Sampling and Data Collection Plan

Topics: Question, Electronics, Electronic engineering Pages: 5 (846 words) Published: October 12, 2014


Sampling and Data collecting Plan
October 9, 2014
University of Phoenix
QNT/561

Team D has chosen to look into whether or not should Pear Inc. should start putting their resources into either Laptops or tablet electronic devices. Putting resources into a venture that may or not pan out could be detrimental to that same company. As an example; Kodak and their choice not to expand into digital, something they created, and stay the course with their polyurethane film (Mui, 2012).In the end the choice the public made was to go to digital and have the image saved for ever instead of the inevitable fading of pictures and the breakdown of the film making Kodak play catchup. Population, Size, and Target

When considering of finding the population we have chosen social media for all sexes, ages, financial, and country demographics. The reasoning behind the choice is Pear’s product utilizes this source of media in particular. In addition social media is heavily used by the same people who frequent these sites. The sampling would reflect on Pear’s target audiences assisting in the choice of which direction to go with Pear’s resources. It determining size team D would like to obtain a min of 10% of the population of random sampling. Due to the time limit restraint of the weeks leading up to week 4 team D would like to see a 300 individual response to the choices of our chosen sampling element. Sample Element

Team D had talked about data mining, survey source, and observation. Data mining would need to look at the sales of both the Pear Book and the Pear Pad in a given business cycle of the most current year (Stat Soft, 2014). This is due the fact technology is growing so fast looking at data, even one year ago, may provide incorrect data in which put Pear on a different direction then it should be taken. Also data mining would only answer the direction the company is being taken too by the market and not determining or understanding the cause of the direction. Data mining is also a form of observation. Pear could send out certain employees to different electronic store, web sites, chat rooms, or to any form of commerce to look at the all sales both Pear’s and its competitors, however still wouldn’t answer the question of where to place Pear’s resources. Pear must determine to logically and have firsthand knowledge of what people are thinking about before they go and purchase. What are their choices and needs to be filled when using their chosen electronic device. Personal lives continue to change. Meaning people might be going to college, High School, into video gaming, or just in need a device as a medium of entertainment. Pear has chosen to perform a targeted survey in which 9 questions would be asked. The survey would ask if they were to look for electronic devices today what they would be looking for. Additional questions would be asked: Age

Income
Laptop or tablet
Gender
Usage (business, pleasure, or both)
Video gaming
How much time on device (laptop or tablet)
Prices of either Laptop or tablet
Is brand type important
Storage capacity
Using the survey is putting the trust in the individual to answer the questions truthfully and accurately. However the moment the individual steps into the business or orders their device their mind may be changed at the point of sale. If there is a change this is the degree of variance and would be used in the confidence makeup of the sampling.

We hope to have a 95% acucuracy. If we only have 300 answered surveys and we wanted to look at the age of those surveyed we would take the age range and find the mean. As an example let call the mean 26.7, then we would find out the standard deviation of those surveyed and receive 2.5. Then we would look to find the 95% interval of 1.96. I would calculate the formula of 1.96(2.5/sq root of 300) and receive the answer of .28 (rounded to the nearest tenth). Then I would take the mean of 26.7and...


References: Mui, C. (2012). How Kodak Failed. Retrieved on October 9, 2014 from
http://www.fo
rbes.com/sites/chunkamui/2012/01/18/how-kodak-failed/
SHMA (2012). 4 Types of Consumer Buying Behavior. Retrieved on October 9, 2014 from
http://managementation.com/4-types-of-consumer-buying-behavior/
Stat Soft (2014). What is Data Mining (Predictive, Analytics, Big Data). Retrieved on October
9, 2014 from http://www.statsoft.com/Textbook/Data-Mining-Techniques
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