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

45 views6 pages
24 May 2018
School
Course
Professor
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.
- 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.
- 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.
- cheap, quick and easy. The researcher can allocate his resources to specific areas/clusters.
Week 4: Sampling
find more resources at oneclass.com
find more resources at oneclass.com
Unlock document

This preview shows pages 1-2 of the document.
Unlock all 6 pages and 3 million more documents.

- Ireasig saple size is a optio eause they’re ore aessile.
- 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.
- 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.
find more resources at oneclass.com
find more resources at oneclass.com
Unlock document

This preview shows pages 1-2 of the document.
Unlock all 6 pages and 3 million more documents.