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Exploring Statistical Concepts and Research Methodologies in Business and Healthcare

This solved assignment examines statistical concepts and research methodologies in business and healthcare applications.

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    Exploring Statistical Concepts and Research Methodologies in Business and Healthcare Week 1 Since statistics is the science of data, th e relationship between a sample, population, descriptive statistics, and inferential statistics is that they are all components of statistics. All of these components involve collecting classifying, summarizing, organizing, and interpreting numerical information. Also, all of these components are connected in some form. For example, a sample is a subset of a population and one or both are included in the elements of descriptive or inferential statistical problems. This means that you can survey iPhone user s but this population is too large so, you can break that down into i Phone users between that ages 25 to 29. According to McClave, Benson, and Sincich ( 2011), a population is a set of units (people, objects, or events) that we are interested in studying (p. 5). A sample as a subset of the units of a population or any set o f output produced by a process. Descriptive utilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set and to pre sent the information in a convenient form (McClave, Benson, & Sincich, 2001, p. 3). The text states that inferent ial statistics utilizes sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.) Boston, MA: Pearson. Week 2 1. Read the Albert Einstein quote in “The Research Process” section in Ch. 4 of Business Research Methods. What is the value of this statement in terms of the research process? What is the relevance and relationship of this statement to the technologically advancing business world? Where do these questions allow us to go? ' A familiar quotation from Albert Einstein, no less apt today than when it was written, supports this view: The formulation of a problem is far more often essential than its solution, which may be merely a matter of mathematical or experimental skill. To raise new questions, new possibilities, to regard old problems from a new angle requires creative imagination and marks real advance in science. 1 In terms of the research process there will be as many probable solutions as there are researchers. For this reason it is imperative that all data extrapolated utilizing the basic formula is verified to be valid. To find new ideas to all problems researche rs must approach the issue with a question attitude. It i s safe to say that the pneumonia has been around since the beginning of time. However, there is continues research that is time consuming and never ending battle to find better ways to treat the illness. Being that the pneumonia is becoming more complex and harder to treat, there is no cure once a person has contracted it , it can be treated but it has to run its course . Although there is a vaccine to combat against whatever strand of the virus exist during that particular calendar year. The questions above allow us to take a long hard look at findin g the proper so lution to problems to benefit the world. These questions allow us to continue to become more creative and to continue to develop new or modern solutions in the business world.

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    2. What are some examples of operational definitions in research design within your profession? An operational definition defines a variable, term, or object in terms of the specific process or set of validation tests used to determine its presence and quantity. Some examples that my organization use is measuring the effectiveness or validity of our electronic medical records. We run a series of validation test to ensure the system is properly working. This in turn tells us about the access, flow and productivity by measuring how long patients have to wait for an appointment to a specialty outpatient clinic or how effectively the flow is in the emergency room or inpatient departments . Other operational definitions in health care include patient and employee safety by measuring how often adverse drug events happen as well as measuring how m any employee injuries/illnesses occur that result in being classified as OSHA recordable. 3. Of the exploratory, formalized, and causal research designs types, which would you use to assess the effectiveness of an aspect of your job? Explain. I build health care electronic medical records. In my job, we are always asking why. So, I would say of the exploratory, formalized, and causal research designs types causal would be would used to assess the effectiveness of my job. Causal research explores the effect of one thing on another or to put it in formal terms the effect of one variable on another. For example, when we have problems with the system, such as failures or workflow issues, we try to determine why it happened so if it happens it again, we can fix the problem. We attempt to recreate the problem in our test system that runs parallel to our live system and document all the steps when trying to create a fix. We found that these problems happen when we install upgrades to our system. Basically, any change can inadvertently effect a different part of our system and cause it to fail and it is our job to find the culprit . 4. What is the purpose of sampling? What are some concerns and dangers of sampling? How important is the sample design to data validity? Explain. Provide an example where a sample might misrepresent data validity. According to McClave, Benson, and Sincich, a sample is “ a subset of the units of a population ” (p. 6) . Also, the sample portion must be carefully selected to represent that population. The purpose of sampling is to learn about the larger population without having to research the entire population. We obtain a sample rather than a complete census of the population because it is cost effective and less time consuming. Some concerns and dangers of sampling is that one formula for a sample may be good for one sample may not be good for another. Also, this formula may part of the population. All kinds of sampling procedures may yield samples which are inaccurate and unreliable. There are techniques which minimize these dangers, but some potential error is the price we must pay for the convenience and savings the samples provide. The sampling design is important to data validity because it includes every person within the target population. For example, when a new medication is created, the research is done on men and women who are both healthy and sick to determi ne side effects of medications and how effect it is to treat an illness. This sample may misrepresent data validity

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    because different people have different reactions to medication and there is always at least one percent of the population that will reject the medication. Week 3 1. How does technological advancement affect the ability to collect data? Provide examples. Does this advancement increase the chance for errors? Explain. Technological advancement affe ct the ability to collect data because it allows us to collect more data while lowering cost and increasing processing speed. Technology also allows store more data than and allow us to make future forecast for companies. For example, I part - time in retail we are able to compute the number of customers that visited our store within the hour with real time data. We can also easily compute sales in order to forecast future progress. Sure, technological advancement increase the chance of error because just like humans it can be unpredictable at times and there is some factor of human error involved. For example, I build electronic medical records, if I get one number, letter, or symbol wrong in the formula it could potentially cause the system to input the information in the wrong areas. 2. Validity is more critical to measurement than reliability. A valid measurement is reliable, but a reliable measurement may not be valid. Do you agree with these statements? Explain your answer. Provide examples to support your rationale. I agree with both statements about valid and reliable measurements. Reliability is more so focused on being consistent or stable but just because the information is consistent does not mean it is vali d. Whereas, validity refers to accuracy. I have an undergraduate degree in Biology and spent numerous amounts of time in the lab. This consist ed of measuring hundreds of chemicals. I once was conductin g a research experiment and my results were consistent but based on other students results my measurements were not valid. When I went back to figure out what went wrong, my measurements were not correct due to the calibration of the balance scale I was using. Although, my information was reliable, it had to be thrown out because it wasn ’t valid. 3. What are some critical decisions involved in selecting an appropriate measurement scale? How do these decisions influence the research design? Some critical decisions when selecting an appropriate measurement scale are 4. What is the importance of pretesting survey questions and instruments? What are risks of not doing this? Provide an example. 5. How do survey question content and wording, response strategy, and preliminary analysis planning affect survey question construction? Provide examples of questions that were positively and negatively influenced by these elements. 6. What is the importance of the null hypothesis? Why has it been the backbone of mainstream hypothesis testing for decades? What are its limitations?

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