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help for wgttest
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Test the impact of sampling weights in regression analysis

        wgttest depvar [varlist] [if exp] [in range] , wgt(wgtvar) [
               cmd(estimation_command) prefix(string) testopt(test_options)
               nonoise estimation_options ]

    by ... : may be used with wgttest; see help by.


Description

    Whether to use sampling weights (pweights) in regression analysis should
    be carefully evaluated. Often, the weights do not have great influence on
    the parameter estimates (see e.g. Winship and Radbill, 1994, to learn
    when and why). In such cases, unweighted estimates are preferable because
    they are more efficient than the weighted estimates.

    wgttest performs a test proposed by DuMouchel and Duncan (1983) to
    evaluate the significance of the impact of sampling weights on estimation
    results. First, a regression model of depvar on varlist including wgtvar
    and the first order interactions between varlist and wgtvar as additional
    covariates will be estimated. Second, the coefficients of these
    covariates (i.e. wgtvar and the interactions) are tested against zero
    using a standard F test (as provided by the post-estimation command
    test). "If the F test is not significant, then the weighted and
    unweighted estimates are not significantly different and the analyst can
    proceed by using unweighted OLS. Weighted and unweighted estimates are
    significantly different if the F test is significant" (Winship and
    Radbill, 1994: 248). In the later case, the unweighted estimators are
    probably biased by sample selection and the weighted estimators are
    preferable. Be aware, however, that significant differences between
    weighted and unweighted estimates may also be due to model
    misspecification.


Options

    cmd(estimation_command) allows users to choose a command other than
        regress for model estimation. Technically, wgttest will work with
        most estimation commands (if not all). This, however, does not mean
        that the test is always valid (DuMouchel and Duncan, 1983, who
        proposed the test, discuss it solely within the framework of linear
        regression).

    nonoise suppresses the estimation results.

    prefix(string) allows users to choose a prefix other than _I for the
        interaction variables. The length of the prefix is restricted to 4
        characters. Note that the interaction variables will only be created
        temporarily.

    testopt(test_options) may be used to pass options thru to the
        post-estimation command test.

    wgt(wgtvar) specifies the sampling weights (mandatory).

    estimation_options are passed thru to the estimation command.


Examples

    Test the impact of the sampling weights (variable pwt) for a linear
    regression model of wages on education and work experience:

        . wgttest wage education experience, wgt(pwt)


References

    DuMouchel, W. H. and G. J. Duncan (1983). Using Sample Survey Weights in
        Multiple Regression Analyses of Stratified Samples. Journal of the
        American Statistical Association 78: 535-543.
    Winship, C. and L. Radbill (1994). Sampling Weights and Regression
        Analysis. Sociological Methods and Research 23: 230-257.


Author

    Ben Jann, ETH Zurich, jann@soz.gess.ethz.ch


Also see

    Manual:  [U] 23 Estimation and post-estimation commands,
             [U] 29 Overview of Stata estimation commands,
             [R] test,
             [R] regress