Group Project -Age Happiness Result Description/ Discussion
Students Name
Institution
Course Name
Instructor
Date
Group Project -Age Happiness Result Description/ Discussion
Problem
Throughout society and the workforce today, we are constantly bombarded with information regarding productivity, efficiency, etc. We are reminded of ways that we can be more productive at work, at home, in our free time, etc. A relatively new threat to the workforce today is the new wave of AI technology. Further reinforcing the need to be more productive and efficient. Throughout this field of research, we see studies that attempt to link two variables. For example, productivity and happiness. According to research at Oxford University, “An extensive study into happiness and productivity has found that workers are 13% more productive when happy” (University of Oxford, 2019)
In our research, we would like to take this one step further and analyze the relation of age and happiness. In healthcare, specifically in Intensive Care Units, we find the average age of an Intensive Care nurse to be 43 years old (Zippia, 2022). Because happiness has been linked to overall productivity, we will be assessing the linear relationship between age and happiness. By assessing these two variables, we will hopefully open the door into future analysis where age and productivity can be looked at in further depth.
Hypothesis:
State your research hypothesis as a null hypothesis and alternate hypothesis (Ho) and (Ha or H1).
Ho: A linear relation exists between age and happiness
H1: No linear relation exists between age and happiness.
Collect data in a way designed to test the hypothesis.
A survey was distributed to ICU RNs asking them to answer how old they are and to rate their level of happiness (1-5).
The data was broken down into categories based on age and level of happiness.
Perform an appropriate statistical test.
Since we are looking at multiple quantitative data, we will use the ANOVA statistical test
P – value: 0.1082
F-stat: 2.624
Using 0.05 level of significance
Decide whether to reject or fail to reject your null hypothesis.
We will not reject the Null hypothesis, there is not enough information to conclude that there is not a linear relationship between age and level of happiness.
Present the findings in your results and discussion section.
Completed in the next section
For our study, we conducted a survey using convenience sampling (n=53). Our target population includes those within the workforce, between the ages of 18-64 (assuming 65 is the common retirement age in America). Within the survey we have asked two simple questions: “Select your age group” with ages being divided into simplified age groups (18-24, 25-34, 35-44, 45-54, 55-64), and “Rate your overall happiness” on a scale of 0-5 (0 being none, 5 being extremely happy). Our explanatory variable is age, and our response variable is overall happiness.
Results
For this study, we received a total of n=53 respondents. 12 respondents within the age group 18-24, 19 respondents within the age group of 24-34, 3 respondents within the age group 35-44, 2 respondents within the age group 45-54, and 17 respondents within the age group 55-64. For analysis, we have assigned a 1, 2, 3, 4, or 5, to weight the age group variable. The sample data is displayed below:
Interpretation:
Analyzing these results, we obtain the following:
Least-Squares Regression Line: Y= -0.013x + 3.925
Interpretation:
Slope= -0.013 (For every x=1 increase in age; overall happiness will decrease by a score of 0.013)
Predicted Values
X=1; y=3.912
X=2; y= 3.899
X=3; y=3.886
X=4: y=3.873
X=5; y=3.860
Confidence Interval for the Slope
95% CI (-0.139, 0.113)
We are 95% confident that the slope is between (-0.139 and 0.113)
Y-intercept= 3.925 (0 is not a likely value for age group {assuming most people enter the workforce around 18 years of age} and therefore we do not need to interpret the y-intercept)
Linear Correlation Coefficient= (r=0.029)
Residual Analysis Requirements:
Constant error variance as demonstrated by the residual plot:
No outliers exist in the data set
Critical Value (at n=53) is 0.271
Because the absolute value of r < 0.271, no linear relation exists between age and happiness.
Although we have found a formula for the least-squares regression line to assist in predicting overall happiness (y) based on age (x), at a 95% CI, our parameters fall between positive and negative numbers, implying that the relationship between age and happiness could either be negatively or positively associated. To conclude, because our correlation coefficient is less than the critical value based on our sample size, we cannot say that a linear relation exists between age and happiness.
Limitations
One limitation of our analysis we encountered was the sample size. Our sample size is not very large and therefore not truly representative of the population. We also have more responses in different age groups than others. For example, there we 19 responses within the age group of 24-34, but only 3 responses within the age group of 35-44. It is unknown if this is now reflective of how many people work in that age group or how many people our survey was able to reach. Another limitation to our survey is happiness is not only subjective but can fluctuate each day. We only surveyed overall happiness at one point in time. A further limitation of our study is that we were only able to send out the survey to people we know. Answers may be different in different hospitals, parts of the country, or social circumstances.
From our research, we can draw the conclusion that no linear relationship exists between age and happiness.
Present the results in a 10-minute Microsoft® PowerPoint® presentation.
For Local Campus students, these are 10-minute oral presentations accompanied by Microsoft® PowerPoint® presentations.
For Online and Directed Study students, these are 5-slide Microsoft® PowerPoint® presentations with notes.
The results of our study indicate that for every increase in age by one year, overall happiness decreases by a score of 0.013. The study findings thus suggest an inverse relationship between age and satisfaction. This implies that for the sampled participants, their happiness levels tend to decrease as age increases. Various factors would describe the outcome of the results. Our study was straightforward and asked the respondent's age and how their level of happiness was measured. The study did not consider causative factors of happiness levels among the employees.
The study assumption that explains the results is that the young employees are probably excited about their jobs and feel happy about securing employment. At a young age, the employees are still learning and adapting to the job environments and have to align to the job environment despite the challenges involved, thus maintaining a state of happiness. The happiness levels thus decrease as the employee's age increase due to increased responsibilities and demands of the job; the unpredictable workforce environments experienced over the years result in reduced happiness levels as employees age.
The study's results are inconsistent with An et al (2020) results that indicated that life satisfaction and happiness increased with age. Our study results differ because the study design did not incorporate other variables like An et al (2020), which considered physical activities as an additional variable for establishing the relationship between life satisfaction and happiness across various age groups among young, middle-aged, and older adults.
The results are, however, consistent, though not in entirety, with Blanchflower's (2020) findings which indicate that happiness is U-shaped. The study findings by Blanchflower (2020) suggest that age and subjective well-being are U-shaped in many countries worldwide, implying that happiness decreases with age to some extent, over which it rises as age increases. The findings are consistent though our study does not incorporate the factor that led to decreasing levels among sampled participants, as Blanchflower (2020) outlines that financial, living standards, home, and work environment contribute to the determination of happiness.
Employee happiness can be attributed to different factors within and without the organization. Our study focused on assessing the relationship between age and satisfaction among employees and did not consider addressing the underlying factors that lead to the difference in happiness levels across different age groups. The study's main limitation is that it ignored the impact of confounding variables that directly affect happiness among people and thus could have been a significant attribute of the results. The study also utilized a small sample of n=53 participants from the intensive care unit; hence the result cannot be generalized to all the employees working in the facility or the healthcare environment.
The study findings are crucial in advancing the research to understand what causes happiness levels to decrease among employees as age increases. The study can also be essential in further analyzing the relationship between age and happiness among employees in the intensive care unit by considering the difference based on the sex of the participants.
Appendix
The excel below reflects on the summary output of the relationship between age and happiness.
References
An, H.-Y., Chen, W., Wang, C.-W., Yang, H.-F., Huang, W.-T., & Fan, S.-Y. (2020). The relationships between physical activity and life satisfaction and happiness among young, middle-aged, and older adults. International Journal of Environmental Research and Public Health, 17(13), 4817. https://doi.org/10.3390/ijerph17134817
Bevans, R. (2022, December 7). Hypothesis testing: A step-by-step guide with easy examples. Scribbr. Retrieved April 12, 2023, from https://www.scribbr.com/statistics/hypothesis-testing/
Blanchflower, D. G. (2020). Is happiness U-shaped everywhere? age and subjective well-being in 145 countries. Journal of Population Economics, 34(2), 575–624. https://doi.org/10.1007/s00148-020-00797-z
University of Oxford. (2019, October 24). Happy workers are 13% more productive. University of Oxford. Retrieved April 13, 2023, from https://www.ox.ac.uk/news/2019-10-24-happy-workers-are-13-more-productive
Zippia. (2022, September 9). Intensive Care Unit Nurse Demographics and Statistics [2023]: Number of Intensive Care Unit Nurses in the US. Intensive Care Unit Nurse Demographics and Statistics [2023]: Number Of Intensive Care Unit Nurses In The US. Retrieved April 13, 2023, from https://www.zippia.com/intensive-care-unit-nurse-jobs/demographics/