Data Analysis using SPSS

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About Course

This course is designed to introduce students to the statistical software SPSS and its applications in data analysis. The course will cover the basics of data management and descriptive statistics, as well as more advanced techniques such as inferential statistics, correlation, and regression analysis.

Throughout the course, students will learn how to use SPSS to import, manipulate, and analyze data and interpret and present the results. The course will be hands-on, focusing on applying the techniques covered to real-world data sets. By the end of the course, students will have the skills and knowledge to use SPSS to analyze data for their research projects or other applications.

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What Will You Learn?

  • Throughout the course, students will learn how to use SPSS at the basic level.

Course Content

Introduction to SPSS
This module will provide an overview of the SPSS software, including its interface, basic data management, and data entry.

Descriptive statistics
This module will cover the basics of descriptive statistics, including measures of central tendency, measures of variability, and frequency distributions. Students will learn how to use SPSS to generate descriptive statistics and interpret the results.

Inferential statistics
This module will cover the basics of inferential statistics, including hypothesis testing and confidence intervals. Students will learn how to use SPSS to perform t-tests, ANOVA, and chi-square tests, and interpret the results.

Correlation and Regression
This module will cover the concepts of correlation and linear regression analysis. Students will learn how to use SPSS to calculate correlation coefficients, create scatterplots, and perform linear regression analysis, and interpret the results

Non-parametric Tests
This module will cover the concepts of non-parametric tests, including chi-square, sign test, wilcoxon rank sum test, and kruskal wallis test.

Advanced statistical techniques
This module will cover advanced statistical techniques such as factorial ANOVA, logistic regression, survival analysis, and factor analysis.

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