Program Overview

Introduction to Data Analysis: SPSS Without Tears

Overview

This short course introduces data analysis using IBM SPSS. Participants will have the opportunity to analyze real-life medical data and support for results discussion and conclusion.

Program Details

• Dates to be announced shortly
• The course will cover data entering, data labeling, 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.

Who Should Attend

• This course is suitable for those who have knowledge of the basic concepts in biostatistics, e.g., presenting data using graphs and numbers, hypothesis test, 95% CI, ANOVA, non-parametric methods, chi-square analysis, relative risk (RR), odds ratio (OR), linear regression, logistic regression, and Cox regression.

What You Will Learn

On completion of this course, participants will be able to:

• 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 the 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.
• Analyze data when normality assumption for data does not hold, i.e., the data does not follow the normal distribution. Thus, the data can be continuous, discrete, or ordinal but asymmetric. The statistical methods used to analyze such data are collectively known as Non-Parametric Methods or distribution-free method.
• Evaluate the effect of exposures on the outcome where the 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 the outcome is categorical BINARY and exposure could be numerical and/or categorical.
• Evaluate the effect of exposures on the outcome where the outcome is categorical Binary and time-dependent (time to event data) or survival analysis.
• Manage data (entering, labeling, creating, cleaning, etc.).

Program Structure

Day 1

• Familiarizing Yourself with SPSS
• Describing Data Using Graphs and Summary Statistics
• Analyzing Data Using Independent and Paired Samples t-Test
• Analyzing Data Using Analysis of Variance ANOVA
• Analyzing Data Using Nonparametric Methods
• Correlation and Linear Regression Analyses

Day 2

• Correlation and Linear Regression Analyses
• Logistic regression
• Chi-square analysis
• Survival analysis
• Data creation, cleaning, and management

Testimonials

Baki has a very good way of explaining complex concepts in easy-to-understand chunks. Thank you!

Very good small group format. Going through questions and examples related to biostats was very relevant. Very attentive teachers.

Very, very helpful – a lot covered in two days.

Instructor

Dr. Baki Billah

Dr. Baki Billah works as a Senior Lecturer at the School of Public Health and Preventive Medicine of Monash University.

Dr. Billah completed his PhD at Monash University, MAS at the Memorial University of Newfoundland, Canada, and Master of Science and Bachelor of Science (Hons) at the University of Dhaka, Bangladesh. Before working for Monash University, Dr. Billah worked at the Carleton University, Ottawa Canada, Industry Canada, Ottawa Canada, Memorial University of Newfoundland, Canada, and the University of Dhaka, Bangladesh.

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