Question.3984 - Assignment 7
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Assignment 7 Anthony Allen Management Research Methods Management Dr. Guclu Atinc November 28th 2024 Question 1 The results reported by Pointsec are questionable due to significant limitations in the sampling method and sample size. The sampling method relied on convenience, with taxi companies connecting researchers to approximately 100 drivers per city. This approach introduces selection bias because the sampled drivers may not accurately represent the entire population of taxi drivers in each city. Variations such as shift patterns, location differences, and fleet characteristics are likely not adequately accounted for in such a limited sample, making the findings less reliable. Additionally, relying on drivers to recall the number of devices left in their cabs over six months introduces recall bias, as human memory is often imprecise, particularly over extended periods. Drivers may forget incidents, overestimate occurrences, or underreport due to lack of accurate tracking. This further undermines the reliability of the collected data. The small sample size compounds the problem. With only 100 drivers surveyed in cities like London or Chicago, where taxi fleets consist of thousands of drivers, the sample size is too small to reliably extrapolate the findings to the entire population. A limited sample cannot fully capture the variability in occurrences of devices being left behind across different drivers, vehicles, or routes. Extrapolating such small-scale data to estimate totals for entire cities, as Pointsec did, significantly increases the potential for error. For instance, calculating 85,619 cell phones left behind in Chicago based on 3.42 devices per cab falsely assumes that the rate is consistent across all taxis and drivers in the city, ignoring any natural variation in behavior or circumstances. Such precise numbers create an illusion of accuracy and precision that is not supported by the underlying data quality. Moreover, the method of multiplying rates by the size of the city’s taxi fleet introduces additional layers of uncertainty. Differences in reporting practices, fleet sizes, and operating conditions between cities could lead to inconsistent or misleading comparisons. While the survey method might be sufficient for drawing attention to the issue of data security in a general sense, it lacks the rigor needed for precise reporting. A more robust sampling method and a larger, more representative sample size would be required to produce results that are both reliable and generalizable. In its current form, the sampling method undermines the credibility of the conclusions drawn. Question 2 To report results with a 95% confidence level, the sampling design and sample size for studying mobile devices left in taxis must be significantly improved. First, a random sampling method should replace the convenience sampling used in the original study. Random sampling involves selecting taxi drivers from a comprehensive list provided by taxi companies, ensuring that each driver has an equal chance of being chosen. This approach reduces selection bias and increases the representativeness of the sample. Moreover, to account for variations in taxi operations, a stratified sampling method can be employed. This involves dividing the population into strata based on factors like shifts (day versus night), locations (urban versus suburban areas), or fleet types (independent drivers versus company-employed), ensuring that all subgroups are proportionally represented in the sample. Relying on objective records rather than subjective driver recall would also improve data reliability. Many taxi companies maintain lost-and-found logs that document the items left behind in vehicles. These logs provide more accurate and consistent data than relying on drivers’ memory, which is prone to recall bias. Using such records would ensure a more robust foundation for extrapolating findings. If direct access to these records is not feasible, conducting interviews or surveys with drivers while providing them with structured prompts or shorter reporting timeframes (e.g., recalling incidents over the past month rather than six months) could help mitigate inaccuracies. Determining the appropriate sample size is also critical for achieving the desired confidence level. The formula for calculating sample size is: n= (Z2 * ?2)/E2 Where, Z represents the Z-value for a 95% confidence level (1.96), ?\sigma is the standard deviation, and E is the margin of error. For example, if a pilot study estimates that drivers encounter an average of 1.5 devices left behind with a standard deviation of 1.5 and a margin of error of 0.5, the required sample size is approximately 35 drivers per city. However, larger cities with more diverse taxi operations, such as London or Chicago, may require larger sample sizes to account for increased variability. Finally, the design should include clear and standardized reporting protocols to ensure consistency. Data validation processes, such as cross-referencing survey responses with lost-and-found records, can further enhance accuracy. By implementing these measures, the company can produce results that are both reliable and precise, making them suitable for critical business predictions.More Articles From Management
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