Data Analysis using SPSS Software
About the course
SPSS is one of the widely used, reliable, and strong data analysis software. The course on “SPSS for data analysis and interpretation of outputs” intends to provide practical hands-on training to enhance the understanding and skills of the participants on how to use SPSS for data processing, analysis, and interpretation of outputs. The computer sessions will be supported with theoretical concepts on biostatistics and epidemiology, especially the use of statistical and epidemiological methods for testing hypothesis. The course will include practical sessions on SPSS for the analysis of data.
Objectives
The aim of this course is to develop skilled professionals in the field of research, especially for data analysis. The overall objective of the course is to empower the participants with necessary but specific knowledge and skills on the use of SPSS for data analysis related to health and social sciences research. On completion of the course, the participants are expected to be able to:
- Use SPSS for data entry, data cleaning, and data management;
- Select appropriate statistical test(s) for hypothesis testing;
- Use SPSS to analyze data using univariate, bivariate, and multivariable statistical methods; and
- Interpret the outputs of data analysis.
Who this course is for
- Anyone wanting to work with SPSS
- Data analysts and scientists
- University students
- Public Health Professionals
- Economists, Social Scientists, Political Scientists, Biostatisticians, and other disciplines
Requirements
- Basic software knowledge
- Statistical knowledge
- Some basic quantitative knowledge
Course outline
- Central tendency and dispersion including quartile and percentile
- Data management (data cleaning, recoding, transformation, etc.)
- Both parametric and non-parametric methods for testing hypothesis will be covered in this course.
- For hypothesis testing, emphasis will be given on what statistics to be used in what situation, how to perform the statistical test by SPSS and interpret the outputs.
- For hypothesis testing, the following statistical methods will be covered.
- Student’s t-test (independent samples and paired t-test)
- One-way ANOVA
- Chi-square test
- Correlation (Pearson’s correlation and Spearman’s correlation)
- Simple linear regression
- Logistic regression with logistic regression diagnostics
- How to understand that the data have come from a normally distributed population (Kolmogorov-Smirnov test, and Shapiro-Wilk test)
Course enrolment
- Date will be announced soon
- Registration link: Will open soon
- Course fee: Will announce soon
Are you curious?
