Professional Research & Communication-Free-Samples for Students

Questions: 1.You will need to understand the limitations of Data Collected in this way in order to be able to explain why the Manipulation in the question is not valid. 2.You will need to consider whether the people filling out the Survey are an accurate representation of all Customers and explain Carefully . 3.Is a little more straightforward. For each sub-question you need to think about what the nature of the data is and which Categorization suits it best. 4.You will need to think about the requirements for each type of Quantitative Study. Answers: 1.The value that has been gathered from the survey is not a valid summary of data because of several reasons. First of all, the value generated is merely a mean rating from the values chosen by the customers during the survey. However, it is not known whether the customers have given biased opinions or not. Secondly, the survey does not reflect the views of the entire customer base as many have not participated in the survey. It is possible that most of customers who participated in the survey have only positive or negative reviews against the company (Krebs Duncan, 2015). Finally, the main problem is that in this survey scale, it has been assumed that the scale is evenly spaced i.e. the data generated is ordinal data but it has been treated as interval data in the survey outcome. A more valid way of representing the same data is to change the answers that are selected by the customers. Instead of predefining the scale of the data (1 to 5), the customers taking part in the survey should be given the chance to give a number in a scale of 1 to 5 or 10 based on their views on each question. This will generate actual interval data that can be then used for analysis. Moreover, there will be less biasing in the values as the customers as they will enter their own values instead of previously fixed values. Hence, this manual scaling method is recommended for representing the collected data from the survey. 2.If the general case is considered, then true reflection is not received from the data collected from the online survey. This is mainly because many customers do not participate in the survey due to lack of sufficient technical support or no interest in conducting surveys. Moreover, some other genuine customers may also have no information regarding such a survey. As a result, there are insufficient amounts of opinion reflected in the survey (Aikens et al., 2014). Again, there is the problem of biasing. There may be some old customers of the company that are biased towards it and hence, will give biased views on the company, reflecting only the positive reviews instead of actual condition of the company. This type of biasing is common in the movie ratings where, no matter how good or bad a movie is, the viewers will always rate that movie based on biased personal preferences and experiences. Similarly, the customers will have biased personal preferences regarding the company. Again, in order to reach a particular conclusion regarding the services of the company, a large sample size necessary instead of a small one in order to remove biasing as well as more variation in the ratings given by the customers (Denscombe, 2014). However, nowadays, almost everyone has a smartphone these days and more and more people can participate in the survey process. As a result, the above discussed limitations are getting more and more irrelevant as more customers are becoming active participants in rating the companys services. 3.Data a In this particular survey, the two responses are male or female. This is an example of Nominal Data. Nominal data is the data that is not based on numerical values and is simply based on some word choices or as commonly called labels (Gravetter Wallnau, 2016). The main significance and the key identification point is that the nominal data has no numeric values and also do not overlap with each other if considered as distinct data sets. Hence, this data is nominal data. Data b Fahrenheit temperature scales provide interval data as the data set is placed in a uniform and constant interval in the scale. Moreover, although absolute zero exists in the scale, but the nominal value at that temperature is never zero. Hence, it can be said that Fahrenheit temperature scale gives interval data. Data c Kelvin scale is not valid for interval data as there is the existence of absolute zero value where the nominal value of the scale is also zero. Moreover, the intervals of the Kelvin scale are not constant and uniform. Hence, it can be concluded that Kelvin thermometric data belongs to ratio data. Data d Initially, bank account balance can seem to be like interval data as it has some distinct set of numerical values. However, the main problem with the interval data is that it can never be zero. On the other hand, bank account balance of an individual can have zero value. Hence, this is not interval data. With further analysis, it can be said that bank account balance is actually ratio data as it fulfills all the perquisites to be a ratio data (Field, 2015). Bank account balance is a set of distinct numerical values and can be have zero value. Moreover, it can be presented in a ratio scale and numerical calculations are also applicable in the same. 4.In the given case, there are three possible options to test whether the hypothesis is correct or not. With descriptive non-experimental study, the coach can interpret that maybe his hypothesis is correct but has no idea whether the performance was enhanced by the orange juice or some other substance taken by the player(s). Again, with quasi-experimental study, the coach can also have an idea that the orange juice may have positive effect in enhancing the performance of the player (Becker et al., 2017). However, with the experimental study, the coach can have a clearer idea whether his hypothesis is correct or not. He will have full control over what can be taken by the players in the week and then, he will test the performances of the players on field. From this test, he will be able to determine whether the performance is enhanced by the orange juice or not as he knows the players have not taken any other food or substances in the same interval of the test period. Hence, for valid ating the accuracy of the hypothesis, the experimental study is the most suitable method. References Aikens, K. A., Astin, J., Pelletier, K. R., Levanovich, K., Baase, C. M., Park, Y. Y., Bodnar, C. M. (2014). Mindfulness goes to work: Impact of an online workplace intervention.Journal of Occupational and Environmental Medicine,56(7), 721-731. Becker, B. J., Aloe, A. M., Duvendack, M., Stanley, T. D., Valentine, J. C., Fretheim, A., Tugwell, P. (2017). Quasi-experimental study designs for evaluating practice, programs and policies: synthesizing evidence for effects collected from quasi-experimental studies presents surmountable challenges.J Clin Epidemiol. Denscombe, M. (2014).The good research guide: for small-scale social research projects. McGraw-Hill Education (UK). Field, T. (2015). The benefits and limitations of quantitative data collection to the literature review data collection. Gravetter, F. J., Wallnau, L. B. (2016).Statistics for the behavioral sciences. Cengage Learning. Krebs, P., Duncan, D. T. (2015). Health app use among US mobile phone owners: a national survey.JMIR mHealth and uHealth,3(4).