Home>Homework Answsers>Nursing homework help3 months ago23.03.202512Report issuefiles (4)WK5DISDATA.docxNURS_8211_WK5_ComparisonofMeansMorethn2.pptxNURS_8211_WK5_Kruskal-WallisOutput.docxNURS_8211_WK5_RRT_ANOVA.docxWK5DISDATA.docxSelf-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal WallisThroughout 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.ResourcesBe sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.WEEKLY RESOURCESRequired Resources· Laerd Statistics: Sign up for a one-month plan using this linkhttps://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.·One Way ANOVA in SPSSLinks to an external site.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· 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)Download Statistical Output: Kruskal-Wallis (Word document)·Document:Statistical Output: ANOVA (Word document)Download Statistical Output: ANOVA (Word document)Required MediaNiedz, B. (2024).Comparison of means, part II: ANOVA and Krustkal Wallis[Video]. Walden University Canvas. https://waldenu.instructure.comPowerPoint PresentationDocument:Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)Required Resources for Topic: ANOVA· 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..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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 attest and an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation on terminal period and grief(ttest).· 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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 at-testand an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation 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-WallisPreviousNextNURS_8211_WK5_ComparisonofMeansMorethn2.pptxNURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-WallisWeek 5:Comparison of Means PartII
ANOVA & Kruskal-WallisObjectivesDifferentiate between ANOVA and Kruskal-Wallis tests of significanceSummarize statistical findings for ANOVA and for Kruskal-Wallis testsComparison of MeansLast week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U testThis week we challenge that by adding additional comparisonsOnce you have three groups to compare, you actually have 3 possible comparisonsPractice-Focused questionIs there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?One-Way anovaUses the F statisticProvides 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 focusHas 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.5Anova MeasurementRapid Response Team (RRT) analysis with 75 RRTsActivating 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 sampleThe 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.6ANOVA ResultsCardiac M: 30.0238 SD 6.42210Respiratory M: 31.00 SD 3.30289Neurology M: 13.9524 SD 12.92082ANOVARRTEvaluationSum of SquaresdfMean SquareFSig.Between Groups4020.39122010.19628.104<.001Within Groups5149.9297271.527Total9170.32074Now 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.7Post hoc: scheffeMultiple ComparisonsDependent Variable: RRTEvaluationScheffe(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= otherMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Boundcardiacrespiratory-.976192.76832.940-7.89585.9434neurology16.07143*2.26032<.00110.421621.7212respiratorycardiac.976192.76832.940-5.94347.8958neurology17.04762*3.06049<.0019.397724.6975neurologycardiac-16.07143*2.26032<.001-21.7212-10.4216respiratory-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).8RRT project small sampleN=25Data did not meet the normality assumptionA nonparametric test is neededKruskal-WallisMeasurement mechanisms: 7-item survey completed by activating nursesThree 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?9Differences in meansCardiac: M=30.0714 SD 5.71724Respiratory: M=29.6667 SD=3.05505Neurology: M=28.2857 SD=10.33948Is 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.10RanksDescription of the call 1= cardiac 2= respiratory 3= neurology 4= otherNMean RankRRTEvalSumcardiac1412.86respiratory39.67neurology713.50all other121.50Total25Test Statisticsa,bRRTEvalSumKruskal-Wallis H2.059df3Asymp. Sig..560a. Kruskal Wallis Testb. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= otherThe 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.11Turgut & Yildez (2023)Table 3N=200 PICU nursesProfessional 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 griefThere 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 unitNurses 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 TRIGNurses 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?13Key PointsWhen there are more than 2 groups, you need a test that can accommodate multiple comparisons at onceBoth 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 necessarySmall samples can potentially lead to14image14.jpgimage18.jpgimage19.jpgimage20.jpgimage21.jpgimage22.jpgimage23.jpgimage24.pngimage25.pngNURS_8211_WK5_Kruskal-WallisOutput.docxThis file is too large to display.View in new windowNURS_8211_WK5_RRT_ANOVA.docxThis file is too large to display.View in new windowNURS_8211_WK5_RRT_ANOVA.docxThis file is too large to display.View in new windowWK5DISDATA.docxSelf-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal WallisThroughout 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.ResourcesBe sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.WEEKLY RESOURCESRequired Resources· Laerd Statistics: Sign up for a one-month plan using this linkhttps://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.·One Way ANOVA in SPSSLinks to an external site.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· 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)Download Statistical Output: Kruskal-Wallis (Word document)·Document:Statistical Output: ANOVA (Word document)Download Statistical Output: ANOVA (Word document)Required MediaNiedz, B. (2024).Comparison of means, part II: ANOVA and Krustkal Wallis[Video]. Walden University Canvas. https://waldenu.instructure.comPowerPoint PresentationDocument:Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)Required Resources for Topic: ANOVA· 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..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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 attest and an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation on terminal period and grief(ttest).· 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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 at-testand an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation 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-WallisPreviousNextNURS_8211_WK5_ComparisonofMeansMorethn2.pptxNURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-WallisWeek 5:Comparison of Means PartII
ANOVA & Kruskal-WallisObjectivesDifferentiate between ANOVA and Kruskal-Wallis tests of significanceSummarize statistical findings for ANOVA and for Kruskal-Wallis testsComparison of MeansLast week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U testThis week we challenge that by adding additional comparisonsOnce you have three groups to compare, you actually have 3 possible comparisonsPractice-Focused questionIs there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?One-Way anovaUses the F statisticProvides 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 focusHas 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.5Anova MeasurementRapid Response Team (RRT) analysis with 75 RRTsActivating 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 sampleThe 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.6ANOVA ResultsCardiac M: 30.0238 SD 6.42210Respiratory M: 31.00 SD 3.30289Neurology M: 13.9524 SD 12.92082ANOVARRTEvaluationSum of SquaresdfMean SquareFSig.Between Groups4020.39122010.19628.104<.001Within Groups5149.9297271.527Total9170.32074Now 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.7Post hoc: scheffeMultiple ComparisonsDependent Variable: RRTEvaluationScheffe(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= otherMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Boundcardiacrespiratory-.976192.76832.940-7.89585.9434neurology16.07143*2.26032<.00110.421621.7212respiratorycardiac.976192.76832.940-5.94347.8958neurology17.04762*3.06049<.0019.397724.6975neurologycardiac-16.07143*2.26032<.001-21.7212-10.4216respiratory-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).8RRT project small sampleN=25Data did not meet the normality assumptionA nonparametric test is neededKruskal-WallisMeasurement mechanisms: 7-item survey completed by activating nursesThree 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?9Differences in meansCardiac: M=30.0714 SD 5.71724Respiratory: M=29.6667 SD=3.05505Neurology: M=28.2857 SD=10.33948Is 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.10RanksDescription of the call 1= cardiac 2= respiratory 3= neurology 4= otherNMean RankRRTEvalSumcardiac1412.86respiratory39.67neurology713.50all other121.50Total25Test Statisticsa,bRRTEvalSumKruskal-Wallis H2.059df3Asymp. Sig..560a. Kruskal Wallis Testb. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= otherThe 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.11Turgut & Yildez (2023)Table 3N=200 PICU nursesProfessional 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 griefThere 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 unitNurses 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 TRIGNurses 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?13Key PointsWhen there are more than 2 groups, you need a test that can accommodate multiple comparisons at onceBoth 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 necessarySmall samples can potentially lead to14image14.jpgimage18.jpgimage19.jpgimage20.jpgimage21.jpgimage22.jpgimage23.jpgimage24.pngimage25.pngNURS_8211_WK5_Kruskal-WallisOutput.docxThis file is too large to display.View in new windowNURS_8211_WK5_RRT_ANOVA.docxThis file is too large to display.View in new windowWK5DISDATA.docxSelf-Study: Comparison of Means, Comparison of Means, Part II: ANOVA and Kruskal WallisThroughout 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.ResourcesBe sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.WEEKLY RESOURCESRequired Resources· Laerd Statistics: Sign up for a one-month plan using this linkhttps://statistics.laerd.com/sign-up.php?code=LS-USD-1M-599Links to an external site.·One Way ANOVA in SPSSLinks to an external site.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· 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)Download Statistical Output: Kruskal-Wallis (Word document)·Document:Statistical Output: ANOVA (Word document)Download Statistical Output: ANOVA (Word document)Required MediaNiedz, B. (2024).Comparison of means, part II: ANOVA and Krustkal Wallis[Video]. Walden University Canvas. https://waldenu.instructure.comPowerPoint PresentationDocument:Comparison of Means, ANOVA and Kruskal Wallis (PowerPoint presentation)Required Resources for Topic: ANOVA· 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..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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 attest and an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation on terminal period and grief(ttest).· 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.Usethis Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.Postanswers 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 at-testand an ANOVA and presented these findings in Table 2 on page 5.· Compare and contrast the comparisons for the impact ofduration of work on the unit, on the Texas Revised Inventory of Grief (ANOVA), and theeducation 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-WallisPreviousNextNURS_8211_WK5_ComparisonofMeansMorethn2.pptxNURS 8211
Research for an evidence-based practice
Week 5:Comparison of Means PartII
ANOVA & Kruskal-WallisWeek 5:Comparison of Means PartII
ANOVA & Kruskal-WallisObjectivesDifferentiate between ANOVA and Kruskal-Wallis tests of significanceSummarize statistical findings for ANOVA and for Kruskal-Wallis testsComparison of MeansLast week we focused on comparing the means of two groups and used the independent samples t test and the Mann Whitney U testThis week we challenge that by adding additional comparisonsOnce you have three groups to compare, you actually have 3 possible comparisonsPractice-Focused questionIs there a difference in perceived satisfaction with the RRT process by activating nurses when compared by RRT type (cardiac, respiratory, or neurology)?One-Way anovaUses the F statisticProvides 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 focusHas 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.5Anova MeasurementRapid Response Team (RRT) analysis with 75 RRTsActivating 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 sampleThe 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.6ANOVA ResultsCardiac M: 30.0238 SD 6.42210Respiratory M: 31.00 SD 3.30289Neurology M: 13.9524 SD 12.92082ANOVARRTEvaluationSum of SquaresdfMean SquareFSig.Between Groups4020.39122010.19628.104<.001Within Groups5149.9297271.527Total9170.32074Now 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.7Post hoc: scheffeMultiple ComparisonsDependent Variable: RRTEvaluationScheffe(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= otherMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Boundcardiacrespiratory-.976192.76832.940-7.89585.9434neurology16.07143*2.26032<.00110.421621.7212respiratorycardiac.976192.76832.940-5.94347.8958neurology17.04762*3.06049<.0019.397724.6975neurologycardiac-16.07143*2.26032<.001-21.7212-10.4216respiratory-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).8RRT project small sampleN=25Data did not meet the normality assumptionA nonparametric test is neededKruskal-WallisMeasurement mechanisms: 7-item survey completed by activating nursesThree 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?9Differences in meansCardiac: M=30.0714 SD 5.71724Respiratory: M=29.6667 SD=3.05505Neurology: M=28.2857 SD=10.33948Is 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.10RanksDescription of the call 1= cardiac 2= respiratory 3= neurology 4= otherNMean RankRRTEvalSumcardiac1412.86respiratory39.67neurology713.50all other121.50Total25Test Statisticsa,bRRTEvalSumKruskal-Wallis H2.059df3Asymp. Sig..560a. Kruskal Wallis Testb. Grouping Variable: Description of the call 1= cardiac 2= respiratory 3= neurology 4= otherThe 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.11Turgut & Yildez (2023)Table 3N=200 PICU nursesProfessional 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 griefThere 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 unitNurses 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 TRIGNurses 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?13Key PointsWhen there are more than 2 groups, you need a test that can accommodate multiple comparisons at onceBoth 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 necessarySmall samples can potentially lead to14image14.jpgimage18.jpgimage19.jpgimage20.jpgimage21.jpgimage22.jpgimage23.jpgimage24.pngimage25.pngNURS_8211_WK5_Kruskal-WallisOutput.docxThis file is too large to display.View in new windowNURS_8211_WK5_RRT_ANOVA.docxThis file is too large to display.View in new window1234Bids(48)Dr. Ellen RMProf Double REmily Clarefirstclass tutorDemi_Rosesherry proffMUSYOKIONES A+Dr ClovergrA+de plusSheryl HoganProWritingGuruDr. Everleigh_JKIsabella HarvardBrilliant GeekWIZARD_KIMPROF_ALISTERAshley ElliePremiumLarry Kellyabdul_rehman_Show All Bidsother Questions(10)AED 222 Exercise Creating a Student Profile for a Mock Case Studyshort paper 350-400 wordsdefend your positionBauer IndustrieshomeworkPayment LinkACC 230 CHECKPOINT DIFFERENTIATING DEPRECIATION METHODSI need essay about ((Wikipedia Analysis Paper)) you have to read the text down for more detailsCritiquing a Change Effort _ 2 of 4 DiscussionANOVA with Repeated Measures
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