Dissimilarity ratings of Second World War politicians by two subjects
12 politicians (Hitler, Mussolini, Churchill, Eisenhower, Stalin, Attlee, Franco, De Gaulle, MaoTseTung, Truman, Chamberlain, Tito), 2 dissimilarity matrices.
CLUSE files:
POLIT.DES - descriptionPOLIT.NAM - names of politiciansPOLIT.ENV - CLUSE environmentPOLIT1.DIS - first dissimilarity matrixPOLIT2.DIS - second dissimilarity matrixSource: B.S. Everitt: Introduction to optimization methods and their application in statistics. Chapman and Hall, London, 1987, p. 72.
version 1: 6. May 1989 POLIT.NAM, POLIT.DES, POLIT1.DIS, POLIT2.DIS
version 2: 23. Feb 1992 POLIT.ENV
version 3: 20. Mar 2018 conversion to R polit.RData
> setwd("C:/Users/batagelj/work/Delphi/Cluse/Cluse/data/Polit")
> a <- scan("Polit1.dis")
Read 78 items
> s <- length(a); n <- round((-1+sqrt(1+8*s))/2); nm <- n-1
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D1 <- D+t(D)
> lab <- c("Hitler", "Mussolini", "Churchill", "Eisenhower", "Stalin", "Attlee",
+ "Franco", "De Gaulle", "MaoTseTung", "Truman", "Chamberlain", "Tito")
> D1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 2 7 8 5 9 2 6 8 8 8 9
[2,] 2 0 8 8 8 9 1 7 9 9 9 9
[3,] 7 8 0 3 5 8 7 2 8 3 5 6
[4,] 8 8 3 0 8 7 7 3 8 2 3 8
[5,] 5 8 5 8 0 7 7 5 6 7 9 5
[6,] 9 9 8 7 7 0 9 7 7 4 7 5
[7,] 2 1 7 7 7 9 0 5 9 8 8 9
[8,] 6 7 2 3 5 7 5 0 6 5 6 5
[9,] 8 9 8 8 6 7 9 6 0 8 8 6
[10,] 8 9 3 2 7 4 8 5 8 0 4 6
[11,] 8 9 5 3 9 7 8 6 8 4 0 8
[12,] 9 9 6 8 5 5 9 5 6 6 8 0
> rownames(D1) <- colnames(D1) <- lab
> a <- scan("Polit2.dis")
Read 78 items
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D2 <- D+t(D)
> rownames(D2) <- colnames(D2) <- lab
> P <- array(0,dim=c(2,n,n),dimnames=list(c("P1","P2"),lab,lab))
> P[1,,] <- D1
> P[2,,] <- D2
> P[1,1:4,1:3]
Hitler Mussolini Churchill
Hitler 0 2 7
Mussolini 2 0 8
Churchill 7 8 0
Eisenhower 8 8 3
> attr(P,"tit") <- "Dissimilarity ratings of Second World War politicians by two subjects"
> attr(P,"refs") <- "B.S. Everitt: Introduction to optimization methods and their application in statistics.
Chapman and Hall, London, 1987, p. 72"
> attr(P,"ver") <- "by Vladimir Batagelj / 20. Mar 2018, 23. Feb 1992, 6. May 1989"
> str(P)
num [1:2, 1:12, 1:12] 0 0 2 3 7 4 8 7 5 3 ...
- attr(*, "dimnames")=List of 3
..$ : chr [1:2] "P1" "P2"
..$ : chr [1:12] "Hitler" "Mussolini" "Churchill" "Eisenhower" ...
..$ : chr [1:12] "Hitler" "Mussolini" "Churchill" "Eisenhower" ...
- attr(*, "tit")= chr "Dissimilarity ratings of Second World War politicians by two subjects"
- attr(*, "refs")= chr "B.S. Everitt: Introduction to optimization methods and their application in statistics.
Chapman and Hall, London, 1987, p. 72"
- attr(*, "ver")= chr "by Vladimir Batagelj / 20. Mar 2018, 23. Feb 1992, 6. May 1989"
> save(P,file="polit.RData",ascii=TRUE)
Rosenberg and Kim (1975) Kinship Terms
The objects (units) in this study were the 15 kinship terms:
The sources of data were k=6 mutually exclusive groups of college students, each of whom received a set of 15 slips of paper, each containing one of the kinship terms. The paradigm for data collection was a “sorting” task, in which a subject is asked to produce a partition of the (15) objects, on the basis of perceived psychological similarity. Eighty-five male and eighty-five female subjects were run in the condition where subjects gave only a single-sort of the terms. A different group of subjects (eighty males and eight females) was told in advance that after making their first sort, they would be asked to give additional subjective partitions of these stimuli using “a different basis of meaning each time”. The authors used only the data of the first and second sorting for these groups of subjects. Thus, we have the k=6 conditions as our source for the analysis.
Each subject's data can be coded as a symmetric 15×15 binary (0,1) co-occurrence matrix in which a one indicates that kinship terms of the corresponding row and column were sorted into the same group by the subject. The resulting co-occurrence matrices are then summed within each condition to yield an aggregate matrix. Thus, each of the six matrices is a similarity matrix. They were converted to dissimilarity ones by subtracting each entry from the number of subjects in the respective conditions.
CLUSE files:
version 1: 14. may 1989 KINSHIP.DES, KINSHIP.NAM, KINSHIP*.DIS
version 2: 23. feb 1992 KINSHIP.ENV
version 3: 20. Mar 2018 conversion to R polit.RData
URLs:
> setwd("C:/Users/batagelj/work/Delphi/Cluse/Cluse/data/Kinship")
> a <- scan("Kinship1.dis")
Read 120 items
> s <- length(a); n <- round((-1+sqrt(1+8*s))/2); nm <- n-1
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D1 <- D+t(D)
> lab <- c("Aunt", "Brother", "Cousin", "Daughter", "Father", "GDaughter", "GFather",
+ "GMother", "GSon", "Mother", "Nephew", "Niece", "Sister", "Son", "Uncle")
> D1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
[1,] 0 79 56 36 76 34 76 36 77 33 57 13 38 77 47
[2,] 79 0 70 66 22 73 35 78 27 68 33 75 48 20 38
[3,] 56 70 0 71 78 70 75 75 71 76 55 54 70 72 59
[4,] 36 66 71 0 63 25 76 34 71 15 74 32 20 48 79
[5,] 76 22 78 63 0 77 32 76 31 50 38 79 67 16 32
[6,] 34 73 70 25 77 0 61 17 49 31 74 31 28 70 78
[7,] 76 35 75 76 32 61 0 48 17 76 37 77 77 35 37
[8,] 36 78 75 34 76 17 48 0 63 30 78 38 34 77 77
[9,] 77 27 71 71 31 49 17 63 0 77 30 73 74 26 35
[10,] 33 68 76 15 50 31 76 30 77 0 80 39 21 62 75
[11,] 57 33 55 74 38 74 37 78 30 80 0 45 77 32 13
[12,] 13 75 54 32 79 31 77 38 73 39 45 0 35 72 57
[13,] 38 48 70 20 67 28 77 34 74 21 77 35 0 66 79
[14,] 77 20 72 48 16 70 35 77 26 62 32 72 66 0 38
[15,] 47 38 59 79 32 78 37 77 35 75 13 57 79 38 0
> a <- scan("Kinship2.dis")
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D2 <- D+t(D)
> a <- scan("Kinship3.dis")
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D3 <- D+t(D)
> a <- scan("Kinship4.dis")
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D4 <- D+t(D)
> a <- scan("Kinship5.dis")
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D5 <- D+t(D)
> a <- scan("Kinship6.dis")
> D <- matrix(0, nrow=n, ncol=n); D[lower.tri(D,diag=TRUE)] <- a; D6 <- D+t(D)
> K <- array(0,dim=c(6,n,n),dimnames=list(paste("K",1:6,sep=""),lab,lab))
> K[1,,] <- D1; K[2,,] <- D2; K[3,,] <- D3; K[4,,] <- D4; K[5,,] <- D5; K[6,,] <- D6
> K[1,1:4,1:3]
Aunt Brother Cousin
Aunt 0 79 56
Brother 79 0 70
Cousin 56 70 0
Daughter 36 66 71
> K[,2,3]
K1 K2 K3 K4 K5 K6
70 63 62 56 77 73
> attr(K,"tit") <- "Rosenberg and Kim (1975) Kinship Terms"
> attr(K,"refs") <- "S. Rosenberg and M. P. Kim (1975). The method of sorting as a data-gathering procedure
in multivariate research. Multivariate Behavioral Research, 10, 489–502."
> attr(K,"ver") <- "by Vladimir Batagelj / 20. Mar 2018, 23. Feb 1992, 14. May 1989"
> labAN <- c("FCA:aunt", "MN*:brot", "*C*:cous", "FND:daug", "MNA:fath", "FGD:Gdaug", "MGA:Gfath",
+ "FGA:Gmoth", "MGD:Gson", "FNA:moth", "MCD:neph", "FCD:niec", "FN*:sist", "MND:son", "MCA:uncl")
> labSI <- c("teta", "brat", "bra/sestrična", "hči", "oče", "vnukinja", "dedek", "babica", "vnuk",
+ "mati", "nečak", "nečakinja", "sestra", "sin", "stric")
> attr(K,"labAN") <- labAN; attr(K,"labSI") <- labSI
> str(K)
num [1:6, 1:15, 1:15] 0 0 0 0 0 0 79 76 78 74 ...
- attr(*, "dimnames")=List of 3
..$ : chr [1:6] "K1" "K2" "K3" "K4" ...
..$ : chr [1:15] "Aunt" "Brother" "Cousin" "Daughter" ...
..$ : chr [1:15] "Aunt" "Brother" "Cousin" "Daughter" ...
- attr(*, "tit")= chr "Rosenberg and Kim (1975) Kinship Terms"
- attr(*, "refs")= chr "S. Rosenberg and M. P. Kim (1975). The method of sorting as a data-"| __truncated__
- attr(*, "ver")= chr "by Vladimir Batagelj / 20. Mar 2018, 23. Feb 1992, 14. May 1989"
- attr(*, "labAN")= chr [1:15] "FCA:aunt" "MN*:brot" "*C*:cous" "FND:daug" ...
- attr(*, "labSI")= chr [1:15] "teta" "brat" "bra/sestrična" "hči" ...
> save(K,file="Kinship.RData",ascii=TRUE)