Overview of employees
plot_pie(dlabels, country)
plot_edu(dlabels, education)
plot_pie2(dlabels, division)
plot_pie(dlabels, gender)
plot_sus(dlabels, sustainability)
plot_pie2(dlabels, age)
plot_position(dlabels, position)
plot_tenure(dlabels, tenure)
plot_size(dlabels, empno)
plot_influence(dlabels, influential)
#awareness of rebound effects
describe(d$reboundaware1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3391 3.94 2.09 4 3.93 2.97 1 7 6 -0.15 -1.33 0.04
describeBy(d$reboundaware1, group = d$country)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 673 4.14 2.23 5 4.18 2.97 1 7 6 -0.25 -1.43 0.09
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 781 3.56 2.1 4 3.46 2.97 1 7 6 0.08 -1.4 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 796 3.85 2.03 4 3.83 2.97 1 7 6 -0.11 -1.32 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 877 4.25 2.02 5 4.31 1.48 1 7 6 -0.33 -1.16 0.07
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 3.81 1.86 4 3.81 1.48 1 7 6 -0.21 -1.01 0.11
#strategies
describe(d$reboundaware2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3391 5.17 2.04 5 5.31 1.48 1 8 7 -0.4 -0.66 0.04
describeBy(d$reboundaware2, group = d$country)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 673 5.47 2 6 5.68 1.48 1 8 7 -0.72 -0.25 0.08
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 781 5.16 2.14 5 5.3 2.97 1 8 7 -0.35 -0.82 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 796 5.15 1.99 5 5.27 1.48 1 8 7 -0.33 -0.6 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 877 5.1 2 5 5.2 1.48 1 8 7 -0.35 -0.67 0.07
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 4.77 2.05 5 4.82 1.48 1 8 7 -0.14 -0.8 0.13
Awareness depending on various factors
Country
#Violin for rebound1
ggplot(d, aes(x = factor(country,
levels = 1:5,
labels = c("UK", "Germany", "NL", "Italy", "Lithuania")), y = reboundaware1, fill = factor(country))) + # Ensure 'country' is #categorical
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(country)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") + # Use Dark2 color palette for fill
scale_color_brewer(palette = "Dark2") + # Use Dark2 color palette for boxplot outlines
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
###recoding answers indicating "I dont know" from 8 to 0
table(d$reboundaware2)
##
## 1 2 3 4 5 6 7 8
## 243 172 276 464 677 567 453 539
d$reboundaware2[d$reboundaware2 == 8] <- 0
describe(d$reboundaware2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3391 3.9 2.35 5 4 1.48 0 7 7 -0.45 -1.09 0.04
#Violin for rebound2
ggplot(d, aes(x = factor(country,
levels = 1:5,
labels = c("UK", "Germany", "NL", "Italy", "Lithuania")), y = reboundaware2, fill = factor(country))) + # Ensure 'country' is categorical
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(country)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") + # Use Dark2 color palette for fill
scale_color_brewer(palette = "Dark2") + # Use Dark2 color palette for boxplot outlines
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
Sector
#Violin for rebound1
ggplot(d, aes(x = industry_new, y = reboundaware1, fill = factor(industry_new))) + # Ensure 'country' is categorical
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(industry_new)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") + # Use Dark2 color palette for fill
scale_color_brewer(palette = "Dark2") + # Use Dark2 color palette for boxplot outlines
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
# Check sig mean differences
anova_rebound1 <- aov(reboundaware1 ~ industry_new, data = d)
summary(anova_rebound1)
## Df Sum Sq Mean Sq F value Pr(>F)
## industry_new 2 80 40.15 9.257 9.79e-05 ***
## Residuals 3388 14696 4.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Violin for rebound2
ggplot(d, aes(x = industry_new, y = reboundaware2, fill = factor(industry_new))) + # Ensure 'country' is categorical
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(industry_new)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") + # Use Dark2 color palette for fill
scale_color_brewer(palette = "Dark2") + # Use Dark2 color palette for boxplot outlines
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
### boxplot
summary_df <- d %>%
group_by(industry_new) %>%
summarise(
mean = mean(reboundaware2, na.rm = TRUE),
sd = sd(reboundaware2, na.rm = TRUE),
n = sum(!is.na(reboundaware2)),
se = sd / sqrt(n)
)
ggplot(summary_df, aes(x = industry_new, y = mean, fill = industry_new)) +
geom_col(alpha = 0.8) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2) +
scale_fill_brewer(palette = "Dark2") +
theme_minimal() +
theme(legend.position = "none") +
labs(
x = "Industry",
y = "Mean reboundaware2 (± SE)"
) +
scale_y_continuous(
breaks = 0:8,
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
)
anova_rebound2 <- aov(reboundaware2 ~ industry_new, data = d)
summary(anova_rebound2)
## Df Sum Sq Mean Sq F value Pr(>F)
## industry_new 2 79 39.65 7.19 0.000765 ***
## Residuals 3388 18685 5.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Awareness differences depending on country and sector
###rebound1
## country
describeBy(d$reboundaware1, group = d$country)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 673 4.14 2.23 5 4.18 2.97 1 7 6 -0.25 -1.43 0.09
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 781 3.56 2.1 4 3.46 2.97 1 7 6 0.08 -1.4 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 796 3.85 2.03 4 3.83 2.97 1 7 6 -0.11 -1.32 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 877 4.25 2.02 5 4.31 1.48 1 7 6 -0.33 -1.16 0.07
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 264 3.81 1.86 4 3.81 1.48 1 7 6 -0.21 -1.01 0.11
# Check sig mean differences
anova_rebound1 <- aov(reboundaware1 ~ country, data = d)
summary(anova_rebound1)
## Df Sum Sq Mean Sq F value Pr(>F)
## country 4 234 58.59 13.64 4.9e-11 ***
## Residuals 3386 14542 4.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey_rebound1 <- TukeyHSD(anova_rebound1)
library(effectsize)
##
## Attaching package: 'effectsize'
## The following object is masked from 'package:psych':
##
## phi
eta_squared(aov(reboundaware1 ~ country, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## -------------------------------
## country | 0.02 | [0.01, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
industry
describeBy(d$reboundaware1, group = d$industry_new)
##
## Descriptive statistics by group
## group: Building
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1262 3.95 2.1 4 3.94 2.97 1 7 6 -0.18 -1.36 0.06
## ------------------------------------------------------------
## group: Manufacturing
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1494 4.06 2.04 4 4.08 2.97 1 7 6 -0.23 -1.23 0.05
## ------------------------------------------------------------
## group: Mobility
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 635 3.64 2.14 4 3.55 2.97 1 7 6 0.1 -1.39 0.08
summary(aov(reboundaware1 ~ industry_new, data = d))
## Df Sum Sq Mean Sq F value Pr(>F)
## industry_new 2 80 40.15 9.257 9.79e-05 ***
## Residuals 3388 14696 4.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey_bio <- TukeyHSD(aov(reboundaware1 ~ industry_new, data = d))
library(effectsize)
eta_squared(aov(reboundaware1 ~ industry_new, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## --------------------------------------
## industry_new | 5.43e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
Company size
d$empno <- as.factor(d$empno)
describeBy(d$reboundaware1, group = d$empno)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 388 3.3 2.07 3 3.14 2.97 1 7 6 0.3 -1.26 0.11
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 558 3.68 2.05 4 3.61 2.97 1 7 6 0.02 -1.34 0.09
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 650 4.03 1.99 4 4.03 2.97 1 7 6 -0.23 -1.17 0.08
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 525 4.37 2 5 4.47 1.48 1 7 6 -0.46 -1.03 0.09
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 414 4.67 1.83 5 4.83 1.48 1 7 6 -0.71 -0.56 0.09
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 856 3.73 2.21 4 3.66 2.97 1 7 6 0.05 -1.48 0.08
summary(aov(reboundaware1 ~ empno, data = d))
## Df Sum Sq Mean Sq F value Pr(>F)
## empno 5 561 112.2 26.72 <2e-16 ***
## Residuals 3385 14216 4.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey_bio <- TukeyHSD(aov(reboundaware1 ~ empno, data = d))
library(effectsize)
eta_squared(aov(reboundaware1 ~ empno, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## -------------------------------
## empno | 0.04 | [0.03, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
#figure
ggplot(d, aes(x = factor(empno,
levels = 1:7,
labels = c("Less than 10", "11-50", "51-150", "151-300","301-500",
"More than 500", "Self-employed")), y = reboundaware1, fill = factor(empno))) +
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(empno)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
division
describeBy(d$reboundaware1, group = d$division)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1301 3.58 2.08 4 3.49 2.97 1 7 6 0.09 -1.38 0.06
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 321 4.19 2.07 5 4.24 1.48 1 7 6 -0.4 -1.16 0.12
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 333 4.75 1.8 5 4.93 1.48 1 7 6 -0.67 -0.43 0.1
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 411 4.02 2.04 4 4.03 2.97 1 7 6 -0.21 -1.27 0.1
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 4.31 2.05 5 4.38 2.97 1 7 6 -0.35 -1.19 0.12
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 365 4.7 1.94 5 4.88 1.48 1 7 6 -0.68 -0.7 0.1
## ------------------------------------------------------------
## group: 7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 362 3.12 2.01 3 2.94 2.97 1 7 6 0.42 -1.16 0.11
summary(aov(reboundaware1 ~ division, data = d))
## Df Sum Sq Mean Sq F value Pr(>F)
## division 1 36 36.02 8.281 0.00403 **
## Residuals 3389 14741 4.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(aov(reboundaware1 ~ division, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## -----------------------------------
## division | 2.44e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
ggplot(d, aes(x = factor(division,
levels = 1:7,
labels = c("Operations", "Finance", "Human Resources (HR)", "Marketing & Sales", "Research & Development", "Information Technology (IT)", "Other:")), y = reboundaware1, fill = factor(division))) +
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(division)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
position
describeBy(d$reboundaware1, group = d$position)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1358 3.36 2.14 3 3.21 2.97 1 7 6 0.26 -1.38 0.06
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1267 4.27 1.95 5 4.34 1.48 1 7 6 -0.39 -1.03 0.05
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 641 4.64 1.91 5 4.8 1.48 1 7 6 -0.58 -0.76 0.08
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 125 3.4 1.99 4 3.3 2.97 1 7 6 0.18 -1.28 0.18
summary(aov(reboundaware1 ~ position, data = d))
## Df Sum Sq Mean Sq F value Pr(>F)
## position 1 498 497.9 118.2 <2e-16 ***
## Residuals 3389 14279 4.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(aov(reboundaware1 ~ position, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## -------------------------------
## position | 0.03 | [0.02, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
ggplot(d, aes(x = factor(position,
levels = 1:4,
labels = c("Team member / Employee (no management responsibilities)", "Middle management (e.g., supervising others, team lead)", "Senior or upper management", "Other")), y = reboundaware1, fill = factor(position))) +
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(position)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
tenure
describeBy(d$reboundaware1, group = d$tenure)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 150 3.1 2.05 3 2.9 2.97 1 7 6 0.46 -1.13 0.17
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 509 3.87 2.11 4 3.84 2.97 1 7 6 -0.1 -1.41 0.09
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 687 4.18 1.96 5 4.23 1.48 1 7 6 -0.37 -1.04 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 689 4.44 2.02 5 4.55 1.48 1 7 6 -0.46 -1.02 0.08
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 479 4.2 2.08 5 4.24 1.48 1 7 6 -0.29 -1.21 0.09
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 346 3.69 2.09 4 3.62 2.97 1 7 6 0.04 -1.42 0.11
## ------------------------------------------------------------
## group: 7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 531 3.23 2.06 3 3.08 2.97 1 7 6 0.31 -1.35 0.09
summary(aov(reboundaware1 ~ tenure, data = d))
## Df Sum Sq Mean Sq F value Pr(>F)
## tenure 1 77 77.19 17.8 2.52e-05 ***
## Residuals 3389 14699 4.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta_squared(aov(reboundaware1 ~ tenure, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## -----------------------------------
## tenure | 5.22e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
#figure
ggplot(d, aes(x = factor(tenure,
levels = 1:7,
labels = c("Less than 12 months", "1-3 years", "4-6 years", "7-10 years", "11-15 years", "15-20 years", "More than 20 years")), y = reboundaware1, fill = factor(tenure))) +
geom_violin(trim = FALSE, alpha = 0.5) + # Violin plot with transparency
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(tenure)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
Sustainability as part of job description
describeBy(d$reboundaware1, group = d$sustainability)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 601 2.05 1.6 1 1.72 0 1 7 6 1.44 0.97 0.07
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 537 3.12 1.96 3 2.97 2.97 1 7 6 0.42 -1.15 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 915 3.98 1.85 4 4.01 1.48 1 7 6 -0.24 -1.04 0.06
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 857 4.8 1.67 5 4.97 1.48 1 7 6 -0.78 -0.06 0.06
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 481 5.63 1.54 6 5.9 1.48 1 7 6 -1.34 1.43 0.07
summary(lm(reboundaware1 ~ sustainability, data = d))
##
## Call:
## lm(formula = reboundaware1 ~ sustainability, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6963 -1.1463 0.0787 1.1912 4.8537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.25875 0.07575 16.62 <2e-16 ***
## sustainability 0.88750 0.02302 38.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.741 on 3389 degrees of freedom
## Multiple R-squared: 0.3049, Adjusted R-squared: 0.3047
## F-statistic: 1487 on 1 and 3389 DF, p-value: < 2.2e-16
eta_squared(lm(reboundaware1 ~ sustainability, data = d))
## For one-way between subjects designs, partial eta squared is equivalent
## to eta squared. Returning eta squared.
## # Effect Size for ANOVA
##
## Parameter | Eta2 | 95% CI
## ------------------------------------
## sustainability | 0.30 | [0.28, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
#figure
ggplot(d, aes(x = factor(sustainability,
levels = 1:5,
labels = c("Not at all", "Hardly", "Somewhat", "Fairly", "Very much")), y = reboundaware1, fill = factor(sustainability))) +
geom_violin(trim = FALSE, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, aes(color = factor(sustainability)), show.legend = FALSE) +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "darkred") + # Mean point
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
scale_y_continuous(
breaks = 0:8, # full scale breaks (adjust if needed)
labels = c(" ", "(1) Not at all", "(2)", "(3)", "(4)", "(5)", "(6)", "(7) To a great extent", "")
) + theme(legend.position = "none")
Correlation matrix
library(corrplot)
## corrplot 0.95 loaded
d$position[d$position == 4] <- NA
d$gender <- as.factor(d$gender)
d$country_base <- as.factor(d$country_base)
d$division <- as.factor(d$division)
d$industry_new <- as.factor(d$industry_new)
d$empno <- as.numeric(d$empno)
d$tenure <- as.numeric(d$tenure)
d$position <- as.numeric(d$position)
#overall table
var <- d[, c("age","gender", "education", "country_base",
"sustainability", "position", "division", "tenure",
"industry_new", "empno",
"bio_val", "ego_val", "pa", "ccb_ar",
"se", "ar3_employee", "ar4_employee",
"sn", "ar1_org", "ar3_org", "ar4_org", "cer1", "cer2",
"reboundaware1")]
var_num <- var[, sapply(var, is.numeric)] # only numerical variables
cor_matrix <- cor(var_num, use = "complete.obs")
corrplot(cor_matrix, method = "color", type = "lower", col = brewer.pal(n = 10, name = "RdYlBu"), addCoef.col = "black", tl.col = "black", tl.srt = 45,diag = FALSE)
apaTables::apa.cor.table(
var_num,
filename = "Table_Correlations.doc",
table.number = 1,
show.conf.interval = TRUE
)
##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. age 3.49 1.33
##
## 2. education 4.90 1.70 -.14**
## [-.17, -.11]
##
## 3. sustainability 3.02 1.30 -.25** .17**
## [-.28, -.22] [.14, .21]
##
## 4. position 1.78 0.75 -.05** .31** .34**
## [-.09, -.02] [.28, .34] [.31, .37]
##
## 5. tenure 4.18 1.77 .52** -.05** -.07** .08**
## [.50, .55] [-.08, -.02] [-.10, -.04] [.04, .11]
##
## 6. empno 3.76 1.72 -.07** .18** .10** .05**
## [-.11, -.04] [.15, .21] [.07, .14] [.01, .08]
##
## 7. bio_val 5.13 1.63 .03 .09** .23** .09**
## [-.01, .06] [.06, .13] [.19, .26] [.06, .13]
##
## 8. ego_val 4.22 1.75 -.21** .16** .41** .17**
## [-.24, -.18] [.12, .19] [.39, .44] [.14, .20]
##
## 9. pa 5.33 1.47 .02 .04** .12** .04*
## [-.02, .05] [.01, .08] [.08, .15] [.01, .07]
##
## 10. ccb_ar 4.72 1.65 -.11** .10** .37** .14**
## [-.14, -.08] [.07, .14] [.34, .40] [.11, .17]
##
## 11. se 5.04 1.41 -.08** .12** .46** .22**
## [-.11, -.04] [.09, .16] [.44, .49] [.19, .25]
##
## 12. ar3_employee 5.17 1.54 -.02 .06** .30** .13**
## [-.05, .02] [.03, .10] [.27, .33] [.10, .16]
##
## 13. ar4_employee 5.24 1.49 .01 .07** .30** .11**
## [-.03, .04] [.04, .10] [.27, .33] [.08, .15]
##
## 14. sn 5.05 1.45 -.07** .10** .48** .21**
## [-.10, -.04] [.07, .13] [.45, .50] [.18, .25]
##
## 15. ar1_org 5.40 1.41 .00 .08** .21** .09**
## [-.03, .04] [.05, .11] [.18, .24] [.06, .12]
##
## 16. ar3_org 5.44 1.39 .01 .07** .23** .08**
## [-.03, .04] [.04, .11] [.19, .26] [.04, .11]
##
## 17. ar4_org 5.37 1.42 .01 .08** .26** .10**
## [-.03, .04] [.05, .11] [.23, .29] [.06, .13]
##
## 18. cer1 5.13 1.52 -.02 .10** .41** .17**
## [-.05, .01] [.07, .13] [.38, .44] [.14, .21]
##
## 19. cer2 5.15 1.50 -.02 .11** .40** .16**
## [-.05, .01] [.07, .14] [.37, .42] [.13, .20]
##
## 20. reboundaware1 3.94 2.09 -.26** .15** .55** .24**
## [-.30, -.23] [.12, .18] [.53, .58] [.21, .28]
##
## 5 6 7 8 9 10
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .06**
## [.03, .10]
##
## .05** .08**
## [.01, .08] [.05, .11]
##
## -.06** .11** .42**
## [-.10, -.03] [.07, .14] [.40, .45]
##
## .04* .06** .45** .26**
## [.01, .08] [.03, .10] [.42, .47] [.23, .30]
##
## -.01 .08** .43** .40** .40**
## [-.04, .02] [.05, .12] [.40, .46] [.37, .43] [.37, .43]
##
## .02 .07** .51** .48** .34** .49**
## [-.02, .05] [.04, .10] [.48, .53] [.45, .50] [.31, .37] [.46, .51]
##
## .04* .06** .40** .37** .28** .36**
## [.01, .07] [.02, .09] [.37, .43] [.34, .40] [.25, .31] [.33, .39]
##
## .05** .08** .48** .39** .31** .38**
## [.01, .08] [.05, .11] [.45, .50] [.36, .41] [.28, .34] [.36, .41]
##
## .04* .06** .49** .47** .34** .50**
## [.01, .07] [.03, .10] [.46, .51] [.44, .50] [.31, .37] [.47, .52]
##
## .04* .15** .42** .28** .27** .32**
## [.01, .08] [.11, .18] [.39, .45] [.25, .31] [.24, .30] [.28, .35]
##
## .06** .15** .44** .31** .31** .32**
## [.03, .10] [.12, .19] [.41, .46] [.28, .34] [.28, .34] [.29, .35]
##
## .06** .14** .43** .34** .28** .34**
## [.02, .09] [.11, .17] [.41, .46] [.31, .37] [.25, .31] [.31, .37]
##
## .04** .16** .37** .39** .23** .35**
## [.01, .08] [.12, .19] [.34, .40] [.36, .42] [.20, .26] [.32, .38]
##
## .05** .17** .38** .38** .25** .34**
## [.02, .09] [.14, .20] [.35, .41] [.35, .41] [.22, .28] [.31, .37]
##
## -.07** .08** .17** .44** .19** .43**
## [-.11, -.04] [.04, .11] [.14, .21] [.41, .47] [.16, .23] [.40, .45]
##
## 11 12 13 14 15 16 17
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .52**
## [.50, .54]
##
## .54** .67**
## [.52, .57] [.65, .69]
##
## .78** .48** .50**
## [.77, .79] [.45, .50] [.48, .53]
##
## .41** .48** .50** .39**
## [.39, .44] [.45, .51] [.48, .53] [.37, .42]
##
## .45** .63** .55** .41** .60**
## [.42, .48] [.61, .65] [.52, .57] [.38, .44] [.58, .62]
##
## .46** .53** .68** .44** .60** .61**
## [.43, .49] [.50, .55] [.66, .70] [.42, .47] [.57, .62] [.58, .63]
##
## .57** .38** .41** .58** .39** .36** .42**
## [.55, .60] [.35, .41] [.38, .43] [.55, .60] [.36, .41] [.33, .39] [.40, .45]
##
## .57** .38** .40** .58** .39** .38** .43**
## [.55, .59] [.35, .41] [.37, .43] [.56, .60] [.36, .42] [.35, .41] [.41, .46]
##
## .42** .27** .25** .43** .13** .17** .19**
## [.40, .45] [.24, .30] [.22, .29] [.41, .46] [.09, .16] [.13, .20] [.16, .22]
##
## 18 19
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .76**
## [.75, .77]
##
## .31** .31**
## [.28, .34] [.27, .34]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
#correlation figure with focus on reboundaware1
target <- "reboundaware1"
df_plot <- map_dfr(
setdiff(names(var_num), target),
function(v) {
ct <- cor.test(
var_num[[v]],
var_num[[target]],
method = "pearson",
use = "pairwise.complete.obs"
)
tibble(
variable = v,
r = unname(ct$estimate),
lower = ct$conf.int[1],
upper = ct$conf.int[2],
p = ct$p.value
)
}
)
library(ggplot2)
ggplot(df_plot, aes(x = r, y = reorder(variable, r))) +
geom_vline(xintercept = 0, linetype = "dashed", color = "grey60") +
geom_errorbarh(aes(xmin = lower, xmax = upper), height = .2) +
geom_point(size = 3) +
labs(
x = "Correlation with Rebound Awareness (r)",
y = NULL
) +
theme_classic(base_size = 12)
## Warning: `geom_errobarh()` was deprecated in ggplot2 4.0.0.
## ℹ Please use the `orientation` argument of `geom_errorbar()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `height` was translated to `width`.
Regression
#complete cases
d_cc <- d[complete.cases(d[, c(
"age","gender","education","country_base",
"sustainability","position","division","tenure",
"industry_new","empno",
"bio_val","ego_val","pa","ccb_ar",
"se","ar3_employee","ar4_employee",
"sn","ar1_org","ar3_org","ar4_org","cer1","cer2",
"reboundaware1"
)]), ]
#model with factors on all levels: 45% explained variance
model <- lm(reboundaware1 ~ age + gender + education + country_base +
sustainability + position + division + tenure +
industry_new + empno +
bio_val + ego_val + pa + ccb_ar +
se + ar3_employee + ar4_employee +
sn + ar1_org + ar3_org + ar4_org + cer1 + cer2, data = d_cc)
summary(model, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
##
## Call:
## lm(formula = reboundaware1 ~ age + gender + education + country_base +
## sustainability + position + division + tenure + industry_new +
## empno + bio_val + ego_val + pa + ccb_ar + se + ar3_employee +
## ar4_employee + sn + ar1_org + ar3_org + ar4_org + cer1 +
## cer2, data = d_cc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.128 -1.012 0.085 1.027 6.246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.486219 0.211117 2.303 0.021337 *
## age -0.173631 0.025867 -6.712 2.25e-11 ***
## gender2 0.010330 0.060761 0.170 0.865018
## gender3 0.398943 0.308548 1.293 0.196115
## gender4 0.757435 0.327171 2.315 0.020670 *
## education 0.008856 0.017923 0.494 0.621267
## country_base2 -0.101654 0.088615 -1.147 0.251408
## country_base3 -0.221784 0.086178 -2.574 0.010110 *
## country_base4 0.248462 0.086008 2.889 0.003893 **
## country_base5 0.083967 0.120218 0.698 0.484939
## sustainability 0.492884 0.027313 18.046 < 2e-16 ***
## position 0.107209 0.041408 2.589 0.009667 **
## division2 0.214739 0.099395 2.160 0.030810 *
## division3 0.454156 0.099244 4.576 4.91e-06 ***
## division4 0.099381 0.090180 1.102 0.270530
## division5 0.222350 0.102272 2.174 0.029770 *
## division6 0.198958 0.097668 2.037 0.041722 *
## division7 -0.280707 0.099489 -2.821 0.004809 **
## tenure 0.013954 0.018751 0.744 0.456830
## industry_newManufacturing 0.022726 0.063266 0.359 0.719459
## industry_newMobility -0.197450 0.080286 -2.459 0.013971 *
## empno 0.023635 0.017902 1.320 0.186841
## bio_val -0.185486 0.022652 -8.189 3.76e-16 ***
## ego_val 0.221633 0.019902 11.136 < 2e-16 ***
## pa 0.082290 0.021901 3.757 0.000175 ***
## ccb_ar 0.242850 0.020890 11.625 < 2e-16 ***
## se 0.104769 0.034539 3.033 0.002438 **
## ar3_employee 0.054520 0.026845 2.031 0.042347 *
## ar4_employee 0.011961 0.029687 0.403 0.687050
## sn 0.126508 0.033197 3.811 0.000141 ***
## ar1_org -0.100657 0.026412 -3.811 0.000141 ***
## ar3_org -0.052349 0.029441 -1.778 0.075483 .
## ar4_org -0.050929 0.029797 -1.709 0.087509 .
## cer1 0.010935 0.029057 0.376 0.706695
## cer2 0.016095 0.029478 0.546 0.585104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.544 on 3231 degrees of freedom
## Multiple R-squared: 0.4593, Adjusted R-squared: 0.4536
## F-statistic: 80.71 on 34 and 3231 DF, p-value: < 2.2e-16
#figure
library(broom)
library(dplyr)
df_coef <- parameters::model_parameters(
model,
standardize = "refit", # gets standardized β
ci = 0.95 # 95% confidence interval
) |>
filter(Parameter != "(Intercept)") # remove intercept
ggplot(df_coef, aes(x = Coefficient, y = reorder(Parameter, Coefficient))) +
geom_vline(xintercept = 0, linetype = "dashed", color = "grey60") +
geom_errorbarh(aes(xmin = CI_low, xmax = CI_high), width = .2) +
geom_point(size = 3) +
labs(
x = "Standardized Regression Coefficient (β)",
y = NULL
) +
theme_classic(base_size = 12)
#model with only numeric variables
model_num <- lm(reboundaware1 ~ age +
sustainability + position + tenure +
empno +
bio_val + ego_val + pa + ccb_ar +
se + ar3_employee + ar4_employee +
sn + ar1_org + ar3_org + ar4_org + cer1 + cer2, data = d_cc)
summary(model_num, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
##
## Call:
## lm(formula = reboundaware1 ~ age + sustainability + position +
## tenure + empno + bio_val + ego_val + pa + ccb_ar + se + ar3_employee +
## ar4_employee + sn + ar1_org + ar3_org + ar4_org + cer1 +
## cer2, data = d_cc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2959 -1.0292 0.1073 1.0316 6.2413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.468283 0.177875 2.633 0.008512 **
## age -0.178786 0.025932 -6.894 6.47e-12 ***
## sustainability 0.515148 0.027162 18.966 < 2e-16 ***
## position 0.128448 0.039197 3.277 0.001060 **
## tenure 0.024336 0.018631 1.306 0.191575
## empno 0.008988 0.016718 0.538 0.590887
## bio_val -0.179873 0.022610 -7.956 2.44e-15 ***
## ego_val 0.225427 0.019948 11.300 < 2e-16 ***
## pa 0.082442 0.021903 3.764 0.000170 ***
## ccb_ar 0.244001 0.020970 11.636 < 2e-16 ***
## se 0.131256 0.034669 3.786 0.000156 ***
## ar3_employee 0.055067 0.027190 2.025 0.042923 *
## ar4_employee 0.020727 0.029969 0.692 0.489235
## sn 0.135168 0.033078 4.086 4.49e-05 ***
## ar1_org -0.114018 0.026691 -4.272 1.99e-05 ***
## ar3_org -0.057278 0.029761 -1.925 0.054362 .
## ar4_org -0.052822 0.030150 -1.752 0.079875 .
## cer1 -0.002879 0.029361 -0.098 0.921888
## cer2 0.008547 0.029773 0.287 0.774068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.566 on 3247 degrees of freedom
## Multiple R-squared: 0.4414, Adjusted R-squared: 0.4383
## F-statistic: 142.5 on 18 and 3247 DF, p-value: < 2.2e-16
df_coef_num <- parameters::model_parameters(
model_num,
standardize = "refit", # gets standardized β
ci = 0.95 # 95% confidence interval
) |>
filter(Parameter != "(Intercept)") # remove intercept
ggplot(df_coef_num, aes(x = Coefficient, y = reorder(Parameter, Coefficient))) +
geom_vline(xintercept = 0, linetype = "dashed", color = "grey60") +
geom_errorbarh(aes(xmin = CI_low, xmax = CI_high), width = .2) +
geom_point(size = 3) +
labs(
x = "Standardized Regression Coefficient (β)",
y = NULL
) +
theme_classic(base_size = 12)
#model for individual-level factors: 32% of explained variance
model_ind <- lm(reboundaware1 ~ age + gender + education + country_base + bio_val + ego_val + pa + ccb_ar, data = d_cc)
summary(model_ind, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
##
## Call:
## lm(formula = reboundaware1 ~ age + gender + education + country_base +
## bio_val + ego_val + pa + ccb_ar, data = d_cc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1502 -1.1735 0.1674 1.2084 6.1159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.69874 0.19785 8.586 < 2e-16 ***
## age -0.25700 0.02402 -10.700 < 2e-16 ***
## gender2 -0.08722 0.06528 -1.336 0.181616
## gender3 0.76678 0.33946 2.259 0.023961 *
## gender4 1.39508 0.35974 3.878 0.000107 ***
## education 0.06598 0.01852 3.562 0.000373 ***
## country_base2 -0.43259 0.09422 -4.591 4.58e-06 ***
## country_base3 -0.26652 0.09334 -2.855 0.004326 **
## country_base4 0.26627 0.09175 2.902 0.003730 **
## country_base5 -0.30881 0.12714 -2.429 0.015200 *
## bio_val -0.16203 0.02319 -6.988 3.37e-12 ***
## ego_val 0.38666 0.02047 18.885 < 2e-16 ***
## pa 0.04990 0.02384 2.093 0.036468 *
## ccb_ar 0.39850 0.02165 18.410 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.71 on 3252 degrees of freedom
## Multiple R-squared: 0.3329, Adjusted R-squared: 0.3302
## F-statistic: 124.8 on 13 and 3252 DF, p-value: < 2.2e-16
#model for job/role-level factors: 37% of variance explained
model_job <- lm(reboundaware1 ~ sustainability + position + division + tenure + se + ar3_employee + ar4_employee, data = d_cc)
summary(model_job, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
##
## Call:
## lm(formula = reboundaware1 ~ sustainability + position + division +
## tenure + se + ar3_employee + ar4_employee, data = d_cc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2459 -1.1289 0.0728 1.1615 6.1792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12953 0.15036 0.861 0.389042
## sustainability 0.67300 0.02682 25.097 < 2e-16 ***
## position 0.10025 0.04220 2.375 0.017588 *
## division2 0.42359 0.10504 4.033 5.64e-05 ***
## division3 0.72821 0.10439 6.976 3.67e-12 ***
## division4 0.29333 0.09568 3.066 0.002190 **
## division5 0.37174 0.10831 3.432 0.000606 ***
## division6 0.43201 0.10209 4.232 2.38e-05 ***
## division7 -0.30225 0.10552 -2.864 0.004205 **
## tenure -0.05836 0.01682 -3.469 0.000529 ***
## se 0.28930 0.02756 10.499 < 2e-16 ***
## ar3_employee 0.04910 0.02646 1.856 0.063544 .
## ar4_employee -0.01185 0.02768 -0.428 0.668592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.665 on 3253 degrees of freedom
## Multiple R-squared: 0.367, Adjusted R-squared: 0.3647
## F-statistic: 157.2 on 12 and 3253 DF, p-value: < 2.2e-16
#model for organizational-level factors: 23% explained variance
model_org <- lm(reboundaware1 ~ industry_new + empno + sn + ar1_org + ar3_org + ar4_org + cer1 + cer2, data = d_cc)
summary(model_org, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
##
## Call:
## lm(formula = reboundaware1 ~ industry_new + empno + sn + ar1_org +
## ar3_org + ar4_org + cer1 + cer2, data = d_cc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6627 -1.3957 0.3082 1.4804 5.5914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.743727 0.170460 4.363 1.32e-05 ***
## industry_newManufacturing -0.000633 0.075043 -0.008 0.99327
## industry_newMobility -0.197908 0.095770 -2.066 0.03886 *
## empno 0.059019 0.020385 2.895 0.00381 **
## sn 0.572619 0.029955 19.116 < 2e-16 ***
## ar1_org -0.129672 0.031485 -4.118 3.91e-05 ***
## ar3_org -0.011179 0.032313 -0.346 0.72940
## ar4_org 0.007196 0.032202 0.223 0.82320
## cer1 0.112608 0.034498 3.264 0.00111 **
## cer2 0.054881 0.035254 1.557 0.11963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.864 on 3256 degrees of freedom
## Multiple R-squared: 0.206, Adjusted R-squared: 0.2038
## F-statistic: 93.86 on 9 and 3256 DF, p-value: < 2.2e-16
