


R_i~={ FUNC {Probability~Of~Reporting~Use~With~The~New~Survey~Methodology}} over
{ FUNC {Probability~Of~Reporting~Use~With~The~Old~Survey~Methodology}}
In equation (1) the constant A is simply a scale factor set equal to
left[ UL right]~ DIV ~left[ (1L)(U1) right]
,
beta
are the model coefficients, and
X_i
is a vector of explanatory variables. The explanatory variables considered in the models consisted of the categorical indicator variables for age group and race/ethnicity. The parameters L and U are the predetermined constants that force the estimated
R_i
to be
L ~<=~ R_i~ <=~ U ~~~~~~~~~ func {for ~\all~ i ~\and~\for~\any~\value~\of~X_i beta}
.
Notice that if the constant Lis set equal to zero and Uapproaches , then the constant A approaches 1, and equation (1) reduces to the familiar, unconstrained exponential model:
R_i~=~{e^{X_i beta} }
.
The model parameter vector
beta
in (1) was estimated by solving the generalized raking equations:
Sum from {i in S_{1994A}} w_i ~R_i~X_i^T~y_i ~~=~~
Sum from {i in S_{1994B}} w_i ~X_i^T~y_i
forces the 1994A estimate to equal the 1994B estimate for any subpopulation represented by an indicator variable in
X_i
. Therefore, for example, if an appropriate indicator for the age group=1217 yearolds was included in
X_i
, then the modelbased estimate of the
R_i
's would produce an adjusted prevalence estimate using the 1994A sample that exactly equaled the prevalence estimate generated from the 1994B sample for the 1217 yearold age group.



This page was last updated on June 16, 2008. 