3 ι ηι ιι η ι -ηι. ι ι ι ι. «η» η ι ι ι ι η ι.,ι ι ι ι ι "ι η ι % ι ι "η ι ι ι η ι ιι. ι ηι ι η ι «ιι ι»

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1 # v ## 3 ι ηι ιι η ι -ηι ι ι ι ιι, ιι ιι-'ι ι, η ι ι ι ι ι, ι ι η η!ιι. ι ιι ι ι «ι» ι «η» ι ι ιι ( 2 ι) η ι η. * ι ι ι ι ι ι ηιι. 6ι η η ηη ι «ι ι» η ι. ιι η ιι η ι ιηι ι η η η ιι ιι ι ι ι ι «η» η ι ι ι ι η ι.,ι ι ι ι ι "ι η ι ι η ηι ι ι ιιι ι % ι ι "η ι ι ι η ι ιι. «η» ι ι ι «ι». $ι ι, ι ι, ι ιη η ηι ι ιι, ιι, 'ι, ιι, ιι η ι ι η ι «ι». «ι» η ι ι ι, ι, ι, η ι ηι. * η ι ιι, 'ι, ιι, ιι ιιι «ι» ι ι η, ι, ιι ι, ι, ι. η ιιι ι ι η η ιι ι η $--ι" Jean Paul Benzecri η η 60 η η ι (Benzecri, 1992) ι ι!! ι., η ιι ι ι η ι * η (1999), Clausen (1998) ι Greenacre (1984). η ιιι ι ι ιι ιηι, ι., ιι Pierre Bourdieu ι ι ηι ι ι ι ιιι ι η!ι ι ι ι ιι η ιη, η ι η η'η ι ιηι ηη ι ιι!ι (ιι!ι, ηιι ) ι ι η ηη ι., Bourdieu ι! ι ι ιι ι η ι ηι ι η ι «ιι ι» ηι ι η ιι ι ιη!ι «ιιι ι» ι η «ιι ηι» ι ι ι 'ι (Bourdieu, 1992). η ιι ' η ι ιιι ι ' η ιι ( Doise, Clemence ι Lorenzi-Cioldi, 1993). < η ιι ι (.. Autiero, Bruno ι Mazzotta, 2000), η η (.. Askell-Williams ι Lawson, 2004), η ι (.. Johansen, Laursen ι Holst, 2004), η ι - ιη (Schwartz, Baldo, Graves ι Brugger (2003), η ι ι (... Vlachová, 2001), ι ιι ι (Guinot.., 2001).,&-@,),η3ο)ο,-ο3ο.η)η,ο($P$$ η η ι ηι ι «ι ηη»! ι η" η ι, ι ι ιη ι ηη (optimal scaling) SPSS η η ι ιηι ι ι ι ιι η η ι, ι (Meulman, ι Heiser, 2001). ι ι ι ιη ι ηη η η ι ιη 154

2 ι ηη ι ι ι -η ηι ι η.,ι ι ι ηι η ι ι " η η η (.. 1=, 2=).,ι ι ι ι η ι ι η η η, «ι»! ι ι ι (.. 1=, 2=, 3=ι...). * ιι ι ηη, ι ι ιη ι ι ι ι ι -η ηι ι η ι ιιη ι ιηι η. ι ι ιι η ιη ι ηη ηι ι η ιι ι η ι ι ι (alternating least squares) ιι ι ι ιι ηι ιι ι η "ηη ι η ι η ι η η η «ι» ι ι ιι ι η ιη ι ηη η ι ιι. η ι ι ι ι ιι ι " ι ιη ι ηη η η SPSS. 2()η,),ο&OdKD;;:9FD8>:8E:A8?GS9B9e η η ιιι ι ι'ι η η ηι ιι η ( ι ι ι η η x 2 ι ι ι ι) ι η ι'ι ι ι -η η. η ιιι ι η ι ι ιι ι ι η ιηη ι ιι, ι, ι, ι, 'ι (.. ιι ιι, ιι 4 ηιι,.). *ι ηι η η ιιι η ι η ι η 'ι ιι η η ηι ιη η ι (.. ηι ιη ι, ι, ιιη..). η ι ι ι ι ι η η η ι ι 1. 6 ι η ηι η ι η '70 ι ιι ηι ι ι.,/η η(ι) ' 483 ιι η 6 η ι η ι η ι! ι ι ιι η! ηι ι η: )η 1. "ηι ι ιι ι ι" )η 2. "ηι ιι ι ι" )η 3. "ηι ιι" )η 4. "ηι ιιι ι ι" )η 5. "ηι ι ιιι ι ι" η ιιι!ι η ηι ι" η ι: % ".!"!! # 0 " " υ " 0 " υ ' # #" % 0"!! " &υ" η )( )% & % 8,2% 25,9% 23,5% 23,5% 11,8% 7,1% )( 1,(, &η % 20,0% 12,4% 13,2% 26,3% 38,5% 33,3% 2!!! " % 1,4% 4,6% 4,1% 4,1% 2,1% 1,2% 17,6% &υ" η & % 7,3% 34,2% 35,5% 12,4% 6,4% 4,3% &η % 48,6% 45,2% 55,0% 38,2% 57,7% 55,6% % 3,5% 16,6% 17,2% 6,0% 3,1% 2,1% 48,4% 1 ι ηι ιη ι ι η Kappa ι! ι. 155

3 υ" &υ" η !!! " % 0"!! " & % 5,7% 40,0% 28,6% 20,0%,0% 5,7% &η % 5,7% 7,9% 6,6% 9,2%,0% 11,1% %,4% 2,9% 2,1% 1,4%,0%,4% 7,2% &υ" η & % 2,5% 45,7% 33,3% 18,5%,0%,0% 16,8% &η % 5,7% 20,9% 17,9% 19,7%,0%,0% %,4% 7,7% 5,6% 3,1%,0%,0% 16,8% &υ" η & % 14,6% 50,0% 22,9% 10,4% 2,1%,0% &η % 20,0% 13,6% 7,3% 6,6% 3,8%,0% % 1,4% 5,0% 2,3% 1,0%,2%,0% 9,9% &υ" η ,2% 36,6% 31,3% 15,7% 5,4% 3,7% 100,0% # " : ) ι ιι ηι ι η '70 η ι, η ηι ι ι ι ι «"» ιι η ι ι ι η η ι ι! x 2 ι. 6ι, ι ιη ι η ( ) η ι η η ( ι ι ι ι η)., ι ι η η «&η.1» η ι 8,2% ι ιι ' ι η ι η ι η η 1. ι η η η ι η ι η ι (. ι )., ι ι η η «&η.1» η ι 20% ηι η η 1 ι ιι ηι ι η '70 ηι ιη η(ι) ι ιι. ( ι η η ι η η. ι, ηι ι ι ιι ηι ι η '70 ', 17,6% " ι ιι ι. ( η η η η ι. ι, ηι ι ι ιι ηι ι η '70 ' ( η. ι), 7,2% ηιη η η 1. η ( η) ι ( ) ι ι " ( ι η) ( η). ( η) η η ι η η ι. ιη! η "ι η η (% ' x 2 ). ι (inertia) ι η ιη η (η η ι η ι ). ι η ι ι ι η η ι Pearson x 2.. η ι η ιιι ι η ιη η ιιη 2 ι ι ι ι ι ι 'η ι ιη ι ι ι ι η. ι ι ι ι! x ι y η 156

4 η ιιη 2. M η ηι ι ι η ι ι! ηι η. ι, η ( η) ι (η. ) "ι ι ιι ι ι. # η ι ιι ι ι ι ηι ι ι ιι η ι ιι ι ι" ιι ι ι-. ι ι ( ηι) ι η &η 3 ι ι ιι ι ι. η 5 ι η ι η ι ι. 2 η ι ι ι" ι. η ι η η ιιι η η SPSS ( correspondence.sav). # ιι ι ιι ι ι ιι 1000 ι ι η (.. ι ιι / #η) ιι ι η ι ι ι η η ι ι ι ηι η ι, ι ι ι ηι ι η ι ". #! ι ι ι η η ιιι. PQA8?GSl:T?<?f:>HE<BD8KD;;:9FD8>:8E:A8?GS9B9Zυ$ι/D%*ι+)/*ω/* 157 *)/%ι4,/,7.υ,%%5,'$υ,% *+ /υ$+ *ι$*ι&ι- /,%$ $* %)ι%υ*ι' %))% #( ι * $+,*ι'*%)4'$,ι)'8+,$,5ω,% * υ)%,%/, #9"=(> d)ω,),%,% )' * )/7υ 9RJHIJSKN RINP:E +,%*8+*2 9"= )%ι,8/%ιrυ)3*+4%)υ5r )4'$,ι )82,* )' * ) D+*2$,%)'*υ%ω*-,%υι.=υ υ*')υ 7%ω ω*)ι $+,*ι'> )82,* υ*/ 2* %4E )'%,E -$*ι%e,'υ$+e *,* ι )% ι*ωι)ι2$,%,%*υι7,)'!.ωιr*$*ι&>,%*8+*2 )%ι,8/%ι q υ)3*+4% %)ι%υ*ι %)ι).υe )' * ^+,*ι'e ω ι %*)*υ&ι. $)υ.e ωι)ι+,.%)'*!.ωι*q>q%*8%/ι)%/,%$*)$ι9q@p:# %ι/(*+,%*8+*2ι

5 *2+(*+,%*8+*29"=#%.&%ι$+,$)7*)7%*2$υ,%*+/7%,%*8+*2(> VQ*/,%*υ,)9T:WB8:f?8^:Z:/*ω)'*)$ι9fD=:>,5D%*ι+/* *)$ι9miaimvmcjkvn:$+,%ι-υ,% *+,ι'*%+ *ι,2 *+,%*8+*2 )υ $* )/%ι4,/, %ι!e )υ *ι$*ι&% $*+ υ)3*+4 9^+,*ι':> *)$ι9mjzimvmcjkvn:$+,%ι-υ,% *+ υ\+'*%+ *ι,2 *+,%*8+*2 )υ %ι q ι *ι$*ι&% $*+ υ)3 *+4 9%*)*υ&ι':> % υ*' * *') D+*/,% $υ,)%ι+57 $*+ /υ$+ '% ι υ)3*+4% *+,%*8+*2*υ%)ι%υ*ι%)ι).υ)υ &+$ι,)ι2$,%#$ q(> 7 7.,% $υ,)%ι/8υ,%,' /)ι% )' *ι υ)3*+4%%)ι%υ*ι%)ι).υ*'*% 7&+$ι,)ι$,%.,ι'*%% #)>&> MIAIMVM cjkvni[ ι MJZIMVM cjkvnio(-$*% )%$υ,% υ*)υ *%%ω$ +,*ι' ι,%*)*υ&ι' )' *+/υ$2,> XQ*/,%9qF>?<::ι$*% [Q9KD8<B8H::> `Q*/,%*υ,)9T:WB8:f?8^:Z:/*ω)'*)$ι9KDGH_8:>,5D%*ι+/*)υ)υ$ι/$,% )+4υ,.ω> +,%ι-υ,%*ι*ι,.!ιr$**/+)$ιe')ω/,%ι$*82,>n> Q*/,%9qF>?<::ι$*% iq9kd8<b8h::> jq *+&ι2/*9kd;;:9fd8>:8e:a8?gs9b9:)*/,%*υ,)9md>:gz:>,5d%*ι+'υ7+/* PQ +,%ι-υ,% *ι %)ι4. )υ )υ$ι/d*ι ι$*%/>,.*+$+ )'$*$+ 4%*ι $*+ 8/$+ *υ $**ι$*ι ι*+υ c )υ )%ι4/\,%$%)+4,%%5/ι> PPQ9KD8<B8H::> PQ *+ &ι2 /* 9KD;;:9FD8>:8E: A8?GS9B9: )*/,% * υ,) 9$<?<B9<BE9Z:>,5D%*ι +'υ7+/* 158

6 PVQ/υ,%*ι%)ι4.)υ$+,%ι-*ι)> PXQ9KD8<B8H::> P[Q *+&ι2/*9kd;;:9fd8>:8e: A8?GS9B9:)*/,%*υ,)9PGD<9Z:>,5D%*ι +'υ7+/* %,5ι$*%.)4*ι' ι/4,,,%*+*υ*'&+ )%ι'ι$+ *ω [ %5-,% *ι υ)3*+4% *ω,%*8+*-> *+%)ι429ibkjsnkpihbp?@hcljbbnhwk@bc: $+,%ι-υ,% % o #,),% $+,%ι-$υ,% )ι2)*% % / '$ )ι,ι' %ι *'$ )ι %υ/4ω$*%%ιιυ)3*+4%$*ι/4,,(> P Q9KD8<B8H::> PiQ *+ &ι2 /* 9KD;;:9FD8>:8E: A8?GS9B9: )*/,% * υ,) 9OK: 4ι 9*.=υ,%: *+ /υ$+ *ι$*ι&ι-)υ$,%,%*ι))/ω%.4%ι%> SPSS ι"ι : Correspondence Table υη 2η υ!υ" Active Margin 0 + #! υη Active Margin , ι ιι ιιι, η ι η η.,ι ι η ιι (.. /ηι) ι! ι ηι ι η (.. ). # «Active Margin» ι ι ι ι -η ι ( /ηι). # ι «Active Margin» ι ι ι ι -η η ( ! η ι ηι ι η). ι"ι ι: 159

7 Row Profiles υη 2η υ!υ" Mass 0 + #! υη Active Margin,023,124,169,328,294,062 1,000,036,152,226,347,178,061 1,000,045,167,242,424,121,000 1,000,090,180,251,323,120,036 1,000,125,205,307,239,114,011 1,000,051,157,229,335,179,048 2 η ι, ι ιη ι ι η η ι ι. $ι ι,, 30,7% ι ιι ι ι ι ι ηι ι η. # η η, η η ι ι 16,9% ιι /ηι ι ι ι ηι ι η. 2, ι ι ι ι ι ι η ι. ι «Mass» (") η η η ηι η ι. 6ι, η η, ι ι, 33,5% ι ιι,! ι ι η. # ι ιι η η. ι"ι η: Column Profiles υη 2η υ!υ" Active Margin 0 + #! υη Mass,078,141,132,174,292,229,178,353,481,493,517,494,625,498,059,071,070,084,045,000,066,294,192,185,162,112,125,168,216,115,119,063,056,021,089 1,000 1,000 1,000 1,000 1,000 1,000 η ι ι ι, η. $ι ι, 51,7% ι! ηι ι η η η ι ιι ι $ι. ι, ι ι ι ι η ι ι,, ιι! ι ι x 2 ( Analyze-Descriptive Statistics- Crosstabs -Cells)., ι ι η ι η (ι ι η, η.! ιι ι ι ηι ι ), ι ιι ι"ι ι ι η η. 160

8 Summary Proportion of Inertia Confidence Singular Value Dimension Total Singular Standard Correlation Value Inertia Chi Square Sig. Accounted for Cumulative Deviation 2,219,048,790,790,031,237 a. 20 degrees of freedom,091,008,138,928,032,062,004,063,991,023,001,009 1,000,061 60,215,000 a 1,000 1,000,ι ι η η «Singular Value» ι ι ι ιη ι η. & ι 'η ι η ι, η. ι ι. ι ι ( x 60,215 η «1nertia») ι η 0, 061. N η η «Proportion of Inertia» ( ι) ι ι η -η «Accounted for» ι ι η η ιη 79% ι η η 13,8% η ι ι. 3ι ( -η «Cumulative») ι ιι,!ιι, 92,8% η ι ι, ι η! η η ι η ιη ι!. ιι ιι chi-square (x 2 ) ηι ιι ι! η ηι η ι ι η ι η. 2 η η η sig. η ι p ι ιη 0,05 ηι ι η ηι η ι ι η ι, ι η ι η "ι!.,ι η ι ηι ι ι η η. υη 2η υ!υ" Active Total a. Symmetrical normalization Overview Row Points a Score in Dimension Contribution Of Point to Inertia of Dimension Of Dimension to Inertia of Point Mass 1 2 Inertia Total,178,673 -,373,021,369,271,859,110,969,498,137,148,004,043,120,503,247,749,066 -,289,678,006,025,335,197,453,651,168 -,503 -,034,010,194,002,959,002,961,089 -,954 -,530,020,368,272,878,113,991 1,000,061 1,000 1,000 ι η η «Score in Dimension» ι"ι ι η ι ι ι ιι -η. η η «Inertia» ι"ι η ι ι ηι η ι. η «Contribution» ι ι η -η «Of Point to Inertia of Dimension» η ι -η ι ι η ι ιη. ι, /ηι ι * ι 36,9% + 36,8% ( 73,7%) η ι ι η η ιη. η η ιη ι ι η η -η -ι (33,5%). -η «Of Dimension to Inertia of Point» η ι η η ιη ηι 85,9% η ι ηι η -η /ηι... 96,9% η ι ι η η /ηι ηι ι ιι (. η Total). ιι, ι, η ι ι η - η -ι ι η ( 65,1%). 161

9 0 + #! υη Active Total a. Symmetrical normalization Score in Dimension Overview Column Points a Of Point to Inertia of Dimension Contribution Of Dimension to Inertia of Point Mass 1 2 Inertia Total,051-1,231 -,669,019,356,252,883,109,992,157 -,303 -,011,003,066,000,995,001,996,229 -,321,027,006,108,002,930,003,933,335,101,327,004,016,393,171,752,922,179,646 -,422,020,342,350,829,148,977,048,718 -,087,009,114,004,641,004,645 1,000,061 1,000 1,000 #ι ι ι η ι. ι"ι ι ι η η ιιι, ι ι ι: 3η ι ι " -η η ι ι ιι. $ι ι η «#»-«* ι» ι ι ι ι ι, ι ηι ι η" ιι. 2()η+οο/dHD_D^:8:B<SA8?GS9B9e η -ι ι ι η η ιιι (correspondence analysis) ηι ι η. & η ι η ηι ι η. η η 13 SPSS! ι η ι «HOMALS»!" η ι, ιη η ι «Multiple Corrspondence Analysis» η, ι η, ι ι ι η η «ηι» η (supplementary variables) η" ηι ι ι"ι ι ι. / ι 162

10 ι ηιι η, ι ι η ι η η. η ιι (Homogeneity Analysis HOMALS η η homogeneity analysis by means of alternating least squares), ι ι η ιιι, ι ι ηι ιηι ι ι ι ι ι (cases) ι ι η. η ιι HOMALS ι ι'ι ι ι ι ηι η ι ι -η η ι ι ι ι ι -η. η ι ηι ι ι ι η η ιι homals.sav. ι ι η η ι (η gender), η ιι η (η marital) ι ι (η personal). 2 ι η ι ηι. ι 500 ι ι (cases), η η ιι. PQA8?GSl:T?<?f:>HE<BD8OF<B_?G$E?GB8^Z,5D%*ι+'υ7+/* Q/υ,%*ι)%)ι4.>)ι.4υ,%*+%)ι429AGG M?;B?JG:9?;: _HG<BFG: 8D_B8?G: 4ι* '% ι υ)3 *+4%*ω *+4ι-,%*8+*-)υ 7.υ,% ι%%υ2$υ,% *+,%*= *υ $&.$+ %ι ι5%*ι.> )$+/υ,%*+%)ι429o8:9:<:4ι*7$υ,%,'.$,%*8+*-> VQ*/,%T:WB8:>,5D%*ι+'υ7+/* XQ %*5.υ,%,% * 8%/ι *ι *%ι,%*8+*.$*%=)$ιι,%*+%)ι42 9T:WB8: f?8^:z: Dυ,%*%*ω*ι,-*+/7%,%*8+*2> ι )/%ι4, +,%*8+*2 9".&%ι% )'!.ω ι r 4ι*.&υ,% ι7,2$%ι r )$ω)ι/ )82,* )' * )* υ)%,%,)$ %)ι.=υ υ*')υ 7%ω$) $+,*ι'> [Q *+ %)ι42 9TB_:89BD89 B8 $DGH<BD8:,),%$υ,%* ι7,'*ωι$*/$%ω*υ,*.υ 2$+)υ7.υ,%%.4=υ,%>,.4ι$* ι7,' ι$*/$%ω )υ,),%$υ,%%ι%*% ( $υι' ι7,'*ω υ)3*+4ι-'ω*ω,%*8+*-,%* ι7,'*ω,%*8+*-#+>![3ni" ι$*/$%ι(#%5'$'%)%ι,8/υ /)ι υ)%,% 2)%ι)*-$%ι( 2 8( ι7,'*ω)%ι)*-$%ω#$* )/%ι4,/,qeeυ)%,%(e/4,%*)ι)'*ι))/ω%)ι4.%ι+,ι'*%+#%-%ι ι7,'"(>d)ω4%*ι*+*'e%)%ι2,.$)'υ*2*+ιι$)$)7,%ι%%υ2$υ,%υ)/&%ι. $&%*ι/,ι' ι7,' υ)%,%ω ι$*/$%ω)υ %,+%%ι'$* υ*',%4*%)$$*'*+ 163

11 164 η &, 2007 ι,$+*ω*ι,-e$υ27ω%)ι.4υ,%[2nι$*/$%ι>-%)ι.=,%[>d$,ι'*%%ιι7,'*ω ι$*/$%ω)υdυ,%*'$)ι%%,+%$ι,%ιι*)*.%$,*+/υ$+,ι4.%ι> ),%%)$+&&'$υ,%,ι2)%ι$$'*%%,%*8+*.,%8/$+*ι)%υ,%**υ#kjsnkc( $*ι*ι,.*υ)4*ιι4/,,*>)%ι2%,),%$υ,%*+ι,%*8+*2)υ&+$ι,)ι,% $*+/υ$+ιω,%*8+*2$*+8/$+*+)7υ,%**υ$*ι*ι,.*υ)4*ιι4/,,*e,),% /υ,%* 'υ7> %*+%*2 *+*/$*$+9RJHIJSKNRINP:*ι4/5υ,%*',**+,%*8+*2fNAhNHι*%)ι,%#%)ι42WJCBN( %=+4+7%υ*ι/*/*$&ι$,'*ω)*%%$,/*ω*+/υ$2,)υυ7%>5+,ιυ42$,%*ι ;(:#,%*ι82,*!e[ ι n)υ 5.,%)ι)/ω(> *)/7υ υ*' ι $*+%)ι42 9I?J:G OJ :E< $ED;:9 PGD<d9e JS6:,%*5.υ,%*+.,%*8+*2$$*)$ι%=ι/ι,%*+%)ι429T:WB8:f?8^:Z:Dυ,%*% *+#$*)/%ι4,/,!3[( ι /υ,% ι 4ι*ι υ)'ι)%[.%,%*8+*. )υ $,% ω 9" ι => *ι/ ι ι*%ι,%*8+*..&υ *ι ι% *ι,. ι υ)3*+4%,% *ι&ι.ι,''4 )υ*ι +,ιυ42$,%%ι 4ι,&+$ι,%$υ ω **ι*ι,-$*ι4/,,* )υ 7 +,ιυ42$υ,% $*+ $υ.&%ι> `Q *+%)ι429of<bd89qqq:,),% %)ι.=υ,% )ι%*ι/ /)ι%%)ι4. 4ι *+ )υ$$+ $**ι$*ι- )*%%$,/*ω Q/υ,%*ι%)ι4.)υ$+,%ι-υ,%%=ι/#$υ*+ 9E?<:^D;S UH?8<BWBE?<BD89:)υ %.&ω $+,%ι-$%ι(> *+ %)ι42 9K;B<:;B?:,),% $υ,% * ι7,'*ω%)2\%ω4ι*+%)*%υ=+*+8.*ι$*+ )$*ι)+$+ *ω *ι,- *ω *+4ι-,%*8+*-,% *+ %)ι42 9M?cB_H_ B<:;?<BD89:> )$+,),% $υ,%,ι *ι,2 $4ι$+ 9ED8M:;^:8E:: /,%$ $*ι *%%υ*% %)2\%ι> /υ$+,ι4.%ι $*,*/'* ι)ι+7.)'*ι*2ι)υ7.$,%> *,*/+2 '*5*/$%ι$*,.4ι$*ι7,'*ω%)2\%ω)υ $,%#%-!ee( 2'* + ι5/ /,%$ $% %)2\%ι %ι,ι'*%+ )' *+ *ι,2 $4ι$+ )υ$,%> *+ %)ι42 9$?M: DJ :E< 9ED;:9:,),% )7+%$υ,%*ι*ι,.*υ/7%υ)%ι,.υ$*ι ι$*/$%ι )υ $,% ω.%,%*8+*. 4ι )%ι*.ω/υ$+>ι.%,%*8+*.)υ/7%5/ +,ιυ4*ι %ι *'$% '$% ι ι7,' *ω ι ι4,,/*ω>,5d%*ι + 'υ7+ /*

12 ι$*/$%ω)υ$,%> iq*/,%9kd8<b8h:: jq ;(:)*/,%9:> SPSS : η &, 2007 #!!!ω # 5 υ Missing Marginal Frequency " "ω Missing Marginal Frequency !. /+ 0 & +η &" Missing Marginal Frequency ι ι"ι ι η ι -η ι η ι η η ιι. ι"ι ιι ' : Iteration History Difference from the Previous Iteration 35 a Fit, Iteration, η.7, η η ιι ι ι ι ' η ι 'ι ι ιη ι ι ιη η. η ι 'ι η η 35 η η'η ι η ι η ηη η'η a. The iteration process stopped because ιη.00001, η. ιη η ι the convergence test value was reached. ιη ( η «Difference from Previous Iteration»). η η «Fit» ι"ι η ιη ηι η η ηι η ηη ιη ι ι ιη η. η ιη η η ι ι 0,84 η ηι 84% η ιη ηι. ι 16% ηι ι ι ι η ιη. ι"ι ι ιιι (eigenvalues) ι ι ι ιι: Eigenvalues,ι ιιι η η ι Dimension Eigenvalue ιη ι η η ι ι ι ιιι ι 1. 1,455 ι ιιι ι ι η η «Fit» 2,390 η. η, η η ιη ηι 45,5% η ιη ι η η 39%.,ι ιιι " ι η. & ι η ι ιιι ιη, ι ι ι (cases) ι ι -η ι (η ι ι) ι ι -η ιι η ι ι ι ι ι (η ι ι ι ι ι ι η ι ι ι ι ι ι η η ). #! ι ι ιιι! ι ι ι ιι (objects scores). & ι ι ι ι η ι ι ι ι η ι ιι : 165

13 ι!ι ι ι ι ι ι η ι ι ι (outliers) η. ι, η η ι"ι. ι η ι ι ι ηι ι ι ι ι ι. ) ιι ι ι ι ι ι ηι ι ι ι ι. η η ι ι ι ιι (η ") η η, ι. ηιι ιι ι η ι. η, ι ι ι ηιι ι"ι. 3!, ι"ι ι ιιη ι η ιη. Discrimination Measures Dimension 1 2,432,090 %+ η,541,517 ω,394,564 ι, 'η ι ιιη ι η ιι η η ι -η η η ι η 'η ιιη ι -η η η η ιη ιη. ι ι ιι η η ιη (dimension 1). ι ι ι η η ιη, η ιι η ι ι ιι. η ι ιιη ι ιη η ( gender, marital ι personal) ιι η ιιι (η η ιη ηι) ι η ιη ιη. ι ιι ι ι ιιη: 166

14 167 η &, η ι η η ιη (ι"ι!) "ι η η ('η ι ιιη η η ιη ι η η η) ι η η ιη ι (!). 6ι ι ι η ι -η ιι ι η ι η η η η ιη. ιι η ι ι 'η ι ι ι ιι ι ι ιιη ι ι ιι.,ι ι ιιη ι ι ι ι ι η ι ιη. ; η ι ι ι - η η η. $ι ι, η ι ι ιη ιι ι ι ιη ι -η ι ι ι ιη ι - ηι ι -η ιι ι ι. ι ι ηη -ηι ι ι η η η ι -η. 6ι ι -η ι η ι"! ι ι ι: ιη ι ι - ηι η ι η ιη ι ι ι ι η ιι ιη. ι!ι η ι ι η η «ι» η - η «η» ι η ι ι "ι η η ιη. ι η η ιη "ι η ι -η «ι» ι ι ι ι!, η ι. «!ι» "ι η η ιη. ι η «ι» ι! ι ι η η ι η η ιη. 6ι, ι η η «ι» (personal) ιι η η η η ιη., η η ιι η η ιη (!) ιι η η ιη. η, ι -η η ιι η ιι ι η ι η η ι ι ι ι η η ιι ι ι ιι. η ι ι η ι ι ι η ι ι η η ι ηι 3 η ι ι gender, marital ι personal. 3 ι ι ι «Object Scores Labeled by ι ι». & ι ι "ι η ι -η η η marit ηι 4 ι ιι ι η ιι

15 η: # η ι ι η η ιη (dimension 1 ι"ι!) ιι ι η ι ι ( ι η ι ι) ι η ι ( ι η!ι ι). η ιη (dimension 2!) ιι ι η ι ι ι η ι. # ι ηι ι ι ι ι η ι ηη ι η ηι η ιι η (η marital): #!! " "ω Missing Marginal Dimension Frequency ,281 -, ,929 -, ,551 1, ,600,918 0 Category Quantifications η η «Category Quantifications» ι η η ιη ι ιιη ι ι ι -. η η ιη ι ιιη ι- ι -. 6ι ιιι ι ι ηι ι. η ι ι" ι ι ι ι η ι «ι»: 168

16 5 υ Missing Category Quantifications Marginal Dimension Frequency ,766 -, ,564,254 0!. /+ 0 & +η &" Missing!ω # Category Quantifications Marginal Dimension Frequency ,101 -, ,239 -,299 77,799 -,069 66,263, ,831 3, ,033,

17 ο(2),,ηο)ο,-ο3ο.η)ηdmhg<b>b_:89bd8?g$e?gb8^e η &, 2007 ιη ι ηη ι ιη ι η ιηη η ι ιι «η» «η» «ι», «ηι», «ι ι» ι. $ι ι, η «ι» ιι η ιι, ι ηι ι ηι η ι ι η ι ιι «ιι» «ι"ι» ιι ιι. η % η ι η η ιη ι ηη ι ι ι η ι η ηη!ιηη «ιι» η «η» «η». $ι ηι ι η η ιη ι ηη SPSS ηι ι ι politicians.sav. ιι 3 "η ηι ι ι 1 9 ι ι 11 ιι ιι!. *ιη ι ι ιη ι ι η ι η ι. # SPSS politicians.sav ι : PQA8?GSl:$E?G:MHG<B>B_:89BD8?G$E?GB8^dPfOXKAIeZ,5D%*ι+'υ7+/* Q *+/*υ*2).)%ι $υ,%*+,52,%*+).&υ,%4-$%ι*%,.,$*&%%,.ω W@KIBIlIJAC>CJc>*F&2E* %,.,+>ι*ι,.$% /7%,%*8+*2%5/Dυ/,%$ %44*+*E)'*%%)ι.4υ,%9TY: >?<??;:F;DcB_B<B:9:>)$+.&υ,%)).,2*% %,.ωι4ιfυ*'*'4 %)ι.4υ,%9mhg<bfg:_?<;bc 9DH;E:9:> */*ω,.*υ )7υ).)%ι )$ι$υ,%)-ι8- %ι4ω,.%υ*.ι,2*% $*&%*ω%,.ω,> )*+2$υ,%*&%, 7,%'*ι.&υ,%n,2*%E '$+2ι*υ)%,% )υ)/*+$$* %ω*+,*'4ι',>υ*.ι,2*%%ι4ω,.%$% $%ι.+,ι/*ω)'*+/+ 170

18 VQ*/,%T:WB8:>,5D%*ι+'υ7+/* XQ%*5.υ,%$*%=)$ι'% *ι,%*8+*.,%*',**ω )ι*ι->1%8ιω7%*%'*ι+$%ι/,% *+),%*5.,%*ι,%*8+*. υ*.%ιι,%*+$%ι/,%*+ )%ιωι)ι+,.%ι,%*8+*.$*&%,>,%*8+*2>%ι*/7%.)' **υ)%,%*+.%υ/,ι 5υ$ι/%$υ,)%ι,8/%*ι$*+ /υ$+>>> [Q*/,%*υ,)9MD>:GZ:>,5D%*ι+'υ7+/* 171

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20 $*( *+%)ι42 9TB_:89BD89: Dυ,%* ι7,'*ω ι$*/$%ω $*ι )% 7.υ,% ))ι2$υ,%* %,.,>,.4ι$*ι7,'ι$*/$%ω)υ,),%$υ,%%ι'$%ι)%ι)*-$%ιE)*+2$%ι2 *ι%,%,%!> *+ $υ4%ι,.+)%)*ω$+.&υ,%!!)%ι)*-$%ι)ι*ι-e%),.ω,.4ι$* ι7,' υ*-ι$*/$%ω%ι!e>$*'$e')ω5.,%$*+&2e$*'&*+/υ$2,%ι)$)72$υ,% ))ι2$υ,%$7%*%,.$%.ι7,'4ωι$*/$%ω)'*%$υ27ω%)ι.4υ,%$,.4ι$*ι7,'[ 2nι$*/$%ι> `Q*/,%9KD8<B8H::> Q)'*&ι')/7υ%)ι.4υ,%*-*υ,)9OF<BD89Z:>,5D%*ι+'υ7+/* -,),% $υ,% ι*2ι4ι*+%)/+\+*υ 4'ι7,υ $4ι$+ *ω,%*$&+,*ι$,.ω *ι,-> ι %)ι4. )υ )υ$ι/d*ι )ι )/ω.&υ )%)ι%4% )' * ;<;;> * )$ι 9I<:;?<BD8 E;B<:;B?: #ι*2ι %)2\%ω( D*ι ι *ι,.*ωι*+ωυ*-> iq*/,%9kd8<b8h::> jq)'*+&ι2/* )*/,%*υ,)9pgd<9z:>,5d%*ι+'υ7+ /* PQ *)/7υυ*',),%$υ,% )ιι4/,,*7 %,5ι$*$* )*%.$,*/,>1$ι/ %)ι.4υ,%*+%)ι42 9KD D89F?E::4ι %,5ι$*%.ι/4,, $%$,,%$+,%)υ D*ι)'*ι $υ*%*4,.%/7% *ι%ι,.υe)*2+$+ 2)%)*ω$+#%-/7% )ι*ι)$-)υ(> )$+,%*+%)ι42 9O;B^B8?GM9Q <;?89WD;_:> F;DcB_B<B:9:,),% )/4υ,%.ι/4,,,%*ι&ι.*ι,.,ι'*+*3,ι'*+*)υ)2,%)'*nυ)%,%)υ $υ,,%*%&$*+.%υ.*ι*ω,%*$&+,*ι$,.ω*ι,-,ι'*+*3,ι'*+*)υυ)4d%ι*;<;;> PPQ*/,%«Continue»> PQ)'*+&ι2/*)*/,%*υ,)9OH<FH<Z:>,5D%*ι+'υ7+/* 173

21 *+/*ι$*%/,),%$υ,%*+ 9)$'*+*:*+ )+5)υ7 )υ$ι$*%$*)/7υ )*%%$,/*ω*υ;<;;> *+%)ι429tb9fg?s: %)ι.4υ,%9kd D8 9F?E:EDD;>B8?<:9:4ι*+ )υ$$+*ω $υ*%*4,.ω*ω *ι%ι,.ωe)*+2$%ω 2)%ι)*-$%ω)υ υ,%>),% %)$+)7+%$υ,% *ι*ι,.υ*.$%.&% 4ι))./υ$+$*+ %)ι429$?m:<dn:= CBG::> *+%)ι429tb9<?8e:9:,),%)/υ,%ω )*.%$,.),% *ι,.)υ%&υ*+%υ7%)'$*$+/,%$$*ι$υ*%*4,.%d%υ4-*ι%ι,.ω$*)4*ι'ι/4,,> *+ %)ι42 9<;?89WD;_:> F;DcB_B<B:9:,),% )/υ,% ω )*.%$,,ι $%ι/ )' )%,% *ι,%*$&+,*ι$,.%*ι,.,ι'*+*3,ι'*+*#+>)'$*$+(e.4ι/7%,ι)'*ι9)+4.:*ω%,.ω,3$*+)%)*ω$+υ*24ι/7%.)'*nυ)%,%)υ$υ,,%*%&$*+.%υ/,> *+%)ι429i<:;?<bd8yb9<d;s:,),%,%%)$+*ι$*ι'*ω%)2\%ω%*.%$+*υ4'ι7,υ 8.*ι$*+)$*ι)+$+*ω*ι,-,ι'*+*3,ι'*+**ω&ι-%,.ω*+.%υ> *+%)ι429$<;:99>:ed_fd9b<bd8:)υ$ι/d%*ι.)*ι,-)υ,),%,%)'$$υ,8/%ι+ /7%9)+42:#+>*/7%υ)%,%*+.%υ$*+$υ4%ι,.+)%)*ω$+($*+ι,'5ω$+*+$υι2 /%ι> PV>*/,%«Continue»> PXQ *+&ι2/*)*/,%*υ,)9:> SPSS ι"ι : Cases Sources Objects Proximities Case Processing Summary Total Proximities Missing Proximities Active Proximities a b a. Active proximities include all non-missing proximities. b. Sum over sources of all strictly lower-triangular proximities., ι ιι.,ι ι ι (cases) ι ι 33 (η. 11 ιι x 3 ).,ι η (sources) ι 3, ι, ι ι (objects) ι 11, η. ι ιι ( ι η ). ι «Total Proximities» ι ι ιη- ιη ι η (η. ). 174

22 ι"ι ι η ιη ηι ι ι ι ι. /η ι ι ηι ι " ι ι ι ιη-ιη 11 ιι. 6ι, ι ι «Stress» ( 4 ι) ι ι ι η. # η ι Stress ι ι 0 ι ι ι ι ιη- ιη η ι ηιη ι η (Kruskal ι Wish, 1981,.25). #ι, ι 'η ι ι ι η ι ι ι η. & ι ι ι ι ι ι η &, 2007 Stress and Fit Measures Normalized Raw Stress Stress-I Stress-II S-Stress Dispersion Accounted For (D.A.F.) Tucker's Coefficient of Congruence,03373,18365 a,50161 a,08804 b,96627,98299 PROXSCAL minimizes Normalized Raw Stress. a. Optimal scaling factor = 1,035. b. Optimal scaling factor =,977. ι ι ι η. * ι η ι!η ι ι'ι η ιη ι ηι ι ιη-ιη 11 ιι. «Iteration History» ι"ι ιι ' η ι ιη ι ηη ι ιη-ιη ι η., η ι ι ι 8 'ι. η 8 η η'η η ιη η ι η η ηη ιη 0,0001 ιι η ι «Options» ι ι. Iteration History Normalized Iteration Raw Stress Improvement 0,28733(a) 1,04739, ,03817, ,03555, ,03455, ,03411, ,03390, ,03379, ,03373,00006(b) a Stress of initial configuration: simplex start. b The iteration process has stopped because improvement has become less than the convergence criterion. ι ι"ι ι ηι ι η (. SRC_1 3) ι ιι ι ι. SRC_2 ι 0,0368 (. ι «Mean») ι ι ι η ι ι. # η «ιι» η Chamberlain, o Franco ι Churchill ι η ι ι (. η «Mean»). Decomposition of Normalized Raw Stress Final Coordinates Object Mean hitler mussolin churchil eisenhow stalin franco degaulle maotsetu truman chamberl tito Source SRC_1 SRC_2 SRC_3 Mean,0265,0205,0206,0225,0214,0529,0293,0345,0338,0321,0574,0411,0278,0275,0141,0231,0188,0514,0275,0326,0547,0443,0302,0431,0245,0118,0155,0173,0374,0346,0491,0404,0361,0335,0255,0317,0366,0667,0297,0443,0607,0291,0313,0404,0344,0368,0300,0337 HITLER MUSSOLIN CHURCHIL EISENHOW STALIN FRANCO DEGAULLE MAOTSETU TRUMAN CHAMBERL TITO Dimension 1 2 -,653 -,010 -,725 -,296,176 -,157,634 -,426 -,472,424 -,450 -,336,111,038,037,947,788,059,187 -,712,368,

23 ,!ι ιι ι η ι"ι ι ι ι ιι ι ι ιι. 3η ι ι ιι ιι ι ι (ι ηι ι). ιι ι ι "η ιι!" ιι ι"ι ι ι! η ι. Distances Hitler,000 Hitler Mussolini Churchill Eisenhower Stalin Franco Degaulle Mao Truman Chamberl Tito Mussolini,295,000 Churchill,842,912,000 Eisenhower 1,353 1,365,531,000 Stalin,471,764,871 1,395,000 Franco,384,278,651 1,088,760,000 Degaulle,766,901,205,699,700,674,000 Mao 1,181 1,459 1,113 1,497,730 1,372,912,000 Truman 1,443 1,554,649,508 1,312 1,299,677 1,163,000 Chamberlain 1,095 1,003,555,531 1,314,740,754 1,666,977,000 Tito 1,128 1,334,655,934,841 1,147,502,581,587 1,195,000 ι ι η ι Hitler ι η ι «ι"ι» ι Mussolini, Franco ι Stalin (η η! ιι ιι ιι ι η ιη! "η) Stalin ηη., ι ι η ι η ι: & η ι ιι "ι ι ι ιη. η η ιη (. ι"ι!) η ι Hitler, Mussolini ι Franco η" ι ι η!ι Eisenhower. $" η ιι ι η η ιι ιι, ι ι ιι ι η ι ιι. ; ι ι ι ι ιι ι "ι ι ι «ιιι» ιη ι η η ιι ιη ι ι η. 176