1 Pacotes

library(tidyverse)
library(kableExtra)
library(data.table)
library(gridExtra)
library(readxl)
library(plotly)
library(psych)
library(stargazer)
library(reshape2)
library(corrplot)
library(scales)
library(GGally)
library(rstatix)

2 Etapa 1 - Validação das Marcas

2.1 Marcas mais relacionadas com o Rugby

2.1.1 Ajustando o conjunto de dados

ResMul <- data.frame(Freq = colSums(Marcas_ASS[2:21]),
                     Pct_Respostas = (colSums(Marcas_ASS[2:21]) / sum(Marcas_ASS[2:21]))*100,
                     Pct_Casos = (colSums(Marcas_ASS[2:21]) / nrow(Marcas_ASS[2:21]))*100)

2.1.2 Tabela

ResMul <- ResMul[order(-ResMul$Freq), ] 
ResMul %>% kbl %>% kable_classic(full_width = F, html_font = "Cambria")

	Freq	Pct_Respostas	Pct_Casos
Adidas	9	18.367347	69.230769
Kipsta	5	10.204082	38.461539
Topper	4	8.163265	30.769231
Gilbert	4	8.163265	30.769231
Canterbury	4	8.163265	30.769231
Nike	4	8.163265	30.769231
Under.Armor	3	6.122449	23.076923
Penalty	2	4.081633	15.384615
Kevingston	2	4.081633	15.384615
Sulback	2	4.081633	15.384615
Kappa	1	2.040816	7.692308
Blk	1	2.040816	7.692308
Flash	1	2.040816	7.692308
Offload	1	2.040816	7.692308
Fila	1	2.040816	7.692308
Umbro	1	2.040816	7.692308
Asics	1	2.040816	7.692308
Soul.Rugby	1	2.040816	7.692308
Puma	1	2.040816	7.692308
Mikasa	1	2.040816	7.692308

2.1.3 Reordenando

setDT(ResMul, keep.rownames = T)

colnames(ResMul)[1] <- 'Marca'

ResMul2 <- transform(ResMul, Marca = reorder(Marca, Freq))

2.1.4 Gráfico

g1 <- ggplot(ResMul2, aes(y = Marca, weight = Freq, fill = Freq)) +
  geom_bar(show.legend = F) +
  scale_fill_continuous(low = "#87b5c5", high = "#3c525a") +
  scale_x_continuous(breaks = c(0,3,6,9)) +
  theme_minimal(base_size = 10) +
  theme(text=element_text(family= "Times New Roman", face="bold"),
        plot.title = element_text(hjust = 0.5))+
  labs(title = "Marcas mais associadas ao Rugby") + xlab("Contagem") + ylab("Marca")

g1

2.2 Marcas menos assocadas com o Rugby

2.2.1 Ajustando o conjunto de dados

ResMul2 <- data.frame(Freq = colSums(Marcas_N_ASS[2:16]),
                     Pct_Respostas = (colSums(Marcas_N_ASS[2:16]) / sum(Marcas_N_ASS[2:16]))*100,
                     Pct_Casos = (colSums(Marcas_N_ASS[2:16]) / nrow(Marcas_N_ASS[2:16]))*100)

2.2.2 Tabela

ResMul2 <- ResMul2[order(-ResMul2$Freq), ] 
ResMul2 %>% kbl %>% kable_classic(full_width = F, html_font = "Cambria")

	Freq	Pct_Respostas	Pct_Casos
Puma	6	18.750	46.153846
Nike	5	15.625	38.461539
Reebok	3	9.375	23.076923
Adidas	2	6.250	15.384615
Fila	2	6.250	15.384615
Under.Armor	2	6.250	15.384615
Umbro	2	6.250	15.384615
Kappa	2	6.250	15.384615
Mizuno	2	6.250	15.384615
Olympikus	1	3.125	7.692308
Peak	1	3.125	7.692308
Skcetchers	1	3.125	7.692308
Diadora	1	3.125	7.692308
Penalty	1	3.125	7.692308
Asics	1	3.125	7.692308

2.2.3 Reordenando

setDT(ResMul2, keep.rownames = T)

colnames(ResMul2)[1] <- 'Marca'

ResMul2 <- transform(ResMul2, Marca = reorder(Marca, Freq))

2.2.4 Gráfico

g2 <- ggplot(ResMul2, aes(y = Marca, weight = Freq, fill = Freq)) +
  geom_bar(show.legend = F) +
  scale_fill_continuous(low = "#87b5c5", high = "#3c525a") +
  scale_x_continuous(breaks = c(0,3,6,9)) +
  theme_minimal(base_size = 10) +
  theme(text=element_text(family= "Times New Roman", face="bold"),
        plot.title = element_text(hjust = 0.5))+
  labs(title = "Marcas menos associadas ao Rugby") + xlab("Contagem") + ylab("Marca")

g2

grid.arrange(g1, g2, ncol = 2, nrow = 1)

3 Data - Rugby

3.1 Organizando os dados

# Recoding variáveis invertidas

data$D2 <- 5 - D2 + 1
data$D5 <- 5 - D5 + 1
data$D6 <- 5 - D6 + 1
data$I2 <- 5 - I2 + 1
data$I4 <- 5 - I4 + 1
data$I7 <- 5 - I7 + 1
data$R3 <- 5 - R3 + 1
data$R6 <- 5 - R6 + 1
data$S4 <- 5 - S4 + 1
data$S6 <- 5 - S6 + 1

data$missing <- rowSums(data[,c(4:38)])

data <- data %>% filter(missing > 1) # 27 casos tiveram que ser removidos da amostra

4 Descrição da Amostra

4.1 Gênero

data$Genero[is.na(data$Genero)] <- 'Não informado'

data$Genero <- factor(data$Genero)

tab <- as.data.frame(table(data$Genero))

blank_theme <- theme_minimal()+
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.border = element_blank(),
    panel.grid=element_blank(),
    axis.ticks = element_blank(),
    plot.title=element_text(size=14, face="bold")
  )

tab2 <- tab %>% 
  mutate(csum = rev(cumsum(rev(Freq))), 
         pos = Freq/3 + lead(csum, 1),
         pos = if_else(is.na(pos), Freq/3, pos))

p1 <- ggplot(tab, aes(x = "", y = Freq, fill = Var1)) +
  geom_col(width = 1, color = 1) +
  geom_text(aes(label = Freq),
            position = position_stack(vjust = 0.5)) +
  coord_polar(theta = "y") +
  guides(fill = guide_legend(title = "Gênero")) +
  scale_y_continuous(breaks = tab2$pos, labels = tab$Var1) +
  blank_theme +
  ggtitle('1) Gênero dos respondentes') + 
  theme(axis.text.x=element_blank())
p1

4.2 Nível de relação com o esporte

data$Nivel_Relacao <- factor(data$Nivel_Relacao)
tab3 <- as.data.frame(table(Nivel_Relacao))
tab3$Nivel_Relacao <- as.character(tab3$Nivel_Relacao)
tab3[3,1] <- 'Outro'
tab3$Nivel_Relacao <- factor(tab3$Nivel_Relacao)

tab4 <- tab3 %>% 
  mutate(csum = rev(cumsum(rev(Freq))), 
         pos = Freq/4 + lead(csum, 1),
         pos = if_else(is.na(pos), Freq/4, pos))

p2 <- ggplot(tab3, aes(x = "", y = Freq, fill = Nivel_Relacao)) +
  geom_col(width = 1, color = 1) +
  geom_text(aes(label = Freq),
            position = position_stack(vjust = 0.5)) +
  coord_polar(theta = "y") +
  guides(fill = guide_legend(title = "Nível da Relação")) +
  scale_y_continuous(breaks = tab4$pos, labels = tab3$Nivel_Relacao) +
  blank_theme +
  ggtitle('2) Relação com o rugby') + 
  theme(axis.text.x=element_blank())

p2

4.3 Idade

describe(Idade)

##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 79 28.8 6.51     29   28.71 5.93  17  44    27 0.04    -0.52 0.73

describe.by(Idade, group = Genero)

## 
##  Descriptive statistics by group 
## group: Feminino
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 22 26.59 6.29     27   26.28 5.93  18  42    24 0.33    -0.43 1.34
## ------------------------------------------------------------ 
## group: Masculino
##    vars  n  mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 57 29.65 6.45     31   29.66 5.93  17  44    27 -0.08    -0.47 0.85

p3 <- data %>% ggplot(aes(y = Idade, fill = 'red')) +
  geom_boxplot() +
  labs(y = 'Anos', x = '') +
  scale_x_discrete(labels = NULL, breaks = NULL) + labs(x = "") +
  guides(fill = F) +
  ggtitle('3) Idade') + 
  theme_bw()

p3

4.4 Tempo de prática

describe(Tempo_Pratica)

##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 79 7.15 3.71      6    6.92 2.97   1  16    15 0.56    -0.57 0.42

describe.by(Tempo_Pratica, group = Genero)

## 
##  Descriptive statistics by group 
## group: Feminino
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 22 4.95 2.34      4    4.78 2.97   2  10     8 0.54    -0.87 0.5
## ------------------------------------------------------------ 
## group: Masculino
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 57    8 3.81      8    7.91 4.45   1  16    15 0.31    -0.86 0.5

p4 <- data %>% ggplot(aes(y = Tempo_Pratica, fill = 'red')) +
  geom_boxplot() +
  labs(y = 'Anos', x = '') +
  scale_x_discrete(labels = NULL, breaks = NULL) + labs(x = "") +
  guides(fill = F) +
  ggtitle('4) Tempo de Prática do Rugby') +
  theme_bw()

p4

grid.arrange(p1,p2,p3,p4)

4.5 Remuneração

(tab10 <- table(Remuneracao))

## Remuneracao
## Não Sim 
##  74   5

5 Análise dos constructos

5.1 Paixão

Paixao <- as.data.frame(data[,c(4:10)])
(Alpha_P <- psych::alpha(Paixao)) # 0.76

## 
## Reliability analysis   
## Call: psych::alpha(x = Paixao)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.71      0.76    0.77      0.31 3.1 0.045  4.5 0.38     0.28
## 
##  lower alpha upper     95% confidence boundaries
## 0.62 0.71 0.8 
## 
##  Reliability if an item is dropped:
##    raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P1      0.69      0.72    0.73      0.30 2.6    0.048 0.015  0.28
## P2      0.69      0.72    0.73      0.30 2.6    0.050 0.019  0.28
## P3      0.69      0.73    0.73      0.31 2.7    0.048 0.015  0.28
## P4      0.69      0.75    0.75      0.33 3.0    0.049 0.019  0.35
## P5      0.63      0.69    0.68      0.27 2.2    0.059 0.011  0.25
## P6      0.66      0.72    0.73      0.30 2.6    0.055 0.017  0.32
## P7      0.71      0.74    0.74      0.33 2.9    0.047 0.015  0.28
## 
##  Item statistics 
##     n raw.r std.r r.cor r.drop mean   sd
## P1 82  0.53  0.64  0.57   0.41  4.8 0.41
## P2 82  0.56  0.65  0.56   0.45  4.9 0.39
## P3 82  0.52  0.64  0.56   0.40  4.9 0.39
## P4 82  0.56  0.55  0.43   0.38  4.6 0.56
## P5 82  0.76  0.75  0.74   0.65  4.6 0.54
## P6 82  0.72  0.65  0.58   0.50  4.1 0.83
## P7 82  0.70  0.57  0.47   0.42  3.8 0.97
## 
## Non missing response frequency for each item
##       1    2    3    4    5 miss
## P1 0.00 0.00 0.01 0.15 0.84    0
## P2 0.00 0.00 0.01 0.12 0.87    0
## P3 0.00 0.00 0.01 0.12 0.87    0
## P4 0.00 0.00 0.04 0.33 0.63    0
## P5 0.00 0.00 0.02 0.35 0.62    0
## P6 0.00 0.05 0.13 0.45 0.37    0
## P7 0.01 0.04 0.43 0.22 0.30    0

(Omega_P <- omega(Paixao, nfactors = 1, poly = T))

## Omega 
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip, 
##     digits = digits, title = title, sl = sl, labels = labels, 
##     plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option, 
##     covar = covar)
## Alpha:                 0.86 
## G.6:                   0.96 
## Omega Hierarchical:    0.86 
## Omega H asymptotic:    1 
## Omega Total            0.86 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##       g  F1*   h2   u2 p2
## P1 0.73      0.54 0.46  1
## P2 0.77      0.59 0.41  1
## P3 0.73      0.53 0.47  1
## P4 0.52      0.27 0.73  1
## P5 0.89      0.79 0.21  1
## P6 0.63      0.40 0.60  1
## P7 0.52      0.27 0.73  1
## 
## With eigenvalues of:
##   g F1* 
## 3.4 0.0 
## 
## general/max  6.103445e+16   max/min =   1
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 14  and the fit is  2.93 
## The number of observations was  82  with Chi Square =  225.96  with prob <  2.6e-40
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.14
## RMSEA index =  0.43  and the 10 % confidence intervals are  0.384 0.483
## BIC =  164.27
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14  and the fit is  2.93 
## The number of observations was  82  with Chi Square =  225.96  with prob <  2.6e-40
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.14 
## 
## RMSEA index =  0.43  and the 10 % confidence intervals are  0.384 0.483
## BIC =  164.27 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            1.01   0
## Multiple R square of scores with factors      1.01   0
## Minimum correlation of factor score estimates 1.03  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.86 0.86
## Omega general for total scores and subscales  0.86 0.86
## Omega group for total scores and subscales    0.00 0.00

stargazer(Paixao, summary = T, type = 'text', title = "Estatísticas descritivas Paixão")

## 
## Estatísticas descritivas Paixão
## =====================================================
## Statistic N  Mean  St. Dev. Min Pctl(25) Pctl(75) Max
## -----------------------------------------------------
## P1        82 4.829  0.410    3     5        5      5 
## P2        82 4.854  0.389    3     5        5      5 
## P3        82 4.854  0.389    3     5        5      5 
## P4        82 4.598  0.563    3     4        5      5 
## P5        82 4.598  0.541    3     4        5      5 
## P6        82 4.134  0.828    2     4        5      5 
## P7        82 3.768  0.972    1     3        5      5 
## -----------------------------------------------------

Paixao$Id <- seq(1:82)
Paixao_long <- melt(Paixao, id.vars = c("Id"))
colnames(Paixao_long) <- c("id", "Item", "Response")

ggplot(Paixao_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

ggplot(Paixao_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

5.2 Integridade

Integridade <- as.data.frame(data[,c(11:17)])
(alpha_I <- psych::alpha(Integridade)) # 0.77

## 
## Reliability analysis   
## Call: psych::alpha(x = Integridade)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.76      0.77    0.78      0.32 3.3 0.04  4.3 0.48     0.29
## 
##  lower alpha upper     95% confidence boundaries
## 0.68 0.76 0.84 
## 
##  Reliability if an item is dropped:
##    raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## I1      0.71      0.71    0.72      0.29 2.5    0.049 0.034  0.25
## I2      0.68      0.70    0.72      0.28 2.3    0.054 0.032  0.24
## I3      0.71      0.71    0.71      0.29 2.4    0.049 0.028  0.25
## I4      0.75      0.76    0.77      0.34 3.2    0.043 0.035  0.29
## I5      0.77      0.78    0.79      0.37 3.6    0.039 0.035  0.44
## I6      0.77      0.78    0.78      0.37 3.5    0.037 0.035  0.44
## I7      0.70      0.71    0.73      0.29 2.5    0.051 0.031  0.25
## 
##  Item statistics 
##     n raw.r std.r r.cor r.drop mean   sd
## I1 82  0.72  0.74  0.72   0.60  4.5 0.71
## I2 82  0.79  0.78  0.75   0.67  4.1 0.81
## I3 82  0.72  0.75  0.74   0.63  4.6 0.55
## I4 82  0.60  0.57  0.46   0.40  4.5 0.84
## I5 82  0.45  0.47  0.31   0.27  4.1 0.68
## I6 82  0.49  0.48  0.34   0.28  4.4 0.80
## I7 82  0.75  0.73  0.70   0.60  4.2 0.84
## 
## Non missing response frequency for each item
##       1    2    3    4    5 miss
## I1 0.00 0.04 0.01 0.41 0.54    0
## I2 0.00 0.04 0.16 0.45 0.35    0
## I3 0.00 0.00 0.04 0.28 0.68    0
## I4 0.02 0.00 0.07 0.24 0.66    0
## I5 0.00 0.01 0.16 0.57 0.26    0
## I6 0.01 0.00 0.12 0.32 0.55    0
## I7 0.02 0.00 0.12 0.45 0.40    0

(omega_I <- omega(Integridade, nfactors = 1, poly = T))

## Omega 
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip, 
##     digits = digits, title = title, sl = sl, labels = labels, 
##     plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option, 
##     covar = covar)
## Alpha:                 0.84 
## G.6:                   0.88 
## Omega Hierarchical:    0.85 
## Omega H asymptotic:    0.99 
## Omega Total            0.85 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##       g  F1*   h2   u2 p2
## I1 0.74      0.55 0.45  1
## I2 0.84      0.71 0.29  1
## I3 0.88      0.78 0.22  1
## I4 0.67      0.45 0.55  1
## I5 0.38      0.14 0.86  1
## I6 0.38      0.15 0.85  1
## I7 0.72      0.52 0.48  1
## 
## With eigenvalues of:
##   g F1* 
## 3.3 0.0 
## 
## general/max  Inf   max/min =   NaN
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 14  and the fit is  1.18 
## The number of observations was  82  with Chi Square =  91.02  with prob <  2.4e-13
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.15
## RMSEA index =  0.259  and the 10 % confidence intervals are  0.211 0.313
## BIC =  29.32
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14  and the fit is  1.18 
## The number of observations was  82  with Chi Square =  91.02  with prob <  2.4e-13
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.15 
## 
## RMSEA index =  0.259  and the 10 % confidence intervals are  0.211 0.313
## BIC =  29.32 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.95   0
## Multiple R square of scores with factors      0.91   0
## Minimum correlation of factor score estimates 0.81  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.85 0.85
## Omega general for total scores and subscales  0.85 0.85
## Omega group for total scores and subscales    0.00 0.00

stargazer(Integridade, summary = T, type = 'text', title = "Estatísticas descritivas Integridade")

## 
## Estatísticas descritivas Integridade
## =====================================================
## Statistic N  Mean  St. Dev. Min Pctl(25) Pctl(75) Max
## -----------------------------------------------------
## I1        82 4.451  0.705    2     4        5      5 
## I2        82 4.122  0.807    2     4        5      5 
## I3        82 4.646  0.553    3     4        5      5 
## I4        82 4.512  0.835    1     4        5      5 
## I5        82 4.073  0.681    2     4       4.8     5 
## I6        82 4.390  0.797    1     4        5      5 
## I7        82 4.207  0.842    1     4        5      5 
## -----------------------------------------------------

Integridade$Id <- seq(1:82)
Integridade_long <- melt(Integridade, id.vars = c("Id"))
colnames(Integridade_long) <- c("id", "Item", "Response")

ggplot(Integridade_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

ggplot(Integridade_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

5.3 Respeito

Respeito <- as.data.frame(data[,c(18:24)])
Respeito_Alpha <- Respeito[,-c(3,6)]
(alpha_R <- psych::alpha(Respeito_Alpha)) # 0.72

## 
## Reliability analysis   
## Call: psych::alpha(x = Respeito_Alpha)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.71      0.72     0.7      0.34 2.6 0.051  4.4 0.45     0.33
## 
##  lower alpha upper     95% confidence boundaries
## 0.61 0.71 0.81 
## 
##  Reliability if an item is dropped:
##    raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## R1      0.64      0.66    0.60      0.32 1.9    0.065 0.0107  0.34
## R2      0.60      0.62    0.56      0.29 1.6    0.072 0.0061  0.31
## R4      0.68      0.70    0.66      0.36 2.3    0.058 0.0148  0.31
## R5      0.73      0.73    0.69      0.40 2.7    0.048 0.0095  0.37
## R7      0.65      0.67    0.62      0.33 2.0    0.064 0.0204  0.32
## 
##  Item statistics 
##     n raw.r std.r r.cor r.drop mean   sd
## R1 82  0.70  0.72  0.64   0.52  4.5 0.61
## R2 82  0.78  0.78  0.74   0.61  4.3 0.68
## R4 82  0.61  0.65  0.50   0.41  4.5 0.57
## R5 82  0.64  0.58  0.40   0.34  4.3 0.81
## R7 82  0.70  0.70  0.59   0.50  4.5 0.63
## 
## Non missing response frequency for each item
##       2    3    4    5 miss
## R1 0.00 0.06 0.43 0.51    0
## R2 0.01 0.09 0.50 0.40    0
## R4 0.00 0.04 0.38 0.59    0
## R5 0.05 0.09 0.43 0.44    0
## R7 0.00 0.07 0.38 0.55    0

(omega_R <- omega(Respeito_Alpha, nfactors = 1, poly = T))

## Omega 
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip, 
##     digits = digits, title = title, sl = sl, labels = labels, 
##     plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option, 
##     covar = covar)
## Alpha:                 0.81 
## G.6:                   0.8 
## Omega Hierarchical:    0.81 
## Omega H asymptotic:    1 
## Omega Total            0.82 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##       g  F1*   h2   u2 p2
## R1 0.73      0.54 0.46  1
## R2 0.87      0.76 0.24  1
## R4 0.62      0.38 0.62  1
## R5 0.49      0.24 0.76  1
## R7 0.68      0.46 0.54  1
## 
## With eigenvalues of:
##   g F1* 
## 2.4 0.0 
## 
## general/max  Inf   max/min =   NaN
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 5  and the fit is  0.16 
## The number of observations was  82  with Chi Square =  12.15  with prob <  0.033
## The root mean square of the residuals is  0.06 
## The df corrected root mean square of the residuals is  0.09
## RMSEA index =  0.131  and the 10 % confidence intervals are  0.035 0.23
## BIC =  -9.88
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5  and the fit is  0.16 
## The number of observations was  82  with Chi Square =  12.15  with prob <  0.033
## The root mean square of the residuals is  0.06 
## The df corrected root mean square of the residuals is  0.09 
## 
## RMSEA index =  0.131  and the 10 % confidence intervals are  0.035 0.23
## BIC =  -9.88 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.93   0
## Multiple R square of scores with factors      0.86   0
## Minimum correlation of factor score estimates 0.72  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.82 0.81
## Omega general for total scores and subscales  0.81 0.81
## Omega group for total scores and subscales    0.00 0.00

stargazer(Respeito, summary = T, type = 'text', title = "Estatísticas descritivas Respeito")

## 
## Estatísticas descritivas Respeito
## =====================================================
## Statistic N  Mean  St. Dev. Min Pctl(25) Pctl(75) Max
## -----------------------------------------------------
## R1        82 4.451  0.612    3     4        5      5 
## R2        82 4.293  0.676    2     4        5      5 
## R3        82 3.500  0.997    1     3        4      5 
## R4        82 4.549  0.570    3     4        5      5 
## R5        82 4.256  0.814    2     4        5      5 
## R6        82 4.171  1.040    1     4        5      5 
## R7        82 4.476  0.633    3     4        5      5 
## -----------------------------------------------------

Respeito$Id <- seq(1:82)
Respeito_long <- melt(Respeito, id.vars = c("Id"))
colnames(Respeito_long) <- c("id", "Item", "Response")

ggplot(Respeito_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

ggplot(Respeito_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

5.4 Disciplina

Disciplina <- as.data.frame(data[,c(25:31)])
alpha_D <- psych::alpha(Disciplina) # 0.73    
omega_d <- omega(Disciplina, nfactors = 1, poly = T)
stargazer(Disciplina, summary = T, type = 'text', title = "Estatísticas descritivas Disciplina")

## 
## Estatísticas descritivas Disciplina
## =====================================================
## Statistic N  Mean  St. Dev. Min Pctl(25) Pctl(75) Max
## -----------------------------------------------------
## D1        82 3.537  0.892    1     3        4      5 
## D2        82 3.293  0.962    1     3        4      5 
## D3        82 3.476  0.984    1     3        4      5 
## D4        82 3.695  0.990    1     3        4      5 
## D5        82 3.976  0.816    2     4       4.8     5 
## D6        82 4.195  0.744    1     4        5      5 
## D7        82 3.598  0.914    1     3        4      5 
## -----------------------------------------------------

Disciplina$Id <- seq(1:82)
Disciplina_long <- melt(Disciplina, id.vars = c("Id"))
colnames(Disciplina_long) <- c("id", "Item", "Response")

ggplot(Disciplina_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

ggplot(Disciplina_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

5.5 Solidariedade

Solidariedade <- as.data.frame(data[,c(32:38)])
(alpha_S <- psych::alpha(Solidariedade)) # 0.84

## 
## Reliability analysis   
## Call: psych::alpha(x = Solidariedade)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.82      0.84    0.84      0.43 5.2 0.03  4.2 0.56     0.42
## 
##  lower alpha upper     95% confidence boundaries
## 0.76 0.82 0.88 
## 
##  Reliability if an item is dropped:
##    raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## S1      0.80      0.82    0.82      0.44 4.7    0.034 0.0145  0.44
## S2      0.78      0.80    0.79      0.39 3.9    0.038 0.0101  0.38
## S3      0.78      0.80    0.79      0.41 4.1    0.038 0.0129  0.38
## S4      0.83      0.83    0.82      0.46 5.0    0.030 0.0101  0.45
## S5      0.81      0.83    0.83      0.45 4.9    0.033 0.0149  0.46
## S6      0.80      0.82    0.81      0.43 4.5    0.034 0.0105  0.42
## S7      0.80      0.81    0.80      0.42 4.3    0.035 0.0098  0.38
## 
##  Item statistics 
##     n raw.r std.r r.cor r.drop mean   sd
## S1 82  0.68  0.68  0.60   0.54  3.8 0.81
## S2 82  0.79  0.81  0.79   0.71  4.3 0.67
## S3 82  0.77  0.78  0.74   0.68  4.3 0.72
## S4 82  0.68  0.62  0.54   0.48  4.1 1.10
## S5 82  0.65  0.65  0.55   0.51  4.1 0.79
## S6 82  0.69  0.71  0.65   0.56  4.3 0.82
## S7 82  0.71  0.74  0.70   0.61  4.5 0.67
## 
## Non missing response frequency for each item
##       1    2    3    4    5 miss
## S1 0.01 0.05 0.23 0.55 0.16    0
## S2 0.00 0.01 0.09 0.52 0.38    0
## S3 0.01 0.00 0.09 0.51 0.39    0
## S4 0.04 0.07 0.12 0.30 0.46    0
## S5 0.00 0.02 0.21 0.45 0.32    0
## S6 0.02 0.01 0.04 0.46 0.46    0
## S7 0.00 0.01 0.06 0.39 0.54    0

(omega_S <- omega(Solidariedade, nfactors = 1, poly = T))

## Omega 
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip, 
##     digits = digits, title = title, sl = sl, labels = labels, 
##     plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option, 
##     covar = covar)
## Alpha:                 0.89 
## G.6:                   0.9 
## Omega Hierarchical:    0.89 
## Omega H asymptotic:    1 
## Omega Total            0.89 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##       g  F1*   h2   u2 p2
## S1 0.66      0.44 0.56  1
## S2 0.89      0.78 0.22  1
## S3 0.76      0.57 0.43  1
## S4 0.66      0.44 0.56  1
## S5 0.63      0.39 0.61  1
## S6 0.74      0.54 0.46  1
## S7 0.78      0.61 0.39  1
## 
## With eigenvalues of:
##   g F1* 
## 3.8 0.0 
## 
## general/max  6.810577e+16   max/min =   1
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 14  and the fit is  0.65 
## The number of observations was  82  with Chi Square =  50.47  with prob <  5.1e-06
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.1
## RMSEA index =  0.178  and the 10 % confidence intervals are  0.128 0.234
## BIC =  -11.23
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14  and the fit is  0.65 
## The number of observations was  82  with Chi Square =  50.47  with prob <  5.1e-06
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.1 
## 
## RMSEA index =  0.178  and the 10 % confidence intervals are  0.128 0.234
## BIC =  -11.23 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.95   0
## Multiple R square of scores with factors      0.91   0
## Minimum correlation of factor score estimates 0.82  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.89 0.89
## Omega general for total scores and subscales  0.89 0.89
## Omega group for total scores and subscales    0.00 0.00

stargazer(Solidariedade, summary = T, type = 'text', title = "Estatísticas descritivas Disciplina")

## 
## Estatísticas descritivas Disciplina
## =====================================================
## Statistic N  Mean  St. Dev. Min Pctl(25) Pctl(75) Max
## -----------------------------------------------------
## S1        82 3.793  0.813    1     3        4      5 
## S2        82 4.268  0.668    2     4        5      5 
## S3        82 4.268  0.721    1     4        5      5 
## S4        82 4.085  1.102    1     4        5      5 
## S5        82 4.061  0.791    2     4        5      5 
## S6        82 4.329  0.817    1     4        5      5 
## S7        82 4.451  0.669    2     4        5      5 
## -----------------------------------------------------

Solidariedade$Id <- seq(1:82)
Solidariedade_long <- melt(Solidariedade, id.vars = c("Id"))
colnames(Solidariedade_long) <- c("id", "Item", "Response")

ggplot(Solidariedade_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

ggplot(Solidariedade_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()

6 Recodificação do one-hot-encoding

data$Forte[is.na(data$Forte)] <- 0
data$Fraca[is.na(data$Fraca)] <- 0
data$Controle[is.na(data$Controle)] <- 0
grupo <- as.data.frame(as.numeric(data$Forte))
grupo$Fraca <- as.numeric(data$Fraca)
grupo$Controle <- as.numeric(data$Controle)
colnames(grupo) <- c('Forte', 'Fraca', 'Controle')
inds <- which(rowSums(grupo) == 1)
grupo$grupo <- toupper(names(grupo)[max.col(grupo)])
data <- cbind(data, grupo$grupo)
colnames(data)[50] <- 'Condição'

7 Correlações

CorMat <- cor(as.matrix(data[,c(4:38)]))

corrplot(CorMat,
         order="original",
         type="lower",
         method="shade",
         diag = FALSE,
         tl.cex = 0.8, 
         title = 'Matriz de Correlação',
         tl.col = "black",
         tl.offset = 0.5,
         mar = c(0, 0, 2, 0))

8 Checks do Experimento

8.1 Condições

table(data$Condição)

## 
## CONTROLE    FORTE    FRACA 
##       28       26       28

8.2 Atenção

data2 <- as.data.frame(cbind(data$Atencao, data$Condição, data$Associacao_Marca))

colnames(data2) <- c('Atenção', 'Condição', 'Associacao_Marca')

data2$Atenção[is.na(data2$Atenção) & data2$Condição == 'CONTROLE'] <- 'CONTROLE'

data2$Atenção[data2$Atenção == 'Adidas'] <- 'FORTE'
data2$Atenção[data2$Atenção == 'Puma'] <- 'FRACA'  

table(Marca = data2$Atenção, 
      Condição = data2$Condição) # Temos 8 errors, mas devido ao pequeno tamanho da amostra, vamos manter

##           Condição
## Marca      CONTROLE FORTE FRACA
##   CONTROLE       28     0     0
##   FORTE           0    24     0
##   FRACA           0     0    19
##   Gilbert         0     1     0
##   Kipsta          0     1     6

1 - 8/82

## [1] 0.902439

8.3 Associação

table(data2$Associacao_Marca, data2$Condição, useNA = 'no')

##                                  
##                                   CONTROLE FORTE FRACA
##   Fortemente associada                   0    12     4
##   Levemente associada                    0     7     5
##   Nem pouco e nem muito associada        0     2    10
##   Nem um pouco associada                 0     1     3
##   Pouco associada                        0     4     5

19/28

## [1] 0.6785714

9/28

## [1] 0.3214286

9 Médias dos constructos

(data$Paixao <- rowMeans(data[4:10]))

##  [1] 4.714286 4.000000 4.571429 4.571429 5.000000 4.571429 4.714286 5.000000
##  [9] 4.142857 5.000000 3.714286 4.857143 5.000000 4.571429 4.285714 4.571429
## [17] 4.428571 3.571429 5.000000 4.285714 4.714286 5.000000 5.000000 4.000000
## [25] 4.142857 4.428571 4.428571 5.000000 4.428571 5.000000 4.285714 4.428571
## [33] 4.000000 4.285714 4.428571 4.428571 4.857143 4.000000 5.000000 4.285714
## [41] 4.714286 4.428571 4.428571 4.285714 4.000000 5.000000 4.428571 4.857143
## [49] 4.428571 5.000000 4.571429 4.857143 4.428571 5.000000 4.857143 5.000000
## [57] 4.000000 4.428571 4.000000 4.857143 3.428571 4.142857 4.000000 4.428571
## [65] 4.857143 4.000000 4.857143 4.142857 4.571429 4.714286 4.571429 4.142857
## [73] 4.857143 4.857143 4.714286 4.142857 4.714286 4.571429 4.428571 4.857143
## [81] 4.285714 5.000000

(data$Integridade <- rowMeans(data[11:17]))

##  [1] 4.428571 4.142857 4.000000 5.000000 4.000000 4.428571 4.000000 4.428571
##  [9] 3.285714 5.000000 3.857143 4.000000 4.428571 4.857143 4.714286 4.571429
## [17] 4.428571 3.857143 4.857143 4.428571 4.714286 4.000000 5.000000 4.142857
## [25] 4.285714 4.714286 4.285714 4.857143 4.571429 4.714286 4.428571 4.857143
## [33] 4.000000 3.571429 4.857143 4.142857 4.142857 4.428571 3.714286 4.857143
## [41] 4.714286 4.714286 5.000000 4.285714 3.857143 4.000000 4.285714 4.571429
## [49] 4.142857 4.428571 3.714286 4.714286 2.857143 4.285714 4.857143 4.714286
## [57] 4.571429 4.428571 4.000000 4.428571 4.285714 4.714286 2.714286 4.714286
## [65] 4.857143 4.285714 4.714286 4.571429 4.142857 3.000000 5.000000 4.571429
## [73] 4.285714 4.571429 4.428571 3.714286 4.714286 5.000000 4.000000 3.857143
## [81] 4.000000 4.428571

(data$Respeito <- rowMeans(data[18:24]))

##  [1] 4.285714 4.285714 3.428571 5.000000 4.571429 3.857143 4.285714 4.000000
##  [9] 3.285714 5.000000 4.571429 4.000000 4.571429 3.857143 4.714286 4.428571
## [17] 3.857143 4.000000 4.428571 4.285714 4.714286 4.428571 4.428571 4.714286
## [25] 4.285714 4.142857 5.000000 4.571429 4.285714 4.714286 3.428571 4.285714
## [33] 4.000000 4.714286 4.714286 4.428571 4.428571 4.571429 3.857143 4.142857
## [41] 4.285714 4.571429 4.571429 4.285714 3.857143 4.000000 4.428571 4.285714
## [49] 3.857143 3.714286 4.142857 4.714286 4.285714 4.000000 4.428571 5.000000
## [57] 4.142857 3.714286 4.000000 4.571429 4.000000 4.000000 3.428571 4.285714
## [65] 4.428571 4.285714 4.571429 4.000000 4.142857 3.428571 4.428571 4.571429
## [73] 3.428571 4.000000 4.714286 4.000000 4.857143 3.714286 3.714286 4.142857
## [81] 4.000000 4.285714

(data$Disciplina <- rowMeans(data[25:31]))

##  [1] 3.571429 3.000000 3.571429 3.571429 3.857143 4.142857 2.857143 2.714286
##  [9] 3.000000 5.000000 2.857143 3.428571 4.428571 3.428571 3.857143 3.428571
## [17] 3.428571 4.285714 4.428571 3.571429 3.428571 3.428571 5.000000 3.857143
## [25] 3.428571 3.428571 4.142857 4.571429 3.857143 3.142857 2.285714 3.571429
## [33] 2.857143 3.428571 4.000000 3.857143 4.000000 3.714286 3.000000 4.000000
## [41] 4.428571 4.571429 3.571429 3.714286 3.571429 3.857143 4.142857 3.142857
## [49] 2.857143 3.142857 2.714286 4.000000 4.428571 3.285714 4.000000 4.714286
## [57] 3.857143 4.000000 3.428571 3.714286 3.857143 3.857143 3.571429 3.714286
## [65] 3.428571 3.000000 4.142857 3.428571 4.000000 2.857143 3.428571 3.571429
## [73] 3.142857 4.285714 3.714286 3.857143 4.571429 3.000000 4.428571 3.285714
## [81] 3.571429 4.571429

(data$Solidariedade <- rowMeans(data[32:38]))

##  [1] 4.857143 4.714286 3.714286 5.000000 5.000000 4.714286 4.142857 3.000000
##  [9] 3.571429 4.428571 4.857143 4.000000 4.857143 3.428571 3.857143 4.000000
## [17] 3.714286 1.857143 3.714286 4.857143 4.857143 4.714286 5.000000 3.857143
## [25] 3.714286 4.285714 4.571429 4.571429 5.000000 5.000000 3.142857 3.857143
## [33] 3.857143 3.714286 4.428571 4.857143 3.571429 3.857143 3.714286 4.000000
## [41] 5.000000 4.142857 4.000000 3.571429 4.142857 4.142857 3.714286 4.142857
## [49] 3.714286 4.000000 4.142857 4.714286 4.714286 4.428571 4.142857 4.857143
## [57] 4.571429 4.000000 3.714286 4.571429 3.285714 4.000000 4.285714 4.142857
## [65] 4.000000 4.000000 4.428571 3.428571 4.571429 3.714286 4.285714 3.857143
## [73] 3.857143 4.428571 5.000000 3.857143 4.857143 4.000000 4.000000 4.428571
## [81] 4.142857 4.857143

10 Normalidade dos constructos

10.1 Paíxão

(shapiro_P <- shapiro.test(data$Paixao)) # Não

## 
##  Shapiro-Wilk normality test
## 
## data:  data$Paixao
## W = 0.93164, p-value = 0.0002947

ggplot(data = data, aes(Paixao)) +
  geom_histogram(aes(y = ..density..), colour = 'lightblue', fill = '#4682b4',
                 bins = 20) +
  labs(x = 'Idade', y = 'Densidade') +
  stat_function(fun = dnorm,
                args = list(mean = mean(data$Paixao, na.rm = T),
                            sd = sd(data$Paixao, na.rm = T)),
                colour = 'black', size = 1) +
  ggtitle('Histograma para Paixão') +
  theme_bw()

10.2 Integridade

(shapiro_I <- shapiro.test(data$Integridade)) # Não

## 
##  Shapiro-Wilk normality test
## 
## data:  data$Integridade
## W = 0.91531, p-value = 4.682e-05

ggplot(data = data, aes(Integridade)) +
  geom_histogram(aes(y = ..density..), colour = 'lightblue', fill = '#4682b4',
                 bins = 20) +
  labs(x = 'Idade', y = 'Densidade') +
  stat_function(fun = dnorm,
                args = list(mean = mean(data$Integridade, na.rm = T),
                            sd = sd(data$Integridade, na.rm = T)),
                colour = 'black', size = 1) +
  ggtitle('Histograma para Integridade') +
  theme_bw()

10.3 Respeito

(shapiro_R <-shapiro.test(data$Respeito)) # Não

## 
##  Shapiro-Wilk normality test
## 
## data:  data$Respeito
## W = 0.96945, p-value = 0.04756

ggplot(data = data, aes(Respeito)) +
  geom_histogram(aes(y = ..density..), colour = 'lightblue', fill = '#4682b4',
                 bins = 20) +
  labs(x = 'Idade', y = 'Densidade') +
  stat_function(fun = dnorm,
                args = list(mean = mean(data$Respeito, na.rm = T),
                            sd = sd(data$Respeito, na.rm = T)),
                colour = 'black', size = 1) +
  ggtitle('Histograma para Respeito') +
  theme_bw()

10.4 Disciplina

(shapiro_D <-shapiro.test(data$Disciplina)) # Sim

## 
##  Shapiro-Wilk normality test
## 
## data:  data$Disciplina
## W = 0.98387, p-value = 0.3942

ggplot(data = data, aes(Disciplina)) +
  geom_histogram(aes(y = ..density..), colour = 'lightblue', fill = '#4682b4',
                 bins = 20) +
  labs(x = 'Idade', y = 'Densidade') +
  stat_function(fun = dnorm,
                args = list(mean = mean(data$Disciplina, na.rm = T),
                            sd = sd(data$Disciplina, na.rm = T)),
                colour = 'black', size = 1) +
  ggtitle('Histograma para Disciplina') +
  theme_bw()

10.5 Solidariedade

(shapiro_S <-shapiro.test(data$Solidariedade)) # Não

## 
##  Shapiro-Wilk normality test
## 
## data:  data$Solidariedade
## W = 0.92827, p-value = 0.0001985

ggplot(data = data, aes(Solidariedade)) +
  geom_histogram(aes(y = ..density..), colour = 'lightblue', fill = '#4682b4',
                 bins = 20) +
  labs(x = 'Idade', y = 'Densidade') +
  stat_function(fun = dnorm,
                args = list(mean = mean(data$Solidariedade, na.rm = T),
                            sd = sd(data$Solidariedade, na.rm = T)),
                colour = 'black', size = 1) +
  ggtitle('Histograma para Solidariedade') +
  theme_bw()

11 Relações Bivariadas entre os valores

ggpairs(data[,c(51:55)],
        title = 'Relação entre os Valores do rugby por condições',
        cardinality_threshold = 15,
        mapping = ggplot2::aes(colour = data$Condição, alpha = 0.1),
        lower = list(continuous = wrap("smooth", alpha = 0.3, size=0.1, se = F)),
        diag = list(continuous = wrap("densityDiag", alpha=0.2)),
        continuous = wrap("cor", method = 'spearman', size = 2, alignPercent = 0.5)) +
  theme_bw()

12 Tabela resumo dos constructos

Lista_Paixao <- c('Paíxão',
                mean(data$Paixao),
                sd(data$Paixao),
                Alpha_P$total[2],
                Omega_P$omega_h,
                if_else(shapiro_P$p.value < 0.05, 'p < 0.05', 'p > 0.05'))

Lista_Integridade <- c('Integridade',
                  mean(data$Integridade),
                  sd(data$Integridade),
                  alpha_I$total[2],
                  omega_I$omega_h,
                  if_else(shapiro_I$p.value < 0.05, 'p < 0.05', 'p > 0.05'))


Lista_Disciplina <- c('Disciplina',
                       mean(data$Disciplina),
                       sd(data$Disciplina),
                       alpha_D$total[2],
                       omega_d$omega_h,
                       if_else(shapiro_D$p.value < 0.05, 'p < 0.05', 'p > 0.05'))

Lista_Respeito <- c('Respeito',
                      mean(data$Respeito),
                      sd(data$Respeito),
                      alpha_R$total[2],
                      omega_R$omega_h,
                      if_else(shapiro_R$p.value < 0.05, 'p < 0.05', 'p > 0.05'))

Lista_Solidariedade <- c('Solidariedade',
                    mean(data$Solidariedade),
                    sd(data$Solidariedade),
                    alpha_S$total[2],
                    omega_S$omega_h,
                    if_else(shapiro_S$p.value < 0.05, 'p < 0.05', 'p > 0.05'))

resumo <- as.data.frame(cbind(Lista_Paixao,
                              Lista_Integridade,
                              Lista_Respeito,
                              Lista_Disciplina,
                              Lista_Solidariedade))

resumo <- t(resumo)
colnames(resumo) <- c("Constructo", "Média", "Desvio Padrão",
                     "Alpha", "Omega", "Normalidade P-Valor")

rownames(resumo) <- NULL

kbl(as.data.frame(resumo)) %>%
  kable_classic(full_width = F, html_font = "Cambria")

Constructo	Média	Desvio Padrão	Alpha	Omega	Normalidade P-Valor
Paíxão	4.519164	0.3773054	0.7559314	0.8623842	p < 0.05
Integridade	4.343206	0.481379	0.7677872	0.8466994	p < 0.05
Respeito	4.24216	0.4001065	0.7217115	0.814672	p < 0.05
Disciplina	3.681185	0.5563707	0.7329862	0.783773	p > 0.05
Solidariedade	4.179443	0.5633516	0.8390173	0.8901089	p < 0.05

13 Médias por grupo

13.1 Paixao

describe.by(data$Paixao, group = data$Condição)

## 
##  Descriptive statistics by group 
## group: CONTROLE
##    vars  n mean  sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 28 4.52 0.4   4.43    4.55 0.42 3.43   5  1.57 -0.58    -0.18 0.08
## ------------------------------------------------------------ 
## group: FORTE
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 26 4.48 0.37    4.5     4.5 0.32 3.57   5  1.43 -0.42    -0.55 0.07
## ------------------------------------------------------------ 
## group: FRACA
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 28 4.55 0.37   4.57    4.57 0.42 3.71   5  1.29 -0.42    -0.93 0.07

ggVP <- ggplot(data, aes(x = Condição, y = Paixao, fill = Condição)) +
  geom_violin(trim = F) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
               geom = 'pointrange', color = 'white') +
  labs(title = 'Valores por Grupos', x = 'Grupos Experimentais',
       y = 'Constructo Paixao') +
    theme_bw() +
  theme(legend.position = 'none')

13.2 Integridade

describe.by(data$Integridade, group = data$Condição)

## 
##  Descriptive statistics by group 
## group: CONTROLE
##    vars  n mean  sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 28 4.26 0.6   4.43    4.32 0.42 2.71   5  2.29 -1.21     0.75 0.11
## ------------------------------------------------------------ 
## group: FORTE
##    vars  n mean   sd median trimmed  mad  min max range skew kurtosis   se
## X1    1 26 4.36 0.42   4.36    4.36 0.53 3.71   5  1.29 0.11    -1.43 0.08
## ------------------------------------------------------------ 
## group: FRACA
##    vars  n mean  sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 28 4.41 0.4    4.5    4.45 0.32 3.29   5  1.71 -0.85     0.25 0.08

ggVI <- ggplot(data, aes(x = Condição, y = Integridade, fill = Condição)) +
  geom_violin(trim = F) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
               geom = 'pointrange', color = 'white') +
  labs(title = 'Valores por Grupos', x = 'Grupos Experimentais',
       y = 'Constructo Integridade') +
  theme_bw() +
  theme(legend.position = 'none')

13.3 Respeito

describe.by(data$Respeito, group = data$Condição)

## 
##  Descriptive statistics by group 
## group: CONTROLE
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 28 4.24 0.39   4.29    4.26 0.42 3.43   5  1.57 -0.27    -0.47 0.07
## ------------------------------------------------------------ 
## group: FORTE
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 26 4.27 0.45   4.21    4.28 0.53 3.43   5  1.57 -0.01       -1 0.09
## ------------------------------------------------------------ 
## group: FRACA
##    vars  n mean   sd median trimmed  mad  min  max range  skew kurtosis   se
## X1    1 28 4.21 0.37   4.29    4.24 0.32 3.29 4.71  1.43 -0.81    -0.09 0.07

ggVR <- ggplot(data, aes(x = Condição, y = Respeito, fill = Condição)) +
  geom_violin(trim = F) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
               geom = 'pointrange', color = 'white') +
  labs(title = 'Valores por Grupos', x = 'Grupos Experimentais',
       y = 'Constructo Respeito') +
  theme_bw() +
  theme(legend.position = 'none')

13.4 Disciplina

describe.by(data$Disciplina, group = data$Condição)

## 
##  Descriptive statistics by group 
## group: CONTROLE
##    vars  n mean   sd median trimmed  mad  min max range skew kurtosis  se
## X1    1 28 3.82 0.52   3.71     3.8 0.42 2.86   5  2.14  0.4    -0.49 0.1
## ------------------------------------------------------------ 
## group: FORTE
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 26 3.64 0.67   3.57    3.64 0.74 2.29   5  2.71 -0.03    -0.77 0.13
## ------------------------------------------------------------ 
## group: FRACA
##    vars  n mean   sd median trimmed  mad  min  max range skew kurtosis   se
## X1    1 28 3.59 0.47    3.5    3.57 0.53 2.86 4.57  1.71 0.24       -1 0.09

ggVD <- ggplot(data, aes(x = Condição, y = Disciplina, fill = Condição)) +
  geom_violin(trim = F) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
               geom = 'pointrange', color = 'white') +
  labs(title = 'Valores por Grupos', x = 'Grupos Experimentais',
       y = 'Constructo Disciplina') +
  theme_bw() +
  theme(legend.position = 'none')

13.5 Solidariedade

describe.by(data$Solidariedade, group = data$Condição)

## 
##  Descriptive statistics by group 
## group: CONTROLE
##    vars  n mean   sd median trimmed  mad  min max range skew kurtosis  se
## X1    1 28 4.24 0.54   4.14    4.26 0.74 3.29   5  1.71 0.02    -1.42 0.1
## ------------------------------------------------------------ 
## group: FORTE
##    vars  n mean   sd median trimmed  mad  min max range  skew kurtosis   se
## X1    1 26 4.06 0.67   4.14    4.12 0.42 1.86   5  3.14 -1.25     2.35 0.13
## ------------------------------------------------------------ 
## group: FRACA
##    vars  n mean   sd median trimmed  mad  min max range skew kurtosis   se
## X1    1 28 4.22 0.48   4.14    4.22 0.64 3.43   5  1.57 0.12    -1.43 0.09

ggVS <- ggplot(data, aes(x = Condição, y = Solidariedade, fill = Condição)) +
  geom_violin(trim = F) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
               geom = 'pointrange', color = 'white') +
  labs(title = 'Valores por Grupos', x = 'Grupos Experimentais',
       y = 'Constructo Solidariedade') +
  theme_bw() +
  theme(legend.position = 'none')

13.6 Final

grid.arrange(ggVP, ggVI, ggVD, ggVR, ggVS)

14 Kruskal-Wallis

14.1 Paíxão

kwP <- kruskal.test(Paixao ~ Condição, data = data)
kwP

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Paixao by Condição
## Kruskal-Wallis chi-squared = 0.4299, df = 2, p-value = 0.8066

kwEP <- kruskal_effsize(data, Paixao ~ Condição, ci = T, nboot = 1000)
kwEP

## # A tibble: 1 x 7
##   .y.        n effsize conf.low conf.high method  magnitude
## * <chr>  <int>   <dbl>    <dbl>     <dbl> <chr>   <ord>    
## 1 Paixao    82 -0.0199    -0.02      0.08 eta2[H] small

14.2 Integridade

kwI <- kruskal.test(Integridade ~ Condição, data = data)
kwI

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Integridade by Condição
## Kruskal-Wallis chi-squared = 0.67867, df = 2, p-value = 0.7122

kwEI <- kruskal_effsize(data, Integridade ~ Condição, ci = T, nboot = 1000)
kwEI

## # A tibble: 1 x 7
##   .y.             n effsize conf.low conf.high method  magnitude
## * <chr>       <int>   <dbl>    <dbl>     <dbl> <chr>   <ord>    
## 1 Integridade    82 -0.0167    -0.02      0.09 eta2[H] small

14.3 Disciplina

kwD <- kruskal.test(Disciplina ~ Condição, data = data)
kwD

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Disciplina by Condição
## Kruskal-Wallis chi-squared = 2.3117, df = 2, p-value = 0.3148

kwED <- kruskal_effsize(data, Disciplina ~ Condição, ci = T, nboot = 1000)
kwED

## # A tibble: 1 x 7
##   .y.            n effsize conf.low conf.high method  magnitude
## * <chr>      <int>   <dbl>    <dbl>     <dbl> <chr>   <ord>    
## 1 Disciplina    82 0.00395    -0.02      0.13 eta2[H] small

14.4 Respeito

kwR <- kruskal.test(Respeito ~ Condição, data = data)  
kwR

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Respeito by Condição
## Kruskal-Wallis chi-squared = 0.10292, df = 2, p-value = 0.9498

kwER <- kruskal_effsize(data, Respeito ~ Condição, ci = T, nboot = 1000)
kwER

## # A tibble: 1 x 7
##   .y.          n effsize conf.low conf.high method  magnitude
## * <chr>    <int>   <dbl>    <dbl>     <dbl> <chr>   <ord>    
## 1 Respeito    82 -0.0240    -0.02      0.08 eta2[H] small

14.5 Solidariedade

kwS <- kruskal.test(Solidariedade ~ Condição, data = data)
kwS

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Solidariedade by Condição
## Kruskal-Wallis chi-squared = 0.48491, df = 2, p-value = 0.7847

kwES <- kruskal_effsize(data, Solidariedade ~ Condição, ci = T, nboot = 1000)
kwES

## # A tibble: 1 x 7
##   .y.               n effsize conf.low conf.high method  magnitude
## * <chr>         <int>   <dbl>    <dbl>     <dbl> <chr>   <ord>    
## 1 Solidariedade    82 -0.0192    -0.02      0.09 eta2[H] small

15 Tabela Final

KWPaixao <- c('Paixão',
              round(kwP$statistic, 4), 
              round(kwP$p.value, 4),
              kwP$parameter,
              round(kwEP$effsize, 4),
              kwEP$magnitude)

KWIntegridade <- c('Integridade',
              round(kwI$statistic, 4), 
              round(kwI$p.value, 4), 
              kwI$parameter,
              round(kwEI$effsize, 4),
              kwEI$magnitude)


KWDisciplina <- c('Disciplina',
                   round(kwD$statistic, 4), 
                   round(kwD$p.value, 4),
                   kwD$parameter,
                   round(kwED$effsize, 4),
                   kwED$magnitude)

KWRespeito <- c('Respeito',
                  round(kwR$statistic, 4),  
                  round(kwR$p.value, 4),
                  kwR$parameter,
                  round(kwER$effsize, 4),
                  kwER$magnitude)

KWSolidariedade <- c('Solidariedade',
                round(kwS$statistic, 4),
                round(kwS$p.value, 4), 
                kwS$parameter,
                round(kwES$effsize, 4),
                kwES$magnitude)


resumoKW <- as.data.frame(cbind(KWPaixao,
                                KWIntegridade,
                                KWDisciplina,
                                KWRespeito,
                                KWSolidariedade))

resumoKW <- t(resumoKW)
colnames(resumoKW) <- c("Constructo", "X²", "P-Value",
                      "GL", "Tamanho do Efeito", "Classificação Cohen (1988)")

rownames(resumoKW) <- NULL

resumoKW[, 6] <- 'Pequeno'

kbl(as.data.frame(resumoKW)) %>%
  kable_classic(full_width = F, html_font = "Cambria")

Constructo	X²	P-Value	GL	Tamanho do Efeito	Classificação Cohen (1988)
Paixão	0.4299	0.8066	2	-0.0199	Pequeno
Integridade	0.6787	0.7122	2	-0.0167	Pequeno
Disciplina	2.3117	0.3148	2	0.0039	Pequeno
Respeito	0.1029	0.9498	2	-0.024	Pequeno
Solidariedade	0.4849	0.7847	2	-0.0192	Pequeno

Marcas de Rugby

Vitor Klein Schmidt

26 julho, 2021

1 Pacotes

2 Etapa 1 - Validação das Marcas

2.1 Marcas mais relacionadas com o Rugby

2.1.1 Ajustando o conjunto de dados

2.1.2 Tabela

2.1.3 Reordenando

2.1.4 Gráfico

2.2 Marcas menos assocadas com o Rugby

2.2.1 Ajustando o conjunto de dados

2.2.2 Tabela

2.2.3 Reordenando

2.2.4 Gráfico

3 Data - Rugby

3.1 Organizando os dados

4 Descrição da Amostra

4.1 Gênero

4.2 Nível de relação com o esporte

4.3 Idade

4.4 Tempo de prática

4.5 Remuneração

5 Análise dos constructos

5.1 Paixão

5.2 Integridade

5.3 Respeito

5.4 Disciplina

5.5 Solidariedade

6 Recodificação do one-hot-encoding

7 Correlações

8 Checks do Experimento

8.1 Condições

8.2 Atenção

8.3 Associação

9 Médias dos constructos

10 Normalidade dos constructos

10.1 Paíxão

10.2 Integridade

10.3 Respeito

10.4 Disciplina

10.5 Solidariedade

11 Relações Bivariadas entre os valores

12 Tabela resumo dos constructos

13 Médias por grupo

13.1 Paixao

13.2 Integridade

13.3 Respeito

13.4 Disciplina

13.5 Solidariedade

13.6 Final

14 Kruskal-Wallis

14.1 Paíxão

14.2 Integridade

14.3 Disciplina

14.4 Respeito

14.5 Solidariedade

15 Tabela Final