/157739/files/12465889?wrap=1)
[Required Resources for Topic: ANOVA](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Turgut, M., & Yıldız, H. (2023).
[Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional studyLinks to an external site.](https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=174028410&site=eds-live&scope=site&custid=s6527200).
_BMC Palliative Care, 22_(1), 195.
**To prepare:**
· Read and view the Learning Resources in Doherty and Skalsky, and Dang et al. (2021) in Required Readings.
· View the video on comparison of means.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t_ test and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t_ test).
· Were these the correct tests to be used in these analyses? Explain why.
For this Self-Study Discussion, you may post throughout Week 5. You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to any or all of the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t-test_ and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t-test_).
· Were these the correct tests to be used in these analyses? Explain why.
Our interactive discussion addresses the following learning objectives:
· Differentiate between ANOVA and Kruskal-Wallis tests of significance
· Summarize statistical findings for ANOVA and for Kruskal-Wallis
[Previous](https://waldenu.instructure.com/courses/157739/modules/items/6373730)
[Next](https://waldenu.instructure.com/courses/157739/modules/items/6373743)
**## NURS_8211_WK5_ComparisonofMeansMorethn2.pptx**
NURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Objectives
Differentiate between ANOVA and Kruskal-Wallis tests of significance
Summarize statistical findings for ANOVA and for Kruskal-Wallis tests
Comparison of Means
Last week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U test
This week we challenge that by adding additional comparisons
Once you have three groups to compare, you actually have 3 possible comparisons
Practice-Focused question
Is there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?
One-Way anova
Uses the F statistic
Provides a p value that encompasses all possible combinations, but does not indicate by itself where those statistically significant findings occur.
Allows for “post hoc” multiple comparisons that do provide that focus
Has multiple assumptions associated with it. Most notably: data must be “normally distributed”
Sample size is large enough and passes the four tests of normality. Observations are all independent (that is, in this sample there were no patients who had both cardiac and respiratory components……..RRTs were classified as one or the other of the three groups.
5
Anova Measurement
Rapid Response Team (RRT) analysis with 75 RRTs
Activating nurse completed a survey with seven questions indicating their level of satisfaction with the RRT (1= very dissatisfied, 5=very satisfied)
Scores were summed yielding a total score on all seven questions and averaged across the entire sample
The highest possible score (very satisfied) was 35 and the lowest possible score was 7.
RRTs were described as cardiac, respiratory, neurology or other.
There were 42 cardiac, 12 respiratory and 21 neurology RRTs in the sample. There were no RRTs described in the other category.
6
ANOVA Results
Cardiac M: 30.0238 SD 6.42210
Respiratory M: 31.00 SD 3.30289
Neurology M: 13.9524 SD 12.92082
| | | | | | |
| — | — | — | — | — | — |
| ANOVA | | | | | |
| RRTEvaluation | | | | | |
| | Sum of Squares | df | Mean Square | F | Sig. |
| Between Groups | 4020.391 | 2 | 2010.196 | 28.104 | <.001 |
| Within Groups | 5149.929 | 72 | 71.527 | | |
| Total | 9170.320 | 74 | | | |
Now there is a lot of information in these results, most of which you do not see mentioned in a published study that used ANOVA. Sometimes, you see the F statistic (28.104). In Salkind & Frey, you can learn how to actually do an ANOVA in excel, which is for your information. In this course we are most concerned about understanding the basic elements of the test, and how to interpret.
So, the F statistic is accompanied by the all-important p value in the table above, and as you can see, it is indicating a statistically significant result. With a p value of <.001 we can be confident that the differences are not due to chance, and more likely to some factor. But the ANOVA level of significance does NOT tell us WHERE those differences are. Is it between cardiac and respiratory, or cardiac and neurology, or between respiratory and neurology? Three groups, three possible comparisons.
Now a one-way ANOVA test is used when there is more than one comparison (that would be two groups, and an independent samples t test). As we have three groups, we also have three comparisons. So, a post hoc test helps to fathom out just where those comparisons are located.
7
Post hoc: scheffe
| | | | | | | |
| — | — | — | — | — | — | — |
| Multiple Comparisons | | | | | | |
| Dependent Variable: RRTEvaluation | | | | | | |
| Scheffe | | | | | | |
| (I) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | (J) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| | | | | | Lower Bound | Upper Bound |
| cardiac | respiratory | -.97619 | 2.76832 | .940 | -7.8958 | 5.9434 |
| | neurology | 16.07143* | 2.26032 | <.001 | 10.4216 | 21.7212 |
| respiratory | cardiac | .97619 | 2.76832 | .940 | -5.9434 | 7.8958 |
| | neurology | 17.04762* | 3.06049 | <.001 | 9.3977 | 24.6975 |
| neurology | cardiac | -16.07143* | 2.26032 | <.001 | -21.7212 | -10.4216 |
| | respiratory | -17.04762* | 3.06049 | <.001 | -24.6975 | -9.3977 |
| *. The mean difference is significant at the 0.05 level. | | | | | | |
As you can see, the Scheffe post hoc test, computes the difference in the means, and then calculates the significance level. (Behind the scenes in the statistical software, there are actually several independent samples t tests that make these 2-group comparisons.)
Note the lack of statistical significance between cardiac and respiratory. That is likely because the difference was so small, less than a point. You would need a huge sample, far greater than 75 cases to be able to see this statistically. But notice that the difference between cardiac and neurology is much bigger and the p value is also statistically significant (p<.001). Finally, notice that the difference between respiratory and neurlogy is also statistically significant (also with a p value of < .001).
8
RRT project small sample
N=25
Data did not meet the normality assumption
A nonparametric test is needed
Kruskal-Wallis
Measurement mechanisms: 7-item survey completed by activating nurses
Three types of RRTs: cardiac (n=14), respiratory (n=3), neurology (n=7), other (n=1) for a total of (N=25)
Much smaller sample, but practice-focused question was the same. Is there a difference by RRT type in activating nurse satisfaction?
9
Differences in means
Cardiac: M=30.0714 SD 5.71724
Respiratory: M=29.6667 SD=3.05505
Neurology: M=28.2857 SD=10.33948
Is there a difference in the means? WOW they are very close……only fractional differences.
The small sample indicates that the data are likely to violate the assumptions of the ANOVA test. So let’s, use the nonparametric equivalent, the Kruksal-Wallis test.
10
| | | | |
| — | — | — | — |
| Ranks | | | |
| | Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | N | Mean Rank |
| RRTEvalSum | cardiac | 14 | 12.86 |
| | respiratory | 3 | 9.67 |
| | neurology | 7 | 13.50 |
| | all other | 1 | 21.50 |
| | Total | 25 | |
| | |
| — | — |
| Test Statisticsa,b | |
| | RRTEvalSum |
| Kruskal-Wallis H | 2.059 |
| df | 3 |
| Asymp. Sig. | .560 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | |
The statistical software discounts the one case in the “all other” category and compares cardiac, respiratory, and neurology. The H statistic used in the statistical software was 2.059 with 3 degrees of freedom and an asymtotic significance level of .560. As this result is far greater than the accepted .05 level of significance, there is no point in going any farther. The difference in the means is not statistically significant.
Degrees of freedom has to do with (essentially) an estimate of the sample size. In this case, all four groups are counted yielding 3 degrees of freedom (1-#of groups).
Salkind and Frey 5th edition has a good explanation of effect size (the magnitude of the difference) on pp243-246.
Read about the word asymptotic on page 183.
Now, because the sample size is so very small, the student took pains to explain that with a small between the groups, she would have needed a huge sample, in fact over 250 in order to see whether this small difference was statistically significant or not.
Thus, this very small dataset exhibits a very likely case of a type II error, when significance was not found, but it may, in fact, be there.
11
Turgut & Yildez (2023)Table 3
N=200 PICU nurses
Professional grief and posttraumatic growth measured against a number of characteristics in nurses.
ANOVA used to determine the impact of duration of work in the unit against professional grief
There will be differences between the way professional grief is experienced based on tenure (duration of work in the PICU unit).
Professional grief & duration of work in the unit
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores.
Nurses who had education on the terminal period and grief had increased score on TRIG
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores, which means their level of professional grief was higher. They used an ANOVA because there are three categories of answers. They must have used a post hoc to localize the differences as this is explained in the narrative of the paper.
Nurses who had education on the terminal period and grief had increased score on TRIG. Which means that their level of grief was lower. They used a t test because there were only two categories: did they receive this specialized type of education yes or no?
13
Key Points
When there are more than 2 groups, you need a test that can accommodate multiple comparisons at once
Both the one-way ANOVA and the Kruskal-Wallis tests have requirements, called assumptions.
The ANOVA has post hoc tests that can pinpoint where the differences are, exactly.
When the Kruskal-Wallis test is not significant, no other analysis is necessary
Small samples can potentially lead to
14
## image14.jpg
## image18.jpg
## image19.jpg
## image20.jpg
## image21.jpg
## image22.jpg
## image23.jpg
## image24.png
## image25.png
**NURS_8211_WK5_Kruskal-WallisOutput.docx**
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**NURS_8211_WK5_RRT_ANOVA.docx**
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**## WK5DISDATA.docx**
Self-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal Wallis
Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered within the assigned week. These Discussions are designed to give you the opportunity to collaborate with your peers and faculty, test your knowledge, ask questions, practice research analysis, and assist your peers.
You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373726)
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Laerd Statistics: Sign up for a one-month plan using this link
[https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.](https://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599)
·
[One Way ANOVA in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php)
https://statistics.laerd.com/premium/spss/owa/one-way-anova-in-spss.php
·
[Kruskal-Wallis Test in SPSSLinks to an external site.](https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php)
https://statistics.laerd.com/premium/spss/kwht/kruskal-wallis-test-in-spss.php
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 14, “Analysis of Variance: Two Groups Too Many?” (pp. 269–273, 279)
·
**Document:** [Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876?wrap=1) [Download Statistical Output: Kruskal-Wallis (Word document)](https://waldenu.instructure.com/courses/157739/files/12465876/download?download_frd=1)
·
**Document:** [Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878?wrap=1) [Download Statistical Output: ANOVA (Word document)](https://waldenu.instructure.com/courses/157739/files/12465878/download?download_frd=1)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Niedz, B. (2024).**
**_Comparison of means, part II: ANOVA and Krustkal Wallis_ [Video]. Walden University Canvas. https://waldenu.instructure.com**
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
**Document:** [**Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)**](https://waldenu.instructure.com/courses/157739/files/12465889?wrap=1)
[Required Resources for Topic: ANOVA](https://waldenu.instructure.com/courses/157739/pages/week-5-learning-resources?module_item_id=6373726)
· Turgut, M., & Yıldız, H. (2023).
[Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional studyLinks to an external site.](https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=174028410&site=eds-live&scope=site&custid=s6527200).
_BMC Palliative Care, 22_(1), 195.
**To prepare:**
· Read and view the Learning Resources in Doherty and Skalsky, and Dang et al. (2021) in Required Readings.
· View the video on comparison of means.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t_ test and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t_ test).
· Were these the correct tests to be used in these analyses? Explain why.
For this Self-Study Discussion, you may post throughout Week 5. You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
**Use** this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
**Post** answers to any or all of the following:
· Summarize the ANOVA or the Kruskal-Wallis output after completing the self-learning module and completing the Required Readings.
· Turgut and Yıldız (2023) used both a
_t-test_ and an ANOVA and presented these findings in Table 2 on page 5.
· Compare and contrast the comparisons for the impact of
_duration of work on the unit_, on the Texas Revised Inventory of Grief (ANOVA), and the
_education on terminal period and grief_ (
_t-test_).
· Were these the correct tests to be used in these analyses? Explain why.
Our interactive discussion addresses the following learning objectives:
· Differentiate between ANOVA and Kruskal-Wallis tests of significance
· Summarize statistical findings for ANOVA and for Kruskal-Wallis
[Previous](https://waldenu.instructure.com/courses/157739/modules/items/6373730)
[Next](https://waldenu.instructure.com/courses/157739/modules/items/6373743)
**## NURS_8211_WK5_ComparisonofMeansMorethn2.pptx**
NURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Week 5:Comparison of Means PartII
ANOVA & Kruskal-Wallis
Objectives
Differentiate between ANOVA and Kruskal-Wallis tests of significance
Summarize statistical findings for ANOVA and for Kruskal-Wallis tests
Comparison of Means
Last week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U test
This week we challenge that by adding additional comparisons
Once you have three groups to compare, you actually have 3 possible comparisons
Practice-Focused question
Is there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?
One-Way anova
Uses the F statistic
Provides a p value that encompasses all possible combinations, but does not indicate by itself where those statistically significant findings occur.
Allows for “post hoc” multiple comparisons that do provide that focus
Has multiple assumptions associated with it. Most notably: data must be “normally distributed”
Sample size is large enough and passes the four tests of normality. Observations are all independent (that is, in this sample there were no patients who had both cardiac and respiratory components……..RRTs were classified as one or the other of the three groups.
5
Anova Measurement
Rapid Response Team (RRT) analysis with 75 RRTs
Activating nurse completed a survey with seven questions indicating their level of satisfaction with the RRT (1= very dissatisfied, 5=very satisfied)
Scores were summed yielding a total score on all seven questions and averaged across the entire sample
The highest possible score (very satisfied) was 35 and the lowest possible score was 7.
RRTs were described as cardiac, respiratory, neurology or other.
There were 42 cardiac, 12 respiratory and 21 neurology RRTs in the sample. There were no RRTs described in the other category.
6
ANOVA Results
Cardiac M: 30.0238 SD 6.42210
Respiratory M: 31.00 SD 3.30289
Neurology M: 13.9524 SD 12.92082
| | | | | | |
| — | — | — | — | — | — |
| ANOVA | | | | | |
| RRTEvaluation | | | | | |
| | Sum of Squares | df | Mean Square | F | Sig. |
| Between Groups | 4020.391 | 2 | 2010.196 | 28.104 | <.001 |
| Within Groups | 5149.929 | 72 | 71.527 | | |
| Total | 9170.320 | 74 | | | |
Now there is a lot of information in these results, most of which you do not see mentioned in a published study that used ANOVA. Sometimes, you see the F statistic (28.104). In Salkind & Frey, you can learn how to actually do an ANOVA in excel, which is for your information. In this course we are most concerned about understanding the basic elements of the test, and how to interpret.
So, the F statistic is accompanied by the all-important p value in the table above, and as you can see, it is indicating a statistically significant result. With a p value of <.001 we can be confident that the differences are not due to chance, and more likely to some factor. But the ANOVA level of significance does NOT tell us WHERE those differences are. Is it between cardiac and respiratory, or cardiac and neurology, or between respiratory and neurology? Three groups, three possible comparisons.
Now a one-way ANOVA test is used when there is more than one comparison (that would be two groups, and an independent samples t test). As we have three groups, we also have three comparisons. So, a post hoc test helps to fathom out just where those comparisons are located.
7
Post hoc: scheffe
| | | | | | | |
| — | — | — | — | — | — | — |
| Multiple Comparisons | | | | | | |
| Dependent Variable: RRTEvaluation | | | | | | |
| Scheffe | | | | | | |
| (I) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | (J) Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| | | | | | Lower Bound | Upper Bound |
| cardiac | respiratory | -.97619 | 2.76832 | .940 | -7.8958 | 5.9434 |
| | neurology | 16.07143* | 2.26032 | <.001 | 10.4216 | 21.7212 |
| respiratory | cardiac | .97619 | 2.76832 | .940 | -5.9434 | 7.8958 |
| | neurology | 17.04762* | 3.06049 | <.001 | 9.3977 | 24.6975 |
| neurology | cardiac | -16.07143* | 2.26032 | <.001 | -21.7212 | -10.4216 |
| | respiratory | -17.04762* | 3.06049 | <.001 | -24.6975 | -9.3977 |
| *. The mean difference is significant at the 0.05 level. | | | | | | |
As you can see, the Scheffe post hoc test, computes the difference in the means, and then calculates the significance level. (Behind the scenes in the statistical software, there are actually several independent samples t tests that make these 2-group comparisons.)
Note the lack of statistical significance between cardiac and respiratory. That is likely because the difference was so small, less than a point. You would need a huge sample, far greater than 75 cases to be able to see this statistically. But notice that the difference between cardiac and neurology is much bigger and the p value is also statistically significant (p<.001). Finally, notice that the difference between respiratory and neurlogy is also statistically significant (also with a p value of < .001).
8
RRT project small sample
N=25
Data did not meet the normality assumption
A nonparametric test is needed
Kruskal-Wallis
Measurement mechanisms: 7-item survey completed by activating nurses
Three types of RRTs: cardiac (n=14), respiratory (n=3), neurology (n=7), other (n=1) for a total of (N=25)
Much smaller sample, but practice-focused question was the same. Is there a difference by RRT type in activating nurse satisfaction?
9
Differences in means
Cardiac: M=30.0714 SD 5.71724
Respiratory: M=29.6667 SD=3.05505
Neurology: M=28.2857 SD=10.33948
Is there a difference in the means? WOW they are very close……only fractional differences.
The small sample indicates that the data are likely to violate the assumptions of the ANOVA test. So let’s, use the nonparametric equivalent, the Kruksal-Wallis test.
10
| | | | |
| — | — | — | — |
| Ranks | | | |
| | Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | N | Mean Rank |
| RRTEvalSum | cardiac | 14 | 12.86 |
| | respiratory | 3 | 9.67 |
| | neurology | 7 | 13.50 |
| | all other | 1 | 21.50 |
| | Total | 25 | |
| | |
| — | — |
| Test Statisticsa,b | |
| | RRTEvalSum |
| Kruskal-Wallis H | 2.059 |
| df | 3 |
| Asymp. Sig. | .560 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= other | |
The statistical software discounts the one case in the “all other” category and compares cardiac, respiratory, and neurology. The H statistic used in the statistical software was 2.059 with 3 degrees of freedom and an asymtotic significance level of .560. As this result is far greater than the accepted .05 level of significance, there is no point in going any farther. The difference in the means is not statistically significant.
Degrees of freedom has to do with (essentially) an estimate of the sample size. In this case, all four groups are counted yielding 3 degrees of freedom (1-#of groups).
Salkind and Frey 5th edition has a good explanation of effect size (the magnitude of the difference) on pp243-246.
Read about the word asymptotic on page 183.
Now, because the sample size is so very small, the student took pains to explain that with a small between the groups, she would have needed a huge sample, in fact over 250 in order to see whether this small difference was statistically significant or not.
Thus, this very small dataset exhibits a very likely case of a type II error, when significance was not found, but it may, in fact, be there.
11
Turgut & Yildez (2023)Table 3
N=200 PICU nurses
Professional grief and posttraumatic growth measured against a number of characteristics in nurses.
ANOVA used to determine the impact of duration of work in the unit against professional grief
There will be differences between the way professional grief is experienced based on tenure (duration of work in the PICU unit).
Professional grief & duration of work in the unit
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores.
Nurses who had education on the terminal period and grief had increased score on TRIG
Nurses who had worked in the unit for more than 3 years…were found to have lower Texas Revised Inventory of Grief (TRIG) scores, which means their level of professional grief was higher. They used an ANOVA because there are three categories of answers. They must have used a post hoc to localize the differences as this is explained in the narrative of the paper.
Nurses who had education on the terminal period and grief had increased score on TRIG. Which means that their level of grief was lower. They used a t test because there were only two categories: did they receive this specialized type of education yes or no?
13
Key Points
When there are more than 2 groups, you need a test that can accommodate multiple comparisons at once
Both the one-way ANOVA and the Kruskal-Wallis tests have requirements, called assumptions.
The ANOVA has post hoc tests that can pinpoint where the differences are, exactly.
When the Kruskal-Wallis test is not significant, no other analysis is necessary
Small samples can potentially lead to
14
## image14.jpg
## image18.jpg
## image19.jpg
## image20.jpg
## image21.jpg
## image22.jpg
## image23.jpg
## image24.png
## image25.png
**NURS_8211_WK5_Kruskal-WallisOutput.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk5-kruskal-wallisoutput-docx)
**NURS_8211_WK5_RRT_ANOVA.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk5-rrt-anova-docx)