Logistic Regression with Stata - Chapter 2 - Logistic Regression with Categorical Predictors

Logistic Regression with Stata Chapter 2 – Logistic Regression with Categorical Predictors. 2.0 Introduction 2.1 One Categorical predictor 2.2 Two Categorical predictors 2.2.1 A 2 by 2 Layout with Only Main Effects 2.2.2 A 2 by 2 Layout with Main Effects and Interaction 2.2.3 A 2 by 3 Layout with Only Main Effects 2.2.4 A 2 by 3 Layout with Main Effects and Interaction 2.3 Categorical and Continuous Predictors 2.3.1 A Continuous and a Two Level Categorical Predictor 2.3.2 A Continuous and a Two Level Categorical Predictor with Interaction 2.3.3 A Continuous and a Three Level Categorical Predictor 2.3.4 A Continuous and a Three Level Categorical Predictor with Interaction 2.4 More on Interpreting Coefficients and Odds Ratios 2.5 Summary. NOTE: This page is under construction!! 2.0 Introduction. In the previous chapter, we looked at logistic regression analyses that used a categorical predictor with 2 levels (i.e. a dummy variable) and a predictor that was continuous. In this chapter, we will further explore the use of categorical predictors, including using categorical predictors with more than 2 levels, 2 categorical predictors, interactions of categorical predictors, and interactions of categorical predictors with continuous predictors. We will focus on the understanding and interpretation of the results of these analyses. We hope that you are familiar with the use of categorical predictors in ordinary least squares (OLS) regression, as described in Chapter 3 of the Regression with Stata book. Understanding how to interpret the results from OLS regression will be a great help in understanding results from similar analyses involving logistic regression. This chapter will use the apilog data that you have seen in the prior chapters. We will focus on four variables hiqual as the outcome variable, and three predictors, the proportion of teachers with full teaching credentials ( cred ), the level of education of the parents ( pared ), and the percentage of students in the school receiving free meals ( meals ). Below we show how you can load this data file from within Stata. 2.1 One Categorical Predictor. First, let’s look at what happens when we use one categorical predictor with three levels. The predictor that we will use is based on the proportion of teachers who have full credentials. We have divided the schools into 3 categories, schools that have a low percentage of teachers with full credentials, schools with a medium percentage of teachers with full credentials and schools with a high percentage of teachers with full credentials. We will refer to these schools as high credentialed , medium credentialed and low credentialed schools. Below we show the codebook information for this variable. The variable cred is coded 1, 2 and 3 representing low , medium and high respectively. Before we run this analysis using logistic regression, let us look at a crosstab of hiqual by cred . Looking at the Pearson Chi Square value (182.9), the results suggest that the quality of the school ( hiqual ) is not independent of the credential status of the teachers ( cred ). But such a way of looking at these results is very limiting. Instead, lets look at this using a regression framework. Lets start by pretending for the moment that our outcome variable is not a 0/1 variable and that it is appropriate to use in a regular OLS analysis. Below we show how we could include the variable cred as a predictor and hiqual as an outcome variable in an OLS regression. We use the xi command with i.cred to break cred into two dummy variables. The variable _Icred_2 is 1 if cred is equal to 2, and zero otherwise. The variable _Icred_3 is one if cred is equal to 3 and 0 otherwise. We can use the adjust command to get the predicted values for the 3 levels of cred as shown below. Note that the low credentialed schools are the omitted group. The coefficient for the constant corresponds to the predicted value for the low credentialed group. The coefficient for I_cred_2 represents the difference between the medium credentialed group and the omitted group (.329 – .081 = .248). Note that the coefficient for I_cred_3 represents the predicted value for group 3 (the high credentialed minus the omitted group (.513 – .081 = .432). Seeing how you interpret the parameter estimates in OLS regression will help in the interpretation of the parameter estimates when using logistic regression. Now let’s run this as a logistic regression and see how to interpret the parameter estimates. As you see below, the syntax for running this as a logistic regression is much like that for an OLS regression, except that we substituted the logit command for the regress command. The results are shown using logistic regression coefficients where the coefficient represents the change in the log odds of hiqual equaling 1 for a one unit change in the predictor. Some prefer to use odds ratios to help make the coefficients more interpretable. The odds ratio is simply the exponentiated version of the logistic regression coefficient. For example, exp(1.715) = 5.557 (shown below). After running the logit command from above, we can type logit , or and the results from the last logit command are shown, except using odds ratios. Let’s interpret these odds ratios. The odds ratio for _Icred_2 is the odds of a medium credentialed school being high quality divided by the odds of a low credentialed school being high quality. Likewise, The odds ratio for _Icred_3 is the odds of a high credentialed school being high quality divided by the odds of a low credentialed school being high quality. Referring back to the crosstabulation of hiqual and cred , we can reproduce these odds ratios. First, using the frequencies from that crosstab, we can manually compute the odds of a school being high-quality school at each level of cred . Cred = Low. Odds or a school being high quality = (31 / 351) = .08831909 Cred = Medium. Odds or a school being high quality = (107 / 218) = .49082569 Cred = High. Odds or a school being high quality = (253 / 240) = 1.0541667. Now, we can see that the odds ratio for _Icred_2 is the odds of a medium credentialed school being high quality divided by the odds of a low credentialed , or (.49082569 / .08831909) = 5.5574134. Likewise, the odds ratio for _Icred_3 is the odds of a high credentialed school being high quality divided by the odds of a low credentialed school being high quality, or (1.0541667 / .08831909) = 11.935887. The above technique works fine in a simple situation, but if we had additional predictors in the model it would not work as easily. Below we demonstrate the same idea but using the adjust command with the exp option to get the predicted odds of a school being high-quality school at each level of cred . The odds ratio for _Icred_2 should be the odds of a medium credentialed school being high quality (.490) divided by the odds of a low credentialed school being high quality (.088). Indeed, we see this is correct. This means that we estimate that the odds of a medium credentialed being high quality (odds = .490) is about 5.6 times that of a low credentialed school being high quality (odds = .088). Likewise, the odds ratio for _Icred_3 should be the odds of a high credentialed school being high quality (1.05) divided by the odds of a low credentialed school being high quality (.088). Indeed, we see this is correct as well. The odds of a high credentialed school being high quality (which is 1.05) is about 11.9 times as high as the odds of a low credentialed school being high quality (which is 0.088). If this were a linear model (e.g. a regression with two dummies, or an ANOVA), we might be interested in the overall effect of cred . We can test the overall effect of cred in one of two ways. First, we could use the test command as illustrated below. This produces a Wald Test . Based on the results of this command, we would conclude that the overall effect of cred is significant. Instead, you might wish to use a likelihood ratio test, illustrated below. We first run the model with all of the predictors, i.e. the full model, and then use the estimates store command to save the results naming the results full (you can pick any name you like). Next, we run the model omitting the variable(s) we wish to test, in this case, omitting i.cred . We can then use the lrtest command to compare the current model (specified as a period) to the model we named full. This test is also clearly significant. If you look back to the crosstab output of hiqual and cred you will see a line that reads. which, interestingly enough, matches the likelihood ratio test shown above. Both of these tests use a likelihood ratio method for testing the overall association between cred and hiqual . 2.2 Two categorical predictors. 2.2.1 A 2 by 2 Layout with Only Main Effects. Now let’s look at an analysis that involves 2 categorical predictors. We have created a variable called cred_hl which is a dummy variable that is 1 if the school has a high percentage of teachers with full credentials ( high credentialed) , and 0 if the school has a low percentage of teachers with full credentials ( low credentialed ). (Note that the medium group has been omitted. This is not a customary thing to do, but this will be useful to us later.) Likewise, we have created a variable called pared_hl which is a binary variable that is coded 1 if the parents education is high (which we will call high parent education , and 0 if the parents education is low (which we will call low parent education . (Again, note that the medium group has been omitted.) The model below looks at the effects of teacher’s credentials and parents education on whether the school is a high quality school, but does not include an interaction term. We then use the logit , or command to obtain odds ratios. To help interpret the odds ratios for cred_hl , let’s look at the predicted odds broken down by cred_hl and pared_hl using the adjust command. For example, the odds ratio for pared_hl is the odds of a school being high quality for high parent education schools divided by the odds of a school being high quality for low parent education schools. Likewise, the odds ratio for cred_hl is the odds of being a high quality school for high credentialed schools divided by the odds of being high quality for low credentialed schools, as illustrated below. Note that the above example used the odds for low parent education schools. Note that we get the same results if we use the odds for high parent education schools, as illustrated below. The above results indicate that the odds of being a high quality school for high credentialed schools is about 15.3 times as high as the odds of low credentialed schools being high quality. Because we did not include an interaction in this model, it assumes that the impact of credentials is the same regardless of the level of education of the parents. As we saw above, the odds ratio comparing high versus low credentialed schools was the same (15.3) for schools with low parent education and schools with high parent education . Let’s look at how reasonable this assumption is by comparing the predicted probabilities of the schools being high quality for the 4 cells with the actual probabilities of the schools being high quality. Below we see the predicted probabilities. Below we see the actual probabilities of the schools being high quality broken down by the 4 cells. Based on these probabilities, let’s look at the odds ratio for cred when parents education is low. When parents education is low, the observed odds ratio is about 27. Let’s compare the above result to the odds ratio for cred when parents education is high . When parents education is high the observed odds ratio for cred is about 7.4. As you see, when we included just main effects in the model, the overall odds ratio for cred was 15.3, but when parents education is low the odds ratio is about 27 and when parents education is high the odds ratio is 7.4. These odds ratios seem considerably different, yet because we only included main effects the model, the model just estimates one overall odds ratio for cred . However, if we include an interaction term in the model, then the model will estimate these odds ratios separately. 2.2.2 A 2 by 2 Layout with Main Effects and Interaction. We will create an interaction term by multiplying cred_hl by pared_hl to create cred_ed . We can then include this interaction term in the analysis. The significant interaction suggest that the effect of cred_hl depends on the level of pared_hl (and likewise, effect of pared_hl depends on the level of cred_hl ). We explore this further using the odds ratio metric below. We can use the adjust command to get the predicted odds broken down by the 4 groups. The odds ratio for pared_hl is the odds of a high parent education school being high quality divided by the odds of a low parent education school being high quality, for low credentialed schools (because low credentialed is coded as 0). Likewise, the odds ratio for cred_hl is the odds of a high credentialed school being high quality divided by the odds of a low credentialed school being high quality, for low parent education schools (because low parent education is coded 0). We can see the meaning of the interaction by comparing the odds ratio for the effect of cred_hl for high parent education schools and for low parent education schools. When parent education is low, we have seen that the odds ratio for cred_hl is 26.99 (see output from the logistic command above). When parent education is high, the odds ratio for cred_hl is shown below. The odds ratio for the interaction is actually the ratio of two odds ratios. Focusing on the effect of cred_hl , the interaction can be thought of as the odds ratio for cred_hl when parents education is high (i.e. 7.4) divided by the odds ratio for cred_hl when parents education is low (i.e., 26.99). As you see below, the ratio of these two odds ratios is the interaction. Here is another way to look at this. We know the odds ratio for cred_hl is 26.99 for low parent education schools. If we multiply this by the interaction term (by .274) we get the odds ratio for the high parent education schools. As we see below, 26.99 * .274 yields the odds ratio (with a touch of rounding error) for high parent education schools. The impact of cred_hl depends on the level of education of the parents. When parent education is low, the impact of cred_hl is much higher than when parent education is high. In particular, when parent education is low, the odds of high credentialed schools being high quality are 27 times than the odds of low credentialed schools being high quality. By contrast, the odds ratio for cred_hl for schools with high parent education is .274 times the low parent education schools . For the high parent education schools, the odds of high credentialed schools being high quality is about 7.4 times that of the low credentialed schools . 2.2.3 A 2 by 3 Layout with Only Main Effects. We can extend the above analysis into a 3 by 2 design by looking at all 3 levels of parent education (low, medium and high) by using the variable pared instead of pared_hl . We will use this example to illustrate how to run and interpret the results of such an analysis. As above, we will start with a model which includes just main effects, and then will move on to a model which includes both main effects and an interaction. We can look at a model which includes cred_hl and pared as predictors as shown below. We use the xi prefix with i.pared to break parent education into two dummy variables _Ipared_2 which is 1 if parent education is medium, 0 otherwise; and _Ipared_3 which is 1 if parent education is high, 0 otherwise. And below we shown the results using odds ratios. These results indicate that cred_hl is significant, and that the odds of a high credentialed school being high quality is about 12.3 times that of low credentialed schools. Neither of the terms for parent education ( _Ipared_2 or _Ipared_3 ) are significant. However, let’s test the joint influence of these two variables using the test command. As we would have expected based on the individual tests, the overall effect of parents education is not significant. Let’s now look at the interpretation of the odds ratios. First, let’s get the predicted odds for the 6 cells of this design using the adjust command. As you would expect, the odds ratio for cred_hl is the odds that a high credentialed school will be high quality divided by the odds that a low credentialed school would be high quality. We illustrate this below. The above odds ratio was computed when parents education is low, but we get the same result if we use medium or high parent education. This is because this model did not contain an interaction between pared and cred_hl . The odds ratio for _Ipared_2 is the odds that a medium parent education school will be high quality divided by the odds that a low parent education school will be high quality, for example. The odds ratio for _Ipared_3 is the odds that a high parent education school will be high quality divided by the odds that a low parent education school will be high quality, for example. These last two effects were computed when credentials was low . If we had computed them when credentials was high , we would have gotten the same result (you can try it for yourself). This model includes only main effects, so it assumes that the effect of cred_hl are the same across the levels of parent education. We can look at the probabilities of being a high quality school by cred_hl and by parent education. Let’s now look at the odds ratio for cred_hl at each level of parent education. This model with main effects is assuming that these odds ratios will be roughly the same, but we can look at them and see if this appears reasonable. Odds ratio for cred_hl when parent education is low. Odds ratio for cred_hl when parent education is medium. Odds ratio for cred_hl when parent education is high. It seems that the odds ratio for cred_hl is much higher when parent education is low as compared to parents with medium and high levels of education. By including an interaction term in the model (as shown below) we can capture these differences in cred_hl across levels of parent education. 2.2.4 A 2 by 3 Layout with Main Effects and Interaction. The analysis above only included main effects of parent education and the credentials of the teachers, but did not include an interaction of these two variables. The analysis below includes this interaction. And here are the odds ratios. Let’s now look at the interpretation of the odds ratios for this analysis. Previously we have used the adjust command to obtain predicted odds. This time, let’s do this a bit different (just for some variety, and to try and see this from a different angle). This time let’s compute the predicted probability of hiqual being 1 using the predict command with the pr option (the default). Below the table command is used to show the predicted probability of hiqual being 1 when broken down by cred_hl and pared . You might think you are having double vision, but note that the top line of the table shows the minimum value of predp and the second line shows the maximum value of predp , both of which are the same, showing that the predicted values are all identical within each cell (as they should be, since there are no other covariates in the model). We can then use these values to illustrate the meaning of the odds ratios from the above model. The odds ratio for _Icred_hl_1 represents the odds ratio of hiqual being 1 for cred_hl when parent education is low (because this was the omitted group for pared ). This is shown below, illustrating that when parent education is low, the odds of a high credentialed school being high quality is about 27 times that of a low credentialed school . The odds ratio for _Ipared_2 is the odds ratio formed by comparing schools with medium parent education with schools with low parent education for schools with low teacher credentials (because this is the reference group for cred_hl ). We illustrate this below, which shows that when when teacher credentials are low, schools with medium parent education have an odds or being high quality that is about 1.9 times of schools with low parent education ; however this effect is not statistically significant. The effect of _Ipared_3 is very similar to _Ipared_2 , except that this compares the effect of high parent education schools with low parent education schools, that is, This effect is not statistically significant. The variable _IcreXpar_. 2 is an interaction term that crosses cred_hl with _Ipared_2 . Because _Ipared_2 compares medium parent education schools with low parent education schools , the odds ratio for _IcreXpar_. 2 is a comparison of the odds ratio for cred_hl for medium parent education schools as compared to low parent education schools. We can illustrate this below. The odds ratio for cred_hl for medium parent education schools is. and the odds ratio for cred_hl for low parent education schools is. So the ratio of these odds is the coefficient for _IcreXpar_. 2 . In other words, the odds ratio for cred_hl when parent education is medium is about .3 (about 30%) of the size of the odds ratio for cred_hl when parent education is low . If we invert this odds ratio (1 / .3017) we get about 3.31, so we could likewise say that the odds ratio for cred_hl for low parent education schools is about 3.3 times that for medium parent education schools. This effect is statistically significant. The interpretation for _IcreXpar_. 3 is similar to _IcreXpar_. 2 , except that it compares the odds ratios for cred_hl for the high parent education schools with the low parent education schools. We should emphasize that when you have interaction terms, it is important to be very careful when interpreting any of the terms involved in the interaction. For example, in the above model you might be tempted to interpret _Ipared_2 as some kind of overall comparison of medium educated to low educated parents, as you normally would. However, because this term was part of an interaction, the interpretation is different. It is not the overall effect of high versus low education, but it is this effect when the other terms in the interaction are at the reference category (i.e., when cred_hl was low ). Likewise, the effect of _Icred_hl_1 is not the overall effect of cred_hl , but it is the effect of cred_hl when pared is at the reference category (i.e., when pared is low ). 2.3 Categorical and Continuous Predictors. All of the prior examples in this chapter have used only categorical predictors. In chapter 1, we saw models which included categorical predictors, continuous predictors, and models that included categorical and continuous predictors. This section will focus on models that include both continuous and categorical predictors, as well as models that include interactions between a continuous and categorical predictor. 2.3.1 A Continuous and a Two Level Categorical Predictor. Let’s first consider a model with one categorical predictor (with 2 levels) and one continuous predictor. The model below predicts hiqual from cred_hl and meals (the percentage of students receiving free meals). And here are the results expressed using odds ratios. Let’s now make a graph of the predicted values showing the predicted logit by meals . We would like to make a graph which shows the predicted value for low credentialed and high credentialed using separate lines for each type of school. To do this, we need to make a separate variable that has the predicted value for the low credentialed and high credentialed schools. We can use the separate command below to take the predicted value ( yhat ) and make separate variables for each level of cred_hl (i.e., making yhat0 for the low credentialed schools, and yhat1 for the high credentialed schools). We can now show a graph of the predicted values using separate lines for the two types of schools. Let’s look at the coefficients for this model, and relate those coefficients to the predicted logits in the graph above. The coefficient for meals is -.106, which reflects the slope of the lines in the above graph. The coefficient for cred_hl represents the difference in the heights of the two lines (with the line for high credentialed ) schools being .984 units higher than the line for the low credentialed schools. (Note that the units in this graph are the log odds of a school being high quality.) Rather than focusing on the particular meaning of these coefficients, we wish to emphasize that the predicted logits in this model for the two groups form 2 parallel lines. The lines are parallel because the outcome is in the form of logits and the model only has main effects. We will soon look at a model which has an interaction of meals and cred_hl , which would then permit the lines to be non-parallel. We can view the same type of graph, except showing the predicted probability (instead of the predicted logit). Rather than making new variables to contain the predicted values, let’s use the same variable names, yhat yhat0 and yhat1 , so let’s drop these variables from the data file so we may use these variable names again. Now let’s generate the predicted value, but this time in terms of the predicted probability, using the pr option. And let’s separate these into two different variables based on cred_hl . And below we see the graph showing the relationship between meals and the predicted probability of being a high quality school, with separate lines for high credentialed and low credentialed schools. Although these lines do not look exactly parallel, they are parallel in that they both reflect the same odds ratio. The odds ratio for meals is .899, so for every unit increase in meals , the odds of a school being high quality changes by .899. This is the same for the high credentialed and low credentialed schools. 2.3.2 A Continuous and a Two Level Categorical Predictor with Interaction. Now let’s include an interaction between cred_hl and meals which allows the relationship between meals and hiqual to be different for the high credentialed and low credentialed schools, i.e., allowing the lines of the predicted values to be non-parallel. We will use the xi command in this model to make it easy to create the interaction of cred_hl and meals . And here are the results expressed as odds ratios. Note that the interaction term is significant. Let’s now make a graph of the predicted values showing the predicted logit by meals . As we have done before, we will use the drop command to drop the variables we have used before. We use the predict command to get the predicted logit. And we use the separate command to make separate variables for the high credentialed and low credentialed schools. Below we graph the relationship between meals and the predicted logit for a school being high quality. You can clearly see that the lines of the predicted logits for the two groups are not parallel. This makes sense since the variable representing the interaction, _IcreXmeal. 1 , was significant. In fact, as you look at the graph above you can see that it looks like there are really two regression lines, one for the low credentialed group and another for the high credentialed group. To make this explicit, let’s re-write the logit model from the results above as two separate equations, one for each group. low credentialed group. logit(hiqual) = 1.86 + -0.0817*meals. high credentialed group. logit(hiqual) = (1.86 + 2.22) + (-0.0817 + -.036)*meals or more simply logit(hiqual) = 4.088 + -.118*meals. Note that the low credentialed group has an intercept of 1.86 and a slope of -.08, while the high credentialed group has an intercept of 4.088 and a slope of -.118. Let’s look at the same graph except substituting the predicted probabilities for the predicted logits by using the pr option on the predict command when we compute the predicted probabilities. You can see that the differences in the shape of these two lines as well. Because we included an interaction term, the odds ratio for the high credentialed schools is different from the odds ratio for the low credentialed schools. In fact, if we look at the results of the logistic command, we can see that the odds ratio for the low credentialed schools (the reference group) is .921. The odds ratio for the high credentialed schools is .921 * .964 or .887. Note that we took the odds ratio for the reference group and then multiplied that by the interaction term, and that yielded the odds ratio for the high credentialed schools (in contrast to when we were dealing with predicted logits we added these terms together, but when we are dealing with predicted probabilities we multiply these together). Another way of thinking about this is that the interaction term is the odds ratio for the high credentialed schools divided by the odds ratio for the low credentialed schools. In this case, the odds ratio for the high credentialed schools is .964 of that of the low credentialed schools. The odds ratio for _Icred_hl_1 is a bit tricky to interpret because it is part of the interaction term. You might be temped to interpret this as a kind of overall effect of cred_hl ; however, this is not the case. The odds ratio for _Icred_hl_1 is the odds ratio when meals is 0. Looking at the graph, think of forming the odds ratio for cred_hl based on the predicted probabilities when meals is 0 (i.e., about .98 versus .84). Based on this rough estimate we can compute the odds ratio for cred_hl when meals is 0 and compare that to the coefficient for _Icred_hl_1 . Indeed, the coefficient corresponds to what we see in the graph. However, very few schools have a value of meals being 0, so this may not be a very useful value for this coefficient. Instead, we can center the variable meals to have a mean of 0 by subtracting the mean, and then this term would represent the odds ratio for cred_hl when meals is at the overall average. First, below we center the variable meals creating a new variable called mealcent . Next, we include mealcent as the continuous variable in our model. And here are the results as odds ratios. Note that the only term that changed in the model was _Icred_hl_1 which now reflects the effect of cred_hl when meals is at the mean (about 52). Note that this effect is not significant. We can eyeball this value by computing the odds ratio for these two groups when meals is 52, which is about .09 versus .13 (see below). This eyeball value is about 1.5, which is close to the actual value (1.38). Now let’s consider a model with a three level categorical predictor. 2.3.3 A Continuous and a Three Level Categorical Predictor. Let us extend this example further to include 3 categories for the variable cred , including schools with low , medium and high credentialed teachers. We start by looking at a model with just main effects (no interaction). And here are the results as odds ratios. First, let’s look at the odds ratios for cred_hl . The odds ratio for _Icred_2 compares the medium credentialed schools to the low credentialed schools (because the low credentialed schools are the reference group). This indicates that a medium credentialed school has an odds of being high quality that is 2.12 times that of the low credentialed schools. Likewise, the effect for _Icred_3 indicates that the odds of being high quality for high credentialed schools is 2.677 that of the low credentialed schools. Note that since we did not have an interaction term in the model, we can talk about these overall effects without needing to worry about other predictors in the model. The effect of mealcent indicates that for every unit increase in mealcent , the odds of being a high quality school changes by a factor of .8999 (about .9). Because this model does not include an interaction term, this model provides a single estimate for the effect of mealcent for all 3 levels of cred . Below we can create and plot the predicted probabilities for the 3 levels of cred . The above graph illustrates that as mealcent increases, the probability of being a high quality school decreases. We can see that the shape of this relationship is basically the same across the three levels of cred (because we have only included main effects in the model). Now let’s look at a model where we include interactions. 2.3.4 A Continuous and a Three Level Categorical Predictor with Interaction. This model is the same as the one we examined above, except that it includes an interaction of cred and mealcent . We now must be much more careful in the interpretation of these results due to the interaction term. But first, let us make a graph of the predicted probabilities to help us picture the results as we interpret them. This graph has 3 lines, but unlike the prior example these lines are not forced to be parallel. Each line has it own odds ratio determining its shape. As you can see, the dashed (cred=medium) and dotted (cred=high) schools have a similar shape, which is different from the solid line (cred=low). If we run the logistic regressions separately for each level of cred we can obtain the odds ratios for each of these 3 lines (the output has been edited to make it more brief). These results indicate the odds ratio is .9215 when cred is low, .9012 when cred is medium, and .8885 when cred is high. Looking back at the graph, you see the dashed and dotted lines (where cred is medium and high) have the steepest descent, which corresponds to them having the smallest odds ratios. By contrast when cred is low, the effect of mealcent is not as strong, and hence the odds ratio for this group is closer to 1. Let’s relate the odds ratios for the 3 groups to the odds ratios that we get from the original logistic regression analysis. First, note that the odds ratio for mealcent represents the odds ratio for the reference group on cred (i.e., when cred is low). Indeed, we see the odds ratio for mealcent is .921. The odds ratio for _IcreXmeal. 2 represents the odds ratio for mealcent for the medium credentialed schools divided by the odds ratio for the low credentialed schools, see below. If the odds ratios for these groups were identical, then this ratio would be 1. This result indicates that the odds ratio for medium credentialed schools is .978 of that for the low credentialed schools, but this is not a significant effect. Likewise, the odds ratio for _IcreXmeal. 3 represents the odds ratio for mealcent for the high credentialed schools divided by the odds ratio for the low credentialed schools, see below. The odds ratio for high credentialed schools is .964 of that for the low credentialed schools, and this is a significant effect. The odds ratios for _Icred_2 and _Icred_3 represent the effects of cred when mealcent is at 0 (which is the mean of meals). In particular, _Icred_2 tests the difference between low credentialed and medium credentialed schools when meals is at the mean. We have repeated the graph from above, but put a vertical line when mealcent is 0 to help you see what is being compared. This odds ratio for _Icred_2 compares the dashed line with the solid line at the vertical line (when mealcent is 0). Likewise, _Icred_3 tests the difference between low credentialed and high credentialed schools when meals is at the mean, so this compares the dotted line with the solid line in the graph above, at the vertical line (when mealcent is 0). Both of these individual effects are not significant. We can test the overall effect of _Icred_2 and _Icred_3 using the test command as shown below. Note we need to first re-run the original logistic regression with all 3 groups since we had run the separate logistic regressions previously, and we use quietly before the command to suppress the output. Note that we could also use the lrtest command as illustrated in lesson 1 to perform this test using a likelihood ratio test. Note that these give much the same result. Note that i.cred|mealcent is the same as i.cred*mealcent but omits the main effects for i.cred . Say that we had wanted to test the effect of cred when meals was 40. We could do this by centering meals around 40 as shown below and then re-running the logistic regression. Instead of the test command, we could have used lrtest to perform a likelihood ratio test as we showed previously. These results show that the overall effect of cred is significant when meals is 40. In particular, odds ratio for _Icred_3 is 2.160339, indicating that high credentialed schools have an odds about 2.16 times that of low credentialed schools of being high quality when the percent of students receiving free meals is 40%. This effect is statistically significant. Likewise the odds ratio for _Icred_2 is about 1.9, indicating that medium credentialed schools have an odds about 1.9 times that of low credentialed schools of being high quality when meals is 40%, and this is also significant. 2.4 More on Interpreting Coefficients and Odds Ratios. At the start of this chapter, we noted that if you understand how to interpret coefficients for models with categorical variables with OLS regression, then this will help you be able to interpret coefficients and odds ratios in logistic regression. In fact, the interpretation of coefficients for OLS and logistic regression are identical, except that in OLS the outcome variable is the dependent variable, whereas in logistic regression the outcome variable is the "log odds of the outcome variable being 1". Aside from this difference, the interpretation of the coefficients is the same because both of these methods are linear models. However, it is much easier to interpret odds ratios than it is to interpret coefficients but the meaning of the odds ratios does not have a direct relationship to OLS like the coefficients. Where OLS (and logistic regression coefficients) form comparisons by subtraction, we have seen that odds ratios form comparisons by division. We illustrate this below with a small fictitious data file that has one outcome variable y , two categorical predictors x1 and x2 and a variable representing the product of these two variables, x12 . You can access this file from within Stata like this. We then analyze this data using OLS (via the regress command), using logistic regression with coefficients (with the logit command) and using logistic regression with odds ratios (via the logistic command). The table below shows the commands issued to obtain these 3 analyses, and the results of the respective 3 regressions and the predicted values broken down by x1 and x2 . We then show the interpretation of the coefficient (in the case of OLS and Logistic using Logits) and the odds ratio (in the case of using Logistic with Odds Ratios). Let’s compare the coefficients/odds ratios for these analyses with respect to the predicted values in each analysis. Note the similarity in the coefficients for OLS and logistic with respect to their predicted values. The coefficient for x1 in OLS compares, when x2 is 0, the predicted value when x1 is 1 minus the predicted value when x1 is 0, .666 – .5. Likewise, the coefficient for x1 in Logistic with Logits compares, when x2 is 0, the predicted value when x1 is 1 minus the predicted value when x1 is 0, .693 – .0. Even though the predicted values are different, the relationship between the predicted values and the coefficients is the same. Now, compare these two methods with Logistic with Odds Ratios. For that analysis, the coefficient for x1 compares, when x2 is 0, the predicted value when x1 is 1 divided by the predicted value x1 is 0, 2 / 1. Note that all three of these methods are comparing, when x2 is 0, the predicted value when x1 is 1 to the predicted value when x1 is 0, but OLS and Logistic with Logits makes this comparison by subtraction whereas Logistic with Odds Ratios makes this comparison by division . If you examine the predicted values and the interpretation of the odds ratios/coefficients for these three methods for x2 and for x12 you will see that this same relationship holds. Likewise, this holds true for the other examples shown in this chapter. If you knew how to interpret the coefficients using OLS regression, you could then infer the interpretation of the coefficients when using Logistic with Logits and when using Logistic with Odds Ratios. The main leap is that when OLS makes comparisons using subtraction, you would substitute the subtraction with division to arrive at the comparisons that would be made using Logistic with Odds Ratios.