Data Analysis

The Data Analysis Course is designed to provide participants with comprehensive theoretical knowledge and practical skills in analyzing quantitative research data using IBM SPSS Statistics. Covering both descriptive and inferential statistics, the course enables learners to organize, prepare, code, clean, summarize, analyze, interpret, and report research data in accordance with academic and research standards. Participants will learn descriptive statistical techniques, including data screening, frequency distributions, percentages, measures of central tendency (mean, median, and mode), measures of dispersion (range, variance, and standard deviation), quartiles, graphical presentations, and descriptive analysis for Likert-scale data. The course also covers inferential statistical methods such as data normality testing, hypothesis testing, Pearson correlation, One-Sample, Independent Samples, and Paired Samples t-tests, One-Way ANOVA, and simple and multiple linear regression for examining relationships, differences, and predictions among variables. Through hands-on analysis of real research datasets, participants will gain the ability to select appropriate statistical techniques, interpret SPSS output accurately, create professional tables, charts, and graphs, draw evidence-based conclusions, make research recommendations, and produce high-quality research reports, dissertations, theses, and journal publications. This course is specifically designed for undergraduate students, master's students, and researchers seeking to strengthen their quantitative data analysis skills and conduct high-quality academic and scientific research.