
Explore SPSS basics—from the data editor window (data view and variable view) to syntax, import data formats, and APA-style outputs.
Explore SPSS basics, customization, and time-saving tools, master data preparation and the big five statistical tests, and learn APA reporting for accurate table styling.
Learn to customize SPSS by removing the welcome dialog, adjusting fonts, showing variable names, opening one data set, and configuring toolbars. Also create a personalized toolbar and install extensions.
Learn how to use the SPSS syntax editor window to create, save, and run commands for data editing and analysis, including frequency tables and bar charts.
Explore the clone variables tool in SPSS to duplicate, recode, and merge categories, compare with originals using crosstabs, and apply value labels for cleaner outputs.
Explore how health behaviors relate to health costs by constructing all scatter plots among four variables in SPSS, including plots with linear fit lines and regression tables.
Master SPSS missing values, distinguishing system from user missing data, detecting them with frequency tables, and handling analyses via listwise, pairwise, or available-case methods.
Organize SPSS projects with a single project folder, subfolders for data and outputs, and an original data subfolder; prefix files with ten folds for replicable order, and use descriptive names.
Inspect the data file, count cases and variables, and ensure a unique identifier. Screen categorical variables for labels and coding, and quantitative variables for distributions and outliers.
Master SPSS data preparation by screening categorical variables with frequency tables and bar charts, handling missing values, merging small categories, and reversing ordinal coding for accurate analysis.
Screen quantitative variables in SPSS, inspect histograms for distributions and outliers, and manage missing data. Learn data prep steps like recoding, renaming, reordering, and applying value labels.
Explore correlation analysis in SPSS by screening with histograms, inspecting scatter plots, and computing Pearson correlations; interpret p-values and confidence intervals among IQ, depression, anxiety, social functioning, and general well-being.
Learn to perform SPSS one-way ANOVA with post hoc tests, interpret p-values and eta squared, and report results in APA style for four medicine groups using BDI scores.
Learn to perform a complete step-by-step multiple linear regression in SPSS, including data screening, scatter plots, correlations, running the model, interpreting coefficients and p-values, and checking assumptions.
Master APA style contingency tables (crosstabs) in SPSS, with three methods: menu, output modify, and a version without totals or decimals.
Learn to generate APA style correlation tables in SPSS using a Python script or manual SPSS and Excel tricks, converting Pearson matrices with precise decimal formatting, p-values, and lower-triangle views.
Learn to report SPSS ANOVA results in APA style using text and three tables, formatting df, f, p, and eta squared, with post-hoc subscripts and table styling.
learn to craft two basic APA style regression tables in SPSS, editing in Excel to report coefficients, standard errors, and p values, with footnotes on R square and sample size.
In this course, you’ll learn everything you need to know for successfully completing data analysis projects in SPSS (“IBM SPSS Statistics”).
Whether you’re a Bachelor’s or Master’s student working on your thesis or a professional in market research, this course will show you how to get things done in SPSS the right way.
This starts off with mastering some fundamentals such as
the basics, tips & tricks for the data editor, syntax and output windows;
how to paste, run, edit & save SPSS syntax and why even do so in the first place;
how to screen your data file and fix any issues you may encounter?
Most other SPSS courses completely ignore these fundamentals and use only nicely prepared, problem-free data files. Sadly, this doesn’t prepare you for working with real-life data because these usually contain issues such as
missing values
string variables
outliers
reverse coded variables
long variable names and absence of variable and/or value labels.
In this course, you’ll quickly learn how to detect and fix any data complications the right way before you proceed to the actual data analyses.
After this data preparation section, I’ll cover the 5 most sought-after statistical analyses (the “big 5 statistical tests”) which are
1. the chi-square independence test
2. the independent samples t-test
3. ANOVA (analysis of variance)
4. (Pearson) correlations
5. multiple linear regression analysis.
You’ll learn how to run, interpret and report each of these tests correctly and completely. In contrast to most other courses, I’ll also cover
which test is suitable for which research question;
how to evaluate if you meet the assumptions required for each test;
which effect size measures are applicable with Cohen’s (1988) rules of thumb for small, medium and large effects
how to visualize the major trends in your data with charts.
After successfully running some test(s), you may need to report your results in APA style. In this course, you’ll learn how to create perfect APA tables with minimal time and effort by using
some handy SPSS tools & tricks for getting the right table contents and layout;
Excel or Googlesheets for styling your tables (numeric formats, alignment, row heights);
WORD or GoogleDocs for column widths, titles and final reporting.
Are you looking for the one course that shows how to deliver outstanding SPSS work with minimal time and effort?
Look no further.
See you in the course!
Ruben (founder, SPSS tutorials)