QUANTITATIVE RESEARCH METHODS
WEM2QRM
2015
Credit points: 15
Subject outline
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, 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.
School: School of Life Sciences
Credit points: 15
Subject Co-ordinator: Warren Paul
Available to Study Abroad Students: Yes
Subject year level: Year Level 2 - UG
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: STA1CTS
Co-requisites: N/A
Incompatible subjects: N/A
Equivalent subjects: N/A
Special conditions: N/A
Learning resources
Readings
| 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 (AVAILABLE AS AN E-BOOK FROM THE LIBRARY) |
Graduate capabilities & intended learning outcomes
01. Design a sample survey, experiment and observational study
- Activities:
- Guidelines for the design of surveys, observational studies, and experiments are given in lectures. A variety of examples and practice problems are given in lectures and tutorials. Students design a study in Assignment #1.
- Related graduate capabilities and elements:
- Inquiry/ Research(Inquiry/ Research)
02. Choose an appropriate analysis for a given research question and data set
- Activities:
- Guidelines for choosing an appropriate analysis are given in lectures. A variety of examples and practice problems are given in lectures and tutorials. Students must choose an appropriate analysis for a given research question and data set in Assignment #2.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
03. Perform data analyses using SPSS and PRIMER statistics packages, interpret the results, and draw conclusions
- Activities:
- A variety of data analysis methods are explained in lectures. Examples and practice problems using the output from SPSS and PRIMER are given in lectures and tutorials. 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.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
04. Communicate statistical analyses in report form
- Activities:
- Instructions for presenting the results of a statistical analysis in report form, with examples to illustrate the process, are given in Assignment #2.
- Related graduate capabilities and elements:
- Writing(Writing)
05. Evaluate statistics reported in the media and scientific journal articles
- Activities:
- Guidelines for evaluating statistics reported in the media and scientific literature are given in lectures. A variety of examples and practice problems are covered in lectures and tutorials. Students critically evaluate journal articles in Assignment #1 and #2.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
Albury-Wodonga, 2015, Semester 1, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Warren Paul
Class requirements
TutorialWeek: 10 - 22
One 1.0 hours tutorial per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
LectureWeek: 10 - 22
Two 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Computer LaboratoryWeek: 10 - 22
Two 1.0 hours computer laboratory per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Assignment 1: Study Design (Week 5) Assignment 2: Data Analysis & Critical Thinking (Week 12) | 40 | 01, 02, 05, 04, 03 | |
| Data Analysis (2 hour final exam.) | 40 | 02, 03 | |
| Weekly online quizzes | 20 | 01, 04, 03, 02, 05 |