5.2.1.2 data list file="des560zeros.dat" free / wunit1 to wunit560. compute hbst4=$casenum. sav out="temp1.sav".
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5.2.1.3 * starting point is defining a short macro which is run for each identified * pseudo-diagonal combination. define psd1 (hoc=!tokens(1) / h=!tokens(1) /w=!tokens(1) ) . if (!hoc=!h) !concat(wunit,!w)=lagwend. !enddefine. * This relies on the existence of a variable, 'lagwend', which starts * equal to 0 for all cases then gets updated by 1 for each new pseudo-diagonal. * For the a priori diagonals, we fill in the values of the pseudo-diagonals * by inspection; ie checking what are the values of the square-autorecoded * variables {h/w}bst4 that correspond to the original * values of the variables {h/w}bst. * This inspection can be achieved by crosstabulating the data in terms * of the new units conditional upon the pseudo-diagonals from the old units. get file="revisions1.sav". weight by freq. * Eg 1 : all diagonal titles. compute diocc=(hocc=wocc). temp. select if (diocc=1). cro hbst4 by wbst4. * Eg 2 : farming semi-diagonal. compute farm=(hocc=110 & (wocc=110 | wocc=160)). temp. select if (farm=1). cro hbst4 by wbst4. * etc etc. weight off. * The crosstabs should not generally cover too many combinations, * as during the data revision section we already 'tidied up' * the treatment of pre-defined problem occuaptions. * Next, we use that information from inspection to fill in the * relevant values (of {h/w}bst4) into the following sequence of macros. * [eg, say the first table revealed three combinations, namely * (1,1), (75,75) and (450,450), and the second table showed one * combination, namely (200,205). get file="temp1.sav". compute lagwend=0. execute. compute lagwend = lagwend +1. psd1 hoc=hbst4 h=1 w=1. compute lagwend = lagwend +1. psd1 hoc=hbst4 h=75 w=75. compute lagwend = lagwend +1. psd1 hoc=hbst4 h=450 w=450. compute lagwend = lagwend +1. psd1 hoc=hbst4 h=200 w=205.
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5.2.1.4 * assumes continuation from stages 1-3 above. * starting value of lagwend is already specified (max value of diags). fre var=lagwend. * Because of the complexity of the multiple data entries at this * stage, we use include files to add in the relevant additional pseudo-diagonals. include file="pseuddiags1.sps". * where the include file has the following structure, and is * updated as we move from each version of the model to the next : .* (Updates from successive versions can either be pasted into each * successively run include files, or pasted into the bottom of the single * include file) * Note that because the include file uses a lot of duplicate * text, it can often be created by using 'replace' functions from * starting data which included just two columns for the combination values. * (this is desirable because it saves time during data entry).
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5.2.1.5 * assumes continuation from stages 1-4 above. * The current SPSS data file now has the form of the desired design matrix. fre var=lagwend. * The value (the max number of pseudo-diagonal combinations named) * is important : it will have to be named in subsequent lEM command files. write out="des560r1u1p1.txt" / wunit1 to wunit560 (560(F4.0)). execute. * (the indicators r1, u1 and p1 will be explained later).
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5.2.1.6 get file="revisions1.sav". * 6.1) first check the initial pseudo-diagonals. * again caculating dummy indicators from original data :. * Eg 1 : all diagonal titles. compute diocc=(hocc=wocc). * Eg 2 : farming semi-diagonal. compute farm=(hocc=110 & (wocc=110 | wocc=160)). * etc etc. * Create an aggregate indicator of all initial pseudo-diagonals. compute psds1=(diocc=1 | farm=1 | etc etc). variable label psds1 "Pseudo-diagonals original sources". weight by freq. tables /format blank missing ('.') /ftotal=ftot1 "Total" /tables (labels) + ftot1 by psds1 > (hbst4 + wbst4) /statistics count ((F5.0) ' Cases ') /title "Male and female title-by-status units". weight off. * 6.2) Then incorporate all the other pseudo-diagonals using * a macro and an adjusted version of the include files. compute h1=hbst4. compute w1=wbst4. compute psds2=psds1. variable label psds2 "All pseudo-diagonals all sources". * macro for identifying other pseudo-diagonals by indicator variable. define idpr2 ( h=!tokens(1) /w=!tokens(1) ). if ( h1=!h & !w1=!w) psds2=1. !enddefine. * Inlcude file idenpsds1.sps as follows :. include file="idenpsds1.sps". * where the include file has the following structure, and is * updated (with more combinations) as we move from each version of the model to the next : * (note that the named values in the file are identical to those * in the pseuddiags1.sps file, so the idenprobs1.sps file can * be created by use of a 'replace' function for the relevant terms).weight by freq. cro psds1 by psds2. weight off. * then check : are any occupational unit distributions * adversely affected (under-represented)after pseudo-diagonal * combinations are excluded. set ovars both onums both tvars both tnumbers labels. weight by freq. tables /format blank missing ('.') /ftotal=ftot1 "Total" /tables (labels) + ftot1 by psds2 > (hbst4 + wbst4) /statistics count ((F5.0) ' Cases ') /title "Male and female title-by-status by exclusion as Pseudo-diagonal". weight off. set ovars both onums both tvars both tnumbers both. * This table is significant : it shows all the base units and the * number of cases with which they are represented in the latest * model version, plus the number of cases excluded as pseudo-diagonals. * We usually save it (eg paste into excel), and use it in the final report. sav out="datamodel1.sav".
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Last modified 14 February 2002
This document is maintained by Paul Lambert (paul.lambert@stirling.ac.uk)