Diabetes Knowledge, Attitudes, and Practices (KAP) Study: Statistical Analysis Using SPSS Among Univ

This project involved a comprehensive analysis of Knowledge, Attitudes, and Practices (KAP) related to diabetes among students at Jomo Kenyatta University of Agriculture and Technology (JKUAT). The study aimed to assess students' awareness of diabetes, attitudes toward its seriousness and preventability, and lifestyle behaviors that influence diabetes risk.

Using data collected through a structured Google Forms questionnaire (n = 384), the project applied statistical techniques using SPSS to evaluate both descriptive trends and inferential relationships across demographic subgroups. The analysis applied a structured analytics pipeline aligned with best practices in public health research and statistical modeling.

Objectives:

Measure students’ knowledge on diabetes symptoms, risk factors, and management

Evaluate student attitudes toward diabetes prevention and screening

Analyze lifestyle behaviors including diet, physical activity, and smoking

Investigate whether demographic factors (age, gender, education, family history) influence diabetes-related KAP

Tools and Methodologies:

Tool Used: SPSS

Study Design: Descriptive cross-sectional

Data Source: Self-reported survey using Google Forms

Analysis Techniques: Descriptive statistics, bar plots, chi-square tests, binary logistic regression

Key Steps:

Data Cleaning & Scoring:

Recoded responses for binary and Likert-scale items

Created composite scores:

Knowledge Score: Sum of 24 binary-coded items

Attitude and Practice Scores: Mean of Likert-scale responses

Binarized outcomes (e.g., Good vs Poor Knowledge) for regression analysis

Descriptive Analysis:

Explored distributions of demographic variables

Visualized knowledge and attitude scores across gender, age, and education groups

Found generally high knowledge levels across the student population

Chi-Square Analysis:

Tested associations between KAP outcomes and demographic factors

No statistically significant associations found (e.g., p = 0.308 for gender vs knowledge)

Logistic Regression Modeling:

Modeled predictors of "Good Knowledge" using demographics

Model accuracy = 76.3%, but this was due to class imbalance (dominance of high-knowledge responses)

Nagelkerke R² = 0.010: Very weak explanatory power

None of the predictors were statistically significant (p > 0.2 for all)

Findings and Interpretation:

The majority of students (76.3%) had “Good Knowledge” about diabetes

Demographic factors (age, gender, education, family history) were not predictive

Despite model accuracy, the logistic regression merely reflected dominant class labels

Results suggest strong, equitable diabetes awareness among students regardless of background

Key Insights:

Logistic regression flagged the model's statistical insignificance, yet still offered a valuable insight: that diabetes knowledge is high and uniformly distributed in the population studied

Highlights the importance of not relying solely on accuracy as a performance metric when classes are imbalanced

This project demonstrates strong skills in health data cleaning, statistical testing, regression modeling, and critical interpretation. It also reflects the ability to distinguish between statistical significance and practical implications, an essential aspect of real-world data analysis in healthcare and education settings.