About this course
For students, researchers and data analysts who don’t have a strong statistical background (or any statistical background), this course teaches you statistical data analysis, interpretation, and APA reporting in a simple, practical approach.
Alexander Mtembenuzeni takes the same simple explanations approach he took with the “Learn SPSS in 15 minutes” video on YouTube (now with over 1.9 million views and so many great comments) and used it to create this course.
The goal of this course is to get you to complete your research project without the need to go through complicated theories!
The course takes you from absolute beginner of SPSS and statistics with lessons such as getting familiar with the SPSS interface, creating variables, entering data, and running, interpreting and reporting basic analyses. From there, you will be introduced to inferential tests and hypothesis testing with statistics such as t-tests, ANOVA and linear regressions.
What you’ll learn
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Data entry, data importing and preparation
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Summarizing data using descriptive statistics
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Exploring relationships between different types of variables
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Choosing appropriate charts and developing them
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Transforming variables and managing the data to suit your analyses
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Choosing the appropriate inferential tests such as chi-square, t-tests and regression and running them
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How to interpret all the statistics presented in the course
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And how to write your results in your reports, dissertations, or thesis using the APA format
Course content
Introduction and getting started
- What is SPSS
- Downloading and installing SPSS
- The SPSS interface
Statistics basics
- Understanding data
- Understanding variables
- Measurement levels – the key to choosing analyses
- Branches of statistics
Getting data into SPSS
- Creating variables
- Creating variables II
- Entering data in SPSS
- Importing data from Excel
Exploring data using descriptive statistics
- Frequencies
- Interpreting and reporting frequencies
- Summary statistics
- Summary statistics II
- Interpreting and reporting summary statistics
Exploring relationships between variables
- Introduction to variable relationships
- Mean comparisons – Relationship between categorical and continuous variables
- Interpreting and reporting mean comparisons
- Crosstabulation – relationships between 2 categorical variables
- Interpreting and reporting crosstabulations
- Correlation – relationship between 2 continuous variables
- Interpreting and reporting correlations
- Using custom tables
- Univariate variable multiple response analysis
- Multiple response crosstabulation
Charts
- Introduction to charts in SPSS
- Charts for single categorical variables – Pie chart and bar chart
- Charts for single continuous variables – Histogram and boxplot
- Charts for relationships between categorical and continuous variables
- Charts for relationships between categorical variables
- Charts for relationships between continuous variables – Scatter plots
- Charts for exploring trends – Line charts
- Customizing charts
Transforming variables
- Introduction to variable transformation
- Ranking
- Binning
- Recoding
- Calculating new variables
Data Management
- Introduction to data management in SPSS
- Filtering using Select-cases
- Disaggregating using Split file
- Merging files by adding cases
- Merging files by adding variables
Getting started with inferential statistics and hypothesis testing
- Fundamentals of inferential statistics and hypothesis testing
- Producing, interpreting and reporting correlations
Inferential statistics: mean comparisons
- One sample t-test
- Interpreting and reporting one sample t-test
- Paired samples t-test
- Interpreting and reporting paired samples t-test
- Independent samples t-test
- Interpreting independent samples t-test: The Levene’s test
- Interpreting and reporting independent samples t-test: The t-test
- Analysis of Variance (ANOVA)
- Interpreting the ANOVA – Levene’s test
- Interpreting the ANOVA – Post Hoc Tests
- Reporting ANOVA
Inferential statistics - linear regressions
- Introduction to linear regression
- Running and interpreting simple linear regression
- Running a multiple linear regression
- Interpreting multiple linear regression
- Basic options for regressions
- Reporting regressions
Inferential statistics - nonparametric tests
- Chi square of independence
- Interpreting and reporting chi-square
Instructor
Alexander Mtembenuzeni
Data Analyst, Instructor
DATAFORDEV Founder
Alexander has over 8 years experience training social impact organizations in data analytics. He also has consulted for small and big organizations alike – developing data collection tools, building M and E systems and analyzing data. He is a 4.6/5-rated instructor on Udemy.com where he has several courses with a combined over 6,000 students. He also loves blogging and creating tutorial videos for the Data for Development YouTube channel.
Frequently asked questions
Does this course offer a certificate?
Yes. You will automatically get a certificate of completion as soon as you complete the course and pass the graded quizzes and project
How long will it take for me to complete the course?
We recommend investing 2 hours of learning per day. With that time investment, you will finish the course including the hands-on practices in 7 days.
Which days and times does the course run?
This course is self paced. Once you enroll, you can go through the learning content at any time, and at your own pace!
Instant access
Start as soon as you enroll
Approximately 21 days to complete
Suggested 2 hours per day
Level
Beginner to intermediate
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