This document summarizes the strengths and weaknesses of basic sampling techniques for probability and non-probability sampling. For non-probability sampling, convenience sampling, judgement sampling, and quota sampling are described as having strengths such as low cost and convenience but weaknesses such as selection bias and lack of representativeness. Snowball sampling can estimate rare characteristics but is time consuming. For probability sampling, simple random sampling, systematic sampling, and stratified sampling are outlined with strengths like representativeness but also weaknesses including difficulties in implementation and costs. The document also briefly describes secondary data as data collected by other parties that is available from sources like government agencies and publications, and notes advantages like easy accessibility but also limitations such as potential lack of accuracy, bias, and

1. 8.0 Coursework
2.1 Please describe strengths and weaknesses of basic sampling techniques.
The following table provides a summary of strengths and weaknesses of basic sampling
techniques.
Non-probability sampling
Techniques Strengths Weaknesses
Convenience sampling Less expensive, less time
consuming, most
convenient
Selection bias, sample not
representative, not
recommended for
descriptive or causal
research
Judgemental sampling Low cost, convenient, not
time consuming
Does not allow
generalisation, subjective
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no
assurance of
representativeness
Snowball sampling Can estimate rare
characteristics
Time consuming

2. Probability sampling
Techniques Strengths Weaknesses
Simple random sampling Easily applied. Result can
be projected on population
Difficult to obtain sampling
frame, expensive,
sometimes no assurance of
representativeness
Systematic sampling Easier to implement than
simple random sampling
Can decrease
representativeness if certain
patterns exist in sampling
frame
Stratified sampling Includes all important
subpopulations, precision is
improved
Difficult to select relevant
stratification variables, not
feasible to stratify on many
variables, expensive
Cluster sampling Easy to implement, cost
effective and work is
reduced
Imprecise, difficult to
compute and to interpret
results

3. 2.2 Please describe secondary data.
Secondary data are normally published data collected by other parties.
Government agencies such as Bank Negara, the Department of Statistics, Ministry of
International Trade and Industry and other agencies publish their data regularly and
provide secondary sources of data to many researchers. In addition, bulletins, journals,
newspapers and other publications also provide useful secondary data to researchers.
However, some of the secondary data are not current. A researcher needs to choose
wisely the secondary data for his research.
One advantage of secondary data is that it is easily accessible from the internet,
journals, annual reports and newspaper. It is relatively inexpensive because there is no
fieldwork required. It also requires less time to collect. Some data such as import and
export data are only available from secondary sources. However, there are some
disadvantages of secondary data as well. The secondary data may lack accuracy because
the measurement procedure and the method of data collection are not explained by the
previous researchers. The data may be biased because the original purpose of data
collection is not known. Finally, the data may not meet the specific needs and objectives
of the current research, or there may be too many constraints involved.