Discuss about the Statistical Method for Business and Sample Size.
This sample size is necessary because large samples are highly nearest to the population. Furthermore, the primary goal of using a larger sample is to collect depth and a wide range of data from a sample to the population. An investigator has selected large sample size to make less assumption in the project. This sample size provides more reliable outcome with high quality and validity, however, there is a need for the high amount of cost and time (Taylor, Bogdan, and DeVault, 2015). The large sample size is more required to generate outcome between variables, which are significantly diverse. For qualitative research, the goal is to ‘decline the possibilities of identifying failure’. A larger sample contains more people as there is a chance of getting the wide range of prospect information and construct picture for evaluation. In addition, large sample size demonstrates the population and limiting the influence of tremendous observation (Lewis, 2015).
Probability sampling is the current sampling method in this research. This method relies on the facts that each participant of the population have a known and equal opportunity of being chosen. Under this research, simple random sampling method has selected as population member are related to one another on the significant variable (Silverman, 2016).
Advantages of using Random Sampling
Random sampling permits an investigator to conduct the assessment of information, which is gathered within the lower margin of error. It is permitted as the sampling occurs within particular boundaries that state the sampling procedure. The whole procedure is randomized and the random sampling demonstrates the whole population and it also permits the facts to give the precise perception of particular subject matters (Smith, 2015).
It is also evaluated that simple random sampling permits each person within targeted area to have an equal probability of being chosen. It aids to generate more accuracy within the pooled information as each participant has 50/50 chances. It is a procedure, which develops the inherent fairness into the conducted research as no earlier facts regarding the involved person are entailed into data gathering procedure (Glaser, and Strauss, 2017).
Another benefit of using simple random sampling method is that there is no need for specific understanding regarding the information being gathered. It could be effective to complete the aim and objectives in an efficient and effective manner. An investigator could ask with employees working in the Belgian banks affiliated to the sector’s joint industrial committee in 2001 without knowing about the Belgium banking structure. In random sampling, a question is asked and then responded. A question is reviewed for a particular purpose. An investigator can attain the aim and objectives of the project due to performing the task and pooling the information by using the simple random sampling process (Neuman, and Robson, 2014).
Disadvantages of Random Sampling
Under this sampling method, each individual should be individually interviewed and reviewed hence the information could be properly gathered. While individuals are in groups, then their answers tend to be persuaded by the responses of others. It shows that an investigator should perform with each person on 1 on 1 basis. It requires more resources, efficiencies and time as compared to other research technique. For this process, a high level of skill is necessary for an investigator as they can separate the feasible information that has been pooled from inappropriate facts and figures. Further, if the skill does not exist then the reliability of conclusion produced by the presented data may be complex (Denzin, 2017).
Reliability is defined as the repeatability of the conclusion. In the given research, there is more than the one person who observes the behavior of participants and recorded the information of participants regarding research issues to get reliable data. Reliability is also applied to measure the individual. When an investigator conducted a survey two times, then their score on two occasions should be homogeneous. In this case, the test would be reliable. When an investigator will give survey question twice to the same person within minimum time then there would be possibilities to get the same outcome and aids to produce the reliable result (Creswell, and Creswell, 2017). For obtaining the reliable outcome, an investigator has supported the primary answer with literature review in this research.
Validity
Validity is the trustworthiness and believability of investigation. It also shows the genuine findings and depicts the valid measure of intelligence. The answer is relied on the degree of investigation to support the relationship (Ary, Jacobs, Irvine, and Walker, 2018).
Following are a different aspect of validity that is used in this research:
Internal validity –
The instruments and process applied in the research to assess what investigator believes to measure. For instance, as part of a survey on banks, employees have shared their belief and ideas regarding research concern. After the study, they are asked how they feel during survey questionnaire and they respond that it has provided me the opportunity to share my belief regarding research concern (Sekaran, and Bougie, 2016). Under this study, the opportunity to share belief shows the good internal validity of the produced outcome.
External validity –
The outcome could be discussed after the collected data. In order to obtain the external validity, the claim that the investigation in different sections could be better as compared to revising the investigation and implement more than one concern. It should also use people, who are beyond the sample in the research to get valid data (Bryman, and Bell, 2015).
When data are valid then it must be reliable. In case, investigator gets different scores from survey through questionnaire in every time then the test is not likely to expect anything. But, if the test is reliable then it does not mean that it is valid. For instance, the investigator can measure grab strength very reliably but it could not valid measure about the intelligence and mechanical competencies (Bryman, 2015). Reliability is required for the survey through questionnaire but there are no chances of getting a valid outcome.
Demographic questions demonstrate the data regarding the characteristics of sample population such as their age, gender, residence, income, smoking status, education level, language spoken, and ethnicity. Demographic data facilitates the facts and figures about the research participants and it is required for assessing whether an individual in research representative sample of the target population for discussing intention. In addition, demographics or participant’s characteristics are significant for a researcher as it serves as an independent variable in the research design (Campbell, and Stanley, 2015). Demographic variables represent the independent variable in the investigation as it could not be manipulated.
The mixed research design is used for this research. This research design entails the gathering, assessing and combining the quantitative such as surveys and qualitative such as literature review research. This research design of research is implemented when this integration facilitates a better knowledge regarding research issue as compared to using one design. Further, Quantitative data entails the close-ended data such as measuring beliefs (e.g., rating scales), behavior (such as observation checklists) and performance instruments. The assessment of this set of information contains the statistical analyze score on an instrument such as questionnaires and checklists to respond research questions. Along with this, qualitative data contains the open-ended data that an investigator collects through literature review and case study (Merriam, and Tisdell, 2015). The assessment of qualitative data considers the way of summative it into the categories of data and demonstrates the diversity of gathered data during data collection.
This research design facilitates the strength that balances the weaknesses of both qualitative and quantitative investigation. For example, quantitative research is weak in comprehending the context and setting in which people respond. In contrast to this, qualitative research could be seen as incomplete data as the chances of bias interpretation made by an investigator and complexity in discussing the findings of the large group. But, at the same time, quantitative research design does not have these limitations. Thus, it is stated that by using both kinds of research, the strength of each approach could decline the possibilities of weaknesses from research. Mixed research design also facilitates more comprehensive and complete knowledge about the knowledge of research issue as compared to using either quantitative or qualitative research design alone. This research design also facilitates an approach to building better and more context particular instruments (Babbie, 2015). For example, by using the qualitative investigation, it is chances to pool the data regarding the certain topic and construct to develop an instrument with high validity. It also aids to describe the findings and how causal procedures work.
There are certain negative sides for using the mixed research design in this research. This research design could be complex. It also takes more time with maximum resources to plan and apply this kind of investigation. It could be complex to plan and execute one technique by depicting the findings of another. It could be unclear that how to deal with the inconsistency that arises in the understanding of the findings (Alvesson and Sköldberg, 2017).
One of the key disadvantages of using this research design is that when an investigator measures the qualitative data then it loses the flexibility and depth, which is key disadvantages of qualitative research. It occurs because qualitative codes are multidimensional. In contrast to this, quantitative codes are based on the one dimensional and static so basically modifying rich qualitative information to dichotomous variables creates one-dimensional unchallengeable information. It is feasible for an investigator to eliminate the measures of qualitative information. However, it could be very time consuming and complex procedure because there is need of analyzing, coding and combining the information from unstructured to structured information (Merriam, and Tisdell, 2015).
Another key challenge is related to mixed research design is that there is a limitation in statistical measuring qualitative information. Hence, it is stated that when qualitative data is measured then it could be weak to co-linearity. Further, it is stated that an investigator uses qualitative data due to declining sample size and less time-consuming. There is also no need to measure the statistical process such as assessing the variance and t-tests. It is a major challenge for this design because an investigator may not have the adequate statistical power to support their investigation. It could be eliminated when an investigator chooses not to conduct mixed research design (Campbell, and Stanley, 2015).
Under the mixed research design, the sequential explanatory design is used by a researcher. This design entails the assortment and assessment of qualitative data. The priority is provided to quantitative data as well as the findings are integrated during the evaluation phase of the research. This kind of mixed research design is to explain, interpret and contextualize the quantitative findings. It is also used to assess the potential result in more detain through quantitative study (Campbell, and Stanley, 2015).
The positive side of using Sequential explanatory design
The positive side of using this research design is that it is easy to execute as the steps fall into unambiguous separate phases. This design is also easy to define and the outcome easy to report (Merriam, and Tisdell, 2015).
The negative side of using Sequential explanatory design
The negative side of using this research design is that it required a substantial length of time for attaining the all pooled data into two separate stages (Bryman, 2015).
Example:
The research gathers the data regarding Associations between Quantitative and Qualitative Job Insecurity and Well-being using survey through a questionnaire. This survey is conducted in the large sample size to get more detail about the research concern.
Gantt chart depicts the graphical representation of the action plan that is implemented to explain how the research activities would be implemented step by step (Campbell, and Stanley, 2015). It could be demonstrated as follow:
From the above time structure, it could be assessed that gathered information associated with research concern and interpretation of data would be attained in longer time as compared to executing other practices.
References
Alvesson, M., & Sköldberg, K. (2017). Reflexive methodology: New vistas for qualitative research. USA: Sage.
Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education. USA: Cengage Learning.
Babbie, E. (2015). The practice of social research. UK: Nelson Education.
Bryman, A. (2015). Social research methods. USA: Oxford university press.
Bryman, A., & Bell, E. (2015). Business research methods. USA: Oxford University Press.
Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. UK: Ravenio Books.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. USA: Sage publications.
Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. UK: Routledge.
Glaser, B. G., & Strauss, A. L. (2017). Discovery of grounded theory: Strategies for qualitative research. UK: Routledge.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), 473-475.
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. USA: John Wiley & Sons.
Neuman, W. L., & Robson, K. (2014). Basics of social research. Canada: Pearson.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. USA: John Wiley & Sons.
Silverman, D. (Ed.). (2016). Qualitative research. USA: Sage.
Smith, J. A. (Ed.). (2015). Qualitative psychology: A practical guide to research methods. USA: Sage.
Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. USA: John Wiley & Sons.
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