1997 Swinburne Higher Education Handbook

esearch. It includes an overview of the procedures used in
survey research, a descriptive approach to methods of
sampling and data collection methods including
questionnaire design and interview techniques (personal and
telephone) mail surveys and census methods survey report
writing and presentation. Other topics may include data
processing including entering, coding, quality control and
analysis of multiple response questions.
Recommended reading
To be advised.
SM733 Demographic Techniques
12.5 credit points 4 hours per week Hawthorn Prerequisite:
SM742 Assessment: assignments
A second year subject in the Graduate Diploma of Applied
Science (Health Statistics and Social Statistics)
0 b jectives
This subject aims to give an understanding of the basic
methods of demographic analysis and to develop an
awareness of the social implications of demographic data.
Content
It will include topics chosen from the following: sources of
demographic data. Census data and use of CD ROM
technology such as CDATA91. Population processes, births
and deaths, size and distribution, mobility, spatial patterns
and models for regional demographic analysis may be
included. Indicators and benchmarks, projections and
forecasts.
Recommended reading
To be advised.
SM735 Survey Sampling
12.5 credit points 4 hours per week Hawthorn
Assessment: assignments and test Prerequisite: SM732,
SM742
A second year subject in the Graduate Diploma of Applied
Science (Social Statistics)
Objectives
Introduce basic methods for sample survey design and
analysis.
Provide practical examples of designing simple sample
surveys.
Content
The basic designs for sample surveys: simple random
samping, stratified sampling, systematic sampling and cluster
sampling.
Estimators for the mean total and proportion for simple
random samples and stratified samples; variance estimation.
The design effect; sample size determination; EPSEM
samples
Ratio estimation;
Cluster sampling, multi-stage sampling, PPS sampling;
variance estimation.
Recommended reading
Cochran, W.G., Sampling Techniques. New York, Wiley, 1977
Jolliffe, F.R. Surwy Design and Analysis. Chichester, Ellis,
Horwood, 1986
Kalton, G. Introduction to Survey Sampling. Beverley Hills, Sage,
1983 -.--
Stuart, A. Basic Ideas of Scientific Sampling. London 3rd edn,
Griffin, 1968
Sudmans, A,, Applied Sampling. New York, Academic Press, 1976
SM742
Elementary Statistical Modelling
12.5 credit points 4 hours per week Hawthorn Prerequisites:
SM750, 94751 Assessment: practical and theoretical tests
A second year subject in the Graduate Diploma of Applied
Science (Social Statistics)
0 b jedives
Provide a computer based introduction to elementary
statistical modelling.
Content
Analysis of variance and regression. Introduction to multiple
regression. Introduction to analysis of categorical data.
Recommended reading
To be advised.
SM743 Multivariate Statistics 1
12.5 credit points 4 hours per wek Hawthorn Prerequisite:
SM742 Assessment: assignments and examination
A second year subject in the Graduate Diploma of Applied
Science (Social Sciences)
Obiectives
This subject aims to identify and apply the multivariate
techniques most commonly used in social research and to
understand the assumptions underlying their use.
Content
The course will include a selection of topics chosen from
multiple regression, statistical inference for multivariate
data, principal component analysis, factor analysis,
discriminant analysis and cluster analysis, multivariate
analysis of variance.
Recommended reading
To be advised.
SM744 Statistical Modelling
12.5 credit points 4 hours per week Hawthorn* Assessment:
assignments m Prerequisite: SM743
A subject in the Master of Applied Science (Social Statistics)
Ob jedives
This subject aims to make an in-depth study of several
statistical modelling techniques for both categorical and
higher level data.
Content
Topics will be chosen from: regression models for
categorical data: log-linear models for multiway contigency
tables, logistic regression for analysing binary response data.
Causal modelling, structural equation models, using
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