QUANTITATIVE RESEARCH METHODS
Credit points: 15
Quantitative Research Methods is concerned with the scientific method, the statistical design and analysis of studies in life sciences, and the critical evaluation of scientific literature. Students will learn how scientists discover causal relationships that explain how the natural world operates. Building upon the foundations learnt in STA1CTS Critical Thinking with Statistics, STA1LS Statistics for Life Sciences, or BIO2POS Practice of Science the subject covers the principles of statistical design for surveys, experiments, and observational studies; some new methods for analysing data, including analysis of variance (ANOVA), multiple regression, analysis of covariance (ANCOVA), and some nonparametric methods that are particularly suited to analysing multispecies ecological data; and how to critically evaluate the statistical arguments made in scientific reports and journal articles. Laboratory sessions and assignments will give students the opportunity to apply what they learn using the statistics packages SPSS and PRIMER.
SchoolSchool of Life Sciences
Subject Co-ordinatorWarren Paul
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Prerequisites STA1CTS, STA1LS or BIO2POS
Incompatible subjects WEM2QRM
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Statistics explained: An introductory guide for life scientists||Recommended||McKillup, S. (2006)||CAMBRIDGE UNIVERSITY PRESS|
Graduate capabilities & intended learning outcomes
01. Design a sample survey, experiment and observational study
- Guidelines for the design of surveys, observational studies, and experiments are given in the subject notes. A variety of examples and practice problems are given in workshops. Students design a study in Assignment #1.
02. Choose an appropriate analysis for a given research question and data set
- Guidelines for choosing an appropriate analysis are given in the subject notes. A variety of examples and practice problems are given in workshops and computer labs. Students must choose an appropriate analysis for a given research question and data set in Assignment #2.
03. Perform data analyses using SPSS and PRIMER statistics packages, interpret the results, and draw conclusions
- A variety of data analysis methods are explained in the subject notes. Examples and practice problems using the output from SPSS and PRIMER are given in workshops and computer labs. Students must choose an appropriate analysis for a given research question and data set and perform that analysis in Assignment #2. They must also interpret the computer output from SPSS and PRIMER and draw conclusions from statistical analyses in the final exam.
04. Communicate statistical analyses in report form
- Instructions for presenting the results of a statistical analysis in report form, with examples to illustrate the process, are given in Assignment #2.
05. Evaluate statistics reported in the media and scientific journal articles
- Guidelines for evaluating statistics reported in scientific literature are given in the subject notes. A variety of examples and practice problems are covered in workshops. Students critically evaluate journal articles in Assignment #1 and #2.
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Melbourne, 2019, Semester 1, Blended
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorWarren Paul
One 3.0 hours computer laboratory per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
"Incorporates a 1 hr online workshop with lecturer via Collaborate. Tutor will be in attendance face to face and lecturer will attend via Collaborate."
|Assignment 1: Study Design (750 words) Assignment 2: Data Analysis & Critical Thinking (750 words)||Assignment 1 is due in week 5, and Assignment 2 is due in week 12.||40||01, 02, 03, 04, 05|
|2 hour final exam (open book)||40||01, 02, 03, 04|
|Ten (10) online quizzes with 5-10 multiple choice questions on each quiz. (750 word equiv)||Due weekly.||20||01, 02, 03, 04, 05|