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hotvalue - v1.0 - 04 Oct 2008

    Generate scales with missing values conditionally imputed

Syntax

        hotvalue varlist [if] [in] , options

Options
    Scale(newvarname) Required: the variable to contain the resultant
        summated rating scale

    MIssing(#) The percentage of missing values allowed for imputation
        (default 40)

    CUToff(#) The maximum probability from the means test before reverting to
        listwise deletion (default 0.05)

    ttest(#) The type of equality of means test (default 2).  See ttest
        command.
        If 0, no test for equality of means is to be performed.
        If 1, a test for equality of the mean of the scale with imputed
        values versus the scale made from complete cases.
        If 2, a test for equality of the imputed values versus the complete
        cases.

    sdtest(#) The type of equality of standard deviation test (default 1).
        See sdtest command.
        If 0, no test for equality of standard deviations is to be performed.
        If 1, test for equality of the standard deviation of the scale with
        imputed values versus the scale made from complete cases.
        If 2, test for equality of the imputed values versus the complete
        cases.

    method(string) The procedure for imputing missing values.  Must be one
        of:
        CM Corrected Item Row-Mean imputation (default)
        PM Person Mean (Row-Mean) imputation
        LW No imputation, complete cases only (listwise deletion of missing
        values).

    int Indicates that imputed values should be rounded to the nearest
        integer.  Statistical rounding is used (eg. 1.75 has a 75% chance of
        being 2 and a 25% chance of being 1).

    err Add an error term to the imputed values, to try and retain similar
        variance.


Description

    hotvalue constructs a summated rating scale with conditional imputation
      of missing values.

    The variables (or items) in varlist are summed to create a summated
      rating scale.

Missing values are handled according to the conditions specified in the options
> :
    1) For complete cases with no missing values on any of the variables (or
      items) the values are summed.

    2) If the criteria for percentage of missing items and equality tests are
      met, missing values will be imputed using the method specified.  In
      this way, the scale is constructed using imputation only if it meets
      the standards specified.

    This method should not be confused with the mean substitution method of
      imputing missing values, which can severely distort model results.  The
      imputation here is row-wise (or case-wise).  Classical measurement
      theory deems all items in a summated rating scale as measures of the
      same thing.  The items are combined to provide a more reliable measure
      of the concept.  By imputing row-wise and placing limits on the
      imputation, the pitfalls of mean substitution can be avoided while
      increasing the cases available for analysis.

 Example


sysuse auto
gen fullscale=mpg+trunk+turn
replace mpg=. in 39/40
gen lwdscale=mpg+trunk+turn
hotvalue mpg trunk turn, scale(scale1)
hotvalue mpg trunk turn, scale(scale2)
pwcorr fullscale lwdscale scale1 scale2, obs


    Coen A. Bernaards and Klaas Sijtsma (2000). Influence of Imputation and
      EM Methods on Factor Analysis when Item Nonresponse in Questionnaire
      Data is Nonignorable. Multivariate Behavioral Research, 35 (3),
      321-364.

Author
Paul Millar
pmillar@brocku.ca