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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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I've attached the first part. Discussion. Please have a look.

Running head: SAMPLING & RESEARCH DESIGN

Discussion Question: Sampling and Research Design
Student’s Name
Institutional Affiliation

1

SAMPLING & RESEARCH DESIGN

2

Discussion Question: Sampling and Research Design
Problem Statement
The current doctor-led approach in hypertension management has resulted in a higher number of
uncontrolled cases which develop into other health complications, than would be expected under
nurse-led interventions.
Research Question
Do nurse-led approaches increase control of hypertensive conditions more than doctor-led
approaches within one year of intervention?
Sampling


How will the sample be selected?

Selection of the sample will be done by randomly selecting 8 hospitals across the state that care
for hypertension patients. In the hospital, 180 new cases of hypertension and pre-hypertension
will be selected. The ratio of hypertension and pre-hypertension patients will be 1:1. The patients
will be from both genders 50-50. 40% will be young adults between 18-39 years. 60% will be
older adults aged 40 years and above. Each group will be divided into two: one will be managed
by doctors and the other nurses. Cases that have deteriorated such as patients who have suffered
stroke or any end-organ damage will be excluded. Patients with other chronic illnesses will also
be excluded from the study.


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