28th April 2014
In the lives of adolescent youth there are a plethora of factors capable of promoting or retarding their educational achievement, and socio-emotional development and functioning. Without a considerable understanding of how the complex interweaving of such factors affects adolescents, the effectiveness of educational and transition environments aimed at promoting the development of positive and productive youth becomes limited. This paper, within the limitations required of a restricted paper, aims to explore and develop valuable insights into the relationship between socio-economic status (SES) on socio-emotional function (SEF), and of substance abuse and parental involvement as it pertains to educational achievement. In the first section of the paper, each of these are examined independently through the lenses of theoretical rationale and the empirical evidence which supports these ideas. The Second section of the paper aims to identify the implications of these findings such that they relate to the teaching, learning and wellbeing of the individual, and to discuss factors which strongly influence the educational achievement and SEF of adolescent youth.
In reading this paper it is important that the reader remain aware of two key ideas. First, that the term adolescent is used in its broadest range to indicate an age range between approximately 10 and 25 years of age and second, that while each factor is interpreted in its singularity, numerous crossovers exist between educational achievement and socio-emotional functioning such that each of these factors hold the potential to directly and indirectly influence each other.
Many family background variables exhibit strong associations with success throughout school, and in young adults’ educational and occupational attainment. Such variables include family structure, parental education level, parental involvement and practices (Hanson et al. 1995). Although the contribution of factors toward education and the reproduction of SES has long been recognised (Albrecht 2010; Jencks & Mayers 1990),the reciprocal influence that it exhibits over SEF through the forms of mental illness, deviant behaviour and minor or non-psychiatric morbidity is noteworthy (Kuruvilla & Jacob 2007; Brooks-Gunn & Duncan 1997)(see table 1). It is factors such as these which collectively contribute to the SEF of adolescents.
Table: 1 The effects of poverty on children (Brooks-Gunn & Duncan 1997)
“A sudden change in an individual’s socio-economic status may result in acute and extreme distress… and [in chronic cases, a sense of] hopelessness [which] has been most consistently linked to poor socio-emotional well-being” (Kuruvilla & Jacob 2007:276). SES is commonly associated with fewer socio-emotional and intellectual resources. Sadly, despite this long standing recognition, empirical, adolescent centered studies continue to be lacking. Existent research frequently pertains to young children, under the age of 13, or at risk and mentored youth. Greater focused empirical study is therefore a crucial step in understanding and responding to the negative influence of SES on SEF.
SEF disadvantages may stem broadly from lack of parent involvement and availability, or fractious parent– child relationships resultant of parental stress, distress, and depression.Relational problems also arise from parents’ perceived isolation and lack of support (Thompson et al. 2013). Parents in such settings reported lower educational expectations, less monitoring of children’s school work and less overall supervision of social activities. Many youth programs focus on reducing socio-emotional and academic risk function on the premise, that youth from low SES homes are more prone to behavioral and academic problems (Thompson et al. 2013).
Daniel Goleman (2006), highlight the connection between SEF and intelligence and learning which can in turn be attributed to SES, creating a self-perpetuating cycle which can be difficult to break. This theoretical chain, from adolescent’s SES to SEF, is a logical series of links, but a comprehensive understand of the entire breadth of empirical support would be crucial before any informed judgment can be concluded.
The research conducted by Thompson et al. Remain while congruent with the trend toward child and at risk and mentored youth provides important relevant information. They conducted a Longitudinal Study of Family SES variables as predictors of socio-emotional resilience among mentored youth in which they explored SES and family structure as “predictors of resiliencies among at-risk youth before and after participating in schools-based mentoring programs” (Thompson et al. 2013:378). Their study consisted of twenty-four youths (13 girls) from the Maine College Community Mentoring Program (see table 2) and housed the goal to “explore how facets of poverty may predict levels of need among at-risk youth and to determine which of these facets best predicts outcomes related to youth assets” (Thompson et al. 2013:385).
Table 2: Descriptive Statistics for Parents and Children (Thompson et al. 2013)
(2) SES scores below 40 are generally considered to reflect moderate to severe poverty
Using families from a low SES range, of the Developmental Assets Profile, they explored variables and hypothesized to predict resilience-related assets as outcomes in high-risk youth. Mothers’ occupational status and the number of children supported in the home were predictors of youths’ perceptions of external assets (Thompson et al. 2013). This produced a plausibly consistent view that low job status among working mothers is a key variable in youth perceptions of support. Thompson et al. (2013)claim that their findings suggest that poverty cannot be assumed to cross configure between mother / father, and education / occupation comparisons.
Data generated supports their theoretical rationale, however the study’s small sample population prevents the use of multiple regression and requires cautious interpretation. Similarly, low SES groups, Thompson et al. (2013:388) explain, “exhibit intergenerational features that were not within the scope of the present study”. Their findings identified, and highlight the need for more empirical exploration into the relationship between ESE and EA.
Adolescence as a developmental period is uniquely marked by, among other things, the initiation and imitation of adult-like behaviors, such as substance use. Australian researcher, Rebecca Huntly (2006:155; Pocock & Clarke 2004) claims that “the cost of being different, of not belonging or keeping up with the consumer habits of your peers, is high. You risk social … alienation and personal happiness”. Adolescents may perceive smoking and other similar actions as a means of improving peer orientated self-images (Elders et al. 1994). Beamon (2001) argues that a sense of community is promoted when adolescents feel personally valued or accepted. Issue of adolescent substance use, while nothing new, remains an important public health concern that is purported to be harmful to adolescent health, brain development, and cognitive abilities (Elders et al. 1994; Jacobs & Harvey 2005).
Adolescence is unique life phase encapsulating cognitive and physical growth.It is vital that academia acknowledges this uniqueness through focused empirical studies into areas of substance use for three reasons. First, many preventative strategies rely upon the enhances perception of risk relative to substance use (Bukstein 2000; Magid & Moreland 2014; Apantaku-Olajide, James & Smyth 2014, Bauman 2001, Yan & Brocksen 2013); second, research suggests that substance use and EA are predictors of continued adulthood development (Bukstein 2000; Bauman 2001; Yan & Brocksen 2013) and finally, it underscores the associated delinquent behaviors common to social groups who exploit these substances (Sen et al. 2009).
Substance use is reported to be the primary source of learning interruptions resulting in substandard EA. While numerous benefits have been noted through adolescent tobacco usage studies analysis, such studies fundamentally note reductions in behavioural issues, and motivational improvements (Elders et al. 1994; Rattermann 2014; Bauman 2001). Identical reasoning applies to adolescent alcoholism (Cook and Moore 1993 cited Yan & Brocksen 2013).
Although a myriad of complex factors exist underscoring the importance of this issue, EA is frequently held as the underlying factor (Rattermann 2014). The research presented here links the consequences associated with teenage substance use as it relate to EA.
Yan & Brocksen (2013)analyzed data provided in the National Longitudinal Survey of Youth between the years 1997 and 2007 (NLSY97). NSY97 is a national representation of 8984 youth. Their studies provide empirical support linking the impact of substance disorder to academic achievement by charting notions of risk awareness and substance use reduction, and by identifying the effect of usage on adolescent delinquent behaviour.
Of additional importance in understanding Yan & Brocksen’s research is that they define a binge drinker, an adolescent who exceeded five drinks on a single occasion bi-monthly, while smokers were those who smoked at least once in the past month. They addressed the issue by posing two research questions, of which only the second is relevant to this factor: “(2) Does reduction in substance use, due to high risk perception among adolescents, improve their educational achievement?” (Yan & Brocksen 2013:1038).
Results attained demonstrate that with adequate risk perception, adolescents became less likely to develop substance use patterns, while simultaneously improving EA. High risk perceptions and, low alcohol and tobacco use, they relate, increased student chances of attaining college acceptance while simultaneously decreasing probabilities associated with dropping out (Yan & Brocksen 2013). This study provides evidence suggestive of enhanced risk perception toward hazards of substance use as being effective intervention policy aimed at reducing adolescent substance use and improving EA.
Research indicates a protracted, deep interest, both by society and more specifically by professionals in the education field, concerning the potential positive, or negative effect of parental involvement in their children’s education. While there is little debate concerning the influence of family socialization patternson children’s cognitive characteristics, there appears to be a general consensus that such patterns are largely constitute the production of specific attitudes, self-concept,beliefs, competence, and causal attributions. This certainty suggests the existence of a theoretical rationale which offers proof of existing relationships between parental involvement and their child’s EA.
In 1995, Epstein disinterred an extensive field of research by defining parental involvement as “families and communities who take an active role in creating a caring educational environment” and her assertions that “parents who are active in their child’s education … show good parenting skills” (Hara & Burke 1998:219). Since then, substantial research propounds the correlation between parental involvement and positive EA. Hoge, Smit, and Crist (1997) also defined parental involvement, which they noted as encompassing the four components of parental expectations, interest, school involvement, and family community. Hara & Burke (1998) contend that increased parental involvement remains the key to improving children’s EA, such that it holds across all levels of parental education, ethnicity, and locale.
While strong empirical support links direct parental involvement to increased EA, it remains equally important to contemplate the limitations. Key issues lie in the fact that much of the information concerning parental involvement is obtained from student responses to questionnaires. Caution is therefore suggest noted since this information may relate a perceived parental involvement rather than actual involvement (as in other works, i.e., Martinez-Pons, 1996 noted Gonzalez et al. 2002).
Despite the multitude of appealingly overlapping links, the theoretical chain which directly links the involvement of parents and their children’s overall educational achievement is cemented firmly in the literature. The more parental involvement, the more positive was children’s academic self-concept, and vice versa.
Using the National Education Longitudinal Survey (NELS) data sets for the years 1990 and 1992, Jeynes (2005) assessed the effects of three aspects of parental involvement and family structure on the academic achievement of children. This data consisted of 18,726 students who responded to survey. Table 3 details specific population data. The study was structured with the intent of determining 3 purposes. Within the grounds of this paper only purpose 1 will be mentioned, that is, “to determine the impact of parental family structure and other aspects of parental involvement on children’s academic achievement” (Jeynes 2005:104). Jeynes recorded a positive result indicative of parental involvement being associated with higher adolescent academic achievement, under the conditions where gender, race, and socioeconomic status are controlled for.
|Student Sample||Parental Education|
|Caucasian||69%||4 year degree||26%|
|Hispanic||13%||High school Dip.||89%|
|Asian||6%||Median family income||40,00-50,000|
Table: 3 Demographic data (Jeynes 2005)
In similar studies conducted by Gonzalez et al. (2002:257), they hypothesized that “the influence of parental involvement on students aptitudes, self-confidence, and casual attributions, as well as of the 3 variables of academic achievement”. Results from their group of 503, 12- to 18 year-old Spanish students (Table 4). Using structural equation modelling to initially deduce the relationship between parental involvement, student’s altitudinal and motivational characteristic, and academic achievement. They determined that the results derived clearly supported the thesis that parental involvement behaviours significantly affect children’s academic achievement via the influence of these behaviours on personal variables, such as their self-concept and self-esteem as students, their typical causal-attribution patterns in specific academic success and failure situations, and their altitudinal competence for academic learning (Figure 1). They maintain that their results “coincide with Patrikakou (1996) [and] … are similar to those obtained by other researchers” (Gonzalez et al. 2002:276-277)
|Semiurban elementary schools (n=163)||Urban high schools (n=340)|
|7th Year||92||18.3%||1st Year||137||27.2%|
|8th Year||71||14.1%||2nd Year||122||24.3%|
Table: 4 Demographic Data (Gonzalez et al. 2002)
Figure: 1 Path coefficients of critical causal paths for latent variable model (Gonzalez et al. 2002)
This empirical evidence supports the hypothesis proposed by various researchers that, the more involved parents are in their children’s education the greater the level of academic achievement exhibited.
A number of implications emerge from the above analysis, affecting teaching, learning and student wellbeing at micro and macro levels. Student achievement is often the most important factor being considered when school leaders and administrators are making their school improvement plans and deciding how to allocate their resources.
Issues 1: Educators should be aware that, regardless of location, that there are public policy implications that focus on family SES, the chronic stress of poverty, self-esteem and depression during the transition to adulthood. Public policy initiatives, social welfare programs and medical interventions not only target the reduction of poverty duration and improve access to mental health treatment but also aim to prevent poverty and mental illness early in the life course. Should this become an issue it is important that educators understand the relative information. In this way it becomes possible to adapt to programs and gain an understanding of the issues that both the student and their family endure.
Issues 2: There are a number of implications that must be taken into account by educators dealing with adolescent students with issues of substance use. The first is the known problems associated with health, mental illness and emotional issues. Secondly it is also important to be aware that in the case any form of treatment administered to adolescents, the action, maintenance and termination stages are not likely to be present (Beauman 2001). The unfortunate problem is the many adolescent students do not want to hear about what authority figures wish to relate, when it comes to the issue of drugs. A final issue for educators to overcome is that while there is plentiful information, that is easily available, not all of it is accurate. Trying to even relay correct material to students who only hear what they want to hears is in itself a challenge.
Issues 3: This issue is more difficult to address in the positive since it is clear through the data related and the breadth of empirical material available that parental involvement makes a difference. So to address this in the negative, it becomes an issue relative to teaching and learning when students do not get the support needed from parents. This acts both directly and indirectly on the achievement of the student. Directly through the work that is produced and indirectly through SEF which is connected directly to their EA. For example the value of a student asking a question is likely to increase both 1. Behaviors that are usually valued and 2 greater success. Often this is then returned as some form of positive reinforcement creating a cycle. Parental modeling of varied school-relevant behaviors is likely to convey a message that the parent value of the time and attention that must be devoted to school work. This creates a powerful motivator for students to imitate adult based behavior, resulting in the child’s sense of increased importance in school
This paper has explored the effect of SES on adolescent SEF, and the effect of both substance use and parental involvement on EA. Each of the factors outlined speak to the educational, social wellbeing, and achievement of adolescent students. While the combination of theoretical rationales and empirical studies applied during the analyses in support of these factors remains sound, alternate research further qualifies or disputes their findings. Following the factorial outline a brief discussion considered the implications toward teaching and learning. This paper has shown that while focused areas of research are still required, correlations can be elucidated from similar internationally relevant data sets, to produce material that is significant in determining possible courses of action for today’s adolescents.
Albrecht, C.M. & Albrecht, D.E (2010). Social Status, Adolescent Behavior, and Educational Attainment, Sociological Spectrum: Mid- South Sociological Association, 31:1, 114-137.
Apantaku-Olajide, T., James, P.D & Smyth, B.P (2014). Association of Educational Attainment and Adolescent Substance Use Disorder in a Clinical Sample, Journal of Child & Adolescent Substance Abuse, 23:3, 169-176.
Beaumon, G.B. (2001). Teaching with Adolescent Learning in mind. Arlington Heights, Illinois: Skylight Professional Development.
Bauman, S., Merta, R.J. & Steiner, R. (2001). The Development of a Measure of Motivation to Change in Adolescent Substance Users: Preliminary Findings, Journal of Child & Adolescent Substance Abuse, 11:2, 19-39.
Brooks-Gunn, J. & Duncan, G.J. (1997). The effects of poverty on children. Children and Poverty, vol.7 No.2, 55-71.
Bukstein, O.G. (2000). Disruptive Behavior Disorders and Substance Use Disorders in Adolescents, Journal of Psychoactive Drugs, 32:1, 67-79.
Elders, M., C. Perry, M. Eriksen, and G. Giovino. (1994). The Report of the Surgeon General: Preventing Tobacco Use Among Young People. American Journal of Public Health 84: 543–547. Retrieved from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1614776/?page=1
Goleman, D. (1995). Emotional Intelligence: Why it can matter more than IQ. New York, New York: Bantom books.
Gonzalez-pienda, J.A., Nunez, J.C., Gonzalez-pumariega, S., Alvarez, L., Roces, C. & Garcia,M. (2002). A Structural Equation Model of Parental Involvement, Motivational and Aptitudinal Characteristics, and Academic Achievement, The Journal of Experimental Education, 70:3, 257-287.
Hanson, T.L., McLanahan, S.S. & Thomson, E. (1995) Economic resources, Parental practices, and child well-being, Office of Population Research, working paper No.95-9
Hara, S. R. (1998). Parent involvement: The key to improved student achievement. School Community Journal, 8, 9-19.
Hoge, D. R., Smit, E., & Crist, J. T. (1997). Four family process factors predicting academic achievement for sixth and seventh grade. Educational Research Quarterly, 21, 27-42.
Huntly, R. (2006). The world according to Y, Sydney, Australia: Allen & Unwin.
Kuruvilla, A. & Jacob, K.S. (2007). Poverty, social stress & mental health Indian Journal of Medical Research 126, 273-278.
Magid, V. & Moreland, A.D. (2014). The Role of Substance Use Initiation in Adolescent Development of Subsequent Substance-Related Problems, Journal of Child & Adolescent Substance Abuse, 23:2, 78-86.
Pocock, B. & Clarke, J. (2004). Can’t buy me love? Young Australians views on parental work, time, guilt and their own consumption, discussion paper No 61. Canberra, Australia: The Australian Institute,.
Thompson, R.B., Corsello, M., McReynolds, S. & Conklin-Powers, B. (2013). A Longitudinal Study of Family Socioeconomic Status (SES) Variables as Predictors ofSocio-Emotional Resilience Among Mentored Youth, Mentoring & Tutoring: Partnership in Learning,21:4, 378-391.
Jacobs, N. & Harvey, D. (2005). Do parents make a difference to children’s academic achievement? Differences between parents of higher and lower achieving students, Educational Studies, 31:4, 431-448.
Jencks, C & Mayers, S.E (1990). The Consequences of Growing Up in a Poor Neighborhood. In Lynn, L.E & McGeary, M.G.H. (Eds.), Inner-City Poverty in the United States, Chapter 4 pp. 111-186. Retrieved from: http://www.nap.edu/openbook.php?record_id=1539&page=111
Jeynes, W.H. (2005) Effects of Parental Involvement and Family Structure on the Academic Achievement of Adolescents, Marriage & Family Review, 37:3, 99-116.
Rattermann, M.J. (2014). Measuring the impact of substance abuse on student academic achievement and academic growth, Advances in School Mental Health Promotion, 7:2, 123-135.
Sen, B., Averett, S., Argys, L. & Rees, D.I. (2009). The effect of substance use on the delinquent behaviour of adolescents, Applied Economics Letters, 16:17, 1721-1729.
Yan, J. & Brocksen, S. (2013). Adolescent risk perception, substance use, and educational attainment, Journal of Risk Research, 16:8, 1037-1055.