And the number of alternatives. Thus if one has sufficient observations

And the RR6 solubility number of alternatives. Thus if one has sufficient observations, it is more fruitful to analyze a sample of many observations with a small number of sampled alternatives rather than fewer observations with a large number of alternatives (Ben-Akiva and Lerman 1985:263). In practice, one can do sensitivity analyses to determine how alternative subsampling probabilities affect the estimated coefficients and standard errors. For example, one can vary the subsampling fraction and pick the smallest fraction that does not result in marked loss of precision of estimates. Choice Based Sampling Many surveys employ a form of stratified sampling that overrepresents some kinds of neighborhoods and underrepresents others. For example, surveys may oversample poor neighborhoods within a city or be drawn from schools or school districts with atypical minority or socioeconomic representation. Whereas this stratification scheme may be exogenous for some analytic purposes, it results in endogenous stratification for the study of neighborhood choice. Neighborhood stratified samples, therefore, are choice-based (Manski and Lerman 1977), in that the sampling procedure is confounded with the residential choices of the respondents. Without correction for sample design, estimates of parameters in discrete choice models are not, in general, consistent. If choice-based sampling probabilities are known, however, one can obtain consistent estimates of the model parameters using sampling weights. Manski and Lerman (1977) introduce an estimator in which each observed residential choice is weighted by its representation in the population as a whole. We define a function for each respondent,(4.5)NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscriptwhere Vi BMS-791325 dose denotes the population shares and Hi denotes the sample shares for that respondent’s type. These weights enter the likelihood function for the model as:(4.6)In practice, the correction weights for choice-based sampling can be estimated using the “importance weights” option in statistical estimation packages. For example, consider a sample of households where the proportion of respondents in high poverty neighborhoods (30 of households below the poverty line) and low poverty neighborhoods (< 30 of households below the poverty line) are each 0.5, whereas the population proportions of households in high and low poverty neighborhoods are 0.3 and 0.7 respectively. In this case, the Manski-Lerman weights are 0.3/0.5 for respondents in high poverty tracts and 0.7/0.5 for respondents in low poverty tracts. Nuances of Behavior Treatment of Own Neighborhood--In most populations the most common choice that an individual makes is his or her own residential location; that is, not to move. This tendency to stay put may be due to the costs of moving as well as familiarity and comfort with one's current location. Nonmoves are informative about residential choice because it is likely that the chances of opting for one's own neighborhood do in fact depend on theSociol Methodol. Author manuscript; available in PMC 2013 March 08.Bruch and MarePagemeasured characteristics of the neighborhood. Models of residential choice, however, should take account of the possibility that the weights that individuals place on neighborhood characteristics may be different for their own neighborhoods than for other potential destinations. We can represent the differential treatment people give to their own housing units or.And the number of alternatives. Thus if one has sufficient observations, it is more fruitful to analyze a sample of many observations with a small number of sampled alternatives rather than fewer observations with a large number of alternatives (Ben-Akiva and Lerman 1985:263). In practice, one can do sensitivity analyses to determine how alternative subsampling probabilities affect the estimated coefficients and standard errors. For example, one can vary the subsampling fraction and pick the smallest fraction that does not result in marked loss of precision of estimates. Choice Based Sampling Many surveys employ a form of stratified sampling that overrepresents some kinds of neighborhoods and underrepresents others. For example, surveys may oversample poor neighborhoods within a city or be drawn from schools or school districts with atypical minority or socioeconomic representation. Whereas this stratification scheme may be exogenous for some analytic purposes, it results in endogenous stratification for the study of neighborhood choice. Neighborhood stratified samples, therefore, are choice-based (Manski and Lerman 1977), in that the sampling procedure is confounded with the residential choices of the respondents. Without correction for sample design, estimates of parameters in discrete choice models are not, in general, consistent. If choice-based sampling probabilities are known, however, one can obtain consistent estimates of the model parameters using sampling weights. Manski and Lerman (1977) introduce an estimator in which each observed residential choice is weighted by its representation in the population as a whole. We define a function for each respondent,(4.5)NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscriptwhere Vi denotes the population shares and Hi denotes the sample shares for that respondent's type. These weights enter the likelihood function for the model as:(4.6)In practice, the correction weights for choice-based sampling can be estimated using the "importance weights" option in statistical estimation packages. For example, consider a sample of households where the proportion of respondents in high poverty neighborhoods (30 of households below the poverty line) and low poverty neighborhoods (< 30 of households below the poverty line) are each 0.5, whereas the population proportions of households in high and low poverty neighborhoods are 0.3 and 0.7 respectively. In this case, the Manski-Lerman weights are 0.3/0.5 for respondents in high poverty tracts and 0.7/0.5 for respondents in low poverty tracts. Nuances of Behavior Treatment of Own Neighborhood--In most populations the most common choice that an individual makes is his or her own residential location; that is, not to move. This tendency to stay put may be due to the costs of moving as well as familiarity and comfort with one's current location. Nonmoves are informative about residential choice because it is likely that the chances of opting for one's own neighborhood do in fact depend on theSociol Methodol. Author manuscript; available in PMC 2013 March 08.Bruch and MarePagemeasured characteristics of the neighborhood. Models of residential choice, however, should take account of the possibility that the weights that individuals place on neighborhood characteristics may be different for their own neighborhoods than for other potential destinations. We can represent the differential treatment people give to their own housing units or.