The index builds on the number of rating matches between pairs of constructs. It is the relation between the total number of matches and the possible number of matches.
Arguments
- x
A
repgridobject.- deviation
Maximal difference between ratings to be considered a match (default
0= identical scores for a match).
Value
List of class indexBieri:
grid: The grid used to calculate the indexdeviationThe deviation parameter.matches_maxMaximum possible number of matches across constructs.matchesTotal number of matches across constructs.constructs: Matrix with no. of matches for constructs.bieri: Bieri index (= matches / matches_max)
Examples
m <- indexBieri(boeker)
# several output options
print(m)
#>
#> ######################
#> BIERI COMPLEXITY INDEX
#> ######################
#>
#> Bieri: 0.24
#>
#> Maximal rating difference to count as match: 0
#> Total no. of matches between constructs: 327
#> Maximum possible no. of matches between constructs: 1365
print(m, output = "IC") # construct matches
#>
#> ######################
#> BIERI COMPLEXITY INDEX
#> ######################
#>
#> Bieri: 0.24
#>
#> Maximal rating difference to count as match: 0
#> Total no. of matches between constructs: 327
#> Maximum possible no. of matches between constructs: 1365
#>
#> MATCHES BETWEEN CONSTRUCTS
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> 1 balanced - get along with conflicts 1 2 3 2 6 5 4 4 6 1 4 5
#> 2 isolated - sociable 2 3 2 4 3 2 3 2 4 1 5
#> 3 closely integrated - excluded 3 4 3 0 5 5 2 2 5 5
#> 4 discursive - passive 4 6 4 4 4 6 1 3 3
#> 5 open minded - indifferent 5 4 5 4 7 2 4 7
#> 6 dreamy - dispassionate 6 4 6 8 4 2 4
#> 7 practically oriented - depressed 7 3 8 1 5 4
#> 8 playful - serious 8 3 3 2 6
#> 9 socially minded - selfish 9 0 2 6
#> 10 quarrelsome - peaceful 10 3 3
#> 11 artistic - technical 11 3
#> 12 scientific - emotional 12
#> 13 introvert - extrovert 13
#> 14 wanderlust - home oriented 14
#> 13 14
#> 1 4 6
#> 2 6 2
#> 3 4 2
#> 4 3 2
#> 5 4 3
#> 6 2 4
#> 7 2 3
#> 8 8 3
#> 9 2 5
#> 10 3 0
#> 11 0 3
#> 12 4 3
#> 13 3
#> 14
# extract the matrix of matches
m$constructs
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] NA 2 3 2 6 5 4 4 6 1 4 5 4
#> [2,] 2 NA 3 2 4 3 2 3 2 4 1 5 6
#> [3,] 3 3 NA 4 3 0 5 5 2 2 5 5 4
#> [4,] 2 2 4 NA 6 4 4 4 6 1 3 3 3
#> [5,] 6 4 3 6 NA 4 5 4 7 2 4 7 4
#> [6,] 5 3 0 4 4 NA 4 6 8 4 2 4 2
#> [7,] 4 2 5 4 5 4 NA 3 8 1 5 4 2
#> [8,] 4 3 5 4 4 6 3 NA 3 3 2 6 8
#> [9,] 6 2 2 6 7 8 8 3 NA 0 2 6 2
#> [10,] 1 4 2 1 2 4 1 3 0 NA 3 3 3
#> [11,] 4 1 5 3 4 2 5 2 2 3 NA 3 0
#> [12,] 5 5 5 3 7 4 4 6 6 3 3 NA 4
#> [13,] 4 6 4 3 4 2 2 8 2 3 0 4 NA
#> [14,] 6 2 2 2 3 4 3 3 5 0 3 3 3
#> [,14]
#> [1,] 6
#> [2,] 2
#> [3,] 2
#> [4,] 2
#> [5,] 3
#> [6,] 4
#> [7,] 3
#> [8,] 3
#> [9,] 5
#> [10,] 0
#> [11,] 3
#> [12,] 3
#> [13,] 3
#> [14,] NA
# CAVEAT: Bieri's index changes when constructs are reversed
nr <- nrow(boeker)
l <- replicate(1000, swapPoles(boeker, sample(nr, sample(nr, 1))))
bieri <- sapply(l, function(x) indexBieri(x)$bieri)
hist(bieri, breaks = 50)
abline(v = mean(bieri), col = "red", lty = 2)
