Quantitative Research (6002HLS)

This course builds upon a number of undergraduate courses in basic statistics and research methods. There will be a strong emphasis on incorporating research design, sampling and sample size concepts into data analysis and interpretation of research results. Through consideration of a variety of statistical methods, this course will provide students with practical experience and skills focussing on analytical planning, result presentation and interpretation. In particular, emphasis will be on specifying appropriate statistical methods and their interpretation within a health framework. A unifying theme of the course will be the relationship between descriptive statistics and inferential statistics in presenting and interpreting quantitative research data. Students will be made aware of the importance of data management in preparation for statistical analysis. Students will also be provided with substantial practical experience to foster their skills in selecting and applying inferential statistics appropriate to specific data sets. In particular, measurement issues, data type (categorical and continuous variables), test assumptions (distribution of the data, equity of variances) will be considered in deciding between parametric and non-parametric statistical approaches to research questions. Students will develop skills in formulation of experimentally testable hypotheses at the bivariate level. The primary statistical techniques to be covered in this course include Chi-square, correlation, t-test, analysis of variance, simple linear regression, Mann-Whitney test, and Kruskal-Wallis test. Analytical strategies in modelling health data are also introduced in this course, with particular emphasis on multiple linear regressions. Students will be provided with substantial experience in the use of the widely used statistical package SPSS to analyse data. This will be achieved through data analysis and interpretation of various research data sets.

Course study information

Credit points (awarded)

10 (10)

Study level

Undergraduate

Student contribution band

Band 1

Usually available

Gold Coast Trimester 1Online Semester 1Online Trimester 2

Course Attributes

Restricted: Course must be listed in Program

Course offerings and timetables

Convenor

Key dates

Course start date
18 July 2022
Last date to add a course
31 July 2022
Last date to drop a course without financial penalty (Census date)
15 August 2022
Last date to drop a course without academic failure
18 September 2022
Class Availability When Where Notes
You must enrol in this Class
Class (46564) Weeks 1 - 4, Mid-Trimester Break, 5 - Study week Course attendance and other requirements will be provided via Course Profile and/or Learning@Griffith.

Convenor

Key dates

Course start date
14 March 2022
Last date to add a course
27 March 2022
Last date to drop a course without financial penalty (Census date)
10 April 2022
Last date to drop a course without academic failure
15 May 2022
Class Availability When Where Notes
You must attend this Lecture
Lecture (11731) Open Wednesday 09:00 - 11:20
Weeks 1 - 3
G01 3.55
Academic 1
Gold Coast Campus
Wednesday 09:00 - 11:20
Weeks 4, 5 - 10
G01 3.37
Academic 1
Gold Coast Campus
Class Availability When Where Notes
You must attend one Tutorial
Tutorial (11733) Open Wednesday 13:30 - 15:20
Weeks 1 - 3
G01 3.37
Academic 1
Gold Coast Campus
Wednesday 13:30 - 14:50
Weeks 4, 5 - 10
G01 3.37
Academic 1
Gold Coast Campus
Tutorial (11732) Wednesday 11:30 - 13:20
Weeks 1 - 3
G01 3.37
Academic 1
Gold Coast Campus
Wednesday 11:30 - 12:50
Weeks 4, 5 - 10
G01 3.37
Academic 1
Gold Coast Campus

Convenor

Key dates

Course start date
14 March 2022
Last date to add a course
27 March 2022
Last date to drop a course without financial penalty (Census date)
10 April 2022
Last date to drop a course without academic failure
15 May 2022
Class Availability When Where Notes
You must enrol in this Class
Class (16805) Open Weeks 1 - 4, Mid-Trimester Break, 5 - 12 Course attendance and other requirements will be provided via Course Profile and/or Learning@Griffith.