Escala de Percepção Social do Uso de Drogas
Este relatório objetiva apresentar as análises introdutória do instrumento ``Escala de percepção social do uso de drogas’’, que está em fase de desenvolvimento pelo Centro de Referência em Pesquisa, Intervenção e Avaliação em Álcool e Outras Drogas (http://www.ufjf.br/crepeia/).
O instrumento está sendo validado para população de educadores. O objetivo da pesquisa é oferecer uma medida confiável para avaliação da percepção social de educadores de um curso à distância oferecido pela Secretaria Nacional de Políticas sobre Drogas para aproximadamente 10.000 educadores dos estados de Minas Gerais e Rio de Janeiro.
Durante todo o processo de desenvolvimento, foram utilizadas ferramentas de código-aberto, para facilitar o re-uso das técnicas e procedimentos desenvolvidos. Todo conteúdo do instrumento e de suas etapas estará disponível para o público no repositório (http://github.com/crepeia/ead-senad). Atualmente, o projeto está hospedado no repositório (http://github.com/henriquepgomide/ead-senad).
Neste relatório são apresentadas, análises da escala com base em uma amostra de 2771 educadores-tutores do curso. As análises foram conduzidas através da linguagem de programação R usando os pacotes car, psych e mirt.
Banco de Dados
O banco de dados da pesquisa, pode ser obtido no seguinte endereço: (https://github.com/crepeia/ead-senad/blob/master/percepcaosocial_df.csv).
Resultados
Os resultados são apresentados por tópicos: caracterização da amostra, avaliação descritiva da escala e análise fatorial exploratória.
Bibliotecas
library(car) # Function Recode
library(psych) # Function Describe##
## Attaching package: 'psych'
##
## The following object is masked from 'package:car':
##
## logitlibrary(mirt) # CFA## Loading required package: stats4
## Loading required package: latticesocialPer <- read.csv("percepcaosocial_df.csv")
## Summing scales to remove NA's
socialPer$scaleSum <- rowSums(socialPer[,34:71])
## Subset completed observations and consented participation
socialPer <- subset(socialPer, subset=socialPer$termo=="Sim" & socialPer$estado=="Finalizadas" & !is.na(socialPer$scaleSum))Sócio-demográficas
Idade
socialPer$idade <- as.numeric(as.character(socialPer$idade))## Warning: NAs introduzidos por coerçãosocialPer$idade[socialPer$idade < 18 | socialPer$idade > 68 ] <- NA
summary(socialPer$idade) # all## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 18.0 34.0 40.0 40.6 47.0 68.0 284by(socialPer$idade, socialPer$sexo, describe) #by sex## socialPer$sexo: Feminino
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 2122 40.74 8.68 41 40.65 10.38 20 68 48 0.08 -0.67
## se
## 1 0.19
## --------------------------------------------------------
## socialPer$sexo: Masculino
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 361 39.86 9.73 39 39.38 10.38 18 67 49 0.39 -0.44
## se
## 1 0.51Sexo
cbind(round(prop.table(sort(table(socialPer$sexo), decreasing = TRUE)),2))## [,1]
## Feminino 0.86
## Masculino 0.14Escolaridade
cbind(round(prop.table(sort(table(socialPer$escolaridade), decreasing = TRUE)),2))## [,1]
## Pós-graduação 0.64
## Ensino Superior Completo 0.29
## Ensino Superior Incompleto 0.05
## Ensino Médio Completo 0.01
## Ensino Fundamental Completo 0.00
## Ensino Médio Incompleto 0.00
## Ensino Fundamental Incompleto 0.00Estado Civil
cbind(round(prop.table(sort(table(socialPer$estadocivil), decreasing = TRUE)),2))## [,1]
## Casado (a) 0.59
## Solteiro (a) 0.21
## Divorciado (a) 0.09
## União Estável 0.08
## Viúvo (a) 0.02
## Outros 0.01Tempo de serviço
timeWorking <- as.numeric(as.character(socialPer$tempodeservico))## Warning: NAs introduzidos por coerçãotimeWorking[timeWorking > 59] <- NA
summary(timeWorking)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 5.0 12.0 12.9 20.0 59.0 545Religião
cbind(round(prop.table(sort(table(socialPer$religiao), decreasing = TRUE)),2))## [,1]
## Católica 0.66
## Evangélica 0.19
## Espírita 0.08
## Sem religião 0.04
## Outras 0.02
## Umbanda 0.00
## Budismo 0.00
## Candomblé 0.00Contato com o tema
cbind(round(prop.table(sort(table(socialPer$contatoanterior), decreasing = TRUE)),2))## [,1]
## Sim 0.62
## Não 0.38Lida com
cbind(round(prop.table(sort(table(socialPer$lidadiretamente), decreasing = TRUE)),2))## [,1]
## Sim 0.64
## Não 0.36Onde lida com
cbind(round(prop.table(sort(table(socialPer$lida.onde), decreasing = TRUE)),2))## [,1]
## Escola 0.34
## Família 0.23
## Comunidade 0.20
## Outros 0.11
## Amigos 0.06
## Serviços de atuação 0.03
## Serviços de saúde 0.02Apresentação dos itens da escala
questions <- read.csv("percepcaosocial_questions.csv", col.names = "Itens", header=TRUE)
print(as.character(questions[1:39,1]), type="html", justify = "left" )## [1] "Usuários de drogas não têm força de vontade."
## [2] "Usuários de drogas têm menor destaque na sociedade."
## [3] "Usuários de drogas não podem ocupar cargos que exigem maior responsabilidade."
## [4] "O uso de drogas representa uma fraqueza de caráter."
## [5] "Usuários de drogas não se preocupam com si mesmos."
## [6] "Usuários de drogas são pessoas moralmente fracas."
## [7] "Usuários de drogas são pessoas sem determinação."
## [8] "Usuários de drogas não querem parar de usá-las."
## [9] "Usuários de drogas raramente prejudicam alguém a não ser a si próprios."
## [10] "A maioria dos usuários de drogas está desempregada."
## [11] "O tratamento raramente ajuda o usuário de drogas."
## [12] "Não se deve ter grandes expectativas na relação com os usuários de drogas."
## [13] "Quem abusa de drogas pode aprender a diminuir o uso, tendo-o sob controle novamente."
## [14] "Usuários de drogas podem ser ajudados antes de chegarem ao “fundo do poço”."
## [15] "As principais causas do uso de drogas é a falta de disciplina e autocontrole."
## [16] "A melhor forma de controlar os usuários de drogas é mantê-los isolados."
## [17] "Existem características que diferenciam os usuários de drogas das pessoas normais."
## [18] "Uma pessoa deve ser hospitalizada assim que apresentar sinais de uso de drogas."
## [19] "A dependência de drogas é uma doença ."
## [20] "Os usuários de drogas são pessoas indesejáveis na sociedade."
## [21] "A sociedade não deveria se preocupar em proteger-se dos usuários de drogas."
## [22] "Os usuários de drogas são responsáveis pelos problemas associados ao uso de drogas."
## [23] "Os usuários de drogas devem ser isolados da sociedade."
## [24] "Uma pessoa seria ingênua em se casar com alguém que tenha sido usuário de drogas, mesmo que estivesse recuperado."
## [25] "As pessoas não gostariam de morar próximo a alguém que tenha sido usuário de drogas."
## [26] "Alguém que tenha um histórico de uso de drogas deve ser impedido de assumir qualquer cargo público."
## [27] "Os usuários de drogas devem ser privados de seus direitos individuais."
## [28] "Usuários de drogas devem ser encorajados a assumir sua responsabilidade por suas atividades diárias."
## [29] "Ninguém tem o direito de excluir os usuários de drogas de sua vizinhança."
## [30] "Os usuários de drogas oferecem mais perigo do que as pessoas imaginam."
## [31] "Os usuários de drogas ainda são ridicularizados."
## [32] "Devem ser gastos mais recursos públicos financeiros no tratamento dos usuários de drogas."
## [33] "A sociedade precisa ser mais tolerante com os usuários de drogas."
## [34] "A sociedade tem a responsabilidade de fornecer o melhor tratamento possível aos usuários de drogas."
## [35] "Os usuários de drogas merecem nossa simpatia."
## [36] "Os usuários de drogas são um peso para a sociedade."
## [37] "Aumentar o investimento nas políticas de drogas é um desperdício de dinheiro público."
## [38] "O numero de serviços de tratamento é suficiente para o número de usuários de drogas ."
## [39] "É melhor evitar alguém que tenha problemas com drogas."Itens
fullScale <- socialPer[,34:71]
describe(fullScale, skew=FALSE)## vars n mean sd median trimmed mad min max range se
## ps001 1 2771 2.26 0.81 2 2.22 0.00 1 5 4 0.02
## ps002 2 2771 3.07 1.02 3 3.09 1.48 1 5 4 0.02
## ps003 3 2771 2.95 0.95 3 2.95 1.48 1 5 4 0.02
## ps004 4 2771 2.27 0.86 2 2.20 0.00 1 5 4 0.02
## ps005 5 2771 2.72 0.98 2 2.70 1.48 1 5 4 0.02
## ps006 6 2771 2.47 0.91 2 2.45 1.48 1 5 4 0.02
## ps007 7 2771 2.49 0.87 2 2.46 1.48 1 5 4 0.02
## ps008 8 2771 2.24 0.76 2 2.22 0.00 1 5 4 0.01
## ps009 9 2771 2.02 0.89 2 1.90 0.00 1 5 4 0.02
## ps010 10 2771 2.83 0.94 3 2.80 1.48 1 5 4 0.02
## ps011 11 2771 2.07 0.78 2 1.99 0.00 1 5 4 0.01
## ps012 12 2771 2.09 0.75 2 2.04 0.00 1 5 4 0.01
## ps013 13 2771 2.84 1.10 3 2.88 1.48 1 5 4 0.02
## ps014 14 2771 4.33 0.60 4 4.35 0.00 1 5 4 0.01
## ps015 15 2771 2.76 0.97 3 2.75 1.48 1 5 4 0.02
## ps016 16 2771 1.83 0.66 2 1.76 0.00 1 5 4 0.01
## ps017 17 2771 3.18 0.99 3 3.24 1.48 1 5 4 0.02
## ps018 18 2771 2.46 0.84 2 2.40 0.00 1 5 4 0.02
## ps019 19 2771 3.93 0.86 4 4.04 0.00 1 5 4 0.02
## ps020 20 2771 2.81 0.99 3 2.83 1.48 1 5 4 0.02
## ps021 21 2771 1.98 0.82 2 1.90 0.00 1 5 4 0.02
## ps022 22 2771 3.04 0.95 3 3.06 1.48 1 5 4 0.02
## ps023 23 2771 1.81 0.64 2 1.75 0.00 1 5 4 0.01
## ps024 24 2771 1.95 0.70 2 1.90 0.00 1 5 4 0.01
## ps025 25 2771 2.55 0.97 2 2.55 1.48 1 5 4 0.02
## ps026 26 2771 2.00 0.72 2 1.95 0.00 1 5 4 0.01
## ps027 27 2771 1.74 0.64 2 1.69 0.00 1 5 4 0.01
## ps028 28 2771 4.18 0.64 4 4.22 0.00 1 5 4 0.01
## ps029 29 2771 4.07 0.70 4 4.13 0.00 1 5 4 0.01
## ps030 30 2771 2.69 0.85 3 2.67 1.48 1 5 4 0.02
## ps031 31 2771 3.65 0.76 4 3.73 0.00 1 5 4 0.01
## ps033 32 2771 3.31 0.88 3 3.34 1.48 1 5 4 0.02
## ps034 33 2771 3.60 0.93 4 3.64 1.48 1 5 4 0.02
## ps035 34 2771 3.54 0.81 4 3.57 1.48 1 5 4 0.02
## ps036 35 2771 2.63 0.92 3 2.62 1.48 1 5 4 0.02
## ps037 36 2771 1.75 0.70 2 1.66 0.00 1 5 4 0.01
## ps038 37 2771 1.75 0.77 2 1.63 0.00 1 5 4 0.01
## ps039 38 2771 2.39 0.97 2 2.30 0.00 1 5 4 0.02Crobach’s alfa
alpha(fullScale)## Warning: Some items were negatively correlated with total scale and were
## automatically reversed.##
## Reliability analysis
## Call: alpha(x = fullScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.87 0.87 0.89 0.15 6.9 0.0044 2.4 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.87 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## ps001 0.86 0.87 0.89 0.15 6.6 0.0045
## ps002 0.87 0.87 0.89 0.16 6.9 0.0044
## ps003 0.86 0.87 0.89 0.15 6.6 0.0045
## ps004 0.86 0.87 0.89 0.15 6.5 0.0046
## ps005 0.86 0.87 0.89 0.15 6.5 0.0046
## ps006 0.86 0.87 0.88 0.15 6.5 0.0046
## ps007 0.86 0.87 0.88 0.15 6.5 0.0046
## ps008 0.86 0.87 0.89 0.15 6.5 0.0045
## ps009 0.87 0.87 0.89 0.16 7.0 0.0043
## ps010 0.86 0.87 0.89 0.16 6.8 0.0044
## ps011 0.86 0.87 0.89 0.15 6.7 0.0045
## ps012 0.86 0.87 0.89 0.15 6.5 0.0046
## ps013- 0.87 0.88 0.89 0.16 7.1 0.0042
## ps014- 0.86 0.87 0.89 0.16 6.8 0.0044
## ps015 0.86 0.87 0.89 0.15 6.8 0.0044
## ps016 0.86 0.87 0.89 0.15 6.6 0.0045
## ps017 0.86 0.87 0.89 0.16 6.8 0.0044
## ps018 0.86 0.87 0.89 0.15 6.8 0.0044
## ps019- 0.87 0.88 0.89 0.16 7.1 0.0043
## ps020 0.86 0.87 0.89 0.15 6.7 0.0045
## ps021 0.87 0.87 0.89 0.16 6.9 0.0044
## ps022 0.86 0.87 0.89 0.15 6.7 0.0045
## ps023 0.86 0.87 0.88 0.15 6.5 0.0046
## ps024 0.86 0.87 0.89 0.15 6.5 0.0045
## ps025 0.86 0.87 0.89 0.16 6.8 0.0044
## ps026 0.86 0.87 0.89 0.15 6.5 0.0046
## ps027 0.86 0.87 0.89 0.15 6.5 0.0045
## ps028- 0.86 0.87 0.89 0.16 6.8 0.0044
## ps029- 0.86 0.87 0.89 0.15 6.7 0.0045
## ps030 0.86 0.87 0.89 0.15 6.6 0.0046
## ps031- 0.87 0.87 0.89 0.16 7.0 0.0044
## ps033- 0.86 0.87 0.89 0.15 6.7 0.0045
## ps034- 0.86 0.87 0.89 0.15 6.8 0.0044
## ps035- 0.86 0.87 0.89 0.15 6.7 0.0045
## ps036 0.86 0.87 0.89 0.15 6.6 0.0046
## ps037 0.86 0.87 0.89 0.15 6.6 0.0045
## ps038 0.86 0.87 0.89 0.15 6.8 0.0044
## ps039 0.86 0.87 0.89 0.15 6.7 0.0045
##
## Item statistics
## n r r.cor r.drop mean sd
## ps001 2771 0.503 0.487 0.4618 2.3 0.81
## ps002 2771 0.282 0.246 0.2411 3.1 1.02
## ps003 2771 0.471 0.452 0.4374 2.9 0.95
## ps004 2771 0.568 0.563 0.5302 2.3 0.86
## ps005 2771 0.543 0.538 0.5129 2.7 0.98
## ps006 2771 0.594 0.599 0.5665 2.5 0.91
## ps007 2771 0.595 0.599 0.5694 2.5 0.87
## ps008 2771 0.549 0.537 0.5054 2.2 0.76
## ps009 2771 0.200 0.155 0.1275 2.0 0.89
## ps010 2771 0.301 0.266 0.2611 2.8 0.94
## ps011 2771 0.413 0.384 0.3503 2.1 0.78
## ps012 2771 0.575 0.564 0.5286 2.1 0.75
## ps013- 2771 0.061 0.003 -0.0011 3.2 1.10
## ps014- 2771 0.310 0.274 0.2307 1.7 0.60
## ps015 2771 0.363 0.333 0.3152 2.8 0.97
## ps016 2771 0.526 0.514 0.4597 1.8 0.66
## ps017 2771 0.341 0.307 0.2988 3.2 0.99
## ps018 2771 0.365 0.330 0.3056 2.5 0.84
## ps019- 2771 0.123 0.071 0.0394 2.1 0.86
## ps020 2771 0.442 0.421 0.4049 2.8 0.99
## ps021 2771 0.232 0.186 0.1578 2.0 0.82
## ps022 2771 0.382 0.350 0.3356 3.0 0.95
## ps023 2771 0.614 0.614 0.5544 1.8 0.64
## ps024 2771 0.570 0.562 0.5129 1.9 0.70
## ps025 2771 0.334 0.304 0.2773 2.6 0.97
## ps026 2771 0.584 0.576 0.5288 2.0 0.72
## ps027 2771 0.582 0.576 0.5168 1.7 0.64
## ps028- 2771 0.336 0.304 0.2578 1.8 0.64
## ps029- 2771 0.456 0.436 0.3847 1.9 0.70
## ps030 2771 0.532 0.518 0.4922 2.7 0.85
## ps031- 2771 0.207 0.161 0.1311 2.3 0.76
## ps033- 2771 0.390 0.367 0.3286 2.7 0.88
## ps034- 2771 0.374 0.343 0.3119 2.4 0.93
## ps035- 2771 0.390 0.366 0.3296 2.5 0.81
## ps036 2771 0.521 0.508 0.4824 2.6 0.92
## ps037 2771 0.496 0.480 0.4256 1.8 0.70
## ps038 2771 0.353 0.320 0.2784 1.7 0.77
## ps039 2771 0.447 0.422 0.3904 2.4 0.97
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## ps001 0.14 0.54 0.25 0.07 0.01 0
## ps002 0.04 0.30 0.24 0.37 0.05 0
## ps003 0.04 0.32 0.32 0.29 0.03 0
## ps004 0.15 0.54 0.21 0.09 0.01 0
## ps005 0.07 0.43 0.24 0.24 0.02 0
## ps006 0.10 0.50 0.23 0.15 0.01 0
## ps007 0.09 0.50 0.27 0.14 0.01 0
## ps008 0.12 0.58 0.24 0.05 0.01 0
## ps009 0.27 0.55 0.09 0.08 0.01 0
## ps010 0.03 0.41 0.27 0.26 0.02 0
## ps011 0.19 0.63 0.11 0.06 0.01 0
## ps012 0.17 0.63 0.14 0.06 0.00 0
## ps013 0.12 0.32 0.21 0.33 0.03 0
## ps014 0.01 0.01 0.02 0.59 0.38 0
## ps015 0.07 0.40 0.27 0.24 0.02 0
## ps016 0.29 0.60 0.09 0.01 0.00 0
## ps017 0.05 0.24 0.24 0.44 0.04 0
## ps018 0.07 0.55 0.24 0.13 0.01 0
## ps019 0.01 0.07 0.11 0.57 0.23 0
## ps020 0.08 0.35 0.28 0.28 0.02 0
## ps021 0.28 0.51 0.15 0.05 0.01 0
## ps022 0.04 0.27 0.32 0.34 0.03 0
## ps023 0.30 0.60 0.08 0.01 0.00 0
## ps024 0.24 0.59 0.14 0.02 0.00 0
## ps025 0.12 0.43 0.25 0.19 0.01 0
## ps026 0.21 0.61 0.13 0.03 0.00 0
## ps027 0.35 0.57 0.06 0.01 0.00 0
## ps028 0.01 0.01 0.05 0.66 0.28 0
## ps029 0.01 0.02 0.11 0.61 0.25 0
## ps030 0.06 0.38 0.40 0.16 0.01 0
## ps031 0.01 0.09 0.23 0.61 0.07 0
## ps033 0.02 0.16 0.37 0.39 0.06 0
## ps034 0.02 0.12 0.25 0.47 0.14 0
## ps035 0.01 0.08 0.35 0.47 0.09 0
## ps036 0.09 0.40 0.33 0.17 0.01 0
## ps037 0.37 0.53 0.08 0.02 0.00 0
## ps038 0.40 0.50 0.06 0.03 0.01 0
## ps039 0.14 0.51 0.23 0.08 0.04 0Análise Fatorial
KMO - Adequação da amostra
KMO(fullScale)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = fullScale)
## Overall MSA = 0.92
## MSA for each item =
## ps001 ps002 ps003 ps004 ps005 ps006 ps007 ps008 ps009 ps010 ps011 ps012
## 0.95 0.87 0.93 0.94 0.95 0.92 0.92 0.95 0.81 0.89 0.92 0.95
## ps013 ps014 ps015 ps016 ps017 ps018 ps019 ps020 ps021 ps022 ps023 ps024
## 0.64 0.91 0.93 0.91 0.93 0.94 0.83 0.91 0.84 0.93 0.92 0.94
## ps025 ps026 ps027 ps028 ps029 ps030 ps031 ps033 ps034 ps035 ps036 ps037
## 0.84 0.94 0.94 0.86 0.93 0.94 0.83 0.87 0.91 0.88 0.93 0.93
## ps038 ps039
## 0.90 0.95Esfericidade
bartlett.test(fullScale)##
## Bartlett test of homogeneity of variances
##
## data: fullScale
## Bartlett's K-squared = 4686, df = 37, p-value < 2.2e-16Cattel’s scree
fa.parallel(fullScale, fm="minres", fa="fa", show.legend=TRUE) # yields 4 components## Loading required package: parallel
## Loading required package: MASS
## Parallel analysis suggests that the number of factors = 9 and the number of components = 5EFA - Análise fatorial exploratória
faAll <- fa.poly(fullScale, nfactors = 2, rotate = "oblimin")## Loading required package: mvtnorm
## Loading required package: GPArotationprint.psych(faAll, digits=2, cut=0.3)## Factor Analysis using method = minres
## Call: fa.poly(x = fullScale, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## ps001 0.55 0.36814 0.63 1.1
## ps002 0.45 0.16470 0.84 1.2
## ps003 0.50 0.27498 0.73 1.0
## ps004 0.64 0.49961 0.50 1.1
## ps005 0.78 0.57678 0.42 1.0
## ps006 0.81 0.66384 0.34 1.0
## ps007 0.83 0.67779 0.32 1.0
## ps008 0.48 0.38785 0.61 1.4
## ps009 0.05152 0.95 1.1
## ps010 0.47 0.17776 0.82 1.2
## ps011 0.36 0.22106 0.78 1.5
## ps012 0.41 0.33 0.40944 0.59 1.9
## ps013 0.00073 1.00 1.5
## ps014 -0.61 0.30293 0.70 1.2
## ps015 0.48 0.21758 0.78 1.0
## ps016 0.62 0.43189 0.57 1.0
## ps017 0.46 0.18196 0.82 1.1
## ps018 0.14828 0.85 2.0
## ps019 -0.42 0.14109 0.86 1.7
## ps020 0.40 0.20716 0.79 1.1
## ps021 0.37 0.11609 0.88 1.1
## ps022 0.37 0.16330 0.84 1.1
## ps023 0.71 0.56540 0.43 1.0
## ps024 0.59 0.44623 0.55 1.1
## ps025 0.11702 0.88 1.4
## ps026 0.59 0.47000 0.53 1.2
## ps027 0.70 0.53918 0.46 1.0
## ps028 -0.59 0.29420 0.71 1.1
## ps029 -0.68 0.41791 0.58 1.0
## ps030 0.35 0.29279 0.71 1.9
## ps031 -0.36 0.10479 0.90 1.2
## ps033 -0.41 0.17639 0.82 1.0
## ps034 -0.39 0.16951 0.83 1.0
## ps035 -0.45 0.19989 0.80 1.0
## ps036 0.31 0.26548 0.73 2.0
## ps037 0.68 0.44302 0.56 1.0
## ps038 0.54 0.26510 0.73 1.0
## ps039 0.43 0.25750 0.74 1.2
##
## MR2 MR1
## SS loadings 6.20 5.21
## Proportion Var 0.16 0.14
## Cumulative Var 0.16 0.30
## Proportion Explained 0.54 0.46
## Cumulative Proportion 0.54 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.00 0.47
## MR1 0.47 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 703 and the objective function was 13.47 with Chi Square of 37128
## The degrees of freedom for the model are 628 and the objective function was 3.17
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic number of observations is 2771 with the empirical chi square 12392 with prob < 0
## The total number of observations was 2771 with MLE Chi Square = 8733 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.751
## RMSEA index = 0.068 and the 90 % confidence intervals are 0.067 0.07
## BIC = 3755
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.95 0.95
## Multiple R square of scores with factors 0.91 0.91
## Minimum correlation of possible factor scores 0.82 0.82RESULTADOS
- Sem fator 9, 13, 18, 25, 32
- Um fatores e com cargas baixas 36
- MR1 : 1,2,3,4,5,6,7,8,10,15,17,20,22,30,
- MR2 : 11,12,-14,16,-19,21,23,24,26,27,-28,-29,-31,-33,-34,-35, 37,38,39
Versão somente com itens com melhores cargas
# Remove items with low loadings
shortScale <- fullScale[, -c(9,13,18,25,32,36)]
faShort <- fa.poly(shortScale, nfactors = 2, rotate = "oblimin", fm="minres")
print.psych(faShort, digits=2, cut=0.3)## Factor Analysis using method = minres
## Call: fa.poly(x = shortScale, nfactors = 2, rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## ps001 0.55 0.37 0.63 1.1
## ps002 0.44 0.16 0.84 1.2
## ps003 0.49 0.27 0.73 1.0
## ps004 0.64 0.50 0.50 1.1
## ps005 0.79 0.58 0.42 1.0
## ps006 0.81 0.67 0.33 1.0
## ps007 0.83 0.68 0.32 1.0
## ps008 0.48 0.39 0.61 1.4
## ps010 0.46 0.17 0.83 1.2
## ps011 0.35 0.22 0.78 1.5
## ps012 0.41 0.33 0.41 0.59 1.9
## ps014 -0.61 0.31 0.69 1.2
## ps015 0.48 0.22 0.78 1.0
## ps016 0.62 0.44 0.56 1.0
## ps017 0.45 0.18 0.82 1.1
## ps019 -0.42 0.14 0.86 1.6
## ps020 0.39 0.20 0.80 1.1
## ps021 0.37 0.12 0.88 1.1
## ps022 0.37 0.16 0.84 1.1
## ps023 0.71 0.57 0.43 1.0
## ps024 0.59 0.44 0.56 1.1
## ps026 0.60 0.48 0.52 1.1
## ps027 0.71 0.55 0.45 1.0
## ps028 -0.61 0.31 0.69 1.1
## ps029 -0.69 0.43 0.57 1.0
## ps030 0.35 0.29 0.71 1.9
## ps031 -0.36 0.10 0.90 1.2
## ps034 -0.36 0.16 0.84 1.1
## ps035 -0.43 0.18 0.82 1.0
## ps036 0.32 0.26 0.74 1.9
## ps038 0.51 0.24 0.76 1.0
## ps039 0.42 0.25 0.75 1.2
##
## MR2 MR1
## SS loadings 5.33 5.09
## Proportion Var 0.17 0.16
## Cumulative Var 0.17 0.33
## Proportion Explained 0.51 0.49
## Cumulative Proportion 0.51 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.00 0.47
## MR1 0.47 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 496 and the objective function was 11.34 with Chi Square of 31281
## The degrees of freedom for the model are 433 and the objective function was 2.03
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 2771 with the empirical chi square 6526 with prob < 0
## The total number of observations was 2771 with MLE Chi Square = 5599 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.808
## RMSEA index = 0.066 and the 90 % confidence intervals are 0.064 0.067
## BIC = 2167
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.95 0.95
## Multiple R square of scores with factors 0.90 0.91
## Minimum correlation of possible factor scores 0.80 0.82Diagrama com fatores
fa.diagram(faShort)
CFA - Análise fatorial confirmatória
### Run de model
cfa <- bfactor(shortScale, c(2,2,2,2,2,2,2,2,2,1,1,1,2,1,2,1,2,1,2,1,1,1,1,1,1,2,1,1,1,2,1,1))##
Iteration: 1, Log-Lik: -103544.755, Max-Change: 1.76197
Iteration: 2, Log-Lik: -96548.071, Max-Change: 0.90102
Iteration: 3, Log-Lik: -94469.577, Max-Change: 0.75520
Iteration: 4, Log-Lik: -93465.431, Max-Change: 0.63965
Iteration: 5, Log-Lik: -92895.605, Max-Change: 0.47738
Iteration: 6, Log-Lik: -92514.467, Max-Change: 0.84933
Iteration: 7, Log-Lik: -92099.845, Max-Change: 0.30583
Iteration: 8, Log-Lik: -91901.458, Max-Change: 0.16194
Iteration: 9, Log-Lik: -91821.112, Max-Change: 0.09384
Iteration: 10, Log-Lik: -91779.646, Max-Change: 0.06948
Iteration: 11, Log-Lik: -91755.993, Max-Change: 0.05183
Iteration: 12, Log-Lik: -91741.342, Max-Change: 0.04594
Iteration: 13, Log-Lik: -91731.714, Max-Change: 0.04084
Iteration: 14, Log-Lik: -91724.980, Max-Change: 0.03369
Iteration: 15, Log-Lik: -91720.515, Max-Change: 0.13180
Iteration: 16, Log-Lik: -91707.664, Max-Change: 0.01972
Iteration: 17, Log-Lik: -91706.100, Max-Change: 0.01904
Iteration: 18, Log-Lik: -91704.719, Max-Change: 0.10750
Iteration: 19, Log-Lik: -91698.349, Max-Change: 0.01769
Iteration: 20, Log-Lik: -91697.686, Max-Change: 0.01089
Iteration: 21, Log-Lik: -91697.166, Max-Change: 0.06331
Iteration: 22, Log-Lik: -91694.784, Max-Change: 0.00840
Iteration: 23, Log-Lik: -91694.465, Max-Change: 0.00701
Iteration: 24, Log-Lik: -91694.252, Max-Change: 0.02632
Iteration: 25, Log-Lik: -91693.576, Max-Change: 0.00576
Iteration: 26, Log-Lik: -91693.412, Max-Change: 0.00472
Iteration: 27, Log-Lik: -91693.310, Max-Change: 0.01518
Iteration: 28, Log-Lik: -91692.999, Max-Change: 0.00367
Iteration: 29, Log-Lik: -91692.931, Max-Change: 0.00369
Iteration: 30, Log-Lik: -91692.859, Max-Change: 0.01759
Iteration: 31, Log-Lik: -91692.563, Max-Change: 0.00333
Iteration: 32, Log-Lik: -91692.524, Max-Change: 0.00309
Iteration: 33, Log-Lik: -91692.486, Max-Change: 0.01871
Iteration: 34, Log-Lik: -91692.289, Max-Change: 0.00291
Iteration: 35, Log-Lik: -91692.269, Max-Change: 0.00175
Iteration: 36, Log-Lik: -91692.252, Max-Change: 0.01138
Iteration: 37, Log-Lik: -91692.171, Max-Change: 0.00188
Iteration: 38, Log-Lik: -91692.160, Max-Change: 0.00140
Iteration: 39, Log-Lik: -91692.153, Max-Change: 0.00790
Iteration: 40, Log-Lik: -91692.119, Max-Change: 0.00057
Iteration: 41, Log-Lik: -91692.116, Max-Change: 0.00056
Iteration: 42, Log-Lik: -91692.114, Max-Change: 0.00099
Iteration: 43, Log-Lik: -91692.112, Max-Change: 0.00144
Iteration: 44, Log-Lik: -91692.111, Max-Change: 0.00040
Iteration: 45, Log-Lik: -91692.110, Max-Change: 0.00143
Iteration: 46, Log-Lik: -91692.109, Max-Change: 0.00067
Iteration: 47, Log-Lik: -91692.106, Max-Change: 0.00090
Iteration: 48, Log-Lik: -91692.105, Max-Change: 0.00093
Iteration: 49, Log-Lik: -91692.104, Max-Change: 0.00102
Iteration: 50, Log-Lik: -91692.103, Max-Change: 0.00142
Iteration: 51, Log-Lik: -91692.103, Max-Change: 0.00009### Summary
summary(cfa)##
## Factor loadings metric:
## G S1 S2 h2
## ps001 0.5287 0.000 0.3856 0.428
## ps002 0.3625 0.000 0.1406 0.151
## ps003 0.5963 0.000 0.0977 0.365
## ps004 0.5666 0.000 0.5349 0.607
## ps005 0.5968 0.000 0.5240 0.631
## ps006 0.6191 0.000 0.6307 0.781
## ps007 0.6507 0.000 0.5893 0.771
## ps008 0.5413 0.000 0.3669 0.428
## ps010 0.3726 0.000 0.1759 0.170
## ps011 0.3831 0.338 0.0000 0.261
## ps012 0.6211 0.319 0.0000 0.488
## ps014 -0.1785 -0.602 0.0000 0.395
## ps015 0.3651 0.000 0.3663 0.267
## ps016 0.4401 0.563 0.0000 0.510
## ps017 0.4137 0.000 0.1423 0.191
## ps019 0.0531 -0.474 0.0000 0.227
## ps020 0.5941 0.000 -0.0413 0.355
## ps021 0.1521 0.402 0.0000 0.185
## ps022 0.4535 0.000 0.0954 0.215
## ps023 0.5674 0.578 0.0000 0.656
## ps024 0.5363 0.481 0.0000 0.519
## ps026 0.5746 0.480 0.0000 0.561
## ps027 0.4902 0.633 0.0000 0.641
## ps028 -0.2057 -0.620 0.0000 0.427
## ps029 -0.4023 -0.580 0.0000 0.499
## ps030 0.6745 0.000 -0.0534 0.458
## ps031 -0.0998 -0.347 0.0000 0.131
## ps034 -0.3312 -0.286 0.0000 0.192
## ps035 -0.3963 -0.248 0.0000 0.218
## ps036 0.7038 0.000 -0.1419 0.515
## ps038 0.2260 0.520 0.0000 0.321
## ps039 0.5540 0.245 0.0000 0.367
##
## SS loadings: 7.299 3.794 1.838
##
## Factor covariance:
## F1 F2 F3
## F1 1 0 0
## F2 0 1 0
## F3 0 0 1### Coefficients
coef(cfa)## $ps001
## a1 a2 a3 d1 d2 d3 d4
## par 1.19 0 0.868 2.395 -1.016 -3.153 -5.883
##
## $ps002
## a1 a2 a3 d1 d2 d3 d4
## par 0.67 0 0.26 3.265 0.649 -0.408 -3.198
##
## $ps003
## a1 a2 a3 d1 d2 d3 d4
## par 1.274 0 0.209 3.851 0.664 -1.021 -4.114
##
## $ps004
## a1 a2 a3 d1 d2 d3 d4
## par 1.539 0 1.453 2.777 -1.3 -3.361 -6.459
##
## $ps005
## a1 a2 a3 d1 d2 d3 d4
## par 1.672 0 1.468 4.296 -0.149 -1.805 -5.599
##
## $ps006
## a1 a2 a3 d1 d2 d3 d4
## par 2.252 0 2.294 4.538 -1.035 -3.441 -8.165
##
## $ps007
## a1 a2 a3 d1 d2 d3 d4
## par 2.313 0 2.095 4.899 -0.835 -3.516 -8.317
##
## $ps008
## a1 a2 a3 d1 d2 d3 d4
## par 1.218 0 0.825 2.606 -1.159 -3.452 -6.008
##
## $ps010
## a1 a2 a3 d1 d2 d3 d4
## par 0.696 0 0.328 3.61 0.208 -1.063 -3.931
##
## $ps011
## a1 a2 a3 d1 d2 d3 d4
## par 0.758 0.669 0 1.703 -1.827 -3.001 -5.305
##
## $ps012
## a1 a2 a3 d1 d2 d3 d4
## par 1.477 0.758 0 2.208 -2.004 -3.63 -6.738
##
## $ps014
## a1 a2 a3 d1 d2 d3 d4
## par -0.391 -1.318 0 5.924 5.254 4.18 -0.599
##
## $ps015
## a1 a2 a3 d1 d2 d3 d4
## par 0.726 0 0.728 3.04 0.143 -1.238 -4.209
##
## $ps016
## a1 a2 a3 d1 d2 d3 d4
## par 1.071 1.369 0 1.219 -3.042 -5.195 -7.156
##
## $ps017
## a1 a2 a3 d1 d2 d3 d4
## par 0.783 0 0.269 3.281 0.987 -0.149 -3.501
##
## $ps019
## a1 a2 a3 d1 d2 d3 d4
## par 0.103 -0.918 0 4.694 2.729 1.675 -1.379
##
## $ps020
## a1 a2 a3 d1 d2 d3 d4
## par 1.259 0 -0.087 3.026 0.288 -1.156 -4.552
##
## $ps021
## a1 a2 a3 d1 d2 d3 d4
## par 0.287 0.757 0 0.973 -1.618 -3.154 -5.373
##
## $ps022
## a1 a2 a3 d1 d2 d3 d4
## par 0.871 0 0.183 3.389 0.851 -0.649 -3.857
##
## $ps023
## a1 a2 a3 d1 d2 d3 d4
## par 1.646 1.677 0 1.344 -3.696 -6.219 -8.421
##
## $ps024
## a1 a2 a3 d1 d2 d3 d4
## par 1.317 1.181 0 1.588 -2.427 -4.761 -7.506
##
## $ps026
## a1 a2 a3 d1 d2 d3 d4
## par 1.475 1.233 0 1.943 -2.456 -4.469 -6.753
##
## $ps027
## a1 a2 a3 d1 d2 d3 d4
## par 1.393 1.799 0 0.924 -3.952 -6.205 -8.258
##
## $ps028
## a1 a2 a3 d1 d2 d3 d4
## par -0.463 -1.395 0 5.83 4.726 3.388 -1.259
##
## $ps029
## a1 a2 a3 d1 d2 d3 d4
## par -0.967 -1.395 0 6.147 4.672 2.645 -1.566
##
## $ps030
## a1 a2 a3 d1 d2 d3 d4
## par 1.559 0 -0.124 3.732 0.308 -2.181 -5.417
##
## $ps031
## a1 a2 a3 d1 d2 d3 d4
## par -0.182 -0.634 0 5.474 2.455 0.872 -2.756
##
## $ps034
## a1 a2 a3 d1 d2 d3 d4
## par -0.627 -0.541 0 4.387 2.056 0.604 -2.018
##
## $ps035
## a1 a2 a3 d1 d2 d3 d4
## par -0.763 -0.477 0 4.853 2.575 0.336 -2.619
##
## $ps036
## a1 a2 a3 d1 d2 d3 d4
## par 1.721 0 -0.347 3.347 0.01 -2.175 -5.541
##
## $ps038
## a1 a2 a3 d1 d2 d3 d4
## par 0.467 1.074 0 0.459 -2.71 -3.818 -5.085
##
## $ps039
## a1 a2 a3 d1 d2 d3 d4
## par 1.185 0.523 0 2.305 -0.878 -2.526 -3.771
##
## $GroupPars
## MEAN_1 MEAN_2 MEAN_3 COV_11 COV_21 COV_31 COV_22 COV_32 COV_33
## par 0 0 0 1 0 0 1 0 1