This short course introduces data analysis using IBM SPSS. Participants will have opportunity to analyse real life medical data and supports for results discussion and conclusion. The short course will cover: data entering, data labelling, data cleaning, data computing / transforming, and data analysis (using commands on menus) including summary statistics, hypothesis test, 95% CI, ANOVA, non-parametric method, RR, OR, correlation, linear regression, logistic regression and Cox's regression. Participants may attend Day 1, Day 2 or both days of this course Learning objectives
- Be familiar with basic SPSS functions and its tools. These functions and tools will enable students to proficiently open and create SPSS data files.
- Present data using SPSS generated graphs and summary statistics.
- Conduct an independent and paired sample t-test to analyze data, where variable is collected on a continuous scale.
- Conduct a One-Way ANOVA to compare more than two groups where the test variable is collected on a continuous scale and the data in each group follows the normal distribution.
- Analyse data when normality assumption for data does not hold, ie. the data does not follow the normal distribution. Thus the data can be continuous, discrete or ordinal but asymmetric. The statistical methods used to analyse such data are collectively known as Non-Parametric Methods or distribution free method.
- Evaluate the effect of exposures on the outcome where outcome is continuous. However, exposure could be numerical or categorical or a combination of both.
- Evaluate the association between an exposure and an outcome variable, where they are either binary or multinomial (more than two categories).
- Evaluate the effect of exposures on the outcome where outcome is categorical BINARY and exposure could be numerical and/or categorical.
- Evaluate the effect of exposures on the outcome where outcome is categorical Binary and time dependent (time to event data) or survival analysis.
- Manage data (entering, labelling, creating, cleaning, etc.)
- Be familiar with basic SPSS functions and its tools. These functions and tools will enable students to proficiently open and create SPSS data files.
- Present data using SPSS generated graphs and summary statistics.
- Conduct an independent and paired sample t-test to analyze data, where variable is collected on a continuous scale.
- Conduct a One-Way ANOVA to compare more than two groups where the test variable is collected on a continuous scale and the data in each group follows the normal distribution.
- Analyse data when normality assumption for data does not hold, ie. the data does not follow the normal distribution. Thus the data can be continuous, discrete or ordinal but asymmetric. The statistical methods used to analyse such data are collectively known as Non-Parametric Methods or distribution free method.
- Evaluate the effect of exposures on the outcome where outcome is continuous. However, exposure could be numerical or categorical or a combination of both.
- Evaluate the association between an exposure and an outcome variable, where they are either binary or multinomial (more than two categories).
- Evaluate the effect of exposures on the outcome where outcome is categorical BINARY and exposure could be numerical and/or categorical.
- Evaluate the effect of exposures on the outcome where outcome is categorical Binary and time dependent (time to event data) or survival analysis.
- Manage data (entering, labelling, creating, cleaning, etc.)
- All participants must bring a laptop (Mac or Windows) to this course which has either SPSS or a trial version of SPSS installed.
- Participants are required to have successfully completed the topics below either through previous study or attendance of the Biostatistics for Clinical and Public Health Research short course
- Key Concepts in Public Health and Clinical Studies
- Statistical Methods for Analysing Continuous Data
- Statistical Methods for Analysing Categorical Data
- Sample size calculation