QNT 561 Discussion Questions
QNT 561 Discussion Questions
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Date Created: 11/14/15
Week 1 Since statistics is the science of data, the 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 users but this population is too large so, you can break that down into iPhone 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 of 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 present the information in a convenient form (McClave, Benson, & Sincich, 2001, p. 3). The text states that inferential 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 1n science. 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 researchers must approach the issue with a question attitude. It is 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 finding the proper solution 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. 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 many 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 determine side effects of medications and how effect it is to treat an illness. This sample may misrepresent data validity 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 affect 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 parttime 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 valid. Whereas, validity refers to accuracy. I have an undergraduate degree in Biology and spent numerous amounts of time in the lab. This consisted of measuring hundreds of chemicals. I once was conducting 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? The null hypothesis is defined as theory about the specific values of one or more population parameters. It is important because it represents the status quo. The null hypothesis has been the backbone of mainstream hypothesis testing for decades because it can be and found to be false, which implies there is a relationship between the observed data. The limitations of the null hypothesis is that it is never exactly correct, meaning it agrees to a certain extent. 7. When would you use a one or twosample hypothesis test? Provide examples. You would use onesample hypothesis test when you want to test whether the mean of a variable is less than, greater than, or equal to a specific value. Basically, it answers questions about the population mean when the data is a random sample of independent observations. The twosample hypothesis test is used to determine whether the difference between means found in the sample is significantly different from the hypothesized difference between means. For example, one of our quiz questions tested the difference between boys and girls being bullied. 8. What is the value of performing hypotheses tests to solve problems related to business and operations management? Provide specific examples. In business and operations management, hypothesis test are used to determine if systematic differences by exploring statistical differences. The value of performing hypotheses tests to solve problems related to business and operations management is critical because it can be used to explain things like labor and productivity. Hypothesis testing is also very valuable in business because it helps examine the causes and effects before making crucial management decisions like increasing production or adding a new product to the business. Basically, hypothesis test in business and operations management are very valuable because it actually shows statistical proof about controlled situations in operations management. 9. What is the relationship between confidence intervals and hypothesis testing? How are they the same? How are they different? Confidence intervals are used when you want to estimate the population parameter. Hypothesis testing refers to the formal procedures used to accept or reject statistical hypotheses. The relationship between confidence intervals and hypothesis testing is that they are both twotailed test, meaning they do the same thing. A hypothesis test assumes the null is correct and sees if the alternative hypothesis is unusual. The confidence interval assumes alternative hypothesis is right and the null hypothesis determines if the null hypothesis is usual. Are hypothesis tests always twotailed tests? Actually, no. Hypothesis testing can be either uppertailed, lowertailed (meaning are directional), or twotailed (nondirectional) depending on how the alternative hypothesis is stated. Week 4 1. What are differences between dependent and independent samples? Provide examples. What are implications for determining the tests used to analyze data? Dependent samples are two samples of data one of which is scored in one sample pair with a specific score in the other sample meaning they are related. For example, Sample A is husbands and sample B is wives. These samples are dependent because they are related, one husband can be placed in sample B and vice versa. One the other hand, with independent samples the scores of one sample do not affect the other. Basically, independent samples is when one sample is unrelated to the other. For example, when measuring the left and right hand of a random sample of people and comparing the average lengths. Since the left arm and the right arm are unrelated these would be two separate averages. 2. What are some concepts behind variance analysis? Why is it important to test for variances in your data? Explain. How is variance analysis used in your profession? Provide an example. Analysis of variance, ANOVA, test the null hypothesis that the means of several independent populations are equal. It is important to test variance in our data so we detect if something is off. When we see variability in the data, we want to know where that variability comes from, and whether something important has happened. Variance allows us to answer questions about our data and produce answer through ANOVA. According to the National Health Institute, variance analysis is used in healthcare to explain the variation between planned and actual costs and charges. It is used to improve efficiency, set priorities for organizational improvement, and explain costs and charges to interested groups such as purchasers and payers. http://www.ncbi.nlm.nih.gov/pubmed/10146167 3. What is the purpose of using nonparametric tests in operations management decisions? Provide examples of what you look for when conducting these tests. The purpose of using nonparametric tests in operations management decisions is to test the equality of variances. Basically, they are useful for testing whether group means or medians are distributed the same across groups. An advantage of nonparametric tests is that sometimes, they give quick answers with little computation work. However, since these tests are nonparametric, it is difficult to quantitatively justify the observed differences. When conducting these test, let’s say for population, we look for differences when assumptions are not satisfied. 4. You observe female sales representatives having lower customer defections than male sales representatives. What concepts and constructs would you use to study this phenomenon? How might the concepts or constructs relate to explanatory hypotheses? Explain. A concept is a generally accepted collection of meanings or characteristics that are concrete whereas a construct is an image or idea invented for a particular theory or research problem. A construct is an abstract concept. To successfully perform a research, we must form common ground; hence the need for concepts and constructs. The concepts and constructs used to test this phenomenon is the male definition, female definition, customer, and customer defections. The concepts or constructs relate to explanatory hypotheses because female reps having lower customer defections than male reps can be tested using research questions that are built from concepts, constructions, and definitions. 5. Provide an example of how you might use coding in your workplace? If we assign a numerical coding scheme to categorical data, can we calculate numerical descriptive statistics (such as the mean) from this coding? Explain why or why not. Coding is essential to my job. We do a lots of categorical coding in health care IT. For example, I can create a field for insurance carrier or location and assign a numerical number to it. Also, in we use codes to determine the level of your visit or diagnosis. I think we can calculate descriptive statistics like the mean for a category because all the codes represent something different. Back to the insurance carrier, I can get the mean for how many patients use a certain insurance carrier because although the field is a numerical it represents a certain category. 6. Which sampling method is the most effective? Why? Describe a situation at your workplace where you would implement this sampling method. I think the best sampling method is determined by what you are looking to study. In health care, the cluster sampling method is used the most often because a random sample of patients can be clustered together. Also, cluster sampling is convenient and inexpensive but they have variables and can be bias. For example, you want to collect data on all patients that had a physical on a certain day. This information can be bias because you are only studying one type of visit. Week 5 1. What is the purpose of using regression analysis? How may it be used to formulate strategies? Provide examples related to strategy formulation and implementation. The purpose of regression analysis is to predict the value of one variable from assumed values of other variables related to it. Regression analysis is used to formulate strategies because it determines the variable that we have data on to forecast the variable that we do not have data on. For example, a company can use general economic indicators like the Nasdaq Stock market and the consumer price index to forecast demand for their product. By using these two input variables, the company can predict demand and find out what percentage of the variation in demand is due to these two variables. 2. How is regression analysis used in forecasting? Provide examples. Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. It measures the strength or correlation between the dependent and independent variables. The purpose of regression analysis is to predict the value of one variable from assumed values of other variables related to it. Regression analysis is used in forecasting because it is an econometric forecasting method. For example, economists use measurements as the Index of Industrial Production and the Consumer Price Index to predict such things as the unemployment rate or the Gross National Product. 3. What is the purpose of using correlation analysis? How may correlation analysis be used in business decisions or in relation to strategy formulation and implementation? The purpose of using correlation analysis is to discover whether there was a relationship between variables, to find out the direction of the relationship, and to find the strength of the relationship between the two variables. Correlation analysis can be used in business decisions to predict the trends and changes that are associated with the variables in question and to inform the business of what needs to be done to keep things moving within the business. As long as the correlation analysis is found then there is no stopping the business from succeeding in everything that they do. 4. How may correlation analysis be misused to explain a causeandeffect relationship? Correlation analysis can be misused to explain a causeandeffect relationship because it is often mistaken as causation. For example, my team is trying to prove correlation between number of banking services offered and the number of years a customer has been a member of the bank. Although, these variables being compared, it does not mean that the reason customers use more or less variables because they have been a member of a bank for a certain amount of years. Basically, a correlation analysis looks at how closely two data sets are related, but further analysis is needed to see if there is a true cause and effect relationship, and which of the two is the cause, and which is the effect. 5. Discuss the relationship between independent and dependent variables with regard to correlation and regression analysis. What is the purpose of these terms? Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables. This relationship is hypothesized and estimates of the parameter values are used to develop an estimated regression equation. Various tests are used to determine if the hypothesis is satisfactory. If the hypothesis is deemed satisfactory, the estimated regression equation can be used to predict the value of the dependent variable given values for the independent variables. Correlation and regression analysis are related in the sense that both deal with relationships among variables. Correlation measures the degree to which changes in one variable are associated with changes in another. It can only indicate the degree of association or covariance between variables. The purpose of both regression analysis and correlation is to predict values for dependent variables when only the independent variable is known. Chapter 10 questions Respond to one of the following: 6. Describe a situation at your workplace where you might use regression analysis to solve a problem. In healthcare information technology, you might want to know if wait time or 30 minutes or more leads to a decrease in patient appointments. While statistical analysis cannot prove that one thing causes another, it can determine if there is a relationship between the variables which gives a direction to the analysis. 7. Using Excel, what is the easiest way to draw a regression line and determine the regression equation? Using Excel, the easiest way to draw a regression line is to go to use the format treadline. 8. Is it necessary to prove a regression is statistically significant? If so, how do we go about performing this proof? Chapter 11 9. What is the difference between simple linear regression and multiple regression? Describe a situation at your workplace where you might use multiple regression to solve a problem. Simple linear regression is used to model the relationship between two continuous variables while multiple regression models includes more than one independent variable. In my field, we use linear regression to understand patient perceptions of inpatient services (surgery or hospital stays), their overall perceptions of health care quality, and also satisfaction with their care and willingness to return or recommend the same hospital's services to others. Week 6 1. How does each key managerial dimension promote effective research? How does each dimension help meet desired results? What is the inherent value of these dimensions to a manager and the decisionmaking process? 2. You have received a business research study done by a consultant for a life insurance company. The study is a survey of customer satisfaction based on a sample of 600. You are asked to comment on its quality. What do you look for? The first thing I would look for is the purpose of the research, making sure it is clearly defined. Next, I would make sure that the process is thoroughly documented. I would make sure that the limitations are revealed and that the research was performed ethically. I would also look for the findings to be straightforward and not confusing. I would also look to see if the conclusions were correct. 3. In your organization's management development program, there was a heated discussion between people who claimed that theory is impractical and not effective, and others who claimed that effective theory is the most practical approach to problems. What position would you take and why? I don’t think I would choose either. For someone to take either side exclusively, they ignore the need for both facts and theory. Theories tend to be complex, be abstract, and involve multiple variables. Theories are the generalizations we make about variables and the relationships among them. We use these generalizations to make decisions and predict outcomes. Our ability to make rational decisions, as well as to develop scientific knowledge, is measured by the degree to which we combine fact and theory. 4. Microsoft PowerPoint is one of the most common presentation tools. Describe a tool that you might use to give a presentation other than PowerPoint. Provide a short example. Although Microsoft PowerPoint is one of the most common presentation tools, there are other options out there. I have used Prezi, a cloudbased presentation software and storytelling tool for presenting ideas on a virtual canvas. Instead of slides, Prezi makes use of one large canvas that allows you to pan and zoom to various parts of the canvas and emphasize the ideas presented there. Prezi supports the use of text, images, and videos and also provides a collection of templates to choose from to help new users get accustomed to the interface. For example, I had to create a presentation on a new method to do build a clinic location, department, and system. The presentation had to be done using Prezi, so I took the informative approach, which was similar to PowerPoint. 5. Reflect on what your have learned in this course. Now that you know more about research, describe how you might take what you've learned and apply it to a management dilemma within your workplace. 6. In preparing PowerPoint presentations, you should use generallyaccepted presentation practices including: No more than five to seven lines of text per slide. Fivetoseven words per line. Use of simple, elegant graphics (but only if needed to clarify the content). Don't use text transitions (e.g., flying or twirling text). If you use a slide transition, use something simple and use it consistently from slide to slide. Don't put anything in the presentation that distracts the audience from the content. Use the "notes" feature to record notes for the speaker, rather than attempting to include too much information on one slide. Remember that the purpose of a presentation is to convey information or to persuade. In most cases, slide and text transitions and other special effects detract from the presentation's purpose. As such, special effects should be avoided unless they aid the viewer in understanding the content or help in making a point. Remember that in a presentation, content and consistency are vital to the presentation's purpose; almost everything else is simply a distraction. In addition to the technical aspects of the presentation given above, be sure to follow something I call the "Golden Rule of Presentations" (which is quite similar to the "Golden Rule of Writing") when creating a presentation: 1. Say what you're going to say. 2. Say it. 3. Say what you said. Translated into English, what this says is that good presentations (similar to good papers) start with an introductory slide that provides the audience with a preview of what the presentation is about (don't give away the farm here, just provide a general overview). This slide (or slides if necessary) is followed by the content of the presentation. The final slide (similar to a paper's conclusion and which provides the "Say what you said" part of the Rule), summarizes the important parts of the presentationthe things you want the audience to remember the most. Remember that we as humans are more likely to remember the first things we see and the last things we see more often than anything in between. To illustrate this, try to remember the last automobile trip you took. You typically remember leaving and you remember getting to your destination, but everything else in between is a somewhat of a blur. In any case, by stressing the most important ideas of the presentation at the beginning and at the end of the presentation, you are more likely to have succeeded in accomplishing what you intended with the presentation. Comments? Thank you for these tips. I have had other instructors that expected my presentations to have tons of special effects. I don’t use these special effects because my company prefers that if special effects are used it should only be used for the slide. I train doctors on electronic medical records and they prefer to keep things simple. Those effects are distractions can cause you to loose your audience. Suggested future research based on your research results, challenges, and implications Canetra
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