A link between success and happiness has been established by numerous studies. Cross-sectional studies confirmed that there is a positive correlation between happiness and success. Bowling et al. (2011) conducted a meta-analysis that established a positive link between subjective well-being and job satisfaction. Hosie et al. (2012) provided evidence that there is a link between the satisfaction of the employees and their productivity.
Since it may be assumed that increased workplace productivity can lead to increased success, there is a positive correlation between subjective well-being and success. Evidence suggests that happy people are less likely to burn out, which positively affects their ability to resist stress and continue productive work (Walkiewicz et al., 2012). Additionally, happy people are less likely to be absent from work or studies, which also contributes to the productivity of employees (Avey et al., 2006). Seligman and Schulman (1986) revealed that happy salespeople sold 37% more life insurance policies. Thus, there is little doubt about the existence of a positive link between happiness and success.
While numerous researchers agree that there is a relationship between the two matters, the direction of the relationship is a matter of dispute. Many people believe in the formula that a person needs to work hard, earn money, and become successful (Achor, 2010). These people believe that success will make them happy (Achor, 2010). However, Walsh et al. (2018) challenged this idea by presenting a systematic review of evidence that happiness leads to success. Cross-sectional studies could not confirm the causal relationships between the two concepts.
Longitudinal studies revealed that happiness comes first and success comes second. Haase et al. (2012) demonstrated that happy people are more likely to find jobs and retain them for a more extended period. Additionally, Burger and Caldwell (2000) revealed that happy college studies were more likely to receive job interviews after graduation. Happy 18-year-olds were also more likely to have the jobs they wanted, retain them for a longer time, and be satisfied with them (Walsh et al., 2018). Thus, longitudinal evidence suggests that being happy leads to financial and career success.
Recent studies also confirm that there is a link between academic success and the happiness of students. Doljanica and Petronijević (2017) demonstrated that there is a positive link between the success of master students and happiness. At the same time, the economic state had an insignificant effect on the happiness of last year’s Master’s students. Thus, it may be assumed that the happier students were, the more successful they were in their studies.
Research Question and Hypotheses
The present paper aims at determining the relationships between happiness and success of students studying in high education institutions. The primary research question is “What is the relationship between happiness and academic success of students?” In particular, the study aims to test if happiness causes academic success in university students. The research uses a cross-sectional design, as it does not require much time and financial resources to be conducted. However, as mentioned by Walsh et al. (2018), cross-sectional studies cannot establish causal relationships; instead, they measure only associations between the variables. Therefore, it was decided to ask questions about the past of the participants and measure their happiness in their childhood. It was expected that the happier the students were in their childhood, the more successful they would be in their university studies. Thus, the hypotheses for the present research are the following:
- H0: There is no significant correlation between the degree of happiness during childhood and the academic success of university students.
- H1: There is a significant correlation between the degree of happiness during childhood and the academic success of university students.
Academic success will be measured using self-perceived academic success and grade point average (GPA). Happiness will be measured using an adapted Oxford Happiness Questionnaire consisting of 12 items (Hills & Argile, 2002). Demographical variables will also be used as controls. The questionnaire and the conceptual model for the project are provided in Sections 2 and 3 of the present report.
Data Collection: Questionnaire
Below is the suggested questionnaire for collecting data.
“The present survey is used for research conducted for the Basic Statistics class at Alkhawarizmi International College. The aim of the research is to explore the relationships between childhood happiness and the academic success of students. This survey consists of three sections. The first section includes questions that collect demographical data of respondents. The second section contains questions that measure how happy the participants were in their childhood. The third section includes questions that measure academic success. Participation in this research is voluntary, and participants may withdraw from participation at any time by closing the browser. As soon as the answers are submitted, there will be no possibility to withdraw from the study as the answered cannot be identified. Please, answer the following questions using your best judgment.
Section 1: Demographic Questions
- What is your gender?
- What is your age? (enter the number of full years)
- For how many full years have you been a student at your university?
- Less than 1
- 1 year
- 2 years
- 3 years
- More than 3 years
- What is your employment status?
- Have a part-time job;
- Have a full-time job.
- What is your monthly household income?
- Less than 5000 AED
- 5,000AED – 10,000 AED
- 10,000AED – 20,000 AED
- 20,000AED – 35,000 AED
- 35,000AED – 50,000 AED
- More than 50,000 AED
- Prefer not to say
- Please, specify your ethnicity.
- Prefer not to say.
Section 2. Measuring Happiness
Below are several questions about happiness during your childhood. Please, indicate how much you agree with the following statements on the scale from 1 to 5, where:
- = Strongly disagree;
- = Disagree;
- = Neither agree nor disagree;
- = Agree;
- = Strongly agree.
- As a child, I was intensely interested in other people.
- As a child, I felt that my life was rewarding.
- As a child, I had warm feelings towards almost everyone.
- As a child, I felt that life was good.
- As a child, I used to laugh a lot.
- As a child, I found most things amusing.
- As a child, I was very happy.
- As a child, I always had a cheerful effect on others.
- As a child, I had great amounts of energy.
- As a child, I felt very healthy.
- As a child, I was able to take anything on.
- As a child, I could find time for everything I wanted to.
Section 3. Measuring Academic Success
- What is your current grade point average (GPA)? (enter a number)
- Please, measure your academic success during the study at the university on a scale from 1 to 5, where 1 is “unsuccessful,” 2 is “somewhat unsuccessful,” 3 is “Neither successful nor unsuccessful,” 4 is “somewhat successful,” and 5 is “Successful.”
Thank you for your participation!”
Data Analysis and Results
The population under analysis are current or former students in the United Arabic Emirates (UAE). The inclusion criteria were being a citizen of UAE, having at least have a year of studies in a higher-education institution, and being 18 or older. Survey replies with no information about GPA were excluded from the analysis. All the replies with missing information were also excluded from the analysis.
Convenience sampling was used to collect primary data. In particular, researchers’ friends in social networks were asked to participate in the survey. The link was distributed through private messages to the selected participants. According to Creswell (2012), convenience sampling is a method of selecting participants that are ready-available for the researchers. This method is associated with decreased cost and increased time efficiency of data collection. Convenience sampling is usually used in qualitative research, as it is associated with significant bias when used for quantitative research (Creswell, 2012).
The problem with convenience sampling is that it is a non-probability sampling method, which implies that members of the population do not have an equal chance to participate in research. Therefore, the sample may have unknown characteristics that may negatively affect the reliability of research results.
Initially, 63 replies were received from participants. However, after cleaning data of invalid replies, the final sample included 46 replies. The acquired sample size is also a source of bias. According to Cochran (1977), the required sample size was calculated using the formula provided below:
- t = t-value corresponding to the alpha level;
- s = standard deviation in the population;
- d = acceptable margin of error for mean being estimated.
With a margin of error of 3%, standard deviation of 0.83, and an alpha level of 0.05, the recommended sample size is 118, which is more than twice the number of replies that were received. Therefore, the sample size is another source of possible reliability issues.
Analysis of Survey Results
Three types of variables were used to answer the research question and conduct hypothesis testing, including a dependent variable, an independent variable, and several control (demographic) variables. Below are the descriptions of how each variable was measured together with descriptive statistics or frequency of responses.
- Academic Success. Academic success was the dependent variable used in the hypotheses testing. It was measured by taking the average value of replies to Questions 1 and 2 from Section 3 of the questionnaire.
- Happiness Index. The Happiness Index was the independent variable used for hypothesis testing. It was measured by taking the average value of replies to Questions 1-12 in Section 2 of the questionnaire.
- Age. Age is a continuous control variable. The age of the respondents was measured using Question 2 from Section 1 of the questionnaire.
The variables discussed above are the only three continuous variables. Descriptive statistics for these variables are provided in Table 1 below.
Table 1. Descriptive statistics.
- Gender. Gender was a categorical control variable that was measured using Question 1 from Section 1. There were 25 (54.3%) females and 21 (45.7%) males among the respondents.
- Years of Study. Years of study was a categorical control variable that was measured using Question 3 of Section 1 of the questionnaire. Frequencies for the variable are provided in Table 2 below.
Table 2. Frequencies of Years of Study.
|Frequency||Percent||Valid Percent||Cumulative Percent|
|Valid||Less than 1 year||3||6,5||6,5||6,5|
|More than 3 years||19||41,3||41,3||100,0|
- Employment Status. Employment status was a categorical control variable measured using Question 4 of Section 1 of the questionnaire. Frequencies for the variable are provided in Table 3 below.
Table 3. Frequencies of Employment Status.
|Frequency||Percent||Valid Percent||Cumulative Percent|
|Have a part-time job||3||6,5||6,5||45,7|
|Have a full-time job||25||54,3||54,3||100,0|
- Monthly Family Income. Monthly family income was a categorical control variable measured using Question 5 of Section 1 of the questionnaire. Frequencies for the variable are provided in Table 4 below.
Table 4. Frequencies of Monthly Family Income.
|Frequency||Percent||Valid Percent||Cumulative Percent|
|Valid||Less than 5000 AED||10||21,7||21,7||21,7|
|5,000AED – 10,000.AED||5||10,9||10,9||32,6|
|10,000AED – 20,000.AED||5||10,9||10,9||43,5|
|20,000AED – 35,000.AED||9||19,6||19,6||63,0|
|35,000AED – 50,000.AED||8||17,4||17,4||80,4|
|More than 50,000.AED||2||4,3||4,3||84,8|
|Prefer not to say||7||15,2||15,2||100,0|
- Ethnicity. Ethnicity was a categorical control variable measured using Question 6 of Section 1 of the questionnaire. Frequencies for the variable are provided in Table 5 below.
Table 5. Frequency table of Ethnicity.
|Frequency||Percent||Valid Percent||Cumulative Percent|
|Prefer not to say||6||13.0||13.0||100.0|
The data were analyzed using multiple regression in SPSS 26. The following regression model was assessed to understand the impact of childhood happiness on the academic success of students in UAE:
The results of the analysis revealed that the created model had a very low predictive ability, which was statistically insignificant. The adjusted R2 coefficient was 0.062, with a p-value (0.23) above the alpha value of 0.05. The estimations of coefficients are provided in Table 6 below.
Table 6. Estimations of Coefficients.
|Model||Unstandardized Coefficients||Standardized Coefficients||t||Sig.|
|Years of Study||.272||.097||.462||2.789||.008|
|Monthly Household Income||.003||.003||.135||.918||.364|
|a. Dependent Variable: Academic Success|
The analysis of coefficients revealed that there was no significant correlation between Happiness Index and academic success, as the p-value (0.621) was below the alpha value of 0.05. Moreover, all the demographic variables were also found to have no effect on the dependent variable except for Years of Study. Pearson’s correlation analysis revealed that there is a significant correlation between Years of Study and Happiness Index (Pearson’s R=0.416, p<0.001). In summary, the analysis of data found no significant evidence to reject the null hypothesis.
The purpose of the study was to explore the relationships between childhood happiness and the academic success of students. Regression analysis revealed that there was no correlation between the two concepts. Therefore, the study concludes that childhood happiness does not lead to academic success in UAE students. However, the study revealed that there is a significant correlation between the years of study and the academic success of students in the UAE.
The results of the present research contradict the previous body of knowledge concerning the link between happiness and success. In particular, Walsh et al. (2018) claimed that the happier people are, the more successful they become. The results of the present study also contradicted the findings of the study by Doljanica and Petronijević (2017), which found a positive link between academic success and the happiness of students. Moreover, no link was found between the academic success of students and their socio-economic status. The data demonstrated that there was no link between monthly family income and the academic achievement of students, which contradicts the current body of knowledge (Doljanica and Petronijević, 2017).
The contradictions of the research results with the current body of literature can be explained in two ways. The contradiction may be caused by the specifics of the population. According to Hofstede’s model of cultural dimensions, national cultures differ considerably in six different aspects, including power distance, uncertainty avoidance, individualism vs. collectivism, masculinity vs. femininity, long-term vs. short-term orientation, and indulgence (Hofstede, 2011).
UAE, for instance, has a very high score in terms of power distance and a very low score in terms of individualism, which are two distinguishing features of the nation (Hofstede Insights, n.d.). All the studies reviewed for the present research were conducted outside the UAE and the Arab world. Since the majority of the sample (58.7%) were from the UAE, cultural differences may have affected the results of the study.
The other explanation is the absence of rigorous control over sampling, which may have led to significant bias and uncertainty in the results. As mentioned in the description of the sampling procedures, there are significant issues with the reliability of findings due to the inadequate sample size and convenience sampling method. The bias associated with the sampling procedure may lead to incorrect results that cannot be used in future research (Creswell, 2012). Additionally, it should be mentioned that the questionnaire was distributed among the citizens of UAE, who are not native English speakers. This may have led to problems with understanding the instructions of the survey, which, in turn, led to biased results. Further research is required to increase the reliability of the present study and select between the two possible explanations.
Achor, S. (2010). The happiness advantage: How a positive brain fuels success in work and life. Crown Business.
Avey, J. B., Patera, J. L., & West, B. J. (2006). The implications of positive psychological capital on employee absenteeism. Journal of leadership & organizational studies, 13(2), 42-60. Web.
Bowling, N. A., Eschleman, K. J., & Wang, Q. (2011). A meta‐analytic examination of the relationship between job satisfaction and subjective well‐being. Journal of Occupational and Organizational Psychology, 83(4), 915-934. Web.
Burger, J. M., & Caldwell, D. F. (2000). Personality, social activities, job-search behavior and interview success: Distinguishing between PANAS trait positive affect and NEO extraversion. Motivation and Emotion, 24(1), 51-62. Web.
Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.
Creswell, J. W. (2012) Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
Doljanica, S., & Petronijević, M. (2017). A relationship between happiness and success of students on master studies. Web.
Haase, C. M., Poulin, M. J., & Heckhausen, J. (2012). Happiness as a motivator: Positive affect predicts primary control striving for career and educational goals. Personality and Social Psychology Bulletin, 38(8). Web.
Hills, P., & Argyle, M. (2002). The Oxford Happiness Questionnaire: a compact scale for the measurement of psychological well‐being. Personality and Individual Differences, 33, 1073–1082. Web.
Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 2307-2333. Web.
Hofstede Insights. (n.d.). Compare cultures. Web.
Hosie, P., Willemyns, M., & Sevastos, P. (2012). The impact of happiness on managers’ contextual and task performance. Asia Pacific Journal of Human Resources, 50(3), 268-287. Web.
Seligman, M. E., & Schulman, P. (1986). Explanatory style as a predictor of productivity and quitting among life insurance sales agents. Journal of personality and social psychology, 50(4), 832- 837.
Walkiewicz, M., Tartas, M., Majkowicz, M., & Budzinski, W. (2012). Academic achievement, depression and anxiety during medical education predict the styles of success in a medical career: a 10-year longitudinal study. Medical teacher, 34(9), e611-e619. Web.
Walsh, L. C., Boehm, J. K., & Lyubomirsky, S. (2018). Does happiness promote career success? Revisiting the evidence. Journal of Career Assessment, 26(2), 199-219. Web.