Construct Validation of a Cybersecurity Awareness Scale for Government Employees

Confirmatory Factor Analysis (CFA) for Informatics and Information Systems

Field: Informatics and Information SystemsLevel: Master's thesis (S2)Method: Confirmatory Factor Analysis (CFA)

0 views

Using synthetic data as an illustration, the analysis seeks to confirm the three-dimension structure of threat knowledge, policy attitude and secure behaviour with CFA. Confirmatory factor analysis (CFA) tests whether the items of an instrument measure the dimensions they were designed for, together with model fit, convergent validity, composite reliability and discriminant validity. It suits theses that develop, adapt or validate a questionnaire.

Construct Validation of a Cybersecurity Awareness Scale for Government Employees: result sheet

Project info

Field of studyInformatics and Information Systems
LevelMaster's thesis (S2)
Method typeSEM and Psychometrics
Estimated build time± 5 working days
Toolslavaan::cfa, lavaan::fitMeasures, lavaan::standardizedSolution, lavaan::lavInspect, ggplot2
PriceUS$25 - US$156

Research goal

Using synthetic data as an illustration, the analysis seeks to confirm the three-dimension structure of threat knowledge, policy attitude and secure behaviour with CFA.

Variables

Role	Variable	Unit
Main	Threat knowledge	Likert 1-5
Supporting 1	Policy attitude	Likert 1-5
Supporting 2	Secure behaviour	Likert 1-5

Example result

1CFI (robust)
1.024TLI (robust)
0RMSEA (robust)
0.031SRMR
0.571Min AVE
0.84Min CR

The three-factor model fits the data well (CFI = 1.000, RMSEA = 0.000, SRMR = 0.031). Standardised loadings range from 0.63 to 0.88, minimum CR is 0.840 and minimum AVE 0.571; the highest inter-factor correlation is 0.560, so Fornell-Larcker discriminant validity holds.

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

About Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis (CFA) tests whether the items of an instrument measure the dimensions they were designed for, together with model fit, convergent validity, composite reliability and discriminant validity. It suits theses that develop, adapt or validate a questionnaire.

Steps

1Prepare the Likert item data according to the designed dimensions
2Write a three-factor correlated CFA model in lavaan syntax
3Estimate with lavaan::cfa (MLR, std.lv = TRUE)
4Assess model fit with CFI, TLI, RMSEA and SRMR
5Compute AVE and CR from the standardised loadings
6Test Fornell-Larcker discriminant validity (square root of AVE against inter-factor correlations)

Assumptions to check

The factor structure is set in advance from theory or the instrument blueprint
Each item loads on one factor only
The sample is adequate (usually at least 5 to 10 respondents per item)
Likert items are treated as continuous with a robust estimator

Reading and reporting the results

Report the fit indices with their cut-offs, then the standardised loadings (ideally above 0.7, at least 0.5). AVE above 0.5 and CR above 0.7 indicate convergent validity and reliability; discriminant validity holds when the square root of each factor's AVE exceeds its correlations with the other factors.

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: Confirmatory Factor Analysis (CFA)
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(lavaan)
})
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: instrumen 12 butir Likert 1-5, tiga dimensi (4 butir per dimensi) ----
n <- 200
rho <- runif(3, 0.3, 0.6)                                       # korelasi antardimensi
R <- matrix(c(1, rho[1], rho[2], rho[1], 1, rho[3], rho[2], rho[3], 1), 3)
eta <- matrix(rnorm(n * 3), n) %*% chol(R)                      # skor laten berkorelasi
likert <- function(e, k) {
  sapply(seq_len(k), function(j) {
    l <- runif(1, 0.68, 0.92)
    s <- l * e + rnorm(length(e), 0, sqrt(1 - l^2))
    findInterval(s, sort(c(-2, -1.1, -0.2, 0.9) + runif(1, -0.3, 0.2))) + 1
  })
}
d <- data.frame(likert(eta[, 1], 4), likert(eta[, 2], 4), likert(eta[, 3], 4))
names(d) <- c(paste0("threat_knowledge", "_", 1:4), paste0("policy_attitude", "_", 1:4), paste0("secure_behaviour", "_", 1:4))
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- CFA tiga faktor berkorelasi ----
model <- paste0(
  "threat_knowledge =~ ", paste(names(d)[1:4], collapse = " + "), "\n",
  "policy_attitude =~ ", paste(names(d)[5:8], collapse = " + "), "\n",
  "secure_behaviour =~ ", paste(names(d)[9:12], collapse = " + "), "\n")
fit <- cfa(model, data = d, estimator = "MLR", std.lv = TRUE)
print(summary(fit, fit.measures = TRUE, standardized = TRUE))
fm <- fitMeasures(fit, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "cfi", "tli", "rmsea", "chisq.scaled", "df", "pvalue.scaled"))
pilih <- function(r, s) if (is.finite(fm[[r]])) fm[[r]] else fm[[s]]
idx <- c(CFI = pilih("cfi.robust", "cfi"), TLI = pilih("tli.robust", "tli"),
         RMSEA = pilih("rmsea.robust", "rmsea"), SRMR = fm[["srmr"]])
print(round(idx, 3))

# ---- Validitas konvergen (AVE), reliabilitas komposit (CR), validitas diskriminan ----
ss <- standardizedSolution(fit)
muat <- ss[ss$op == "=~", c("lhs", "rhs", "est.std", "pvalue")]
fak <- c("threat_knowledge", "policy_attitude", "secure_behaviour")
ave <- sapply(fak, function(f) mean(muat$est.std[muat$lhs == f]^2))
cr <- sapply(fak, function(f) { l <- muat$est.std[muat$lhs == f]; sum(l)^2 / (sum(l)^2 + sum(1 - l^2)) })
kor <- lavInspect(fit, "cor.lv")[fak, fak]                      # korelasi antarfaktor
fl <- kor; diag(fl) <- sqrt(ave)                                # matriks Fornell-Larcker
print(round(rbind(AVE = ave, CR = cr), 3)); print(round(fl, 3))
diskr <- all(sqrt(ave) > apply(abs(kor - diag(3)), 1, max))     # akar AVE > korelasi dengan faktor lain


# ---- Grafik ----
singkat <- function(s, w = 18) {                                # nama dimensi pendek untuk grafik
  k <- strsplit(s, " ")[[1]]
  if (nchar(s) > w) k <- k[!tolower(k) %in% c("of", "to", "the", "and", "for", "in", "on", "with")]
  i <- length(k)
  while (nchar(paste(k, collapse = " ")) > w && i >= 1) {
    if (nchar(k[i]) > 5) k[i] <- paste0(substr(k[i], 1, 4), ".")
    i <- i - 1
  }
  substr(paste(k, collapse = " "), 1, w)
}
nm_k <- make.unique(unname(sapply(c("threat_knowledge", "policy_attitude", "secure_behaviour"), singkat)), sep = " ")
muat$dimensi <- factor(nm_k[match(muat$lhs, fak)], levels = nm_k)
muat$butir <- paste0(rep(c("D1.", "D2.", "D3."), each = 4), rep(1:4, 3))
muat$butir <- factor(muat$butir, levels = rev(muat$butir))
p1 <- ggplot(muat, aes(est.std, butir, fill = dimensi)) +
  annotate("rect", xmin = 0, xmax = 0.5, ymin = -Inf, ymax = Inf, fill = warna$grid, alpha = 0.6) +
  geom_col(width = 0.68) +
  geom_vline(xintercept = c(0.5, 0.7), linetype = c("dotted", "dashed"), colour = warna$teks) +
  geom_text(aes(label = sprintf("%.2f", est.std)), hjust = -0.2, size = 2.8, colour = warna$teks) +
  scale_fill_manual(values = pal[1:3], name = NULL) +
  scale_x_continuous(limits = c(0, 1.08), breaks = seq(0, 1, 0.25)) +
  labs(title = "Standardised loadings (cut-offs 0.5 / 0.7)", x = "Loading", y = NULL)
lab3 <- paste0("D", 1:3)
dh <- expand.grid(a = lab3, b = lab3)
dh$nilai <- as.vector(fl)
dh$jenis <- ifelse(dh$a == dh$b, "sqrt(AVE)", "Correlation")
dh$b <- factor(dh$b, levels = rev(lab3))
p2 <- ggplot(dh, aes(a, b, fill = nilai)) +
  geom_tile(colour = warna$panel, linewidth = 1.2) +
  geom_text(aes(label = sprintf("%.2f", nilai), fontface = ifelse(jenis == "sqrt(AVE)", "bold", "plain")),
            colour = warna$panel, size = 3.6) +
  scale_fill_gradient(low = pal[4], high = pal[1], limits = c(0, 1), guide = "none") +
  labs(title = "Fornell-Larcker (diagonal = sqrt AVE)", x = NULL, y = NULL,
       subtitle = paste(strwrap(paste0(lab3, " = ", nm_k, collapse = "; "), 48), collapse = "\n"))
df <- data.frame(ukuran = factor(c(names(idx), paste("AVE", lab3), paste("CR", lab3)),
                                 levels = c(names(idx), paste("AVE", lab3), paste("CR", lab3))),
                 nilai = c(unname(idx), ave, cr),
                 grup = rep(c("Fit index", "AVE", "CR"), c(4, 3, 3)))
df$lolos <- ifelse(df$ukuran %in% c("RMSEA", "SRMR"), df$nilai <= 0.08,
                   ifelse(df$grup == "AVE", df$nilai >= 0.5, ifelse(df$grup == "CR", df$nilai >= 0.7, df$nilai >= 0.9)))
df$ukuran <- factor(df$ukuran, levels = rev(levels(df$ukuran)))
p3 <- ggplot(df, aes(nilai, ukuran, fill = lolos)) +
  geom_col(width = 0.65) +
  geom_text(aes(label = sprintf("%.3f", nilai)), hjust = -0.15, size = 2.7, colour = warna$teks) +
  scale_fill_manual(values = c(`TRUE` = pal[3], `FALSE` = pal[2]), labels = c(`TRUE` = "Meets cut-off", `FALSE` = "Below cut-off"),
                    name = NULL, drop = FALSE) +
  scale_x_continuous(limits = c(0, 1.18), breaks = seq(0, 1, 0.25)) +
  labs(title = "Fit, convergent validity and reliability", x = "Value", y = NULL)
p_hasil <- if (tata == 1) p1 | (p2 / p3) else (p1 | p2) / 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$25

excl. 11% VAT

Duration: 5 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for Confirmatory Factor Analysis (CFA)
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$72

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$156

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. Confirmatory Factor Analysis (CFA) is commonly used at this level in Informatics and Information Systems. 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$25, Package 2 (your data analysis and interpretation) US$72 and Package 3 (full guidance) US$156, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

Similar projects

See all

PreviousDeterminants of negative review counts at nature attractions in East Java

NextMeta-Analysis of the Effect of Influencer Marketing on Consumer Purchase Intention

The result sheet uses illustrative synthetic data. R is a programming language of the R Foundation project; RStudio and Posit are trademarks of Posit PBC.

Back to R Studio portfolio

Memuat