help ckvar_overview
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Help for the ckvar system

Description

    This help file gives an overall description of how ckvar and its
    associated commands can be used to keep information about data validation
    and scoring connected to the variables themselves.

Remarks
    Remarks are presented under the headings

    1. Introduction
    1.1. Background
    1.2. Overview

    2. Quick Start

1. Introduction

1.1. Background

    Data validation is an important part of working with any external
    dataset. Unfortunately, validation rules are rarely passed along when
    data are shared. When they are, they are typically in a form which forces
    the receiver of the data to write validation routines from scratch,
    wasting precious time. To make matters worse, sometimes the validation
    depends on other variables which are not passed along.  In the worst-case
    (and most common) scenaraio, validation rules are not passed along
    whatsoever. This, of course, impedes reproducibility and lowers the value
    of the data.  Using characteristics to store the data validation routines
    (or error-checking or scoring routines) allows the valiation rules to be
    passed along with the data to the data user, saving time and frustration.
    It also allows passing on all variables needed for validation.

1.2. General Overview

    The ckvar command uses characteristics to store the information it needs
    to validate variables. Thus, passing along data also passes along the
    ability to validate the data, even if new observations are added to
    existing variables. There are also some helper commands which can keep
    things running smoothly: ckkeep, ckdrop, and ckrename.

2. Quick Start

    To add validation rules to a dataset which is already in use, run the
    ckvaredit command (click here for help instead). It will open up a dialog
    box which will allow you to add or edit rules for validation for as many
    as you would like. At its simplest, you may
        optionally give a validation rule,
        optionally state whether the variable is required to be non-missing,
            and
        optionally state if there are other variables needed to validate this
            variable.
    When you are finished, typing ckvar will then validate your data. The
    help file for ckvar gives more details about what else your validation
    can involve.

    You may also use this system for scoring variables as one might score an
    exam or a survey instrument. The distinction between validation (or
    error-checking) and scoring is this:  validation and error-checking
    result in something which is two-valued (either 0 or 1), whereas scoring
    can result in any value.  The idea is that in validation, one would
    normally like to create a new error variable containing 1's for errors
    and 0's for valid values, wheras for scoring, there could be multiple
    possible values depending on the value of the original variable (think:
    partial credit).  The distinction is not large; it has been made because
    most people think of validation as right or wrong, errors as existing or
    not, but think of scores as multivalued.  The help files for ckvar will
    primarily talk of validation, since 99% of all users will use ckvar for
    validation.  Scoring simply involves writing more complicated
    `validation' rules.

Also see

    Online: ckvaredit for ways to set up validation rules using a dialog box,
             along with how validation rules work.

Author

    Bill Rising, StataCorp
    email: brising@stata.com
    web: http://homepage.mac.com/brising