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MODULE 9

Logistic Regression (Binary)

Binary (also called binomial) Logistic regression is appropriate when the outcome is a dichotomous variable (i.e. categorical with only two categories) and the predictors are of any type: nominal, ordinal, and / or interval/ratio (numeric). Either Multi-nomial Logistic Regression or Discriminant Function Analysis is appropriate when the outcome variable is polytomous (i.e. categorical with more than two categories). Standard multiple regression can only accommodate an outcome variable which is continuous or nearly continuous (i.e. interval/ratio in scale) and it works best with continuous or nearly continuous predictor variables. Although standard regression can accommodate categorical predictors using one of the following strategies for those types of predictors: dummy coding, effects coding, orthogonal coding, or criterion coding. Binary logistic and multinomial logistic regression can also accommodate categorical predictors, but categorical predictors must be identified in the menu system when the analysis is being specified. 

For the duration of this tutorial we will be using the logreg1.sav file; which contains 1 dichotomous categorical outcome variable (y) and 4 predictor variables (x1 - x4). The outcome (y) contains the values 0 and 1.

Begin by clicking on Analyze, Regression, Binary Logistic...

Next, highlight / select the outcome variable (y) and use the top arrow button to move it to the Dependent: box. Then, highlight the four predictor variables (x1, x2, x3, x4) and use the second arrow button to move them to the Covariates: box. Notice, if one or more of the predictors was categorical, we would need to click on the Categorical... button to specify them as such. Click on the Options... button and select Classification plots, Hosmer-Lemeshow goodness-of-fit, Casewise listing of residuals, Correlations of estimates, Iteration history, and CI for exp(B):. Then, click the Continue button, then click the OK button.

    

The output should be similar to what is displayed below.

The Case Processing Summary table provides an overview of missing data; here there was no missing data. The Dependent Variable Encoding table shows how the outcome variable was coded, if it was coded. Here, the outcome variable was not coded; therefore, the values are listed in both columns. If, for example, the outcome variable represented responses yes and no, then the left column (Original Value) would show the associated value labels, 'yes' and 'no' while the right column (Internal Value) would show the values 0 and 1 for each. By default, the binary logistic regression predicts the odds of membership in the outcome category with the highest value; here predicting membership in the 1 value, as opposed to membership in the 0 value.

      

The Beginning Block evaluates our model with only the constant in the equation (sometimes called the null model). The constant is analogous to the y-intercept in OLS regression. The iteration history was specified in the options, is displayed throughout the output file. The first Iteration History table (directly below) shows that estimation was terminated at iteration # 1 because the parameter estimates did not change by more than 0.001. The -2 Log likelihood (-2 LL) is a likelihood ratio and represents the unexplained variance in the outcome variable. Therefore, the smaller the value, the better the fit. The Classification Table shows how well our null model correctly classifies cases. The rows represent the number of cases in each category in the actual data and the columns represent the number of cases in each category as classified by the null model. The key piece of information is the overall percentage in the lower right corner which shows our null model is only 50% accurate; which is equal to the accuracy of random guessing.

    

The Variables in the Equation table shows the logistic coefficient (B) associated with the intercept as it is included in the model. This table is similar to and contains analogous information as the coefficients table in a standard regression. The logistic coefficient for the constant is similar to the y-intercept term in standard regression. The Wald statistic is a chi-square 'type' of statistic and is used to test the significance of the variable in the model. The Exp(B) refers to the change in odds ratio attributed to the variable. The Variables not in the Equation table simply lists the Wald test score, df, and p-value for each of the variables not included in the beginning block model. Notice the Overall Statistics is not a total, but rather an estimate of overall Wald statistic associated with the model had all the variables been included. 

    

The number of blocks will increase with and correspond to the number of blocks of covariates or predictors entered into the model. Meaning, when specifying the variable for inclusion into the model, you notice above in the second figure (Logistic Regression dialog box) we could have clicked the Next button and entered more variables as a distinct block (as is done in sequential or hierarchical regression).  Here, we only have one set of predictors so there is only the intercept model block (Block 0) above and the complete model (Block 1) below.

    

The iteration history (above) was specified in the options, is displayed throughout the output file. The Iteration History table (above left) shows that estimation was terminated at iteration # 11 because the parameter estimates did not change by more than 0.001. The -2 LL is a likelihood ratio and represents the unexplained variance in the outcome variable. Therefore, the smaller the value, the better the fit. Notice here the -2 LL (57.759) is substantially lower than that given above for the null model (554.518). The Omnibus Tests of Model Coefficients table reports the chi-square associated with each step in a stepwise model. Here, there is only one step from the constant model to the block containing predictors so all three values are the same. The significance value or p-value indicates our model (Block 1; with predictors) is significantly different from the constant only model; meaning there is a significant effect for the combined predictors on the outcome variable.

    

The Model Summary table displays the -2 LL as was shown and discussed directly above. The two R estimates are not truly R estimates; they are pseudo-R; meaning they are analogous to R in standard multiple regression, but do not carry the same interpretation. They are not representative of the amount of variance in the outcome variable accounted for by all the predictor variables. The Nagelkerke estimate is calculated in such a way as to be constrained between 0 and 1. So, it can be evaluated as indicating model fit; with a better model displaying a value closer to 1. The larger Cox & Snell estimate is the better the model; but it can be greater than 1. These metrics should be interpreted with caution and although not ignored, they offer little confidence in interpreting the model fit. The Hosmer and Lemeshow Test table (above right) is the preferred test of goodness-of-fit. As with most chi-square based tests however, it is prone to inflation as sample size increases. Here, we see model fit is acceptable χ (9) = 14.559, p = .068, which indicates our model predicts values not significantly different from what we observed. To be clear, you want the p-value to be greater than your established cutoff (generally 0.05) to indicate good fit. The Contingency Table for Hosmer and Lemeshow Test (below left) simply shows the observed and expected values for each category of the outcome variable as used to calculate the Hosmer and Lemeshow chi-square.

    

The Classification Table (above right) shows how well our full model correctly classifies cases. A perfect model would show only values in the diagonal--correctly classifying all cases. Adding across the rows represents the number of cases in each category in the actual data and adding down the columns represents the number of cases in each category as classified by the full model. The key piece of information is the overall percentage in the lower right corner which shows our model (with all predictors & the constant) is 98.3% accurate; which is excellent. One way of assessing the model's fit is to compare the overall percentage in the full model's table to the overall percentage in the null model table. Another, more highly regarded way is to compare the full model's overall percentage to the chance percentage (50% in this example) plus 25% =  75%. Note, chance percentage can be weighted by the proportion of cases in each category of the outcome variable; thus making it more conservative. For instance, if we had 250 cases (62.5%) in the 1 category and 150 cases (37.5%) in the 0 category, then we would square and sum those proportions to arrive at a more conservative chance percentage for comparison (53.2%). Taking the weighted chance percentage and adding 25% brings the comparison to 78.2 %; still far below what our full model is capable of.

Weighted chance percentage: .625 + .375 = .391 + .141 = .532 = 53.2%

   

The Variables in the Equation table (above), shows the logistic coefficient (B) for each predictor variable. The logistic coefficient is the expected amount of change in the logit for each one unit change in the predictor. The logit is what is being predicted; it is the odds of membership in the category of the outcome variable with the numerically higher value (here a 1, rather than 0). The closer a logistic coefficient is to zero, the less influence it has in predicting the logit. The table also displays the standard error, Wald statistic, df, Sig. (p-value); as well as the Exp(B) and confidence interval for the Exp(B). The Wald test (and associated p-value) is used to evaluate whether or not the logistic coefficient is different than zero. The Exp(B) is the odds ratio associated with each predictor. We expect predictors which increase the logit to display Exp(B) greater than 1.0, those predictors which do not have an effect on the logit will display an Exp(B) of 1.0 and predictors which decease the logit will have Exp(B) values less than 1.0. Note that the Exp(B) is wildly large for the x3 predictor. This is due to a combination of the strong relationship between that variable and the outcome variable; and the fact that x3 is nearly categorical itself. Generally, when using continuous variables as predictors, you will not see such large Exp(B). 

     

The Correlation Matrix table simply shows the correlations between each of the predictor variables and the constant (note the outcome is not included).

    

The graph (above) shows how our full model predicts membership. It is unusually clear in the middle because our model was so accurate. When a model is less accurate, more symbols (here 1 and 0) would appear in the middle, displaying their probability (x-axis). The better the model, the clearer the middle of the graph.

    

The Casewise List table displays cases which were incorrectly classified by the model. Here, we only have four cases mis-classified.

As with most of the tutorials / pages within this site, this page should not be considered an exhaustive review of the topic covered and it should not be considered a substitute for a good textbook.

 

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Contact Information

Jon Starkweather, PhD

Jonathan.Starkweather@unt.edu

940-565-4066

Richard Herrington, PhD

Richard.Herrington@unt.edu

940-565-2140

Last updated: 01/21/14 by Jon Starkweather.

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