QUANTITATIVE DATA ANALYSIS
Credit points: 15
In this subject, you will develop your quantitative data analysis skills and learn techniques which are commonly used by researchers to answer important questions and by senior managers in important business decision making. Topics in the first component of this subject include; descriptive statistics, probability distributions, parametric estimation methods, parametric and non-parametric hypothesis tests, and both simple and multiple regression analysis. This knowledge will then be extended with advanced techniques including logit and probit models, panel data analysis, and clusterings. Authentic assessments in this subject will promote critical thinking and inquiry whilst strengthening your analytical, report writing and communication skills.
SchoolLa Trobe Business School
Subject Co-ordinatorPetko Kalev
Available to Study Abroad StudentsNo
Subject year levelYear Level 5 - Masters
Prerequisites BUS1BAN or approval by subject coordinator
Graduate capabilities & intended learning outcomes
01. Develop effective questionnaires and survey procedures to acquire high quality, valid and reliable primary data.
- Students will be taught in the very first lecture the underlying principles of a good survey design and its implementation. In a computer lab activity, a model questionnaire and associated raw data will be used to code, recode and summarise information using graphical and descriptive statistic tools in a software.
02. Provide a full and accurate explanation of relevant univariate and multivariate techniques to analyse different types of data and the underlying model assumptions that underpin these techniques.
- Learning activities will be based upon face-to-face lectures, labs sessions, online videos, readings, group discussions, and assessment tasks.
03. Demonstrate the ability to apply various data analysis techniques using relevant statistical software.
- There will be an hour long weekly interactive lab session for 12 weeks where students will use real world data to estimate, interpret and analyse various statistical tools using relevant software.
04. Demonstrate the ability to test hypotheses whilst interpreting, reporting and presenting data analysis results in a scholarly way.
- The learning will be developed through weekly lecture and workshop activities that will be assessed through authentic assessments. In particular, students will replicate or extend a published research work and disseminate their findings in the final lecture in week 12 as part of their project.
05. Gain insight into various quantitative methods that are frequently used in applied economics, finance and business research.
- Learning activities include lectures, online videos, and different research articles.
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Melbourne, 2018, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorPetko Kalev
One 2.0 hours seminar per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
One 1.0 hours laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
|Two data analysis exercises, between 1200 to 1500 words each (15% each)||30||01, 02, 03, 04, 05|
|Data analysis project, 2000 words (25% report + 5% presentation during week 12)||30||01, 02, 03, 04, 05|
|Final Examination (2 h)||40||02, 03, 04, 05|