
Explore descriptive statistics, including mean, median, mode, range, and standard deviation. Learn when to use each measure, including variance, and how SPSS summarizes data.
Learn to run frequencies and descriptives in SPSS to summarize categorical and numeric data, view distribution, and obtain statistics like mean and standard deviation for cleaning and reporting.
Explore the SPSS Explore tool to generate descriptive statistics, tests of normality, outlier detection, and plots like boxplots and histograms for continuous data across groups.
Learn to read SPSS output tables in the output viewer from frequencies, descriptives, and explore analyses, including mean, standard deviation, and minimum and maximum, for clear data interpretation.
Learn to create bar charts, histograms, and pie charts in SPSS to visualize data and communicate patterns clearly. Build, customize, and export publication-ready visuals for reports and presentations.
Customize graphs in SPSS to improve readability, align with branding, highlight key information, and create professional visuals by editing titles, axis labels, colors, and data labels, then export for reports.
Explore SPSS exploratory visuals, including boxplots, histograms, stem-and-leaf plots, and Q-Q plots, to reveal distribution and outliers. Learn to create, interpret, and compare boxplots, preparing for follow-on correlation analysis.
Learn to export SPSS charts for reports by copying into Word or PowerPoint, exporting as images, and saving full outputs as PDFs.
Explore the meaning of correlation, its strength and direction, and the Pearson and Spearman approaches, with scatter plots to prep for SPSS analysis.
Compare Pearson and Spearman correlation in SPSS, understand when to use linear versus monotonic relationships, and interpret r values while considering outliers and data distribution.
Run a correlation matrix in SPSS to analyze relationships among multiple numeric variables, decide between Pearson or Spearman based on data, and interpret correlation coefficients and significance in context.
Interpret the SPSS coefficient table by examining the correlation coefficient r, its strength and direction, the p-value, and the sample size, while noting the table's symmetry.
“This course contains the use of artificial intelligence.”
In this course, you’ll learn how to use SPSS to perform descriptive statistics, create professional charts, and run correlation analysis with clarity and confidence.
This is Part B of the “Getting Started with SPSS” series and focuses on helping you summarize, visualize, and interpret your data effectively.
We begin with core descriptive statistics. You will learn how to calculate and interpret mean, median, mode, and standard deviation, and understand when to use each measure. You’ll practice generating summary tables using the Frequencies, Descriptives, and Explore functions in SPSS and learn how to interpret output tables correctly.
Next, we move into data visualization. You’ll create bar charts, histograms, pie charts, and boxplots. You’ll also learn how to customize titles, labels, and colors to make your charts clear, professional, and presentation-ready. By the end of this section, you’ll be able to explore data distributions and identify patterns or outliers visually.
In the final module, you’ll explore correlation analysis. You’ll understand the difference between Pearson and Spearman correlation, learn how to run a correlation matrix in SPSS, and interpret the coefficient table—focusing on strength, direction, and statistical significance.
This course is ideal for students writing a thesis or research paper, professionals analyzing survey data, and beginners who want a practical, step-by-step guide to descriptive statistics and correlation in SPSS.
By the end of this course, you’ll be able to confidently summarize datasets, visualize data effectively, and interpret relationships between variables using SPSS.