Applied Multivariable Statistical Analysis: Online (3 Days)

Multivariate statistics provide researchers with the ability to analyse complex data sets. It allows them the ability to plot large sets of data, reduce the number of variables, predict and identify groups of inter-related variables, and detect natural groups of observations.
 

*This course will be run over 3 days in the following sessions:

  • 9.30 am - 11.00 am: Instructional Zoom session
  • 11.30am - 12.30 pm: Instructional Zoom session
  • 1.30 pm - 3.00 pm: Instructional Zoom session
  • 3.30 pm - 4.30 pm: Exercises

 

*Please note: Courses will run on Australian Eastern Daylight Time (GMT +11)

(ie Melbourne, Sydney, Canberra daylight savings time. There will be 5 different time zones to consider in Summer)

 

 

 
Level 3 - runs over 3 days
Course dates: Wednesday 10 February 2021 - Friday 12 February 2021
Instructor: 

Dr Joanna Dipnall is a biostatistician with the School of Public Health and Preventative Medicine (SPHPM) at Monash University and Honorary Research Fellow with School of Medicine at Deakin University. She holds a B.Ec(Honours) from Monash University, and a PhD from the School of Medicine at Deakin University. She also lectures and tutors with the Department of Statistics, Data Science and Epidemiology at Swinburne University. Joanna has developed a novel Risk Index for Depression (RID) utilising SEM and machine learning techniques that brought together five key determinants of depression. She has been a teacher of Stata software for over 15 years, training across Australia and overseas and was a member of the Scientific Committee for the Oceania Stata Users Group Meeting in 2017.

Venue: 
Online
Week: 
Week 3
About this course: 

The aim of the course is to provide the participants with an understanding of different multivariable analysis techniques, sufficient to determine the appropriate technique for a given problem, format data as required for analysis, run the analysis using the Stata statistical program, and interpret the results.

Stata is a comprehensive integrated package for data management, analysis and graphics. Sample datasets will be provided.

 

The course is suitable for researchers of varying disciplines.

 

Course syllabus: 

Day 1
Overview of Multivariate analysis: An introduction to multivariate analysis and the different issues.

Issues with data: There are many issues analysts face when deciding on the appropriateness of different multivariate analysis techniques. A brief introduction to these issues and discussion of data integration, missing data, and an introduction to Stata's multiple imputation techniques.

Multiple Regression: Multiple regression analysis is often used to model the relationship between a single dependent interval variable with several varying types of independent variables. This technique is often used in economics for prediction and forecasting (e.g. national economy), and in social research for evaluating what determines an effective program (e.g. the best predictors of success in high-school), or determining which personality variable best predicts a social trait.

 

Day 2
Binary Logistic Regression: Binary logistic regression is used when there is a binary dependent variable and several varying types of independent variables. Logit analysis is used to predict the probability of an event in the dependent variable. The analysis is used widely in health research where the dependent variable is the outcome of a disease or health condition (e.g. lung cancer), or in social research where the outcome is a certain event, (e.g. employment status).

Ordinal Logistic Regression: Ordinal logistic regression is used when there is an ordinal dependent variable and several varying types of independent variables. Logit analysis is used to predict the probability of an event in the dependent variable.

Survival analysis: Survival analysis data deals with the outcome being the waiting time until the occurrence of a well-defined event. Observations are censored, in the sense that for some units the event of interest has not occurred at the time the data are analysed and explanatory variables are used to control for the effect on the waiting time. The point of survival analysis is to follow subjects over time and observe at which point in time they experience the event of interest (e.g. cancer). Survival analysis is often referred to as time to event analysis, mainly used in biomedical sciences where the interest is in observing time to death. However, over the past few years this analysis has been extended to other areas of research such as the social sciences (e.g. forensic analysis, employment analysis, marriage) and even engineering sciences (e.g. failure time analysis). An introduction to competing risks survival analysis will also be covered in this course.

 

Day 3
Exploratory Factor analysis: Exploratory Factor analysis is used to obtain distinct new variables of factors. Factor analysis looks at the interrelationships among a large number of variables and explains them in terms of their underlying factors or dimensions. This technique is often used in social science to measure a trait that cannot be measured directly (e.g. self-esteem).

Cluster analysis: Cluster analysis is an exploratory technique that uses a number of different algorithms and methods to combine observations into previously unknown mutually exclusive natural groups or clusters based on specific similarities. For example, social researchers have used this technique to produce unique groups based on socio-economic profiles.

Correspondence analysis: Simple correspondence analysis provides graphical representations of two-way frequency tables to improve the researcher’s understanding of any similarities and associations between the variables. Thus, it is especially good for the analysis of large contingency tables. For example, it could be used to investigate various crimes across the different states.

 

Course format: 

This course will take place online and uses Stata software.

Notes and sample datasets will be provided.

Recommended Background: 

Participants should have completed an intermediate statistics course covering at least some of the syllabus of Data Analysis using Stata. Experience with Stata will be assumed (e.g. use of Stata’s Do files).

Recommended Texts: 

The instructor's bound, book length notes will serve as the course text.

The notes will be posted to you in advance.

For an overview of the Stata package, please visit http://survey-design.com.au or http://stata.com.

Course fees
Early bird Member: 
$1,570
Early bird Non Member: 
$2,870
Early bird full time student Member: 
$870
Member: 
$2,020
Non Member: 
$3,270
Full time student Member: 
$1,720
FAQ: 

Q: Was this course named something else?

A: yes this course used to be called 'Applied Multivariate Analysis Using Stata'

 

Q: Do I have to have any prerequisites to do this course?

A: Please check the recommended background section for details.

Participant feedback: 

It served as a helpful exposure to a wide range of statistical techniques (Summer 2015)

 

Covered some new things that I hadn’t known about before (Summer 2015)

 

Theory & pracs good - lots of emphasis on pre & post testing of data before analysis !!! great. (Summer 2014)

 

Syntax, course notes, broad coverage, so very helpful. (Summer 2014)

 

Exposure to someone who really knows her stuff. (Summer 2014)

 

catered to diff students but seemed to suit all our levels. We all got what we needed out of it. (Summer 2014)

Supported by: 

Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.