Unit VI Scholarly Activity: A Local Retail Store Wants To Ex

Unit VI Scholarlyactivity A Local Retail Store Wants To Expand Its Off

A local retail store aims to expand its product offerings beyond women’s fashions and accessories by introducing either girls’ clothing or men’s clothing. The store seeks public opinion to determine which expansion is more needed and sustainable. The primary concerns include deciding which offering to add, determining the number of items to include in the survey, and selecting an appropriate sampling plan and respondent demographic. The store owner has requested assistance to identify the target population, the sampling frame, the type of sampling (probability or non-probability), and the suitable sampling design method. Additionally, guidance is needed on how to select respondents—whether current customers should be approached during shopping or through other methods—and how to determine an appropriate sample size.

Paper For Above instruction

The decision to expand a retail store’s product offerings necessitates a thorough understanding of the target market and appropriate data collection methods. The first step involves clearly defining the target population, which in this case includes current and potential customers within the store’s geographic or demographic reach. Given the store’s current customer base and the local community, the target population encompasses women, men, girls, boys, and their guardians or parents who are likely to shop at or be interested in the store’s new offerings.

The sampling frame refers to the actual list or representation of the population from which the sample will be selected. For a retail store, potential sampling frames could include customer databases, mailing lists, loyalty program memberships, or even residents in the store’s vicinity if expanding to new markets. An accurate sampling frame is vital because it ensures that every member of the target population has an equal chance of being selected, thereby enhancing the representativeness of the survey results.

Sampling is crucial in research because it allows for efficient data collection while providing insights that reflect the larger population’s preferences. Proper sampling reduces bias, saves resources, and enables accurate generalizations. When choosing between probability and non-probability sampling, probability sampling is preferred for this context because it ensures that each individual has a known, non-zero chance of selection, thus leading to more statistically valid inferences about the target population. Non-probability sampling, such as convenience sampling, might be quicker and less costly but risks bias and reduced representativeness, which could compromise the decision-making process regarding store expansion.

Different sampling designs include simple random sampling, stratified sampling, cluster sampling, or convenience sampling. For this scenario, stratified sampling would be most appropriate because it involves dividing the population into distinct strata—such as current customers, potential new customers, or residents in specific age groups—and selecting samples proportionally from each group. This approach enhances the representativeness of the sample by acknowledging the diversity within the target population. Given that the store may want insights from both existing customers and new demographics, stratified sampling ensures comprehensive coverage and comparability among different customer segments.

Respondent selection should be carefully planned. To maximize response rates and relevance, current customers could be approached during their visits through in-store surveys or at checkout, providing immediate feedback on potential expansions. Alternatively, surveys can be distributed via mail or telephone, offering convenience and privacy, especially if targeting residents beyond current customers. Combining these approaches can optimize response rates and data richness. Using multiple methods helps reach a broader demographic, ensuring that the survey captures opinions from various segments of the population.

Regarding sample size, the store should conduct a statistical power analysis considering the population size, expected response rate, margin of error, and confidence level. For example, assuming a population of approximately 10,000 residents within the store’s reach, a 5% margin of error, and a 95% confidence level, a sample size calculator suggests roughly 370 respondents are necessary. Adjustments should be made for anticipated non-responses or incomplete data, possibly increasing the sample size by 10-20%.

In selecting sampling units, the store might utilize systematic sampling—such as selecting every kth individual from a stratified list—or randomly select individuals within each stratum. The factors influencing sample size include population heterogeneity, desired precision, and resource constraints. Employing stratified random sampling ensures each subgroup is appropriately represented, thereby enhancing the accuracy and reliability of the findings. Therefore, a well-planned sampling approach, combined with an adequately sized sample, provides meaningful insights into customer preferences and helps the store make informed expansion decisions.

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