This page shows an example of an ordered logistic regression analysis with footnotes explaining the output.
The data were collected on 200 high school students and are scores on various tests, including science, math,
reading and social studies. The outcome measure in this analysis is socio-economic status (**ses**)- low, medium and
high- from which we are going to see what relationships exist with science test scores (**science**), social science
test scores (**socst**) and gender (**female**). Our response variable, **ses**, is going to be treated as ordinal under the
assumption that the levels of **ses** status have a natural ordering (low to high), but the distances between
adjacent levels are unknown. The dataset used in this page can be downloaded from
SAS Web Books Regression with SAS.

proc logistic data = "C:\temp\hsb2" descending; model ses = science socst female; run;The LOGISTIC Procedure Model Information Data Set TMP1.HSB2 Response Variable ses Number of Response Levels 3 Number of Observations 200 Model cumulative logit Optimization Technique Fisher's scoring Response Profile Ordered Total Value ses Frequency 1 3 58 2 2 95 3 1 47 Probabilities modeled are cumulated over the lower Ordered Values. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Score Test for the Proportional Odds Assumption Chi-Square DF Pr > ChiSq 2.1498 3 0.5419 Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 425.165 399.605 SC 431.762 416.096 -2 Log L 421.165 389.605 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 31.5604 3 <.0001 Score 28.9853 3 <.0001 Wald 29.0022 3 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 3 1 -5.1055 0.9226 30.6238 <.0001 Intercept 2 1 -2.7547 0.8607 10.2431 0.0014 science 1 0.0300 0.0159 3.5838 0.0583 socst 1 0.0532 0.0149 12.7778 0.0004 female 1 -0.4824 0.2785 3.0004 0.0832 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits science 1.030 0.999 1.063 socst 1.055 1.024 1.086 female 0.617 0.358 1.066 Association of Predicted Probabilities and Observed Responses Percent Concordant 68.1 Somers' D 0.368 Percent Discordant 31.3 Gamma 0.370 Percent Tied 0.6 Tau-a 0.235 Pairs 12701 c 0.684

## Model Information

Model Information Data Set^{a}TMP1.HSB2 Response Variable^{b}ses Number of Response Levels^{c}3 Number of Observations^{d}200 Model^{e}cumulative logit Optimization Technique^{f}Fisher's scoring Response Profile Ordered Total Value^{g}ses^{g}Frequency^{i}1 3 58 2 2 95 3 1 47 Probabilities modeled are cumulated over the lower Ordered Values.

a. **Data Set** – This is the SAS dataset that the ordered logistic regression was done on.

b. **Response Variable** – This is the dependent variable in the ordered logistic regression.

c. **Number of Response Levels** – This is the number of levels of the dependent variable. Our dependent variable has three levels: low, medium and high.

d. **Number of Observations** – This is the number of observations used in the ordered logistic regression.
It may be less than the number of cases in the dataset if there are missing
values for some variables in the equation. By default, SAS does a listwise
deletion of incomplete cases.

e. **Model** – This is the model that SAS is fitting.

f. **Optimization Technique** – This refers to the iterative method of estimating the regression parameters. In SAS, the default is method is Fisher’s
scoring method, whereas in Stata, it is the Newton-Raphson algorithm. Both
techniques yield the same estimate for the regression coefficient; however, the
standard errors differ between the two methods. For further discussion, see
Regression Models for Categorical and Limited Dependent Variables by J.
Scott Long (page 56).

g. **Ordered Value** and **ses**– **Ordered value** refers to how SAS orders/models the
levels of the dependent variable, **ses**. When we specified the **
descending** option in the procedure statement, SAS treats the levels of **ses** in a descending order
(high to low), such that when the ordered logit regression coefficients are
estimated, a positive coefficient corresponds to a positive relationship for **ses** status
(i.e., increase values of the respective variable produces higher levels of **
ses**) and a negative coefficient has a negative relationship with **ses**
status (i.e., increase values of the respective variable produces lower levels of
**ses**). Special attention needs to
be placed on the ordered value since it can lead to erroneous interpretation.

i. **Total Frequency – **This is the observed frequency distribution of subjects in the dependent variable.
Of our 200 subjects, 47 were
of low **ses**, 95 were of middle **ses** and 58 reported high **ses**.

## Model Fit Statistics

Model Convergence Status^{k}Convergence criterion (GCONV=1E-8) satisfied. Score Test for the Proportional Odds Assumption^{l}Chi-Square DF Pr > ChiSq 2.1498 3 0.5419 Model Fit Statistics Intercept Intercept and Criterion^{m}Only^{n}Covariates^{o}AIC 425.165 399.605 SC 431.762 416.096 -2 Log L 421.165 389.605 Testing Global Null Hypothesis: BETA=0 Test^{p}Chi-Square^{q}DF^{q}Pr > ChiSq^{q}Likelihood Ratio 31.5604 3 <.0001 Score 28.9853 3 <.0001 Wald 29.0022 3 <.0001

k. **Model Convergence Status** – This describes whether the maximum-likehood algorithm has converged or not and what kind of convergence
criterion is used for convergence. The default convergence criterion is the relative gradient convergence criterion** (GCONV)**, and the default
precision is 10^{-8}.

l. **Score Test for the Proportional Odds Assumption** – This is the Chi-Square Score Test
for the Proportional Odds Assumption. Since the ordered logit model estimates
one equation over all levels of the dependent variable (as compared to the
multinomial logit model, which models, assuming low ses is our referent level,
an equation for medium ses versus low ses, and an equation for high ses versus low ses),
the test for proportional odds tests whether our one-equation model is valid. If
we were to reject the null hypothesis, we would conclude that ordered logit
coefficients are not equal across the levels of the outcome and we would fit a
less restrictive model (i.e., multinomial logit model). If we
fail to reject the null hypothesis, we conclude that the assumption holds. For our model, the Proportional Odds Assumption appears to have held.

m. **Criterion** – Underneath are various measurements used to assess the model fit. The first two, Akaike Information Criterion (**AIC**) and Schwarz
Criterion (**SC**) are deviants of negative two times the Log-Likelihood (**-2 Log L**).
**AIC** and **SC** penalize the Log-Likelihood by the number of predictors in the model.

** AIC – **This is the Akaike Information Criterion. It is calculated as AIC = -2 Log L + 2((*k*-1) +
*s*), where *k* is the number of levels
of the dependent variable and *s* is the number of predictors in the model.
**AIC** is used for the comparison of models from different samples or nonnested models.
Ultimately, the model with the smallest **AIC** is considered the best.

** SC – **This is the Schwarz Criterion. It is defined as – 2 Log L + ((*k*-1) +
*s*)*log(Σ* f _{i}*), where

*f*‘s are the frequency values of the

_{i}*i*

^{th}observation, and

*k*and

*s*were defined previously. Like

**AIC**,

**SC**penalizes for the number of predictors in the model and the smallest

**SC**is most desireable.

** -2 Log L** – This is negative two times the log likelihood. The
**-2 Log L** is used in hypothesis tests for nested models.

n. **Intercept Only** – This column refers to the respective **criterion** statistics
with no predictors.

o. **Intercept and Covariates – **This column corresponds to the
respective **criterion** statistics
for the fitted model. A fitted model
includes all independent variables and the intercept. We can compare the values
in this column with the criteria corresponding **Intercept Only** value to
assess model fit/significance.

p. **Test** – These are three asymptotically equivalent Chi-Square tests.
They test against the null hypothesis that all of the predictors’ regression coefficient
are equal to zero
in the model. The alternative hypothesis is that at least one of the predictors’
regression coefficients is not equal to zero. The difference between them are where on the log-likelihood function
they are evaluated at. For further discussion, see Categorical Data Analysis, Second Edition, by
Alan Agresti (pages 11-13).

** Likelihood Ratio – **This is the Likelihood Ratio (LR) Chi-Square test that at least one of the predictors’ regression coefficient is
not equal to zero in the model. The LR Chi-Square statistic can be calculated by -2 Log L(null model) – 2 Log L(fitted model) =
421.165 – 389.605 = 31.5604, where L(null model) refers to the **Intercept Only** model and L(fitted model)
refers to the **Intercept and Covariates** model.

** Score** – This is the Score Chi-Square Test that at least one of the predictors’ regression coefficient is not equal to zero in the
model.

** Wald** – This is the Wald Chi-Square Test that at least one of the predictors’ regression coefficient is not equal to zero in the
model.

q. **Chi-Square, DF and Pr > ChiSq – **These are the **Chi-Square** test statistic, Degrees of Freedom (**DF**) and associated p-value
(**PR>ChiSq**) corresponding to the specific **test** that all of the
predictors are simultaneously equal to zero. We are testing the probability (**PR>ChiSq**)
of observing a **Chi-Square** statistic as extreme as, or more so, than the observed one under the null hypothesis; the null hypothesis is that all of the regression coefficients in the model are equal to zero. The **DF** defines the distribution of the Chi-Square test statistics and is defined
by the number of predictors in the model. Typically, **PR>ChiSq** is compared to a specified alpha level, our willingness to accept a type I error,
which is typically set at 0.05 or 0.01. The small p-value from the all three **tests** would lead us to conclude that at least one of the regression
coefficients in the model is not equal to zero.

## Analysis of Maximum Likelihood Estimates

Analysis of Maximum Likelihood Estimates Standard Wald Parameter^{s}DF^{t}Estimate^{u}Error^{v}Chi-Square^{w}Pr > ChiSq^{w}Intercept 3 1 -5.1055 0.9226 30.6238 <.0001 Intercept 2 1 -2.7547 0.8607 10.2431 0.0014 science 1 0.0300 0.0159 3.5838 0.0583 socst 1 0.0532 0.0149 12.7778 0.0004 female 1 -0.4824 0.2785 3.0004 0.0832 Odds Ratio Estimates Point 95% Wald Effect^{x}Estimate^{y}Confidence Limits^{z}science 1.030 0.999 1.063 socst 1.055 1.024 1.086 female 0.617 0.358 1.066

s. **Parameter** – These refer to the independent variables in the model as well as
intercepts (a.k.a. constants) for the
adjacent levels of the dependent variable.

t. **DF** – This column gives the degrees of freedom corresponding to the **Parameter**. For each **Parameter** estimated in the model, one **DF**
is required, and the **DF** defines the Chi-Square distribution to test whether the individual regression coefficient is zero given the other variables are in the
model, superscript w.

u. **Estimate** -These are the ordered log-odds (logit) regression coefficients.

Intercept 3 and Intercept 2 are the estimated ordered logits for the
adjacent levels of the dependent variable, high versus med and low, and high and med versus
low, respectively, when the independent variables are evaluated at zero. To identify this model, SAS set the
first intercept, β_{0 }, to zero. This constraint is not unique to
identify the model; Stata sets the first cutpoint (a.k.a., thresholds) to zero. The different constraints do not
result in different regression parameter estimates or predicted probabilities.
For further discussion of the parameterization with respect to intercepts and cutpoints,
we refer to Regression Models for Categorical and Limited Dependent Variables
by J. Scott Long and the Stata FAQ: Fitting ordered logistic
and probit models with constraints.

** Intercept 3 – **This is the estimated log odds for
high **ses** versus low and middle **ses** when the predictor variables are
evaluated at zero. The log odds of high **ses** versus low and middle **ses**
for a male (female variable evaluated at zero) with a zero **science** and **
socst** test score is -5.11. Note, evaluating **science** and **socst**
at zero is out of the range of plausible test scores and if the test scores were
mean-centered, the intercept would have a natural interpretation: log odds of
high **ses** versus low & middle **ses** for a male with average **science** and **socst** test score.

** Intercept 2 – **This is the estimated log odds for
high and middle **ses** versus low **ses** when the predictor variables are
evaluated at zero. The log odds of high and middle **ses** versus low **ses**
for a male with a zero **science** and **
socst** test score is -2.75.

Standard interpretation of an ordered logit coefficients is that for a one unit increase in the predictor, the dependent variable level is expected to change by its respective regression coefficient in the ordered logit scale while the other variables in the model are held constant.

** science** – This is the ordered log-odds estimate for a one unit increase in **science** score on the expected **ses** level given the
other variables are held constant in the model. If a subject were to increase
his **science** score by one point, you’d expect his **ses** score
would result in a 0.03 unit increase in the ordered log-odds scale while the other variables in the model are held constant.

** socst** – This is the ordered log-odds estimate for a one unit increase in **socst** score on the expected **ses** level given the other
variables are held constant in the model. A one unit increase in **socst** test scores would result in a 0.053 unit
increase
in the expected value of **ses** in the ordered logit scale while the other variables in the model are held constant.

** female** – This is the ordered log-odds estimate of comparing females to males on expected **ses** given the other variables are held
constant in the model. As one goes from males to females, we expect a -0.4824 unit decrease in the expected value of **ses** in the
ordered logit scale while the other variables in the model are held constant.

v. **Standard Error** – These are the standard error of the individual regression coefficients. They are used in both the calculation of the **Wald
Chi-Square **test statistic, superscript w, and the **95% Wald Confidence Limits**,
superscript z.

w. **Wald Chi-Square & Pr > ChiSq – **These are the test statistics and p-values, respectively,
for the hypothesis test that an individual
predictor’s regression coefficient is zero given the rest of the predictors are in the model. The **Wald Chi-Square** test statistic is the
squared ratio of the **Estimate** to the **Standard Error **of the
respective predictor. The
probability that a particular **Wald Chi-Square** test statistic is as extreme as, or more so, than what has been observed under the null hypothesis
is given by **Pr > ChiSq**.
The **Wald Chi-Square** test statistic for the predictor **science** (0.030/0.016)^{2} is 3.584 with an associated
p-value of 0.0583. If we set our alpha level to 0.05, we would fail to reject the null hypothesis and conclude that the regression coefficient for
**science** has not been found to be statistically different from zero in estimating **ses **given **socst** and **female** are in the model.
The **Wald Chi-Square** test statistic for the predictor **socst **(0.053/0.015)^{2} is 12.78 with an associated p-value
of 0.0004. If we again set our alpha level to 0.05, this time we would reject the null hypothesis and conclude that the regression coefficient
for **socst** has been found to be statistically different from zero in estimating **ses** given **science** and **female** are in the model.
The interpretation for a dichotomous variable parallels the continuous variable.

x. **Effect** – Underneath are the independent variables that are to be
interpreted in terms of proportional odds.

y. **Point Estimate – ** These are the proportional odds ratios. They can be obtained by exponentiating the **estimate**, e** ^{estimate}**.
Since the response variable has multiple levels and the model assumes that as one moves to different levels of the response variable, the regression coefficients
are the same and the only thing that changes is the intercept. If we view the change in levels in a cumulative sense and interpret the coefficients in odds, we
are comparing the people who are in groups greater than

*k*versus those who are in groups less than or equal to

*k*, where

*k*is a the level of the response variable. A standard interpretation is that for a one unit change in the predictor variable, the odds for cases in the level of the outcome that is greater than

*k*versus less than or equal to

*k*are the proportional odds times larger.

** science** – This is the proportional odds for a one unit increase in **science** score on **ses** level given the
other variables are held constant in the model. For a one unit increase in science test score, the odds of
high** ses**
are 1.03 times greater than for the combined effect of middle and low **ses** given the all the other variables are held constant.
Likewise, for a one unit increase in **science** test score, the odds of
middle and high **ses** versus low** ses** is 1.03
times greater given all the other variables are held constant.

** socst** – This is the proportional odds for a one unit increase in **socst** score on **ses** level given the
other variables are held constant in the model. For a one unit increase in **socst** test score, the odds of
high** ses**
versus the combined effect of middle and low **ses** is 1.05 times greater given all the other variables are held constant.
Likewise, for a one unit increase in **socst** test score, the odds of middle
and high** ses** versus low **ses** is 1.05
times greater given all the other variables are held constant.

** female** – This is the proportional odds of comparing females to males on **ses** given the other variables are held
constant in the model. As one goes from males to females, the odds of high** ses**
versus the combined
effect of middle and low **ses** is 0.6173
times lower given all the other variables are held constant. Likewise, as one goes from males to females, the odds of
middle and high **ses**
versus low** ses** is 0.6173 times lower given all the other variables are held constant.

z. **95% Wald Confidence Limits – **This is the Confidence Interval (CI) for the proportional odds ratio given the other predictors are in the model.
For a given predictor with a level of 95% confidence, we say that we are 95% confident that the “true” population proportional odds ratio lies
between the lower and upper limit of the interval. The CI is equivalent to the **Wald
Chi-Square** test statistic; if the CI includes 1, we would fail to
reject the null hypothesis that a particular ordered logit regression coefficient is zero given the other predictors are in the model
at an alpha level of 0.05. The CI is more illustrative than the **Wald
Chi-Square** test statistic.

## Association of Predicted Probabilities and Observed Responses

Association of Predicted Probabilities and Observed Responses Percent Concordant^{a1}68.1 Somers' D^{e1}0.368 Percent Discordant^{b1}31.3 Gamma^{f1}0.370 Percent Tied^{c1}0.6 Tau-a^{g1}0.235 Pairs^{d1}12701 c^{h1}0.684

a1. **Percent Concordant – **A pair of observations with different observed responses
is said to be concordant if the observation with the lower ordered response value has a lower
predicted mean score than the observation with the higher ordered response value.

b1. **Percent Discordant – **If the observation with the lower ordered
response value has a higher predicted mean score than the observation with the
higher ordered response value, then the pair is discordant.

c1. **Percent Tied – **If a pair of observations with different responses
is neither concordant nor discordant, it is a tie.

d1. **Pairs – **This is the total number of distinct pairs.

e1. **Somer’s D** – Somer’s D is used to determine the strength and direction of relation between pairs of variables. Its values range from -1.0 (all
pairs disagree) to 1.0 (all pairs agree). It is defined as (n_{c}-n_{d})/t where n_{c} is the number of pairs that are concordant,
and n_{d} the number of pairs that are discordant, and t is the number of total number of pairs with different responses. In our example, it equals
the difference between the percent concordant and the percent discordant divided by 100: (68.1-31.3)/100 =
0.368.

f1. **Gamma** – The Goodman-Kruskal Gamma method does not penalize for ties on either variable. Its values range from -1.0 (no association) to 1.0 (perfect
association). Because it does not penalize for ties, its value will generally be greater than the values for Somer’s D.

g1. **Tau-a – **Kendall’s Tau-a is a modification of Somer’s D to take into the account the difference between the number of possible paired
observations and the number of paired observations with different response. It is defined to be the ratio of the difference between the number of concordant
pairs and the number of discordant pairs to the number of possible pairs (2(n_{c}-n_{d})/(N(N-1)). Usually Tau-a is much smaller than Somer’s D since there would be
many paired observations with the same response.

h1. **c – **Another measure of rank correlation of ordinal variables. It ranges from 0 to (no association) to 1 (perfect
association). It is a variant of Somer’s D index.