Statistical Data Analysis with SPSS: a practical approach
From comparative analyses to multiple predictive models
Ratings 4.65 / 5.00
What You Will Learn!
- To handle data in SPSS
- To perform basic and advanced analysis in SPSS
- To perform comparative analyses using t-tests, ANOVA, ANCOVA and multivariate ANOVA
- To test simple and multiple linear regression models, logistic models and many other models
Description
In this course, you will learn how to perform statistical data analysis with one of the best statistical software in the world, called SPSS (Statistical Package for Social Sciences).
SPSS is indeed one of the best statistical software for many fields of research, such as social sciences, business intelligence, psychology, forensic research, medical research, and many others.
This course will present you with a research project that you will follow from the primary data generated by a survey, to a large set of statistical procedures able to extract valuable information from this data.
In this journey, you will learn to perform the following procedures in SPSS: to define variables and to introduce data, to import data, to transform and recode variables, to reverse and compute variables, to perform descriptive analysis, to perform independent and repeated measures t-tests, to perform independent and repeated Analysis of Variance (ANOVA), to perform multivariate ANOVA analysis, to perform ANCOVA analysis, to perform correlations, to test simple and multiple linear regression models, to test logistic models, and all other procedures that a well-trained analyst should know.
For each presentation, there are several exercises and databases attached as downloadable resources, which you can use to consolidate your knowledge about how to use SPSS properly.
Finally, this course is an ongoing process, which means that each new week or month, there will be constantly uploaded new lectures in order to upgrade your knowledge continuously.
So, what are we waiting for? Let's go!
Who Should Attend!
- Students, professionals, entrepreneurs and any other person interested in understanding data
- Psychologists, sociologists, business researchers, data scientists, data analysts, entrepreneurs
