Conduct and Interpret a One-Way ANOVAWrite up the results in APA format. This method of estimating the variance IS sensitive to solutionw mean differences! A clinical vitamins and testosterone has run a between-subjects experiment comparing two treatments for depression cognitive-behavioral therapy CBT and client-centered therapy CCT against a control condition. Subjects were randomly assigned to the experimental condition. The data one way anova questions and solutions summarized as follows:. An education researcher is comparing four solutinos algebra curricula. Eighth grade students are randomly assigned to one one of the four groups.
More Practice Problem ANSWERS: 1-Way ANOVA - korean-war.info: Learning Statistics
The main purpose of an ANOVA is to test if two or more groups differ from each other significantly in one or more characteristics. The way this works is that the factors sort the data points into one of the groups and therefore they cause the difference in the mean value of the groups. Let us claim that woman have on average longer hair than men. We find twenty undergraduate students and measure the length of their hair. A conservative statistician would then claim we measured the hair of ten female and ten male students, and that we conducted an analysis of variance and found that the average hair of female undergraduate students is significantly longer than the hair of their fellow male students.
Most statisticians fall into the second category. In more statistical terms it tests the effect of one or more independent variables on one or more dependent variables. This is due to the fact that it only requires a nominal scale for the independent variables — other multivariate tests e.
This following table shows the required scales for some selected tests. This happens if the independent variable for the ANOVA has only two factor steps, for example male or female as a gender. The T-test compares the means of two and only two groups when the variances are not equal. Whereas the ANOVA can have one or more independent variables, it always has only one dependent variable. Do the standardized math test scores differ between students that passed the exam and students that failed the final exam?
This question indicates that our independent variable is the exam result fail vs. We must now check the assumptions. First we examine the multivariate normality of the dependent variable. Both plots show a somewhat normal distribution, with a skew around the mean. An alternative to the K-S test is the Chi-Square goodness of fit test, but the K-S test is more robust for continuous-level variables. If normality is not present, we could exclude the outliers to fix the problem, center the variable by deducting the mean, or apply a non-linear transformation to the variable creating an index.
As described in the research question we want to test, the math test score is our dependent variable and the exam result is our independent variable. This would be enough for a basic analysis. But the dialog box has a couple more options around Contrasts, post hoc tests also called multiple comparisons , and Options.
In the dialog box options we can specify additional statistics. If you find it useful you might include standard descriptive statistics. Generally you should select the Homogeneity of variance test which is the Levene test of homoscedasticity , because as we find in our decision tree the outcome of this test is the criterion that decides between the t-test and the ANOVA.
Post Hoc tests are useful if your independent variable includes more than two groups. In our example the independent variable just specifies the outcome of the final exam on two factor levels — pass or fail. If more than two factor levels are given it might be useful to run pairwise tests to test which differences between groups are significant. Because executing several pairwise tests in one analysis decreases the degrees of freedom, the Bonferoni adjustment should be selected, which corrects for multiple pairwise comparisons.
Another test method commonly employed is the Student-Newman-Keuls test or short S-N-K , which pools the groups that do not differ significantly from each other. Therefore this improves the reliability of the post hoc comparison because it increases the sample size used in the comparison. The last dialog box is contrasts. Contrasts are differences in mean scores. It allows you to group multiple groups into one and test the average mean of the two groups against our third group.
Please note that the contrast is not always the mean of the pooled groups! It is only equal to the pooled mean, if the groups are of equal size. It is also possible to specify weights for the contrasts, e. We do not specify contrasts for this demonstration. Medicine — Does a drug work? Does the average life expectancy significantly differ between the three groups that received the drug versus the established product versus the control?
Sociology — Are rich people happier? Do different income classes report a significantly different satisfaction with life? Management Studies — What makes a company more profitable? A one, three or five-year strategy cycle? Options Post Hoc Tests Post Hoc tests are useful if your independent variable includes more than two groups. Contrasts The last dialog box is contrasts. Pin It on Pinterest.