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MAT FPX 2001 Assessment 5 Evaluating Studies

Name

Capella University

MAT FPX 2001 Statistical Reasoning

Prof. Name

Date

Evaluating Studies

Purpose and Summary of the Selected Gallup Poll

The Gallup poll selected for evaluation is titled “U.S. Employee Engagement Needs a Rebound in 2023” (Gallup, 2023). This study primarily focuses on workers from various organizations. Conducting a survey among a diverse workforce ensures that the selected population is representative, allowing for the generalization of findings. The study investigates the impact of employee interaction on business outcomes, noting a 36% decline in employee engagement over the past decade. Data were collected from a random sample of 15,000 U.S. full and part-time employees. The results indicate that organizations are adapting to new hybrid work arrangements, with 21% of remote-ready jobs functioning on-site, 53% of employees working in hybrid arrangements, and 26% working remotely. This stabilization in work patterns introduces predictability but also demands high levels of coordination. To maintain engagement, organizations rely on their culture and values, which guide business decisions and foster strong connections between managers and employees.

Appropriateness of Sample

The Gallup poll presents key findings, including a decrease in the ratio of actively engaged to disengaged workers in the U.S. from 2.1 to 1 in 2021 to 1.8 to 1 in the current year, marking the lowest ratio of disengaged workers in the United States. The margin of error, which is influenced by sample size, affects the accuracy of the population parameter. Larger samples reduce the margin of error, providing more precise information and mitigating random sampling errors. Various factors, such as data variations and confidence levels, must be considered when interpreting survey results (Story & Tait, 2019).

Rationale for Sampling Technique

The choice of sampling technique depends on research questions, population traits, and available resources. The Gallup survey employs random sampling, ensuring an unbiased representation of the entire population, making it superior to cluster, stratified, and simple sampling approaches. This method focuses on randomly sampling the working population, assessing elements such as customer service and productivity in the workspace.

Comparison With Other Techniques

Random sampling, as used in this Gallup survey, is considered superior to cluster, stratified, and simple sampling. Unlike stratified sampling, which may not represent the overall population accurately, random sampling ensures that each sample is equally observed, providing an unbiased representation of the entire population (Lakens, 2022).

Interpretation of Confidence Interval

Confidence intervals indicate the range of values suggesting the probability that the population parameter lies within that range. In this survey, confidence intervals highlight variations in engagement among different age groups. For instance, young workers experienced a four-point decrease in engagement, while active disengagement increased by four points.

Effect of Study Design on Margin of Error

Random changes in the sampling process influence margins of error, with larger sample sizes leading to smaller margins. Modifications in the study design can alter marginal errors, emphasizing the importance of relevant samples to accurately reflect the population (Etikan & Babatope, 2019).

Impact of Question-Wording

Question wording significantly influences participant responses. Inaccurate phrasing can lead to unreliable results. Careful selection of neutral and clear questions ensures unbiased participant responses, contributing to the validity of survey outcomes (Henriques et al., 2019).

Impact of Question-Wording on Statistical Results

The phrasing of questions directly affects statistical outcomes. Various question types yield different insights. The survey in question explores employee engagement, emphasizing the importance of selecting wording that aligns with desired survey outcomes (DeJonckheere & Vaughn, 2019).

Evaluation of Bias

Determining the presence of bias in the study is challenging, but the chosen random sampling method aims to minimize bias. To ensure an unbiased survey, the study utilizes random sampling (Kyriazos, 2018).

Avoidance of Bias

Careful consideration of question wording and survey design helps mitigate biases. The study employs a well-constructed questionnaire with neutral questions to obtain precise answers, reducing the impact of potential biases (Boutron et al., 2019).

Conclusion

In conclusion, the study “U.S. Employee Engagement Needs a Rebound in 2023” offers valuable insights into the engagement levels of U.S. workers. By employing random sampling and avoiding biases, the study underscores the significance of hybrid work arrangements. The use of descriptive statistics enhances the evaluation of both quantitative and qualitative data, contributing to the reliability of the study’s findings.

References

Boutron, I., Page, M. J., Higgins, J. P., Altman, D. G., Lundh, A., & Hróbjartsson, A. (2019). Considering bias and conflicts of interest among the included studies. Cochrane Handbook for Systematic Reviews of Interventions, 177–204. https://doi.org/10.1002/9781119536604.ch7

DeJonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: A balance of relationship and rigor. Family Medicine and Community Health, 7(2). Bmj.

MAT FPX 2001 Assessment 5 Evaluating Studies

Etikan, I., & Babatope, O. (2019). A basic approach in sampling methodology and sample size calculation review article. MedLife Clinics, 1, 1006. http://www.medtextpublications.com/open-access/a-basic-approach-in-sampling-methodology-and-sample-size-calculation-249.pdf

Gallup. (2023, January 25). U.S. Employee engagement needs a rebound in 2023. Gallup.comhttps://www.gallup.com/workplace/468233/employee-engagement-needs-rebound-2023.aspx

Henriques, A., Silva, S., Severo, M., Fraga, S., & Ramos, E. (2019). The influence of question-wording on interpersonal trust. Methodology, 15(2), 56–66. https://doi.org/10.1027/1614-2241/a000164

Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (efa, cfa) and sem in general. Psychology, 09(08), 2207–2230. https://doi.org/10.4236/psych.2018.98126

Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267

MAT FPX 2001 Assessment 5 Evaluating Studies

Story, D. A., & Tait, A. R. (2019). Survey research. Anesthesiology, 130(2), 192–202. https://doi.org/10.1097/aln.0000000000002436

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