In Week 4, we used epsilons and 10-percent-point rule to determine if a potential relationship between two variables is worth examining further. During Week 5, we studied tests of significance. In this week's discussion, we will apply these tests of significance to our project variables. We will also run measures of association to determine the strength and direction of the relationship between our variables. As we discussed previously, the levels of measurement of our variables determine which test of significance works for the research project. Here is the guideline:  1. Before-and-after design and the DV is at I/R level: Dependent Sample T-test 2. DV and IV are BOTH categorical variables (nominal/ordinal): Chi-square *Special note for Chi-square: you should have less than 20% of the cells with an expected count of 5 or less. This information is reported automatically, right below the chi-square output table. If your chi-square test fails to meet this requirement, it is necessary to use "recoding" to combining certain answer categories together so the expected counts would increase.   3. DV and IV are both continuous (interval/ratio) variables: regression 4. Comparison of groups (when IV is categorical - nominal/ordinal and DV is continuous - interval/ratio):           a. Between 2 groups: Independent Sample T-test           b. Among 3 or more groups: ANOVA Why do we need to run tests of significance? They allow us to see if our relationship is "statistically significant." To be more specific, these tests tell us if a relationship observed in a sample, like your research project based on GSS 2016 data set, is generalizable to the population from which this sample was drawn (US adults). Test results reported under "p" in the SPSS output tells us the chances that a relationship observed in the sample is not real, but rather due to factors like a sampling error. We compare this "chance" with level of significance, commonly set as .05 or .01. If this chance is smaller than level of significance, we can reject the null hypothesis, and keep the research hypothesis. Next, we'll use tests of "measures of association" to figure out the exact strength of a relationship between two variables. In addition, we'll learn how to interpret SPSS outputs for measures of association tests such as lambda, gamma, and Pearson's r, along with other possible tests. These tests are also specific to the level of measurement of your variables. Here are the guidelines: Both DV and IV are nominal variables: Lambda (when it is not a 2X2 table) If it is a 2X2 table: Phi Both DV and IV are ordinal variables: Gamma One variable ordinal AND the other variable dichotomous nominal (like Yes/No, male/female, etc.): Gamma One variable ordinal AND the other variable nominal (not dichotomous, has more than 2 categories): Cramer's V. Both DV and IV are I/R variables: Pearson's r To interpret the output, see attached handout. Keep in mind measures of association is a statistical procedure based on Proportional Reduction of Error (PRE). Thus the format of interpretation will be: Knowing the IV will reduce error in predicting the DV by *%.  Please note: Don't just say "IV" and "DV" in your explanation. You need to enter your variables names for IV and DV, and replace * for the exact test value from the output. If the value of Lambda is .34, then it will be interpreted as 34%. ****Ok, now it is time for you to try! For this week's discussion, be sure to perform the correct test of significance (choose one) and measure of association (choose one) on your variables for the final project. You can download the class handout attached at the bottom of the page.