1 Carregar pacotes

library(tidyverse)

2 Carregar dados

Uma vez que os pacotes foram solicitados, agora é necessário carregar a base de dados

df = read.csv("https://osf.io/download/4dg7b/")
df

3 O CVC não ajustado de clareza

Cada um dos juízes deu uma nota entre 1 e 5 para a clareza dos itens. Por sua vez, 54 itens foram avaliados neste quesito e foram codificados com x_1, x_2, até x_54. Uma maneira de verificar o quão claro o item é, é fazendo uma média das respostas dos juízes.

cvc_clareza_nao_ajustado = df %>% 
  select(contains("x_")) %>%
  summarise_all(
    ~mean(.)/5 #funcao pega a média e divide por 5
    )
cvc_clareza_nao_ajustado

4 PEi

Neste momento, o PEI será criado. Este é um indicador de erro de polarização. Ele será utilizado para subtrair o CVC não ajustado.

pei
[1] 3.504939e-12

5 CVC Clareza ajustado (Final)

cvc_clareza_nao_ajustado-pei

6 Apresentação tabular

df_cvc_clareza = df_cvc_clareza %>%
  t() %>% #transpor
  as.data.frame() %>% #transformar em base de dados
  rownames_to_column("item")

7 Relatorio da clareza

df_cvc_clareza %>%
  arrange(desc(V1))
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