Case Study: Automating Likert-Scale Data Transformation & Analysis in a Public Health Dissertation

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1. Project & Data Context

This case study focuses on automating likert‑scale analysis for a Public Health PhD dissertation surveyed 413 healthcare workers across urban and rural hospitals to assess their perceptions of standard operating protocols via a 10-item, 5-point Likert scale. The goals were to:

• Collapse and aggregate responses into meaningful categories
• Generate reproducible dashboards of item and composite scores
• Execute inferential tests on the derived categorical outcomes

This case study details the end-to-end software stack and workflow that automated each step—maximizing consistency, auditability, and efficiency.

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2. Software Ecosystem for focusing on automating likert‑scale analysis

• R (Base + tidyverse)
  • Data import (read.csv) and reshaping (pivot_longer)
  • Custom functions for scale collapsing and composite scoring
  • Inferential tests: chisq.test(), t.test(), glm()
• Microsoft Excel + VBA
  • “Masterchart” creation macro: merges pre-survey demographic files with cleaned Likert scores
  • Automated dashboard macro: iterates over item groups to produce summary tables and charts
• R Markdown & Git
  • Parameterized .Rmd templates for generating HTML/PDF reports with embedded code, tables, and narrative
  • Git version control for script integrity and reproducibility

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3. Data Ingestion & Masterchart Standardization for automating likert‑scale analysis

• Raw Data Import

raw_df <- read.csv(\"survey_responses.csv\") demo_df <- read.csv(\"demographics.csv\")

• Schema Validation

validate_schema(raw_df, expected_cols = paste0(\"Q\", 1:10))

• Excel Masterchart Macro
  • VBA script CreateMasterchart() reads both CSVs, standardizes column names, and outputs masterchart.csv with one row per respondent, demographic fields + raw item scores.

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4. Likert-Scale Transformation Pipeline for automating likert‑scale analysis

All transformations occur in likert_transform.R:

• Collapsing 5-Point → 3-Point Scale

collapse_likert <- function(x) 
   { 
    case_when
      ( 
       x >= 4 ~ 3, # Agree 
       x == 3 ~ 2, # Neutral 
       x <= 2 ~ 1 # Disagree 
     )
   } 
df3 <- raw_df %>% mutate(across(Q1:Q10, collapse_likert))

• Composite Scoring of Related Items

df3 <- df3 %>% 
rowwise() %>% 
mutate
( 
 composite_training = mean(c(Q2, Q5), na.rm=TRUE), 
 composite_protocol = mean(c(Q1, Q4, Q7), na.rm=TRUE) 
) # Convert composites back into 3-point categories 
df3 <- df3 %>% 
mutate(across(starts_with(\"composite_\"), ~ case_when
 ( 
   . > 2.33 ~ 3, 
   . >= 1.67 ~ 2, 
   TRUE ~ 1 
)))

• Export Cleaned Masterchart

write.csv(df3, \"likert_masterchart.csv\", row.names=FALSE)

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5. Automated Dashboard Generation

• Excel VBA Macro:
  • Loops over each Q1:Q10 and composite_* column
  • Builds a pivot table of frequency counts (1, 2, 3) by hospital_type
  • Creates a clustered bar chart for each pivot and labels axes/text
• Output:
  • 13 dashboards (10 individual items + 3 composites), each on its own worksheet, ready for inclusion in R Markdown reports.

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6. Inferential Testing Pipeline

In inferential_tests.R, the cleaned masterchart is used to test whether high-response rates differ by hospital type:

df3 <- read.csv(\"likert_masterchart.csv\")

# Define “high agreement” as category 3
df3 <- df3 %>% mutate(high_composite_protocol = if_else(composite_protocol == 3, 1, 0))

# Chi-square test
cs_result <- chisq.test(table(df3$hospital_type, df3$high_composite_protocol))
write.csv(as.data.frame(cs_result[c(\"statistic\",\"p.value\")]), \"chi_square_protocol.csv\")

Diagnostic plots (mosaic plots, residual heatmaps) are generated automatically and saved to /results/.

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7. Reproducible Reporting & Audit

• R Markdown Template:
  • Parameters: data_path, output_format
  • Sections: Data summary, Dashboard gallery, Inferential results, Executive summary
• Git Workflow:
  • main branch holds stable release; feature branches (feature/likert-transform, feature/dashboard-macro) undergo pull-request reviews.
• Audit Report:
  • A final Data Audit PDF includes schema checks, transformation logs, macro version stamp, and test assumptions (e.g., expected cell counts for chi-square) .

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8. Lessons & Best Practices

• Centralize Data Prep: Use a “masterchart” as the single data source to avoid divergence.
• Automate Repetitive Tasks: VBA for dashboards, R functions for transformations ensure uniform output.
• Parameterize Reports: R Markdown templates make it trivial to re-run analyses on updated data.
• Version Control Everything: Preserves a complete audit trail of data changes and script edits.

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This Software & Workflow Walkthrough illustrates how combining Excel macros with R scripting creates a robust, transparent pipeline for Likert-scale data in a Public Health dissertation—delivering audit-ready dashboards and statistical outputs with minimal manual effort.

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