Determinants of User Satisfaction Levels with Mobile Banking Apps Among University Students

Ordinal Logistic Regression for Informatics and Information Systems

Field: Informatics and Information SystemsLevel: Master's thesis (S2)Method: Ordinal Logistic Regression

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Through synthetic data, the illustrated aim is to estimate the odds of a higher level of user satisfaction level given app loading time and usability score. Ordinal logistic regression (the proportional odds model) models an ordered categorical outcome, such as low, medium and high, from several predictors. It suits theses whose outcome is a level, grade or ordered rating.

Determinants of User Satisfaction Levels with Mobile Banking Apps Among University Students: result sheet

Project info

Field of studyInformatics and Information Systems
LevelMaster's thesis (S2)
Method typeRegression
Estimated build time± 5 working days
ToolsMASS::polr, ordinal::clm, stats::anova, ggplot2
PriceUS$19 - US$128

Research goal

Through synthetic data, the illustrated aim is to estimate the odds of a higher level of user satisfaction level given app loading time and usability score.

Variables

Role	Variable	Unit
Main	User satisfaction level	class
Supporting 1	App loading time	s
Supporting 2	Usability score	score

Example result

4.843OR/SD App loading time
<0.001p-value App loading time
1.432OR/SD Usability score
<0.001LR test p-value
0.28Prop. odds test p
0.615Accuracy

A one-SD increase in App loading time multiplies the odds of a higher User satisfaction level category by 4.84 (p < 0.001); the OR for Usability score is 1.43. The model is significant (LR test p < 0.001, McFadden R2 = 0.215) and the proportional odds test (p = 0.280) supports the assumption. Classification accuracy is 61.5%.

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

About Ordinal Logistic Regression

Ordinal logistic regression (the proportional odds model) models an ordered categorical outcome, such as low, medium and high, from several predictors. It suits theses whose outcome is a level, grade or ordered rating.

Steps

1Convert the outcome to an ordered factor with the correct category order
2Fit the model with MASS::polr (Hess = TRUE) and compute p-values from the t values
3Compute proportional odds ratios with 95% confidence intervals
4Test the model with a likelihood ratio test against the null model (ordinal::clm)
5Test the proportional odds assumption by comparing the standard clm with a nominal-effects model
6Compute classification accuracy and plot the predicted probability of each category

Assumptions to check

The outcome categories have a meaningful order
Proportional odds: each predictor has the same effect at every category threshold
Observations are independent and predictors are not highly collinear

Reading and reporting the results

An OR above 1 means a higher predictor value shifts respondents towards higher categories; report the OR, 95% confidence interval and p-value. Include the likelihood ratio test, the proportional odds test (p > 0.05 means the assumption holds) and a plot of predicted category probabilities.

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: Ordinal Logistic Regression
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(MASS)
  library(ordinal)
})
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 ----
n <- 200
d <- data.frame(app_loading_time = rnorm(n, 50, 10),
                usability_score = rnorm(n, 30, 6))
z1 <- as.numeric(scale(d$app_loading_time))
z2 <- as.numeric(scale(d$usability_score))
b1 <- runif(1, 0.9, 1.6)
b2 <- runif(1, 0.3, 0.8) * sample(c(-1, 1), 1)
laten <- b1 * z1 + b2 * z2 + rlogis(n)                    # variabel laten kontinu di balik kategori
batas <- quantile(laten, c(runif(1, 0.25, 0.4), runif(1, 0.6, 0.75)))
kat <- c("A", "B", "C")                     # urutan rendah ke tinggi
d$user_satisfaction_level <- factor(kat[findInterval(laten, batas) + 1], levels = kat, ordered = TRUE)
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Analisis: regresi logistik ordinal (proportional odds, MASS::polr) ----
d$user_satisfaction_level <- factor(d$user_satisfaction_level, levels = kat, ordered = TRUE)  # pastikan urutan kategori benar (mis. setelah read.csv)
fit <- MASS::polr(user_satisfaction_level ~ app_loading_time + usability_score, data = d, Hess = TRUE)
print(summary(fit))
ct <- coef(summary(fit))
pval <- 2 * pnorm(abs(ct[, "t value"]), lower.tail = FALSE)   # p-value aproksimasi normal
print(cbind(ct, "p value" = round(pval, 4)))
or_tab <- exp(cbind(OR = coef(fit), confint.default(fit)))    # odds ratio per satuan + CI Wald 95%
print(round(or_tab, 4))

# OR per 1 simpangan baku (sebanding antarprediktor)
sdx <- c(sd(d$app_loading_time), sd(d$usability_score))
se_b <- ct[1:2, "Std. Error"]
or_sd <- data.frame(var = c("app_loading_time", "usability_score"),
                    or = exp(coef(fit) * sdx), lo = exp((coef(fit) - 1.96 * se_b) * sdx),
                    hi = exp((coef(fit) + 1.96 * se_b) * sdx), p = pval[1:2])

# ---- Uji model: LR terhadap model null dan uji asumsi odds proporsional (ordinal::clm) ----
f_clm <- ordinal::clm(user_satisfaction_level ~ app_loading_time + usability_score, data = d)
f_null <- ordinal::clm(user_satisfaction_level ~ 1, data = d)
lr <- anova(f_null, f_clm)
print(lr)
p_lr <- lr[2, "Pr(>Chisq)"]
r2_mcf <- 1 - as.numeric(logLik(f_clm)) / as.numeric(logLik(f_null))
p_po <- tryCatch({
  f_nom <- ordinal::clm(user_satisfaction_level ~ 1, nominal = ~ app_loading_time + usability_score, data = d)   # ambang berbeda per prediktor
  a <- anova(f_clm, f_nom); print(a); a[2, "Pr(>Chisq)"]
}, error = function(e) {
  nt <- ordinal::nominal_test(f_clm); print(nt); min(nt[-1, "Pr(>Chi)"], na.rm = TRUE)
})
if (!is.finite(p_po)) p_po <- 1
prob <- predict(fit, type = "probs")
d$prediksi <- factor(kat[max.col(prob)], levels = kat)
tab <- table(Aktual = d$user_satisfaction_level, Prediksi = d$prediksi)
print(tab)
akurasi <- mean(as.character(d$prediksi) == as.character(d$user_satisfaction_level))


# ---- Grafik ----
# Peluang prediksi tiap kategori menurut X1 (X2 pada rata-rata), area bertumpuk
g <- data.frame(app_loading_time = seq(min(d$app_loading_time), max(d$app_loading_time), length.out = 120), usability_score = mean(d$usability_score))
pg <- predict(fit, newdata = g, type = "probs")
dg <- data.frame(x = rep(g$app_loading_time, 3), kategori = factor(rep(kat, each = nrow(g)), levels = rev(kat)),
                 p = as.numeric(pg))
p1 <- ggplot(dg, aes(x, p, fill = kategori)) +
  geom_area(alpha = 0.8, colour = warna$panel, linewidth = 0.3) +
  scale_fill_manual(values = setNames(pal[1:3], kat), breaks = kat, name = NULL,
                    labels = function(s) substr(s, 1, 18)) +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "Predicted category probabilities", x = "app_loading_time", y = "Probability")
# Distribusi kategori: teramati vs prediksi model
dk <- rbind(data.frame(kategori = kat, jenis = "Observed", jml = as.numeric(table(d$user_satisfaction_level))),
            data.frame(kategori = kat, jenis = "Predicted", jml = as.numeric(colSums(prob))))
dk$kategori <- factor(dk$kategori, levels = kat)
p2 <- ggplot(dk, aes(kategori, jml, fill = jenis)) +
  geom_col(position = position_dodge(width = 0.75), width = 0.7, alpha = 0.85) +
  scale_fill_manual(values = c(Observed = pal[4], Predicted = pal[5]), name = NULL) +
  scale_x_discrete(labels = function(s) substr(s, 1, 12)) +
  labs(title = "Category distribution", x = NULL, y = "Count")
p3 <- ggplot(or_sd, aes(or, var)) +
  geom_vline(xintercept = 1, linetype = "dashed", colour = warna$redup) +
  geom_segment(aes(x = lo, xend = hi, yend = var), colour = pal[1], linewidth = 1.1) +
  geom_point(colour = pal[2], size = 3.2) +
  geom_text(aes(label = sprintf("%.2f", or)), vjust = -1.1, colour = warna$teks, size = 3.2) +
  scale_x_log10() +
  labs(title = "Proportional odds ratio per 1 SD (95% CI)", x = "Odds ratio (log scale)", 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$19

excl. 11% VAT

Duration: 5 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for Ordinal Logistic Regression
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$128

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. Ordinal Logistic Regression 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$19, Package 2 (your data analysis and interpretation) US$56 and Package 3 (full guidance) US$128, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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