Two part assignment one page for each assignment, will provide example. Please include
reference.
Discussion Question
Begin your discussion by sharing your problem statement and research question. Next, discuss
your sampling plan. In addition, discuss your research design. Consider the following as you
craft your response.
Sampling
•
How will the sample be selected?
•
What type of sampling method is used? Is it appropriate to the design?
•
Does the sample reflect the population as identified in the problem or purpose statement?
•
Is the sample size appropriate? Why or why not?
•
To what population may the findings be generalized? What are the limitations in
generalizability?
Research design
•
What type of design will be used?
•
Does the design seem to flow from the proposed research problem, theoretical
framework, literature review, and hypothesis?
Assignment 2: Research Proposal Draft
By, write a 1-page paper addressing the sections below of the research proposal.
Methodology
•
Sample/Setting: Number and criteria for inclusion and description of place in which data
will be collected.
•
Sampling Strategy
•
Research Design: Type (e.g., Quasi-Experimental), description, and rationale for
selection.
Post your assignment to the W6: Assignment 2 Dropbox.
Assignment 2 Grading Criteria
Maximum
Points
Sample discussion includes justification for number of
subjects and criteria for inclusion/exclusion.
5
Setting discussion includes an overview and rationale for
setting location
5
Sampling Strategy is fully explained and appropriate to the
10
study focus.
Research Design is described in detail and is appropriate to
5
answer the research question.
Followed APA guidelines for writing style, spelling and
grammar, and citation of sources.
5
Total:
30
Notes from the class if this will help with assignment:
A population is a set of individuals that meets sampling criteria. The target population is the
entire population that the researcher would like to make generalizations about. The accessible
population is the one that meets the criteria established and is also accessible, considering
constraints of time, money, and researcher availability.
Generalizability
Generalizability is extending findings from the sample to the larger population.
Sampling Criteria
A well-defined set that meets very specific criteria. This criteria must be very well defined and
must have limiting factors so that persons not meeting the criteria will be excluded. The
sampling criteria must also be designed to control for homogeneity by excluding from the
desired population anyone who would bring in a confounding variable
Sample Criteria Example
An example of inclusion criteria is from the study on Meniere’s Disease (MD) below.
Inclusion criteria for men and women participants included:
(a) self-reported healthcare provider diagnosis of MD
(b) no previous otologic surgical treatment for MD
(c) unilateral involvement during the evaluation period
(d) has no use of antidepressants or corticosteroids for 3 months before study onset, and
(e) Internet/e-mail access or a modem with e-mail soft- ware.
Additional inclusion criteria for women subjects were:
(a) ages 18–45 years
(b) cycle regularity with lengths between 23 and 35 days
(c) has not been pregnant or lactating for 3 months before study onset
(d) had not used hormones or oral contraceptives for 3 months before study onset, and
(e) no major gynecological illnesses. (Morse, 2001, p. 288).
Morse, G. &, House, J. (2001). Changes in Meniere’s disease responses as a function of the
menstrual cycle. Nursing Research, 50(5), 286-292.
Representativeness
The extent to which the sample and the population are alike.
Nonprobability and Probability Sampling
The difference between probability and nonprobability sampling has to do with a basic
assumption about the nature of the population under study. In probability sampling, every item
has an equal chance of being selected. In nonprobability sampling, there is an assumption that
there is an even distribution of characteristics within the population. For probability sampling,
randomization is a feature of the selection process, rather than an assumption about the structure
of the population.
Nonprobability Sampling: Convenience Sampling
Uses the most readily available subjects and is the easy method to obtain subjects
Examples:
•
The female moviegoers sitting in the first row of a movie theatre
•
The first 100 customers to enter a department store
•
The first three callers in a radio contest.
Problem:
•
Risk of bias is very great, sample tends to be self selecting.
•
What motivated people to volunteer?
•
What sample of the population is missed because they were not available?
Nonprobability Sampling: Quota Sampling
Sampling is done until a specific number of units (quotas) for various sub-populations have been
selected. Since there are no rules as to how these quotas are to be filled, quota sampling is really
a means for satisfying sample size objectives for certain sub-populations. The quotas may be
based on population proportions. For example, if there are 100 men and 100 women in a
population and a sample of 20 are to be drawn to participate in a cola taste challenge, you may
want to divide the sample evenly between the sexes—10 men and 10 women.
Nonprobability Sampling: Purposive Sampling
Researcher handpicks subjects to participate in the study based on identified variables under
consideration. Used when the population for study is highly unique.
Example:
•
Parents of children with ADHD.
Uses for purposive sampling:
•
validation of a test or instrument with a known population
•
collection of exploratory data from an unusual population
•
use in qualitative studies to study the lived experience of a specific population
Purposive restricts the sample population to a very specific population and then tends to use all
of the subjects available
Probability Sampling: Simple Random Sampling
In simple random sampling, each member of a population has an equal chance of being included
in the sample. Also, each combination of members of the population has an equal chance of
composing the sample. Those two properties are what defines simple random sampling. To select
a simple random sample, you need to list all of the units in the survey population.
Example:
•
To draw a simple random sample from a telephone book, each entry would need to be
numbered sequentially. If there were 10,000 entries in the telephone book and if the
sample size were 2,000, then 2,000 numbers between 1 and 10,000 would need to be
randomly generated by a computer. Each number will have the same chance of being
generated by the computer (in order to fill the simple random sampling requirement of an
equal chance for every unit). The 2,000 telephone entries corresponding to the 2,000
computer-generated random numbers would make up the sample.
Random Numbers Generator: http://www.random.org/
Probability Sampling: Systematic Sampling
Sometimes called interval sampling, systematic sampling means that there is a gap, or interval,
between each selected unit in the sample. The sample selection of the population must start at a
random point—if you had an alphabetical listing of all subjects, you would not start with the
"A"—but rather with a random point in the list and then go by the sampling interval (K).
Sampling interval (K)
Total population (N) ÷ sample size (n) = sampling interval
N÷n=K
= 10,000 ÷ 500
= 20
This method is often used in industry, where an item is selected for testing from a production line
to ensure that machines and equipment are of a standard quality. For example, a tester in a
manufacturing plant might perform a quality check on every 20th product in an assembly line.
The tester might choose a random start between the numbers 1 and 20. This will determine the
first product to be tested; every 20th product will be tested thereafter.
Probability Sampling: Cluster Sampling
Sometimes it is too expensive to spread a sample across the population as a whole. Travel costs
can become expensive if interviewers have to survey people from one end of the country to the
other. To reduce costs, statisticians may choose a cluster sampling technique.
Cluster sampling divides the population into groups or clusters. A number of clusters are selected
randomly to represent the total population, and then all units within selected clusters are included
in the sample. No units from non-selected clusters are included in the sample—they are
represented by those from selected clusters.
Example:
•
Suppose you are a representative from an athletic organization wishing to find out which
sports Grade 11 students are participating in across the U.S. It would be too costly and
lengthy to survey every American in Grade 11, or even a couple of students from every
Grade 11 class in the U.S. Instead, 100 schools are randomly selected from all over the
U.S.
•
These schools provide clusters of samples. Then every Grade 11 student in all 100
clusters is surveyed. In effect, the students in these clusters represent all Grade 11
students in the U.S.
Probability Sampling: Stratified Random Sampling
Using stratified sampling, the population is divided into homogeneous, mutually exclusive
groups called strata, and then independent samples are selected from each stratum. Any of the
sampling methods can be used to sample within each stratum. The sampling method can vary
from one stratum to another. When simple random sampling is used to select the sample within
each stratum, the sample design is called stratified simple random sampling. A population can be
stratified by any variable that is available for all units on the sampling frame prior to sampling
(e.g., age, sex, state of residence, income, etc.).
Sample Size
In quantitative studies, the larger the sample the greater likelihood will it be non-biased: in
qualitative studies, the sample size is generally very small. The smaller the expected differences
in subject response to the intervention, the larger the sample size needed to demonstrate a
significantly different response. If the study has been well designed, a smaller sample size can
produce good results
Power Analysis
Power is determined by the following:
•
Alpha level
•
Effect size
•
Sample size
Generally speaking, when the alpha level, the effect size, or the sample size increases, the power
level increases.
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