Statistical analysis involves collecting data to discover trends and patterns. Proper collection and data analysis are critical to ensure that the final claims match the results' analysis. There can be errors introduced in research in hypothesis testing and interpreting the statistical significance of data. This paper analyzes scenario 1 and scenario 4 to evaluate the sample size, statements for meaningfulness, statements for statistical significance, and to explain the implications for social change.
Scenario 1
Evaluation of the Sample Size
The sample size in statistical research should be well representative of the population. The sample size in the given study was 65 for the traditional state university and 69 for the online classes. It is expected that the average number of students in a class to be about 50. Therefore, the sample size was a good representation of the study.
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Evaluation of the Statements for Meaningfulness
The study examined whether student satisfaction through quantitative reasoning was different between the traditional classroom and online classroom environment. Statistics can be used to analyze the data's meaningfulness and better understand its application (Kamper, 2019). The study would provide useful information to educators regarding which type of teaching is more satisfactory for learners.
Evaluation of the Statements for Statistical Significance
The study's analysis showed that it was significant with a t value (132) = 1.8 and p= 0.074. Face-to-face learners reported lower satisfaction with means of 3.39 and standard deviations of 1.8, while online learners had a mean of 3.89 and a standard deviation of 1.4. The conclusion was that online quantitative reason classes reported higher satisfaction levels. The findings from the study were statistically significant because of the larger sample size. Therefore, there is a strong relationship between the sample size and the actual population.
Implications for Social Change
The study's implication presents a good indication that students can learn better in the online environment than in the traditional setting. The application of the study would imply reducing social interactions by having a further application of technology. It also shows that the current generation has adapted to the increased use of technology, impacting their social wellbeing.
Scenario 4
Evaluation of the Sample Size
The scenario involved examining the relationship between job satisfaction and income level. The sample involved 432 employees who represented the public, private, and non-profit sectors. While the sample size was adequate, it could have been taken separately from each sector to compare the organizations' differences. Increasing the sample size would have provided more accurate results with few errors.
Evaluation of the Statements for Meaningfulness
The study examined a practical aspect of job satisfaction to determine whether the income level influences it. The study showed that job satisfaction increases with the income level. The study would be meaningful to organizations that want to improve job satisfaction or reduce employee turnover rates by improving wages.
Evaluation of the Statements for Statistical Significance
The study results showed a strong positive correlation since r = .87 and p<.01, indicating that job satisfaction increases with income. A significance level of 0.01 would mean that the null hypothesis would be rejected (Szucs & Ioannidis, 2017). The correlation of 0.87 and a significance level of 0.01 thus led to the null hypothesis being rejected.
Implications for Social Change
The study implies that organizations should focus on increasing the level of income to improve job satisfaction. The socioeconomic aspects of society would be impacted as more companies can increase the income level to enhance job satisfaction. Additionally, the study indicates why many people would prefer going for higher-paying jobs as those jobs have higher satisfaction.
References
Kamper, S. J. (2019). Interpreting outcomes 2—statistical significance and clinical meaningfulness: linking evidence to practice. Journal of Orthopedic & Sports Physical Therapy , 49 (7), 559-560. https://www.jospt.org/doi/10.2519/jospt.2019.0704
Szucs, D., & Ioannidis, J. (2017). When null hypothesis significance testing is unsuitable for research: a reassessment. Frontiers in Human Neuroscience , 11 , 390. https://doi.org/10.3389/fnhum.2017.00390
