Job Satisfaction, Commitment and Intention to Stay across Three Generations: MANOVA

MANOVA for Human Resource Management

Field: Human Resource ManagementLevel: Master's thesis (S2)Method: MANOVA

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The goal is to test simultaneous differences in three work attitudes across employee generations; the data are synthetic and for illustration only. MANOVA tests whether the mean vector of several outcomes measured together differs across groups, taking the correlation among outcomes into account. It suits theses comparing several groups on a set of outcomes at once, such as several performance indicators or clinical measures.

Job Satisfaction, Commitment and Intention to Stay across Three Generations: MANOVA: result sheet

Project info

Field of studyHuman Resource Management
LevelMaster's thesis (S2)
Method typeInferential Statistics
Estimated build time± 5 working days
Toolsstats::manova, stats::summary.aov, MASS::lda, MASS::mvrnorm, ggplot2
PriceUS$19 - US$125

Research goal

The goal is to test simultaneous differences in three work attitudes across employee generations; the data are synthetic and for illustration only.

Variables

Role	Variable	Unit
Main	Job satisfaction	score
Supporting 1	Organisational commitment	score
Supporting 2	Intention to stay	score

Example result

0.383Pillai trace
<0.001Pillai p-value
0.621Wilks lambda
<0.001Wilks p-value
0.32Max partial eta2
0.561LDA accuracy

MANOVA shows that the mean vector of the three outcomes differs significantly across groups (Pillai = 0.383, p < 0.001; Wilks = 0.621). Follow-up ANOVAs find 2 of 3 outcomes significantly different, largest for Job satisfaction (partial eta2 = 0.320). The discriminant functions classify 56.1% of units correctly.

Computed from illustrative synthetic data, not from a real study.

About MANOVA

MANOVA tests whether the mean vector of several outcomes measured together differs across groups, taking the correlation among outcomes into account. It suits theses comparing several groups on a set of outcomes at once, such as several performance indicators or clinical measures.

Steps

1Prepare data with a group factor and three continuous outcomes
2Check correlations among outcomes and descriptive statistics per group
3Run manova() and read the Pillai and Wilks statistics with summary()
4Run follow-up ANOVAs per outcome with summary.aov() and compute partial eta squared
5Run linear discriminant analysis (MASS::lda) to see how groups separate
6Plot standardised means, discriminant scores and the correlation matrix

Assumptions to check

Observations are independent
Outcomes are multivariate normal within each group
Covariance matrices are homogeneous across groups (can be checked with Box's M)
Outcomes are moderately correlated but not perfectly collinear

Reading and reporting the results

Report Pillai's trace (most robust to assumption violations) or Wilks' lambda with F and p for the multivariate effect. If significant, report follow-up ANOVAs per outcome with partial eta squared to show which outcomes separate the groups most.

Example R code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

Show code

# Contoh kode R: MANOVA
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(MASS)
  library(scales)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: tiga variabel terikat yang diukur bersama pada tiga kelompok ----
n <- 200
lv <- c("A", "B", "C")
kelompok <- factor(sample(rep(lv, length.out = n)), levels = lv)
r12 <- runif(1, 0.2, 0.55); r13 <- runif(1, -0.3, 0.4); r23 <- runif(1, 0.1, 0.45)
S <- matrix(c(1, r12, r13, r12, 1, r23, r13, r23, 1), 3)              # korelasi antar-variabel terikat
galat <- MASS::mvrnorm(n, mu = rep(0, 3), Sigma = S)
geser <- matrix(runif(9, -0.55, 0.55), 3)                              # profil rata-rata tiap kelompok (dalam SD)
geser[, 1] <- geser[, 1] + c(-0.35, 0, 0.35)[sample(3)]                # minimal satu DV jelas berbeda
geser <- sweep(geser, 2, colMeans(geser))
z <- geser[as.integer(kelompok), ] + galat * runif(1, 0.75, 0.95)
d <- data.frame(kelompok = kelompok,
                job_satisfaction = round(100 + 15 * z[, 1], 2),
                organisational_commitment = round(50 + 10 * z[, 2], 2),
                intention_to_stay = round(30 + 6 * z[, 3], 2))
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Analisis: MANOVA satu arah ----
fit <- manova(cbind(job_satisfaction, organisational_commitment, intention_to_stay) ~ kelompok, data = d)
s_pillai <- summary(fit, test = "Pillai")
s_wilks <- summary(fit, test = "Wilks")
print(s_pillai); print(s_wilks)

# ---- ANOVA lanjutan per variabel terikat + partial eta kuadrat ----
s_aov <- summary.aov(fit)
print(s_aov)
lanjut <- do.call(rbind, lapply(s_aov, function(a) data.frame(F = a[1, "F value"], p = a[1, "Pr(>F)"],
                                                              peta2 = a[1, "Sum Sq"] / sum(a[, "Sum Sq"]))))
lanjut$dv <- make.unique(c("job_satisfaction", "organisational_commitment", "intention_to_stay"), sep = " ")
print(lanjut)

# ---- Analisis diskriminan linear (visualisasi pemisahan kelompok) ----
ld <- MASS::lda(kelompok ~ job_satisfaction + organisational_commitment + intention_to_stay, data = d)
print(ld)
skor_ld <- predict(ld)
akurasi <- mean(skor_ld$class == d$kelompok)
cat("Akurasi klasifikasi LDA (resubstitusi):", round(akurasi, 3), "\n")


# ---- Grafik ----
pendek <- function(s, w = 14) ifelse(nchar(s) > w, paste0(trimws(substr(s, 1, w - 1)), "."), s)
dv <- c("job_satisfaction", "organisational_commitment", "intention_to_stay")
lab_dv <- make.unique(c("job_satisfaction", "organisational_commitment", "intention_to_stay"), sep = " ")
panjang <- do.call(rbind, lapply(seq_along(dv), function(j) {
  zz <- as.numeric(scale(d[[dv[j]]]))                                  # skala terstandar (z) karena satuan berbeda
  a <- aggregate(zz, list(kelompok = d$kelompok), function(v) c(m = mean(v), se = sd(v) / sqrt(length(v))))
  data.frame(dv = lab_dv[j], kelompok = a$kelompok, m = a$x[, "m"], se = a$x[, "se"])
}))
panjang$dv <- factor(panjang$dv, levels = unique(lab_dv))
p1 <- ggplot(panjang, aes(dv, m, colour = kelompok)) +
  geom_hline(yintercept = 0, colour = warna$redup, linetype = "dashed") +
  geom_pointrange(aes(ymin = m - 1.96 * se, ymax = m + 1.96 * se), position = position_dodge(width = 0.55),
                  size = 0.45, linewidth = 0.9) +
  scale_colour_manual(values = pal[1:3], name = NULL, labels = pendek) +
  scale_x_discrete(labels = scales::label_wrap(12)) +
  labs(title = "Group means of each outcome (z, 95% CI)", x = NULL, y = "Standardised mean (z)") +
  theme(legend.position = "bottom")
dl <- data.frame(LD1 = skor_ld$x[, 1], LD2 = skor_ld$x[, 2], kelompok = d$kelompok)
prop <- 100 * ld$svd^2 / sum(ld$svd^2)
p2 <- ggplot(dl, aes(LD1, LD2, colour = kelompok)) +
  geom_point(alpha = 0.55, size = 1.4) +
  stat_ellipse(level = 0.68, linewidth = 1) +
  geom_point(data = aggregate(cbind(LD1, LD2) ~ kelompok, dl, mean), shape = 23, size = 3.2,
             aes(fill = kelompok), colour = warna$teks, stroke = 1) +
  scale_colour_manual(values = pal[1:3], guide = "none") +
  scale_fill_manual(values = pal[1:3], guide = "none") +
  labs(title = "Discriminant function scores (68% ellipses)",
       x = sprintf("LD1 (%.1f%%)", prop[1]), y = sprintf("LD2 (%.1f%%)", prop[2]))
kr <- cor(d[, dv])
dk <- expand.grid(a = seq_along(dv), b = seq_along(dv))
dk$r <- kr[cbind(dk$a, dk$b)]
dk$a <- factor(lab_dv[dk$a], levels = unique(lab_dv)); dk$b <- factor(lab_dv[dk$b], levels = rev(unique(lab_dv)))
p3 <- ggplot(dk, aes(a, b, fill = r)) +
  geom_tile(colour = warna$panel, linewidth = 1.2) +
  geom_label(aes(label = sprintf("%.2f", r)), fill = warna$panel, colour = warna$teks, size = 3.3,
             label.size = 0, alpha = 0.85) +
  scale_fill_gradient2(low = pal[1], mid = warna$grid, high = pal[2], limits = c(-1, 1), guide = "none") +
  scale_x_discrete(labels = scales::label_wrap(12)) + scale_y_discrete(labels = scales::label_wrap(12)) +
  labs(title = "Correlation among outcomes", x = NULL, y = NULL) +
  theme(panel.grid = element_blank())
dd <- data.frame(dv = factor(lanjut$dv, levels = unique(lab_dv)), peta2 = lanjut$peta2, p = lanjut$p)
p4 <- ggplot(dd, aes(dv, peta2)) +
  geom_col(fill = pal[4], width = 0.6, alpha = 0.85) +
  geom_text(aes(label = ifelse(p < 0.001, "p<0.001", sprintf("p=%.3f", p))), vjust = -0.4, colour = warna$teks, size = 3) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.18))) +
  scale_x_discrete(labels = scales::label_wrap(12)) +
  labs(title = "Follow-up ANOVA: partial eta squared", x = NULL, y = "Partial eta squared")
p_hasil <- if (tata == 1) (p1 | p2) / (p3 | p4) else (p2 | p1) / (p4 | p3)

print(p_hasil)

The project file you can download

The original project file is available. It is not just the picture above: you get the working R project. It can be downloaded after payment, and we send it by WhatsApp or email.

ZIP: analisis.R + data_contoh.csv + BACA-DULU.txt

What is inside the ZIP

The complete analysis script (analisis.R), commented step by step, ready to run in RStudio.
A sample dataset (data_contoh.csv) so the script runs straight away.
A short guide (BACA-DULU.txt): which packages to install, how to run it and how to swap in your own data.

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

Package 1

Code and Explanation

US$19

excl. 11% VAT

Duration: 5 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for MANOVA
Example data and step-by-step run instructions
Ready-to-use result figures and metrics table
Short explanation of the method, its assumptions and how to read the results

Choose Package 1

Package 2 · most chosen

Your Data Analysis + Interpretation

US$56

excl. 11% VAT

Duration: 9 working days

Output: Code + analysis + interpretation notes

Includes all of Package 1, plus

Adapting the code to your own research dataset (CSV or Excel)
Assumption checks, result validation and data cleaning
Tables and figures formatted for your manuscript
Interpretation notes for the methods and discussion sections, for you to write up in your own words

Choose Package 2

Package 3

Full Guidance

US$125

excl. 11% VAT

Duration: 14-17 working days

Output: Code + analysis + guidance until ready to present

Includes all of Packages 1 & 2, plus

Comparison against one alternative method with the reasoning for the choice
Two 60-minute online coaching sessions so you fully understand the code and results
Revisions following your supervisor feedback (up to 3 rounds)
Practice Q&A to prepare for your seminar and defence
Full documentation (README, R package list (sessionInfo)) so the results can be reproduced

Choose Package 3

Payment: 50% down payment before work starts, balance after the deliverables are handed over
Tax: Prices exclude 11% VAT
Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions

Do I get the real project file?

Yes. The original project file is a ZIP containing the complete analisis.R script, a data_contoh.csv sample dataset and a BACA-DULU.txt guide. It can be downloaded after payment, and we send it by WhatsApp or email. For your own data (Package 2 and 3) the script is adapted to your dataset.

Is this suitable for a Master's thesis (S2)?

Yes. MANOVA is commonly used at this level in Human Resource Management. We adapt the analysis to your research question, your own data and your supervisor's requirements.

Is the result sheet real data?

No. The sheet is computed from illustrative synthetic data so you can see the kind of output R Studio gives. For your order we run the same method on your own data.

Will I understand the code?

That is the point of the packages. Every script is commented, and Package 3 includes two coaching sessions plus practice questions so you can explain the method and the results at your seminar and defence.

How is the price decided?

Each project has three packages: Package 1 (code and explanation) US$19, Package 2 (your data analysis and interpretation) US$56 and Package 3 (full guidance) US$125, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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