QUANTITATIVE DATA ANALYSIS
Credit points: 15
In this subject, you will explore the quantitative analysis techniques that are commonly used by researchers, analysts and managers to analyse data to provide answers to important research questions. You will be expected to develop the ability to: collect data using survey methods; prepare data for analysis;demonstrate technical competence in the application of univariate and multivariate techniques to data, using suitable statistical software; interpret the results of the analysis accurately; and write a report detailing a data analysis method and results in an accurate and scholarly way. Some basic Quantitative skills are pre-required.
SchoolLa Trobe Business School
Subject Co-ordinatorIshaq Bhatti
Available to Study Abroad StudentsNo
Subject year levelYear Level 5 - Masters
Graduate capabilities & intended learning outcomes
01. Conduct a survey that produces high quality, valid and reliable data.
- Explicitly teach and model questionnaire design; explicitly teach and model scale development.
02. Provide a full and accurate explanation of relevant univariate and multivariate techniques and the assumptions that underpin these techniques.
- Seminars, readings, class discussions and both assessment items.
03. Prepare data for analysis [including data cleaning and checking]
- Explicitly teaching the key assumptions and principles; modelling of techniques.
04. Demonstrate the ability to apply data analysis techniques using relevant statistical software.
- Explicitly teaching and modelling techniques; applying techniques to secondary data using relevant software
05. Demonstrate the ability to correctly interpret the output of data analysis procedures.
- Explicitly teaching and modelling interpretation of outputs; explaining meaning of data analysis output listed in point 4 to classmates.
06. Write a report detailing a data analysis method and results in an accurate and scholarly way.
- Explicitly teaching characteristics of scholarly reporting of data analysis; modelling of reporting.
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Melbourne, 2017, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorIshaq Bhatti
One 2.0 hours seminar per week on weekdays during the day and delivered via face-to-face.
One 1.0 hours laboratory class per week on weekdays during the day and delivered via face-to-face.
|Two data analysis exercises, 1000 words each (10% each)||Hurdle: Students must attempt all assessment elements.||20||01, 02, 03, 04, 05|
|Data analysis project, 2000 words||Hurdle: Students must attempt all assessment elements.||30||01, 02, 03, 04, 05, 06|
|Final Examination (3 h)||Hurdle: Students must attempt all assessment elements.||50||01, 04, 05|