Educators’ Low Salaries: The Main Reasons

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Table of Contents

Population
Sampling Methods
Data Analysis
References

Population

For this study, it is essential to conduct interviews with a vast number of different teachers who would help gain an understanding of the identified issue. Overall, a specific school district is a population from which the sample will be drawn. Noticeably, since the research question focuses on identifying the reasons behind educators’ low salaries, it is required not to exclude participants who can potentially bring clarity through their personal characteristics. Thus, the population will include teachers of various races, ages, and genders.

Sampling Methods

To begin with, it is vital to mention that there are two categories of sampling methods – probability and non-probability. The main difference between them is that the former group of methods is based on randomness (Berndt, 2020). In other words, if this study used any probability sampling technique, all teachers in the U.S. or a particular state would have relatively equal chances of being selected. The main methods included in this group are systematic, simple random, cluster, and stratified sampling (Berndt, 2020). At the same time, in non-probability sampling, researchers trust their knowledge and expertise and select certain participants based on numerous characteristics (Lamm & Lamm, 2019). The key methods are snowball, convenience, quota, and purposive samples.

With this being a qualitative study, a non-probability method will be used. None of the probability methods can guarantee the required variety in the selected participants (Berndt, 2020). It is possible to say that purposive sampling should be quite informative and useful. As stated by Campbell et al. (2020), purposive sampling is “used to select respondents that are most likely to yield appropriate and useful information” (pp. 653-654). This method will allow choosing those teachers who are likely to share the most interesting and relevant facts (Beaumont & Rao, 2021; Lamm & Lamm, 2019). However, convenience sampling, which is based on selecting the most accessible individuals, would not be appropriate for this study because these persons can appear to be very similar in their characteristics and fail to bring variety to the sample.

Like any sampling method, the purposive one has some advantages and disadvantages. For instance, it is stated by Lamm and Lamm (2019) that the main benefit of this technique is that it increases the chances of receiving the needed data and information. However, this method may be considered rather judgmental and cause some biases (Campbell et al., 2020). In order to be selected for the study, the participants will need to work in public rather than private schools and have at least seven years of teaching experience. Then, an equal number of individuals will be in the following groups: African American, White, and Asian American teachers; females and males; and physical education, mathematics, and English teachers. Finally, the sampling size for this study should also be mentioned and justified. According to Vasileiou et al. (2018), “qualitative studies require a minimum sample size of at least 12 to reach data saturation” (para. 4.2). At the same time, Hennink and Kaiser (2021) state that 20-40 participants allow achieving saturation. Therefore, an appropriate sample for this study would be 25 participants.

Data Analysis

Each research design requires a specific type of data analysis method, and since this research involves a case study, a thematic analysis will be used. As stated by Kiger and Varpio (2020), the thematic analysis will include the following steps: familiarization with the data, generation of initial codes, themes search, review, naming, and definition, and report production. After conducting interviews, it will be essential to search for repetitions in the responses, identify items of interest, explore whether the themes provide an answer to the research question, and create a report.

References

Beaumont, J. F., & Rao, J. N. K. (2021). Pitfalls of making inferences from non-probability samples: Can data integration through probability samples provide remedies? The Survey Statistician, 83, 11-22.

Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226. Web.

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652-661. Web.

Hennink, M., & Kaiser, B. N. (2021). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 114523.

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846-854. Web.

Lamm, A. J., & Lamm, K. W. (2019). Using non-probability sampling methods in agricultural and extension education research. Journal of International Agricultural and Extension Education, 26(1), 52-59.

Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and justifying sample size sufficiency in interview-based studies: Systematic analysis of qualitative health research over a 15-year period. BMC Medical Research Methodology, 18(148). Web.

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Educators’ Low Salaries: The Main Reasons

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