{smcl} {* Last revised Aug 30, 2016}{...} {hline} help for {hi:oglm} version 2.30 released August 30, 2016 {hline} {title:Ordinal Generalized Linear Models} {p 8 15 2} {cmdab:oglm} {it:depvar} [{it:indepvars}] [{it:weight}] [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,} {cmd:link(}{it:logit/probit/cloglog/loglog/cauchit/log}{cmd:)} {cmd:force} {cmdab:lrf:orce} {cmdab:sto:re(}{it:name}{cmd:)} {cmdab:c:onstraints:(}{it:clist}{cmd:)} {cmdab:r:obust} {cmdab:cl:uster:(}{it:varname}{cmd:)} {cmdab:l:evel:(}{it:#}{cmd:)} {opt or} {opt irr} {opt rrr} {opt eform} {opt hr} {cmdab:log} {cmdab:het:ero(}{it:varlist}{cmd:)} {opt scale(varlist)} {opt eq2(varlist)} {opt hc} {opt ls} {opt flip} {it:display_options} {it:maximize_options} ] {p 4 4 2} {cmd:oglm} supports factor variables and the {cmd:margins} command and requires Stata version 11.2 or later. Those with Stata 9 or 10 should use {cmd:oglm9} instead. {p 4 4 2} {cmd:oglm} shares the features of all estimation commands; see help {help est}. {cmd:oglm} typed without arguments redisplays previous results. The following options may be given when redisplaying results: {p 8 8 2} {cmdab:sto:re} {cmd:or} {opt irr} {opt rrr} {opt hr} {opt eform} {cmdab:l:evel:(}{it:#}{cmd:)} {p 4 4 2} {opt by}, {opt svy}, {opt nestreg}, {opt stepwise}, {opt xi} and possibly other prefix commands are allowed; see help {help prefix}. {p 4 4 2} {cmd:fweight}s, {cmd:iweight}s, and {cmd:pweight}s are allowed; see help {help weights}. {title:Syntax for predict} {p 8 16 2} {cmd:predict} {dtype} {it:{help newvar:newvars}} {ifin} [{cmd:,} {it:statistic} {opt o:utcome(outcome)} ] {p 8 16 2} {cmd:predict} {dtype} {c -(}{it:stub*}{c |}{it:newvar_reg} {it:newvar_k1} ... {it:newvar_kk-1}{c )-} {ifin} {cmd:,} {opt sc:ores} {pstd} where k is the number of outcomes in the model. {synoptset 11 tabbed}{...} {synopthdr :statistic} {synoptline} {syntab :Main} {synopt :{opt p:r}}predicted probabilities; the default{p_end} {synopt :{opt xb}}linear prediction{p_end} {synopt :{opt s:igma}}the standard deviation{p_end} {synopt :{opt stdp}}standard error of the linear prediction{p_end} {synoptline} {p2colreset}{...} {p 4 6 2} Note that with the {opt pr} option, you specify one or k new variables depending on whether the {opt outcome()} option is also specified (where k is the number of categories of {depvar}). With {opt xb} and {opt stdp}, one new variable is specified.{p_end} {p 4 4 2} These statistics are available both in and out of sample; type "{cmd:predict} {it:...} {cmd:if e(sample)} {it:...}" if wanted only for the estimation sample. {title:Description} {p 4 4 2}{cmd:oglm} estimates Ordinal Generalized Linear Models. When these models include equations for heteroskedasticity they are also known as heterogeneous choice/ location-scale / heteroskedastic ordinal regression models. {cmd:oglm} supports multiple link functions, including logit (the default), probit, complementary log-log, log-log and cauchit. {p 4 4 2}When an ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are biased. Heterogeneous choice/ location-scale models explicitly specify the determinants of heteroskedasticity in an attempt to correct for it. Further, these models can be used when the variance/variability of underlying attitudes is itself of substantive interest. Alvarez and Brehm (1995), for example, argued that individuals whose core values are in conflict will have a harder time making a decision about abortion and will hence have greater variability/error variances in their responses. {p 4 4 2}Several special cases of ordinal generalized linear models can also be estimated by {cmd:oglm}, including the parallel lines models of {cmd:ologit} and {cmd:oprobit} (where error variances are assumed to be homoskedastic), the heteroskedastic probit model of {cmd:hetprob} (where the dependent variable must be a dichotomy and the only link allowed is probit), the binomial generalized linear models of {cmd:logit}, {cmd:probit} and {cmd:cloglog} (which also assume homoskedasticity), as well as similar models that are not otherwise estimated by Stata. This makes {cmd:oglm} particularly useful for testing whether constraints on a model (e.g. homoskedastic errors) are justified, or for determining whether one link function is more appropriate for the data than are others. {p 4 4 2}Other features of {cmd:oglm} include support for linear constraints, making it possible, for example, to impose and test the constraint that the effects of x1 and x2 are equal. {cmd:oglm} works with several prefix commands, including {cmd:by}, {cmd:nestreg}, {cmd:xi}, {cmd:svy} and {cmd:sw}. Its {cmd:predict} command includes the ability to compute estimated probabilities. The actual values taken on by the dependent variable are irrelevant except that larger values are assumed to correspond to "higher" outcomes. Up to 20 outcomes are allowed. {cmd:oglm} was inspired by the SPSS PLUM routine but differs somewhat in its terminology, labeling of links, and the variables that are allowed when modeling heteroskedasticity. {title:Options} {p 4 8 2} {opt link(logit/probit/cloglog/loglog/cauchit/log)} specifies the link function to be used. The legal values are {opt link(logit)}, {opt link(probit)}, {opt link(cloglog)}, {opt link(loglog)} and {opt link(cauchit)} which can be abbreviated as {opt link(l)}, {opt link(p)}, {opt link(c)}, {opt link(ll)} and {opt link(ca)}. {opt link(logit)} is the default if the option is omitted. {p 8 8 2} NOTE: {opt link(log)} is also available but is considered experimental (and possibly wrong) at this point. Stata’s {cmd:glm} program successfully uses the log link with dichotomous dependent variables but it is not clear how and how well it generalizes to the ordinal case. {p 8 8 2} The following advice is adapted from Norusis (2005, p. 84): Probit and logit models are reasonable choices when the changes in the cumulative probabilities are gradual. If there are abrupt changes, other link functions should be used. The log-log link may be a good model when the cumulative probabilities increase from 0 fairly slowly and then rapidly approach 1. If the opposite is true, namely that the cumulative probability for lower scores is high and the approach to 1 is slow, the complementary log-log link may describe the data. {p 8 8 2} WARNING: Programs differ in the names used for some links. Stata's loglog link corresponds to SPSS PLUM's cloglog link; and Stata's cloglog link is called nloglog in SPSS. {p 4 8 2} {opt hetero(varlist)}, {opt scale(varlist)} and {opt eq2(varlist)} are synonyms (use only one of them) and can be used to specify the variables believed to affect heteroskedasticity in heterogeneous choice/ location-scale models. In such models the model chi-square statistic is a test of whether the choice/location parameters and the heteroskedasticity/scale parameters differ from zero; this differs from {cmd:hetprob}, where the model chi-square only tests the choice/location parameters. The more neutral-sounding {opt eq2(varlist)} alternative is provided because it may be less confusing when using the {opt flip} option. {p 8 8 2} WARNING: The default Wald tests conducted by the {opt nestreg} and {opt sw} prefix commands can give incorrect results when the same variable appears in both the location and scale equations. In such cases it is recommended that you use {opt nestreg}'s and {opt sw}'s likelihood ratio test options. {p 4 8 2} {opt flip} causes the command-line placement of the location and scale variables to be reversed, i.e. what would normally be the location variables will instead be the scale variables, and vice- versa. This is primarily useful if you want to use the {opt sw} or {opt nestreg} prefix commands to do stepwise selection or hierarchical entry of the heteroskedasticity/scale variables. (Just be sure to keep straight which set of variables is which!) Again, if you do this, remember to use the likelihood ratio test options of {opt nestreg} or {opt sw}, because the default Wald tests may be wrong otherwise. {p 4 8 2} {opt hc} and {opt ls} affect how the equations are labeled. If {opt hc} is used, then, consistent with the literature on heterogeneous choice, the equations are labeled "choice" and "variance". If {opt ls} is used, the equations are labeled "location" and "scale", which is consistent with SPSS PLUM and other published literature. If neither option is specified, then the scale/heteroskedasticity equation is labeled "lnsigma", which is consistent with other Stata programs such as {cmd:hetprob}. {p 4 8 2} {opt force} can be used to force {cmd:oglm} to issue only warning messages in some situations when it would normally give a fatal error. By default, the dependent variable can have a maximum of 20 categories. A variable with more categories than that is probably a mistaken entry by the user, e.g. a continuous variable has been specified rather than an ordinal one. But, if your dependent variable really is ordinal with more than 20 categories, {opt force} will let {cmd:oglm} analyze it (although other practical limitations, such as small sample sizes within categories, may keep it from coming up with a final solution.) Obviously, you should only use {opt force} when you are confident that you are not making a mistake. {opt trustme} can be used as a synonym for {opt force}. {p 4 8 2} {cmd:lrforce} forces Stata to report a Likelihood Ratio Statistic under certain conditions when it ordinarily would not. Some types of constraints can make a Likelihood Ratio chi-square test invalid. Hence, to be safe, Stata reports a Wald statistic whenever constraints are used. But, for many common sorts of constraints (e.g. constraining the effects of two variables to be equal) an LR chi- square statistic is probably appropriate. Note that the {cmd:lrforce} option will be ignored when robust standard errors are specified either directly or indirectly, e.g. via use of the {cmd:robust} or {cmd:svy} options. Use this option with caution. {p 4 8 2} {cmd:store(}{it:name}{cmd:)} causes the command {cmd:estimates store {it:name}} to be executed when {cmd:oglm} finishes. This is useful for when you wish to estimate a series of models and want to save the results. {p 8 8 2} WARNING: The {opt store} option may not work correctly when the {opt svy} prefix is used. {p 4 8 2} {cmd:log} displays the iteration log. By default it is suppressed. {p 4 8 2} {cmd:coeflegend} specifies that the legend of the coefficients and how to specify them in an expression be displayed rather than displaying the statistics for the coefficients. {p 4 8 2} {it:display_options} control how results are displayed. These are generic options in Stata and not all have been tested with oglm. In some cases the option may already be specified and cannot be overridden. See {help estimation options##display_options}. A few other options, along with abbreviations for commands, are listed at {help _get_diopts}. {p 4 8 2} {opt or} reports the estimated coefficients transformed to odds ratios, that is, exp(b) rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. {opt or} may be specified at estimation or when replaying previously estimated results. Options {cmd:rrr}, {cmd:eform}, {cmd:irr} and {cmd:hr} produce identical results (labeled differently) and can also be used. It is up to the user to decide whether the exp(b) transformation makes sense given the link function used, e.g. it probably doesn't make sense when using the probit link. {p 4 8 2} {cmd:constraints(}{it:clist}{cmd:)} specifies the linear constraints to be applied during estimation. The default is to perform unconstrained estimation. Constraints are defined with the {help constraint} command. {cmd:constraints(1)} specifies that the model is to be constrained according to constraint 1; {cmd:constraints(1-4)} specifies constraints 1 through 4; {cmd:constraints(1-4,8)} specifies 1 through 4 and 8. {p 4 8 2} {cmd:robust} specifies that the Huber/White/sandwich estimator of variance is to be used in place of the traditional calculation. {cmd:robust} combined with {cmd:cluster()} allows observations which are not independent within cluster (although they must be independent between clusters). If you specify {cmd:pweight}s, {cmd:robust} is implied. {p 4 8 2} {cmd:cluster(}{it:varname}{cmd:)} specifies that the observations are independent across groups (clusters) but not necessarily within groups. {it:varname} specifies to which group each observation belongs; e.g., {cmd:cluster(personid)} in data with repeated observations on individuals. {cmd:cluster()} affects the estimated standard errors and variance-covariance matrix of the estimators (VCE), but not the estimated coefficients. {cmd:cluster()} can be used with {cmd:pweight}s to produce estimates for unstratified cluster-sampled data. {p 4 8 2} {cmd:level(}{it:#}{cmd:)} specifies the confidence level in percent for the confidence intervals of the coefficients; see help {help level}. {p 4 8 2} {cmd:startvals(}{it:matname}{cmd:)} specifies that the matrix {it:matname} (a row vector) contains starting values. Column names must correspond to parameters estimated in the model. Named parameters will be assigned the corresponding starting value. Any parameters in the model not included in the matrix {it: matname} will have default starting values of 0. Extra paramaters named in the matrix {it:matname} but not included in the model will be ignored. This is equivalent to passing a matrix to {help ml init} with the {opt skip} option but not the {opt copy} option specified. If {opt startvals()} is not supplied, {cmd:oglm} uses the estimated thresholds from a constant-only model as starting values for the thresholds, with 0s for all other parameters. {p 8 8 2} WARNING: The {opt startvals()} option should only be used if you are confident you do not want to use the default starting values. Specifying some starting values without including starting values for the thresholds will probably result in worse performance than specifying no starting values at all. {p 4 8 2} {it:maximize_options} control the maximization process; see help {help maximize}. You should never have to specify most of these. However, the {opt difficult} option can sometimes be useful with models that are running very slowly or not converging at all. {title:Options for predict} {phang} {opt pr}, the default, calculates the predicted probabilities. If you do not also specify the {opt outcome()} option, you must specify either one or else k new variables, where k is the number of categories of the dependent variable. Say that you fitted a model by typing {cmd:oglm result x1 x2}, and {opt result} takes on three values. Then you could type {cmd:predict p1 p2 p3} to obtain all three predicted probabilities. If you specify the {opt outcome()} option, you must specify one new variable. Say that {opt result} takes on the values 1, 2, and 3. Typing {cmd:predict p1, outcome(1)} would produce the same {opt p1} If you do not specify outcome(), pr (with one new variable specified) assumes outcome(#1). {phang} {opt xb} calculates the linear prediction. You specify one new variable, for example, {cmd:predict linear, xb}. The linear prediction is defined, ignoring the contribution of the estimated cutpoints. {phang} {opt sigma} calculates the standard deviation, also known as the scale. You specify one new variable, for example, {cmd:predict sigma, s}. If the model does not include an equation for heteroskedasticity then the predicted sigma value is missing for all cases. {phang} {opt stdp} calculates the standard error of the linear prediction. You specify one new variable, for example, {cmd:predict se, stdp}. {phang} {opt outcome(outcome)} specifies for which outcome the predicted probabilities are to be calculated. {opt outcome()} should contain either a single value of the dependent variable or one of {opt #1}, {opt #2}, {it:...}, with {opt #1} meaning the first category of the dependent variable, {opt #2} the second category, etc. {phang} {opt scores} calculates equation-level score variables. {title:Examples} {p 4 4 2} {it}Example 1. Basic models. {sf} By default, {cmd:oglm} will estimate the same models as {cmd:ologit}. The {opt store} option is convenient for saving results if you want to contrast different models. {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, store(m1)}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, robust}{p_end} {p 4 4 2} {it}Example 2. Survey data estimation.{sf} {p 8 12 2}{cmd:. webuse nhanes2f, clear}{p_end} {p 8 12 2}{cmd:. svy: oglm health i.female i.black age c.age#c.age}{p_end} {p 8 12 2}{cmd:. svy, subpop(female): oglm health i.black age c.age#c.age}{p_end} {p 4 4 2} {it}Example 3. The {cmd:predict} command. {sf} {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. quietly oglm warm i.yr89 i.male i.white age ed prst}{p_end} {p 8 12 2}{cmd:. predict p1 p2 p3 p4}{p_end} {p 4 4 2} {it}Example 4. Constrained logistic regression. {sf} {cmd:logit}, {cmd:ologit}, {cmd:probit} and {cmd:oprobit} provide other and generally faster means for estimating non-heteroskedastic models with logit and probit links; but none of these commands currently supports the use of linear constraints, such as two variables having equal effects. {cmd:oglm} can be used for this purpose. For example, {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. recode warm (1 2 = 0)(3 4 = 1), gen(agree)}{p_end} {p 8 12 2}{cmd:. * Constrain the effects of male and white to be equal}{p_end} {p 8 12 2}{cmd:. constraint 1 1.male = 1.white}{p_end} {p 8 12 2}{cmd:. oglm agree i.yr89 i.male i.white age ed prst, lrf store(constrained) c(1)}{p_end} {p 8 12 2}{cmd:. oglm agree i.yr89 i.male i.white age ed prst, store(unconstrained) }{p_end} {p 8 12 2}{cmd:. lrtest constrained unconstrained}{p_end} {p 4 4 2} {it}Example 5. Other link functions. {sf} By default, {cmd:oglm} uses the logit link. If you prefer, however, you can specify probit, complementary log log, log log or log links. In the following example, the same model is estimated using each of the links supported by oglm (note that {opt link(log)} is considered experimental and possibly wrong.} {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, link(l)}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, link(p)}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, link(c)}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, link(ll)}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, link(ca)}{p_end} {p 4 4 2} {it}Example 6. Prefix commands. {sf} {cmd:oglm} supports many of Stata's prefix commands. But remember that many prefix commands do not support factor variables. For example, {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. sw, pe(.05): oglm warm yr89 male}{p_end} {p 8 12 2}{cmd:. nestreg: oglm warm (yr89 male white age) (ed prst)}{p_end} {p 4 4 2} {it}Example 7. heteroskedasticity/scale/eq2 option. {sf} The {opt het}, {opt scale} and {opt eq2} options are synonyms, use whichever one you prefer. {opt ls} and {opt hc} are optional and affect whether the equations are labeled consistently with the heterogeneous choice or location-scale literature. If also using the {opt sw} or {opt nestreg} prefix commands, you should use their likelihood ratio test options since the default Wald tests can be wrong when the same variable appears in both the location and scale equations. Note that it is possible to estimate a heteroskedasticity-only model, and that the variables in the two equations do not need to be the same. {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst, het(i.yr89) hc}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male white age ed prst, scale(i.male i.white) ls link(p)}{p_end} {p 8 12 2}{cmd:. oglm warm, eq2(i.male)}{p_end} {p 8 12 2}{cmd:. sw, pe(.05) lr: oglm warm yr89 male white age ed prst, het(yr89 male white) }{p_end} {p 8 12 2}{cmd:. nestreg, lr: oglm warm yr89 male white age ed prst, het(yr89 male white)}{p_end} {p 4 4 2} {it}Example 8. The flip option. {sf} In the last two examples, we did stepwise selection and hierarchical entry of the choice/location variables. Suppose we wanted to do stepwise selection or hierarchical entry of the heteroskedasticity/scale variables instead? We can use the {opt flip} option, which causes the command-line placement of the location and scale variables to be reversed. Just make sure you specify each variable list correctly - while the {opt hetero}, {opt scale} and {opt eq2} options are all synonyms, you may find it less confusing if you use {opt eq2} with {opt flip}. Also remember to use the likelihood ratio test options with {opt nestreg} or {opt sw}. In the following examples, because of the {opt flip} option, the choice variables are yr89, male, white, age, ed, and prst, while the hetero variables are yr89, male, and white. {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. sw, pe(.05) lr: oglm warm yr89 male white, eq2(yr89 male white age ed prst) flip}{p_end} {p 8 12 2}{cmd:. nestreg, lr: oglm warm yr89 male white, eq2(yr89 male white age ed prst) flip}{p_end} {p 4 4 2} {it}Example 9. Factor variables and the margins command. {sf} {cmd:oglm} works with factor variables and the {cmd:margins} command. Like {cmd:ologit}, {cmd:oglm} is a multiple outcome command, so you have to tell {cmd:margins} which outcome you want the predictive margins or marginal effects for. Also, keep in mind that not all prefix commands work when factor variables are used. {p 8 12 2}{cmd:. use http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta, clear}{p_end} {p 8 12 2}{cmd:. recode warm (1 2 = 0)(3 4 = 1), gen(agree)}{p_end} {p 8 12 2}{cmd:. oglm agree i.yr89 i.male i.white age ed prst}{p_end} {p 8 12 2}{cmd:. margins yr89 male white, predict(outcome(1))}{p_end} {p 8 12 2}{cmd:. margins, dydx(*) predict(outcome(1))}{p_end} {p 8 12 2}{cmd:. oglm warm i.yr89 i.male i.white age ed prst}{p_end} {p 8 12 2}{cmd:. margins yr89 male white, predict(outcome(1))}{p_end} {p 8 12 2}{cmd:. margins yr89 male white, predict(outcome(2))}{p_end} {p 8 12 2}{cmd:. margins yr89 male white, predict(outcome(3))}{p_end} {p 8 12 2}{cmd:. margins yr89 male white, predict(outcome(4))}{p_end} {p 8 12 2}{cmd:. margins, dydx(*) predict(outcome(1))}{p_end} {p 8 12 2}{cmd:. margins, dydx(*) predict(outcome(2))}{p_end} {p 8 12 2}{cmd:. margins, dydx(*) predict(outcome(3))}{p_end} {p 8 12 2}{cmd:. margins, dydx(*) predict(outcome(4))}{p_end} {title:Author} {p 5 5} Richard Williams{break} Notre Dame Department of Sociology{break} Richard.A.Williams.5@ND.Edu{break} {browse "http://www.nd.edu/~rwilliam/oglm/"}{p_end} {title:Acknowledgements} {p 5 5}The documentation and source code for several Stata commands (e.g. {opt ologit_p}) were major aids in developing the {cmd:oglm} documentation and in adding support for the {cmd:predict} command. Much of the code is adapted from {it}Maximum Likelihood Estimation with Stata, Third Edition{sf}, by William Gould, Jeffrey Pitblado and William Sribney. SPSS's PLUM routine helped to inspire {opt oglm} and provided a means for double-checking the accuracy of the program. {p 5 5} Joseph Hilbe, Mike Lacy, Rory Wolfe, and Sean Reardon provided stimulating and helpful comments. Jeff Pitblado helped me with several programming issues. Kerry Kammire played a major role in updating oglm to work with factor variables and the margins command. Ben Shear from Stanford provided the code and help file documentation for the {it:startvals} option. {title:References} {p 5 5}Alvarez, R. Michael and John Brehm. 1995. "American Ambivalence towards Abortion Policy: Development of a Heteroskedastic Probit Model of Competing Values." American Journal of Political Science 39(4):1055-82. {p 5 5}Hardin, James and Joseph Hilbe. 2001. "Generalized Linear Models and Extensions." College Station, TX: Stata Press. {p 5 5}Long, J. Scott and Jeremy Freese. 2014. "Regression Models for Categorical Dependent Variables Using Stata, 3rd Edition." College Station, Texas: Stata Press. {p 5 5}Norusis, Marija. 2005. "SPSS 13.0 Advanced Statistical Procedures Companion." Upper Saddle River, New Jersey: Prentice Hall. See especially the chapter on SPSS PLUM, available on the web at{break} {browse "http://www.norusis.com/pdf/ASPC_v13.pdf"}{p_end} {title:Suggested citations if using {cmd:oglm} in published work } {p 5 5}{cmd:oglm} is not an official Stata command. It is a free contribution to the research community, like a paper. Please cite it as such. {p 5 5}Williams, Richard. 2009. "Using Heterogeneous Choice Models To Compare Logit and Probit Coefficients Across Groups" Sociological Methods & Research 37(4): 531-559. A pre-publication version is available at {break} {browse "http://www.nd.edu/~rwilliam/oglm/RW_Hetero_Choice.pdf"}. {p 5 5}Williams, Richard. 2010. "Fitting Heterogeneous Choice Models with oglm." The Stata Journal 10(4):540-567. The published article can be found at {break} {browse "http://www.stata-journal.com/article.html?article=st0208"} {p 5 5}Williams, Richard. 2006. "Generalized Ordered Logit/ Partial Proportional Odds Models for Ordinal Dependent Variables." The Stata Journal 6(1):58-82. A pre-publication version that includes information on updates to the program since the article was published is available at {break} {browse "http://www.nd.edu/~rwilliam/gologit2/gologit2.pdf"}. {break}The published article can be found at {break} {browse "http://www.stata-journal.com/article.html?article=st0097"} {p 5 5} {cmd:gologit2} is a related program and may be more appropriate than {cmd:oglm} for some purposes. The two programs can also be used together if you wish to contrast heterogeneous choice / location-scale models with gologit models. {p 5 5}I would appreciate an email notification if you use oglm in published work, as well as a citation of one or more of the sources listed above. Also feel free to email me if you have comments about the program or its documentation. {title:Also see} {p 4 13 2} Online: {help estcom}, {help postest}, {help constraint}, {help ologit}, {help oprobit}, {help hetprob}, {help svy}, {help margins}, {help fvvarlist}, {help prefix}, {help gologit2} (if installed).