Educational Research Analytics Using SPSS: A Statistical Investigation of Student Performance and Pr

This project is a complete statistical analysis of high school and college student data using SPSS, conducted for a research course in educational statistics. It explores key relationships among academic performance indicators, demographic variables, motivation factors, and behavioral patterns. The project demonstrates strong proficiency in statistical reasoning, data handling, and research interpretation using real-world educational data. Objective: To conduct an in-depth statistical examination of academic performance (e.g., GPA, math achievement), demographic attributes (e.g., gender, ethnicity, parental education), and psychological factors (e.g., motivation, competence, pleasure), and to identify predictors of educational outcomes using SPSS. Tools & Technologies Used: SPSS: For data preprocessing, visualization, descriptive statistics, inferential statistics, correlation matrices, and regression modeling. Datasets: Two sample datasets were analyzed: hsbdata.sav (high school students) and college student.sav (college students). Key Analysis Components: Data Cleaning & Handling Missing Values Used descriptive statistics and SPSS's missing values analysis Employed mean imputation for missing data to maintain dataset integrity Descriptive and Exploratory Data Analysis Computed central tendency, dispersion, skewness, and kurtosis Used bar charts and stem-and-leaf plots to analyze gender, ethnicity, parental education, and height distributions Central Tendency and Normality Assessment Analyzed mean, median, and mode across variables Checked normality through skewness, kurtosis, and graphical methods Frequency Analysis of Nominal Variables Gender and ethnicity distributions were analyzed to assess representation and balance Findings revealed overrepresentation of certain demographics (e.g., female and Euro-American students) Psychological Variable Analysis EDA conducted on motivation, pleasure, and competence Visual and numerical analysis revealed distribution patterns and skewness Computed Measures Developed a new variable aveEval as an average of four evaluation components Compared with meanEval using SPSS’s MEAN function to highlight effects of missing data Categorical Reclassification Recoded GPA into three performance categories: Low, Moderate, High Created visual frequency tables for GPA classification Correlation Matrix Explored relationships among GPA, study time, work hours, institutional evaluations, and more Found significant correlations between work hours and GPA, and between positive institutional evaluation and GPA Regression Analysis Multiple regression was performed to predict GPA from study hours, work hours, and TV watching Found that none of the predictors significantly explained GPA variation (R² = 10.2%, p > 0.05) Highlighted the complexity of academic performance and the importance of deeper psychological and environmental factors Predictive Modeling of Physical Traits Analyzed correlation between student and same-sex parent height Found a strong correlation (r = 0.842, p < 0.01), confirming hereditary influence Built a regression model with gender and parent height as predictors (R² = 0.748), showing both variables significantly contribute to height prediction Outcomes: Delivered a comprehensive educational research report guided by scientific inquiry and data. Demonstrated mastery of SPSS for statistical modeling, interpretation, and data storytelling. Identified key demographic and environmental predictors of academic and physical traits. Showed ability to handle missing data, recode variables, and interpret multiple statistical outputs. This project highlights the application of quantitative research methods and data-driven insights in the field of education, emphasizing both technical SPSS proficiency and interpretative clarity. It is a strong example of using statistics and data analysis to address complex human-centered questions in academic settings.