The Influence of Parents on a Child’s Development

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Wiley Swain

Wiley Swain

Project Management

This study examines the direct and indirect influences on childhood educational development.  Several research studies that attempt to quantify and measure the influence of parents on child development were consulted to identify factors that play a role in this.  Child education is increasingly coming under scrutiny because of the globalization of the world economy.  Education has suddenly become paramount in order for an individual to gain a high socioeconomic status as the business structures of most marketplaces are becoming more and more technology driven.  This switch to a technology-driven society will continue to become an ever increasing influence on the policies that govern communities and countries.

It is often said the apple doesn’t fall very far from the tree.  It goes without saying that the influence of family, parents in particular, has a tremendous effect on an individual.  However, modern culture has seen the family unit transformed over the 20th century.  So, how much influence do a person’s parents have over his or her development and has that changed over time?  The hypothesize being that even given the fractured nature of today’s families, family is still paramount.

To test this hypothesis, data from the General Social Surveys was used to do a time-series study of individuals to see the effects a parent has on his or her child and also to see if the effect has waned in the latter quarter of the 20th century.  The individual’s income was used as dependent variable.  Although this is by no means the best indicator it provides the most consistent basis for analysis.  Using the same line of reasoning, the parents’ education level was chosen as a means of measuring influence.  The logic being that the more education the parents have the more resources available to them to influence their children.  The other independent variables will be age and education.  Also, dummy variables were used for male and female to try and adjust for the inherit income difference between the sexes.  Along with studying the GSS data, other research studies were examined to gain a more holistic understanding of the various influences on child development.

The importance of acquiring the necessary skill sets to compete in a global marketplace is growing day by day.  Therefore, it becomes paramount to study and understand the factors that influence the development of a person’s skills and abilities.  The focus was put on a child development, in particular the influence of education, because it is during these formative years that a person develops the vast majority of the skills he or she will have to use to compete in society.

Literature Review

It is not hard to imagine that are plenty of research studies done on the influence of the parent-child relationship.  The question is not if there is an influence on children by their parents, but rather the interest is more in defining the relationship and the key elements as such.  Two of the main topics of interest for researchers are parent nurturing of children and the socio-economic status of the family unit.

Several studies have noted the education of the parents to be critical in the educational success of the child.  In fact, a parent’s employment and income status has been show to be less of an influence than their educational level.  Seventy-three percent of children with a parent with full-time, year-round employment that does not have a high school diploma live in low-income households while only 17% of children whose parents have some college education live in low-income households (Douglass-Hall & Chau, 2007).  Statistics suggest that parental education is also growing in importance on the economic attainments of children.  Over the past two decades the number of children in low-income households has increased by almost 14% if the parents do not have a high school diploma; even if the parents have full-time, year-round employment.

Of the parents, the mother has been noted by several studies to be the dominate factor on childhood educational attainment.  The higher the mother’s educational level, the more was expected of the children (Davis-Kean, 2005).  Some information also suggests that the importance of the maternal educational level is even greater than the income level of the family.  The role of parental education plays in childhood development appears to be most important in creating a nurturing, supportive environment for the child.

Besides parental education, variables such as child health, childcare arrangements, home environment, and food security are identified as key to child development.  Another factor that has to be accounted for is race because the cultural and social perceptions about education among different races can greatly affect the educational growth of a child.  The warmth and stability provided to a child can often mitigate the disadvantages of being in a low-income household (Davis-Kean 2005).  If children are provided a stable environment the negative effects of financial constraints can be reduced especially as the child ages.

Although the research reviewed on the subject matter has gone to great pains to quantify the pertinent variables, the data is often based on subjective analysis.  For instance, the aforementioned factors such as food security and home environment are often quantified through surveys that rely on the subject to somehow assign a numeric value to a feeling or emotion.  This could lead to inconsistent results especially when one considers that cultural norms can greatly influence a person’s perception of a factor.  Added to all these potential problems is the fact that child development is a time-consuming process with a variety of direct and indirect factors to consider.  Given all of these considerations, this data analysis attempts to review the end results of the parent-child relationship.  First, we start with the income level of the adult and then identify the educational level of the parents to try and establish some form of direct correlation between the parents’ educational level and the socio-economic status of the adult offspring.

Data and Methods

Data from the General Social Surveys was used to compile these results.  The General Social Surveys, or GSS for short, were started in 1972 and are given out annually.  It consists of over 5300 variables and provides a cross-section of information on a variety of societal trends.  Areas covered by the GSS include race, religion, healthcare, emotions, and multiculturalism among others.  The survey has been given every year since 1972 (every other year since 1994) except for 1979, 1981, and 1992.  The core questions of the GSS center around demographic, behavioral, and attitudinal questions with other subjects (i.e.:  abortion, cultural issues, social networks, etc.) being added over the years.  The survey is conducted by the National Opinion Research Center at the University of Chicago using in-person interviews.  Twenty-six national samples have been taken (as of 2007) with over 51,000 respondents to the survey.

Starting with 1977, data from every three years was taken (or the next available year) to produce the results of the study.  The year 1977 was chosen as the initial data point because this was the first year income was adjusted for inflation allowing for a more consistent comparison of results.  Respondent income is used as the dependent variable because income among respondents provides a more level field of comparison of socioeconomic attainment by an individual.  The educational levels of both parents as well as the respondent are used as independent variables so as to analyze the effects of each on respondent income.  Age of respondent is also included because the age and experiences of an individual greatly influences his or her income earning potential.  Finally, a dummy variable was included for gender to adjust for the income difference between similarly situated males and females.

Equation

EQUATION:

Income= B0 + B1Education (M) + B2Education (F) + B3Education(R) + B4Age + B5Male + e

Empirical results:

The above chart plots the p-values for the years sampled.  For the purposes of this study a p-value at or below .05 is considered statistically significant. The results of this study should be considered preliminary at best, but they tend to support the conclusions draw by other studies.  The mother’s educational level appears to be more statistically significant than the father’s educational level.  Also, by performing a linear regression on the data, the importance of both parents educational attainment seems to increase over the last thirty years.

Conclusion

The correlation between a person’s educational level and socioeconomic status are becoming stronger and stronger.  With the research being done on parental influence on child development, it is becoming apparent that not only does the individual’s education matter but that of the parent is also paramount in a person’s development.  Studies that help us better identify both direct and indirect factors on child development can help with the allocation of resources and allow government to make more efficient decisions regarding educational and social policies.

Bibliography

Kao, G., & Tienda, M. (1998). “Educational Aspirations of Minority Youth.”  American Journal of Education, 106, 349-383.

Kirsch, I., Braun, H., & Yamamota, K. (2007).  “America’s Perfect Storm:  Three Forces Changing Our Nation’s Future.”  Princeton, NJ:  Educational Testing Service Policy Information Center, 1-27.

Cherubini, L. & Hodson, J. (August 2, 2008).  “Ontario Ministry of Education Policy and Aboriginal Learners’ Epistemologies:  A Fundamental Disconnect.”  Canadian Journal of Educational Administration and Policy.

Juslin, R. W. & Brembreg, S. (2005).  “Greater parental influence enhances educational achievement:  A Systematic Review.”  Swedish National Institute of Public Health, 03.

Davis-Kean, P. (2005).  “The Influence of Parent Education and Family Income on Child Achievement:  The Indirect Role of Parental Expectations and the Home Environment.”  Journal of Family Psychology, 19(2), 294-304.

Douglas-Hall, A. &  Chau, M. (November, 2007).  “Parents’ Low Education Leads to Low Income, Despite Full-Time Employment.”  National Center for Children in Poverty.

Yao, X., Hongbin, L., Zhang, J., & Zhou, L.  (October 2005).  “Parental childcare and children’s educational attainment:  evidence from China.”  Applied Econonmics, 37.18, 2067 (10).

Sawhill, I. & Morton, J. (2007).  “Economic Mobility:  Is the American Dream Alive and Well?”  The Pew Charitable Trusts.

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1906.054 5 381.211 49.209 .000a
Residual 5105.083 659 7.747
Total 7011.137 664
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1854.880 5 370.976 43.272 .000a
Residual 5641.048 658 8.573
Total 7495.928 663
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 2019.985 5 403.997 44.680 .000a
Residual 6989.540 773 9.042
Total 9009.525 778
a. Predictors: (Constant), if r=1 then male, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER, HIGHEST YEAR SCHOOL COMPLETED, MOTHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1723.224 5 344.645 36.091 .000a
Residual 7066.579 740 9.549
Total 8789.803 745
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1801.650 5 360.330 38.573 .000a
Residual 8201.802 878 9.341
Total 10003.452 883
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1349.928 5 269.986 34.674 .000a
Residual 6182.467 794 7.786
Total 7532.395 799
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1349.928 5 269.986 34.674 .000a
Residual 6182.467 794 7.786
Total 7532.395 799
a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 698.654 5 139.731 16.401 .000a
Residual 11475.986 1347 8.520
Total 12174.640 1352
a. Predictors: (Constant), if male r=1, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER
b. Dependent Variable: RESPONDENTS INCOME
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 691.176 4 172.794 24.791 .000a
Residual 9207.331 1321 6.970
Total 9898.507 1325
a. Predictors: (Constant), =1 if r is male, HIGHEST YEAR SCHOOL COMPLETED, FATHER, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, MOTHER
b. Dependent Variable: RESPONDENTS INCOME
Year Father’s Ed p-value Mother’s Ed p-value
1977 0.549 0.338
1980 0.506 0.672
1982 0.862 0.898
1985 0.278 0.086
1987 0.919 0.166
1993 0.499 0.092
1996 0.449 0.233
2002 0.1 0.26
2004 0.533 0.538
Variable Name Description
Education(M) mother’s highest educational level
Education(F) father’s highest educational level
Education ( R ) respondent’s highest educational level
Male  = 1 if R is male, 0 otherwise
Age respondent’s age