1. When should ANOVA be used? 2. What is the difference between one-way and two-
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1. When should ANOVA be used? 2. What is the difference between one-way and two-way ANOVA? 3. What are "multiple comparison tests" and why are they used? 4. What is the difference between a main effect and an interaction effect? 5. Use the "ANOVA Data for Question 1" file for this question. The data in the table presents the measures of pain (on the NRS) to palpation of a trapezius tender point (TP). The table has the baseline measure for pain, pain measure post treatment, and the change value from treatment. Asyou can see from the table, there are three groups: Group 1 received PRT, Group 2 received ischemic compression, and Group 3 was the control group (i.e, did not receive treatment). You want to determine if there are differences between groups in regards to the total change in their pain scale from treatment (ie, run the analysis on the "NRS change score" only) a. What type of ANOVA is appropriate for this scenano? b. What are your results (eg, mean difference with standard deviation, p-value, & Cls)? c. What do your results mean? What are the main findings?Explanation / Answer
a)The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. Basically, you’re testing groups to see if there’s a difference between them.
b)
One-Way ANOVA
A one-way ANOVA has just one independent variable. For example, difference in IQ can be assessed by Country, and County can have 2, 20, or more different categories to compare.
Two-Way ANOVA
A two-way ANOVA refers to an ANOVA using two independent variables. Expanding the example above, a 2-way ANOVA can examine differences in IQ scores (the dependent variable) by Country (independent variable 1) and Gender (independent variable 2). Two-way ANOVA can be used to examine the interaction between the two independent variables. Interactions indicate that differences are not uniform across all categories of the independent variables. For example, females may have higher IQ scores overall compared to males, but this difference could be greater (or less) in European countries compared to North American countries. Two-way ANOVAs are also called factorial ANOVAs.
c)When processes are compared and the null hypothesis of equality (or homogeneity) is rejected, all we know at that point is that there is no equality amongst them. But we do not know the form of the inequality.
Questions concerning the reason for the rejection of the null hypothesis arise in the form of:
This is when we use Multiple Comaprisons
d)A main effect is the effect of a single independent variable on a dependent variable – ignoring all other independent variables.
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