As análises serão feitas com as rotinas dos pacotes psych e ltm. A importação dos dados será feita usando rotinas do pacote foreign
install.packages("psych").
install.packages("ltm").
install.packages("foreign").
Antes de começar as análises é preciso “chamar” os pacotes
library(foreign)
library(psych)
library(ltm)
Configurando o diretório a partir do qual o R irá ler e salvar os arquivos das análises
getwd()
## [1] "/Users/rprimi/Dropbox/TRI/ri_rld"
setwd("~/Dropbox/TRI/ri_rld/")
Lendo um arquivo do SPSS
ri <- read.spss("ri_exerc1.sav", to.data.frame = TRUE, use.missings = TRUE)
## re-encoding from CP1252
Lendo dados copiados do EXCEL para o clipboard
ri.keys <- read.clipboard.tab()
ri.keys <- as.vector(ri.keys$Key)
“Vendo” o conteúdo do arquivo
ri.keys
## [1] 6 8 1 6 2 1 4 5 3 8 2 5 4 4 2 1
names(ri)
## [1] "id" "i01" "i02" "i03" "i04" "i05" "i06" "i07" "i08" "i09" "i10"
## [12] "i11" "i12" "i13" "i14" "i15" "i16"
head(ri)
## id i01 i02 i03 i04 i05 i06 i07 i08 i09 i10 i11 i12 i13 i14 i15 i16
## 1 B0001 6 8 1 6 2 1 5 5 8 8 2 NA 1 4 1 3
## 2 B0004 6 8 NA 6 2 2 4 2 3 8 5 5 1 4 2 1
## 3 B0005 1 8 1 6 2 1 4 5 3 8 5 5 1 3 2 1
## 4 B0009 6 8 1 6 2 1 2 5 3 8 5 3 8 2 3 8
## 5 B0010 6 8 1 6 2 1 2 5 3 8 5 6 1 3 3 5
## 6 B0011 1 8 1 6 2 1 2 5 4 8 5 3 1 3 2 8
tail(ri)
## id i01 i02 i03 i04 i05 i06 i07 i08 i09 i10 i11 i12 i13 i14 i15 i16
## 435 B1425 6 5 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 436 B1431 6 8 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 437 B1433 6 8 1 6 4 1 4 8 NA NA NA NA NA NA NA NA
## 438 B1437 6 8 5 6 2 1 5 8 NA NA NA NA NA NA NA NA
## 439 B1438 6 4 4 5 6 3 5 8 3 6 NA NA NA NA NA NA
## 440 B1441 6 7 1 5 2 7 4 3 5 8 2 5 6 1 6 3
describe(ri)
## var n mean sd median trimmed mad min max range skew
## id* 1 440 220.50 127.16 220.5 220.50 163.09 1 440 439 0.00
## i01 2 433 5.82 1.33 6.0 6.07 0.00 1 8 7 -2.34
## i02 3 435 7.27 1.80 8.0 7.81 0.00 1 8 7 -2.39
## i03 4 428 1.70 1.67 1.0 1.23 0.00 1 8 7 2.63
## i04 5 432 5.48 1.35 6.0 5.82 0.00 1 8 7 -2.21
## i05 6 430 3.06 1.67 2.0 2.78 0.00 1 8 7 1.21
## i06 7 428 1.69 1.37 1.0 1.38 0.00 1 8 7 2.45
## i07 8 427 3.77 1.21 4.0 3.72 0.00 2 8 6 0.65
## i08 9 428 5.22 1.48 5.0 5.24 0.00 1 8 7 0.03
## i09 10 418 3.69 1.85 3.0 3.43 0.00 1 8 7 1.50
## i10 11 416 6.32 2.78 8.0 6.76 0.00 1 8 7 -1.19
## i11 12 405 4.28 2.04 5.0 4.12 2.97 1 8 7 0.30
## i12 13 405 4.73 1.47 5.0 4.76 0.00 1 8 7 -0.36
## i13 14 407 3.22 2.18 4.0 2.95 4.45 1 8 7 0.61
## i14 15 401 3.68 1.48 4.0 3.60 1.48 1 8 7 0.67
## i15 16 391 3.30 1.99 2.0 3.02 1.48 1 8 7 1.11
## i16 17 397 4.19 2.61 4.0 4.12 4.45 1 8 7 0.09
## kurtosis se
## id* -1.21 6.06
## i01 6.02 0.06
## i02 4.12 0.09
## i03 6.16 0.08
## i04 4.49 0.07
## i05 0.28 0.08
## i06 6.44 0.07
## i07 2.30 0.06
## i08 0.91 0.07
## i09 1.20 0.09
## i10 -0.37 0.14
## i11 -0.96 0.10
## i12 0.01 0.07
## i13 -0.53 0.11
## i14 1.11 0.07
## i15 0.15 0.10
## i16 -1.49 0.13
Análises psicométricas clássicas com o psych
ri_classicas1 <- score.multiple.choice(ri.keys, ri[, c(2:17)], score = FALSE,
short = FALSE)
ri_classicas2 <- score.multiple.choice(ri.keys, ri[, c(2:17)], score = TRUE,
short = FALSE)
head(ri_classicas1)
## i01 i02 i03 i04 i05 i06 i07 i08 i09 i10 i11 i12 i13 i14 i15 i16
## [1,] 1 1 1 1 1 1 0 1 0 1 1 NA 0 1 0 0
## [2,] 1 1 NA 1 1 0 1 0 1 1 0 1 0 1 1 1
## [3,] 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1
## [4,] 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0
## [5,] 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0
## [6,] 0 1 1 1 1 1 0 1 0 1 0 0 0 0 1 0
head(ri_classicas2)
## $scores
## Averages <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.7500 0.7500 0.5625 0.5625 0.5000 0.3750 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.7500 0.6875 0.0000 0.1250 0.5000 0.1875 0.8125 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.6250 0.3750 0.5000 0.6875 0.4375 0.3750 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.4375 0.7500 0.8125 0.9375 0.7500 0.6250 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.2500 0.5000 0.5625 0.3125 0.6250 0.6250 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.6875 0.6250 0.6875 0.7500 0.9375 0.9375 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8750 0.6250 0.6250 0.8750 0.8750 0.8125 0.6875 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.6875 0.1875 0.6250 0.1875 0.4375 0.0625 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.4375 0.6250 0.8125 0.3750 0.4375 0.3125 0.8125
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.1250 0.5000 0.8125 0.3750 0.4375 0.5000 0.1250 0.8125
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.6250 0.9375 0.8750 0.8125 0.8125 1.0000 0.5000
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.3750 0.3750 0.8750 0.6875 0.7500 0.6875 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.0625 0.5625 0.4375 0.8750 0.6875 0.8125 0.4375 0.1875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.1875 0.6250 0.7500 0.5625 0.6250 0.6875 0.8125
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.5625 0.7500 0.6875 0.6250 1.0000 0.8125 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.3125 0.6250 0.7500 0.1875 0.5625 0.4375 0.4375
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8750 0.9375 0.8750 0.8125 0.6250 0.8125 0.5625 0.8125
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.3125 0.5625 0.5625 0.6250 0.5625 0.8125 0.2500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.1875 0.5625 0.6250 0.3750 0.3125 0.0000 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.7500 0.4375 0.6250 0.9375 0.6875 0.8125 0.7500 0.5000
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.8750 0.5625 0.6250 0.6875 0.6250 0.9375 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.3750 0.3125 0.5625 0.6250 0.8750 0.6875 0.8750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.8125 0.7500 0.6875 1.0000 0.7500 0.8750 0.5625
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.4375 0.6875 0.8125 0.8750 0.8125 0.1250 0.4375 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.5000 0.6875 0.6875 0.6875 0.3750 0.2500 0.5625
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.2500 0.4375 0.5625 0.0625 0.7500 0.8125 0.6250 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3750 0.5625 0.2500 0.5000 0.6250 0.4375 0.6875 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.9375 0.5625 0.7500 0.9375 0.8750 0.6875 0.5625
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.7500 0.4375 0.6875 0.6250 0.6875 0.8125 0.3125 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.1875 0.6875 0.2500 0.8125 1.0000 0.6875 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.6250 0.6875 0.5625 0.8125 0.5000 0.4375 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.3750 0.8125 0.2500 0.6875 0.7500 0.2500 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.2500 0.6875 0.6875 0.5625 0.2500 0.7500 0.4375
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.5625 0.6250 0.4375 0.2500 0.3750 0.8125 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.8750 0.6250 0.7500 0.6250 0.1875 0.7500 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.5625 0.5000 0.6250 0.7500 0.8750 0.8750 0.8750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.7500 0.6875 0.4375 0.8125 0.6250 0.1875 0.1875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3125 0.5000 0.5625 0.9375 0.6250 0.2500 0.3750 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.2500 0.5000 0.3125 0.6250 0.7500 0.7500 0.5000
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.1875 0.5000 0.6250 0.7500 0.7500 0.2500 0.8750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.3750 0.1875 0.5625 0.8750 0.6250 0.1250 0.4375 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.7500 0.5625 0.7500 0.8125 0.5625 0.0625 0.4375
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.8125 0.5625 0.6875 0.4375 0.3750 0.5000 0.3125
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.2500 0.5625 0.6875 0.4375 0.5625 0.5625 0.8125 1.0000
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.6875 0.9375 0.5625 0.7500 0.6250 0.4375 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.8750 0.5000 0.3750 0.7500 0.8125 0.8750 0.5000
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8750 0.5000 0.7500 0.0625 0.5000 0.3750 0.9375 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.8125 0.9375 0.8750 0.8750 0.6875 0.6250 0.1250 0.7500
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6250 0.3750 0.3125 0.7500 0.8750 0.9375 0.6875 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5000 0.4375 0.7500 0.8125 0.2500 0.7500 0.8125 0.6250
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.4375 0.7500 0.8750 0.6875 0.6250 0.6875 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.7500 0.6875 0.6250 0.6875 0.5625 0.4375 0.5625 0.6875
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.6875 0.3125 0.5625 0.5625 0.5000 0.1875 0.4375 0.3750
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.2500 0.1875 0.2500 0.2500 0.6875 0.6875 0.2500 0.4375
## <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 0.5625 0.5625 0.5625 0.6875 0.5625 0.5000 0.1875 0.4375
##
## $missing
## [1] 1 1 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 16 0 0
## [24] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 0 0 0 0 1 0
## [47] 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0
## [70] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [93] 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 6 0
## [116] 0 0 1 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [139] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 3
## [162] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [185] 1 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## [208] 0 0 0 0 0 0 0 0 0 2 0 0 2 0 3 0 0 0 0 0 0 1 0
## [231] 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 1 3 0 0
## [254] 4 0 2 0 4 0 16 5 0 0 0 0 0 0 0 0 0 0 4 16 1 0 1
## [277] 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
## [300] 0 0 0 0 0 0 0 2 0 0 1 4 0 0 0 0 0 0 0 1 0 0 0
## [323] 2 0 2 0 7 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [346] 0 4 0 0 0 0 4 0 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0
## [369] 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [392] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6
## [415] 8 12 9 4 7 7 6 2 10 4 3 0 7 4 8 11 5 12 12 7 13 13 8
## [438] 8 6 0
##
## $item.stats
## key 1 2 3 4 5 6 7 8 miss r n mean sd
## i01 6 0.04 0.01 0.03 0.01 0.01 0.73 0.13 0.03 0.02 0.52 433 0.73 0.44
## i02 8 0.00 0.08 0.01 0.01 0.00 0.03 0.06 0.80 0.01 0.57 435 0.80 0.40
## i03 1 0.79 0.03 0.07 0.02 0.01 0.03 0.00 0.04 0.03 0.52 428 0.79 0.40
## i04 6 0.05 0.02 0.03 0.02 0.09 0.76 0.01 0.02 0.02 0.53 432 0.76 0.43
## i05 2 0.01 0.67 0.00 0.04 0.21 0.02 0.03 0.02 0.02 0.56 430 0.67 0.47
## i06 1 0.72 0.07 0.14 0.03 0.02 0.00 0.02 0.01 0.03 0.46 428 0.72 0.45
## i07 4 0.00 0.23 0.01 0.63 0.10 0.00 0.01 0.02 0.03 0.40 427 0.63 0.48
## i08 5 0.02 0.04 0.05 0.03 0.67 0.01 0.06 0.13 0.03 0.52 428 0.67 0.47
## i09 3 0.05 0.05 0.67 0.06 0.03 0.01 0.01 0.13 0.05 0.45 418 0.67 0.47
## i10 8 0.18 0.00 0.01 0.05 0.04 0.00 0.01 0.70 0.05 0.46 416 0.70 0.46
## i11 2 0.01 0.35 0.02 0.04 0.40 0.02 0.05 0.11 0.08 0.39 405 0.35 0.48
## i12 5 0.03 0.02 0.20 0.03 0.50 0.07 0.13 0.01 0.08 0.47 405 0.50 0.50
## i13 4 0.40 0.01 0.05 0.37 0.02 0.04 0.05 0.06 0.07 0.36 407 0.37 0.48
## i14 4 0.08 0.05 0.33 0.36 0.05 0.06 0.03 0.03 0.09 0.48 401 0.36 0.48
## i15 2 0.09 0.44 0.11 0.16 0.02 0.08 0.03 0.07 0.11 0.46 391 0.44 0.50
## i16 1 0.29 0.03 0.14 0.08 0.11 0.04 0.18 0.13 0.10 0.44 397 0.29 0.45
## skew kurtosis se
## i01 -1.04 -0.91 0.02
## i02 -1.53 0.35 0.02
## i03 -1.45 0.11 0.02
## i04 -1.21 -0.54 0.02
## i05 -0.71 -1.50 0.02
## i06 -0.96 -1.08 0.02
## i07 -0.52 -1.74 0.02
## i08 -0.72 -1.48 0.02
## i09 -0.71 -1.50 0.02
## i10 -0.89 -1.21 0.02
## i11 0.61 -1.63 0.02
## i12 0.00 -2.00 0.02
## i13 0.55 -1.70 0.02
## i14 0.56 -1.69 0.02
## i15 0.23 -1.95 0.03
## i16 0.94 -1.12 0.02
##
## $alpha
## [1] 0.77
##
## $av.r
## [1] 0.17
print.psych(ri_classicas2, short = FALSE)
## Call: NULL
##
## (Unstandardized) Alpha:
## [1] 0.77
##
## Average item correlation:
## [1] 0.17
##
## item statistics
## key 1 2 3 4 5 6 7 8 miss r n mean sd
## i01 6 0.04 0.01 0.03 0.01 0.01 0.73 0.13 0.03 0.02 0.52 433 0.73 0.44
## i02 8 0.00 0.08 0.01 0.01 0.00 0.03 0.06 0.80 0.01 0.57 435 0.80 0.40
## i03 1 0.79 0.03 0.07 0.02 0.01 0.03 0.00 0.04 0.03 0.52 428 0.79 0.40
## i04 6 0.05 0.02 0.03 0.02 0.09 0.76 0.01 0.02 0.02 0.53 432 0.76 0.43
## i05 2 0.01 0.67 0.00 0.04 0.21 0.02 0.03 0.02 0.02 0.56 430 0.67 0.47
## i06 1 0.72 0.07 0.14 0.03 0.02 0.00 0.02 0.01 0.03 0.46 428 0.72 0.45
## i07 4 0.00 0.23 0.01 0.63 0.10 0.00 0.01 0.02 0.03 0.40 427 0.63 0.48
## i08 5 0.02 0.04 0.05 0.03 0.67 0.01 0.06 0.13 0.03 0.52 428 0.67 0.47
## i09 3 0.05 0.05 0.67 0.06 0.03 0.01 0.01 0.13 0.05 0.45 418 0.67 0.47
## i10 8 0.18 0.00 0.01 0.05 0.04 0.00 0.01 0.70 0.05 0.46 416 0.70 0.46
## i11 2 0.01 0.35 0.02 0.04 0.40 0.02 0.05 0.11 0.08 0.39 405 0.35 0.48
## i12 5 0.03 0.02 0.20 0.03 0.50 0.07 0.13 0.01 0.08 0.47 405 0.50 0.50
## i13 4 0.40 0.01 0.05 0.37 0.02 0.04 0.05 0.06 0.07 0.36 407 0.37 0.48
## i14 4 0.08 0.05 0.33 0.36 0.05 0.06 0.03 0.03 0.09 0.48 401 0.36 0.48
## i15 2 0.09 0.44 0.11 0.16 0.02 0.08 0.03 0.07 0.11 0.46 391 0.44 0.50
## i16 1 0.29 0.03 0.14 0.08 0.11 0.04 0.18 0.13 0.10 0.44 397 0.29 0.45
## skew kurtosis se
## i01 -1.04 -0.91 0.02
## i02 -1.53 0.35 0.02
## i03 -1.45 0.11 0.02
## i04 -1.21 -0.54 0.02
## i05 -0.71 -1.50 0.02
## i06 -0.96 -1.08 0.02
## i07 -0.52 -1.74 0.02
## i08 -0.72 -1.48 0.02
## i09 -0.71 -1.50 0.02
## i10 -0.89 -1.21 0.02
## i11 0.61 -1.63 0.02
## i12 0.00 -2.00 0.02
## i13 0.55 -1.70 0.02
## i14 0.56 -1.69 0.02
## i15 0.23 -1.95 0.03
## i16 0.94 -1.12 0.02
irt.responses(ri_classicas2$scores, ri[, c(2:17)], breaks = 11)
Calibração dos parâmetros TRI de modelo TRI de 3 parâmetros com o ltm e fazendo os gráficos das CCI's
ri_descri <- descript(ri_classicas1, n.print = 10)
print(ri_descri)
##
## Descriptive statistics for the 'ri_classicas1' data-set
##
## Sample:
## 16 items and 440 sample units; 359 missing values
##
## Proportions for each level of response:
## logit
## i01 0.2679 0.7321 1.0053
## i02 0.1954 0.8046 1.4153
## i03 0.2056 0.7944 1.3516
## i04 0.2407 0.7593 1.1486
## i05 0.3326 0.6674 0.6966
## i06 0.2827 0.7173 0.9311
## i07 0.3747 0.6253 0.5121
## i08 0.3294 0.6706 0.7107
## i09 0.3325 0.6675 0.6967
## i10 0.2957 0.7043 0.8680
## i11 0.6469 0.3531 -0.6055
## i12 0.4988 0.5012 0.0049
## i13 0.6339 0.3661 -0.5490
## i14 0.6359 0.3641 -0.5577
## i15 0.5575 0.4425 -0.2312
## i16 0.7128 0.2872 -0.9092
##
## Missing responses:
## i01 i02 i03 i04 i05 i06 i07 i08 i09 i10
## Freq 7.000 5.000 12.000 8.000 10.000 12.000 13.000 12.000 22 24.000
## (%) 1.591 1.136 2.727 1.818 2.273 2.727 2.954 2.727 5 5.455
## i11 i12 i13 i14 i15 i16
## Freq 35.000 35.000 33.0 39.000 49.00 43.000
## (%) 7.955 7.955 7.5 8.864 11.14 9.773
##
##
## Frequencies of total scores:
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## Freq 1 4 5 11 14 14 23 24 22 35 44 45 33 38 24 14 3
##
##
## Point Biserial correlation with Total Score:
## Included Excluded
## i01 0.5289 0.4256
## i02 0.5588 0.4730
## i03 0.4873 0.3928
## i04 0.5291 0.4320
## i05 0.5677 0.4627
## i06 0.4228 0.3073
## i07 0.4075 0.2809
## i08 0.5334 0.4263
## i09 0.4613 0.3433
## i10 0.4492 0.3348
## i11 0.3717 0.2423
## i12 0.4690 0.3435
## i13 0.3423 0.2087
## i14 0.4628 0.3415
## i15 0.4628 0.3375
## i16 0.4398 0.3241
##
##
## Cronbach's alpha:
## value
## All Items 0.7980
## Excluding i01 0.7868
## Excluding i02 0.7801
## Excluding i03 0.7837
## Excluding i04 0.7837
## Excluding i05 0.7813
## Excluding i06 0.7876
## Excluding i07 0.7916
## Excluding i08 0.7821
## Excluding i09 0.7884
## Excluding i10 0.7835
## Excluding i11 0.7952
## Excluding i12 0.7873
## Excluding i13 0.7970
## Excluding i14 0.7886
## Excluding i15 0.7894
## Excluding i16 0.7919
##
##
## Pairwise Associations:
## Item i Item j p.value
## 1 5 13 0.266
## 2 13 16 0.264
## 3 11 16 0.244
## 4 9 11 0.224
## 5 12 13 0.181
## 6 9 15 0.142
## 7 7 10 0.134
## 8 10 11 0.117
## 9 4 13 0.090
## 10 9 10 0.086
rim3 <- tpm(ri_classicas1, max.guessing = 0.4, IRT.param = TRUE)
plot(ri_descri, includeFirstLast = TRUE, type = "b", lty = 1, pch = 1:6)
plot(rim3, lwd = 2, legend = TRUE, ncol = 2)
print(rim3)
##
## Call:
## tpm(data = ri_classicas1, max.guessing = 0.4, IRT.param = TRUE)
##
## Coefficients:
## Gussng Dffclt Dscrmn
## i01 0.227 -0.526 1.841
## i02 0.001 -1.082 2.238
## i03 0.101 -1.013 1.843
## i04 0.000 -1.063 1.478
## i05 0.000 -0.639 1.470
## i06 0.212 -0.581 1.269
## i07 0.000 -0.742 0.758
## i08 0.008 -0.720 1.235
## i09 0.191 -0.361 1.275
## i10 0.107 -0.729 1.189
## i11 0.015 0.979 0.760
## i12 0.000 0.007 1.049
## i13 0.000 0.979 0.623
## i14 0.000 0.639 1.151
## i15 0.081 0.461 1.294
## i16 0.000 1.062 1.065
##
## Log.Lik: -3740
Salvando os objetos
save.image("~/Dropbox/TRI/ri_rld/ri_exemplo.RData")