This workshop on Research Methodology, Research Design, Data Analysis, and Visualization using SPSS and MS Excel is designed to provide participants with practical knowledge and hands-on skills essential for conducting high-quality research. The course introduces the fundamentals of research planning, formulation of research problems, selection of appropriate research designs, and methods of data collection. Participants will gain experience in organizing, analyzing, and interpreting data using SPSS and MS Excel. The workshop also emphasizes effective data visualization techniques to communicate research findings clearly and professionally, enabling participants to strengthen their academic, research, and evidence-based decision-making capabilities. Learning Outcomes By the end of the workshop, participants will be able to: Explain key concepts of research methodology and identify suitable research designs for various research problems. Develop a structured research plan including problem formulation, objectives, hypotheses, and data collection strategies. Perform statistical data analysis using SPSS and MS Excel, including data entry, processing, and interpretation. Create meaningful data visualizations and reports to effectively communicate research findings and support decision-making.
Course Details
Explore the comprehensive course modules
Overview of research, significance of scientific inquiry, types of research (basic, applied, qualitative, quantitative, mixed methods), and stages of the research process.
Identifying research gaps, framing research problems, defining objectives and hypotheses, and conducting systematic literature reviews.
Concepts of research design, exploratory, descriptive and experimental designs; probability and non-probability sampling methods.
Primary and secondary data sources, questionnaire design, surveys, interviews, reliability and validity of instruments.
Research ethics, plagiarism, informed consent, referencing styles, and components of research proposals.
Types of variables, scales of measurement (nominal, ordinal, interval, ratio), operationalization and research workflow planning.
Familiarization with SPSS and Excel environment, data sheets, variable view, importing datasets and workflow.
Data coding, handling missing values, identifying outliers, data transformation and cleaning procedures.
Understanding variable types and preparing datasets for statistical analysis.
Measures of central tendency, dispersion, frequency distribution and summary statistics in SPSS and Excel.
Principles of effective visualization; creating bar charts, pie charts, histograms and frequency plots.
Scatter plots, box plots, trend lines, dashboards and formatting visual outputs in Excel and SPSS.
Normal distribution, skewness, kurtosis and assumptions required for statistical testing.
Shapiro–Wilk, Kolmogorov–Smirnov tests, Q-Q plots and interpretation for selecting analysis methods.
Null and alternative hypotheses, p-values, confidence intervals, Type I and Type II errors.
Conditions, assumptions and selection criteria for choosing appropriate statistical tests.
Comparing means between groups using independent and paired sample t-tests with SPSS demonstrations.
Pearson correlation, interpretation of correlation coefficients and practical applications.
Chi-square tests for categorical data and measures of association.
Comparing means among multiple groups, assumptions and interpretation.
Comparing means among multiple groups, assumptions and interpretation.
Tukey, Bonferroni methods and interpretation of effect size statistics.
Mann–Whitney U test and Wilcoxon Signed Rank test for non-normal datasets.
Kruskal–Wallis and Friedman tests for multiple group comparisons.
Simple and multiple linear regression, assumptions, interpretation and prediction models.
Polynomial and non-linear models, curve fitting and practical applications.
Binary logistic regression, odds ratios, classification and interpretation of outputs.
End-to-end analysis of a real dataset using SPSS and Excel including visualization, interpretation and reporting.
CA1
CA2
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Expert in data analysis and research methods
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