Course Title: Statistical Analysis 2

Part A: Course Overview

Course Title: Statistical Analysis 2

Credit Points: 12.00

Terms

Teaching Period(s)

MATH1281

Bundoora Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH1281

Bundoora Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2018

MATH1282

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH1282

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2018

Course Coordinator: Associate Professor Cliff Da Costa

Course Coordinator Phone: +61 3 9925 6114 / 9925 2277

Course Coordinator Email: cliff.dacosta@rmit.edu.au

Course Coordinator Location: 201.03.24

Course Coordinator Availability: Contact via email for an appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

This is a follow-up course to MATH1279/1280. We will assume you are familiar with the topics taught in MATH1279/1280, which require you to have done MATH1275/1276, MATH1277/1278 or equivalent first year statistics courses.

Course Description

The purpose of this course is to introduce you to further applications of statistical methods in research beyond that covered in MATH1279/1280 Statistical Analysis 1. The intent is to enable you to read and understand current research literature in your respective fields of specialisation especially with regard to the use of statistical methods. Among the statistical methods that you will be exposed to are the following: MANOVA, two-way Repeated Measures ANOVA, Mixed Designs ANOVA, one-way ANCOVA. The testing of assumptions underlying the use of statistical tests will be emphasised. Extensive use will be made of the MINITAB and/or SPSS statistical packages to illustrate applications.

Objectives/Learning Outcomes/Capability Development

On completion of the course, you should be able to:

1. Perform exploratory analysis of data collected within your field of specialisation;
2.  Determine effect size, power on existing research and estimating sample sizes for new research;
3.  Apply advanced inferential statistical methods via hypothesis testing and confidence interval estimation;
4.  Test for statistical assumptions underlying the use of advanced inferential statistical methods on the data;
5.  Proficiently use statistical packages such as SPSS in analysing data using advanced statistical methods.

MATH1281/ MATH 1282 is offered as a Program Option to Psychology and other Health Science students. It contributes to Program Learning Outcomes in the following domains:

1.0 Critical Analysis and Problem Solving
1.1 Ability to apply scientific principles and methods to describe and analyse research data
1.2 Ability to know what questions to ask, who to ask and how to ask them.

2.0 Teamwork & Leadership
2.1 Ability to work in collaboration with others on data analysis tasks

3.0 Communication and Presentation
3.1 Ability to communicate in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to the circumstances of the situation and capabilities and sensitivities of the psychological disciplines.
3.2 Ability to constructively give and receive feedback

4.0 Self- management
4.1 Ability to take personal responsibility for decisions and actions while being aware of limits of knowledge and skill and when to seek help

Overview of Learning Activities

The learning activities included in this course are:

• attendance at lectures where syllabus material will be presented and explained and  topics illustrated with demonstrations via java applets, statistical packages, simulations and worked examples;
• completion of tutorial/practice questions and data analysis computer laboratory sessions are designed to give further practice in the application of theory and procedures, and to provide feedback on your progress and understanding;
• in-lecture review questions on topics completed so as to enable you to gauge progress in your learning;
• guided private study through the provision of lecture summaries that indicate follow-up reading and practice problems to attempt on the material taught.

Lecture: 2 hours/week, Labs: 1 hour/week; Independent  study: 5 hours/week
Total : 96 hours per semester

Overview of Learning Resources

You will be able to access online course information and learning materials through Canvas. Additional learning materials will be provided in lectures via appropriate handouts. Lists of relevant texts, resources in the library and freely accessible Internet sites will be provided. You will also be able to use computer software within the School’s computer laboratories.
http://rmit.libguides.com/mathstats

Overview of Assessment

The assessment for this course comprises the following:

There will be three assessment tasks undertaken to assess your learning in the course.

ASSESSMENT TASK 1 (WEIGHTED 10%)

SPSS Lab sessions (Covers CLOs 1 – 5) due every 2nd week

ASSESSMENT TASK 2 (WEIGHTED 45%)

Data analysis report (Covers CLOs 1 – 5) due after semester break

ASSESSMENT TASK 3 (WEIGHTED 45%)

Data analysis report (Covers CLOs 1 – 5) due end of SWOT VAC

You need to attempt ALL assessment tasks and obtain a weighted average of at least 50% in order to pass the course.