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Bivariate Categorical Tests Bivariate Categorical Tests Program Transcript [MUSIC PLAYING] MATT JONES: Up to this point, we’ve been focusing on statistical tests that require metric or variables-- that is, variables measured at the interval or ratio level. But there are a lot of categorical variables that are of use to the social scientist. We’re going to cover the chi-square test for independence and associated measures of effect of Cramer’s V in SPSS. Let’s go to SPSS. We can test for the relationship between two variables by using the chi-square test for independence. Let’s go ahead and test the relationship between gender and views on marijuana legalization. To do this, we go to Analyze, Descriptive Statistics, and Crosstabs. Here, you will see a place to put a variable in a row and a column. I’m going to scroll down and find my SHOULD MARIJUANA BE MADE LEGAL variable and enter that into my row, and I will scroll down to find the respondent’s gender and place that into my column. I’m going to go ahead and hit OK to show you the output that we receive. And here, you will see some output that are basic Descriptive Statistics. These are counts of the number of males and the number of females who felt that marijuana should be either LEGAL or NOT LEGAL. However, this does not statistically test for a relationship between these variables. We can request the chi-square statistic by, again, going back into our Crosstabs box. So I perform the same procedure of going to Descriptive Statistics, Crosstabs, and all of my information is still there, so I can select Statistics. You’ll see that the Chi-square statistic comes first, but I have to go ahead and activate that. I’m also going to go into the section Nominal to ask for Phi and Cramer’s V. This will tell me something about the strength of the relationship between the two variables. As you know from your reading, the chi-square tells us whether there is a relationship, but it doesn’t tell us anything about the strength of that relationship. Find Cramer’s V help us with that follow-up should we have a significant relationship with a chi-square. Continue. OK. So I’m going to hit Cells. Just for ease of interpretation, I’m going to request Percentages for Columns. I’ll hit Continue and OK. Here, you see, I receive some Case Processing Summary. This tells me that there are 920 valid cases in this analysis. 580 cases are missing. So out of the 1,500 cases or respondents of the survey, we have ©2016 Laureate Education, Inc. 1 Bivariate Categorical Tests quite a few of them that either didn’t answer, refused to answer, or just left that blank. The next piece of output is the Crosstabulation table. You can see this looks similar to the Crosstabs I asked for in Descriptive Statistics with just the raw counts, but now, I also requested for the percent within respondent’s sex. This tells me 55% of the males believe that marijuana should be made LEGAL and 44.6% of the males believe that marijuana should be NOT LEGAL for a cumulative percentage of 100%. I can interpret the female column as the same way. 41% of females believe that marijuana should be LEGAL while 59% believe that it should be NOT LEGAL. If there was no relationship between these two variables, we would see approximately equal percentages. To statistically test for this, we can look to our chi-square statistic. Here, we see a critical value of 18.993 with an associated p-value of 0.001. This test is significant at the 0.01 level and certainly well below the common 0.05 threshold. Therefore, we can reject the null hypothesis that there is no relationship between the two variables assuming that there is some sort of relationship between gender and position on marijuana legalization. But once again, we don’t know the strength of that relationship. We can scroll down to our Cramer’s V correlation, which tells us about the strength of this relationship. A value of 0 indicates no relationship whatsoever, and a value of 1.0 indicates a very strong, perfect relationship. We can see here, we have a value of 0.144. So while there is a relationship, it’s important to do the follow-up test to determine the strength of that relationship. In this case, the relationship between these two variables, which is statistically significant at the 0.01 level, is rather weak. [MUSIC FADING] ©2016 Laureate Education, Inc. 2
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