Sampling Proposal on Death Anxiety in Computer Tech Personnel

Sampling Proposal on Death Anxiety in Computer Tech Personnel

The well-being of workers in any field is a fundamental aspect that has drawn attention from scholars and researchers due to the emergence of several factors that affect the mental and psychological health of workers. Death anxiety is one of the causes of mental disturbance among employees not only in the tech industry but also in work contexts. Recently, the emergence of Covid-19 has left a toll on workers through impacts on financial, social, and career life, which has largely affected people, as Zhao, Liu, and Wang (2022) noted. Due to the high number of deaths that occurred as a result of COVID-19 globally, it invoked feelings of death anxiety, depression, uncertainty, insomnia, and other psychological effects. People were worried about the death of other people and themselves as well. Compared to tech personnel, the impact of death anxiety was largely felt among healthcare workers, especially those who were on the frontline in the provision of care for Covid-19 patients (Lalloo et al., 2022). However, besides COVID-19, tech personnel may be faced with several psychological hazards as a result of increased workload, adverse working patterns, and an imbalance between work and personal and family life. This causes burnout, work-related stress, and mental illnesses.

Population in research regards the collection of individuals or objects that form the center of interest in scientific inquiry. A population contains similar attributes that meet the researcher’s criteria for suitability for inclusion in a study (Saunders, Lewis, & Thornhill, 2016). For instance, a common attribute that forms the basic criteria for the population selected for the current study is that every research participant must be working in a tech-related sector. If research is interested in studying attributes contained in teachers, the population will comprise of teachers. In the study, the population of interest will be different employees in tech and tech-related sectors. These will include data scientists, software developers, information security analysts, computer systems analysts, web developers, information technology managers, computer research scientists, network and system administrators, computer support specialists, and several other professions in the technology industry.

A sampling frame is the actual collection of units that will form a sample of a research study. A sampling frame contains all the relevant subjects that can potentially be part of the research and can provide appropriate information for research. Essentially, a sampling frame should contain all elements that can yield a representative sample for a population (Black, 2016). Identifying an appropriate sampling frame determines the representativeness and scope of the population under study. A good sampling frame reflects the general population under study and includes all the potential participants of the study. In this case, the sampling frame for the study should contain all the professionals in the technology industry who can fit the study from various departments and perform various roles, taking the general population of professionals in the tech industry as the general sample of the population. A sample frame can be viewed as a population that has been organized in a certain manner to enhance the drawing of a sample. This entails excluding the units that do not provide the essential information for the study.

A sampling frame partly determines the profoundness of a research study and the sampling error from the study. Coverage error and sampling error are the two most common types of sampling error that occur when a sampling frame does not include all the potential units that can provide the relevant information related to the phenomena under study (Black, 2016). In the context of the study, a coverage error would occur if the researcher omits some categories of tech personnel, such as omitting data science analysts or web developers in the population of study. On the other hand, sampling error may occur when the sample selected from a sampling frame is not representative of the population of interest (Black, 2016). This may occur as a result of systematic bias or random chance in the sampling process. For instance, sampling bias may occur when the researcher chooses tech personnel from a few selected companies and leaves other tech companies. This may lead to an error in the final findings since this may not be representative of the population.

Several ways can be used to determine the appropriate sample size for a study. One of the most common and reliable ways to determine a sample size is the use of statistical power analysis through the use of statistical formulas to estimate the appropriate size of the sample based on the population of interest. A sample of 100 tech personnel will be appropriate for the study. Yamane’s formula will be used to calculate the sample size (Adam, 2020);

n=

where; n= Sample size

N = Population of Study

e = Margin of Error

The sampling approach used for the study will be a purposive sampling strategy. This is a non-probability sampling approach that requires the researcher to rely on their own judgment in selecting the appropriate sample for the study. This method of sampling is suitable since the researcher has prior knowledge about the study’s purpose to include only the participants that will contribute to the study’s purpose, as Saunders, Lewis, and Thornhill (2016) suggest. Purposive sampling is suitable for the study since the research targets a specific group of people, and the most suitable sample cannot be obtained through a probability-based approach such as random sampling. For instance, employing a random sampling strategy may lead to obtaining participants who do not work in tech-related fields. Therefore, the core reason for choosing the sampling strategy is to obtain participants with the characteristics required for the research, that is, working in tech departments.

The purposive sampling strategy will be implemented using a multi-level approach. Firstly, tech-related organizations and institutions will be selected purposively due to their relevance in the research. A sample of 50 organizations will be selected to draw 100 participants. Participants will also be obtained from organizations and institutions with tech personnel, although not dedicated to tech functions. A sample of participants will be obtained from the selected organizations purposively since only persons working in technology functions will be deemed suitable for the research. For instance, for an organization with employees performing several functions, only employees from technology-related functions and departments will be involved in the study.

The research project will be implemented qualitatively through interviews. After identifying the organizations and institutions with tech personnel performing various functions, an invitation letter will be sent to the departments explaining the purpose of the research and requesting the research participants in the tech sector to participate in the study. The contact details will be provided so that willing participants can reach out. The interviews will be conducted by telephone. From the targeted sample of 100 participants, an expected response rate of 80% will be the requirement for the study, as Field (2018) recommends. To increase the response rate, the participants will be assured of the confidentiality and protection of their personal information. Follow-up reminders will be issued through emails and phone calls to encourage more participants to participate in the research. The invitation letters will indicate that the research will not be time-consuming and that the research is participant-friendly. Besides, testimonials from other participants willing to participate in the study will be used to convince other participants.

Some of the advantages of employing a qualitative approach through interviews to obtain the relevant information for the study include the suitability of the method to conduct an in-depth study to identify the factors that contribute to death anxiety and the extent to which these factors affect the study participants. Telephone interviews will also save time and resources for the researcher. The approximate budget to complete the study will be $5000, which will be mainly spent on facilitating communication with the research participants and printing the research materials and final report.

References

Adam, A. (2020). Sample Size Determination in Survey Research. Journal of Scientific Research and Reports. 26. 90-97. 10.9734/JSRR/2020/v26i530263.

Black, K. (2016). Business Statistics: Contemporary Decision Making. (6th edition) John Wiley & Sons.

Field, A. (2018). Introduction to Statistics with IBM SPSS Statistics. London: Sage Publishers.

Lalloo D. et al. (2022). Comparing Anxiety and Depression in Information Technology Workers with Others in Employment: A UK Biobank Cohort Study. Ann Work Expo Health; 66(9):1136-1150. doi: 10.1093/annweh/wxac061. PMID: 36029464; PMCID: PMC9664232.

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students. (6th edition) Pearson Education Limited.

Zhao, N., Liu, B., Wang, Y. (2022). Examining the Relationship between Death Anxiety and Well-Being of Frontline Medical Staff during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 19, 13430. https://doi.org/10.3390/

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Define the population of interest. As discussed in class, this should be as specific as possible.

Identify and describe the sampling frame that will allow you the most representative sample practically possible.

Discuss coverage error and, to a lesser extent, sampling error that may result from using this particular sampling frame.

Propose an adequate sample size. Explain your reasoning, including the source used for determining this sample size.

Present your choice of the best sampling approach with justification (probability or non-probability and which type).

If you need to stratify, for example, explain what the relevant strata might be. Again, stratifying is not a method to make your population/sampling frame SMALLER nor are the strata variables that you want to TEST/analyze. Instead, it is a strategy to contribute to representativeness.

Be careful that your tone is not one of persuasion or your own personal justification but is grounded in what is known about these different types of sampling. i.e., which approach makes the most sense for your particular population and sampling frame.

Explain the steps taken to implement this approach

g.. Will you stratify and THEN randomly sample every 20^th person for a total of your proposed 850 people?

g., will you use judgment sampling in selecting/recruiting your proposed 150 participants?

g., will you use convenience sampling within 5 randomly selected districts?

Propose the mode or modes of administration by outlining a plan for implementing the survey project. Include a general timeline. Considering the response rate likely from this mode, how many individuals will you need to contact to ‘ensure’ that you receive an adequate number of responses?

What are some disadvantages that you anticipate using this mode (assuming that the advantages outweigh the disadvantages)?

What decisions/factors will you include that are likely to increase your response rate?

All things considered, what would you estimate to be the budget needed to implement such a study?