2. Utilise effective communication practice in organisations.
Likert Scale according to Kostoulas (2014), is an ordinal psychometric measurement of opinions, attitudes, and beliefs. The problem with Likert items is that calculating average scores provides a middle ground it can sometimes skew the results. In our case, the obtained mean is not a representative of the feelings/attitude/opinions of the staff members regarding the store products. The answer between the two extremes could be interpreted as the best and realistic outcome for this study. For instance, an in-depth an
alysis of the survey reveals that over 48% of the sampled individual agree that there are cognizant of the store products.
On the other hand, 35% of respondents disagree that they are not aware of the existence of the store productions. This information contradicts the interpretation that would be made with a mean of 3.19, i.e. 63.8 of the respondents are not sure whether the store products exist. In such a case, a wrong interpretation would be made, and hence the use of average scores is not an excellent way to summarise the Likert data. According to Purdal (2013), the most appropriate measure to use in this case would be mode (the most frequent responses), or the median. In our case, our mode would be 32% which gives a more accurate interpretation than the use of mean. Alternatively, we could use a non-parametric test like Levene's test or Kruskal Wallis to summarise the data.
The collected data does not provide an accurate reflection of the likely outcome of the election for because there is a high probability of the occurrence of sampling error. For instance, by using the simple random sampling technique in our case, it would be possible to draw people of the same political affiliations even if the population was more general or also interview the same respondents twice as there is not controlling factor. For this reason, simple random sampling would not work to give the correct predictions of the upcoming election. Given that, the best way of collecting the views of the citizens about the upcoming election would be to use a different sampling technique, for example, stratified random sampling, which would help to account for the differences within the population, for instance, political affiliations, age, gender or race.
Interval data. According to Kostoulas (2014), interval data is measured along a scale in which each position is equidistant from one another. In our case, the calculation of cars that passes the given intersection is done after every single hour.
Ratio data. With ratio data, the difference between values is meaningful and can be ordered (Kostoulas, 2014). Besides doubling in ratio scale is also significant and there is an absolute zero. For instance, when using the Ke1vin scale to measure temperature, 40K is a twice as hot as 20K.
Interval data. The temperature in degrees Fahrenheit is measured along a scale in which each position is equidistant from one another.
Nominal Data. Nokia, Huawei, Samsung, Blackberry, Apple, Other are items which are differentiated by just naming. According to Purdal (2013), the only thing a nominal scale does is to say that stuff being measured have a standard feature. In our case, they are just phones from different types of manufacturers
Ratio data- In a ratio scale according to Purdal (2013), numbers can be linked as multiples of each another. In our case, one individual can be twice as tall as a different person.
Observation. As a teacher, I would review the results of individual students for a given duration to know whether or not they attend class to make conclusions.
Correlation. I would correlate the results of students who attend classes with the results of students who do not attend classes to interpret the effects.
Advanced statistical test such as T-test, F-test and regression analysis to test the significance level of the model of the study.
An experimental study would give more accurate data as the subjects of study are not manipulated or easily predictable as they occur in their natural case (Grolemund & Wickham, 2014). The other two methods may be subject to personal biases.
Grolemund, G., & Wickham, H. (2014). A Cognitive Interpretation of Data Analysis. International Statistical Review, 82(2), 184-204. doi:10.1111/insr.12028
Kostoulas A. (2014, September 20). On Likert scales, ordinal data and mean values. Retrieved August 18, 2018, from https://achilleaskostoulas.com/2013/02/13/on-likert-scales-ordinal-data-and-mean-values/
Purdal S. (2013, January 13). Scales of Measurement: Nominal, Ordinal, Interval, Ratio. Retrieved from https://communitymedicine4asses.com/2013/01/13/scales-of-measurement-nominal-ordinal-interval-ratio/
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