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SPSS for Data Analysis: Complete SPSS Tutorials

Name: SPSS for Data Analysis: Complete SPSS Tutorials
Rating: 4.8 (17 reviews)

Analyze data with SPSS, discover valuable insights, create data visualisations, and professionally report your results.

Highest Rated

Created byHabibur Rahman

Last updated 3/2026

English

What you'll learn

Explore the SPSS interface, key functions, and features
Prepare datasets by importing, cleaning, and recoding variables
Independently run commonly used statistical analyses
Interpret results and present them clearly
Create easy-to-understand graphs and charts

Course content

14 sections • 82 lectures • 7h 10m total length

Downloading and Installing SPSS0:59
Study Guide0:02

Introduction to SPSS Data Editor7:30
The SPSS Data Editor provides a simple and user-friendly interface for entering, editing, and managing datasets. Explore how the Data Editor works and familiarize yourself with its key functionalities.
Introduction to Output Viewer6:24
The SPSS Output Viewer presents the results of your analysis in one place in an organized manner. Learn how to navigate the Output Viewer to understand and interpret the results of your statistical analyses.
Entering Data in SPSS- Numeric Variables8:05
Understand the different properties in the Variable View, set up variable properties, and enter numeric data in SPSS—quickly and easily!
Entering Data in SPSS- Categorical and Date5:41
Categorical and date variables are commonly used in SPSS. Learn how to define their properties and enter these types of data properly.
Editing Data in SPSS2:12
Take your SPSS skills one step further with the following essential skills that will significantly improve your productivity.
Edit, delete, update contents in the data editor
Copy and paste data and variable attributes
Delete or hide, reposition variables
Importing Data from Excel2:55
Learn how to import data from Excel into SPSS in just a few clicks—no need to copy and paste dozens of variables or manually enter the data.
Merging Data Files3:46
Learn to combine datasets in SPSS by matching cases with a shared identifier.
Automatic Recode4:10
Learn how to use the Automatic Recode feature in SPSS to convert string (text) variables into numeric format. String variables can limit your ability to perform statistical analyses, but with Automatic Recode, you can quickly assign numeric codes to text values or categories—making your data suitable for a wide range of analyses.
Manual Recode7:00
Manual Recode can also be used in SPSS to convert string (text) variables into numeric format. Manual recoding is especially important when dealing with ordinal variables, such as Likert scale responses, where the order of categories matters. Unlike Automatic Recode, Manual Recode gives you full control over how each value is coded—ensuring accuracy and consistency in your data.
Reverse Coding Items3:22
Reverse coding is an important step in calculating total scores for many survey scales. Learn how to perform reverse coding in SPSS using the manaul recode command.
Compute Variable5:46
If you use multi-item survey instruments, you will typically need to create a combined score from the individual items. Two common methods are calculating the sum or the mean. In this lesson, you will learn how to do both using the Compute Variable command in SPSS.
Assignment 1: Essential Data Preparation
Categorizing Variables3:56
Learn how to categorize a scale variable in SPSS using the manual Recode command. This can simplify your analysis and offer greater flexibility in your analytical process.
Split File3:22
The Split File feature in SPSS allows you to divide your dataset into subgroups based on one or more categorical variables. When Split File is active, SPSS runs separate analyses for each subgroup, making it easier to compare results across categories.
Select Cases: Part 16:16
The Select Cases feature in SPSS works similarly to the IF function in Excel. It allows you to specify conditions to create subsets of your dataset for targeted analysis.
Select Cases: Part 24:26
Learn how to select cases in SPSS using and and or operators to apply multiple conditions, such as age from 25 to 35 and product categories makeup or skin care.
Combining Categories4:39
Duplicate categories, too few cases in one or more categories, or simply too many categories? The obvious solution is to combine them—without unnecessarily removing cases. Learn how to do it properly in this lesson.
Define Missing Values2:49
Sometimes, you may need to exclude an invalid category, value, or range from a variable. In such cases, you can define these values as missing in SPSS. This ensures they are ignored during analyses and do not affect your results.
Introduction to Table Editor2:56
Table editors can be used to customize tables within SPSS. In this lesson, you will become familiar with the interface of the SPSS Table Editor
Introduction to Graph Editor3:19
This lesson introduces you to the SPSS Chart Editor, a tool that lets you modify and enhance the appearance of your graphs. You'll learn how to navigate its interface and make basic customizations to your charts.
Basic of Syntax3:25
Assignment 2: Transforming Data

Steps in Data Preparation2:40
Export survey data and import it into SPSS to begin data preparation. Clean data, convert string variables to numeric, reverse code Likert items, assess reliability, and compute scores for analysis.
Data Cleaning7:30
Reliability analysis4:32
Basics of Descriptive Statistics2:31
Descriptive Statistics6:39
Analyzing Multiple Response Questions7:20
Analyze multiple response questions in SPSS by creating binary variables for each category, and use frequency tables or cross tabulations after defining the variable set.
Descriptive Statistics

Introduction to Data Visualization1:29
Histogram6:08
Learn to create and interpret histograms in SPSS, visualizing the distribution of continuous variables. Explore normal and skewed distributions, compare subgroups, and edit charts for reports.
Box Plots5:06
Bar Chart3:30
Clustered Bar Chart9:01
Line Chart4:53
Learn to create and interpret line charts in SPSS, including simple and multiple line charts, and use them to visualize relationships and interaction effects between employment status, stress, and gender.
Pie Chart1:02
Scatterplot5:33
Explore how scatter plots visualize the relationship between two continuous variables. See how a best fit line clarifies negative, positive, or weak relationships in self-esteem, stress, and procrastination.
Editing Graphs and Charts4:54
Data Visualization

Normality4:53
Learn to assess normality as a key statistical assumption for parametric tests in SPSS using histograms, normal qq plots, and Kolmogorov-Smirnov and Shapiro-Wilk tests.
Linearity4:48
Outliers (detection and handling)7:50
Identify univariate, bivariate, and multivariate outliers using histogram, box plot, z-score, and scatter plots, then decide to delete or keep based on context.
Independence of Observations1:01
Assumptions Basics

Basics of Linear Regression5:04
Assumptions of Linear Regression6:49
Learn to check linear regression assumptions in SPSS using histogram, pp plot, and scatter plot, including residual normality, linearity, homoscedasticity, independence, outliers, and multicollinearity checks.
Simple Linear Regression8:32
Multiple Linear Regression Analysis6:24
Multiple Regression with Categorical Variables7:17
Learn to include categorical variables in linear regression by creating dummy variables and choosing reference categories, with gender and ethnicity as examples, and interpret coefficients and model significance.
Linear Regression Assignment

Overview of Logistic Regression0:34
Basics of Binary Logistic Regression4:29
Explore the basics of binary logistic regression in SPSS to predict a binary outcome with one or more predictors, and learn assumptions, coding, model fit, and odds-ratio interpretation.
Binary Logistic Regression: Example18:10
Learn to run and interpret binary logistic regression for anxiety, using gender, socioeconomic status, age, social media usage, duration, and online comparison, with dummy coding and odds ratios.

Requirements

Access to IBM SPSS Statistics (any version).
No prior knowledge of statistics is required.

Description

Learn how to take a raw dataset, prepare it in SPSS, choose the appropriate statistical test, run the analysis, interpret the output, and report your findings in APA style. This course provides a structured, hands-on approach to learning SPSS, from the fundamentals to more advanced statistical techniques.

The course is suitable for bachelor's, master's, and PhD students, as well as researchers who need to analyze data for theses, dissertations, research projects, assignments, and homework. You do not need previous experience with SPSS, statistics, or data analysis.

You will begin with the basic concepts of statistics, including types of data, descriptive and inferential statistics, hypothesis testing, significance levels, and other essential concepts that will help you understand the analyses covered in the course.

You will then learn the fundamentals of SPSS, including how to navigate the interface, import and organize data, define variables, and perform common data preparation tasks. You will also learn how to clean datasets, recode variables, create composite scores, and assess the reliability of multi-item scales.

Once your data are prepared, the course moves into descriptive and inferential statistics. You will learn how to summarize and visualize your data, check important assumptions, and select and perform appropriate statistical analyses based on your research questions.

The course covers a core set of statistical techniques, including correlation, multiple regression, logistic regression, mediation, moderation, t-tests, ANOVA, non-parametric tests, and exploratory factor analysis. Each method is explained through practical examples so that you can see not only how to run the analysis in SPSS, but also how to understand and interpret the results.

You will also learn how to report statistical findings in APA format and use appropriate reporting practices for academic work. Practical datasets are provided throughout the course, and assignments give you opportunities to apply these methods on your own.

What You Will Learn

Basic statistical concepts and terminology
How to navigate and work with SPSS
Importing, organizing, and managing datasets
Data cleaning and preparation
Coding and recoding variables
Creating and scoring composite scales
Reliability analysis using Cronbach's alpha
Descriptive statistics and exploratory data analysis
Creating and interpreting charts and graphs
Checking assumptions such as normality and linearity
Pearson, Spearman, and partial correlation
Multiple linear regression
Binary logistic regression
Mediation and moderation analysis
Independent- and paired-samples t-tests
One-way and factorial ANOVA
Non-parametric statistical tests
Exploratory factor analysis
Reading and interpreting SPSS output
Reporting statistical results in APA style
Best practices for data analysis

By the end of the course, you will be fully capable of working with SPSS, including preparing datasets, conducting a range of statistical analyses, interpreting statistical output, and communicating your findings clearly.

Join today and build the skills you need to analyze data and uncover meaningful insights with SPSS.

Who this course is for:

Bachelor’s, Master’s, and PhD students working on theses, dissertations, or research projects.
Researchers and professionals conducting quantitative research.
Students completing statistics and SPSS homework, assignments, or lab reports.
Anyone interested in learning data analysis with SPSS.

SPSS for Data Analysis: Complete SPSS Tutorials

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 1min

Basic Concepts in Statistics6 lectures • 10min

Getting Started with SPSS20 lectures • 1hr 32min

Preliminary Analysis6 lectures • 31min

Data Visualization9 lectures • 42min

Introduction to Statistical Assumption4 lectures • 19min

Correlation Analysis3 lectures • 15min

Linear Regression Analysis5 lectures • 34min

Logistic Regression Analysis3 lectures • 23min

T tests4 lectures • 20min

Requirements

Description

Who this course is for: