categorical tests.

(( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( (( (( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( 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 RR 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'r e going to cover(the chi R 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 R 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 MARIJ UANA(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 r equest the chi R 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 R squar e 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 R 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 R up should we have a significant relationship with a chi R square. Continu e.

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(analysi s. 580 cases( are missing. So out of the 1,500 cases(or(respondents(of the survey, we have ©201 6 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(lo oks( 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 male s 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 relati onship between these two variables, we would see approximately(equal percentages.

To statistically(test for(this, we can look(to our(chi R square statistic. Here, we see a critical value of 18.993 with an associated p R value of 0.001. This(test is(significan t 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 mar ijuana 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 R up test to determine the strength of that relationship. In this(case, the relationship betwe en these two variables, which is(statistically( significant at the 0.01 level, is(rather(weak.