Traditional Techniques For Sampling And Estimating Population Characteristics

According to Schutt (2008), sampling is defined as a subset of population used in a study to be a representation of the population as a whole. My final project is a pre-hire assessment which analyzes potential risky pattern behaviors and emotions in the work place. One of the most important considerations related to sampling that will need to be addressed in my final project is defining the population that will be taking the assessment.

Probability sampling, also known as random sampling, requires that every member of the study population have an equal opportunity to be chosen as a study subject. For each member of the population to have an equal opportunity to be chosen, the sampling method must select members randomly. Probability sampling allows every facet of the study population to be represented without researcher bias. Four common sampling designs have been developed for selection of a random sample: simple random sampling, stratified random sampling, cluster sampling, and systematic sampling (Burns & Grove,

A Solution: “Respondents were sampled using…random digit dialing. This procedure…avoids the use of directories with their inherent problem of the exclusion of unlisted numbers while guaranteeing equal probability of selection for all telephone numbers. Such an approach has been used in a number of telephone surveys with Hispanics and has been shown to be not only feasible but

Coverage error, the failure to give some persons in a target population a chance to be selected into the sample, was present in the survey because insecure areas were not represented. The survey attempted to mitigate coverage errors by conducting face to face interviews to eliminate the coverage bias introduced by phone interviews. The response rate was not reported in the methodology page. However, if the response rate was 25%, that would indicate that the 1,000-sample size was taken from 4,000 individuals. Whether response rate affects data quality is a matter of debate, yet, according to Hillygus, “multiple studies have found that lower response rates do not indicate the results are inaccurate” (Hillygus 42). The role response rates have on the accuracy of results is uncertain, but inclusion of it provides insight into the quality of the methods.

Sample Survey: An easier and cheaper solution could be a sample survey. A benefit of a sample survey would be that it could account for individuals that are homeless and that have the Zika virus, because they would be left out in a census and do not have the opportunity to get medical attention (have medical records). Downside would be that a sample survey is not very reliable and the whole population would not participate (just a

In population-based studies, instead of looking at a small group of individuals to make an assumption on the entire population, we are taking numbers that represent the population and determining

Part I: According to a 2013 census of Hot Springs, Arkansas, the demographics compared to San Diego City, California varied substantially. In education, 84.1% of people were a high school graduate or higher, while in San Diego City it was 87%. Kindergarteners in public school in Hot Springs were up to 97.6% in public schools and 2.4% in private schools, while San Diego City’s public school was only 90.5% and private school was 9.5%. Financial Characteristics on household incomes of $75,000-$99,000 of owned houses in Hot Springs was 9.7% and rent owned houses were 3.5% while San Diego’s percentage of owned houses was a whopping 14.1% and rented ones were 12.0%. Overall

Certain populations of homeless people are resistant to being tracked or recorded by any government agency, which creates a sense of caution for workers approaching these people (Agans 1). The difficulty of tracking homeless people complicates processes such as census work, as well as gathering information over an extended period of time from individuals for the purposes of studying homelessness. Because homeless people often change location repeatedly within a short time frame, getting accurate data on the population becomes exceedingly challenging. Information can be gathered through extensive face to face interviews with homeless individuals, both by walking around cities trying to find these individuals, and by targeting shelters. These types of surveys, such as the one conducted by the Survey Research Unit, University of North Carolina, show that historically, some of the key factors causing homelessness are lack of affordable housing, drug addiction, unemployment rates, and lack of housing for the mentally ill (Agans 3).

To identify people to include in the interviewing process, a purposive type of sampling will be done with the use of a pre-survey. Purposive sampling is used when a researcher wants access to a particular subset of people, in this case

This document will discuss the relevancy of the population chosen and the disease of interest. The vulnerable population that was chosen is the homeless. The disease related to this population is HIV/AIDS. It will define and describe epidemiology regarding HIV/AIDS and the steps and methods used. This document will also define the Epidemiological Triangle and the different types of epidemiology. It will also discuss how the population chosen characteristics influences vulnerability and how the cultural considerations may influence health status of the individual. It will explain the ethical and legal considerations and how to overcome

A snowball sample is when participants of a certain priority population are recruited for the program and then provide the names of people they know to participate (Cohen, 2011). This method of sampling allows for a program to take place where it might be difficult to conduct because of a lack of participation. This could be the case in low-income areas of New Hanover County. However, a disadvantage to snowball sampling is that it is hard to determine sampling error (Cohen, 2011). Because of snowball sampling, participants are not randomly selected therefore the all individuals in the target population do not have an equal chance of participating in the study. Delimitations of this study included women aged 25-50 who had an income less than $11,670 per

It can be argued that the chosen sampling method proved to be suitable since it produced twenty-nine male participants and eleven female ones, all from various backgrounds and ages, as a result of interviews conducted in six different geographical, social and economic areas. They all adhered to the chosen criteria, homeless drug users that have used hostels or night shelters as accommodation, which resulted in mixed experiences and provisions. The researchers have made the method explicit by presenting the layout of the interviews and its duration and indicating the topic guide. Methods were altered during the study in order to insert further data in regard to spatial needs. Although the form of data appears to be clear - the study has used audio files that have been ulterior processed and indexed, no mention of

To improve “sampling representativeness” a “proportional stratified sample” will be done to determine how many surveys to send to each city (Clemons & McBeth, 2001, p. 277). The formula will calculate the population of each city in 2010 and divide it by the total population of the ten cities to come up with a percentage. That percentage is multiplied by 2,000 to determine the number of surveys each community will receive and is available for review in Appendix II. COMPASS will then work with member cities and randomly select households through the use of “systematic random sampling” by dividing each city’s population by the number of surveys it receives for the survey (Clemons & McBeth, 2001, p.

Hinduja and Patchin (2013) used random sampling from 33 middle and high schools in a large school district. The administrators randomly selected two or three classes from each grade level, generating a sample of 4,441 students (Hinduja & Patchin, 2013). Random sampling is effective because each student from the population pool has a chance to be selected. The large sample size creates the likelihood that the sample is representative of the population, making it more generalizable (Sheperis, Young, & Daniels, 2013). Yet, the randomized selection was left to the administrator at each school, which could have decreased the randomization because they could have decided to use specific criteria, such as high referral rate classes, which would have made the chances of selection unequal. The selection could have been improved by assigning classes specific numbers that are inputed into a random number generator for selection (Sheperis, Young, & Daniels, 2010). This would help to ensure the selection was truly a random sample.

1975 University of Texas – Austin, nd) ) There was no available data for the number of