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  • 00:04

    HERSCHEL KNAPP: Welcome to practical statisticsfor nursing using SPSS.This video shows how to process the ANCOVA test.You can watch the entire video or use the time sliderto navigate directly to any time point.

  • 00:24

    HERSCHEL KNAPP [continued]: The ANCOVA test is similar to ANOVA test.Before proceeding, it's recommended that you first viewthe video Ch 06 - ANOVA.mp4.In terms of setup and results, the ANCOVA test and the ANOVAtest are quite similar.

  • 00:45

    HERSCHEL KNAPP [continued]: Remember that the ANOVA test assesses three or more groupsto detect statistically significant differencesbetween the pairs of groups, specificallythe results of ANOVA test are based on the effectthat the independent variable, the anti-hypertensive drug,had on the dependent variable, systolic blood pressure.

  • 01:08

    HERSCHEL KNAPP [continued]: The ANCOVA statistic allows us to includea potentially confounding variableinto the model, which we expect mayinfluence the dependent variable, such as smoking rate.The ANCOVA test then adjusts the resultsin the dependent variable accordingly.

  • 01:29

    HERSCHEL KNAPP [continued]: The ANCOVA test has two pretest criteria,homogeneity of regression slopes and homogeneityof variance, Levene's test.We'll check for homogeneity of regression slopes now.We'll see the results of the homogeneity of variance testwhen we run the ANOVA test.This example uses the dataset Ch 07 - Example 01 -ANCOVA.sav.

  • 01:57

    HERSCHEL KNAPP [continued]: This dataset contains three variables.Group is a categorical variable containing three values, drugA, drug B, and drug C. Systolic BPis a continuous variable that contains the systolic bloodpressure of each participant at the conclusion of the study.

  • 02:19

    HERSCHEL KNAPP [continued]: And smoking is a continuous variablethat contains the mean number of cigarettesthat each participant smoked on a daily basis.To check for the homogeneity of regression slopes,click on Analyze, General Linear Model, Univariate.Move Group to Fixed Factors.

  • 02:42

    HERSCHEL KNAPP [continued]: Move Systolic BP to Dependent Variable.And move Smoking to Covariance.Click on Model, select Custom, move Group and Smokingto Model.Next, hold down the Shift key, and click on Group and Smoking.

  • 03:05

    HERSCHEL KNAPP [continued]: This will select both Group and Smoking togetherto signify the interaction term.And move them to Model.Click Continue, click OK, and it'll process.We look to the Test Between Subject Effects Table.If the P value for the group smoking interaction term

  • 03:25

    HERSCHEL KNAPP [continued]: is greater than 0.05, then this wouldindicate that there is no statistically significantdifference in the regression slopes among the variablesinvolved in this model, and the assumptionof homogeneity of regression slopes would be satisfied.In this case, the P value is 0.028.

  • 03:46

    HERSCHEL KNAPP [continued]: Since this is less than or equal to 0.05,this indicates that there is a statistically significantdifference between the regression slopesfor systolic BP and smoking.This violation makes sense, as the covariateis somewhat atypical.The covariate in this model is smoking,

  • 04:07

    HERSCHEL KNAPP [continued]: the number of cigarettes that each participantsmokes in a typical day.Since more than 90% of the participants are nonsmokers,this finding is not unexpected.Since this pretest criteria is not fully satisfied,this should be noted in the results section.

  • 04:27

    HERSCHEL KNAPP [continued]: To run the ANCOVA test, click on Analyze, General Linear Model,Univariate.Click on Model, select Full Factorial,click Continue, click Options, move Group to Display Means 4,

  • 04:49

    HERSCHEL KNAPP [continued]: check the Compare Main Effects checkbox.In the Confidence Interval Adjustmentpull down menu, select Bonferroni.In the Display Options, check Homogeneity Tests.Click Continue, click OK, and it'll process.

  • 05:13

    HERSCHEL KNAPP [continued]: To finalize the pretest checklist,we see that the homogeneity of variance testproduced a P value of 0.791.Since this is greater than 0.05, thisindicates that there is no statistically significantdifference between the variances,hence this criteria is satisfied.Next, we look to the Test of Between Subjects Effects Table.

  • 05:38

    HERSCHEL KNAPP [continued]: The P value of 0.004 indicates that a statisticallysignificant difference has been detectedamong the adjusted means for the groups.In the Estimates Table, we see the adjusted meansfor each group.These means have been adjusted to account for the smokingcovariate.

  • 05:58

    HERSCHEL KNAPP [continued]: These figures will be useful when documenting the results.Finally, we look to the Pairwise Comparisons Table.This table is read in the same wayas the Multiple Comparisons table producedby the ANCOVA post-hoc test.To identify the pairs of groups thatare statistically significantly different from each other,

  • 06:19

    HERSCHEL KNAPP [continued]: we look for P values that are less than or equal to 0.05.We see that there is a statistically significantdifference in the adjusted means between drug A and drug Band between drug A and drug C.This concludes this video.

Video Info

Series Name: Practical Statistics for Nursing Using SPSS

Episode: 8

Publisher: SAGE Publications, Inc.

Publication Year: 2016

Video Type:Tutorial

Methods: Analysis of covariance

Keywords: blood pressure; blood pressure measurement; mathematical concepts; mathematical models; nursing; Smoking; Smoking and health; Software ... Show More

Segment Info

Segment Num.: 1

Persons Discussed:

Events Discussed:

Keywords:

Abstract

Professor Herschel Knapp offers an analysis approach to use when a potentially confounding variable mixes with the data set. This 8th chapter of the nursing statistics series focuses on how to run an ANCOVA test in SPSS.

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ANCOVA

Professor Herschel Knapp offers an analysis approach to use when a potentially confounding variable mixes with the data set. This 8th chapter of the nursing statistics series focuses on how to run an ANCOVA test in SPSS.

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