Which effects are significant and what does the pattern of means indicate?

This assignment will involve using JASP to answer a number of research questions using one data set
that you will find on Blackboard (CA 3 Cancer FALL 2021.csv). In order to answer the questions below you will run a one-way ANOVA, a two-way ANOVA, as well as bivariate correlations, and linear regression. The data come from a study of care-seeking for breast cancer symptoms by a group of women in an urban hospital.

Data Summary: A subset of 54 women was drawn from a larger study for this exercise.

1. Delay in seeking care.
The number of days between noticing a symptom and seeking medical assistance.

2. Optimism.
Measured with the Life Orientation Test. Scores range from 0-4, higher scores mean greater optimism.

3. Age. Measured in years.

4. Race. Add value labels: 0=White, 1=Black or African American, 2 = Hispanic

5. Education. Add value labels: 1= Less than HS, 2=High School diploma, 3=College

Your tasks:

1. Open the data and add value labels for race and education level. Check the data – under Frequencies run Contingency Tables – race by education and you should get 6 people in each cell. Check the distribution of the DV “Delay”. The mean for delay should be 38.67, with a standard deviation of 24.55. The data are positively skewed but the skew index is under 2 so use ANOVA anyway. The homogeneity of variance assumption is also violated in these data, but proceed with the analyses requested anyway.

2. First, use One-way ANOVA to test whether Education level has any significant effects on women’s delay in seeking treatment. Under options include Descriptives and use a Tukey post hoc test to compare groups if the F-test is significant. Provide a written description of the ANOVA results, including the post-hoc tests.

3. Next, do a Two-way ANOVA to test whether effects of education on delay of treatment differ between race groups. Under options choose display means for education, and make a plot (with Education on X, and Race groups as separate lines). Again use Tukey post hoc tests when F’s are significant. Write a complete summary of the ANOVA results – which effects are significant and what does the pattern of means indicate?

4. Then, test whether delay of treatment is correlated with education level, age, and optimism score. Describe in simple language what these three correlations mean (the direction of the relationship). Also describe whether the correlations are significant. (What does significant mean here – significantly different than what?)

5. Run two linear regressions (under stats choose estimates & model fit), first with delay as the dependent variable and age as the predictor. Then run it a second time adding optimism score (along with age) as predictors of delay. [The second example is called multiple regression because you have more than one predictor.] Look at the model summary boxes and compare the R2 for the two regressions. How much variance is accounted for by age alone? By both age and optimism? How much additional variance in predicting delay is accounted for when optimism is added?
6. Using APA format, prepare a summary of the results (#2, 3, 4, 5). What do these findings suggest about the factors that influence seeking treatment for breast cancer symptoms?

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