 One final variation of regression that we can get in Jmovi, that really is kind of surprising considering it's not always available in other programs is ordinal regression or specifically ordinal logistic regression. And the idea here is you're trying to take several variables to predict where a particular case falls in ordered categories, say, for instance, from lowest to middle to highest. Now, you could theoretically have an entire ordinal scale where you rank every case from one to however many there are. But when you have a small number of cases, this is where ordinal logistic regression is going to be most useful. And another time that it shows up as really handy is when you have data that really deviates from normality. Now I'm going to use this example data of state data that I uploaded. And we have among other things, how much different states search for different things. And one that's kind of interesting here at the very end is searching for modern dance. Now, let me show you what this looks like, because then you'll understand why the procedure is necessary. If we put modern dance right here, it'll give us some statistics. But what we're really interested in here is the plot, the density, Instagram. So we have data from 48 contiguous states. And you can see it's mostly normal. But man, we got this big gap in this bump up here. I happen to know that this is Utah where I live. Utah, it turns out leads the national mind share for modern dance, which is kind of shocking. Anyhow, this is not a normal distribution. And outliers like this can really cause problems with irregular analyses. And so we doing the data transforming it and possibly putting into categories that might be one method of dealing with this and ordinal logistic regression makes that possible. Now, what I want to do to make that happen is I want to split it up into categories. So for instance, let me come back to this just for a moment where I was. And let's go to statistics and say, what are the cut points to create four equal sized groups? So we're going to make quartiles. And what this says is, well, the minimum z score is minus 1.44, the 25th percentile, the first quartile is negative 0.618. The 50th is about negative 0.23. And the 75th is positive 0.45. And it goes up to 4.7. I think that's where Utah is way up here. Now, I'm going to round these off a little bit. And what I did is I came in here and I created this new variable and calling it modern dance quartiles. And what I'm saying is, if the z score is less than negative five, put it in one that is put it in the first quartile. If modern dance is less than zero, but above negative 0.5 put in the second quartile. If modern dance is less than positive 0.5 put in the third quartile, and otherwise put it in the fourth quartile. And so that's a way of nesting if statements to create these categories. And when I do that, I can come back and do exploration again, except this time we'll do it for the quartiles. I'll put modern dance quartiles right there. Look at a frequency table. And then we'll get a bar plot. And then you'll be able to see that it's a little better behaved, because we've brought in the outliers. And we've created four groups that are approximately equal size, they're pretty close. And the reason they're not exactly the same is because I intentionally chose to make the cutoff points, something that seemed a little more sensible. But now we have an ordinal variable, we have the states categorized into the lowest quarter, the second lowest, the second highest, and the highest quartile, in terms of their relative search preference for modern dance. And so I'm going to come here to regression and come to this last option here, ordinal outcomes. And when I open that up, the first thing I need to do is specify the dependent variable or the outcome that we're looking at, and that's going to be modern dance quartiles. I'm going to come back up here, I got selected, I'll just click it over here. And then we put in covariates, we can put in a lot of variables if we want, but because I have a small sample size, only 48 cases, I'm going to limit it. Now, modern dance, because it's an art form should be most associated with openness, because in certain studies on big five personality characteristics, openness seem to also have something to do with art. So I'm going to click that and put it over here and covariates. And then also, I'm going to show you how to use a factor, I'm going to take the psych regions, because that included friendly and conventional, but also relaxed and creative, which seems like it would be important for something like performing arts. And so if we take those, we have this table right here, it lets us know about the deviance and the AIC, which are methods of assessing the fit of the overall model. We have these predictors, openness, it turns out is significantly associated with modern dance quartiles. So that's something that we were kind of hoping for that openness, or being open to new ideas and possibly to aesthetic experiences seems to be associated with the number of searches relatively speaking that is state does for modern dance. We also see that the psych regions matter, that the relaxing creative and the friendly and conventional, it makes a difference. And what's interesting, actually in both cases is that relaxing creative does more than friendly and conventional does, that's because this is positive, and also temperamental and uninhibited does more than friendly and conventional does in both cases. So let's take a look at some of the options. We have the model builder. If I wanted to put things in separate blocks, I could. Let's go to model fit. This is where we have the choices, the deviance, the AIC and the McFadden's R squared all seem like great ideas. So I'm going to leave those and the model coefficients. That's this table we have right here. I'm just going to throw one more thing onto it. I'm going to do the odds ratios and the confidence intervals for the odds ratios. Remember, the null value for the odds ratio is one. And so if we are consistently below one in the confidence interval or above, that lets us know that we have something important here. And for instance, let me close this so we can see more of this output here. I'll drag this over. So openness, the odds ratio on both ends of the confidence interval are above one. So, you know, it's significant. But really the amazing one is this here, temperamental and uninhibited states versus friendly and conventional states, the odds ratio is 17.54. That's really big. And the confidence interval goes all the way up to 91. And so this is a very influential difference in trying to predict what a state's ranked category would be in terms of the quartiles one through four, and their searches for modern dance on Google. And so it turns out that this is actually a really easy analysis to set up. We could theoretically have a lot more options available to us. But Jamowi's keeping it pretty simple. And this is a good way to get started in dealing with ranked information, as opposed to the outliers that we had in the original distribution, and using a regression model with both a quantitative or continuous predictor variable, and a categorical predictor to try to find out where states would fall into that ranked continuum that we created.