<div dir="ltr"><div><br></div><div>Does this 3.2 structure do what you need? it can be referenced from any variable, noted as the default missing values for a LogicalRecord and a Physical Instance. </div><div><br></div><div>
<r:ManagedMissingValuesRepresentation> (note I've left off the identification and other versionable type information)</div><div> <r:ManagedMissingValuesRepresenntationName>Combined Missing Types</r:ManagedMissingValuesRepresentationName></div>
<div> <r:MissingCodeRepresentation></div><div> <r:RecommendedDataType>integer</r:RecommendedDataType></div><div> <r:CodeListReference/> to a CodeList with name Missing at Random</div>
<div> </r:MissingCodeRepresentation></div><div> <r:MissingCodeRepresentation><div><div> <r:RecommendedDataType>integer</r:RecommendedDataType></div> <r:CodeListReference/> to a CodeList with name Missing by Design</div>
<div> </r:MissingCodeRepresentation></div></div><div></r:ManagedMissingValuesRepresentation> </div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Wed, Jun 25, 2014 at 2:39 AM, Adrian Dușa <span dir="ltr"><<a href="mailto:dusa.adrian@unibuc.ro" target="_blank">dusa.adrian@unibuc.ro</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear All,<div><br></div><div>Following a private discussion, an idea emerged that i think it's useful to circulate and discuss.</div>
<div><br></div>From what I understand, SAS codes special missing values as extremely low values, while Stata went for the opposite way, coding them as extremely large values.<div>
<br></div><div>Those are decisions which are software specific, and it is unlikely that other software packages will follow one trend or another.</div><div><br></div><div>There might be a way to solve all particular needs, using DDI as a mediator and most importantly using only "normal" values.</div>
<div><br></div><div>The main quest is to differentiate between missing values. In R, and I'm sure DDI can do that too, each variable can be attached with a list of attributes. One such component of the list of attributes could be dedicated to the missing values, and further differentiate within:</div>
<div>- "missing at random": 1, 5, 9</div><div>- "missing by design": 8, 15, 78</div><div><br></div><div>Here, the (simple integer) numbers 1, 5, 8, 9, 15 and 78 are nothing but the indexes of the line numbers (ie the cases) where the missing values reside in a particular variable.</div>
<div><br></div><div>If I had this kind of information in the DDI XML file, I could then instruct my R function to create <specific> setup files for SAS or Stata using .r and .d in those specific cases, while in R all missing values could remain as simple NAs but users can still differentiate between missings by just looking at the list of attributes.</div>
<div><br></div><div>This way it would accomplish the other need to avoid accidental mistakes, and it is both package independent and specific in the same time, using DDI as an exchange platform.</div><div><br></div><div>
Recoding specific missing values is trivial in R, but I have to confess I don't know if and how this might be done in other software via setup files. People using specific software packages might confirm if this approach is possible or not. Raw data should be read by all packages from a .csv file where missing values are system missing (empty) values.</div>
<div><br></div><div>Best wishes,</div><div>Adrian</div><span class="HOEnZb"><font color="#888888"><br><br>-- <br><div dir="ltr">Adrian Dusa<br>University of Bucharest<br>Romanian Social Data Archive<br>1, Schitu Magureanu Bd.<br>
050025 Bucharest sector 5<br>Romania<br>
Tel.:+40 21 3126618 \<br> +40 21 3120210 / int.101<br>Fax: +40 21 3158391</div><br>
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