Correção e psicometria clássica com testes de múltipla escolha
- Importa dicionário do Excel
load("ibap.RData")
library(readxl)
dic <-read_excel("lego.xlsx", col_names = TRUE, sheet = "Sheet2")
library(dplyr)
dic <- tbl_df(dic)
dic
## Source: local data frame [118 x 15]
##
## Bloco2 CodItem Ord VarLabel Key0 Key1 Key2 b senna54 senna84
## 1 RV ahv_1 1 1 2 NA NA -3.36 NA NA
## 2 RV ahv_2 2 2 4 NA NA -2.47 NA NA
## 3 RV ahv_3 3 3 3 NA NA -1.39 NA NA
## 4 RV ahv_4 4 4 3 NA NA -0.99 NA NA
## 5 RV ahv_5 5 5 1 NA NA -0.91 NA NA
## 6 RV ahv_6 6 6 5 NA NA -0.38 NA NA
## 7 RV ahv_7 7 7 2 NA NA 0.39 NA NA
## 8 RV ahv_8 8 8 5 NA NA 0.33 NA NA
## 9 RV ahv_9 9 9 1 NA NA 1.07 NA NA
## 10 RA aha_1 1 1 3 NA NA -3.28 NA NA
## .. ... ... ... ... ... ... ... ... ... ...
## Variables not shown: OrdSenna84 (dbl), domain (chr), facet (chr), pole
## (dbl), P_S (dbl)
- Cria vetor com gabarito
- Nessa base há 3 subtestes (RV, RA, RE) com 9 itens cada. Assim temos 27 itens (os 27 primeiros do dataframe base
dic)
key <- dic[1:27 ,]$Key0
key
## [1] 2 4 3 3 1 5 2 5 1 3 5 4 2 4 3 3 5 1 1 2 5 3 4 2 2 5 1
- Análises psicométricas clássicas com o psych
names(df1)
## [1] "id" "IDA_SEXO" "IDA_DT_NASCIMENTO"
## [4] "IDA_SERIE" "IDA_TURMA" "IDA_TURNO"
## [7] "ahv_1" "ahv_2" "ahv_3"
## [10] "ahv_4" "ahv_5" "ahv_6"
## [13] "ahv_7" "ahv_8" "ahv_9"
## [16] "aha_1" "aha_2" "aha_3"
## [19] "aha_4" "aha_5" "aha_6"
## [22] "aha_7" "aha_8" "aha_9"
## [25] "ahe_1" "ahe_2" "ahe_3"
## [28] "ahe_4" "ahe_5" "ahe_6"
## [31] "ahe_7" "ahe_8" "ahe_9"
## [34] "ise_1" "ise_2" "ise_3"
## [37] "ise_4" "ise_5" "ise_6"
## [40] "ise_7" "ise_8" "ise_9"
## [43] "ise_10" "ise_11" "ise_12"
## [46] "ise_13" "ise_14" "ise_15"
## [49] "ise_16" "ise_17" "ise_18"
## [52] "ise_19" "ise_20" "ise_21"
## [55] "ise_22" "ise_23" "ise_24"
## [58] "ise_25" "ise_26" "ise_27"
## [61] "ise_28" "ise_29" "ise_30"
## [64] "ise_31" "ise_32" "ise_33"
## [67] "ise_34" "ise_35" "ise_36"
## [70] "ise_37" "ise_38" "ise_39"
## [73] "ise_40" "ise_41" "ise_42"
## [76] "ise_43" "ise_44" "ise_45"
## [79] "ise_46" "ise_47" "ise_48"
## [82] "ise_49" "ise_50" "ise_51"
## [85] "ise_52" "ise_53" "ise_54"
## [88] "ise_55" "ise_56" "ise_57"
## [91] "ise_58" "ise_59" "ise_60"
## [94] "ise_61" "ise_62" "ise_63"
## [97] "ise_64" "ise_65" "ise_66"
## [100] "ise_67" "ise_68" "ise_69"
## [103] "ise_70" "ise_71" "ise_72"
## [106] "ise_73" "ise_74" "ise_75"
## [109] "ise_76" "ise_77" "ise_78"
## [112] "ise_79" "ise_80" "ise_81"
## [115] "ise_82" "ise_83" "ise_84"
## [118] "ise_85" "ise_86" "ise_87"
## [121] "ise_88" "ise_89" "ise_90"
## [124] "ise_91"
library(psych)
?score.multiple.choice
bpr_psicom <- score.multiple.choice(key, df1[ ,c(7:33)], score=TRUE, short=FALSE)
print.psych(bpr_psicom, short = FALSE)
## Call: NULL
##
## (Unstandardized) Alpha:
## [1] 0.77
##
## Average item correlation:
## [1] 0.11
##
## item statistics
## key 1 2 3 4 5 miss r n mean sd skew
## ahv_1 2 0.08 0.70 0.09 0.07 0.06 0.41 0.07 2098 0.70 0.46 -0.86
## ahv_2 4 0.16 0.08 0.21 0.48 0.06 0.43 0.62 2054 0.48 0.50 0.08
## ahv_3 3 0.16 0.10 0.44 0.12 0.18 0.44 0.59 2020 0.44 0.50 0.25
## ahv_4 3 0.08 0.37 0.36 0.10 0.08 0.46 0.54 1930 0.36 0.48 0.60
## ahv_5 1 0.40 0.08 0.19 0.09 0.25 0.49 0.53 1833 0.40 0.49 0.40
## ahv_6 5 0.13 0.20 0.16 0.15 0.35 0.51 0.44 1749 0.35 0.48 0.62
## ahv_7 2 0.57 0.09 0.15 0.06 0.12 0.52 0.18 1705 0.09 0.29 2.77
## ahv_8 5 0.33 0.26 0.24 0.08 0.09 0.54 0.17 1649 0.09 0.29 2.86
## ahv_9 1 0.41 0.21 0.21 0.07 0.09 0.54 0.44 1628 0.41 0.49 0.36
## aha_1 3 0.06 0.08 0.79 0.03 0.05 0.41 0.12 2117 0.79 0.41 -1.41
## aha_2 5 0.07 0.08 0.18 0.13 0.54 0.41 0.06 2103 0.54 0.50 -0.16
## aha_3 4 0.17 0.15 0.16 0.30 0.22 0.42 0.55 2066 0.30 0.46 0.87
## aha_4 2 0.06 0.36 0.18 0.31 0.09 0.43 0.51 2044 0.36 0.48 0.58
## aha_5 4 0.17 0.07 0.14 0.34 0.27 0.47 0.48 1896 0.34 0.48 0.66
## aha_6 3 0.05 0.42 0.34 0.10 0.10 0.48 0.50 1862 0.34 0.47 0.69
## aha_7 3 0.14 0.14 0.50 0.11 0.11 0.49 -0.05 1833 0.50 0.50 -0.01
## aha_8 5 0.16 0.17 0.19 0.33 0.14 0.50 0.31 1803 0.14 0.35 2.06
## aha_9 1 0.14 0.13 0.18 0.29 0.26 0.50 0.24 1776 0.14 0.34 2.10
## ahe_1 1 0.49 0.12 0.18 0.10 0.11 0.42 0.54 2079 0.49 0.50 0.05
## ahe_2 2 0.20 0.37 0.13 0.11 0.19 0.43 0.47 2035 0.37 0.48 0.55
## ahe_3 5 0.25 0.33 0.16 0.07 0.19 0.44 0.32 2015 0.19 0.39 1.62
## ahe_4 3 0.14 0.11 0.32 0.21 0.22 0.45 0.47 1969 0.32 0.47 0.77
## ahe_5 4 0.18 0.22 0.22 0.27 0.11 0.47 0.45 1914 0.27 0.45 1.01
## ahe_6 2 0.14 0.35 0.24 0.18 0.10 0.48 0.45 1861 0.35 0.48 0.64
## ahe_7 2 0.16 0.30 0.21 0.19 0.15 0.50 0.46 1803 0.30 0.46 0.89
## ahe_8 5 0.27 0.17 0.25 0.16 0.15 0.50 0.28 1778 0.15 0.36 1.94
## ahe_9 1 0.21 0.17 0.24 0.19 0.19 0.51 0.31 1750 0.21 0.41 1.44
## kurtosis se
## ahv_1 -1.27 0.01
## ahv_2 -1.99 0.01
## ahv_3 -1.94 0.01
## ahv_4 -1.64 0.01
## ahv_5 -1.84 0.01
## ahv_6 -1.62 0.01
## ahv_7 5.68 0.01
## ahv_8 6.16 0.01
## ahv_9 -1.87 0.01
## aha_1 -0.01 0.01
## aha_2 -1.98 0.01
## aha_3 -1.24 0.01
## aha_4 -1.66 0.01
## aha_5 -1.57 0.01
## aha_6 -1.52 0.01
## aha_7 -2.00 0.01
## aha_8 2.23 0.01
## aha_9 2.43 0.01
## ahe_1 -2.00 0.01
## ahe_2 -1.69 0.01
## ahe_3 0.63 0.01
## ahe_4 -1.40 0.01
## ahe_5 -0.97 0.01
## ahe_6 -1.59 0.01
## ahe_7 -1.22 0.01
## ahe_8 1.76 0.01
## ahe_9 0.08 0.01
str(bpr_psicom)
## List of 5
## $ scores : Named num [1:3578] 0.111 0.148 0.296 0.148 0.37 ...
## ..- attr(*, "names")= chr [1:3578] "Averages" NA NA NA ...
## $ missing : num [1:3578] 20 27 0 27 0 0 0 27 27 27 ...
## $ item.stats:'data.frame': 27 obs. of 14 variables:
## ..$ key : num [1:27] 2 4 3 3 1 5 2 5 1 3 ...
## ..$ 1 : num [1:27] 0.08 0.16 0.16 0.08 0.4 0.13 0.57 0.33 0.41 0.06 ...
## ..$ 2 : num [1:27] 0.7 0.08 0.1 0.37 0.08 0.2 0.09 0.26 0.21 0.08 ...
## ..$ 3 : num [1:27] 0.09 0.21 0.44 0.36 0.19 0.16 0.15 0.24 0.21 0.79 ...
## ..$ 4 : num [1:27] 0.07 0.48 0.12 0.1 0.09 0.15 0.06 0.08 0.07 0.03 ...
## ..$ 5 : num [1:27] 0.06 0.06 0.18 0.08 0.25 0.35 0.12 0.09 0.09 0.05 ...
## ..$ miss : num [1:27] 0.41 0.43 0.44 0.46 0.49 0.51 0.52 0.54 0.54 0.41 ...
## ..$ r : num [1:27] 0.07 0.62 0.59 0.54 0.53 0.44 0.18 0.17 0.44 0.12 ...
## ..$ n : num [1:27] 2098 2054 2020 1930 1833 ...
## ..$ mean : num [1:27] 0.7 0.48 0.44 0.36 0.4 0.35 0.09 0.09 0.41 0.79 ...
## ..$ sd : num [1:27] 0.46 0.5 0.5 0.48 0.49 0.48 0.29 0.29 0.49 0.41 ...
## ..$ skew : num [1:27] -0.86 0.08 0.25 0.6 0.4 0.62 2.77 2.86 0.36 -1.41 ...
## ..$ kurtosis: num [1:27] -1.27 -1.99 -1.94 -1.64 -1.84 -1.62 5.68 6.16 -1.87 -0.01 ...
## ..$ se : num [1:27] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ...
## $ alpha : num 0.77
## $ av.r : num 0.11
## - attr(*, "class")= chr [1:2] "psych" "mchoice"
head(bpr_psicom$scores)
## Averages <NA> <NA> <NA> <NA> <NA>
## 0.1111111 0.1481481 0.2962963 0.1481481 0.3703704 0.4444444
- Análises gráficas dos itens
bpr_df <- score.multiple.choice(key, df1[ ,c(7:33)], score=FALSE)
head(bpr_df)
## ahv_1 ahv_2 ahv_3 ahv_4 ahv_5 ahv_6 ahv_7 ahv_8 ahv_9 aha_1 aha_2
## [1,] NA NA NA NA NA NA NA NA NA 0 0
## [2,] NA NA NA NA NA NA NA NA NA NA NA
## [3,] 1 1 1 0 0 0 0 0 1 1 1
## [4,] NA NA NA NA NA NA NA NA NA NA NA
## [5,] 1 0 1 1 1 0 1 0 1 1 1
## [6,] 1 0 0 1 0 0 1 1 0 1 1
## aha_3 aha_4 aha_5 aha_6 aha_7 aha_8 aha_9 ahe_1 ahe_2 ahe_3 ahe_4
## [1,] 0 1 0 0 NA 0 NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA
## [3,] 0 0 1 0 1 0 0 0 0 0 0
## [4,] NA NA NA NA NA NA NA NA NA NA NA
## [5,] 1 0 1 0 0 0 0 0 0 0 0
## [6,] 1 0 1 0 1 0 0 1 0 0 0
## ahe_5 ahe_6 ahe_7 ahe_8 ahe_9
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] 0 0 0 0 0
## [4,] NA NA NA NA NA
## [5,] 0 0 0 0 0
## [6,] 0 1 1 0 0
irt.responses(bpr_psicom$scores, df1[ ,c(7:33)],breaks=9)
TRI via análise fatorial
?irt.fa
bpr_irt <- irt.fa(bpr_df)
print.psych(bpr_irt, short=FALSE)
## Item Response Analysis using Factor Analysis
##
## Call: irt.fa(x = bpr_df)
## Item Response Analysis using Factor Analysis
##
## Summary information by factor and item
## Factor = 1
## -3 -2 -1 0 1 2 3
## ahv_1 0.07 0.14 0.20 0.19 0.13 0.07 0.03
## ahv_2 0.04 0.10 0.19 0.25 0.20 0.11 0.05
## ahv_3 0.04 0.09 0.16 0.21 0.18 0.11 0.05
## ahv_4 0.04 0.07 0.11 0.15 0.14 0.11 0.06
## ahv_5 0.04 0.05 0.06 0.07 0.07 0.06 0.04
## aha_1 0.11 0.30 0.44 0.29 0.11 0.03 0.01
## aha_2 0.02 0.11 0.47 0.81 0.33 0.06 0.01
## aha_3 0.01 0.05 0.16 0.37 0.43 0.22 0.07
## aha_4 0.04 0.06 0.08 0.09 0.09 0.07 0.05
## aha_5 0.04 0.05 0.07 0.09 0.08 0.07 0.05
## aha_6 0.04 0.06 0.10 0.12 0.12 0.09 0.06
## aha_7 0.03 0.11 0.33 0.53 0.32 0.10 0.03
## ahe_4 0.03 0.05 0.06 0.07 0.07 0.06 0.05
## ahe_5 0.03 0.05 0.06 0.08 0.08 0.07 0.05
## ahe_7 0.03 0.05 0.08 0.09 0.09 0.08 0.06
## Test Info 0.61 1.33 2.58 3.40 2.44 1.31 0.68
## SEM 1.28 0.87 0.62 0.54 0.64 0.87 1.22
## Reliability -0.64 0.25 0.61 0.71 0.59 0.24 -0.48
##
## Item discrimination and location for factor MR1
## discrimination location
## ahv_1 0.53 -0.58
## ahv_2 0.59 0.06
## ahv_3 0.54 0.18
## ahv_4 0.46 0.41
## ahv_5 0.31 0.26
## ahv_6 0.23 0.39
## ahv_7 -0.01 1.31
## ahv_8 -0.03 1.34
## ahv_9 0.09 0.22
## aha_1 0.78 -1.02
## aha_2 1.06 -0.14
## aha_3 0.79 0.67
## aha_4 0.35 0.38
## aha_5 0.35 0.43
## aha_6 0.41 0.46
## aha_7 0.85 -0.01
## aha_8 0.24 1.10
## aha_9 0.01 1.09
## ahe_1 0.29 0.04
## ahe_2 0.23 0.35
## ahe_3 0.12 0.90
## ahe_4 0.32 0.49
## ahe_5 0.33 0.63
## ahe_6 0.26 0.40
## ahe_7 0.36 0.57
## ahe_8 0.13 1.04
## ahe_9 0.10 0.82
##
## These parameters were based on the following factor analysis
## Factor Analysis using method = minres
## Call: fa(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate,
## fm = fm)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## ahv_1 0.47 2.2e-01 0.78 1
## ahv_2 0.51 2.6e-01 0.74 1
## ahv_3 0.47 2.2e-01 0.78 1
## ahv_4 0.42 1.7e-01 0.83 1
## ahv_5 0.30 8.9e-02 0.91 1
## ahv_6 0.22 5.0e-02 0.95 1
## ahv_7 -0.01 1.6e-04 1.00 1
## ahv_8 -0.03 9.7e-04 1.00 1
## ahv_9 0.09 8.9e-03 0.99 1
## aha_1 0.62 3.8e-01 0.62 1
## aha_2 0.73 5.3e-01 0.47 1
## aha_3 0.62 3.8e-01 0.62 1
## aha_4 0.33 1.1e-01 0.89 1
## aha_5 0.33 1.1e-01 0.89 1
## aha_6 0.38 1.5e-01 0.85 1
## aha_7 0.65 4.2e-01 0.58 1
## aha_8 0.23 5.3e-02 0.95 1
## aha_9 0.01 3.7e-05 1.00 1
## ahe_1 0.28 7.7e-02 0.92 1
## ahe_2 0.22 4.9e-02 0.95 1
## ahe_3 0.12 1.5e-02 0.98 1
## ahe_4 0.30 9.2e-02 0.91 1
## ahe_5 0.31 9.9e-02 0.90 1
## ahe_6 0.25 6.2e-02 0.94 1
## ahe_7 0.34 1.2e-01 0.88 1
## ahe_8 0.13 1.8e-02 0.98 1
## ahe_9 0.10 9.7e-03 0.99 1
##
## MR1
## SS loadings 3.69
## Proportion Var 0.14
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 351 and the objective function was 4.63 with Chi Square of 16506.24
## The degrees of freedom for the model are 324 and the objective function was 2.08
##
## The root mean square of the residuals (RMSR) is 0.07
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 3578 with the empirical chi square 10699.12 with prob < 0
## The total number of observations was 3578 with MLE Chi Square = 7402.08 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.525
## RMSEA index = 0.078 and the 90 % confidence intervals are NA NA
## BIC = 4750.93
## Fit based upon off diagonal values = 0.81
## Measures of factor score adequacy
## MR1
## Correlation of scores with factors 0.92
## Multiple R square of scores with factors 0.84
## Minimum correlation of possible factor scores 0.69
plot(bpr_irt,type="IIC")
plot(bpr_irt,type="test")
plot(bpr_irt,type="ICC")
Correção e psicometria clássica com escalas likert e várias dimensões
- SENNA (v1.0) Sistema de Avaliação e Monitoramento de Competências Socioemocionais
- Seis dimensões: C:Grit, E:Engajamento, N: Estabilidade Emocional, 4: A:Colaboração, 5:O: Abertura, 6: Loc:MindSet
- Dois layers
P_S 0: tema nuclear da personalidade e 1: auto eficácia
- Versão com 83 itens informatizado (itens oralizados)
- Aplicado em crianças com oito (3o ano) e dez anos (5o ano) em projeto de avaliaçào de impacto do usdo dos materias da LEGO (LEGO Zoom)
names(dic)
## [1] "Bloco2" "CodItem" "Ord" "VarLabel" "Key0"
## [6] "Key1" "Key2" "b" "senna54" "senna84"
## [11] "OrdSenna84" "domain" "facet" "pole" "P_S"
names(df1)[42:124]
## [1] "ise_9" "ise_10" "ise_11" "ise_12" "ise_13" "ise_14" "ise_15"
## [8] "ise_16" "ise_17" "ise_18" "ise_19" "ise_20" "ise_21" "ise_22"
## [15] "ise_23" "ise_24" "ise_25" "ise_26" "ise_27" "ise_28" "ise_29"
## [22] "ise_30" "ise_31" "ise_32" "ise_33" "ise_34" "ise_35" "ise_36"
## [29] "ise_37" "ise_38" "ise_39" "ise_40" "ise_41" "ise_42" "ise_43"
## [36] "ise_44" "ise_45" "ise_46" "ise_47" "ise_48" "ise_49" "ise_50"
## [43] "ise_51" "ise_52" "ise_53" "ise_54" "ise_55" "ise_56" "ise_57"
## [50] "ise_58" "ise_59" "ise_60" "ise_61" "ise_62" "ise_63" "ise_64"
## [57] "ise_65" "ise_66" "ise_67" "ise_68" "ise_69" "ise_70" "ise_71"
## [64] "ise_72" "ise_73" "ise_74" "ise_75" "ise_76" "ise_77" "ise_78"
## [71] "ise_79" "ise_80" "ise_81" "ise_82" "ise_83" "ise_84" "ise_85"
## [78] "ise_86" "ise_87" "ise_88" "ise_89" "ise_90" "ise_91"
dic_senna <- dic[dic$Bloco2=="SENNA" & dic$senna84==1, c(2, 4, 11:15) ]
dic_senna$Ord2 <- dic_senna$OrdSenna84*ifelse(dic_senna$pole == 0 , -1, 1)
names(dic_senna)
## [1] "CodItem" "VarLabel" "OrdSenna84" "domain" "facet"
## [6] "pole" "P_S" "Ord2"
dic_senna$VarLabel2 <-paste("i", dic_senna$domain,
dic_senna$P_S,
dic_senna$pole,
dic_senna$VarLabel, sep="_")
library(psych)
senna83.key1 <-make.keys(nvars=83,
list(F1.Cons=dic_senna[dic_senna$domain =="C" , ]$Ord2,
F2.Extr=dic_senna[dic_senna$domain =="E" , ]$Ord2,
F3.Neur=dic_senna[dic_senna$domain =="N" , ]$Ord2,
F4.Agre=dic_senna[dic_senna$domain =="A" , ]$Ord2,
F5.Opns=dic_senna[dic_senna$domain =="O", ]$Ord2,
F6.ELoc=dic_senna[dic_senna$domain =="NV" , ]$Ord2),
item.labels=dic_senna[ dic_senna$Bloco2=="SENNA", ]$VarLabel2)
senna83.key1
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## [1,] 1 0 0 0 0 0
## [2,] 0 1 0 0 0 0
## [3,] 0 0 1 0 0 0
## [4,] 1 0 0 0 0 0
## [5,] 0 1 0 0 0 0
## [6,] 0 0 1 0 0 0
## [7,] 1 0 0 0 0 0
## [8,] 0 1 0 0 0 0
## [9,] 0 0 1 0 0 0
## [10,] 1 0 0 0 0 0
## [11,] 0 1 0 0 0 0
## [12,] 0 0 1 0 0 0
## [13,] 1 0 0 0 0 0
## [14,] 0 1 0 0 0 0
## [15,] 1 0 0 0 0 0
## [16,] 0 1 0 0 0 0
## [17,] 1 0 0 0 0 0
## [18,] 0 0 -1 0 0 0
## [19,] 0 0 0 1 0 0
## [20,] 0 0 0 0 1 0
## [21,] 1 0 0 0 0 0
## [22,] 0 0 -1 0 0 0
## [23,] 0 0 0 0 0 1
## [24,] 0 0 0 1 0 0
## [25,] 0 0 0 0 1 0
## [26,] 1 0 0 0 0 0
## [27,] 0 0 -1 0 0 0
## [28,] 0 0 0 0 0 1
## [29,] 0 0 0 1 0 0
## [30,] -1 0 0 0 0 0
## [31,] 0 0 -1 0 0 0
## [32,] 0 0 -1 0 0 0
## [33,] 0 0 0 1 0 0
## [34,] 0 0 0 0 1 0
## [35,] 1 0 0 0 0 0
## [36,] 0 0 0 1 0 0
## [37,] 0 0 0 0 1 0
## [38,] 0 0 0 1 0 0
## [39,] 1 0 0 0 0 0
## [40,] 0 0 -1 0 0 0
## [41,] 0 0 -1 0 0 0
## [42,] 0 0 0 1 0 0
## [43,] 0 0 0 0 1 0
## [44,] -1 0 0 0 0 0
## [45,] 0 0 -1 0 0 0
## [46,] 0 0 0 0 0 1
## [47,] 0 0 0 1 0 0
## [48,] 1 0 0 0 0 0
## [49,] -1 0 0 0 0 0
## [50,] 0 0 -1 0 0 0
## [51,] 0 0 0 1 0 0
## [52,] 0 0 0 0 1 0
## [53,] -1 0 0 0 0 0
## [54,] 0 0 -1 0 0 0
## [55,] 0 0 0 -1 0 0
## [56,] 0 0 0 0 1 0
## [57,] -1 0 0 0 0 0
## [58,] 0 0 -1 0 0 0
## [59,] 0 0 0 0 0 1
## [60,] 0 0 0 1 0 0
## [61,] -1 0 0 0 0 0
## [62,] 0 0 0 0 0 1
## [63,] 0 0 0 -1 0 0
## [64,] 0 0 0 0 1 0
## [65,] 0 1 0 0 0 0
## [66,] 0 0 0 0 0 1
## [67,] 0 0 0 1 0 0
## [68,] 0 0 0 0 1 0
## [69,] 0 -1 0 0 0 0
## [70,] 0 0 0 0 0 1
## [71,] 0 0 0 0 1 0
## [72,] 0 1 0 0 0 0
## [73,] 0 0 0 0 0 1
## [74,] 0 0 0 0 1 0
## [75,] 0 -1 0 0 0 0
## [76,] 0 0 0 0 0 1
## [77,] 0 0 0 0 1 0
## [78,] 0 1 0 0 0 0
## [79,] 0 0 0 0 1 0
## [80,] 0 -1 0 0 0 0
## [81,] 0 0 0 0 0 -1
## [82,] 0 1 0 0 0 0
## [83,] 0 1 0 0 0 0
psicom_senna <-scoreItems(senna83.key1,
df1[, 42:124],
missing=TRUE, imput="none")
str(psicom_senna)
## List of 17
## $ scores : num [1:3578, 1:6] 1.93 NaN 3.82 NaN NaN ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ missing : num [1:3578, 1:6] 4 18 1 18 18 18 5 18 18 18 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ alpha : num [1, 1:6] 0.742 0.526 0.613 0.695 0.848 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "alpha"
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ av.r : num [1, 1:6] 0.1375 0.0733 0.0956 0.1494 0.2997 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "average.r"
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ sn : num [1, 1:6] 2.87 1.11 1.59 2.28 5.56 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "Signal/Noise"
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ n.items : Named num [1:6] 18 14 15 13 13 10
## ..- attr(*, "names")= chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ item.cor : num [1:83, 1:6] 0.542 0.295 0.2 0.481 0.212 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:83] "ise_9" "ise_10" "ise_11" "ise_12" ...
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ cor : num [1:6, 1:6] 1 0.336 0.161 0.54 0.403 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ corrected : num [1:6, 1:6] 0.742 0.336 0.161 0.54 0.403 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ G6 : num [1, 1:6] 0.814 0.662 0.715 0.774 0.865 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "Lambda.6"
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ item.corrected: num [1:83, 1:6] 0.511 0.327 0.221 0.435 0.235 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:83] "ise_9" "ise_10" "ise_11" "ise_12" ...
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ response.freq : num [1:83, 1:6] 0.0799 0.0808 0.2209 0.1208 0.1514 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:83] "ise_9" "ise_10" "ise_11" "ise_12" ...
## .. ..$ : chr [1:6] "1" "2" "3" "4" ...
## $ raw : logi FALSE
## $ alpha.ob : num [1, 1:6] 0.694 0.46 0.557 0.642 0.81 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "alpha.observed"
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ num.ob.item : Named num [1:6] 14.19 10.75 11.89 10.22 9.98 ...
## ..- attr(*, "names")= chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ ase : num [1, 1:6] 0.00746 0.01271 0.0107 0.0091 0.00559 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:6] "F1.Cons" "F2.Extr" "F3.Neur" "F4.Agre" ...
## $ Call : language scoreItems(keys = senna83.key1, items = df1[, 42:124], missing = TRUE, impute = "none")
## - attr(*, "class")= chr [1:2] "psych" "score.items"
print.psych(psicom_senna, short=FALSE, sort=TRUE, cut=.20)
## Call: scoreItems(keys = senna83.key1, items = df1[, 42:124], missing = TRUE,
## impute = "none")
##
## (Standardized) Alpha:
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## alpha 0.74 0.53 0.61 0.7 0.85 0.59
##
## Standard errors of unstandardized Alpha:
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## ASE 0.0075 0.013 0.011 0.0091 0.0056 0.012
##
## Standardized Alpha of observed scales:
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## alpha.observed 0.69 0.46 0.56 0.64 0.81 0.53
##
## Average item correlation:
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## average.r 0.14 0.073 0.096 0.15 0.3 0.13
##
## Guttman 6* reliability:
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## Lambda.6 0.81 0.66 0.72 0.77 0.87 0.69
##
## Signal/Noise based upon av.r :
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## Signal/Noise 2.9 1.1 1.6 2.3 5.6 1.4
##
## Scale intercorrelations corrected for attenuation
## raw correlations below the diagonal, alpha on the diagonal
## corrected correlations above the diagonal:
##
## Note that these are the correlations of the complete scales based on the correlation matrix,
## not the observed scales based on the raw items.
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## F1.Cons 0.742 0.54 0.24 0.75 0.51 -0.10
## F2.Extr 0.336 0.53 -0.39 0.81 0.81 0.64
## F3.Neur 0.161 -0.22 0.61 -0.36 -0.54 -0.95
## F4.Agre 0.540 0.49 -0.24 0.70 0.90 0.58
## F5.Opns 0.403 0.54 -0.39 0.69 0.85 0.79
## F6.ELoc -0.066 0.36 -0.57 0.37 0.56 0.59
##
## Item by scale correlations:
## corrected for item overlap and scale reliability
## F1.Cons F2.Extr F3.Neur F4.Agre F5.Opns F6.ELoc
## ise_9 0.51 0.34 -0.02 0.45 0.34 0.17
## ise_10 0.33 0.48 -0.11 0.41 0.37 0.22
## ise_11 0.22 0.17 -0.04 0.24 0.23 0.19
## ise_12 0.44 0.31 -0.07 0.36 0.33 0.17
## ise_13 0.23 0.46 -0.15 0.31 0.34 0.26
## ise_14 0.29 0.31 -0.04 0.35 0.30 0.22
## ise_15 0.56 0.36 0.01 0.48 0.37 0.16
## ise_16 0.37 0.51 -0.09 0.48 0.41 0.24
## ise_17 0.28 0.37 -0.03 0.35 0.32 0.21
## ise_18 0.50 0.40 -0.07 0.43 0.37 0.17
## ise_19 -0.12 0.21 -0.21 0.05 0.12 0.31
## ise_20 0.22 0.34 -0.07 0.23 0.22 0.19
## ise_21 0.53 0.41 -0.03 0.45 0.40 0.18
## ise_22 0.24 0.46 -0.18 0.35 0.29 0.28
## ise_23 0.49 0.32 -0.08 0.44 0.39 0.21
## ise_24 0.18 0.40 -0.17 0.26 0.26 0.25
## ise_25 0.64 0.46 -0.11 0.58 0.49 0.16
## ise_26 -0.10 0.27 -0.45 0.13 0.18 0.34
## ise_27 0.48 0.46 -0.16 0.56 0.47 0.21
## ise_28 0.38 0.44 -0.21 0.47 0.49 0.32
## ise_29 0.53 0.34 -0.16 0.53 0.47 0.25
## ise_30 -0.05 0.23 -0.53 0.14 0.21 0.40
## ise_31 -0.14 0.20 -0.46 0.10 0.18 0.41
## ise_32 0.40 0.39 -0.18 0.57 0.48 0.31
## ise_33 0.44 0.40 -0.20 0.52 0.55 0.31
## ise_34 0.55 0.42 -0.19 0.59 0.51 0.30
## ise_35 -0.04 0.26 -0.53 0.20 0.28 0.48
## ise_36 -0.14 0.09 -0.36 0.08 0.11 0.35
## ise_37 0.33 0.33 -0.17 0.54 0.42 0.26
## ise_38 -0.09 0.21 -0.46 0.20 0.23 0.46
## ise_39 -0.07 0.23 -0.60 0.20 0.27 0.47
## ise_40 -0.04 0.29 -0.50 0.20 0.30 0.49
## ise_41 0.49 0.43 -0.19 0.62 0.52 0.27
## ise_42 0.21 0.35 -0.31 0.38 0.45 0.36
## ise_43 0.44 0.34 -0.22 0.48 0.47 0.27
## ise_44 -0.06 0.18 -0.52 0.18 0.21 0.40
## ise_45 0.24 0.33 -0.35 0.44 0.48 0.40
## ise_46 0.16 0.21 -0.28 0.37 0.32 0.34
## ise_47 0.49 0.36 -0.15 0.60 0.47 0.20
## ise_48 -0.08 0.16 -0.54 0.20 0.26 0.45
## ise_49 0.38 0.45 -0.24 0.56 0.54 0.32
## ise_50 0.40 0.44 -0.22 0.64 0.57 0.36
## ise_51 0.28 0.43 -0.26 0.52 0.56 0.37
## ise_52 -0.28 0.14 -0.46 0.07 0.17 0.48
## ise_53 -0.18 0.23 -0.59 0.14 0.28 0.53
## ise_54 0.12 0.35 -0.36 0.39 0.43 0.45
## ise_55 0.15 0.30 -0.24 0.41 0.41 0.42
## ise_56 0.04 0.28 -0.42 0.32 0.44 0.50
## ise_57 -0.14 0.21 -0.50 0.25 0.33 0.53
## ise_58 0.29 0.24 -0.18 0.51 0.50 0.33
## ise_59 0.36 0.44 -0.24 0.62 0.58 0.37
## ise_60 0.28 0.37 -0.24 0.49 0.56 0.38
## ise_61 -0.31 0.13 -0.46 0.04 0.24 0.51
## ise_62 -0.19 0.12 -0.50 0.12 0.28 0.52
## ise_63 -0.24 0.12 -0.45 0.02 0.21 0.49
## ise_64 0.33 0.44 -0.24 0.57 0.56 0.37
## ise_65 -0.27 0.14 -0.49 0.12 0.27 0.57
## ise_66 0.04 0.28 -0.48 0.36 0.45 0.50
## ise_67 -0.15 0.27 -0.51 0.21 0.36 0.58
## ise_68 0.24 0.43 -0.26 0.49 0.52 0.41
## ise_69 -0.31 0.10 -0.41 0.02 0.21 0.53
## ise_70 -0.23 0.16 -0.49 0.11 0.27 0.58
## ise_71 -0.17 0.24 -0.52 0.14 0.32 0.53
## ise_72 0.27 0.45 -0.32 0.51 0.62 0.48
## ise_73 0.37 0.52 -0.21 0.56 0.61 0.38
## ise_74 0.11 0.35 -0.34 0.41 0.49 0.53
## ise_75 0.31 0.50 -0.27 0.57 0.60 0.44
## ise_76 0.24 0.43 -0.30 0.50 0.67 0.51
## ise_77 0.22 0.23 -0.22 0.49 0.49 0.41
## ise_78 0.28 0.43 -0.14 0.53 0.56 0.38
## ise_79 0.22 0.27 -0.23 0.42 0.53 0.41
## ise_80 0.32 0.50 -0.19 0.50 0.62 0.39
## ise_81 -0.04 0.25 -0.39 0.29 0.45 0.54
## ise_82 0.15 0.39 -0.37 0.39 0.56 0.48
## ise_83 0.23 0.23 -0.24 0.42 0.53 0.45
## ise_84 -0.03 0.22 -0.39 0.26 0.45 0.51
## ise_85 0.17 0.40 -0.28 0.40 0.59 0.45
## ise_86 0.16 0.46 -0.29 0.39 0.51 0.45
## ise_87 0.26 0.47 -0.26 0.45 0.57 0.38
## ise_88 -0.04 0.13 -0.39 0.25 0.39 0.50
## ise_89 0.09 0.25 -0.33 0.34 0.45 0.47
## ise_90 0.28 0.53 -0.29 0.54 0.59 0.41
## ise_91 0.06 0.38 -0.36 0.32 0.44 0.47
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## ise_9 0.08 0.07 0.20 0.23 0.42 0.48
## ise_10 0.08 0.12 0.17 0.27 0.36 0.48
## ise_11 0.22 0.19 0.25 0.16 0.18 0.49
## ise_12 0.12 0.12 0.24 0.25 0.26 0.49
## ise_13 0.15 0.13 0.17 0.23 0.32 0.49
## ise_14 0.15 0.13 0.19 0.23 0.29 0.49
## ise_15 0.08 0.07 0.20 0.28 0.37 0.48
## ise_16 0.10 0.13 0.20 0.23 0.34 0.50
## ise_17 0.17 0.15 0.21 0.22 0.26 0.50
## ise_18 0.10 0.08 0.15 0.22 0.44 0.49
## ise_19 0.55 0.10 0.12 0.11 0.13 0.50
## ise_20 0.27 0.15 0.15 0.16 0.26 0.50
## ise_21 0.08 0.08 0.21 0.25 0.38 0.51
## ise_22 0.21 0.13 0.20 0.19 0.27 0.51
## ise_23 0.10 0.12 0.20 0.22 0.35 0.51
## ise_24 0.24 0.11 0.16 0.17 0.32 0.49
## ise_25 0.13 0.08 0.17 0.21 0.41 0.49
## ise_26 0.30 0.18 0.21 0.13 0.18 0.50
## ise_27 0.08 0.12 0.22 0.23 0.35 0.50
## ise_28 0.12 0.13 0.24 0.23 0.28 0.51
## ise_29 0.10 0.11 0.21 0.23 0.36 0.51
## ise_30 0.28 0.16 0.21 0.13 0.22 0.50
## ise_31 0.34 0.17 0.18 0.13 0.18 0.51
## ise_32 0.11 0.13 0.24 0.21 0.31 0.50
## ise_33 0.10 0.11 0.20 0.23 0.36 0.51
## ise_34 0.09 0.13 0.22 0.23 0.34 0.51
## ise_35 0.27 0.16 0.21 0.15 0.21 0.50
## ise_36 0.42 0.13 0.17 0.11 0.17 0.50
## ise_37 0.13 0.14 0.21 0.19 0.33 0.50
## ise_38 0.27 0.15 0.25 0.14 0.19 0.50
## ise_39 0.29 0.16 0.21 0.13 0.20 0.51
## ise_40 0.28 0.16 0.19 0.14 0.23 0.52
## ise_41 0.12 0.12 0.21 0.21 0.34 0.52
## ise_42 0.20 0.14 0.23 0.18 0.25 0.52
## ise_43 0.12 0.12 0.26 0.22 0.29 0.53
## ise_44 0.28 0.15 0.22 0.15 0.21 0.53
## ise_45 0.16 0.16 0.24 0.19 0.25 0.53
## ise_46 0.25 0.12 0.22 0.16 0.26 0.53
## ise_47 0.10 0.10 0.20 0.22 0.39 0.52
## ise_48 0.30 0.13 0.18 0.13 0.25 0.51
## ise_49 0.20 0.12 0.17 0.17 0.34 0.53
## ise_50 0.11 0.15 0.21 0.23 0.30 0.54
## ise_51 0.13 0.12 0.23 0.21 0.31 0.55
## ise_52 0.32 0.18 0.21 0.13 0.16 0.55
## ise_53 0.27 0.16 0.22 0.14 0.21 0.55
## ise_54 0.18 0.17 0.22 0.18 0.25 0.54
## ise_55 0.17 0.19 0.29 0.16 0.19 0.55
## ise_56 0.20 0.17 0.28 0.15 0.21 0.56
## ise_57 0.24 0.19 0.23 0.14 0.19 0.56
## ise_58 0.16 0.17 0.23 0.18 0.25 0.55
## ise_59 0.12 0.13 0.19 0.24 0.32 0.56
## ise_60 0.13 0.12 0.18 0.21 0.36 0.57
## ise_61 0.28 0.18 0.22 0.13 0.19 0.57
## ise_62 0.27 0.19 0.22 0.15 0.17 0.56
## ise_63 0.38 0.15 0.18 0.13 0.16 0.55
## ise_64 0.11 0.12 0.15 0.20 0.42 0.56
## ise_65 0.30 0.14 0.23 0.15 0.18 0.56
## ise_66 0.17 0.16 0.25 0.17 0.24 0.56
## ise_67 0.24 0.16 0.22 0.16 0.22 0.57
## ise_68 0.15 0.11 0.20 0.21 0.34 0.57
## ise_69 0.34 0.17 0.20 0.13 0.17 0.57
## ise_70 0.29 0.17 0.23 0.13 0.18 0.55
## ise_71 0.30 0.18 0.20 0.15 0.18 0.57
## ise_72 0.13 0.13 0.22 0.22 0.31 0.56
## ise_73 0.10 0.10 0.17 0.21 0.42 0.57
## ise_74 0.15 0.14 0.25 0.21 0.26 0.55
## ise_75 0.11 0.12 0.19 0.23 0.35 0.56
## ise_76 0.13 0.13 0.21 0.19 0.34 0.57
## ise_77 0.14 0.13 0.24 0.20 0.29 0.57
## ise_78 0.12 0.11 0.22 0.22 0.34 0.57
## ise_79 0.18 0.13 0.21 0.19 0.29 0.57
## ise_80 0.11 0.11 0.18 0.21 0.39 0.58
## ise_81 0.19 0.14 0.26 0.18 0.22 0.58
## ise_82 0.14 0.14 0.24 0.22 0.26 0.58
## ise_83 0.15 0.16 0.22 0.19 0.28 0.58
## ise_84 0.21 0.17 0.24 0.16 0.23 0.57
## ise_85 0.14 0.14 0.20 0.20 0.32 0.57
## ise_86 0.11 0.12 0.19 0.21 0.36 0.58
## ise_87 0.11 0.11 0.18 0.19 0.41 0.58
## ise_88 0.25 0.14 0.22 0.17 0.22 0.58
## ise_89 0.20 0.14 0.24 0.16 0.27 0.58
## ise_90 0.10 0.10 0.19 0.23 0.38 0.58
## ise_91 0.17 0.11 0.23 0.18 0.31 0.56
multi.hist(psicom_senna$scores)
