Chapter 12 If we want to compare more than 2 groups, why can’t we just use multi
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Question
Chapter 12
If we want to compare more than 2 groups, why can’t we just use multiple t-tests?
Compare and contrast the F-distributions to the z- and t-distributions.
If I was conducting an ANOVA with 2 nominal independent variables (IVs), 1 scale dependent variable, and all my participants were in each level/group of my IVs, what would be the appropriate name for my ANOVA?
I am conducting and 1-way between groups ANOVA where I want to compare 4 different depression groups (no, low, medium, and high depression levels) on their enjoyment of the cinematic masterpiece Ghostbusters (1984). I have 50 individuals in each group, with a total of 200 individuals (N = 200). I want to know if my depression groups are significantly different from one another.
State the null and alternative hypothesis for this test in mathematical notation.
Based on the information provided, complete the following ANOVA source table.
Source
SS
df
MS
F
Between
100
Within
Total
150
Find the critical value for the ANOVA in question 6. Is our ANOVA significant? What conclusions can we draw about our depression groups?
Calculate the effect size of this ANOVA.
When and why should we use post-hoc tests?
Chapter 17
In which situations would you choose a nonparametric statistical test over a parametric statistical test?
Compare and contrast the Chi-square test for goodness of fit and the Chi-square test for independence.
What is the measure of effect size for a chi-square goodness of fit test? What is the measure of effect size for a chi-square test of independence?
Source
SS
df
MS
F
Between
100
Within
Total
150
Explanation / Answer
dear student please post the question one at a time
If we want to compare more than 2 groups, why can’t we just use multiple t-tests?
A standard approach when you have three or more groups and a numeric dependent variable is to first test the null hypothesis that all group means are equals. ANOVA provides a significance test for this. Then, if the ANOVA is then this is followed up by some procedure to understand the pattern of group means (e.g., post hoc tests, contrasts and so on). The rationale for this approach is that the overall ANOVA provides some initial overall check of group mean differences before you commence examination of the group mean differences.
Furthermore, there is a difference between performing all possible pairwise comparisons using a t-test versus using common post-hoc tests (e.g., Tukey's). Post-hoc tests generally have some built-in component to protect against type-1 errors.
That said, you don't have to perform an ANOVA before performing follow-up tests. In fact, if there are particular comparisons that interest you, then you might want to perform those first, or you might argue that you have no interest in the overall ANOVA test. The main thing is that you have a rationale for how you are doing your inference.
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