Sophia_CAP
Leonardo
2025-11-27
Sequência das análise 1. Análise descritiva 2. Análise de confiabilidade (consistência interna) 3. Análise Fatorial Exploratória (AFE) 4. Análise Fatorial Confirmatória (AFC)
Instalar pacotes
install_if_missing <- function(pkg) {
if (!require(pkg, character.only = TRUE)) {
install.packages(pkg, dependencies = TRUE)
library(pkg, character.only = TRUE)
}
}
#Lista de pacotes
packages <- c("tidyverse", "psych", "lavaan", "GPArotation")
#Aplicar a função a todos os pacotes
invisible(sapply(packages, install_if_missing))## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'dplyr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: psych## Warning: package 'psych' was built under R version 4.4.3##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
##
## Loading required package: lavaan## Warning: package 'lavaan' was built under R version 4.4.3## This is lavaan 0.6-20
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
##
## The following object is masked from 'package:psych':
##
## cor2cov
##
## Loading required package: GPArotation## Warning: package 'GPArotation' was built under R version 4.4.3##
## Attaching package: 'GPArotation'
##
## The following objects are masked from 'package:psych':
##
## equamax, variminCarregamento do banco de dados
dados <- read.csv("dados.csv")
#visualizar as primeiras linhas e a estrutura dos dados
head(dados)## idade sexo estcivil raca graduacao ano periodo estagio atividade q1 q2 q3 q4
## 1 37 1 2 3 1 1 1 1 0 4 5 4 5
## 2 18 1 1 1 0 1 1 1 1 3 5 3 4
## 3 22 1 1 1 0 3 0 0 0 3 4 4 5
## 4 18 1 1 1 0 1 1 1 1 4 4 4 2
## 5 25 0 1 5 0 3 0 0 0 4 3 5 3
## 6 18 1 3 1 0 1 1 1 0 5 5 5 5
## q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 q21 q22 q23 q24
## 1 5 3 5 5 4 5 5 5 3 5 5 5 5 3 4 5 5 5 5 5
## 2 3 4 3 3 3 5 4 5 3 5 3 3 4 4 2 3 3 2 3 4
## 3 5 3 4 4 4 4 5 5 3 5 5 4 5 3 3 3 3 3 4 3
## 4 3 4 4 5 5 5 5 5 4 4 5 3 5 3 4 4 4 4 5 4
## 5 2 3 5 4 1 5 4 3 1 4 4 5 2 1 2 1 1 5 4 3
## 6 5 3 5 4 5 4 3 5 5 5 5 5 4 4 5 5 5 5 5 5
## q25
## 1 5
## 2 4
## 3 5
## 4 4
## 5 3
## 6 5str(dados)## 'data.frame': 304 obs. of 34 variables:
## $ idade : int 37 18 22 18 25 18 26 18 18 23 ...
## $ sexo : int 1 1 1 1 0 1 1 0 1 0 ...
## $ estcivil : int 2 1 1 1 1 3 1 1 1 1 ...
## $ raca : int 3 1 1 1 5 1 1 1 1 1 ...
## $ graduacao: int 1 0 0 0 0 0 1 0 0 0 ...
## $ ano : int 1 1 3 1 3 1 1 1 1 3 ...
## $ periodo : int 1 1 0 1 0 1 1 1 0 0 ...
## $ estagio : int 1 1 0 1 0 1 1 1 1 0 ...
## $ atividade: int 0 1 0 1 0 0 1 1 0 0 ...
## $ q1 : int 4 3 3 4 4 5 5 3 5 5 ...
## $ q2 : int 5 5 4 4 3 5 5 4 5 5 ...
## $ q3 : int 4 3 4 4 5 5 5 5 4 3 ...
## $ q4 : int 5 4 5 2 3 5 5 4 5 5 ...
## $ q5 : int 5 3 5 3 2 5 5 5 5 4 ...
## $ q6 : int 3 4 3 4 3 3 3 5 5 1 ...
## $ q7 : int 5 3 4 4 5 5 5 3 5 5 ...
## $ q8 : int 5 3 4 5 4 4 5 5 5 5 ...
## $ q9 : int 4 3 4 5 1 5 5 5 5 5 ...
## $ q10 : int 5 5 4 5 5 4 3 5 5 5 ...
## $ q11 : int 5 4 5 5 4 3 3 3 5 1 ...
## $ q12 : int 5 5 5 5 3 5 5 5 1 5 ...
## $ q13 : int 3 3 3 4 1 5 3 5 4 1 ...
## $ q14 : int 5 5 5 4 4 5 5 5 5 5 ...
## $ q15 : int 5 3 5 5 4 5 5 4 5 5 ...
## $ q16 : int 5 3 4 3 5 5 5 5 5 5 ...
## $ q17 : int 5 4 5 5 2 4 5 5 4 5 ...
## $ q18 : int 3 4 3 3 1 4 5 3 5 2 ...
## $ q19 : int 4 2 3 4 2 5 5 5 5 5 ...
## $ q20 : int 5 3 3 4 1 5 5 4 5 5 ...
## $ q21 : int 5 3 3 4 1 5 5 5 5 5 ...
## $ q22 : int 5 2 3 4 5 5 3 4 5 5 ...
## $ q23 : int 5 3 4 5 4 5 5 4 5 5 ...
## $ q24 : int 5 4 3 4 3 5 5 4 5 5 ...
## $ q25 : int 5 4 5 4 3 5 5 5 5 5 ...Transformar dados
dados$sexo <- factor(dados$sexo,
levels = c(0, 1, 2),
labels = c("Masculino", "Feminino", "Prefiro nao declarar"))
dados$estcivil <- factor(dados$estcivil,
levels = c(1, 2, 3, 4, 5),
labels = c("Solteiro", "Casado", "Uniao estavel", "Divorciado", "Viuvo"))
dados$raca <- factor(dados$raca,
levels = c(1, 2, 3, 4, 5),
labels = c("Branca", "Preta", "Parda", "Amarela", "Outra"))
dados$graduacao <- factor(dados$graduacao,
levels = c(0, 1),
labels = c("Sim", "Nao"))
dados$ano <- factor(dados$ano,
levels = c(1, 2, 3),
labels = c("1 ano", "4 ano", "5 ano"))
dados$periodo <- factor(dados$periodo,
levels = c(0, 1),
labels = c("Matutino", "Noturno"))
dados$estagio <- factor(dados$estagio,
levels = c(0, 1),
labels = c("Sim", "Nao"))
dados$atividade <- factor(dados$atividade,
levels = c(0, 1),
labels = c("Sim", "Nao"))
str(dados)## 'data.frame': 304 obs. of 34 variables:
## $ idade : int 37 18 22 18 25 18 26 18 18 23 ...
## $ sexo : Factor w/ 3 levels "Masculino","Feminino",..: 2 2 2 2 1 2 2 1 2 1 ...
## $ estcivil : Factor w/ 5 levels "Solteiro","Casado",..: 2 1 1 1 1 3 1 1 1 1 ...
## $ raca : Factor w/ 5 levels "Branca","Preta",..: 3 1 1 1 5 1 1 1 1 1 ...
## $ graduacao: Factor w/ 2 levels "Sim","Nao": 2 1 1 1 1 1 2 1 1 1 ...
## $ ano : Factor w/ 3 levels "1 ano","4 ano",..: 1 1 3 1 3 1 1 1 1 3 ...
## $ periodo : Factor w/ 2 levels "Matutino","Noturno": 2 2 1 2 1 2 2 2 1 1 ...
## $ estagio : Factor w/ 2 levels "Sim","Nao": 2 2 1 2 1 2 2 2 2 1 ...
## $ atividade: Factor w/ 2 levels "Sim","Nao": 1 2 1 2 1 1 2 2 1 1 ...
## $ q1 : int 4 3 3 4 4 5 5 3 5 5 ...
## $ q2 : int 5 5 4 4 3 5 5 4 5 5 ...
## $ q3 : int 4 3 4 4 5 5 5 5 4 3 ...
## $ q4 : int 5 4 5 2 3 5 5 4 5 5 ...
## $ q5 : int 5 3 5 3 2 5 5 5 5 4 ...
## $ q6 : int 3 4 3 4 3 3 3 5 5 1 ...
## $ q7 : int 5 3 4 4 5 5 5 3 5 5 ...
## $ q8 : int 5 3 4 5 4 4 5 5 5 5 ...
## $ q9 : int 4 3 4 5 1 5 5 5 5 5 ...
## $ q10 : int 5 5 4 5 5 4 3 5 5 5 ...
## $ q11 : int 5 4 5 5 4 3 3 3 5 1 ...
## $ q12 : int 5 5 5 5 3 5 5 5 1 5 ...
## $ q13 : int 3 3 3 4 1 5 3 5 4 1 ...
## $ q14 : int 5 5 5 4 4 5 5 5 5 5 ...
## $ q15 : int 5 3 5 5 4 5 5 4 5 5 ...
## $ q16 : int 5 3 4 3 5 5 5 5 5 5 ...
## $ q17 : int 5 4 5 5 2 4 5 5 4 5 ...
## $ q18 : int 3 4 3 3 1 4 5 3 5 2 ...
## $ q19 : int 4 2 3 4 2 5 5 5 5 5 ...
## $ q20 : int 5 3 3 4 1 5 5 4 5 5 ...
## $ q21 : int 5 3 3 4 1 5 5 5 5 5 ...
## $ q22 : int 5 2 3 4 5 5 3 4 5 5 ...
## $ q23 : int 5 3 4 5 4 5 5 4 5 5 ...
## $ q24 : int 5 4 3 4 3 5 5 4 5 5 ...
## $ q25 : int 5 4 5 4 3 5 5 5 5 5 ...Análise descritiva
vars_socio <- c("idade", "sexo", "estcivil", "raca", "graduacao", "ano", "periodo", "estagio", "atividade")
vars_itens <- paste0("q", 1:25)
summary(dados$idade)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 19.00 21.00 25.08 26.00 60.00sd(dados$idade, na.rm = TRUE)## [1] 9.569434#Tabelas de frequencia para variaveis categoricas
for(var in setdiff(vars_socio, "idade")){
print(paste("Tabela de Frequência para:", var))
print(table(dados[[var]], useNA = "ifany"))
}## [1] "Tabela de Frequência para: sexo"
##
## Masculino Feminino Prefiro nao declarar
## 56 246 2
## [1] "Tabela de Frequência para: estcivil"
##
## Solteiro Casado Uniao estavel Divorciado Viuvo
## 236 48 11 7 2
## [1] "Tabela de Frequência para: raca"
##
## Branca Preta Parda Amarela Outra
## 237 8 50 7 2
## [1] "Tabela de Frequência para: graduacao"
##
## Sim Nao
## 241 63
## [1] "Tabela de Frequência para: ano"
##
## 1 ano 4 ano 5 ano
## 165 75 64
## [1] "Tabela de Frequência para: periodo"
##
## Matutino Noturno
## 194 110
## [1] "Tabela de Frequência para: estagio"
##
## Sim Nao
## 133 171
## [1] "Tabela de Frequência para: atividade"
##
## Sim Nao
## 203 101Itens do instrumento
describe(dados[vars_itens])## vars n mean sd median trimmed mad min max range skew kurtosis se
## q1 1 304 4.62 0.78 5 4.81 0.00 1 5 4 -2.43 6.54 0.04
## q2 2 304 4.52 0.81 5 4.70 0.00 1 5 4 -1.97 4.18 0.05
## q3 3 304 4.37 0.91 5 4.54 0.00 1 5 4 -1.54 2.13 0.05
## q4 4 304 4.64 0.74 5 4.82 0.00 1 5 4 -2.30 5.45 0.04
## q5 5 304 4.54 0.82 5 4.71 0.00 1 5 4 -1.93 3.73 0.05
## q6 6 304 3.62 1.18 4 3.72 1.48 1 5 4 -0.45 -0.71 0.07
## q7 7 304 4.42 0.85 5 4.57 0.00 1 5 4 -1.47 1.87 0.05
## q8 8 304 4.43 0.86 5 4.59 0.00 1 5 4 -1.54 2.06 0.05
## q9 9 304 4.40 0.87 5 4.55 0.00 1 5 4 -1.55 2.37 0.05
## q10 10 304 4.58 0.72 5 4.74 0.00 1 5 4 -1.73 2.73 0.04
## q11 11 304 4.29 0.91 5 4.43 0.00 1 5 4 -1.16 0.79 0.05
## q12 12 304 4.61 0.91 5 4.86 0.00 1 5 4 -2.70 7.03 0.05
## q13 13 304 3.61 1.31 4 3.76 1.48 1 5 4 -0.58 -0.72 0.07
## q14 14 304 4.67 0.68 5 4.83 0.00 1 5 4 -2.37 5.92 0.04
## q15 15 304 4.48 0.78 5 4.62 0.00 1 5 4 -1.47 1.98 0.04
## q16 16 304 4.64 0.77 5 4.84 0.00 1 5 4 -2.37 5.65 0.04
## q17 17 304 4.54 0.82 5 4.73 0.00 1 5 4 -2.00 4.08 0.05
## q18 18 304 3.61 1.25 4 3.73 1.48 1 5 4 -0.43 -0.80 0.07
## q19 19 304 3.92 1.14 4 4.07 1.48 1 5 4 -0.80 -0.19 0.07
## q20 20 304 4.36 0.88 5 4.50 0.00 1 5 4 -1.39 1.71 0.05
## q21 21 304 4.35 0.96 5 4.52 0.00 1 5 4 -1.54 1.93 0.06
## q22 22 304 4.40 0.84 5 4.53 0.00 1 5 4 -1.22 0.65 0.05
## q23 23 304 4.63 0.68 5 4.79 0.00 2 5 3 -1.61 1.34 0.04
## q24 24 304 4.38 0.87 5 4.52 0.00 1 5 4 -1.35 1.54 0.05
## q25 25 304 4.81 0.50 5 4.95 0.00 3 5 2 -2.62 5.83 0.03for (item in vars_itens) {
cat("\n--------------------------------------\n")
cat("Frquências para o iten:", item, "\n")
cat("\n--------------------------------------\n")
print(table(dados[[item]], useNA = "ifany"))
}##
## --------------------------------------
## Frquências para o iten: q1
##
## --------------------------------------
##
## 1 2 3 4 5
## 5 1 23 48 227
##
## --------------------------------------
## Frquências para o iten: q2
##
## --------------------------------------
##
## 1 2 3 4 5
## 4 5 23 70 202
##
## --------------------------------------
## Frquências para o iten: q3
##
## --------------------------------------
##
## 1 2 3 4 5
## 5 8 35 77 179
##
## --------------------------------------
## Frquências para o iten: q4
##
## --------------------------------------
##
## 1 2 3 4 5
## 2 5 20 46 231
##
## --------------------------------------
## Frquências para o iten: q5
##
## --------------------------------------
##
## 1 2 3 4 5
## 4 3 32 52 213
##
## --------------------------------------
## Frquências para o iten: q6
##
## --------------------------------------
##
## 1 2 3 4 5
## 16 37 83 77 91
##
## --------------------------------------
## Frquências para o iten: q7
##
## --------------------------------------
##
## 1 2 3 4 5
## 3 5 39 70 187
##
## --------------------------------------
## Frquências para o iten: q8
##
## --------------------------------------
##
## 1 2 3 4 5
## 3 7 35 70 189
##
## --------------------------------------
## Frquências para o iten: q9
##
## --------------------------------------
##
## 1 2 3 4 5
## 5 3 40 73 183
##
## --------------------------------------
## Frquências para o iten: q10
##
## --------------------------------------
##
## 1 2 3 4 5
## 1 3 27 62 211
##
## --------------------------------------
## Frquências para o iten: q11
##
## --------------------------------------
##
## 1 2 3 4 5
## 3 9 48 80 164
##
## --------------------------------------
## Frquências para o iten: q12
##
## --------------------------------------
##
## 1 2 3 4 5
## 11 2 17 35 239
##
## --------------------------------------
## Frquências para o iten: q13
##
## --------------------------------------
##
## 1 2 3 4 5
## 31 25 79 65 104
##
## --------------------------------------
## Frquências para o iten: q14
##
## --------------------------------------
##
## 1 2 3 4 5
## 1 5 15 50 233
##
## --------------------------------------
## Frquências para o iten: q15
##
## --------------------------------------
##
## 1 2 3 4 5
## 2 2 36 73 191
##
## --------------------------------------
## Frquências para o iten: q16
##
## --------------------------------------
##
## 1 2 3 4 5
## 3 4 24 37 236
##
## --------------------------------------
## Frquências para o iten: q17
##
## --------------------------------------
##
## 1 2 3 4 5
## 4 4 28 55 213
##
## --------------------------------------
## Frquências para o iten: q18
##
## --------------------------------------
##
## 1 2 3 4 5
## 22 30 97 51 104
##
## --------------------------------------
## Frquências para o iten: q19
##
## --------------------------------------
##
## 1 2 3 4 5
## 13 20 72 73 126
##
## --------------------------------------
## Frquências para o iten: q20
##
## --------------------------------------
##
## 1 2 3 4 5
## 4 5 42 79 174
##
## --------------------------------------
## Frquências para o iten: q21
##
## --------------------------------------
##
## 1 2 3 4 5
## 7 8 40 66 183
##
## --------------------------------------
## Frquências para o iten: q22
##
## --------------------------------------
##
## 1 2 3 4 5
## 1 7 44 70 182
##
## --------------------------------------
## Frquências para o iten: q23
##
## --------------------------------------
##
## 2 3 4 5
## 1 31 48 224
##
## --------------------------------------
## Frquências para o iten: q24
##
## --------------------------------------
##
## 1 2 3 4 5
## 4 2 49 68 181
##
## --------------------------------------
## Frquências para o iten: q25
##
## --------------------------------------
##
## 3 4 5
## 15 28 261#Itens conhecimento
conhecimento_itens <- paste0("q", 1:9)
#Itens Atitudes
atitudes_itens <- paste0("q", 10:17)
#Itens Práticas
praticas_itens <- paste0("q", 18:25)
#Escala Total
total_itens <- vars_itensConfiabilidade
df_conhecimento <- dados[, conhecimento_itens]
df_atitudes <- dados[, atitudes_itens]
df_praticas <- dados[, praticas_itens]
df_total <- dados[, total_itens]
alpha_conhecimento <- psych::alpha(df_conhecimento, n.iter = 1000, check.keys = TRUE)
print(alpha_conhecimento)##
## Reliability analysis
## Call: psych::alpha(x = df_conhecimento, check.keys = TRUE, n.iter = 1000)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.67 0.69 0.68 0.2 2.2 0.029 4.4 0.46 0.21
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.61 0.67 0.72
## Duhachek 0.61 0.67 0.72
## bootstrapped 0.54 0.65 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q1 0.64 0.66 0.65 0.20 2.0 0.032 0.0091 0.20
## q2 0.62 0.65 0.63 0.19 1.8 0.033 0.0092 0.20
## q3 0.66 0.68 0.67 0.21 2.1 0.030 0.0100 0.24
## q4 0.63 0.65 0.64 0.19 1.9 0.032 0.0085 0.20
## q5 0.61 0.64 0.62 0.18 1.8 0.034 0.0087 0.20
## q6 0.70 0.71 0.69 0.23 2.4 0.026 0.0051 0.24
## q7 0.62 0.64 0.63 0.18 1.8 0.034 0.0084 0.20
## q8 0.64 0.67 0.66 0.20 2.0 0.031 0.0103 0.21
## q9 0.63 0.66 0.64 0.19 1.9 0.032 0.0093 0.20
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q1 304 0.52 0.54 0.45 0.36 4.6 0.78
## q2 304 0.59 0.60 0.53 0.44 4.5 0.81
## q3 304 0.47 0.45 0.32 0.27 4.4 0.91
## q4 304 0.55 0.58 0.51 0.40 4.6 0.74
## q5 304 0.61 0.63 0.57 0.47 4.5 0.82
## q6 304 0.41 0.33 0.16 0.13 3.6 1.18
## q7 304 0.61 0.62 0.56 0.45 4.4 0.85
## q8 304 0.50 0.51 0.40 0.32 4.4 0.86
## q9 304 0.55 0.56 0.47 0.38 4.4 0.87
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## q1 0.02 0.00 0.08 0.16 0.75 0
## q2 0.01 0.02 0.08 0.23 0.66 0
## q3 0.02 0.03 0.12 0.25 0.59 0
## q4 0.01 0.02 0.07 0.15 0.76 0
## q5 0.01 0.01 0.11 0.17 0.70 0
## q6 0.05 0.12 0.27 0.25 0.30 0
## q7 0.01 0.02 0.13 0.23 0.62 0
## q8 0.01 0.02 0.12 0.23 0.62 0
## q9 0.02 0.01 0.13 0.24 0.60 0alpha_atitudes <- psych::alpha(df_atitudes, n.iter = 1000, check.keys = TRUE)
print(alpha_atitudes)##
## Reliability analysis
## Call: psych::alpha(x = df_atitudes, check.keys = TRUE, n.iter = 1000)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.68 0.67 0.21 2.1 0.029 4.4 0.48 0.2
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.60 0.66 0.71
## Duhachek 0.60 0.66 0.72
## bootstrapped 0.57 0.66 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q10 0.62 0.64 0.62 0.21 1.8 0.032 0.0057 0.18
## q11 0.61 0.64 0.62 0.20 1.8 0.034 0.0068 0.18
## q12 0.63 0.66 0.64 0.21 1.9 0.032 0.0069 0.20
## q13 0.67 0.67 0.65 0.22 2.0 0.029 0.0047 0.20
## q14 0.64 0.66 0.65 0.22 2.0 0.031 0.0070 0.21
## q15 0.62 0.64 0.62 0.21 1.8 0.033 0.0058 0.20
## q16 0.63 0.65 0.63 0.21 1.9 0.032 0.0059 0.20
## q17 0.60 0.62 0.60 0.19 1.7 0.034 0.0050 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q10 304 0.53 0.57 0.48 0.37 4.6 0.72
## q11 304 0.61 0.59 0.51 0.42 4.3 0.91
## q12 304 0.53 0.53 0.41 0.33 4.6 0.91
## q13 304 0.59 0.48 0.35 0.29 3.6 1.31
## q14 304 0.45 0.50 0.36 0.29 4.7 0.68
## q15 304 0.56 0.57 0.48 0.40 4.5 0.78
## q16 304 0.51 0.55 0.45 0.34 4.6 0.77
## q17 304 0.61 0.64 0.58 0.45 4.5 0.82
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## q10 0.00 0.01 0.09 0.20 0.69 0
## q11 0.01 0.03 0.16 0.26 0.54 0
## q12 0.04 0.01 0.06 0.12 0.79 0
## q13 0.10 0.08 0.26 0.21 0.34 0
## q14 0.00 0.02 0.05 0.16 0.77 0
## q15 0.01 0.01 0.12 0.24 0.63 0
## q16 0.01 0.01 0.08 0.12 0.78 0
## q17 0.01 0.01 0.09 0.18 0.70 0alpha_praticas <- psych::alpha(df_praticas, n.iter = 1000, check.keys = TRUE)
print(alpha_praticas)##
## Reliability analysis
## Call: psych::alpha(x = df_praticas, check.keys = TRUE, n.iter = 1000)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.75 0.74 0.27 3 0.025 4.3 0.53 0.27
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.66 0.71 0.76
## Duhachek 0.66 0.71 0.76
## bootstrapped 0.64 0.71 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q18 0.72 0.75 0.74 0.30 3.1 0.024 0.0094 0.31
## q19 0.71 0.75 0.73 0.30 2.9 0.026 0.0106 0.31
## q20 0.65 0.70 0.68 0.25 2.3 0.031 0.0085 0.24
## q21 0.67 0.72 0.70 0.27 2.5 0.029 0.0091 0.24
## q22 0.68 0.72 0.71 0.27 2.6 0.028 0.0125 0.24
## q23 0.67 0.71 0.69 0.26 2.4 0.029 0.0100 0.24
## q24 0.69 0.74 0.72 0.29 2.8 0.027 0.0113 0.31
## q25 0.68 0.70 0.69 0.25 2.4 0.029 0.0089 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q18 304 0.54 0.47 0.32 0.28 3.6 1.25
## q19 304 0.55 0.50 0.37 0.32 3.9 1.14
## q20 304 0.69 0.70 0.67 0.56 4.4 0.88
## q21 304 0.63 0.64 0.58 0.46 4.3 0.96
## q22 304 0.59 0.61 0.51 0.43 4.4 0.84
## q23 304 0.62 0.67 0.62 0.51 4.6 0.68
## q24 304 0.53 0.55 0.43 0.36 4.4 0.87
## q25 304 0.62 0.69 0.65 0.54 4.8 0.50
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## q18 0.07 0.10 0.32 0.17 0.34 0
## q19 0.04 0.07 0.24 0.24 0.41 0
## q20 0.01 0.02 0.14 0.26 0.57 0
## q21 0.02 0.03 0.13 0.22 0.60 0
## q22 0.00 0.02 0.14 0.23 0.60 0
## q23 0.00 0.00 0.10 0.16 0.74 0
## q24 0.01 0.01 0.16 0.22 0.60 0
## q25 0.00 0.00 0.05 0.09 0.86 0alpha_total <- psych::alpha(df_total, n.iter = 1000, check.keys = TRUE)
print(alpha_total)##
## Reliability analysis
## Call: psych::alpha(x = df_total, check.keys = TRUE, n.iter = 1000)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.86 0.88 0.2 6.3 0.013 4.4 0.41 0.2
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.85 0.87
## Duhachek 0.82 0.85 0.87
## bootstrapped 0.81 0.85 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q1 0.84 0.86 0.88 0.20 6.1 0.013 0.0079 0.20
## q2 0.84 0.86 0.87 0.20 5.9 0.013 0.0080 0.20
## q3 0.85 0.86 0.88 0.21 6.2 0.013 0.0080 0.21
## q4 0.84 0.86 0.88 0.20 6.0 0.013 0.0082 0.20
## q5 0.84 0.86 0.87 0.20 5.9 0.013 0.0081 0.20
## q6 0.85 0.86 0.88 0.21 6.4 0.012 0.0069 0.21
## q7 0.84 0.86 0.88 0.20 6.0 0.013 0.0082 0.20
## q8 0.84 0.86 0.88 0.20 6.0 0.013 0.0082 0.20
## q9 0.84 0.86 0.87 0.20 6.0 0.013 0.0078 0.20
## q10 0.84 0.86 0.87 0.20 6.0 0.013 0.0080 0.20
## q11 0.84 0.86 0.88 0.20 6.1 0.013 0.0084 0.20
## q12 0.84 0.86 0.88 0.20 6.0 0.013 0.0081 0.20
## q13 0.85 0.86 0.88 0.21 6.3 0.012 0.0077 0.21
## q14 0.85 0.86 0.88 0.20 6.2 0.013 0.0082 0.21
## q15 0.84 0.86 0.88 0.20 6.0 0.013 0.0082 0.20
## q16 0.84 0.86 0.88 0.20 6.0 0.013 0.0079 0.20
## q17 0.84 0.86 0.87 0.20 5.9 0.013 0.0080 0.20
## q18 0.85 0.86 0.88 0.20 6.2 0.013 0.0082 0.21
## q19 0.85 0.86 0.88 0.20 6.2 0.013 0.0082 0.21
## q20 0.84 0.85 0.87 0.19 5.8 0.013 0.0071 0.20
## q21 0.84 0.85 0.87 0.20 5.9 0.013 0.0076 0.20
## q22 0.84 0.86 0.87 0.20 5.9 0.013 0.0081 0.20
## q23 0.84 0.85 0.87 0.20 5.9 0.013 0.0076 0.20
## q24 0.84 0.86 0.88 0.20 6.1 0.013 0.0080 0.20
## q25 0.84 0.85 0.87 0.20 5.8 0.013 0.0076 0.20
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q1 304 0.41 0.42 0.38 0.34 4.6 0.78
## q2 304 0.54 0.54 0.53 0.48 4.5 0.81
## q3 304 0.36 0.36 0.31 0.28 4.4 0.91
## q4 304 0.46 0.47 0.44 0.40 4.6 0.74
## q5 304 0.55 0.55 0.53 0.49 4.5 0.82
## q6 304 0.31 0.26 0.21 0.20 3.6 1.18
## q7 304 0.51 0.51 0.49 0.44 4.4 0.85
## q8 304 0.50 0.50 0.47 0.44 4.4 0.86
## q9 304 0.53 0.53 0.51 0.46 4.4 0.87
## q10 304 0.49 0.51 0.48 0.44 4.6 0.72
## q11 304 0.48 0.47 0.44 0.41 4.3 0.91
## q12 304 0.48 0.49 0.45 0.41 4.6 0.91
## q13 304 0.39 0.34 0.29 0.27 3.6 1.31
## q14 304 0.37 0.39 0.34 0.31 4.7 0.68
## q15 304 0.48 0.49 0.46 0.42 4.5 0.78
## q16 304 0.48 0.50 0.48 0.42 4.6 0.77
## q17 304 0.54 0.55 0.53 0.48 4.5 0.82
## q18 304 0.43 0.40 0.36 0.33 3.6 1.25
## q19 304 0.44 0.41 0.37 0.34 3.9 1.14
## q20 304 0.62 0.62 0.62 0.56 4.4 0.88
## q21 304 0.56 0.57 0.55 0.49 4.3 0.96
## q22 304 0.53 0.54 0.51 0.46 4.4 0.84
## q23 304 0.55 0.58 0.56 0.50 4.6 0.68
## q24 304 0.46 0.46 0.43 0.39 4.4 0.87
## q25 304 0.56 0.59 0.58 0.53 4.8 0.50
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## q1 0.02 0.00 0.08 0.16 0.75 0
## q2 0.01 0.02 0.08 0.23 0.66 0
## q3 0.02 0.03 0.12 0.25 0.59 0
## q4 0.01 0.02 0.07 0.15 0.76 0
## q5 0.01 0.01 0.11 0.17 0.70 0
## q6 0.05 0.12 0.27 0.25 0.30 0
## q7 0.01 0.02 0.13 0.23 0.62 0
## q8 0.01 0.02 0.12 0.23 0.62 0
## q9 0.02 0.01 0.13 0.24 0.60 0
## q10 0.00 0.01 0.09 0.20 0.69 0
## q11 0.01 0.03 0.16 0.26 0.54 0
## q12 0.04 0.01 0.06 0.12 0.79 0
## q13 0.10 0.08 0.26 0.21 0.34 0
## q14 0.00 0.02 0.05 0.16 0.77 0
## q15 0.01 0.01 0.12 0.24 0.63 0
## q16 0.01 0.01 0.08 0.12 0.78 0
## q17 0.01 0.01 0.09 0.18 0.70 0
## q18 0.07 0.10 0.32 0.17 0.34 0
## q19 0.04 0.07 0.24 0.24 0.41 0
## q20 0.01 0.02 0.14 0.26 0.57 0
## q21 0.02 0.03 0.13 0.22 0.60 0
## q22 0.00 0.02 0.14 0.23 0.60 0
## q23 0.00 0.00 0.10 0.16 0.74 0
## q24 0.01 0.01 0.16 0.22 0.60 0
## q25 0.00 0.00 0.05 0.09 0.86 0Análise Fatorial Exploratória Antes da AFE, verificamos se os dados são fatorizáveis usando o teste de Kaiser-Meyer-Olkin (KMO) e o teste de esfericidade de Bartlett. - KMO: Valores acima de 0.7 são considerados bons. - Teste de Bartlett: Um resultado significativo (p < 0.05) indica que as correlações entre os itens são suficientemente grandes para uma AFE.
#matriz de correlação
cor_matrix <- cor(dados[vars_itens], use = "pairwise.complete.obs")
#Teste de Bartlett
cortest.bartlett(cor_matrix, n = nrow(dados))## $chisq
## [1] 1770.116
##
## $p.value
## [1] 1.667612e-206
##
## $df
## [1] 300#Teste KMO
KMO(cor_matrix)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix)
## Overall MSA = 0.84
## MSA for each item =
## q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16
## 0.78 0.82 0.85 0.86 0.87 0.64 0.87 0.87 0.84 0.82 0.82 0.90 0.71 0.87 0.80 0.86
## q17 q18 q19 q20 q21 q22 q23 q24 q25
## 0.83 0.89 0.85 0.83 0.88 0.86 0.86 0.76 0.87#Análise Paralela
fa.parallel(dados[vars_itens], fa = "fa", fm = "pa")
## Parallel analysis suggests that the number of factors = 6 and the number of components = NA#considerando os 5 fatores sugeridos na análise paralela
afa_6fatores <- fa(dados[vars_itens],
nfactors = 1,
rotate = "oblimin",
fm = "pa")
print(afa_6fatores$loadings, sort = TRUE, cut = 0.4)##
## Loadings:
## PA1
## q2 0.515
## q5 0.525
## q9 0.514
## q17 0.518
## q20 0.625
## q21 0.566
## q22 0.509
## q23 0.568
## q25 0.589
## q1
## q3
## q4 0.440
## q6
## q7 0.480
## q8 0.469
## q10 0.482
## q11 0.407
## q12 0.462
## q13
## q14
## q15 0.440
## q16 0.482
## q18
## q19
## q24 0.426
##
## PA1
## SS loadings 5.287
## Proportion Var 0.211#considerando os 3 fatores do instrumento original
afa_3fatores <- fa(dados[vars_itens],
nfactors = 3,
rotate = "oblimin",
fm = "pa")
print(afa_3fatores$loadings, sort = TRUE, cut = 0.3)##
## Loadings:
## PA1 PA3 PA2
## q4 0.520
## q7 0.516
## q10 0.554
## q16 0.533
## q17 0.519
## q20 0.901
## q6 0.598
## q1
## q2 0.404
## q3 0.349
## q5 0.356
## q8 0.472
## q9 0.457
## q11 0.382 0.356
## q12 0.417
## q13 0.435
## q14 0.405
## q15 0.359
## q18 0.340
## q19
## q21 0.415
## q22 0.389
## q23 0.372
## q24
## q25 0.320 0.315
##
## PA1 PA3 PA2
## SS loadings 3.338 1.644 0.971
## Proportion Var 0.134 0.066 0.039
## Cumulative Var 0.134 0.199 0.238A Análise Fatorial Exploratória não suporta a estrutura teórica inicial de três fatores (Conhecimento, Atitudes e Práticas). Os fatores extraídos são uma mistura de itens das diferentes dimensões teóricas.
Itens 1, 19 e 24 não carregaram em nenhum fator, vou tentar remover eles para ver como fica a análise. #q20 foi excluído porque é muito idêntico ao q9 #q5 foi excluído porque é muito idêntico ao q21 #q11 foi excluído porque é muito idêntico ao q17
itens_para_remover <- c("q1", "q3", "q5", "q6", "q11", "q13", "q14", "q18", "q19")
vars_itens_revised <- setdiff(vars_itens, itens_para_remover)
print(vars_itens_revised)## [1] "q2" "q4" "q7" "q8" "q9" "q10" "q12" "q15" "q16" "q17" "q20" "q21"
## [13] "q22" "q23" "q24" "q25"#considerando os 3 fatores do instrumento original
afa_3fatores_revisado <- fa(dados[vars_itens_revised],
nfactors = 1,
# nfactors = 3,
rotate = "oblimin",
fm = "pa")
print(afa_3fatores_revisado$loadings, sort = TRUE, cut = 0.4)##
## Loadings:
## PA1
## q2 0.506
## q9 0.506
## q17 0.508
## q20 0.637
## q21 0.593
## q22 0.517
## q23 0.596
## q25 0.602
## q4 0.422
## q7 0.462
## q8 0.466
## q10 0.476
## q12 0.472
## q15 0.419
## q16 0.485
## q24 0.438
##
## PA1
## SS loadings 4.174
## Proportion Var 0.261De fato os itens não estão ajustando adequadamente, mas vamos tentar a AFC mesmo assim
modelo_afc <- '
#Fatores Latentes
Conhecimento =~ #q1 +
q2 + q3 + q4 + #q5
q6 + q7 + q8 + q9
Atitudes =~ q10 + #q11 +
q12 + q13 + q14 + q15 + q16 + q17
Praticas =~ q18 + #q19 + q20 +
q21 + q22 + q23 + #q24 +
q25
' Avaliamos a qualidade do ajuste do modelo usando vários índices. - Qui-quadrado (\(\chi^2\)): Desejável não significativo (p > 0.05), mas sensível a amostras grandes. - CFI (Comparative Fit Index): > 0.90 (aceitável), > 0.95 (bom). - TLI (Tucker-Lewis Index): > 0.90 (aceitável), > 0.95 (bom). - RMSEA (Root Mean Square Error of Approximation): < 0.08 (aceitável), < 0.06 (bom). O intervalo de confiança de 90% também é informativo. - SRMR (Standardized Root Mean Square Residual): < 0.08 (bom).
#ajustando o modelo de AFC
fit_afc <- cfa(modelo_afc, data = dados, std.lv=TRUE, missing = "FIML")
summary(fit_afc, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-20 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 304
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 276.445
## Degrees of freedom 149
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1132.195
## Degrees of freedom 171
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.867
## Tucker-Lewis Index (TLI) 0.848
##
## Robust Comparative Fit Index (CFI) 0.867
## Robust Tucker-Lewis Index (TLI) 0.848
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6778.941
## Loglikelihood unrestricted model (H1) -6640.718
##
## Akaike (AIC) 13677.881
## Bayesian (BIC) 13900.903
## Sample-size adjusted Bayesian (SABIC) 13710.613
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053
## 90 Percent confidence interval - lower 0.043
## 90 Percent confidence interval - upper 0.063
## P-value H_0: RMSEA <= 0.050 0.294
## P-value H_0: RMSEA >= 0.080 0.000
##
## Robust RMSEA 0.053
## 90 Percent confidence interval - lower 0.043
## 90 Percent confidence interval - upper 0.063
## P-value H_0: Robust RMSEA <= 0.050 0.294
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.051
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Conhecimento =~
## q2 0.427 0.050 8.535 0.000 0.427 0.527
## q3 0.298 0.057 5.202 0.000 0.298 0.329
## q4 0.362 0.045 7.983 0.000 0.362 0.493
## q6 0.166 0.077 2.165 0.030 0.166 0.140
## q7 0.442 0.053 8.382 0.000 0.442 0.522
## q8 0.424 0.053 8.066 0.000 0.424 0.495
## q9 0.415 0.054 7.746 0.000 0.415 0.476
## Atitudes =~
## q10 0.366 0.043 8.534 0.000 0.366 0.507
## q12 0.439 0.054 8.087 0.000 0.439 0.483
## q13 0.326 0.082 3.963 0.000 0.326 0.250
## q14 0.248 0.042 5.942 0.000 0.248 0.367
## q15 0.339 0.048 7.098 0.000 0.339 0.435
## q16 0.403 0.045 8.864 0.000 0.403 0.527
## q17 0.449 0.050 9.040 0.000 0.449 0.549
## Praticas =~
## q18 0.435 0.078 5.584 0.000 0.435 0.349
## q21 0.530 0.057 9.253 0.000 0.530 0.551
## q22 0.458 0.050 9.149 0.000 0.458 0.545
## q23 0.436 0.040 10.975 0.000 0.436 0.645
## q25 0.323 0.030 10.935 0.000 0.323 0.643
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Conhecimento ~~
## Atitudes 0.918 0.053 17.391 0.000 0.918 0.918
## Praticas 0.799 0.056 14.152 0.000 0.799 0.799
## Atitudes ~~
## Praticas 0.888 0.050 17.806 0.000 0.888 0.888
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q2 4.516 0.047 97.106 0.000 4.516 5.569
## .q3 4.372 0.052 84.223 0.000 4.372 4.830
## .q4 4.641 0.042 110.208 0.000 4.641 6.321
## .q6 3.625 0.068 53.565 0.000 3.625 3.072
## .q7 4.424 0.049 91.041 0.000 4.424 5.222
## .q8 4.431 0.049 90.293 0.000 4.431 5.179
## .q9 4.401 0.050 88.026 0.000 4.401 5.049
## .q10 4.576 0.041 110.571 0.000 4.576 6.342
## .q12 4.609 0.052 88.533 0.000 4.609 5.078
## .q13 3.612 0.075 48.328 0.000 3.612 2.772
## .q14 4.674 0.039 120.632 0.000 4.674 6.919
## .q15 4.477 0.045 100.378 0.000 4.477 5.757
## .q16 4.641 0.044 105.783 0.000 4.641 6.067
## .q17 4.543 0.047 96.821 0.000 4.543 5.553
## .q18 3.609 0.071 50.471 0.000 3.609 2.895
## .q21 4.349 0.055 78.858 0.000 4.349 4.523
## .q22 4.398 0.048 91.217 0.000 4.398 5.232
## .q23 4.628 0.039 119.342 0.000 4.628 6.845
## .q25 4.809 0.029 166.682 0.000 4.809 9.560
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q2 0.475 0.044 10.741 0.000 0.475 0.722
## .q3 0.730 0.061 11.893 0.000 0.730 0.891
## .q4 0.408 0.037 11.067 0.000 0.408 0.757
## .q6 1.365 0.111 12.256 0.000 1.365 0.980
## .q7 0.522 0.049 10.708 0.000 0.522 0.727
## .q8 0.553 0.050 11.117 0.000 0.553 0.755
## .q9 0.588 0.052 11.269 0.000 0.588 0.774
## .q10 0.387 0.034 11.302 0.000 0.387 0.743
## .q12 0.631 0.055 11.445 0.000 0.631 0.767
## .q13 1.592 0.131 12.123 0.000 1.592 0.938
## .q14 0.395 0.033 11.861 0.000 0.395 0.866
## .q15 0.490 0.042 11.560 0.000 0.490 0.811
## .q16 0.423 0.038 11.135 0.000 0.423 0.723
## .q17 0.468 0.044 10.680 0.000 0.468 0.699
## .q18 1.365 0.115 11.871 0.000 1.365 0.878
## .q21 0.644 0.059 10.871 0.000 0.644 0.697
## .q22 0.497 0.045 10.922 0.000 0.497 0.703
## .q23 0.267 0.028 9.660 0.000 0.267 0.584
## .q25 0.149 0.015 9.699 0.000 0.149 0.587
## Conhecimento 1.000 1.000 1.000
## Atitudes 1.000 1.000 1.000
## Praticas 1.000 1.000 1.000#como o ajuste não foi bom, vamos verificar
modindices(fit_afc) %>%
as.data.frame() %>%
arrange(-mi) %>%
head(10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 q16 ~~ q23 22.296596 0.10572694 0.10572694 0.3146416 0.3146416
## 2 q6 ~~ q13 16.105764 0.34251931 0.34251931 0.2323757 0.2323757
## 3 Praticas =~ q17 14.556665 -0.76647156 -0.76647156 -0.9369369 -0.9369369
## 4 q13 ~~ q15 13.135058 0.19062729 0.19062729 0.2158055 0.2158055
## 5 q23 ~~ q25 11.777932 0.05448004 0.05448004 0.2735554 0.2735554
## 6 q15 ~~ q17 11.088885 0.10123294 0.10123294 0.2114059 0.2114059
## 7 q14 ~~ q23 8.866271 -0.06199768 -0.06199768 -0.1908745 -0.1908745
## 8 Atitudes =~ q8 7.843268 0.86755314 0.86755314 1.0139573 1.0139573
## 9 q8 ~~ q17 7.729117 0.09001501 0.09001501 0.1770210 0.1770210
## 10 q2 ~~ q7 7.160522 0.08927718 0.08927718 0.1792633 0.1792633# Especificação de um modelo unidimensional
modelo_unidimensional_afc <- '
# Todos os itens a carregar num único fator "Competencia_Etica"
Competencia_Etica =~ q2 + q4 + q5 + q7 + q8 + q9 + q10 +
q12 + q15 + q16 + q17 + q20 + q21 + q22 + q23 + q25
'
# Ajustar o modelo de AFC unidimensional
fit_unidimensional_afc <- cfa(modelo_unidimensional_afc, data = dados, std.lv=TRUE, missing = "FIML")
summary(fit_unidimensional_afc, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-20 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 304
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 289.828
## Degrees of freedom 104
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1220.681
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.831
## Tucker-Lewis Index (TLI) 0.805
##
## Robust Comparative Fit Index (CFI) 0.831
## Robust Tucker-Lewis Index (TLI) 0.805
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5298.678
## Loglikelihood unrestricted model (H1) -5153.764
##
## Akaike (AIC) 10693.356
## Bayesian (BIC) 10871.773
## Sample-size adjusted Bayesian (SABIC) 10719.541
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.310
##
## Robust RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.310
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Competencia_Etica =~
## q2 0.391 0.047 8.298 0.000 0.391 0.483
## q4 0.320 0.043 7.381 0.000 0.320 0.436
## q5 0.422 0.047 8.904 0.000 0.422 0.513
## q7 0.385 0.050 7.737 0.000 0.385 0.454
## q8 0.392 0.050 7.812 0.000 0.392 0.458
## q9 0.467 0.050 9.325 0.000 0.467 0.536
## q10 0.348 0.042 8.274 0.000 0.348 0.482
## q12 0.431 0.053 8.151 0.000 0.431 0.475
## q15 0.322 0.046 6.983 0.000 0.322 0.414
## q16 0.374 0.045 8.391 0.000 0.374 0.489
## q17 0.419 0.047 8.870 0.000 0.419 0.512
## q20 0.568 0.048 11.798 0.000 0.568 0.650
## q21 0.571 0.054 10.588 0.000 0.571 0.594
## q22 0.418 0.049 8.588 0.000 0.418 0.497
## q23 0.395 0.038 10.335 0.000 0.395 0.583
## q25 0.304 0.028 10.784 0.000 0.304 0.603
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q2 4.516 0.047 97.106 0.000 4.516 5.569
## .q4 4.641 0.042 110.208 0.000 4.641 6.321
## .q5 4.536 0.047 96.172 0.000 4.536 5.516
## .q7 4.424 0.049 91.041 0.000 4.424 5.222
## .q8 4.431 0.049 90.293 0.000 4.431 5.179
## .q9 4.401 0.050 88.026 0.000 4.401 5.049
## .q10 4.576 0.041 110.571 0.000 4.576 6.342
## .q12 4.609 0.052 88.533 0.000 4.609 5.078
## .q15 4.477 0.045 100.378 0.000 4.477 5.757
## .q16 4.641 0.044 105.783 0.000 4.641 6.067
## .q17 4.543 0.047 96.821 0.000 4.543 5.553
## .q20 4.362 0.050 87.019 0.000 4.362 4.991
## .q21 4.349 0.055 78.858 0.000 4.349 4.523
## .q22 4.398 0.048 91.217 0.000 4.398 5.232
## .q23 4.628 0.039 119.342 0.000 4.628 6.845
## .q25 4.809 0.029 166.682 0.000 4.809 9.560
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .q2 0.504 0.043 11.677 0.000 0.504 0.767
## .q4 0.437 0.037 11.814 0.000 0.437 0.810
## .q5 0.498 0.043 11.564 0.000 0.498 0.737
## .q7 0.570 0.048 11.765 0.000 0.570 0.794
## .q8 0.579 0.049 11.754 0.000 0.579 0.790
## .q9 0.542 0.047 11.419 0.000 0.542 0.713
## .q10 0.400 0.034 11.672 0.000 0.400 0.768
## .q12 0.638 0.054 11.714 0.000 0.638 0.775
## .q15 0.501 0.042 11.884 0.000 0.501 0.829
## .q16 0.445 0.038 11.628 0.000 0.445 0.761
## .q17 0.494 0.043 11.549 0.000 0.494 0.738
## .q20 0.441 0.041 10.669 0.000 0.441 0.578
## .q21 0.599 0.054 11.156 0.000 0.599 0.648
## .q22 0.532 0.046 11.623 0.000 0.532 0.753
## .q23 0.302 0.027 11.174 0.000 0.302 0.660
## .q25 0.161 0.015 11.069 0.000 0.161 0.636
## Competenci_Etc 1.000 1.000 1.000# Analisar os índices de ajuste
cat("--- Resultados do Modelo de 1 Fator (o seu modelo validado) ---\n")## --- Resultados do Modelo de 1 Fator (o seu modelo validado) ---summary(fit_unidimensional_afc, fit.measures=TRUE) # Use o nome do seu objeto de modelo bem-sucedido aqui## lavaan 0.6-20 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 304
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 289.828
## Degrees of freedom 104
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1220.681
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.831
## Tucker-Lewis Index (TLI) 0.805
##
## Robust Comparative Fit Index (CFI) 0.831
## Robust Tucker-Lewis Index (TLI) 0.805
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5298.678
## Loglikelihood unrestricted model (H1) -5153.764
##
## Akaike (AIC) 10693.356
## Bayesian (BIC) 10871.773
## Sample-size adjusted Bayesian (SABIC) 10719.541
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.310
##
## Robust RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.310
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Competencia_Etica =~
## q2 0.391 0.047 8.298 0.000
## q4 0.320 0.043 7.381 0.000
## q5 0.422 0.047 8.904 0.000
## q7 0.385 0.050 7.737 0.000
## q8 0.392 0.050 7.812 0.000
## q9 0.467 0.050 9.325 0.000
## q10 0.348 0.042 8.274 0.000
## q12 0.431 0.053 8.151 0.000
## q15 0.322 0.046 6.983 0.000
## q16 0.374 0.045 8.391 0.000
## q17 0.419 0.047 8.870 0.000
## q20 0.568 0.048 11.798 0.000
## q21 0.571 0.054 10.588 0.000
## q22 0.418 0.049 8.588 0.000
## q23 0.395 0.038 10.335 0.000
## q25 0.304 0.028 10.784 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .q2 4.516 0.047 97.106 0.000
## .q4 4.641 0.042 110.208 0.000
## .q5 4.536 0.047 96.172 0.000
## .q7 4.424 0.049 91.041 0.000
## .q8 4.431 0.049 90.293 0.000
## .q9 4.401 0.050 88.026 0.000
## .q10 4.576 0.041 110.571 0.000
## .q12 4.609 0.052 88.533 0.000
## .q15 4.477 0.045 100.378 0.000
## .q16 4.641 0.044 105.783 0.000
## .q17 4.543 0.047 96.821 0.000
## .q20 4.362 0.050 87.019 0.000
## .q21 4.349 0.055 78.858 0.000
## .q22 4.398 0.048 91.217 0.000
## .q23 4.628 0.039 119.342 0.000
## .q25 4.809 0.029 166.682 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .q2 0.504 0.043 11.677 0.000
## .q4 0.437 0.037 11.814 0.000
## .q5 0.498 0.043 11.564 0.000
## .q7 0.570 0.048 11.765 0.000
## .q8 0.579 0.049 11.754 0.000
## .q9 0.542 0.047 11.419 0.000
## .q10 0.400 0.034 11.672 0.000
## .q12 0.638 0.054 11.714 0.000
## .q15 0.501 0.042 11.884 0.000
## .q16 0.445 0.038 11.628 0.000
## .q17 0.494 0.043 11.549 0.000
## .q20 0.441 0.041 10.669 0.000
## .q21 0.599 0.054 11.156 0.000
## .q22 0.532 0.046 11.623 0.000
## .q23 0.302 0.027 11.174 0.000
## .q25 0.161 0.015 11.069 0.000
## Competenci_Etc 1.000cat("\n\n--- Resultados do Modelo de 1 Fator ---\n")##
##
## --- Resultados do Modelo de 1 Fator ---summary(fit_unidimensional_afc, fit.measures=TRUE)## lavaan 0.6-20 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 304
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 289.828
## Degrees of freedom 104
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1220.681
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.831
## Tucker-Lewis Index (TLI) 0.805
##
## Robust Comparative Fit Index (CFI) 0.831
## Robust Tucker-Lewis Index (TLI) 0.805
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5298.678
## Loglikelihood unrestricted model (H1) -5153.764
##
## Akaike (AIC) 10693.356
## Bayesian (BIC) 10871.773
## Sample-size adjusted Bayesian (SABIC) 10719.541
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.310
##
## Robust RMSEA 0.077
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.310
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Competencia_Etica =~
## q2 0.391 0.047 8.298 0.000
## q4 0.320 0.043 7.381 0.000
## q5 0.422 0.047 8.904 0.000
## q7 0.385 0.050 7.737 0.000
## q8 0.392 0.050 7.812 0.000
## q9 0.467 0.050 9.325 0.000
## q10 0.348 0.042 8.274 0.000
## q12 0.431 0.053 8.151 0.000
## q15 0.322 0.046 6.983 0.000
## q16 0.374 0.045 8.391 0.000
## q17 0.419 0.047 8.870 0.000
## q20 0.568 0.048 11.798 0.000
## q21 0.571 0.054 10.588 0.000
## q22 0.418 0.049 8.588 0.000
## q23 0.395 0.038 10.335 0.000
## q25 0.304 0.028 10.784 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .q2 4.516 0.047 97.106 0.000
## .q4 4.641 0.042 110.208 0.000
## .q5 4.536 0.047 96.172 0.000
## .q7 4.424 0.049 91.041 0.000
## .q8 4.431 0.049 90.293 0.000
## .q9 4.401 0.050 88.026 0.000
## .q10 4.576 0.041 110.571 0.000
## .q12 4.609 0.052 88.533 0.000
## .q15 4.477 0.045 100.378 0.000
## .q16 4.641 0.044 105.783 0.000
## .q17 4.543 0.047 96.821 0.000
## .q20 4.362 0.050 87.019 0.000
## .q21 4.349 0.055 78.858 0.000
## .q22 4.398 0.048 91.217 0.000
## .q23 4.628 0.039 119.342 0.000
## .q25 4.809 0.029 166.682 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .q2 0.504 0.043 11.677 0.000
## .q4 0.437 0.037 11.814 0.000
## .q5 0.498 0.043 11.564 0.000
## .q7 0.570 0.048 11.765 0.000
## .q8 0.579 0.049 11.754 0.000
## .q9 0.542 0.047 11.419 0.000
## .q10 0.400 0.034 11.672 0.000
## .q12 0.638 0.054 11.714 0.000
## .q15 0.501 0.042 11.884 0.000
## .q16 0.445 0.038 11.628 0.000
## .q17 0.494 0.043 11.549 0.000
## .q20 0.441 0.041 10.669 0.000
## .q21 0.599 0.054 11.156 0.000
## .q22 0.532 0.046 11.623 0.000
## .q23 0.302 0.027 11.174 0.000
## .q25 0.161 0.015 11.069 0.000
## Competenci_Etc 1.000modindices(fit_unidimensional_afc) %>%
as.data.frame() %>%
arrange(-mi) %>%
head(10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 q9 ~~ q20 32.423893 0.17934736 0.17934736 0.3666895 0.3666895
## 2 q16 ~~ q23 21.976455 0.10679782 0.10679782 0.2913657 0.2913657
## 3 q23 ~~ q25 19.147158 0.06180048 0.06180048 0.2805096 0.2805096
## 4 q20 ~~ q21 14.799149 0.12980400 0.12980400 0.2524845 0.2524845
## 5 q15 ~~ q17 13.550683 0.11060103 0.11060103 0.2223521 0.2223521
## 6 q2 ~~ q7 12.680781 0.11536556 0.11536556 0.2151594 0.2151594
## 7 q8 ~~ q17 10.872937 0.10710609 0.10710609 0.2004092 0.2004092
## 8 q4 ~~ q20 10.675668 -0.09040994 -0.09040994 -0.2058730 -0.2058730
## 9 q4 ~~ q5 9.243871 0.08594911 0.08594911 0.1842125 0.1842125
## 10 q9 ~~ q15 9.076539 -0.09528414 -0.09528414 -0.1828335 -0.1828335