Assigning Successive Integers to Satisfaction Levels
It cannot be said that the value obtained based on the collected data set is a valid summary because the value is not a true representative of the various response or rather say satisfaction level of the customers. The procedure of assigning successive integers to the various satisfaction levels is inconsistent as one cannot assume that the difference between the two successive integers are equal which means the sequential satisfaction level of the customer is exactly the same.
Hence, it can be concluded that the existing method does not provide valid summary of the collected data as it is a method of convenience not the way of true representation of data (Flick, 2015). In order to find the valid summary of the data, it is essential to assign the weights to each of the given response based on underling requirement. Further, a single value cannot show the accurate conclusion because the same average value can easily be derived from multiple response combination. Therefore, it is recommended that significant weights need to be assigned to the responses of the customers by considering the underlying utility rather than just assigning random number. Further, graphical summary is also another way to represent the valid summary of the collected data (Eriksson & Kovalainen, 2015).
Question 2
The survey would not provide reliable estimate of the satisfaction level of the store customers primarily because of the non-representative sample used. There is greater likelihood for the younger age customers to participate in the online survey through the website owing to their tech-savvy nature which is not the case for people those who are aged and are typically less tech savvy. Further, there is a high likelihood that the sample for which responses are obtained is not representative of the customer population for the store since the participation in the online survey is left to the choice of the individual consumers. Instead, it would have been better to select a representative sample from amongst the customers and then obtain their responses regarding customer satisfaction (Hair et. al., 2015).
Question 3
- The gender would be expressed in categorical form i.e labels (non-numeric) and there is no particular superiority order and thus data type is nominal (Flick., 2015).
- The Fahrenheit thermometer is an interval data as the scale represents numeric data but does not possess true zero. Also, it is essential for a data to be recognised as interval data that the difference between any two values which is easy to determine here (Eriksson & Kovalainen, 2015).
- The Kelvin thermometer is a ratio data as the scale represents numeric number and also possesses a true zero. Presence of true zero in case of Kelvin thermometer has changed the data type from interval to ratio data (Flick, 2015).
- The number of items bought would assume only numeric values and thus the relevant option would be either ratio or interval. However, since a true zero can be defined, thus data would be ratio (Lieberman et. al., 2013).
- The bank account balance would be a numeric value and hence the two possible choices are ratio or interval. However, when the account balance will have no money, then the balance would assume a true zero. Further, values can be compared which implies that the data would be ratio type (Hillier, 2016).
Question 4
- Descriptive non-experimental study
In order to perform the above study, there would be observation of the two groups of players or players who are in the same team. The players in one group would not consume orange juice thrice a day on four days of the week while the players in other group would consume orange juice thrice a day on four days of the week. The hypothesis testing can be done by comparing the performance of these two groups on the weekend game (Eriksson & Kovalainen, 2015).
- Quasi experimental study
Assigned Weights Based on Underlying Requirements
In order to perform the above study, two groups of players would be formed from sports team who are playing weekend matches. Out of these two groups, one group would work as control group and other would work as intervention group. It is essential that the selection of players for the group would not be based on random selection and rather based on any specified criteria or choice of the researcher. The players of the intervention group would be given orange juice thrice a day on four days of the week while the players of control group would not be given orange juice thrice a day on four days of the week. The hypothesis testing can be done by comparing the performance of these two groups on the weekend game (Flick, 2015).
- Experimental study
In order to perform the above study, two groups of players would be formed from sports team who are playing weekend matches. Out of these two groups, one group would work as control group and other would work as intervention group. It is essential that the selection of players for the group would be based on random selection from the matched pairs formed based on comparable ability, experiences and other relevant aspects.
The players of the intervention group would be given orange juice thrice a day on four days of the week while the players of control group would not be given orange juice thrice a day on four days of the week. The hypothesis testing can be done by comparing the performance of these two groups on the weekend game (Hair et. al., 2015).
It can be concluded based on the above analysis that the most suitable method for the hypothesis test is experimental study. It is because the selected groups are comprised of matched pair which also prevents the effect of other irrelevant external factors. Further, random selection of the students for the two groups also reduces the researcher bias and enhances reliability (Hillier, 2016).
References
Eriksson, P. & Kovalainen, A. (2015).Quantitative methods in business research (3rd ed.). London: Sage Publications.
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project (4th ed.). New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015) Essentials of business research methods (2nd ed.). New York: Routledge.
Hillier, F. (2016) Introduction to Operations Research (6th ed.). New York: McGraw Hill Publications.
Lieberman, F. J., Nag, B., Hiller, F.S. & Basu, P. (2013) Introduction To Operations Research (5th ed.). New Delhi: Tata McGraw Hill Publishers.
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