Diversity & Equality in Health and Care Open Access

Keywords

Low-income family; T-test; ANOVA; Correlation quantitative design

Introduction

In the last decades, there was a phenomenon of economic insecurity and declining wages in the United States [1]. Childcare is unaffordable and inaccessible, which affects the community and drain on U.S. working families and employers’ bottom lines (Child Care Aware of America, 2018). This research investigates how family income has critical implications for children’s development in a family from different racial backgrounds. The National Center for Children in Poverty has payed attention to family economic security for a long time. The research shows that about 15 million children and 43% of children live in lowincome families in the United States [2]. It is important to help families meet their financial needs and promote well-being for their children. The government needs to pay attention to ensure families to gain adequate income and have enough savings to raise their children.

The outcomes are based on larger sample sizes, which can represent a large group of the population [3]. There are two research questions needed to be addressed in order to find options for the lower income families. First, what are the trends in income segregation between different races? Second, is there any relationship between family income and the number of children they are willing to raise? This research seeks to identify the problems that families might encounter and provide suggestions for policy makers to find the best way to establish fair support obligations for families, as well as secure cooperation between different departments.

Study population and quantitative design

The income data was chosen from General Social Survey, and income levels vary across different races. This data analysis will help the policy makers when they try to provide children’s medical insurance; it will also provide ideas of how to help these families who are experiencing medical hardships, have obstacles to support the children because of a single family, or the parents are disabled and cannot work enough time to cultivate their children [4]. Lowincome single families often face many barriers. The ignorance of enriching home environments may result in growing gaps in society and it might lead to low socioeconomic mobility [1]. Also, low-income parents often lack resources to achieve their parenting goals [4]. The state government needs to invest in parents so they can afford the family expenses while cultivating their children. In addition, class gaps between different races and the gaps between low and high-income families are predominant in the United States [5]. For example, policy approaches have to address how to increase equity between different families and races, and to help the family in supporting children’s development, as well as provide regular in-home visits by trained nurses for families who are experiencing financial or physical difficulties.

T-test design

The T-test is a type of inferential statistics. It has been used to determine if there is a significant difference between the means of different groups. It helps to compare whether two groups’ averages are unlikely to occur because of random sample selection [6].

The chosen variables for this T-Test are sex and age of respondents. The mean score of females is higher than the mean of males. The table shows that the mean for males is 46.5 and the mean for females is 47.2. The null hypothesis is that there is no difference between the two groups. However, the p value for the 2-tailed test is 0.44, which means that the null hypothesis cannot be rejected. There is no statistical difference when comparing the means of the two groups (Figure 1).

Figure 1: Age and Sex of respondent.

ANOVA quantitative design

The purpose of using a two-way ANOVA is to understand the data by illustrating t-tests, ANOVAs, and correlations (Field, 2018). The ANOVA can examine potential differences between different variables and examine the interaction between variables (Healey, 1990). The table below shows the Between-Subjects Factors. The number of children has 8 levels from 0 to 8, and race has 5 levels, such as white, black or African American, American Indian or Alaskan native, Hispanic American, and Asian and Pacific Islander Americans [7].

The p value for the number of children is 0.1, which means that there is a significant relationship between children and family income. The p value of race is 0.00, which also indicates that there is a significant relationship between income and race. If we look at both variables of race and children, the significance number is 0.231. It means that the relationship between all the variables is not significant. When comparing the combination of race and children with the single variable of either race or children separately, the combination variables of race and children are less significant than comparing children or race with family income separately [8].

From this graph, it indicates that the mean of 2 children has the highest score of 16.603, and the lowest mean score is 10 for 7 children. The highest mean for the number of children is 16.603. When we look at the Pairwise Comparisons, it does not have a statistically significant difference between how many and total family income because the significance number ranges from 0.233 to 1, which is higher than 0.05, meaning that the family with 7 children have less income than the family with less than 7 children or have 8 or more. The families with 8 or more children have less income than the families with less than 6 children with the mean of 11.5.

The Univariate Tests show that the p value is 0.016, which means that it successfully rejects the null hypothesis. It indicates that there is a relationship between the number of children and family income.

This table indicates that Asian and Pacific Islander Americans have the highest income mean of 16.859, and Black or African Americans have the lowest mean of 12.092. People should pay attention to support Black or African American families (Table 1).

Table 1: Pairwise Comparisons.

In terms of family income, comparing white Americans with Black or African Americans, there is a statistically significant relationship between the two populations, as well as comparing white Americans with Hispanic Americans. The p value shows 0.007, which means that there is a statistically significant difference between the two populations (Table 2).

Table 2: Number of children * what is rs race 1st mention.

Income also varies by race and ethnicity. When examining the interaction factors between the number of children and race, the lowest mean score of 5 is for comparing 3 children with American Indian or Alaskan Native, and the mean of 7.5 is for comparing 6 children with black or African Americans. However, the higher mean score of 24 is for comparing Asian and Pacific Americans with 5 children, and the mean score of 19.8 is for comparing Asian and Pacific Islander Americans with 1 child (Table 3).

Table 3: Report.

The table above illustrates that there is not much difference when comparing 0 to 3 children with family income, but the difference is significant when comparing 8 children with 2 children. Families with 8 or more children have the lowest mean family income. However, the total family income for a family with 2 children has the highest mean. It illustrates that families with more children might experience more financial difficulties. Policy makers need to update policy when it comes to supporting families with multiple children (Figures 2 and 3).

Figure 2: Estimated marginal means of total family income (Number of children).

Figure 3: Estimated marginal means of total family income (Race).

When looking at white Americans, the means do not demonstrate a significant difference between 0 children and eight children in terms of family incomes. When analyzing Asian and Pacific Islander Americans for the means of family income and number of children, 4 children families have the lowest score for a family income, and 5 children’s families have the highest income mean. American Indian or Alaskan native family income mean reaches the lowest score for 3 children families compared with the income means of other races. However, the income mean of 5 children families reaches the highest score of around 24 compared with other races.

Correlation quantitative design

A correlation test measures the relationship between different variables and to determine if two variables are correlated. The correlation is a number between -1 and 1, which indicates that the two variables are linearly related. Positive coefficients indicate that when one variable number increases, other variables also increase. Negative coefficients produce a downward slope, which means that when the value of one variable increases, the value of the other variable decreases (Figure 4).

Figure 4: Estimated marginal means of total family income (Race and children).

In this case, the correlation will be used to measure the relationship between two variables (Chava & Anna, 2014). In this correlation test, looking at the variables of number of children and family income based on the General Social Survey database, p value is lower than 0.05, which means that there is a statistically significant relationship between the number of children and family income. However, the Pearson correlation value is - 0.105, it illustrates that there is a negative correlation relationship between the number of children and family income, which means that the more children a family has, the more likely the family will face financial challenges, making them a low-income family.

Figure 5: Simple Histogram of number of children.

Conclusion

Establishing child support orders and ensuring that child support agencies can develop well with welfare agencies is essential for establishing a long-term peaceful community and a harmonious society. Affordable childcare has long-term benefits for people. Government policies can play a critical role in supporting lowincome families. Policy makers should be concerned about how to help the families to meet their day-to-day needs, such as food and housing. In addition, the state governor should consider how to improve children’s economic security by increasing their family income. By encouraging the earned income tax credits, increasing unemployment insurance, initiating financial work, updating minimum wage standards, providing childcare, and housing assistance will help in easing financial burdens to help them gain access to higher education for their children. Additionally, state policy should ensure job equality among workers, and the agency needs to have goals to make plans for providing health coverage for families so that they can access health care services and financial support.

References

1. Jane W, Elizabeth W (2011) Early Years Policy. Child Dev Res 2011: 343016.
2. The National Center for Children in Poverty (2012) Child Poverty.
3. Healey JF (1990) Statistics: a Tool for Social Research. Belmont Wadsworth, California, USA.
4. Phillipe B, Lance L (2007) The Changing Role of Family Income and Ability in Determining Educational Achievement.  J Human Capital 1: 37-89.
5. Annette L (2003) Unequal Childhoods: Class, Race, and Family Life. CA: University of California Press, USA.
6. Field A (2018) Discovering Statistics Using IBM SPSS Statistics. SAGE Publications, London, UK.
7. Child Care Aware of America (2019) The U.S. and the High Cost of Child Care: A Review of  Prices and Proposed Solutions for a Broken System, USA.
8. Frankfort-Nachmias C, Leon-Guerrero A (2014) Social Statistics for a Diverse Society. Pine Forge Press, California, USA.