Advance biostats spss (two-way anova) use tukey
DUE 3/17/19 9P.M EST
BE ON TIME AND ORIGINAL WORK!!
HAVE SPSS AND READ DIRECTIONS!!
DATA IS ATTACHED AND STEP BY STEP GUIDE IS ATTACHED
Two-way ANOVA enables researchers to study the effects of a variable upon two independent variables at multiple levels. Researchers might wish to compare the exercise habits (represented by number of steps taken per month) of individuals, based on their gender and education. Two categories of gender and three education levels may be assessed. Two-way ANOVA can account for the effects of these groups, independently, on the number of steps taken each month. It can also help to determine whether interaction exists.
For this Assignment, you use two-way ANOVA with interaction. Be sure to complete all of the parts of the assignment listed below. As this is an ANOVA, you also use multiple comparisons to determine for which factors the differences are significant. Also, to avoid additional type 1 errors, you must use Tukey, one of a number of possible methods to adjust for your multiple comparisons.
1. Provide numeric descriptive statistics (include skewness and kurtosis if appropriate) and graphic descriptions for Sex, Educ, and Exercise.
2. Create histograms of the number of steps (Exercise) (dependent variable) for each combination of levels for the two independent variables. Describe the data and shape of the distributions.
3. Discuss whether the assumptions of homogeneity of variance of the groups and normality of the data on Exercise are met. Be sure to include output to support your decision on whether the assumptions have been met. (Continue with the analyses even if assumptions are not met.)
4. Conduct two-way ANOVA with interaction and post hoc analysis (as appropriate) using Tukey to correct for multiple comparisons. Provide relevant SPSS output.
5. Interpret the analysis results in the context of the research question: Is there a difference in the level of exercise based on a person’s sex and level of education? Include important statistics from your analysis results to support your conclusion and generalize your results, if appropriate, to the relevant population(s).