MAT FPX 2001 Assessment 5 Evaluating Studies
Capella University
MAT FPX 2001 Statistical Reasoning
Prof. Name
November, 2024
Evaluating Studies
Purpose and Summary of the Selected Gallup Poll
One of the Gallup polls to be reviewed is “Trends in U.S. Job Satisfaction and Workforce Well-being in 2023” (Zhao & Jeon, 2023). This study delves into the satisfaction and well-being level of America’s workforce across varying industries. By eliciting an opinion from a random sample of 18,000 employees in the private sector, this poll surveys the general situation of job satisfaction and how issues such as compensation, work-life balance, and job security affect it. Results showed that employees’ overall job satisfaction remains mostly stable, while employees also report a growing need for better mental health support and greater flexibility in their work arrangements. With 45 percent of employees fearing burnout and stress, the findings emphasize the increasing significance of organizational support in the enhancement of employee well-being as well as productivity at work. Therefore, the output of this survey is useful for businesses in their quest to better their retention and engagement policies for employees.
Appropriateness of Sample
The sample size for the study is very aptly dealt with within Gallup’s titled Trends in U.S. Job Satisfaction and Workforce Well-being in 2023: encompassing 18,000 employees from various industries and demographic backgrounds. Such a huge and diversified sample ensures that the findings are representative of the broader workforce in the United States, capturing the whole range of job satisfaction levels and experiences concerning feelings of well-being (James et al., 2021). This survey cuts across different industries, focusing on full-time and part-time workers with different jobs, income levels, and types of work environments, all factors that can determine overall job satisfaction and mental health. This approach makes the sample very suitable for giving reliable insights that could generalize to the larger population of American workers, thus establishing the results as relevant and applicable to organizational and policy efforts meant to improve employee engagement and workplace well-being.
Rationale for Sampling Technique
The Gallup poll titled Trends in U.S. Job Satisfaction and Workforce Well-being in 2023 employed the random sampling technique, which is highly fitting to ensure that the study’s findings are both reliable and generalizable. For instance, every single individual in the population of interest gets an equal chance of being selected through the application of random sampling, thereby minimizing biases that could distort the outcome of a study (Sirwan Khalid Ahmed, 2024). A representative sample of 18,000 employees, randomly picked from various industries and backgrounds to ensure broad coverage of the broader U.S. workforce, was utilized here. This is because the major consideration in using random sampling is that it eliminates the over- or under-representation of specific groups and allows for an adequate reflection of the population’s characteristics.
Through random sampling, the Gallup poll captures the diverse perspectives of employees from different job roles, and income levels, working in varied industries, and across multiple geographical locations. Diversity is critical in the factors analyzed here, such as job satisfaction, employee well-being, and the impact of workplace policies on mental health; otherwise, these factors vary significantly across different sectors and demographic groups. Thus, random sampling enhances the external validity of the study in other words, the outcome can be applied safely to the larger population of U.S. workers.
In addition, the large sample of 18,000 employees will make sure that the study gets much closer to perfection in terms of accuracy and precision (Surya Sunilkumar, 2022). In such larger sample sizes, the margin of error becomes very small, so the estimates become reliable and sometimes may even detect the trends or differences buried in the data. This will become even more crucial when evaluating very complex issues like job satisfaction and mental health, which can always involve some kind of multifactorial influence. Generally speaking, the random sampling technique used in this Gallup poll ensures that the findings of the study are robust, and valid, and can thereby inform organizational strategies and national policy decisions regarding workforce well-being.
Comparison With Other Techniques
Random sampling proves to be different from other methods, such as stratified sampling and convenience sampling, in several ways when it comes to aspects of accuracy, bias, and applicability. For instance, in stratified sampling, for proper stratification, the population is divided into clear-cut subgroups or strata based on some defined characteristics, such as age, income, or job type. The researchers then randomly sample from each subgroup with proportional representation. Where stratified sampling provides greater insight into the subgroup at hand, its design might necessitate more intricate planning and analysis, making it less practical in other studies. Convenience sampling selects participants mainly due to ease of access; this can be hugely biased and limits generalizability (Golzar et al., 2022). This method more often leads to a sample that not at all could represent the population. The results are bound to skew because it would be highly unreliable. On the other hand, the Gallup poll relies on random sampling which is a very simple and less costly process that still allows providing the participant with the assurance of a very broad, unbiased representation of the population. Although it does not obtain the particular characteristics of subgroups as effectively as stratified sampling, random sampling reduces selection bias, where there is an equal chance of including each individual, thereby providing a more reliable system of generalization of results for the entire population.
Interpretation of Confidence Interval
Interpretation of a confidence interval would essentially mean understanding the range within which the value of some population parameter, like a mean or proportion, would probably lie at a given level of confidence (Schober et al., 2021). For instance, if a survey mentions a 95% confidence interval for employee satisfaction to be between 45% and 55%, it would mean that there is a 95% chance that the true satisfaction of all the population surveyed lies between 45% and 55%. This range reflects the uncertainty inherent in sampling, as we cannot be sure of an exact value for the whole population but can estimate within some degree of confidence. The size of the confidence interval is altered by factors such as sample size and variability; the larger the sample size, the more likely one would obtain a smaller interval, meaning their estimates are more precise. A larger interval implies greater uncertainty about the value for which the process was estimated, whereas a more narrow one indicates greater precision. Lastly, note that the confidence level that makes all such intervals possible and useful means that the given percent level (e.g., 95% or 99%) denotes the probability that the interval if repeated over very many samples, would include the true population parameter. Therefore, the confidence interval helps researchers and decision-makers know whether their estimates are reliable and to make sound conclusions from the data gathered.
Effect of Study Design on Margin of Error
Generally speaking, study design plays an important role in deciding on the margin of error for research. In most cases, an effective design with an adequately sized sample to engage in random sampling will yield a smaller margin of error. This is because a larger sample size presents more data points, reduces variability and hence increases the precision of estimates (Lakens, 2022). Conversely, a not-so-well-designed study that uses convenience sampling or has a relatively small sample size may end up with a bigger margin of error. That is to say, the estimates are less reliable because they depend on whether or not the sample represents the general population. Other factors such as survey methods, clarity of questions, and nature of data collection could also have impacts on the precision of the results and thus their margin of error. Therefore, a proper study design will necessarily have to minimize the margin of error and support the truthfulness of conclusions drawn about the data.
Impact of Question-Wording
The wording of questions in a survey or study can considerably make the outcome vary as it influences the understanding and response of the respondents. Inappropriately worded questions-for example, biased questions, or ambiguous, leading questions-can skew the data and lead to inaccurate conclusions. For example, a question like “Don’t you think employees deserve better benefits?” might push the respondents towards some answers because of its suggestive wording. Instead, clear neutral, and specific wording helps ensure that the question has been well understood by respondents, and hence it yields better, unbiased responses. Another factor is the way questions are stated, which may introduce social desirability bias. People may answer such questions in a socially preferred manner rather than truthfully. Moreover, sophisticated or double-barreled questions that ask something more than one at once can confuse those respondents and lead to unreliable data. Thus, a careful thought process in asking questions is very important in the design of any research so as not to influence the outcome and make sure that the data collected would indeed reflect respondents’ real thoughts and experiences.
Impact of Question-Wording on Statistical Results
The wording of a given question in a survey may have a very profound effect on the statistical results since the wordings determine how respondents will understand and comprehend the question. Questions that are biased or ambiguously constructed may lead to biased data which often influences the correctness of the statistical analysis. For instance, leading or emotive language may push the respondent toward a certain answer. This could culminate in distorted results and influence what is concluded after critically analyzing the data.
Biasness Analysis
Such survey design bias may be generated due to the wording of questions, the selection of samples, or the method used in conducting the survey. The evaluation of bias would involve identifying areas where there is distortion in leading questions, in non-random sampling, or even in the choice of survey methods that can favor one group over another. This helps researchers to analyze, in the end, how the biases may have affected findings and thus the reliability of the findings.
Avoidance of Biasness
To avoid bias, a survey will require clear, neutral, and unambiguous questions. For random sampling in research to guarantee that respondents represent segments of the population equally, careful attention should be paid to how the survey is drafted and processed. Any factor that may unduly influence the questions asked to any respondent must be eliminated. All these ensure enhancement in the validity and accuracy of data collected and lead to more reliable statistical results.
MAT FPX 2001 Assessment 5 Conclusion
Design and implementation play a pivotal role in the reliability and validity of a survey or study, and impacts concerning the wording of questions, potential biases in sample selection, and the way questions are formulated can have drastic alterations to statistical outcomes to an extent where conclusions will be inaccurate and/or misleading (Cash et al., 2022). To safeguard the soundness of research findings, sources of bias must be assessed and checked through the careful development of a question, random sampling, and strict methodologies in conducting the survey. Being able to address these factors, researchers enhance the accuracy of their data and, thus acquire truer insights for better-informed decision making.
MAT FPX 2001 Assessment 5 Reference
Cash, P., Isaksson, O., Maier, A., & Summers, J. (2022). Sampling in design research: Eight key considerations. Design Studies, 78(1), 101077. Sciencedirect. https://doi.org/10.1016/j.destud.2021.101077
Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling. International Journal of Education & Language Studies, 1(2), 72–77. https://doi.org/10.22034/ijels.2022.162981
James, S. L., Yorgason, J. B., Holmes, E. K., Johnson, D. R., & Busby, D. M. (2021). Is it still possible to collect nationally representative marriage data in the United States? A case study from the create project. Family Relations, 71(4), 1428–1443. https://doi.org/10.1111/fare.12577
Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1). https://doi.org/10.1525/collabra.33267
