# 314512 Lecture Notes - Lecture 4: Simple Random Sample, Research Question, Uptodate

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Research Procedure:

1. Research question

2. Hypothesis

3. Sampling → before they design, they need to get participants in the study

4. Design → 5 Levels

Probability Sampling:

Simple random sampling

Each member of the population has an equal chance of being selected. Each

subject is selcted independantly of the other members of the population. A

random selection is carried out (such as taking numbers out of a hat) with

larger populations a computer-aided random sampling selection is preffered.

Advantages:

- assembling the sample is simple, easy and cheap

- good representativeness of the population. Luck is involved which is the only aspect that can

make a sampling error occur.

- Generalisations for the larger population is possible due to high representitiveness.

Disadvantages:

- Need a complete list of the population. Must be up-to-date which is harder for large

populations.

Multistage

Combination of two or more sampling techniques. Most of the researches are done in different

stages with each stage applying a different random sample technique → cluster, systemic, stratified

& simple.

Cluster

The researcher takes several steps in gathering the sample population. The researcher selects

groups of people (clusters) and the picks individual subjets via. Simple or

systemic random sampling methods, for his sample group.

The mot popular cluster is the geographical cluster; separating groups of

people based on where they live.

This sampling technique is to give all the clusters equal chance of being

selected.

One-Stage Cluster Sampling Method

The researcher uses the cluster groups as his smaple population.

Two-Stage Cluster Sampling Method

The researcher selects, at random (simple or systemic), individuals from each cluster.

Advantages:

- cheap, quick and easy. The researcher can allocate his resources to specific areas/clusters.

Week 4: Sampling

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- Ireasig saple size is a optio eause they’re ore aessile.

Disadvantages:

- Least representative due to populations having similar characteristics within a cluster, which

can skew results.

- High sampling error.

Stratified Random

The researcher divides the entire population into subgroups/strata then

randomly selects (simple sampling method) the same amount of

individuals from each.

Advantages:

- The presence of the key subgroup is ensured. Good

representativeness

- Higher statistical precision because of the variability in each

subgroup.

Proportionate Stratified Random Sampling

The sample size in each stratum in this technique is proportionate to the ssample size. Each group

has the same amount of individuals chosen.

For example, you have 3 strata with 100, 200 and 300 population sizes respectively. And the

researcher chose a sampling fraction of ½. Then, the researcher must randomly sample 50, 100 and

150 subjects from each stratum respectively.

Stratum

A

B

C

Population Size

100

200

300

Sampling Fraction

½

½

½

Final Sample Size

50

100

150

The important thing to remember in this technique is to use the same sampling fraction for each

stratum regardless of the differences in population size of the strata. It is much like assembling a

smaller population that is specific to the relative proportions of the subgroups within the

population.

Disproportinate Stratified Random Sampling

The only difference between proportionate and disproportionate stratified random sampling is their

sampling fractions. With disproportionate sampling, the different strata have different sampling

fractions.

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