Professional Research and Communication Survey Process

Question: Discuss about Professional Research and Communication for Survey Process. Answer: Likert scale is a psychometric scale using to accumulate responses in the survey process. The Likert scale facilitates to execute the sum of all Likert items (Wakita, Ueshima Noguchi, 2012). In the particular survey process, total 100 responses have been selected for accumulating responses on the topic. In this context, the survey questionnaire includes the option like below: 1: The staff member appears knowledgeable regarding the stores product. Likert Items Responses (%) Total number of respondents Number of Responses 1- Strongly Disagree 10 100 10 2- Disagree 25 100 25 3- Unsure 17 100 17 4-Agree 32 100 32 5-Strongly agree 16 100 16 Table 1 By analyzing the above table, it can be assessed that the particular arrangement of the Likert items is wrong. Consequently, it is unable to provide the valid calculation. The summary of the collected data does not indicate a valid way of calculation. The Likert scaling is a bipolar scaling, which facilitates to measure either positive or negative responses of the respondents (Boone Boone, 2012). In this particular survey question, the arrangement of the Likert scale needs to be following manner: Likert Items Total Number of respondents Number of Responses Responses (%) 1- Strongly agree 100 16 16 2- Agree 100 32 32 3- Unsure 100 17 17 4- Disagree 100 25 25 5-Strongly disagree 100 10 10 Table 2 For accumulating the information from the positive type of question, the surveyor needs to arrange the options from positive to negative. Consequently, it would facilitate in providing valid data and information on the topic (Hair et al., 2016). For example, by calculating the responses from the table 1, we get the 3.19 as an average score. On the contrary, by calculating the responses from table 2, we get 2.81 as an average score. Calculation from Table 1: (10+50+51+128+80)/100=3.19 Calculation from Table 2: (16+64+51+100+50)/100=2.81 2: In the survey process, there are several techniques to accumulate the data from the respondents. Most o the time, the respondents is not aware of the products or the services (Mackey Gass, 2015). Consequently, they randomly provide feedbacks to the survey questions. It decreases the data reliability. Hence, the interested customers can obtain the knowledge on the products or services before participating in the survey process. For example, if a customer wants to provide feedback on the particular product without having knowledge, he might not be able to deliver the accurate information on the topic. Hence, the organization or the surveyor can involve those customers, who have adequate knowledge of the product and services. On the contrary, it is not possible for the surveyor for engaging a huge number of respondents, who have adequate knowledge of the particular product or services (Taylor, Bogdan DeVault, 2015). Hence, it can be assessed that the random selection of the respondent s would facilitate the surveyor for conducting a large assessment process. Sometimes, large organizations conduct a survey on the online platform for accumulating a huge amount of data on the research topic. However, some organizations believe that the survey process must involve the interested customers having adequate knowledge on the topic. It facilitates in obtaining the valid and reliable data on the research topic. On the contrary, it has been seen that the customers, who have the product knowledge, provide a biased answer to the surveyor. Generally, in the survey process, they show their liability on the particular brand. Consequently, it decreases the data validity and reliability (Panneerselvam, 2014). Hence, it can be assessed that the new survey techniques need to utilize for obtaining accurate information on the research topic. In recent years, an online survey has been increasingly popular due its easy accessible nature. On the other hand, it facilitates to conduct the survey process in both domestic and international platform. 3: There are four types of the quantitative data including nominal, ordinal, interval and ratio (Treiman, 2014). The first survey question indicates the subtype nominal measurement scale, as it does not include the numerical significance. The sub-type of the nominal scale includes the two categories. For example, the demographic question on the gender includes two options such as male and female. Hence, it indicates the nominal scale. The nominal subtype scale is often called the dichotomous. The Fahrenheit thermometer is related to the interval scale, as the difference between each degree is known. In the interval scale, we know the numeric order along with its exact differences between values. Kelvin thermometer is also related with the interval scale. In the Kelvin thermometer, the exact difference between each degree is measurable. The fourth question is related to the numeric value, which can be related with the ratio scale. In the ratio scale, a wide range of the interferential and descriptive data can be applied. The particular question would consist of the numeric option. Moreover, the option can be started from the Zero. Hence, it can be assessed that the particular question is related to the ration scale (Zhu, 2014). The bank account balances can be related to the ratio scale, as the ratio between the both accounts can be evaluated. Moreover, it also can be absolute zero. The ratio analysis suggests the possibilities for conducting the statistical analysis (Grbich, 2012). On the other hand, these variables can be subtracted, added, multiplied and divided. 4: Descriptive non-experimental study: In the non-experimental research design, no external variable are introduced (Gelman et al., 2014). In this particular design, the variables are not manipulated or controlled in a systematic manner. In this scenario, drinking the orange juice three times per day would facilitate the players in performing in the better manner. In the non-experimental study, no values are included. Hence, it can be assessed that the hypothesis testing may not provide the proper outcome in the non-experimental study. On the contrary, the research situation does not allow conducting the research or experiment in an effectual manner (Creswell, 2013). However, in this scenario, the non-experimental study should not be followed. It should be evaluated whether the players can perform in a better manner after having the orange juice or not. Consequently, the descriptive design would not be applicable for this particular research. Quasi-experimental study The quasi-experimental design is one of the important parts of the research, as it facilitates in evaluating the proper analysis of the research. However, the particular research design does not include key ingredients of the research. The quasi-experiment research is feasible, as it does not include the time and logistical barriers (Ioannidis et al., 2014). However, the particular research design is based on the random assignment selection. Consequently, the randomization would cause the lack of data validation (Tibshirani, 2014). In this scenario, the data has been given, and the research needs to be executed based on that provided data. Hence, it can be assessed that the quasi-experiment is not the right approach to analysis the particular research scenario. Experimental study: The experimental research study is the most authentic method of analyzing the research topic. The prime benefit of the experimental study is that it facilitates to gain the insight into the instruction method (Bechhofer Paterson, 2012). In this scenario, the data are pre-validate. Consequently, it will enhance the research activity in an effectual manner. In the experimental process, two examinations need to be conducted. First, the coach needs to conduct the experiment on the performance of the players before taking orange juice. Second, an examination of the performance of the players needs to be taken post drinking of juice. Hence, it can be assessed that the experimental study would be the best possible solution for the particular research, as the variables can be controlled in an effectual manner. References: Bechhofer, F., Paterson, L. (2012).Principles of research design in the social sciences. Routledge. Boone, H. N., Boone, D. A. (2012). Analyzing likert data.Journal of extension,50(2), 1-5. Creswell, J. W. (2013).Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014).Bayesian data analysis(Vol. 2). Boca Raton, FL, USA: Chapman Hall/CRC. Grbich, C. (2012).Qualitative data analysis: An introduction. Sage. Hair Jr, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M. (2016).A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications Ioannidis, J. P., Greenland, S., Hlatky, M. A., Khoury, M. J., Macleod, M. R., Moher, D., ... Tibshirani, R. (2014). Increasing value and reducing waste in research design, conduct, and analysis.The Lancet,383(9912), 166-175 Mackey, A., Gass, S. M. (2015).Second language research: Methodology and design. Routledge. Panneerselvam, R. (2014).Research methodology. PHI Learning Pvt. Ltd.. Taylor, S. J., Bogdan, R., DeVault, M. (2015).Introduction to qualitative research methods: A guidebook and resource. John Wiley Sons. Treiman, D. J. (2014).Quantitative data analysis: Doing social research to test ideas. John Wiley Sons Wakita, T., Ueshima, N., Noguchi, H. (2012). Psychological distance between categories in the likert scale comparing different numbers of options.Educational and Psychological Measurement,72(4), 533-546. Zhu, J. (2014).Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets(Vol. 213). Springer.