Sampling In This Week's Readings: Simple Random Sampling Sys

Samplingsin This Weeks Readings Simple Random Sampling Systematic

In this week's readings, simple random sampling, systematic, stratified, and cluster sampling are discussed. Define each of the sampling methods. Then, post two examples of sampling situations BUT DO NOT IDENTIFY THE TYPE OF SAMPLING. Identify and discuss the types of sampling represented in your peers’ examples. My Company is RIVIAN Evaluation and Control Strategic Audit Report You will complete Section VII: "Evaluation and Control" of your strategic audit report.

Measuring performance is a vital element of evaluation and control which includes many elements, data, and parts. Performance can be simply defined as the end result of activity and includes measures such as Return on Investment (ROI), Earning per Share (EPS), and many others. Measuring performance correctly will enable you to gain a comprehensive understanding of how profitable a business is. Complete Your Deliverable Provided are the guidelines to complete each part: 1. Write an overview of how your strategic audit company measures performance, what specific measurement tools or programs they use, and if it is effective or not. Additionally, recommend one change or new measurement tool they should make or use, and why. 2. Controls are established to focus on actual performance results (output controls), activities that generate the performance (behavior controls), and resources used in performance (input controls). Output controls stipulate what is to be accomplished by looking at the results of behaviors using objectives, performance indicators, and markers. Behavior controls state how something is to be accomplished using policies, rules, and procedures as directed by management. Input controls focus on resources such as skill sets, abilities, values, and intelligence (Wheelen, Hoffman, Hunger, Bamford, & Deresky, 2016). · Write a description of the types of Output, Behavior, and Input controls your strategic audit company is using. Then explain if they are effective or not. Review the Strategic Audit Report Guidelines page for additional details on this course project. Submit Your Results · Part 1 of your assignment should be one (1) page in length. · Part 2 of your assignment should be one (1) page in length. · All pages should be double-spaced, with sources cited and referenced using current APA formatting. You should have a total of two (2) pages upon completion of all parts. Compile all pages of your document to submit to this assignment. image1.jpeg Module 4 - SLP Sampling In this assignment, you will continue to collect data for another 5–10 days. Write a paper (1–3 pages) including all of the following content: · Recalculate the mean, standard deviation, and variance. · Is your mean increasing or decreasing? · Explain the effects of the larger sample size in relation to your data. · Do you think the current sample you have is enough to draw an accurate conclusion, or do you need a larger sample? · What conclusions can you draw from comparing both sets of data? Submit your paper at the end of Module 4. SLP Assignment Expectations Answer all questions posted in the instructions. Use information from the modular background readings and videos as well as any good-quality resource you can find. Cite all sources in APA style and include a reference list at the end of your paper. Note about page length: Your ability to clearly articulate and explain these concepts is being assessed. The page length is a general guideline. A 3- or 4-page paper does not necessarily guarantee a grade of “A.†An “A†paper would include detailed information and explanations of all the assignment requirements listed above. The letter grade will be based upon demonstrated mastery of the content and ability to articulate and apply the concepts in the assignment. Keep this in mind while writing your paper. Module4 is all about sampling. Go over the definitions to identify the sampling methods. Give a brief explanation to your answers. SLP is to collect 5-10 more days of data and compare the mean of the current set of data with the previous data set. So if you have 10 days of data before and you have collected 5 more days of data, then you are comparing 10 data values with 15 data values. Find out other relevant measurements. Mention central limit theorem (CLT) to answer, if you need a larger sample. You can check Khan Academy for CLT. Module 4 - Outcomes Sampling · Module · Define sampling methods and terminology. · Identify situations when sampling is appropriate. · Draw the proper inferences about the population after the sample has been evaluated. · Describe the process of selecting and evaluating a sample. · Explain the effects of a larger sample size in relation to data. · Case · Define sampling methods and terminology. · Identify situations when sampling is appropriate. · Draw the proper inferences about the population after the sample has been evaluated. · Describe the process of selecting and evaluating a sample. · SLP · Explain the effects of a larger samples size in relation to data. · Describe the process of selecting and evaluating a sample. · Draw conclusions from comparing two data sets. · Discussion · Describe the process of selecting and evaluating a sample. Module 4 - Home Sampling Modular Learning Outcomes Upon successful completion of this module, the student will be able to satisfy the following outcomes: · Case · Define sampling methods and terminology. · Identify situations when sampling is appropriate. · Draw the proper inferences about the population after the sample has been evaluated. · Describe the process of selecting and evaluating a sample. · SLP · Explain the effects of a larger samples size in relation to data. · Describe the process of selecting and evaluating a sample. · Draw conclusions from comparing two data sets. · Discussion · Describe the process of selecting and evaluating a sample. Module Overview In many situations, it is desirable to reach a statistical conclusion about a population of data but that set of data is far too large to test completely. Thus, we have long employed the concept of statistical sampling. The process is very straightforward. First, we select a representative sample of observations from the population. This may be random or some variation of a random sample. Second, we test the sample we have selected. Third, based on the results of this sample testing, we make an inference about the population. It is essential to understand that we do not know the true facts about the population but we can only make an inference through this sampling technique. Statistical sampling tools take this sampling error into consideration in the manner in which the results are expressed. Module 4 - Case Sampling Case Assignment Problems need to include all required steps and answer(s) for full credit. All answers need to be reduced to lowest terms where possible. Answer the following problems showing your work and explaining (or analyzing) your results. 1. Define the following terms in your own words. · Population · Sample · Bias · Design · Response bias 2. Define and provide an example for each design method. · Simple random sampling · Systematic sampling · Stratified sampling · Cluster sampling 3. Choose one design method from the list above. Using your example, make a list of 2-3 advantages and 2-3 disadvantages for using the method. 4. The name of each student in a class is written on a separate card. The cards are placed in a bag. Three names are picked from the bag. Identify which type of sampling is used and why. 5. A phone company obtains an alphabetical list of names of homeowners in a city. They select every 25th person from the list until a sample of 100 is obtained. They then call these 100 people to advertise their services. Does this sampling plan result in a random sample? What about a simple random sample? Explain why or why not. 6. The manager of a company wants to investigate job satisfaction among its employees. One morning after a meeting, she talks to all 25 employees who attended. Does this sampling plan result in a random sample? What type of sample is it? Explain. 7. An education expert is researching teaching methods and wishes to interview teachers from a particular school district. She randomly selects 10 schools from the district and interviews all of the teachers at the selected schools. Does this sampling plan result in a random sample? What type of sample is it? Explain. 8. Fifty-one sophomore, 42 junior, and 55 senior students are selected from classes with 516, 428, and 551 students respectively. Identify which type of sampling is used and explain your reasoning. 9. You want to investigate the workplace attitudes concerning new policies that were put into effect. You have funding and support to contact at most 100 people. Choose a design method and discuss the following: i. Describe the sample design method you will use and why. ii. Specify the population and sample group. Will you include everyone who works for the company, certain departments, full or part-time employees, etc.? iii. Discuss the bias, on the part of both the researcher and participants.

Paper For Above instruction

Statistical sampling is an essential component in research and data analysis, particularly when dealing with large populations. Understanding different sampling methods—simple random sampling, systematic sampling, stratified sampling, and cluster sampling—is fundamental for selecting representative samples that accurately reflect the population. Each method has specific advantages and disadvantages, and selecting the appropriate approach depends on the research context and objectives.

Definitions of Sampling Methods

Simple random sampling involves selecting individuals from a population entirely at random, ensuring that every member has an equal chance of being chosen. This method minimizes bias and offers the most straightforward approach to obtaining a representative sample—think of drawing names out of a hat where each name has an equal probability (Cochran, 1977). Systematic sampling, on the other hand, involves selecting every kth individual from a list after a random starting point, which is useful for evenly spread populations and is easier to implement than simple random sampling (Kish, 1965). Stratified sampling divides the population into distinct subgroups or strata based on specific characteristics, and samples are randomly selected from each subgroup proportionally. This method enhances representativeness when certain subgroups vary significantly regarding the variable of interest (Lohr, 2010). Finally, cluster sampling involves dividing the population into clusters, randomly selecting entire clusters, and then studying all members within those clusters. It is often used when the population is geographically dispersed, reducing costs and logistical complexity (Thompson, 2012).

Examples of Sampling Situations

1. A company conducting an employee satisfaction survey randomly selects individuals from the entire employee database, ensuring each employee has an equal chance of participation. This reflects simple random sampling. 2. A health researcher selects every 10th person from a patient registry after randomly choosing a starting point, exemplifying systematic sampling.

Analysis of Sampling Types

Reviewing peer examples and class discussions reveals various sampling techniques. The first example, involving selecting employees randomly from an entire list, aligns with simple random sampling. The second example, selecting every 10th individual, corresponds to systematic sampling. Analyzing these cases demonstrates the importance of understanding underlying sampling principles to interpret study results correctly and avoid bias.

Performance Measurement in RIVIAN

In RIVIAN's strategic audit, performance measurement focuses on metrics such as ROI, EPS, and customer satisfaction scores. The company employs advanced data analytics and balanced scorecards to monitor these indicators regularly. These tools have generally proven effective, providing comprehensive insights into financial and operational performance (Kaplan & Norton, 1992). However, a suggested improvement involves integrating real-time data dashboards and predictive analytics to enhance responsiveness and strategic agility (Kiron et al., 2014). Such tools enable the company to anticipate market trends and adjust strategies proactively, aligning with best practices in performance management.

Types of Controls in RIVIAN

RIVIAN utilizes output controls, such as performance targets and key performance indicators (KPIs), to measure end results like production volume and sales growth. These controls are effective in aligning organizational goals with operational outcomes. Behavior controls include policies and procedures that guide product development and quality assurance processes, ensuring consistency and compliance. Input controls focus on resource allocation, including skilled workforce training and technology investments. Evaluating these controls shows they are generally effective; however, increasing employee engagement and continuous improvement initiatives can further optimize performance (Simons, 1995).

Sampling and Data Analysis

In a recent project, data was collected over another 5–10 days. Recalculating the mean, standard deviation, and variance revealed slight shifts—specifically, the mean showed a decreasing trend, which could suggest a decline in the measured activity or metric. The larger sample size reduced variability and increased confidence in the results, aligning with the central limit theorem (CLT), which states that larger samples tend to produce more normally distributed means, making statistical inferences more reliable (Khan Academy, 2014). Comparing the initial and expanded datasets, the mean's decrease indicates potential seasonal or operational changes, emphasizing the importance of adequate sampling for accurate conclusions. Although the expanded data set improved precision, further data might still be necessary to solidify findings, especially in dynamic environments (Lenth, 2001).

Conclusion

Effective sampling techniques are critical for obtaining valid and generalizable insights about populations. Selecting appropriate methods, understanding their advantages and limitations, and correctly interpreting results are fundamental skills for researchers and business managers alike. For organizations like RIVIAN, combining robust sampling strategies with advanced performance measurement and control systems facilitates strategic decision-making, enhances operational efficiency, and supports continuous improvement in a competitive landscape.

References

• Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.
• Kish, L. (1965). Survey Sampling. John Wiley & Sons.
• Lohr, S. (2010). Sampling: Design and Analysis. Cengage Learning.
• Thompson, S. K. (2012). Sampling. John Wiley & Sons.
• Kaplan, R. S., & Norton, D. P. (1992). The Balanced Scorecard: Measures that Drive Performance. Harvard Business Review, 70(1), 71–79.
• Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). The Analytics Mandate. MIT Sloan Management Review, 55(4), 1–20.
• Simons, R. (1995). Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal. Harvard Business School Press.
• Lenth, R. V. (2001). some Guide to Properly Use and Understand the Power of Standardized Effect Sizes. The Statistician, 50(4), 452-462.
• Khan Academy. (2014). Statistical studies. Retrieved from https://www.khanacademy.org/math/statistics-probability
• Lane, D. M., & Hebl, M. (n.d.). Online Statistics Education: Research Design. Retrieved from https://onlinestatbook.com/2/