While Confidence Intervals are used when polling sample data to make a decision for a bigger population, Denis Cousineau 92017), states that the golden rule for Confidence Intervals is “If a given value is within the interval of a result, the two can be informally assimilated as being comparable.” This applies when interpreting all intervals. I currently work for an Occupational Health Clinic at a research hospital, therefore nearly everything we do is related to collecting data and measuring outcomes. One sample that is measured is related to employees who develop occupational asthma because of exposure to animals in the research labs. Using a Redcap animal allergy survey, we take a random sample of employees who are identified as having
Let’s say you want to do research to learn about the causes of drug use among teenagers in Connecticut. Explain how you could create a sample of teens to study using random sampling, convenience sampling, and snowball sampling, and discuss a limitation of each sampling method.
The research process is mainly from online websites. The data and information collected will be from government health, asthma foundation and national health websites. The websites are carefully picked to prevent incorrect or biased data. Websites such as Wikipedia or personal blogs will be prevented as much as possible.
The advances in technology have provided a number of ways to collect and interpret data in regards to scientific research. According to Cope (2014) using paper and pencil surveys is the tried and true method of collecting data; However, technology is quickly becoming a popular and at times a more efficient way to collect data. The use of technology allows interpreting data to become simpler, allows the researcher to reach a larger sample group and quicker compared
An investigation of 150 randomly selected local restaurants concluded that 42% of local restaurants have serious health code violations. Is this a population or a sample; explain your answer.
Provide at least three examples or problem situations in which statistics was used or could be used.
As a population, we are bombarded with percentages and statistics, but how does one know if what we are being told is correct? The book How to Lie With Statistics by Darrel Huff was written to help readers better understand statistics especially when they are presented to us in ways that can be misleading or misunderstood. The book is not meant as a guide on how to change or manipulate statistical numbers. However, if statistics are not presented properly or perhaps purposely misleading people, this book will help readers question or form their own opinions from data. Most people simply are not that interested when you hear the word statistics and many times people do not believe the numbers presented. This mistrust occurs most often for two reasons: the person not being able to see the raw data and where or how it was collected and the person not being able to verify the credibility of the information presented. Throughout the book, Huff discusses different statistical techniques that can be used improperly and how one can discern good statistics from those that may have been manipulated.
Sampling is the raw resource which enables the quantitative researcher gain insight on the target population. In the past half-century, Haer & Becher (2012) note that surveys have become the ubiquitous data gathering devices serving many researchers purposes for assembling data in person or by mail. Nevertheless, the purpose of the survey is designed to gather valuable data, however, even more important is the design and in the way it is conducted ethically. Sampling strategies can be diverse depending on the resources and time available to a researcher. The surroundings in which the survey is conducted play in important role easy and participation which the data can be collected.
As it is impossible for researchers to study an entire population, sampling theory studies a target population under study. A target population refers to a group of individuals who meet the sampling criteria for a particular study. For example, male, 20 years with type 1 diabetes. The two different types of sampling design are probability and nonprobability sampling. Probability sampling is the type of sampling plan where each person in the population has an equal opportunity to be selected for the sample, whereas in nonprobability sampling methods, not every element or person in the population has a chance to be selected for a study. This type is a more common method used in nursing research because of the limitations of the availability of
The main sources of data I am planning on using are data regarding academic performance in gender, but also looking at a variety of student created artifacts (drawings specifically), and anecdotal evidence
Sampling bias (pg. 112) – a sampling method can be called biased if the results of the research found favors the outcome the researcher is looking for. The researcher ultimately controls/influences whether the results are biased and potentially misleading. If a researcher thinks that football players are more susceptible to concussions, the researcher may only look at specific positions where the players take more hits to the head for their research which could affect the results looking at football players as a whole.
Survey research was used to gather information about people’s beliefs, attitudes, behaviors, and demographic composition. Survey data was collected by asking participants from a population a set of questions, which could be administered in a questionnaire that was mailed, emailed, or in an interview over the phone or in person (Leedy & Ormrod, 2013). In a sample survey researcher attempted to infer information about a population based on a representative sample drawn from that population. To generalize the findings from a sample to a population, the sample should accurately represent the population (Salkind, 2012). Robson (2011) stated that the details of the design should be fully pre-specified before the beginning of data collection. Gay et al. (2011) informed that correlational research involved data collection to determine relationship existence between two or more quantifiable
According to David (2008), health assessments are red-hot right now- and for good reason. With health care costs estimated to increase to more than $4 trillion by 2016, personal health assessments will help to ameliorate some of these costs by providing individuals with much needed health information so that they can manage their current health status and, perhaps even more importantly, prevent health problems before they occur. Moreover, personal health assessments, if used strategically, will aid employers in better understanding the collective health risks of their employee populations – thus allowing them to incorporate important and necessary interventions that will effectively address unhealthy behaviours at the workplace. Health assessment
It is useful because different observers will not interpret the answers the same way. In this scene, some may differ and therefore give different opinions over the same issue. An example of inter-rater reliability is when different raters evaluate the degree to which particular portfolio meet specific art standards. It is thus worth to note that this kind of reliability is sufficient to use in cases of art and not Math.
Researchers, professionals and others use statistics to prove their claims or findings. Even though statistics are not an absolute fact because the conclusion is mostly drawn from a sample group – representative of a specific population subjected to the research, it is commonly used as the basis of decision making or alternating choices in daily living, studies, works, scientific research, politics and other planning. The inventor of a documentary film called “An inconvenient truth”, Mr. Al Gore, for instance, in his campaign to educate people about the climate change, used statistics to alert people that everyone on earth is polluting the environment and should participate in solving the problem. He collected data from many different countries with an in...
Sampling is to draw a conclusion efficiently about a population of interest by testing the acquired assurance on a subset of that population, which is called the sample, at less cost of time and money than those of testing the whole population. However, the trade-off between efficiency and effectiveness always exists. In the other words, sampling would be used when the gains of efficiency exceed the loss of effectiveness.