{smcl} {* 26Jun2006}{...} {cmd:help multin} {hline} {title:Title} {p 4 8 2} {bf: multin -- Grouped conditional logit regression} {title:Syntax} {p 8 15 2} {cmdab:multin:} {depvar} [{indepvars}] {ifin} {cmd:,} {bind:{cmdab:gr:oup:(}{varname}{cmd:)} [{it:options}]} {title:Description} {p 4 4 2} {cmd:multin} fits the grouped conditional logit regression model. It obtains the same results as conditional logit regression but it requires a smaller data set. The data is prepared as for conditional logit regression but each block of independent variables is unique and the dependent variable is a count with the number of times each choice is selected. An example of a dataset prepared for estimation with {cmd:multin} is: x1 x2 gid cid y 0 4 1 1 0 1 4 1 2 1 1 6 1 3 1 2 2 2 1 2 1 4 2 2 0 3 5 2 3 0 2 6 4 1 0 3 7 4 2 1 2 5 4 3 0 1 5 4 4 0 The variable gid is a unique group identifier and the dependent variable y shows the number of times that each choice was selected. If your data is in the format required by clogit you can convert it to the above format using {helpb groupdata}. {title:Options} {p 4 8 2}{cmd:addcon} Evaluates the likelihood with all constant factors added to the likelihood. The results are the same but the reported likelihood is different. {p 4 8 2}{cmd:pearson} reports the Pearson chi-squared statistic. {title:Examples} {p 4 8 2}{cmd:. multin y x1 x2, gr(id)} {title:Author} Paulo Guimaraes, Division of Research, University of South Carolina. Email: {browse "mailto:guimaraes@moore.sc.edu":guimaraes@moore.sc.edu} {title:Also See} {p 4 13 2} {helpb groupdata} {helpb dirmul}