help rrlogit                                  also see:  rrlogit postestimation
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Title

rrlogit -- Logistic regression for randomized response data

Syntax

rrlogit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model pwarner(#|varname) probability of non-negated question in Warner's model; default is pwarner(1) pyes(#|varname) probability of a surrogate "yes"; default is pyes(0) pno(#|varname) probability of a surrogate "no"; default is pno(0) noconstant suppress constant term offset(varname) include varname in model with coefficient constrained to 1 asis retain perfect predictor variables constraints(constraints) apply specified linear constraints

SE/Robust vce(vcetype) vcetype may be oim, robust, opg, bootstrap, or jackknife robust synonym for vce(robust) cluster(varname) adjust standard errors for intragroup correlation

Reporting level(#) set confidence level; default is level(95) or report odds ratios

Max options maximize_options control the maximization process; seldom used ------------------------------------------------------------------------- depvar and indepvars may contain time-series operators; see tsvarlist. bootstrap, by, jackknife, rolling, statsby, stepwise, and xi are allowed; see prefix. fweights, iweights, and pweights are allowed; see weight. See rrlogit postestimation for features available after estimation.

Description

rrlogit fits a maximum-likelihood logistic regression for randomized response data following (see, e.g., Maddala 1983:54-56). depvar=0 indicates a negative outcome (a "no" answer); depvar!=0 & depvar<. (typically depvar=1) indicates a positive outcome (a "yes" answer).

rrlogit is suited for the analysis of data from Warner's RRT scheme, the forced-response RRT, or the unrelated-question RRT with a known distribution for the non-sensitive question (see, e.g., Fox and Tracy 1986).

Options

+-------+ ----+ Model +------------------------------------------------------------

pwarner(#|varname) specifies the probability of the non-negated question in Warner's RRT scheme. # must be in [0,1] and may not be 0.5. The default is pwarner(1). Individually varying probabilities may be specified by pwarner(varname), where varname holds the individual probabilities.

pwarner() is conditional on not being directed to the surrogate "yes" or "no". That is, the overall probability of the non-negated question is (1-pyes-pno)*pwarner.

pyes(#|varname) specifies the probability of a surrogate "yes" answer. # must be in [0,1]. The default is pyes(0). Individually varying probabilities may be specified by pyes(varname), where varname holds the individual probabilities.

pno(#|varname) specifies the probability of a surrogate "no" answer. # must be in [0,1]. The default is pno(0). Individually varying probabilities may be specified by pno(varname), where varname holds the individual probabilities.

pyes() and pno() are unconditional (overall) probabilities. For example, in an unrelated-question RRT where the probability to be directed to the non-sensitive question is 0.4 (i.e. the probability to answer the sensitive question is 60%) and the probability to answer "yes" to the non-sensitive question is known to be, say, 0.75, the overall probability of a surrogate "yes" is 0.4*0.75 = 0.3. Likewise, the overall probability of a surrogate "no" is 0.4*(1-0.75) = 0.1.

noconstant, offset(varname); see estimation options.

asis forces retention of perfect predictor variables and their associated perfectly predicted observations and may produce instabilities in maximization; see probit.

constraints(constraints); see estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype); see vce_option.

robust, cluster(varname); see estimation options.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see estimation options.

or reports the estimated coefficients transformed to odds ratios, i.e., 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. or may be specified at estimation or when replaying previously estimated results.

+-------------+ ----+ Max options +------------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, hessian, gradient, showstep, tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#), nonrtolerance, from(init_specs); see maximize. These options are seldom used.

Examples

. rrlogit infidelity male age, pyes(0.5)

References

Fox, James Alan, and Paul E. Tracy. 1986. Randomized response: A method for sensitive surveys. London: Sage.

Maddala, G. S. 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.

Author

Ben Jann, Institute of Sociology, University of Bern, jann@soz.unibe.ch

Also see

Online: rrlogit postestimation; constraint, logistic, logit, rrreg (if installed)