Preliminiary Results: Self concept and motivation to play tabletop games
Vittorio Guerrieri, Guyonne Rogier, Thomas Tacchino
Last updated: 14-09-2024
## [1] "52 people participated to the study so far."Help us! Answer the questionnaire HERE
Sociodemographics
Gender
Currently we have a rather balanced sample in regards to gender.
## Gender N
## 1 Femmina 27
## 2 Maschio 25
Age
At the moment we have manged to contact a very young sample, we will need to focus on older portions of population.
## [1] "The sample's mean age is 26.06 ± 5.64"
Correlation matrix
corMat <- dataframe %>%
select_if(is.numeric) %>%
cor(use = "pairwise.complete.obs") %>%
round(2)
heatmap.2(1-abs(corMat),
Rowv = F,
Colv = F,
dendrogram="none",
symm=T,
scale="none",
revC=F,
cellnote=matrix(gsub("0.",".",corMat,fixed=T),ncol=ncol(corMat),nrow=ncol(corMat)),
trace = "none",
notecol = "black",
notecex=0.5,
key=F,
keysize=0.4,
margins = c(6,6),
col=hcl.colors(100,"mint"),
cexRow = 0.5,
cexCol = 0.5,
offsetRow = 0.01,
offsetCol = 0.01
)
Scale validation
Correlation matrix and reduntant items
TGMI
#matrice di correlazione per la TGMI
data[,121:172] %>%
lapply(function(x){gsub("Fortemente in disaccordo",1,x)}) %>%
lapply(function(x){gsub("Abbastanza in disaccordo",2,x)}) %>%
lapply(function(x){gsub("Un po' in disaccordo",3,x)}) %>%
lapply(function(x){gsub("Né in disaccordo, né in accordo",4,x)}) %>%
lapply(function(x){gsub("Un po' d'accordo",5,x)}) %>%
lapply(function(x){gsub("Abbastanza d'accordo",6,x)}) %>%
lapply(function(x){gsub("Fortemente d'accordo",7,x)}) %>%
lapply(as.numeric) %>%
do.call(cbind,.) %>%
unname %>%
assign("TGMI",.,1)%>%
cor(use="pairwise.complete.obs") %>%
round(.,2) %T>%
assign("corMat_TGMI",.,1) %T>%
print %>%
#flag reduntant items
(function(x){abs(x)>.7&abs(x)<1}) %>%
replace_triangle(by=F) %>%
as.matrix %T>%
{assign("cor",corMat_TGMI[.],1)} %>%
which(arr.ind=T) %>%
unname %>%
cbind(cor) %T>%
{print("Redundant pairs of items")} %>%
print## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1.00 -0.41 0.53 0.46 0.55 0.64 0.70 0.60 0.58 0.05 -0.07 -0.16
## [2,] -0.41 1.00 -0.53 -0.40 -0.25 -0.35 -0.31 -0.29 -0.32 0.21 0.12 0.10
## [3,] 0.53 -0.53 1.00 0.61 0.62 0.75 0.59 0.64 0.52 0.04 0.07 -0.08
## [4,] 0.46 -0.40 0.61 1.00 0.73 0.62 0.51 0.66 0.46 0.30 0.20 0.20
## [5,] 0.55 -0.25 0.62 0.73 1.00 0.73 0.62 0.66 0.38 0.31 0.25 0.11
## [6,] 0.64 -0.35 0.75 0.62 0.73 1.00 0.69 0.65 0.49 0.18 0.18 -0.05
## [7,] 0.70 -0.31 0.59 0.51 0.62 0.69 1.00 0.70 0.38 0.35 -0.02 -0.12
## [8,] 0.60 -0.29 0.64 0.66 0.66 0.65 0.70 1.00 0.49 0.24 0.06 -0.05
## [9,] 0.58 -0.32 0.52 0.46 0.38 0.49 0.38 0.49 1.00 -0.29 0.17 0.05
## [10,] 0.05 0.21 0.04 0.30 0.31 0.18 0.35 0.24 -0.29 1.00 -0.04 0.00
## [11,] -0.07 0.12 0.07 0.20 0.25 0.18 -0.02 0.06 0.17 -0.04 1.00 0.58
## [12,] -0.16 0.10 -0.08 0.20 0.11 -0.05 -0.12 -0.05 0.05 0.00 0.58 1.00
## [13,] 0.06 0.05 0.10 0.21 0.17 0.16 0.01 0.11 0.26 -0.23 0.62 0.59
## [14,] 0.14 0.24 0.16 0.16 0.28 0.34 0.32 0.21 0.00 0.29 0.39 0.20
## [15,] 0.18 0.00 0.20 0.36 0.38 0.44 0.21 0.31 0.18 0.19 0.46 0.24
## [16,] -0.03 0.06 0.15 0.26 0.23 0.24 -0.01 0.15 0.27 -0.08 0.59 0.61
## [17,] 0.38 -0.28 0.50 0.56 0.53 0.49 0.35 0.49 0.45 0.10 0.28 0.15
## [18,] 0.45 -0.27 0.46 0.61 0.58 0.52 0.42 0.60 0.45 0.15 0.29 0.12
## [19,] 0.41 -0.29 0.54 0.55 0.43 0.49 0.40 0.59 0.45 0.05 0.24 0.13
## [20,] 0.34 -0.15 0.40 0.47 0.46 0.43 0.50 0.54 0.45 0.17 0.34 0.17
## [21,] 0.28 -0.13 0.29 0.23 0.49 0.47 0.44 0.21 0.21 0.12 0.10 0.11
## [22,] 0.03 -0.02 0.06 -0.07 0.09 0.22 0.22 -0.10 -0.15 0.20 -0.03 -0.04
## [23,] 0.16 -0.19 0.43 0.31 0.38 0.40 0.46 0.26 0.10 0.32 0.00 0.00
## [24,] 0.18 -0.02 0.17 0.02 0.22 0.29 0.43 0.08 -0.08 0.32 -0.14 -0.16
## [25,] 0.27 0.05 0.27 0.26 0.23 0.31 0.41 0.38 -0.04 0.52 -0.19 -0.21
## [26,] 0.16 0.06 0.16 0.25 0.21 0.14 0.32 0.28 -0.09 0.33 -0.11 0.02
## [27,] 0.32 -0.05 0.56 0.42 0.51 0.59 0.53 0.62 0.31 0.27 0.15 -0.10
## [28,] 0.37 -0.12 0.56 0.50 0.55 0.49 0.54 0.57 0.24 0.28 0.14 0.11
## [29,] 0.43 -0.26 0.40 0.35 0.21 0.29 0.37 0.37 0.29 0.16 -0.20 -0.14
## [30,] 0.30 -0.13 0.29 0.31 0.18 0.27 0.36 0.32 0.29 0.24 -0.24 -0.06
## [31,] 0.63 -0.31 0.44 0.27 0.40 0.58 0.50 0.39 0.43 0.10 0.00 -0.17
## [32,] 0.20 -0.22 0.37 0.35 0.32 0.35 0.19 0.41 0.33 0.16 0.02 -0.09
## [33,] 0.48 -0.32 0.70 0.50 0.63 0.72 0.62 0.64 0.34 0.26 0.05 -0.11
## [34,] 0.48 -0.31 0.60 0.39 0.44 0.55 0.47 0.56 0.33 0.07 0.01 -0.14
## [35,] 0.36 -0.09 0.53 0.49 0.61 0.57 0.46 0.61 0.32 0.35 0.18 -0.01
## [36,] 0.32 -0.08 0.60 0.54 0.54 0.50 0.38 0.60 0.33 0.26 0.06 0.01
## [37,] 0.21 -0.28 0.25 0.21 0.12 0.13 0.17 0.23 0.10 0.06 -0.25 -0.27
## [38,] 0.58 -0.36 0.51 0.29 0.46 0.54 0.63 0.55 0.18 0.27 -0.25 -0.31
## [39,] 0.61 -0.32 0.45 0.36 0.46 0.57 0.72 0.59 0.21 0.36 -0.20 -0.35
## [40,] 0.17 -0.32 0.26 0.25 0.15 0.29 0.25 0.40 0.19 0.10 -0.01 0.01
## [41,] 0.43 -0.25 0.44 0.38 0.45 0.38 0.44 0.33 0.36 0.14 0.16 0.01
## [42,] 0.45 -0.11 0.31 0.32 0.35 0.36 0.47 0.36 0.30 0.27 0.19 -0.01
## [43,] 0.47 -0.28 0.35 0.27 0.35 0.51 0.59 0.50 0.20 0.24 -0.05 -0.06
## [44,] 0.45 -0.13 0.45 0.33 0.40 0.40 0.49 0.46 0.32 0.18 -0.05 -0.12
## [45,] 0.41 -0.10 0.26 0.32 0.42 0.41 0.35 0.35 0.38 0.08 0.21 0.07
## [46,] 0.32 -0.15 0.29 0.25 0.33 0.45 0.33 0.35 0.19 0.19 0.08 -0.10
## [47,] 0.34 -0.10 0.36 0.38 0.42 0.48 0.25 0.35 0.15 0.26 0.01 -0.01
## [48,] 0.51 -0.36 0.49 0.29 0.37 0.60 0.36 0.41 0.30 -0.07 0.01 -0.15
## [49,] 0.31 -0.21 0.31 0.52 0.42 0.28 0.40 0.53 0.32 0.20 0.23 -0.03
## [50,] 0.48 -0.35 0.38 0.39 0.50 0.58 0.64 0.52 0.23 0.30 0.14 -0.13
## [51,] 0.52 -0.44 0.56 0.40 0.40 0.51 0.55 0.44 0.35 0.07 0.05 0.08
## [52,] 0.61 -0.25 0.34 0.38 0.39 0.51 0.66 0.60 0.28 0.33 -0.08 -0.15
## [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] 0.06 0.14 0.18 -0.03 0.38 0.45 0.41 0.34 0.28 0.03 0.16 0.18
## [2,] 0.05 0.24 0.00 0.06 -0.28 -0.27 -0.29 -0.15 -0.13 -0.02 -0.19 -0.02
## [3,] 0.10 0.16 0.20 0.15 0.50 0.46 0.54 0.40 0.29 0.06 0.43 0.17
## [4,] 0.21 0.16 0.36 0.26 0.56 0.61 0.55 0.47 0.23 -0.07 0.31 0.02
## [5,] 0.17 0.28 0.38 0.23 0.53 0.58 0.43 0.46 0.49 0.09 0.38 0.22
## [6,] 0.16 0.34 0.44 0.24 0.49 0.52 0.49 0.43 0.47 0.22 0.40 0.29
## [7,] 0.01 0.32 0.21 -0.01 0.35 0.42 0.40 0.50 0.44 0.22 0.46 0.43
## [8,] 0.11 0.21 0.31 0.15 0.49 0.60 0.59 0.54 0.21 -0.10 0.26 0.08
## [9,] 0.26 0.00 0.18 0.27 0.45 0.45 0.45 0.45 0.21 -0.15 0.10 -0.08
## [10,] -0.23 0.29 0.19 -0.08 0.10 0.15 0.05 0.17 0.12 0.20 0.32 0.32
## [11,] 0.62 0.39 0.46 0.59 0.28 0.29 0.24 0.34 0.10 -0.03 0.00 -0.14
## [12,] 0.59 0.20 0.24 0.61 0.15 0.12 0.13 0.17 0.11 -0.04 0.00 -0.16
## [13,] 1.00 0.29 0.29 0.69 0.07 0.17 0.21 0.23 0.06 -0.08 -0.09 -0.28
## [14,] 0.29 1.00 0.38 0.34 0.00 0.14 0.12 0.10 0.36 0.36 0.29 0.41
## [15,] 0.29 0.38 1.00 0.54 0.22 0.27 0.09 0.15 0.28 0.15 0.33 0.23
## [16,] 0.69 0.34 0.54 1.00 0.19 0.25 0.20 0.28 0.11 -0.02 0.11 -0.03
## [17,] 0.07 0.00 0.22 0.19 1.00 0.86 0.73 0.61 0.26 -0.13 0.24 0.09
## [18,] 0.17 0.14 0.27 0.25 0.86 1.00 0.76 0.71 0.18 -0.13 0.14 0.09
## [19,] 0.21 0.12 0.09 0.20 0.73 0.76 1.00 0.71 0.10 -0.20 0.12 -0.03
## [20,] 0.23 0.10 0.15 0.28 0.61 0.71 0.71 1.00 0.14 -0.08 0.25 0.05
## [21,] 0.06 0.36 0.28 0.11 0.26 0.18 0.10 0.14 1.00 0.63 0.58 0.67
## [22,] -0.08 0.36 0.15 -0.02 -0.13 -0.13 -0.20 -0.08 0.63 1.00 0.53 0.76
## [23,] -0.09 0.29 0.33 0.11 0.24 0.14 0.12 0.25 0.58 0.53 1.00 0.62
## [24,] -0.28 0.41 0.23 -0.03 0.09 0.09 -0.03 0.05 0.67 0.76 0.62 1.00
## [25,] -0.31 0.42 0.15 -0.12 0.12 0.14 0.09 0.15 0.21 0.32 0.31 0.44
## [26,] -0.11 0.33 0.23 0.03 0.15 0.18 -0.04 0.03 0.33 0.31 0.32 0.42
## [27,] 0.03 0.31 0.38 0.13 0.29 0.31 0.29 0.35 0.22 0.07 0.36 0.19
## [28,] 0.09 0.30 0.45 0.23 0.37 0.37 0.25 0.23 0.18 0.12 0.32 0.30
## [29,] 0.02 0.05 -0.03 -0.01 0.23 0.28 0.41 0.36 0.00 -0.06 0.10 0.15
## [30,] -0.08 0.01 -0.07 -0.01 0.27 0.33 0.29 0.28 0.09 0.00 0.12 0.18
## [31,] 0.03 0.28 0.13 0.00 0.19 0.29 0.24 0.27 0.37 0.13 0.17 0.26
## [32,] -0.08 -0.08 0.07 0.09 0.34 0.39 0.35 0.34 0.13 -0.04 0.08 0.04
## [33,] -0.07 0.17 0.26 0.06 0.58 0.55 0.47 0.42 0.31 0.11 0.37 0.32
## [34,] 0.00 0.05 0.28 0.16 0.56 0.53 0.49 0.34 0.18 0.03 0.31 0.25
## [35,] 0.07 0.45 0.24 0.22 0.54 0.62 0.58 0.48 0.29 0.11 0.41 0.33
## [36,] -0.02 0.24 0.27 0.16 0.52 0.51 0.47 0.30 0.27 0.02 0.40 0.25
## [37,] -0.14 -0.18 -0.27 -0.29 0.09 0.17 0.30 0.27 0.04 -0.04 -0.03 0.12
## [38,] -0.18 0.16 0.05 -0.10 0.32 0.45 0.44 0.36 0.29 0.20 0.28 0.41
## [39,] -0.22 0.16 0.13 -0.11 0.28 0.40 0.38 0.32 0.24 0.13 0.24 0.34
## [40,] -0.02 -0.17 0.02 0.02 0.21 0.25 0.38 0.39 0.20 0.15 0.13 0.12
## [41,] 0.12 0.04 0.11 0.05 0.38 0.35 0.16 0.30 0.22 0.20 0.20 0.18
## [42,] 0.09 0.20 0.34 0.02 0.33 0.30 0.11 0.22 0.32 0.33 0.34 0.26
## [43,] -0.04 0.10 0.30 -0.04 0.15 0.18 0.15 0.26 0.33 0.29 0.29 0.27
## [44,] 0.04 0.10 0.21 0.00 0.12 0.18 0.09 0.20 0.34 0.34 0.35 0.26
## [45,] 0.24 0.22 0.29 0.22 0.17 0.23 0.29 0.30 0.24 0.02 0.20 0.18
## [46,] 0.05 0.11 0.24 0.19 0.10 0.21 0.27 0.27 0.20 0.12 0.22 0.27
## [47,] -0.06 0.25 0.06 0.06 0.35 0.30 0.38 0.20 0.28 0.08 0.22 0.13
## [48,] -0.10 0.13 0.22 0.06 0.24 0.29 0.31 0.19 0.30 0.19 0.19 0.27
## [49,] 0.16 0.10 0.16 0.18 0.32 0.50 0.42 0.50 -0.15 -0.22 0.17 -0.12
## [50,] 0.08 0.23 0.22 0.09 0.33 0.47 0.47 0.47 0.32 0.06 0.25 0.20
## [51,] 0.04 0.20 0.11 -0.01 0.34 0.35 0.38 0.28 0.43 0.20 0.20 0.26
## [52,] -0.17 0.22 0.16 -0.04 0.19 0.37 0.34 0.39 0.14 0.04 0.08 0.21
## [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
## [1,] 0.27 0.16 0.32 0.37 0.43 0.30 0.63 0.20 0.48 0.48 0.36 0.32
## [2,] 0.05 0.06 -0.05 -0.12 -0.26 -0.13 -0.31 -0.22 -0.32 -0.31 -0.09 -0.08
## [3,] 0.27 0.16 0.56 0.56 0.40 0.29 0.44 0.37 0.70 0.60 0.53 0.60
## [4,] 0.26 0.25 0.42 0.50 0.35 0.31 0.27 0.35 0.50 0.39 0.49 0.54
## [5,] 0.23 0.21 0.51 0.55 0.21 0.18 0.40 0.32 0.63 0.44 0.61 0.54
## [6,] 0.31 0.14 0.59 0.49 0.29 0.27 0.58 0.35 0.72 0.55 0.57 0.50
## [7,] 0.41 0.32 0.53 0.54 0.37 0.36 0.50 0.19 0.62 0.47 0.46 0.38
## [8,] 0.38 0.28 0.62 0.57 0.37 0.32 0.39 0.41 0.64 0.56 0.61 0.60
## [9,] -0.04 -0.09 0.31 0.24 0.29 0.29 0.43 0.33 0.34 0.33 0.32 0.33
## [10,] 0.52 0.33 0.27 0.28 0.16 0.24 0.10 0.16 0.26 0.07 0.35 0.26
## [11,] -0.19 -0.11 0.15 0.14 -0.20 -0.24 0.00 0.02 0.05 0.01 0.18 0.06
## [12,] -0.21 0.02 -0.10 0.11 -0.14 -0.06 -0.17 -0.09 -0.11 -0.14 -0.01 0.01
## [13,] -0.31 -0.11 0.03 0.09 0.02 -0.08 0.03 -0.08 -0.07 0.00 0.07 -0.02
## [14,] 0.42 0.33 0.31 0.30 0.05 0.01 0.28 -0.08 0.17 0.05 0.45 0.24
## [15,] 0.15 0.23 0.38 0.45 -0.03 -0.07 0.13 0.07 0.26 0.28 0.24 0.27
## [16,] -0.12 0.03 0.13 0.23 -0.01 -0.01 0.00 0.09 0.06 0.16 0.22 0.16
## [17,] 0.12 0.15 0.29 0.37 0.23 0.27 0.19 0.34 0.58 0.56 0.54 0.52
## [18,] 0.14 0.18 0.31 0.37 0.28 0.33 0.29 0.39 0.55 0.53 0.62 0.51
## [19,] 0.09 -0.04 0.29 0.25 0.41 0.29 0.24 0.35 0.47 0.49 0.58 0.47
## [20,] 0.15 0.03 0.35 0.23 0.36 0.28 0.27 0.34 0.42 0.34 0.48 0.30
## [21,] 0.21 0.33 0.22 0.18 0.00 0.09 0.37 0.13 0.31 0.18 0.29 0.27
## [22,] 0.32 0.31 0.07 0.12 -0.06 0.00 0.13 -0.04 0.11 0.03 0.11 0.02
## [23,] 0.31 0.32 0.36 0.32 0.10 0.12 0.17 0.08 0.37 0.31 0.41 0.40
## [24,] 0.44 0.42 0.19 0.30 0.15 0.18 0.26 0.04 0.32 0.25 0.33 0.25
## [25,] 1.00 0.71 0.33 0.37 0.15 0.15 0.14 0.08 0.41 0.11 0.38 0.41
## [26,] 0.71 1.00 0.30 0.43 -0.04 0.03 -0.03 -0.02 0.34 0.21 0.31 0.49
## [27,] 0.33 0.30 1.00 0.50 0.25 0.17 0.34 0.40 0.68 0.47 0.54 0.61
## [28,] 0.37 0.43 0.50 1.00 0.27 0.25 0.16 0.28 0.59 0.48 0.52 0.58
## [29,] 0.15 -0.04 0.25 0.27 1.00 0.72 0.56 0.47 0.28 0.38 0.38 0.19
## [30,] 0.15 0.03 0.17 0.25 0.72 1.00 0.40 0.57 0.27 0.35 0.38 0.30
## [31,] 0.14 -0.03 0.34 0.16 0.56 0.40 1.00 0.43 0.36 0.20 0.32 0.07
## [32,] 0.08 -0.02 0.40 0.28 0.47 0.57 0.43 1.00 0.46 0.34 0.44 0.39
## [33,] 0.41 0.34 0.68 0.59 0.28 0.27 0.36 0.46 1.00 0.67 0.57 0.72
## [34,] 0.11 0.21 0.47 0.48 0.38 0.35 0.20 0.34 0.67 1.00 0.53 0.64
## [35,] 0.38 0.31 0.54 0.52 0.38 0.38 0.32 0.44 0.57 0.53 1.00 0.72
## [36,] 0.41 0.49 0.61 0.58 0.19 0.30 0.07 0.39 0.72 0.64 0.72 1.00
## [37,] 0.21 0.07 0.02 0.13 0.39 0.17 0.14 0.40 0.22 0.14 0.23 0.21
## [38,] 0.33 0.16 0.24 0.31 0.51 0.44 0.52 0.33 0.54 0.53 0.49 0.36
## [39,] 0.36 0.23 0.42 0.32 0.39 0.29 0.46 0.22 0.52 0.45 0.48 0.37
## [40,] 0.13 0.09 0.26 0.19 0.29 0.29 0.18 0.64 0.37 0.31 0.32 0.31
## [41,] 0.20 0.28 0.36 0.48 0.28 0.34 0.34 0.30 0.48 0.43 0.35 0.32
## [42,] 0.32 0.40 0.42 0.43 0.18 0.16 0.33 0.13 0.42 0.31 0.26 0.29
## [43,] 0.35 0.28 0.42 0.26 0.34 0.27 0.45 0.29 0.46 0.38 0.23 0.17
## [44,] 0.37 0.47 0.47 0.40 0.20 0.17 0.33 0.26 0.50 0.38 0.28 0.48
## [45,] 0.07 -0.02 0.35 0.21 0.19 -0.02 0.32 0.06 0.35 0.23 0.39 0.27
## [46,] 0.18 0.06 0.43 0.21 0.32 0.27 0.44 0.42 0.48 0.33 0.38 0.30
## [47,] 0.38 0.30 0.37 0.30 0.13 0.23 0.26 0.33 0.46 0.29 0.48 0.49
## [48,] 0.30 0.19 0.45 0.28 0.16 0.14 0.42 0.36 0.56 0.38 0.34 0.39
## [49,] 0.00 -0.08 0.29 0.31 0.20 0.05 0.20 0.18 0.20 0.28 0.34 0.19
## [50,] 0.11 -0.10 0.35 0.21 0.23 0.16 0.49 0.27 0.44 0.34 0.33 0.18
## [51,] 0.32 0.29 0.35 0.35 0.29 0.25 0.33 0.22 0.45 0.41 0.38 0.45
## [52,] 0.54 0.32 0.34 0.35 0.26 0.27 0.40 0.26 0.44 0.23 0.37 0.31
## [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
## [1,] 0.21 0.58 0.61 0.17 0.43 0.45 0.47 0.45 0.41 0.32 0.34 0.51
## [2,] -0.28 -0.36 -0.32 -0.32 -0.25 -0.11 -0.28 -0.13 -0.10 -0.15 -0.10 -0.36
## [3,] 0.25 0.51 0.45 0.26 0.44 0.31 0.35 0.45 0.26 0.29 0.36 0.49
## [4,] 0.21 0.29 0.36 0.25 0.38 0.32 0.27 0.33 0.32 0.25 0.38 0.29
## [5,] 0.12 0.46 0.46 0.15 0.45 0.35 0.35 0.40 0.42 0.33 0.42 0.37
## [6,] 0.13 0.54 0.57 0.29 0.38 0.36 0.51 0.40 0.41 0.45 0.48 0.60
## [7,] 0.17 0.63 0.72 0.25 0.44 0.47 0.59 0.49 0.35 0.33 0.25 0.36
## [8,] 0.23 0.55 0.59 0.40 0.33 0.36 0.50 0.46 0.35 0.35 0.35 0.41
## [9,] 0.10 0.18 0.21 0.19 0.36 0.30 0.20 0.32 0.38 0.19 0.15 0.30
## [10,] 0.06 0.27 0.36 0.10 0.14 0.27 0.24 0.18 0.08 0.19 0.26 -0.07
## [11,] -0.25 -0.25 -0.20 -0.01 0.16 0.19 -0.05 -0.05 0.21 0.08 0.01 0.01
## [12,] -0.27 -0.31 -0.35 0.01 0.01 -0.01 -0.06 -0.12 0.07 -0.10 -0.01 -0.15
## [13,] -0.14 -0.18 -0.22 -0.02 0.12 0.09 -0.04 0.04 0.24 0.05 -0.06 -0.10
## [14,] -0.18 0.16 0.16 -0.17 0.04 0.20 0.10 0.10 0.22 0.11 0.25 0.13
## [15,] -0.27 0.05 0.13 0.02 0.11 0.34 0.30 0.21 0.29 0.24 0.06 0.22
## [16,] -0.29 -0.10 -0.11 0.02 0.05 0.02 -0.04 0.00 0.22 0.19 0.06 0.06
## [17,] 0.09 0.32 0.28 0.21 0.38 0.33 0.15 0.12 0.17 0.10 0.35 0.24
## [18,] 0.17 0.45 0.40 0.25 0.35 0.30 0.18 0.18 0.23 0.21 0.30 0.29
## [19,] 0.30 0.44 0.38 0.38 0.16 0.11 0.15 0.09 0.29 0.27 0.38 0.31
## [20,] 0.27 0.36 0.32 0.39 0.30 0.22 0.26 0.20 0.30 0.27 0.20 0.19
## [21,] 0.04 0.29 0.24 0.20 0.22 0.32 0.33 0.34 0.24 0.20 0.28 0.30
## [22,] -0.04 0.20 0.13 0.15 0.20 0.33 0.29 0.34 0.02 0.12 0.08 0.19
## [23,] -0.03 0.28 0.24 0.13 0.20 0.34 0.29 0.35 0.20 0.22 0.22 0.19
## [24,] 0.12 0.41 0.34 0.12 0.18 0.26 0.27 0.26 0.18 0.27 0.13 0.27
## [25,] 0.21 0.33 0.36 0.13 0.20 0.32 0.35 0.37 0.07 0.18 0.38 0.30
## [26,] 0.07 0.16 0.23 0.09 0.28 0.40 0.28 0.47 -0.02 0.06 0.30 0.19
## [27,] 0.02 0.24 0.42 0.26 0.36 0.42 0.42 0.47 0.35 0.43 0.37 0.45
## [28,] 0.13 0.31 0.32 0.19 0.48 0.43 0.26 0.40 0.21 0.21 0.30 0.28
## [29,] 0.39 0.51 0.39 0.29 0.28 0.18 0.34 0.20 0.19 0.32 0.13 0.16
## [30,] 0.17 0.44 0.29 0.29 0.34 0.16 0.27 0.17 -0.02 0.27 0.23 0.14
## [31,] 0.14 0.52 0.46 0.18 0.34 0.33 0.45 0.33 0.32 0.44 0.26 0.42
## [32,] 0.40 0.33 0.22 0.64 0.30 0.13 0.29 0.26 0.06 0.42 0.33 0.36
## [33,] 0.22 0.54 0.52 0.37 0.48 0.42 0.46 0.50 0.35 0.48 0.46 0.56
## [34,] 0.14 0.53 0.45 0.31 0.43 0.31 0.38 0.38 0.23 0.33 0.29 0.38
## [35,] 0.23 0.49 0.48 0.32 0.35 0.26 0.23 0.28 0.39 0.38 0.48 0.34
## [36,] 0.21 0.36 0.37 0.31 0.32 0.29 0.17 0.48 0.27 0.30 0.49 0.39
## [37,] 1.00 0.40 0.29 0.57 0.07 -0.20 0.10 0.08 0.18 0.19 0.19 0.24
## [38,] 0.40 1.00 0.82 0.38 0.32 0.25 0.45 0.42 0.32 0.43 0.33 0.48
## [39,] 0.29 0.82 1.00 0.35 0.24 0.32 0.49 0.38 0.37 0.41 0.33 0.41
## [40,] 0.57 0.38 0.35 1.00 0.14 0.04 0.45 0.25 0.18 0.53 0.29 0.39
## [41,] 0.07 0.32 0.24 0.14 1.00 0.76 0.33 0.67 0.26 0.25 0.25 0.30
## [42,] -0.20 0.25 0.32 0.04 0.76 1.00 0.41 0.75 0.17 0.15 0.17 0.19
## [43,] 0.10 0.45 0.49 0.45 0.33 0.41 1.00 0.49 0.11 0.39 0.06 0.33
## [44,] 0.08 0.42 0.38 0.25 0.67 0.75 0.49 1.00 0.18 0.28 0.14 0.28
## [45,] 0.18 0.32 0.37 0.18 0.26 0.17 0.11 0.18 1.00 0.59 0.38 0.51
## [46,] 0.19 0.43 0.41 0.53 0.25 0.15 0.39 0.28 0.59 1.00 0.46 0.61
## [47,] 0.19 0.33 0.33 0.29 0.25 0.17 0.06 0.14 0.38 0.46 1.00 0.60
## [48,] 0.24 0.48 0.41 0.39 0.30 0.19 0.33 0.28 0.51 0.61 0.60 1.00
## [49,] 0.13 0.26 0.33 0.07 0.29 0.23 0.12 0.19 0.26 0.11 0.04 0.03
## [50,] 0.07 0.49 0.57 0.32 0.17 0.26 0.42 0.20 0.32 0.47 0.22 0.29
## [51,] 0.25 0.40 0.44 0.45 0.34 0.30 0.40 0.41 0.14 0.18 0.28 0.36
## [52,] 0.24 0.48 0.61 0.32 0.21 0.25 0.54 0.35 0.21 0.34 0.35 0.47
## [,49] [,50] [,51] [,52]
## [1,] 0.31 0.48 0.52 0.61
## [2,] -0.21 -0.35 -0.44 -0.25
## [3,] 0.31 0.38 0.56 0.34
## [4,] 0.52 0.39 0.40 0.38
## [5,] 0.42 0.50 0.40 0.39
## [6,] 0.28 0.58 0.51 0.51
## [7,] 0.40 0.64 0.55 0.66
## [8,] 0.53 0.52 0.44 0.60
## [9,] 0.32 0.23 0.35 0.28
## [10,] 0.20 0.30 0.07 0.33
## [11,] 0.23 0.14 0.05 -0.08
## [12,] -0.03 -0.13 0.08 -0.15
## [13,] 0.16 0.08 0.04 -0.17
## [14,] 0.10 0.23 0.20 0.22
## [15,] 0.16 0.22 0.11 0.16
## [16,] 0.18 0.09 -0.01 -0.04
## [17,] 0.32 0.33 0.34 0.19
## [18,] 0.50 0.47 0.35 0.37
## [19,] 0.42 0.47 0.38 0.34
## [20,] 0.50 0.47 0.28 0.39
## [21,] -0.15 0.32 0.43 0.14
## [22,] -0.22 0.06 0.20 0.04
## [23,] 0.17 0.25 0.20 0.08
## [24,] -0.12 0.20 0.26 0.21
## [25,] 0.00 0.11 0.32 0.54
## [26,] -0.08 -0.10 0.29 0.32
## [27,] 0.29 0.35 0.35 0.34
## [28,] 0.31 0.21 0.35 0.35
## [29,] 0.20 0.23 0.29 0.26
## [30,] 0.05 0.16 0.25 0.27
## [31,] 0.20 0.49 0.33 0.40
## [32,] 0.18 0.27 0.22 0.26
## [33,] 0.20 0.44 0.45 0.44
## [34,] 0.28 0.34 0.41 0.23
## [35,] 0.34 0.33 0.38 0.37
## [36,] 0.19 0.18 0.45 0.31
## [37,] 0.13 0.07 0.25 0.24
## [38,] 0.26 0.49 0.40 0.48
## [39,] 0.33 0.57 0.44 0.61
## [40,] 0.07 0.32 0.45 0.32
## [41,] 0.29 0.17 0.34 0.21
## [42,] 0.23 0.26 0.30 0.25
## [43,] 0.12 0.42 0.40 0.54
## [44,] 0.19 0.20 0.41 0.35
## [45,] 0.26 0.32 0.14 0.21
## [46,] 0.11 0.47 0.18 0.34
## [47,] 0.04 0.22 0.28 0.35
## [48,] 0.03 0.29 0.36 0.47
## [49,] 1.00 0.56 0.05 0.33
## [50,] 0.56 1.00 0.38 0.55
## [51,] 0.05 0.38 1.00 0.49
## [52,] 0.33 0.55 0.49 1.00
## [1] "Redundant pairs of items"
## cor
## [1,] 4 5 0.73
## [2,] 3 6 0.75
## [3,] 5 6 0.73
## [4,] 17 18 0.86
## [5,] 17 19 0.73
## [6,] 18 19 0.76
## [7,] 18 20 0.71
## [8,] 19 20 0.71
## [9,] 22 24 0.76
## [10,] 25 26 0.71
## [11,] 29 30 0.72
## [12,] 6 33 0.72
## [13,] 33 36 0.72
## [14,] 35 36 0.72
## [15,] 7 39 0.72
## [16,] 38 39 0.82
## [17,] 41 42 0.76
## [18,] 42 44 0.75#item response distribution TGMI
par(mfrow=c(3,4))
for(i in 1:ncol(TGMI)){
hist(TGMI[,i],main=paste("Item",i))
}
PAI
#PAI
data[,63:81] %>%
lapply(function(x){gsub("Fortemente in disaccordo",1,x)}) %>%
lapply(function(x){gsub("In disaccordo",2,x)}) %>%
lapply(function(x){gsub("Né in accordo né in disaccordo",3,x)}) %>%
lapply(function(x){gsub("D'accordo",4,x)}) %>%
lapply(function(x){gsub("Fortemente d'accordo",5,x)}) %>%
lapply(as.numeric) %>%
do.call(cbind,.) %>%
unname %T>%
assign("PAI",.,1) %>%
cor(use="pairwise.complete.obs") %>%
round(.,2) %T>%
assign("corMat_PAI",.,1) %T>%
print %>%
#flag reduntant items
(function(x){abs(x)>.7&abs(x)<1}) %>%
replace_triangle(by=F) %>%
as.matrix %T>%
{assign("cor",corMat_PAI[.],1)} %>%
which(arr.ind=T) %>%
unname %>%
cbind(cor) %>%
assign("flagReduntant_PAI",.,1) %>%
print## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1.00 0.28 0.09 0.26 0.23 0.38 0.24 0.37 0.36 0.35 0.18 0.27
## [2,] 0.28 1.00 0.34 0.46 0.49 0.30 0.38 0.48 0.27 0.14 0.15 0.16
## [3,] 0.09 0.34 1.00 0.63 0.55 0.15 0.23 0.58 0.35 0.23 0.57 0.02
## [4,] 0.26 0.46 0.63 1.00 0.52 0.41 0.24 0.71 0.39 0.26 0.55 0.24
## [5,] 0.23 0.49 0.55 0.52 1.00 0.40 0.29 0.62 0.48 0.32 0.43 0.05
## [6,] 0.38 0.30 0.15 0.41 0.40 1.00 0.37 0.45 0.18 0.19 0.06 0.28
## [7,] 0.24 0.38 0.23 0.24 0.29 0.37 1.00 0.31 0.06 -0.03 0.06 0.24
## [8,] 0.37 0.48 0.58 0.71 0.62 0.45 0.31 1.00 0.42 0.30 0.50 0.25
## [9,] 0.36 0.27 0.35 0.39 0.48 0.18 0.06 0.42 1.00 0.67 0.54 0.16
## [10,] 0.35 0.14 0.23 0.26 0.32 0.19 -0.03 0.30 0.67 1.00 0.52 0.12
## [11,] 0.18 0.15 0.57 0.55 0.43 0.06 0.06 0.50 0.54 0.52 1.00 0.17
## [12,] 0.27 0.16 0.02 0.24 0.05 0.28 0.24 0.25 0.16 0.12 0.17 1.00
## [13,] 0.10 -0.04 0.31 0.31 0.28 0.16 -0.13 0.37 0.18 0.18 0.22 0.12
## [14,] 0.12 0.05 0.44 0.36 0.34 0.22 -0.08 0.42 0.28 0.28 0.31 0.26
## [15,] 0.10 -0.17 -0.02 -0.06 0.08 -0.05 0.11 -0.07 0.27 0.12 0.08 0.07
## [16,] 0.08 0.05 0.03 0.16 0.21 -0.06 0.06 0.17 0.19 0.08 0.18 0.03
## [17,] 0.36 0.10 0.48 0.49 0.39 0.33 -0.01 0.32 0.38 0.46 0.41 0.23
## [18,] -0.15 -0.40 0.08 -0.10 -0.09 -0.13 -0.17 0.00 0.02 0.10 0.18 0.03
## [19,] -0.10 0.08 0.02 0.06 0.08 -0.12 0.19 0.10 0.19 -0.11 -0.03 -0.01
## [,13] [,14] [,15] [,16] [,17] [,18] [,19]
## [1,] 0.10 0.12 0.10 0.08 0.36 -0.15 -0.10
## [2,] -0.04 0.05 -0.17 0.05 0.10 -0.40 0.08
## [3,] 0.31 0.44 -0.02 0.03 0.48 0.08 0.02
## [4,] 0.31 0.36 -0.06 0.16 0.49 -0.10 0.06
## [5,] 0.28 0.34 0.08 0.21 0.39 -0.09 0.08
## [6,] 0.16 0.22 -0.05 -0.06 0.33 -0.13 -0.12
## [7,] -0.13 -0.08 0.11 0.06 -0.01 -0.17 0.19
## [8,] 0.37 0.42 -0.07 0.17 0.32 0.00 0.10
## [9,] 0.18 0.28 0.27 0.19 0.38 0.02 0.19
## [10,] 0.18 0.28 0.12 0.08 0.46 0.10 -0.11
## [11,] 0.22 0.31 0.08 0.18 0.41 0.18 -0.03
## [12,] 0.12 0.26 0.07 0.03 0.23 0.03 -0.01
## [13,] 1.00 0.82 0.17 0.08 0.40 0.24 0.02
## [14,] 0.82 1.00 0.01 -0.05 0.46 0.23 -0.11
## [15,] 0.17 0.01 1.00 0.56 0.24 0.48 0.52
## [16,] 0.08 -0.05 0.56 1.00 0.16 0.47 0.49
## [17,] 0.40 0.46 0.24 0.16 1.00 0.07 -0.10
## [18,] 0.24 0.23 0.48 0.47 0.07 1.00 0.23
## [19,] 0.02 -0.11 0.52 0.49 -0.10 0.23 1.00
## cor
## [1,] 4 8 0.71
## [2,] 13 14 0.82#item response distribution TGMI
par(mfrow=c(3,4))
for(i in 1:ncol(PAI)){
hist(PAI[,i],main=paste("Item",i))
}
Number of factors
fa.parallel(TGMI,fa="fa",main="TGMI")## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## In factor.scores, the correlation matrix is singular, the pseudo inverse is used## In smc, smcs > 1 were set to 1.0
## In smc, smcs > 1 were set to 1.0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0## In smc, smcs > 1 were set to 1.0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs > 1 were set to 1.0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0## In smc, smcs > 1 were set to 1.0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): The estimated weights for the factor scores are
## probably incorrect. Try a different factor score estimation method.## In smc, smcs > 1 were set to 1.0## In smc, smcs > 1 were set to 1.0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs > 1 were set to 1.0
## In smc, smcs < 0 were set to .0## In smc, smcs > 1 were set to 1.0## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Parallel analysis suggests that the number of factors = 3 and the number of components = NAfa.parallel(PAI,fa="fa",main="PAI")## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Parallel analysis suggests that the number of factors = 3 and the number of components = NA