Mastery IBM-SPSS in 30 Days: The Ultimate Beginner’s Guide

Step-by-step guide to mastering data entry, cleaning, and preparation in IBM SPSS Statistics for Absolute Beginners

Created byTraining Que

Last updated 4/2026

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What you'll learn

Understand what SPSS is and how it compares to other statistical tools like Excel, R, and Python.
Navigate the SPSS interface confidently, including Data View and Variable View.
Import, label, and organize datasets from Excel or CSV files for analysis
Clean and prepare data by handling missing values, recoding variables, and computing new ones.
Apply a structured data analysis workflow to summarize, visualize, and interpret results accurately.

Course content

4 sections • 12 lectures • 1h 6m total length

What is SPSS?5:29
Learn SPSS, the statistical package for the social sciences, for quantitative data analysis from data entry to descriptive statistics, charts, correlations, and tests like t tests, ANOVA, and regression.
Key Features and Who Uses SPSS6:14
Explore the key features of SPSS and who uses it. See how a point-and-click interface, data management, labeling, statistics, and visualization serve students, researchers, healthcare professionals, and businesses.
SPSS vs. Other Tools (Excel, R, Python)6:00
Compare SPSS with Excel, R, and Python to decide when SPSS fits your needs, focusing on ease of use, data management, statistics, visualization, and reproducibility.
Overview of the Data Analysis Workflow5:57
Navigate the six-step SPSS data analysis workflow from defining questions to interpreting results, preparing data, labeling variables, cleaning data, running analyses, and reporting insights.
Question 1

SPSS Interface: Data View vs. Variable View6:01
Learn the SPSS interface by using data view and variable view to define variables (name, type, label, values, missing, measure) and enter data with clear metadata.
Manual Data Entry Step-by-Step Importing Data from Excel & CSV4:26
Learn to manually enter data in SPSS from scratch by defining variables in variable view, entering data in data view, using value labels, and saving as a sav file.
Understanding SPSS File Types5:23
Learn the three core SPSS file types—sav data files with metadata, SPV output files for results, and SPS syntax files for reusable commands—along with organizing and naming practices.
Importing Data from Excel & CSV5:35
Import data into SPSS from Excel and CSV, review in data view and variable view, clean headers and types, set missing values, and save as SPSS data file.
Quiz: Import & Entry Checkpoint

Labeling Variables and Assigning Value Labels5:24
Learn how labeling variables and assigning value labels in SPSS improves clarity, readability, and interpretation of dataset outputs, with hands-on steps for variable view, label creation, and value labels.
Handling Missing Data5:32
Learn how to detect, define, and handle missing data in SPSS, including system missing values, coded values like -99 or 999, and strategies such as deletion, imputation, and variable exclusion.
Recoding and Computing Variables5:19
Recoding into variables preserves raw data while computing metrics like total scores and body mass index (bmi). Group ages into categories, reverse Likert items, and label values for reproducible analysis.
Best Practices for Data Preparation5:39
Keep data clean by following a practical checklist to label, define missing values, and document changes for accurate, ready-to-analyze SPSS outputs.
Quiz: Data Preparation Review

Requirements

No prior SPSS or statistics experience required — this course is designed for absolute beginners.
You will need a computer with IBM SPSS Statistics (version 24 or higher) installed.
A basic understanding of what data looks like (e.g., rows = participants, columns = variables) will be helpful.

Description

Are you new to SPSS and not sure where to start? This beginner-friendly course provides a clear, step-by-step introduction to SPSS data analysis software, helping you build confidence in using IBM SPSS Statistics from day one.

You’ll begin by learning what SPSS is, how IBM SPSS Statistics compares to tools like Excel, R, or Python, and why it is widely trusted by researchers, analysts, and professionals across multiple industries. From there, you’ll learn how to enter, import, label, and organize data correctly within SPSS data analysis software. By the end of this course, you’ll be able to clean and prepare datasets for analysis with confidence—even if you’ve never used SPSS before.

Using real-world examples and hands-on demonstrations, you’ll discover how to define variables, manage and handle missing data, recode values, compute new variables, and apply best practices for data preparation in IBM SPSS Statistics. Each module includes short quizzes to reinforce learning and help you track your progress as you build essential data analysis skills.

Whether you’re a student, researcher, or working professional, this course gives you a strong foundation in data analysis using SPSS data analysis software—without writing a single line of code. You’ll gain the skills needed to work efficiently with datasets, prepare accurate reports, and interpret statistical results clearly.

By the end of the course, you’ll have the knowledge and confidence to organize, analyze, and interpret data using IBM SPSS Statistics, empowering you to make data-driven decisions and advance your academic or professional work.

AI Disclaimer:
Some elements of this course (such as narration, visuals, or supporting materials) were created or enhanced with the assistance of artificial intelligence tools, under the instructor’s guidance and review

Who this course is for:

Students and researchers who need to analyze data for assignments, theses, or dissertations.
Professionals in education, healthcare, business, or social sciences who want to use SPSS for real-world data analysis.
Beginners who have never used SPSS but want a simple, step-by-step introduction.
Anyone tired of searching online tutorials and ready to learn SPSS properly from the ground up.

Mastery IBM-SPSS in 30 Days: The Ultimate Beginner’s Guide

What you'll learn

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Course content

Module 1: Introduction to SPSS4 lectures • 24min

Module 2: Data Entry and Importing Files4 lectures • 21min

Module 3: Data Cleaning and Preparation4 lectures • 22min

Final Quiz0

Requirements

Description

Who this course is for: