7.1 Score Transformations: SPSS syntaxget file="fulldata.sav". * This file has the original and new occupational unit values, * indicators of pseudo-diagonality, and the derived CAMSIS scale values. * (ie as in the example for section 6.3 above). * the 'raw' scores from the association models are in variables {h/w}i{t/s}d1. * note that in some cases care should be taken that the differently derived * original scales all have the same positive to negative ranking - it could be * the case that different versions have comparable structures but some have * been arbitrarily reversed in their magnitude, in which case it is sensible to * re-sign the data to a consistent schema. ** 1) Mean Standardisation plus range cropping : * THIS IS THE METHOD USED IN ALL CAMSIS VERSIONS DISTRIBUTED FROM THIS SITE. * standard normal transformation, mean 50, standard deviation 15. * precision 1 decimal point and range cropped to 1 to 99. * (since standardisation reflects population distribution, we must use weights). * Use total sample weights rather than non-pseudos only :. * also assumes that any cases with missing 'raw' scores are recognised by SPSS as missing. weight by freq. descriptives var=htd1 wtd1 hsd1 wsd1 /save /statistics=all . * (creates by default standard normal vars, mean 0 sd 1, prefixed by z). descriptives var=zhtd1 zwtd1 zhsd1 zwsd1 /statistics=all. * then generate to fit within desired range and precision :. compute hitcs=(trunc(10*((15*zhitd1)+50)) )/10. compute witcs=(trunc(10*((15*zwitd1)+50)) )/10. compute hiscs=(trunc(10*((15*zhisd1)+50)) )/10. compute wiscs=(trunc(10*((15*zwisd1)+50)) )/10. descriptives var=hitcs witcs hiscs wiscs /statistics=all. * finally, 'crop' any extreme values that exist. recode hitcs witcs hiscs wiscs (lo thru 1=1) (99 thru hi=99). descriptives var=hitcs witcs hiscs wiscs /statistics=all. weight off. ** 2) Range transformations (all values to be integers, range 1 to 999). * (range transformations independent of population distribution, so don't need weights). * [THIS IS INCLUDED AS AN EXAMPLE BUT IS NO LONGER THE PREFERRED CAMSIS METHOD]. compute valone=1. aggregate outfile="mtch3.sav" /break=valone /htd1ma=max(htd1) /htd1mi=min(htd1) /wtd1ma=max(wtd1) /wtd1mi=min(wtd1) /hsd1ma=max(hsd1) /hsd1mi=min(hsd1) /wsd1ma=max(wsd1) /wsd1mi=min(wsd1). match files file=* /table="mtch3.sav" /by=valone. * the above is run to automate identification of minimum and maximum values. compute htd1cs=trunc ( 999 - ( (htd1ma - htd1)*(998 / (htd1ma - htd1mi) ) ) ). compute wtd1cs=trunc ( 999 - ( (wtd1ma - wtd1)*(998 / (wtd1ma - wtd1mi) ) ) ). compute hsd1cs=trunc ( 999 - ( (hsd1ma - hsd1)*(998 / (hsd1ma - hsd1mi) ) ) ). compute wsd1cs=trunc ( 999 - ( (wsd1ma - wsd1)*(998 / (wsd1ma - wsd1mi) ) ) ). * these commands compute the range transformed values. descriptives /var=htd1cs wtd1cs hsd1cs wsd1cs /statistics=all. weight by freq. descriptives /var=htd1cs wtd1cs hsd1cs wsd1cs /statistics=all. *note that the population mean value of these scores doesn't mean much anymore. weight off. sav out="fulldataandscores.sav".