Literature Review
The appearance of children in families can be associated with severe stress, which significantly increases the level of life satisfaction and brings happiness to people. The level of satisfaction determines an individual’s ability to assess various aspects of their life positively and to be inspired by the events taking place. Patterns in life satisfaction do not show stability: for some communities, this indicator has gradually fallen over the past years, according to Eurostat (2022), whereas for Canada, for example, satisfaction has shown an increase over the past ten years, according to Su et al. (2022).
In their study, Su et al. (2022) used national health data for a nearly complete (97%) Canadian population and found through regression analysis that women consistently reported higher life satisfaction than men, with the level of perceived social support positively influencing this parameter. However, the results of one national community with difficulty can be generalized to the whole world.
Joshanloo and Jovanović (2020) suggest that globally, women were more satisfied with life across all age, employment, and education cohorts but inferior to older men in African regions. In this study, the authors used a sample of more than 1.8 million participants from 166 countries and used multilevel modeling for analysis. Thus, the academic discourse is characterized by the notion that women, on average, have higher life satisfaction than men.
Regarding having children in the family, life satisfaction trends are less homogeneous. Having children has been shown to be negatively related to life satisfaction among single and full-time working individuals (Ugur, 2020). Similarly, the authors used a regression model on data from 1980 to 2009 to show that for cohabiting parents, having children increases life satisfaction, and this effect is particularly strong for older parents. These findings are supported by a study that also found that older adults are more likely to have higher levels of life satisfaction if they have children (Yang et al., 2022).
Yang et al. (2022) conducted a relationship analysis on a sample of Chinese elderly respondents with a mean age of 69 based on 2,968 data points. A reasonable conclusion about the relationship between having children and life satisfaction can be found in an article by Stieg (2021), who reports that a childless life turns out to be much more stable, and a person’s level of happiness drops off quickly when they discover all the worries associated with caring for a child. In other words, this relationship is heterogeneous and depends on many related factors.
Sources have demonstrated a dearth of helpful knowledge about the relationship between gender and having children in the context of affecting satisfaction. Satisfaction is a dependent factor measured in this research paper on a hundred-point scale. The choice of this scale was based on the experience of previous studies that have demonstrated the usefulness of subjectively assessing satisfaction on a continuous scale (Sirgy, 2020; Li et al., 2019). An individual’s gender and the presence (or absence) of children are explanatory dichotomous variables for identifying differences in satisfaction levels.
As shown in the literature search, women with children are expected to have higher life satisfaction than men both with and without children; similarly, having children, in general, is expected to lead to higher satisfaction (Ugur, 2020; Joshanloo & Jovanović, 2020). Testing these hypotheses will confirm or refute previous studies’ results and identify life satisfaction patterns depending on the demographic attribute.
Methods
Participants
Participation in the observational study was implemented through Qualtrics’ online platform, where individuals were warned that there was no compensation or reward for the survey. The final sample size (after initial data processing) was 39, of which 66.7% (n = 26) were female and 33.3% (n = 13) were male. The mean age of the sample was 24.9 years (SD = 7.3), with a minimum participant age of 16 and a maximum of 55. In terms of race (Figure 1), most participants identified themselves as White (28.2%, n = 11), whereas 25.6% (n = 10) were Black or African American, 20.5% (n = 8) were American Indian or Alaska Native, and the same number were Asian. 5.1% (n = 2) identified themselves as Native Hawaiian or Pacific Islanders.
When participants were asked about their educational status, the majority of the sample (43.6%, n = 17) had an undergraduate degree, and the fewest participants had a graduate degree (2.6%, n = 1), as shown in the figure below.
Finally, Figure 3 reports that the sample was equally represented by single individuals and those in relationships with partners (41.0%, n = 16 each) and had the fewest widows/widowers (2.6%, n = 1). The descriptive results obtained for the sample may indicate its overall representativeness, as different demographic cohorts were represented.
Materials
The primary material for this research project was a short questionnaire created on the Qualtrics platform. The questionnaire included the questions shown in Table 1. Only Q1, Q7, and Q8 were used to perform the statistical analysis based on the research question, and the remaining questions were buffered. In addition, MS Excel (for primary data processing and coding) and SPSS v.28 (to perform the test) were required to perform the analysis, and MS Word was used to write the research report.
Table 1. Elements of the Survey Used in the Project
Procedure
A systematic probability mechanism was used to generate the sample, which increases the likelihood of having a representative study group and also equalizes the chances of candidates from the population being included in the sample. To implement this mechanism, all social contacts personally known to the authors of the research project on social media platforms, whether Facebook, Twitter, Instagram, or LinkedIn, were written out in an Excel spreadsheet.
Each candidate in the population was entered into the spreadsheet only once. Using a random number assignment function, each candidate was assigned a number between 0 and 1, followed by an automatic ascending sort function. Of the 183 candidates, an invitation link was sent to each fourth candidate to take the short survey on Qualtrics.
If a candidate declined, the link was sent to the next person on the list; thus, the sample would presumably consist of 45 respondents. Following the anonymous link, which did not collect any personal information about the respondent, the individual had to complete a survey consisting of eight questions. Before doing so, they were asked to carefully read the informed consent and confirm their willingness to participate voluntarily (Bazzano et al., 2021).
Five survey questions included demographic information (gender, age, ethnicity, education, and marital status), and three questions were related to the primary survey (having pets, having children, and life satisfaction). Notably, most of the survey questions were buffer questions: the purpose of using such survey elements is to reduce respondents’ biases, increase their comfort during participation, and increase their likelihood of engagement. After completing the questionnaire, the individual clicked to submit the results and was thanked for participating.
The exported data was subjected to initial processing, which was aimed at improving its quality. This included removing blank and blank questionnaires, as well as entries in which respondents indicated irrelevant, erroneous information. A two-factor ANOVA test was used to analyze the data. This test is used to test the effect of categorical variables on the dependent variable, which includes assessing the effect of each explanatory variable as well as the joint effect (SL, 2021). The analysis was conducted in SPSS v. 28, and the results are obtained and interpreted in this report.
Results
The purpose of this analysis was to determine the differences in life satisfaction levels between individuals as a function of their demographic attributes of gender and having children. In addition, it was necessary to determine how each of the independent variables might affect these levels. The first hypothesis was that women with children were more satisfied with life than men with children. In addition, it was hypothesized that women were generally more satisfied with life and that having children had a positive effect on this indicator.
Descriptive Statistics
In the context of the interest variables investigated, it was found that each respondent completed the appropriate question, and no answers were omitted. Thus, 33.3% (n = 13) of respondents said they had children, and the overall mean level of life satisfaction for the entire sample was 71.3 (SD = 18.5). Interestingly, extreme values were observed: for example, when assessing their own life satisfaction, respondents indicated a value of both 1 (“critically low level of satisfaction”) and 100 (“the highest level of satisfaction”).
Table 2 contains the mean values and standard deviations for satisfaction levels according to combinations of the two categorical variables. At first glance, it appears that satisfaction for men (M = 73.8, SD = 12.1) is generally higher than for women (M = 70.1, SD = 21.1), and satisfaction for people with children (M = 75.9, SD = 12.2) seems to be higher than for those without children (M = 69.0, SD = 20.8). However, the use of inferential statistics is required to determine more significant differences.
Table 2. Descriptive Statistics for Interest Variables
Inferential Statistics
A two-factor ANOVA test was used to determine the difference in mean values between the four groups of the two categorical variables, as well as for each dichotomous variable separately. No significant differences were found between the two gender groups (t(37) = -0.581, p <.564). In other words, women (M = 70.1, SD = 21.1) did not show significantly lower levels of life satisfaction than men (M = 73.8, SD = 12.1).
No such difference was also shown for the factor of having children (t(37) = 1.103, p <.277), meaning that having children (M = 75.9, SD = 12.2) and not having children (M = 69.0, SD = 20.8) did not significantly affect life satisfaction levels. For the combination of the two categorical variables, it was also confirmed that there were no significant differences (F(1, 35) = 0.861, p <.360). From the results of the general inferential analysis, it appears that life satisfaction did not differ between the gender groups, on the criterion of having children, and on the interaction of these variables.
Regression Analysis with Dummy Variables
It was also of interest to determine the effect of each variable and their interaction on life satisfaction using regression analysis. However, because the independent variables were represented by dichotomous categories, it was initially necessary to recode them into new categories (UCLA, 2021). Regression analysis demonstrated overall model insignificance, (F(2, 36) = 0.666, p <.520), with coefficients of such a model also showing no significance: for female gender versus male gender, (B = -2.45, p <.707), and for having children versus not having children, (B = 6.45, p <.325). Thus, it was confirmed that none of the variables, as well as their interaction, affect the respondents’ level of life satisfaction.
Discussion
The study sought to identify differences in satisfaction levels between the four cohorts formed on two dichotomous variables: gender and having children. It was found that there were no significant differences in life satisfaction between all groups. Regression analysis also showed no significant effect of the explanatory variables.
In other words, neither gender, having children, nor their interaction had any effect on the level of life satisfaction. A potential reason for the lack of significance in any results could be the fact that there are many more factors influencing life satisfaction, and this equation needs to be reducible to just two variables. It is possible that adding variables to the multiple regression models could cause the factors already studied to be significant. The study had several limitations that may have affected the accuracy of the results.
First, the sample was represented by only 39 participants, which could have biased the results. More specifically, only one-third of the sample had children, which may indicate that this cohort is underrepresented. Second, although all questionnaires were anonymous, the Hawthorne effect may have acted on respondents, leading to decreased honesty in assessing satisfaction. Third, life satisfaction is a dynamic parameter and can change depending on an individual’s mood or circumstances, so the results are difficult to generalize.
Based on the findings, the first step in expanding the scope of the study is to increase the number of variables used in order to determine moderating effects. In addition, using standardized ways of assessing satisfaction, such as the Likert scale, may also be good practice for future research.There was no separation in the number of children the respondent had, which may serve as a competent expansion of the model in the future to assess how exactly their number may influence satisfaction.
References
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Eurostat. (2022). Quality of life indicators — overall experience of life. Eurostat Statistics Explained. Web.
Joshanloo, M., & Jovanović, V. (2020). The relationship between gender and life satisfaction: Analysis across demographic groups and global regions. Archives of Women’s Mental Health, 23, 331-338. Web.
Li, Q., Stoeckl, N., & King, D. (2019). Using the life-satisfaction approach to quantify the complex interrelated impacts of coal mining on host communities: A case study in Shanxi, China. Resources Policy, 62, 305-316. Web.
Sirgy, M. J. (2020). Positive balance at the meta-cognitive level: Life satisfaction. In M. J. Sirgy (Ed.), Positive balance: A theory of well-being and positive mental health (pp. 73-93). Springer Cham.
SL. (2021). Two-way ANOVA in SPSS Statistics. Laerd Statistics. Web.
Stieg, C. (2021). Why having kids doesn’t necessarily make you happier, according to research. CNBC. Web.
Su, Y., D’Arcy, C., Li, M., & Meng, X. (2022). Trends and patterns of life satisfaction and its relationship with social support in Canada, 2009 to 2018. Scientific Reports, 12(1), 1-11. Web.
UCLA. (2021). Coding systems for categorical variables in regression analysis. Statistical Methods and Data Analytics. Web.
Ugur, Z. B. (2020). Does having children bring life satisfaction in Europe? Journal of Happiness Studies, 21(4), 1385-1406. Web.
Yang, C., Sun, X., & Duan, W. (2022). Widowhood and life satisfaction among Chinese elderly adults: The influences of lifestyles and number of children. Frontiers in Public Health, 9, 1-9. Web.