How Does Counselling In A Stationary Health Care Setting Affect The Attendance In A Standardised Sports Club Programme?

Use of Likert Scale in Customer Satisfaction Surveys

The data that is collected after the survey being considered about the perception of the customers at the local store. With the use of Likert Scale, the customer could respond to the score by using the survey given to them. The score that is represented on the survey could be Strongly Disagree, Disagree, Unsure, Agree, or Strongly Agree. The data that has been provided is not a valid data as shown in the example because if there are only 100 customer than how can it be possible that number of responded could be 319. If customer is 100 than responded could not be more than that. The probability would be either 100 or less than 100. As it may happen that either all customer had responded or the other case would be some might have responded and some might have not responded. Thus, to make a better customer satisfaction survey, the question should not be out of place which is the intended topic for the survey section. Likert scale is categorically similar which should have a reliable measurement of a particular behavior. We need to get the accurate data in either case to pick a topic and stick with it. It would be better if the scales are not mixed with the survey where rating and scaling could differ. We can use bipolar survey and have more clarity on Likert questions that need to be used in the survey. In bipolar, the response that is chosen is based on neutrality. The customer can either be extremely happy or can be extremely sad. The response gather must be accurate as the final result provide is easy to understand and even easy to respond.

The upcoming election of a polling company has employees whom they send to the corners of the street in every week-day between 9 and 12 were instruction are given to stop passer-by. They have been asked which party they are going to vote for the election. Before the employees comes to the polling booth, they must know some important aspects as an elector. The data collection process is the fact that has data to be provided in a different format. There are many people who simply survey and many will not or cannot vote and they won’t have any impact on getting elected (Thies, 2018). For those who are the registered voters who are legally eligible to cast a vote during the election at the polling station where larger fraction of eligible voters from the employee has not actually vote. Therefore, a statistical model is generated as the final option that could vote on the Election Day. There had been a problem in predicting who is going to vote on the election. The wild difference in the polls is the factors accounting to believe that the voters of Democratic-learning might turn out, while some believe that the voters of Republican-learning might turn out. A voting equipment is set up and employees has to pick up the materials in the Election. The employees are the elector who can immediately check though the name list whether included in the electoral roll. It might happen that some employees vote for one candidate while other may oppose to it and vote for the other candidate. The location will be determine for the Election and voter eligibility will be depend on the maximum vote. The voting being done of which who has got the maximum vote is definitely get selected for the election. While approaching for the election entry where regulated by queues. At a time 3-4 voters will be allowed into the polling station. It is expected by every employee as an elector to maintain the secrecy of voting. If in failure case, secrecy could be maintain and elector would not be permitted to vote. Employees should not disclosed whom they have voted for or else it will be count as offence that has committed by the employees.

Prediction of Voter Turnout in Upcoming Elections

There are different types of data collected which need to be categorized. In this section, we will categories the data with ordinal, nominal, interval and ratio (Cherney et al., 2018).

The number of cars passing through an intersection in an hour, in whole numbers:

The first data gives the number of cars that passes through where there is an intersection. The car passes in an hour is differentiated and since it starts with a whole number so the data can be taken as ‘interval’ (Ong & Puteh, 2017).

The type of mobile phone someone has:

It can be considered as nominal. As it is being provided with the brand name on it. As it use the name to label the data. It uses the brand name which is classified as per the choice of the mobile.

Kelvin thermometers:

The values of Kelvin thermometers can be taken from an absolute zero. It can be considered into interval. As it has the absolute zero value. It produces variables order along with true zero. This variable has the option for zero. In interval scale everything can be done which include nominal, ordinal and interval scale and establish the value of zero.

Fahrenheit thermometers:

Similarly Fahrenheit thermometers has the value for absolute zero. It has interval were the temperature scale has the difference between one temperatures and has the same as the difference between the other. The temperature negative values for 0 value is arbitrary. Thus the Fahrenheit temperature scale is an interval scale. The difference between variables can be achieved by Interval scale but cannot be achieved by ordinal or nominal scale.

A person’s height:

The height of the person is taken under the ratio as ratio data give quantitative value. The measurement of height is done is ratio. The exact value is being provided by units. It has a mathematical comparison in which the information of the height of the person is gathered. Suppose if the person height is 100 cm and the other person height is 50 cm then the person with 100 cm is taller in comparison to the person having 50 cm.

non-experimental descriptive study

In Non-Experimental Studies the educational method is done through survey research, observational research and analyzing the existing data sets. In survey research, flexibility is designed and appear in various forms (Mabusela & Adams, 2017). It is the easiest form were organizations design surveys in-house for variety of questions.

A quasi experimental study

In quasi-experimental’ design study, the student were assessed for pre and post-test which was randomly assigned to one of two groups. The student for pre-test talk-times found in the range of 16-24 % (Titze et al., 2018). The lecture has to spend less time in talking and more time in organizing the activities for the student (Pålsson et al., 2017).

An experimental study

The activities develop in class with paper and pencil has focus on the design of the experimental study (Routen et al., 2017). An active learning approach is done which effectively increases the learning process of the student. The student can better understand through experimental study and improve their skills by working in a group (Fonger, Davis & Rohwer, 2018).

References

Cherney, A., Belton, E., Leslie, E., Bell, J., Cherney, L., & Mazerolle, L. (2018). Countering Violent Extremism: Data Collection and Analysis Manual. Australian and New Zealand Counter-Terrorism Committee, National Countering Violent Extremism Evaluation Framework and Guide. This work was funded by the Countering Violent Extremism Centre, Department of Home Affairs, Canberra.

Fonger, N. L., Davis, J. D., & Rohwer, M. L. (2018). Instructional Supports for Representational Fluency in Solving Linear Equations with Computer Algebra Systems and Paper?and?Pencil. School Science and Mathematics, 118(1-2), 30-42.

Mabusela, S. M., & Adams, J. D. (2017). Students’ experience of e-learning at a rural university in South Africa. Gender and Behaviour, 15(4), 10221-10235.

Ong, M. H. A., & Puteh, F. (2017). Quantitative Data Analysis: Choosing Between SPSS, PLS and AMOS in Social Science Research. International Interdisciplinary Journal of Scientific Research, 3(1), 14-25.

Pålsson, Y., Mårtensson, G., Swenne, C. L., Ädel, E., & Engström, M. (2017). A peer learning intervention for nursing students in clinical practice education: A quasi-experimental study. Nurse education today, 51, 81-87.

Routen, A. C., Biddle, S. J., Bodicoat, D. H., Cale, L., Clemes, S., Edwardson, C. L., … & Salmon, J. (2017). Study design and protocol for a mixed methods evaluation of an intervention to reduce and break up sitting time in primary school classrooms in the UK: The CLASS PAL (Physically Active Learning) Programme. BMJ open, 7(11), e019428.

Thies, C. F. (2018). Polls and Elections: The Chicago Record Poll and the Election of 1896. Presidential Studies Quarterly, 48(1), 127-138.

Titze, S., Lackinger, C., Grossschaedl, L., Strehn, A., Dorner, T. E., Niebauer, J., & Schebesch-Ruf, W. (2018). How Does Counselling in a Stationary Health Care Setting Affect the Attendance in a Standardised Sports Club Programme? Process Evaluation of a Quasi-Experimental Study. International journal of environmental research and public health, 15(1), 134.