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»

# 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

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 1970-1979 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 '70. 1. 2. 3. 4. 5. 6 # #" % 0"!! " &υ" η 7 22 20 20 10 6 85 )( )% & % 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 17 80 83 29 15 10 234 & % 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

υ" &υ" η 2 14 10 7 0 2 35 2!!! " % 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 37 27 15 0 0 81 & % 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 7 24 11 5 1 0 48 & % 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 35 177 151 76 26 18 483 7,2% 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

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

$*)$ι9y@kvma:# *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

PVQ/υ,%*ι%)ι4.)υ$+,%ι-*ι)> PXQ9KD8<B8H::> P[Q *+&ι2/*9kd;;:9fd8>:8e: A8?GS9B9:)*/,%*υ,)9PGD<9Z:>,5D%*ι +'υ7+/* P`Q/υ,%*ι)%)ι4.>^ι.4υ,%9BIWK@B:4ι %,5ι$*%.)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 4 22 30 58 52 11 177 18 75 112 172 88 30 495 3 11 16 28 8 0 66 15 30 42 54 20 6 167 11 18 27 21 10 1 88 51 156 227 333 178 48 993, 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 (.. 177 /ηι). # ι «Active Margin» ι ι ι ι -η η (.. 51 993! η ι ηι ι η). ι"ι ι: 159

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

Summary Proportion of Inertia Confidence Singular Value Dimension 1 2 3 4 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 993 2 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 1 2 1 2 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

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 1 2 1 2 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

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ι$*/$%ω)υ$,%> iq*/,%9kd8<b8h:: jq *+&ι2/*9h@m@fnanibzaajkzcic#homa ;(:)*/,%9:> SPSS : η &, 2007 #!!!ω # 5 υ Missing Marginal Frequency 211 289 0 " "ω Missing Marginal Frequency 273 51 74 102 0!. /+ 0 & +η &" Missing Marginal Frequency 103 197 77 66 10 45 2 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,845642 Iteration,000009 2 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

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

167 η &, 2007 3η 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

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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) 1,04739,23994 2,03817,00922 3,03555,00262 4,03455,00100 5,03411,00044 6,03390,00021 7,03379,00011 8,03373,00006(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 -,712,368,469 175

,!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