TRAINING DATA ANALYSIS FOR QUALITY ASSURANCE

Training on data analysis for quality assurance held at Federal University of Lafia, Nigeria

1. QUANTITATIVE AND QUALITATIVE DATA ANALYSIS FOR QUALITY ASSURANCE Facilitator: Dr. Abdulkareem Alhassan Department of Economics, Federal University of Lafia Contact [email protected] , [email protected] [phone] Presented during A 2-day In-house Training organized by the Directorate of Staff Training and Development (DST&D) for Staff of the Directorates of Quality Assurance, Academic Planning and Members of Quality Assurance Board

2. Approach: What to do it ? Not How to do it? Outline

3. Introduction Quality assurance (QA) in the university ensures that teaching, research, and community service meet established standards. To achieve this, data analysis —both quantitative and qualitative—is critical for monitoring performance, identifying gaps, and informing policy . Quantitative data analysis provides objective, numerical indicators of performance, while qualitative analysis offers context, meaning, and stakeholder perspectives . Together , they form a robust QA framework that the university needs to meet both national (NUC) and international standards . Thus, QA solely depends on Input-Process-Output of data analysis QA = f(DATA ANALYSIS)

4. Input–Process–Output (IPO) of data analysis Input (Data Sources for QA ): These are the raw materials needed for analysis Process (Data Analysis Activities) This is how the input data is transformed into useful information Output (Results of QA Data Analysis) These are the products delivered from the process :

5. INPUT (Data Sources for QA) PROCESS (Data Analysis Activities) OUTPUT (Results of QA Data Analysis) Student-related Data - Course evaluation forms - Examination results and GPA records - Graduation and dropout rates - Student feedback on teaching & learning resources Data Collection & Validation - Administering surveys, evaluation forms, and structured interviews - Extracting records from MIS/databases - Cleaning data to ensure accuracy Reports & Dashboards - Annual Quality Assurance report - Faculty/departmental performance scorecards - Student satisfaction reports Staff-related Data - Staff qualifications and publications - Training and development records - Staff-to-student ratio - Performance appraisal and promotion data Quantitative Analysis - Descriptive statistics (mean, percentages, distributions) - Trend analysis (enrollment, graduation, dropout) - Performance indicators (pass rate, employability rate) Key Performance Indicators (KPIs) - Student-to-staff ratio - Graduation and retention rates - Research output & staff training levels Institutional Data - Accreditation reports - Curriculum documents - Facilities and infrastructure audits (classrooms, ICT, laboratories) - Budget and resource allocation Qualitative Analysis - Thematic analysis of student/staff feedback - SWOT & PESTLE analysis for institutional planning - Benchmarking results against NUC/quality standards Actionable Insights - Recommendations for curriculum review - Identification of training needs for staff - Infrastructure and resource improvement plans External Data - Employers’ feedback on graduates - Alumni tracer studies - Benchmarking against other universities Triangulation & Synthesis - Integrating multiple data sources (students, staff, stakeholders) - Identifying strengths, weaknesses, gaps, and opportunities Strategic Outcomes - Evidence for accreditation and ranking - Policy decisions on teaching, research, and community engagement - Enhanced student learning experience and employability

6. Type of Data Description Examples in University QA Discrete Data (Count Data) Whole numbers that cannot be meaningfully divided. Number of students enrolled in ECO 101 - Number of classrooms in Faculty of Social Sciences - Number of PhD staff in a department Continuous Data (Measurement Data) Values that can take on any number within a range, including decimals. Students ’ GPA scores (e.g., 3.45 ) Staff salaries - Age of students or staff Time taken to complete a degree Interval Data Equal intervals between values, but no true zero point. Classroom temperature in °C Academic calendar dates (exam dates, workshop dates) Ratio Data Equal intervals with a true zero , allowing “twice as much” comparisons. Student-to-staff ratio - Budget allocations (₦) Duration of lectures (hours/minutes) Number of research publications Quantitative Data by Measurement Scale

7. Quantitative Data by Structure / Time Dimension Type of Data Description Examples in University QA Cross-sectional Data Data collected at one point in time across multiple units. Student satisfaction survey in 2025 across faculties Accreditation status of all departments in a single year Number of staff trained in ICT in 2024 Time Series Data Data collected over time for a single unit. Graduation rates from 2015–2025 Annual staff training budget from 2010–2025 Enrollment trends in Faculty of Social Sciences across 10 years Panel (Longitudinal) Data Combines cross-sectional and time series : multiple units observed over time. GPA progression of students across departments over several years Faculty research output tracked for 5 years Alumni employment rates by department from 2018–2025

8. Quantitative Data Analysis for Quality Assurance in the University Definition Quantitative analysis involves numerical data to measure, compare, and evaluate university performance objectively. Example/Sources of quantitative Data for QA Student enrollment figures Staff–student ratios Staff mix by rank and qualifications Number and size of offices, classrooms, laboratories, etc Equipment Results (Faculty, Departments, Programmes, courses ) Graduation rates and dropout rates Research output (publications, citations, grants) Accreditation scores (e.g., NUC accreditation reports) Employability statistics of graduates

9. Quantitative Data Analysis for Quality Assurance in the University Techniques Descriptive statistics : Mean(average), percentage, ratios (e.g., % of accredited programs). Trend analysis : Growth of enrollment vs. staff recruitment. Benchmarking: Comparing FULafia’s graduate employment rate with other state and federal universities . FULafia Examples Between 2015 and 2024 , FULafia’s student enrollment grew from about 2,500 to over 8 ,000 , requiring close monitoring of infrastructure expansion. The staff–student ratio in the Faculty of Social Sciences in 2024 was 1:65 , which is above the NUC benchmark of 1:30 , signaling the need for more staff recruitment. Accreditation results for 2024 showed that 98% of FULafia programmes received full accreditation, while a few were given interim status due to gaps in facilities and staffing.

10. Tools Excel/Google Sheets SPSS, Stata , R, etc. Dashboards for performance tracking Usefulness Provides objective evidence for accreditation, policy, and funding. Helps in measuring efficiency (e.g., cost per graduate). Supports accountability and decision-making.

11. Qualitative Data Analysis in Quality Assurance Definition: Qualitative analysis involves non-numerical data to understand perceptions, experiences, and processes within universities. Example/Sources of Qualitative Data Interviews with staff, students, alumni, employers Focus group discussions Course evaluations and student feedback forms External examiners’ reports Observation of teaching/learning environments Institutional self-study reports

12. FULafia Examples Student feedback on General Studies (GST) courses revealed complaints about overcrowded lecture halls , stress during examination, leading to management introducing e-learning and online examination. Focus group discussions with alumni highlighted the need to strengthen entrepreneurship education, prompting the university to redesign its Entrepreneurship Centre curriculum. Employer surveys in Nasarawa State showed that FULafia graduates are strong in theoretical knowledge but require better digital and practical skills . It can be Faculty/department/programme-based

13. Techniques of qualitative data analysis Thematic analysis: Identifying patterns in student/staff feedback Content analysis: Reviewing policy documents, curricula, mission statements Case studies: In-depth study of a department/programme SWOT analysis: Strengths, Weaknesses, Opportunities, Threats PESTLE Analysis: Political, Economic, Social, Technological, Legal, Environmental Tools NVivo , ATLAS.ti , MAXQDA (for coding and categorization) Manual coding with matrices and charts

14. SWOT Analysis (FULafia – Faculty of Agriculture Example) Strengths: Well-trained academic staff; strong demand for agriculture-related programs. Weaknesses: Limited laboratory facilities and outdated equipment. Opportunities: Government support for food security and agribusiness start-ups. Threats: Competition from other universities offering modern agricultural technology programs. PESTLE Analysis (FULafia Example) Political: Federal government’s policies on expanding access to tertiary education. Economic: Fluctuating funding and rising costs of maintaining infrastructure. Social: Growing demand for university education in Nasarawa and surrounding states. Technological: Increasing reliance on ICT and e-learning, but slow adoption in some faculties. Legal: Compliance with NUC accreditation standards and labor laws. Environmental: Pressure to integrate sustainable practices (solar energy, green campus).

15. Usefulness of Qualitative data analysis Provides insights into teaching effectiveness and learning experiences. Captures stakeholders’ perspectives (students, employers, alumni). Identifies hidden challenges not captured by numbers (e.g., staff morale, curriculum relevance).

16. Integration of Quantitative and Qualitative Approaches (Mixed Methods - Triangulation) For effective QA , both approaches should be combined Use quantitative data to measure outcomes (e.g., graduation rates). Use qualitative data to explain why outcomes occur (e.g., students dropping out due to poor facilities). Triangulation improves validity and reliability of findings . Areas of integration Accreditation: NUC requires both statistics (e.g., staff strength) and narratives (e.g., teaching quality). Student evaluation of courses: Combines Likert -scale responses (quantitative) with open comments (qualitative). Graduate employability surveys: Tracks employment rates (quantitative) and employer satisfaction feedback (qualitative). Internal QA units: Collect data to monitor compliance, academic standards, and service delivery.

17. Challenges Limited capacity in data analysis among QA staff Poor data culture and incomplete records Underutilization of ICT and software tools Resistance to transparent feedback mechanisms

18. Recommendations Strengthen QA units with training in both quantitative and qualitative analysis. Invest in ICT platforms for real-time data collection and analysis. Encourage stakeholder participation in QA through surveys and interviews. Promote a culture of evidence-based decision making.