Data Analysis with SPSS (Basic to Intermediate)

⏱ Duration: 16 Contact Hours (2 hours per Session) | Mode: offline

Instructors :

• Sujit Kumar Banik: Deputy Programme Manager, Society for Health Extension and Development (SHED); BSc in Nutrition and Food Science (DU), MS in Public Health (NUB), MS in Development Studies (DU), MS in Applied Statistics and Data Science (JU), PGD in Project Management (UK).
• Munmun Akter: Lecturer (Senior-scale), Department of Nutrition and Food Engineering, Daffodil International University.
• Lipi Rani Mondal: Lecturer (Senior-scale), Department of Nutrition and Food Engineering, Daffodil International University.

Course Description

The micro-credential course “Data Analysis with SPSS (Basic to Intermediate)” is designed to develop students’ fundamental skills in data management, statistical analysis, and interpretation using SPSS software. The course aims to help learners conduct academic and research-based data analysis efficiently and accurately, enabling them to perform evidence-based research, prepare research reports, and enhance their academic and professional competencies in various fields such as Nutrition, Public Health, Business, and Social Sciences.

Evidence of Demand

• The course is highly necessary for university students in Bangladesh because research, data analysis, and evidence-based decision-making have become essential components of higher education and professional practice.
• Many students collect survey or laboratory data without having the practical skills to analyze and interpret the results correctly, limiting their research capability.
• The demand for SPSS skills is increasing rapidly in disciplines such as Nutrition, Public Health, Epidemiology, Business, Pharmacy, Sociology, Psychology, and Agricultural Sciences.
• Employers in NGOs, healthcare organizations, research institutions, and corporate sectors frequently seek graduates who possess basic data analysis competencies.
• The growing number of undergraduate and postgraduate research activities in Bangladeshi universities has created a strong practical demand for SPSS training.

Course Objectives

Upon completion of this course, participants will be able to:

• Introduce students to the basic features and functions of SPSS software.
• Teach students how to conduct descriptive and basic inferential statistical tests using SPSS.
• Help students interpret SPSS outputs and present findings effectively.
• Enhance students’ research and analytical capabilities for academic projects and thesis work.

Course Content & Class Plan (Modules)

• Module 1: Introduction to Statistics and SPSS: Overview of statistics, types of data, measurement scales, installation, Variable vs Data view, and importing data.
• Module 2: Data Preparation and Data Entry: Designing a data sheet, coding variables, data entry techniques, missing data handling, and cleaning/error checking.
• Module 3: Data Transformation: Recoding variables, computing new variables, categorizing continuous variables, sorting, filtering, and split file function.
• Module 4: Reliability and Scale Analysis: Concept of reliability, Cronbach’s Alpha interpretation, item-total statistics, and scale refinement.
• Module 5: Descriptive Statistics: Frequency distribution, central tendency, dispersion, graphical presentation, and reporting of results.
• Module 6: Inferential Statistics I: t-tests (independent and paired), F-test (One-way ANOVA), post-hoc tests, test assumptions, and APA style reporting.
• Module 7: Inferential Statistics II: Chi-square test, correlation analysis, interpretation, and non-parametric tests (Mann-Whitney U, Kruskal-Wallis).
• Module 8: Advanced Analysis (Introductory Level): Exploratory Factor Analysis, simple linear regression, model interpretation, and APA reporting.

Practical & Field Work

• The course follows a blended instructional method emphasizing practical application in a computer laboratory setting.
• Learners will engage heavily with SPSS software for hands-on data entry, cleaning, and statistical testing.
• Practical exercises account for 20% of the overall evaluation, while an extensive final project/presentation requires learners to apply these skills practically, accounting for 40% of the final grade.

Learning Outcomes

After completing the course, participants will be able to:

• Explain fundamental concepts of statistics and their applications in research.
• Operate SPSS for data entry, coding, management, and cleaning efficiently.
• Organize and prepare datasets for analysis using SPSS functions.
• Perform descriptive statistical analyses and present data using tables and charts.
• Conduct reliability analysis using Cronbach’s Alpha.
• Apply appropriate inferential statistical tests (t-test, ANOVA, Chi-square, correlation).
• Interpret SPSS output accurately and perform introductory factor and regression analysis.
• Prepare and present statistical results in academic reporting formats.

Target Audience & Entry Requirements

• Target Audience: Undergraduate and postgraduate students, researchers, faculty members, healthcare professionals, and beginners needing practical SPSS knowledge. It is particularly suitable for learners in Nutrition, Public Health, Food Science, Pharmacy, Business, Sociology, Psychology, and related fields. Class size is optimized for 20-25 participants.
• Entry Requirements: Basic knowledge of Statistics.
• Minimum Age: 18 years old.

Career Pathways

• Academic Pathway: Undergraduate thesis/dissertation work, Master's research projects, PhD research and publications, and Scientific manuscript preparation.
• Professional Pathway: Research Assistant, Data Analyst, Monitoring and Evaluation Officer, Public Health Researcher, Market Research Executive, Lecturer/Academic Researcher, Statistical Consultant.

Tools & Resources

• Computer laboratory with projector, whiteboard, and internet access.
• Desktop/laptop for each learner with adequate specifications.
• IBM SPSS Statistics software and Microsoft Office package (Word, Excel, PowerPoint).
• Course materials including syllabus, handouts, sample datasets, practice exercises, and project guidelines.

Assessment Criteria & Certification

• Attendance & participation: 10%
• Practical exercises: 20%
• Daily Quiz: 30%
• Final project/presentation: 40%
• Certification: Successful completion will earn a micro-credential certificate.

Financial Information

• Tentative Course Fee: 2,500 Taka