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Briefly advise each of the following two (2) people on specific research studies that he or she might find useful. For each person, propose a reporting, descriptive, explanatory, and predictive study.

• a. Manager of a full-service restaurant with high employee turnover (the management decision problem is known)
• b. Director of Big Brothers/Big Sisters in charge of sponsor recruiting (the management decision problem has not yet been specified)

Distinguish between the items in the following sets and describe the significance of each in a research context:

• a. Concept and construct
• b. Deduction and induction
• c. Concept and variable
• d. Hypothesis and proposition
• e. Theory and model

The quarterly production of pine lumber, in millions of board feet, by Northwest Lumber since 1996 is shown in the following table: Quarter Year Winter Spring Summer Fall 1996 7.8 10.2 14.7 9.9 11.6 17.5 9.9 9.7 15.3 10.7 12.4 16.8 10.2 13.6 17.1 10.3.

a. Determine the typical seasonal pattern for the production data using the ratio-to-moving average method.

b. Interpret the pattern.

c. Deseasonalize the data and determine the linear trend equation.

d. Project the seasonally adjusted production for the four quarters of 2001.

Sales of roof material, by quarter, since 1994 for Carolina Home Construction, Inc., are shown below (in $000): Quarter Year I II III IV

a. Determine the typical seasonal patterns for sales using the ratio-to-moving average method.

b. Deseasonalize the data and determine the trend equation.

c. Project the sales for 2001, and then seasonally adjust each quarter.

The following is the number of retirees receiving benefits from the State Teachers Retirement System of Ohio from 1991 until 2000:

a. Determine the least squares trend equation. Use a linear equation.

b. Estimate the number of retirees that will be receiving benefits in 2003. Does this seem like a reasonable estimate based on the historical data?

c. By how much has the number of retirees increased or decreased (per year) on average during the period?

Paper For Above instruction

In the realm of research studies, tailoring specific investigations to meet the needs of particular stakeholders is crucial. This paper provides tailored research study proposals for two individuals: a restaurant manager facing high employee turnover and the director of Big Brothers/Big Sisters focusing on sponsor recruitment. Additionally, it explores fundamental concepts in research methodology—such as concepts vs. constructs, deduction vs. induction, and hypotheses vs. propositions—highlighting their significance in conducting effective research.

Research Study Proposals

1. Restaurant Manager

The restaurant manager, grappling with high employee turnover, can benefit from comprehensive research encompassing descriptive, explanatory, and predictive analyses. A descriptive study might involve surveying current employees to understand satisfaction levels, reasons for leaving, and organizational climate. An explanatory study could explore relationships between employee satisfaction and turnover rates, examining factors like wage structures, work environment, and management style. Lastly, a predictive study might develop a model to forecast future turnover, considering variables such as employee demographics, tenure, and engagement scores.

2. Director of Big Brothers/Big Sisters

Since the management decision problem is not yet specified, the first step involves exploratory research to identify key challenges in sponsor recruitment. A descriptive study could gather data on current recruitment sources, target demographics, and retention rates. An explanatory study might investigate factors influencing sponsor commitment, such as perceived program value or community engagement levels. A predictive study could then use data on existing sponsors to forecast potential recruitment success, enabling targeted outreach efforts.

Key Concepts in Research Methodology

Concept vs. Construct: A concept is an abstract idea, like "leadership," while a construct operationalizes this idea into measurable elements, such as leadership quality assessed through specific behaviors.

Deduction vs. Induction: Deduction involves reasoning from general principles to specific instances (theory testing), whereas induction derives general principles from specific observations (theory development).

Concept vs. Variable: A concept is an overarching idea, while a variable is a measurable attribute that can vary, such as "employee satisfaction" (concept) operationalized as a satisfaction score (variable).

Hypothesis vs. Proposition: A hypothesis is a testable prediction derived from a theory, whereas a proposition is a statement expressing a relationship between concepts that may or may not be testable.

Theory vs. Model: A theory provides an overarching explanation of phenomena, while a model is a simplified representation or simulation of a system based on that theory.

Time Series Data and Seasonal Pattern Analysis

The quarterly pine lumber production from 1996 onwards displays seasonal fluctuations. Using the ratio-to-moving average method helps identify these patterns. For example, calculating centered moving averages smoothens the data, rounding out seasonal effects. By dividing actual values by these averages, the seasonal indices reveal periods of high or low production, characteristic of seasonal industries.

Interpreting the pattern shows that production peaks typically occur in the summer and spring, indicating higher demand during these periods, possibly due to construction activity. The observed seasonal pattern assists in planning inventory and managing resources.

Deseasonalizing involves dividing actual production figures by seasonal indices, removing seasonal effects and unveiling the underlying trend. A linear trend equation can then be fitted to this adjusted data, often using least squares regression, to forecast future values. Projecting the seasonally adjusted data into 2001 helps anticipate future production, enabling better strategic planning.

Sales Data and Trend Prediction

Similar to lumber production, analyzing quarterly sales involves calculating seasonal indices using the ratio-to-moving average method. Deseasonalizing sales figures reveals the underlying sales trend, critical for understanding long-term growth or decline. Fitting a trend line, typically linear, offers projections into future years, such as 2001.

Forecasts enable the company to adjust inventory, staff, and marketing efforts proactively. Seasonal adjustments reflect periodic fluctuations, ensuring management decisions are based on underlying sales performance rather than seasonal effects.

Retiree Benefits Over Time

Analyzing retiree data from 1991 to 2000 using least squares regression provides a trend equation, describing whether the retiree count is increasing or decreasing. For example, a linear equation of the form y = mx + b quantifies yearly changes. Estimating the 2003 value involves extrapolating this trend. The reasonableness of this estimate depends on the linearity of past data and potential external influences.

Calculating the average yearly change indicates whether the retiree population is expanding or contracting, informing policy and financial planning within the retirement system. Such analyses underpin resource allocation and sustainability strategies.

Conclusion

Effective research in business and social sciences hinges on designing studies aligned with decision-makers' needs and understanding core concepts in methodology. Accurate pattern analysis, trend estimation, and forecasting foster informed strategic decisions, ultimately contributing to organizational success.

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