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Academic Leadership: The Online Journal Academic Leadership: The Online Journal
Volume 9
Issue 1
Winter 2011
Article 38
1-1-2011
Parents’ Education, Occupation and Real Mother’s Age as Parents’ Education, Occupation and Real Mother’s Age as
Predictors of Students’ Achievement in Mathematics in Some Predictors of Students’ Achievement in Mathematics in Some
Selected Secondary Schools in Ogun State, Nigeria. Selected Secondary Schools in Ogun State, Nigeria.
K.O. Muraina
Kassim Ajayi
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Muraina, K.O. and Ajayi, Kassim (2011) "Parents’ Education, Occupation and Real Mother’s Age as
Predictors of Students’ Achievement in Mathematics in Some Selected Secondary Schools in Ogun State,
Nigeria.,"
Academic Leadership: The Online Journal
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Academic Leadership Journal
Parents’ Education, Occupation and Real Mother’s Age as
Predictors of Students’ Achievement in Mathematics in Some
Selected Secondary Schools in Ogun State, Nigeria.
Issues: Winter 2011 - Volume 9 Issue 1
Posted On 2011-03-24 06:59:00
Author(s): K.O. Muraina and Kassim Ajayi
Background to the study
The importance of Mathematics transcends all definitions and the prosperity of any country depends on
the volume and quality of Mathematics offered in its school system. Obe (1996) conceptualises
Mathematics as the master and servant of most disciplines and thus, a source of enlightenment and
understanding of the universe. He further opines that without it, the understanding of national problems
would be superficial. Graeber and Weisman (1995) agree that Mathematics helps the individual to
understand his/her environment and to give accurate account of the physical phenomena around
him/her. To this end, Setidisho (2001) submits that no other subject forms a strong binding force
among various branches of science as Mathematics, and without it, knowledge of the sciences often
remains superficial.
Emphasising the importance of the subject to the society, Robert (1987) stated that in the United
States, Mathematics has come to play important roles: in the engineering of highways, the search for
energy, the designing of television sets, the profitable operation of most businesses, astronauts flying
space-craft, the study of epidemics, the navigation of ships at sea, etc., all depend on the study of
Mathematics. Ogunbanjo (1998) opines that all over the world, science has been accepted as a vehicle
of technology, social and economic development. Mathematics is not only basic to these but is the
language of science. In another related study, Igbokwe (2003) highlights the intricate link of
Mathematics to science and technology, and contends that without Mathematics there will be no
science and without science there will be no technology, and without technology there will be no modern
society. These and many more reasons are why the Nigerian government believed that the subject
should be taken seriously in our school system; and Nigeria, in her march towards technological
development, has not only made Mathematics a compulsory subject in the curriculum of the primary and
secondary school levels of her educational system (Federal Republic of Nigeria, 2004) but also as a
prerequisite to the study of science courses in her colleges, polytechnics and universities (JAMB
Brochure 1992-2007).
However, many academically capable students prematurely restrict their educational and career
options by discontinuing their mathematical learning early in the high school. The poor results in this
subject have continued to be stumbling-blocks in the realisation of the educational and employment
desire of many candidates because it is a gatekeeper for many careers. What then could be
responsible for this poor performance despite its recognition in the society and various efforts made by
the Federal Government of Nigeria since the inception of the new policy on education? This is one of
the questions to be answered in this study.
Over the years, the investigations of the factors that affected academic achievement of students in
Mathematics have attracted the interest and concern of teachers, psychologists, researchers, parents
and school administrators in Nigeria (Sogbetan, 1981). This is because of the public outcries
concerning the poor performance of students in Mathematics in the country (Igbokwe, 2003). Some of
the factors identified are low socio-economic status of the family, students’ attitude, poor family
structure, poor study habit, intellectual ability, parents’ education, income and occupation as well as the
age of the mother at the birth of the child (Sogbetan, 1981; Hassan, 1983; Maple and Stage, 1991;
Steinberg, 1993; Brooks-Gunn and Chase-Lansdale, 2001). Emeke (1984) has attributed the cause of
poor academic performance to a combination of personal and institutional factors. Personal factors
relate to the individual’s intelligence, knowledge and ability while the institutional factors are family or
parental influences, societal influences and school related factors among others. Ajila and Olutola
(2000) categorise problems responsible for students’ poor performance as their environment, which
include availability of suitable learning environment, adequacy of educational infrastructure like
textbooks and society at large among others.
This study would only be restricted to variables like parents’ education, occupation and real mother’s
age as the factors affecting the academic achievement of students in Mathematics while other
variables will not be considered in this study because of the limited time the study have. Socio-
economic status like parents’ education, occupation, income and standard of living have shown to be
related to students’ outcomes, such that students from middle to upper class families tend to
outperform those from less advantaged background (Jaffe, 1985; Rani, 1998; Simon, 2004).
However, the most important effect of socio-economic pressure is that it generally makes parents less
available to support and encourage their children in their schooling (Baker and Sodem, 1997). Also,
literatures reveal that the home background variables have a great influence on the students’
psychological, emotional, social and economic state (Onocha, 1985; Crane, 1993; Rani, 1998; Dubey,
1999; Mitchell, 1999; Musgrave, 2000; Neil and Keddie 2001; Grissmer, 2003; Teese, 2004; Sharma,
2004). This means the family background and context of a child affect his/her reaction to life situations
and level of performance. Thus, Ichado (1998) concludes that the environment from which a student
comes can greatly influence his/her performance in school. The family lays the psychological and moral
foundations in the overall development of the child while the mother’s significant role in this cannot be
overemphasised (Agulanna, 1999).
There is evidence that parents’ education will affect students’ academic achievement in Mathematics.
According to Grissmer (2003), parents’ level of education is the most important factor affecting
students’ academic achievement. Taiwo (1993) submits that parents’ educational background
influence the academic achievement of students. Musgrave (2000) states that a child that comes from
an educated home would like to follow the steps of his/her family and by this, work actively in his/her
studies. Onocha (1985) concludes that a child from a well educated family with high socio-economic
status is more likely to perform better than a child from an illiterate family. Similar results were found by
Teese (2004), in his analysis of the students’ performance where he found clear and consistent trends
for children from lower socio-economic background. Coleman (1998) state that the relationship
between socio-economic disadvantage and learning outcomes has been accepted almost as an
article of faith by educators. This was supported by the Children’s Defence Fund (1995) “Year Book” on
the State of America’s children which made the following observations:
In 1993, there were 15.7 million poor children in the United States. This was the highest number
in 30 years;
The inflation-adjusted median income of young families with children declined to 34% between
1973 and 1992;
In 1992, 66.2% of the children who lived in a family headed by a person who dropped out of
school were in poverty. Poverty rates for other levels of education were as follows: high school
graduates, 40.2%; some college graduates, 22.4%; and university graduates, 7.5%.
In 1993, almost one in every seven children, 9.4 million, had no health insurance. This
represented an increase of 800,000 from 1992; and
The birth rate among unmarried teens was 15.5 births per 1,000 in 1959. The figure in 1992
was 44.6.
From the above “Year Book”, the economic factor which refers to family characteristic is the most
powerful predictor of school performance. Careful consideration of the socio-economic status of
parents reveals that the higher the standard of living of the parents, the higher the academic
performance of the child. These relationships have been documented in countless studies and seem to
hold, no matter what measure of status is used (occupation of principal bread winner, family income,
parents’ education or a combination of these).
Researchers have shown that family’s socio-economic status is based on parents’ income, education
and occupation. Thus, a family with high socio-economic status is often more successful in preparing
its young children for school because they typically have access to a wide range of resources to
promote and support their development. They are able to provide their young children with high quality
child care, books and toys to encourage them in various learning activities at home. This in turn, will
affect the students’ academic achievement in Mathematics. According to Marjoribanks (2003), the high
achievers had a high socio-economic status and they hailed from highly educated families. Lockheed,
Fuller and Nyirongo (1989) show that students belonging to upper socio-economic status groups
showed better academic achievement than students belonging to lower socio-economic status groups.
With reference to achievement in Mathematics, Howley (1989) and House (2002) contend that students
learn better if they are from above average or average income family, with well-educated parents who
participate in the school’s education process and encourage their children to learn. They established
that the socio-economic status of students affected their achievement.
For families in poverty, basic necessities are lacking, parents may place top priority on housing,
clothing and health care. Educational toys, games and books may appear to be luxuries. This point was
supported by Bookcock (2000) and Lloyd (2002) on the relationship between school performance and
parental socio-economic condition where they conclude that students with high achievement values
tend to come from families that are more educated and with higher status of occupation.
Again, literature revealed that the age at which a mother gives birth to their young ones affect their
academic performance either positively or negatively. According to Moore (1993), income, family size
and the mother’s age at child birth were modestly related to students’ academic achievement. This
implies that early age or old age has its significance in students’ academic achievement. Hayes and
Bronzaft (2006) contend that factors such as the mother’s age at birth of the child, number of siblings,
genetics and environment have more to do with academic achievement. Moore (1993) opines that
early birth has been disadvantageous to a young mother’s children as well as the woman herself. One
key reason is that early childbearing interferes with the process of schooling and human capital
development which means that the mother’s ability to gather resources will be reduced. She is
therefore likely to be poorer than a woman who delays childbearing. For this reason, he concludes that
the age at first childbirth may prevent a teenage mother from providing resources that promote
cognitive development, such as a high-quality child-care arrangement and a stimulating home
environment that can improve a child’s academic performance especially in Mathematics.
William and Chelser (2005) view the mother as the first child educator and the age at which she gives
birth to the child matters in her life. This allows her to have a stable or unstable mind which affects the
mother’s instinct and love towards the child. Ninio (1979) and Benjamin (1993) observe that mother’s
age enhances the cognitive development of her child. According to Brooks-Gunn and Chase- Lansdale
(2001), young mothers are socially and emotionally immature; we would expect them to have limited
parenting ability. They said further that coping with the demands of an infant is likely to be far more
challenging for a teenager than for an older woman. Inconsistent and arbitrary discipline which is more
common among young mothers, has a negative impact on children’s behaviour and on their social and
emotional development. As a result, Brooks-Gunn and Chase-Lansdale (2001), expect a young age at
first birth to adversely affect children’s social and emotional adjustment. Even if a teenage mother has
additional children when she is older, she may continue the patterns of parenting she established with
her first child. Teenage mothers also tend to provide their children with less cognitive stimulation and
less emotional support than do older mothers.
Psychological factors may also be involved since many teenage pregnancies are unplanned, unwanted
or discovered late. A pregnant teenager may lack the emotional maturity to take responsibility for a
pregnancy even after she has decided to carry it to term. All these affect the composition of the child’s
brain which eventually affects the academic performance of the child. William and Decoufle (1999)
asserted that teenage pregnancy is a multifaceted problem with no single cause or cure. According to
them, for a teenager, pregnancy comes at a time when her physical development is incomplete and
available support systems may be limited. Again, pregnancy interrupts her education and makes it
tremendously difficult for her to complete the developmental tasks of adolescence as well as those
related to pregnancy and parenthood; all these affect the academic achievement of the child.
Brooks-Gunn and Chase-Lansdale (2001) compared the birth outcomes of teenage mothers and older
mothers and concludes that the teenage mothers are most likely to be at risk both biologically and
socially for poor birth outcomes. This is because the older mothers are more likely to be married and to
have a wanted pregnancy which makes them psychologically balance than the teenage mothers who
have unplanned pregnancy and who are likely to be undereducated or live in areas with limited access
to resources and services. In another related study, Rothenberg and Varga (1981) found that scores on
a global measure of parenting were lower for the homes of children of teenage parents than for the
homes of other children.
Delayed childbearing poses its own biological risks, such as an increased likelihood of medical
conditions like hypertension and diabetes which in turn may affect the brain composition of the child
leading to congenital aberrations like hydrocephaly (mental retardation resulting from accumulation of
fluid in the brain); microcephaly (mental retardation associated with a small skull and brain) and down’s
syndrome which are common with mothers over 35years especially when the mother has not borne her
first child at that age and these may eventually affect the child’s academic achievement. They therefore
concluded that psychologically, the best time to have a child is probably between the ages 22 and 29.
Several studies (Frazer, Brockert and Ward, 2004; Lee, Ferguson and Corpuz, 1988; Wadsworth,
Osborn and Taylor, 1984) indicate that young age by itself is not a risk factor for poor outcome of
children from young mothers, but those young mothers who are from lower socioeconomic
backgrounds may eventually leave their children in the hands of grand mothers who do not understand
much about education. Thus, Brooks-Gunn and Fursternberg (1986) concurred that young mother is at
higher risk for social and economic disadvantages than her teenage counterpart who is not pregnant
and lives in the same social environment. According to them, being forced into adult roles before
completing adolescent developmental tasks cause a series of events that affects the teenage mother’s
entire life. These events may result in a prolonged dependence on parents, lack of stable relationships,
and lack of economic and social stability. In addition, many teenage mothers drop out of school during
their pregnancy. This tendency may have as much to do with low academic achievement and low
academic commitment as it does with the pregnancy. This is because many teenage mothers never
completed their education and lack of education reduces the quality of jobs available to these
individuals which in turn affects the academic achievement of their child.
Frazer, Brockert and Ward, (2004) asserted that childbearing at an early age is a strong predictor for
need for assistance, especially in lower socioeconomic groups and when the pregnant teenage family
will not support her. Schooling is critical to a young woman’s prospects throughout her life and the
amount of schooling a woman obtains affects her occupation, her income, her chances of marriage, her
risk of poverty and welfare dependence, and more generally, the quality of her own life and that of her
children. Failure to be self-supporting logically follows lack of education and lost of career goals. In
general, children of teenage mothers are found to be at a developmental disadvantage compared to
children whose mothers were older at the time of their birth.
Statement of the Problem
Observations and reports from examining bodies like WAEC, NECO and JAMB revealed that a high
percentage of secondary school students continue to perform poorly in Mathematics examinations.
This poor performance is likely to be caused by some factors such as age of the mother at birth of the
child, parents’ education or parents’ occupation. As a result of these factors, this study sought to
investigate the extent to which these variables determine the Mathematics achievement of secondary
school students in Ogun State, Nigeria.
Research Hypothesis
There is no joint and relative effect of parents’ education, occupation and real mother’s age on
students’ academic achievement in Mathematics in Ogun State, Nigeria.
Significance of the Study
The outcome of this study is significant to researchers in that it provides additional empirical data for a
better understanding of some of the factors that account for different levels of students’ performance in
Mathematics. More importantly, this study differs from related studies in that it adds real mother’s age
to the study of Mathematics.
Methodology
Research Design
The study is a non-experimental type and an ex-post facto research design was adopted.
Population
The target population for this study comprised all the senior secondary school one students (SSS 1) in
Ogun State. The choice of this group of students was based on the assumption that majority of the
students would be able to read printed materials with little assistance from the researchers and that the
students would have completed their course contents in Junior Secondary School three (JSS 3)
Mathematics curriculum. This was considered important because the achievement test will reflect the
JSS 3 Mathematics curriculum as a unit.
Population Sample
The sample of the study was selected using the multi-stage sampling procedure. At the first stage, nine
local government areas were purposively selected from twenty local government areas in Ogun State.
At the second stage, the stratified random sampling technique was used to select a total of 60 senior
secondary schools from 147 senior secondary schools in the 9 LGAs selected in Ogun State, Nigeria
and this represented a total of 40 per cent of the entire schools in the nine local government areas
selected. At the third stage, simple random sampling technique was employed to select a total of 40
SS1 comprising male and female students from each of the participating schools. Altogether, a total of
nine local government areas, 60 schools and 2,400 students were involved in the study.
Instrumentation
In order to collect data and provide answers to the research hypothesis, Students’ Questionnaire (SQ)
and Students’ Mathematics Achievement Test (SMAT) research instruments were developed and
employed by the researchers in gathering data. Under the students’ questionnaire (SQ) instrument, the
researchers created three sections for measuring variables that related to the students. These are: (a)
Demographic Data; (b) Parents’ Qualification and (c) Parents’ Occupation.
Demographic Data: The demographic data questionnaire was designed to collect information from
the students on the following items: (i) School name; (ii) Type of school;
(iii) School location; (iv) Gender; (v) Age; (vi) Class; and
(vii) Mother’s age at the birth of the child.
The item which addressed the issue of mother’s age at birth of the child was calculated by subtracting
the age of the child from the age of the mother to get the real age of the mother at the birth of the child.
The mother’s age was grouped into three categories-young teenage mother (age 11-19 years), mid-
age group (ages 20-35) and older mothers (ages above 35 years).
Parents’ Qualification: The students were asked to provide only the highest qualification of their
parents. The issue of parents’ qualification was given a score ranging from zero (0) to 11, where 0 was
awarded to no schooling and 11 to Ph.D. parent. Thus, a minimum score of zero and a maximum score
of 22 was given to the parents.
Parents’ Occupation: This section obtained information about the occupational levels of the parents
of each student. The parents’ occupations were given scores from one to seven, where one was
awarded to parents that were farmers and seven was awarded to parents that were in professional and
managerial occupations, thus giving a minimum score of two and a maximum score of fourteen.
Students’ Mathematics Achievement Test (SMAT): To measure the achievement of students in the
subject, a Mathematics Achievement Test (MAT) was developed by the researchers using a table of
specifications to generate 20 items out of the 50 items formerly prepared for students’ Mathematics
tasks. This is a multiple-choice objective test, made up of 40 items with four options A, B, C and D.
Each item has one correct option (the key) and three distracters. The correct option attracts 1 mark and
the total mark obtainable is 20. Kuder Richardson formula 20 was used to establish the internal
consistency of the instrument.
Validity of the Instruments: For the purpose of this study, both the face and content validity of the
instruments were ensured. To ensure validity of the instruments, the initial drafts of the instruments were
scrutinised by four experts in questionnaire and content construction who were required to check for all
non-technical flaws in the instruments. Such inputs enhanced a thorough validation in order to ensure
that the instruments actually measured what they were intended to measure in relation to the research
hypothesis. Based on the suggestions and comments of these experts, the necessary corrections were
made and the final version of the instruments was trial tested on a sample of 50 students who were not
part of the real study sample, in Ijebu-Ode LGA of Ogun State, Nigeria. The data collected showed that
the students did not have problems responding to the items in the questionnaire.
Reliability of the Instruments: In computing the reliability of this research instruments, Cronbach’s
alpha (a) was utilised in estimating the reliability coefficient. The scores for each item were encoded in
SPSS software. The Cronbach alpha reliability of the instruments was established as SQ = 0.87 while
the reliability of the test was estimated as 0.84. The construct, content and criterion related validities
were found to be adequate.
Data Collection Procedure
The necessary data for this study were obtained from students of the selected schools in the selected
local government areas. After collection of data, questionnaire responses without corresponding
responses to achievement tests were discarded. The idea was to have complete sets of the students’
related instruments. 2,400 copies of the questionnaire were distributed to the selected students in the
60 schools in the 9 local government areas and a total of 2,317 (96.5%) questionnaire were returned,
among which 366 (15.3%) badly filled ones were discarded. A total of 1951 (81.2%) questionnaire, fully
responded to, were utilized and data collection lasted for 28 working days.
Results
Ho1:There is no joint and relative effect of parents’ education, occupation and real mother’s age on
students’ academic achievement in Mathematics in Ogun State, Nigeria.
Table 1 shows R (0.066) with adjusted R2 (0.003) which shows that only 0.3% not up to 1.0% of the
variance in academic achievement is jointly accounted for by the three independent variables. The f-
value (2.875) which is significant at 0.05, (p<0.05) indicates that though the percentage joint effect is
small, the effect is still significant on academic achievement.
The beta values – 0.046, 0.030 and -0.008 for parents’ education, parents occupation and real
mother’s age respectively shows that parents’ education has the highest effect or predicts students’
academic achievement most, followed by parents’ occupation and real mother’s age the least. But
while parents’ occupation has positive effect on students’ academic achievement, both parents’
education and real mother’s age have negative effect on it. None of the variables has significant
relative effect on achievement.
Discussion
The result in Table 1 reveals that parents’ education has significant influence on the academic
achievement of students in Mathematics. This is because parents’ education has highest effect or
predicts students’ academic achievement in Mathematics most. This observation provides the
evidence that students of educated parents might performed better than students of uneducated
parents in Mathematics achievement. The findings lend support to the results of Onocha (1985),
Carlson (1997), Musgrave (2000) and Grissmer (2003) which reported that parents’ level of education
was the most important factor affecting students’ academic achievement.
The results in Table 1 also reveal that parents’ occupation is next to parents’ education that predicts
academic achievement in Mathematics. The result provide evidence that students whose parents
belong to the high ranking occupational status might a better grade in Mathematics than their
counterparts whose parents belong to the low ranking occupational status. This is because parents with
high ranking occupational status might have enough income which can be used to provide the needed
materials and support for their children in order to arouse their interest in Mathematics than their
counterparts in low ranking occupation whose major obligation is to provide shelter and food for the
family. The findings was supported by that of Jaffe (1985), Rain (1998), Simon (2004), Teese (2004),
Sharma (2004), Dubey (1999) and Crane (1993).
With respect to the effect of age of mother at birth of the child and the child’s academic achievement in
Mathematics; Table 1 shows that the effect of mother’s age on students’ academic achievement in
Mathematics had the least effect among the home background variables which exerted significant
effects on students’ academic achievement in Mathematics. Additionally, the result shows that the
effect of mother’s age at birth of the child and the child’s academic achievement in Mathematics is very
low and this indicates that in the face of other home background variables, its effects are very minimal.
The implication of this however is that the age at which a mother gives birth to her child may contribute
to low maternal education, unmarried status and/or poverty factor with known, large, negative effects on
educational disabilities. This finding was supported by that of Moore (1993), Brook-Gun and Chase-
Lansdale (2001), Hayes and Bronzaft (2006), William and Decoufle (1999), Frazer, Brockert and Ward
(2004) all agreed that maternal age does not directly influence the achievement of students in
Mathematics, it may have an indirect effect through the intermediate socio-demographic factors such
as maternal education.
Summary of Findings
The major findings are summarised below:
(i). When the predictor variables; age of mother at birth of the child, parents’ education and
occupation are taken together, they effectively predicted the academic achievement of students in
Mathematics.
(ii). The variable, parents’ education was the most potent predictor of students’ achievement in
Mathematics while parents’ occupation, and age of mother at birth of the child in a decreasing order of
magnitude, made significant contributions to the prediction of students’ academic achievement in
Mathematics.
Recommendation
From the findings of this study, the following recommendations were reached:
i. Given that the present study is limited to senior secondary schools in Ogun State, similar studies
could be carried out in other parts of the country to affirm or refute the conclusion reached.
ii. Since parents education influences students academic achievement in Mathematics, the
government and all stakeholders in education sector should endeavour to implement its policy on basic
education for all and thus create an enlighten society in which every parent would be educated enough
to have a positive influence on their children especially in their attitude towards Mathematics which in
turn would lead to better achievement in the subject.
REFERENCES
Agulanna, G. G. 1999. Family Structure and Prevalence of Behavioural Problems among Nigerian
Adolescents. The Counselor, 17(1), 154-159.
Ajila, C. and Olutola, A. 2000. Impact of Parents’ Socio-economic Status on University Students’
Academic Performance. Ife, Journal of Educational Studies, 7(1), 31 – 39.
Baker, A. J. L. and Sodem, L. M. 1997. Parent Involvement in Children’s Education: A critical
assessment of the knowledge base. (Report No. PS-025357). Paper Presented at the Annual
Meeting of the American Educational Research Association, Chicago, IL. (ERIC Document
Reproduction Service No. ED407127).
Benjamin, L. A. 1993. Parents’ Literacy and their Children’s Success in School: Education Research
Report, Washington, DC.
Bookcock, O. J. 2000. Relationship between Academic Expectations and School Attainment of
Children. Sociology of Education, 96, 174 – 188.
Brooks-Gunn J. and Chase-Lansdale P. L. 2001. Adolescent Parenting: In M. Bornstein, (Ed.) Vol. 3:
Handbook of Parenting, Status and Social Conditions of Parenting. International Journal of Lawrence
Erlbanm and Associates.
Brooks-Gunn, J and Fursternberg, F. F. 1986. The Children of Adolescent Mothers – Physical,
Academic and Psychological Outcomes. Journal of Population Development Review, 6, 224 – 251.
Children’s Defence Fund. 1995. The State of America’s Children: Year Book, Washington, D.C.
Coleman, L. J. 1998. The Ability of African Children to Assimilate the Teaching of Science. Journal of
Science Education in African, 34(2) 77- 86.
Crane, J. 1991. The Epidemic Theory of Ghettos and Neighbourhood Effects on Dropping Out and
Teenage Childbearing. American Journal of Sociology, 96, 1226 – 1256.
Dubey, K. R. 1999. Effects of Home Environment on the Mental Development of Children. Journal of
Social Psychology, 174, 221 – 236.
Emeke, E. A. 1984. Relationship between Personal Problems and Study Habits. Journal of Applied
Psychology, 3, 113 – 129.
Federal Republic of Nigeria FRN, 2004. National Policy on Education. Lagos. NERC Press.
Frazer, A. M., Brockert, J. E. and Ward, R. H. 2004. Association of Young Maternal Age with Adverse
Reproductive Outcomes. NEngl J Med, 332, 1113 – 1117.
Graeber, P. and Weisman, C. Y. 1995. School Science and Mathematics. Journal for Research in
Mathematics Education, 7, 324 – 336.
Grissmer, R. H. 2003. Beyond Helping with Homework: Parents and Children Doing Mathematics at
Home. Teaching Children Mathematics, 14, 120 – 131.
Hassan, T. 1983. Psychosocial Predictors of Academic Achievement. Psychology of Everyday
Living, 2(2), 155 – 159.
Hayes, R. F. and Bronzaft, A. L. 2006. Birth Order and Related Variables in an Academically Elite
Sample. Journal of Individual Psychology, 135, 214-231.
House, W. H. 2002. Educational Environment, Social Class and Cognitive Development. Journal of
Social Psychology, 68(2), 160 – 167.
Howley, G. 1989. The Motivation Factor: A Theory of Personal Investment. Journal of Personality and
Social Psychology, 44, 103 – 121.
Ichado, S. M. 1998. Impact of Broken Home on Academic Performance of Secondary School
Students in English Language. Journal of Research in Counseling Psychology, 4 (1), 84 – 87.
Igbokwe, D. I. 2003. An Assessment of the Foundation for a Sustainable Scientific and Technological
Development in Nigeria. Journal of Issues on Mathematics. Vol. 6(1) 18-30.
Jaffe, B. D. 1985. The Relationship between two Aspects of Socio-Economic Disadvantage and the
School Success of 8th Grade Negro Students in a Detroit Junior High School. Doctoral Dissertation,
Wayne State University.
Lloyd, A. K. 2002. Factors Influencing Academic Achievement of Students in Science and
Mathematics. Journal of Educational Psychology, 102, 56 – 71.
Lockheed, M. E.; Fuller, B and Nyirongo, L. 1989. Family Effects on Students’ Achievement in
Thailand and Malawi. Journal of Sociology of Education, 62, 239 – 256.
Maple, S. A. and Stage, F. K. 1991. Influences of the Choice of Mathematics and Science Major by
Gender and Ethnicity. American Educational Research Journal, 28, 37 – 60.
Marjoribanks, K. 2003. Birth Order, Family Environment, Academic and Affective Outcomes. Journal
of Psychological Reports, 92, 1284 – 1298.
Mitchell, D. 1999. Neighbourhood Effects and Community Spillover in the Australian Youth Labour
Market. Journal of Australian Council for Education Research, 24(1), 8 – 18.
Moore, K. A. 1993. Age at First birth and Later Poverty. Journal of Research on Adolescence, 3(4),
393 – 422.
Musgrave, C. B. 2000. Environmental Factors Affecting Attitude towards Science and Mathematics.
Journal of Educational Psychology, 91(1) 382 – 394.
Neil, S. R and Keddie P. V. 2001. Synchronies in Mental Development: An epigenetic perspective.
Journal of Science Education. 202,439-457
Ninio, A. 1979. The Native theory of the Infant and Other Maternal Attitude in Two Sub-Group in Israel.
Journal of Child Development. Vol. 50, p. 976 – 980.
Obe, E. O. 1996. Remedies for Mass Failure and Examination Malpractices. In E.O. Obe (Ed),
“School Indiscipline and Remedies”. Lagos Premier Press ad Publishers.
Ogunbanjo, F. O. 1998. Evaluation of Factors Affecting Effective Teaching and Learning Science in
Primary Schools in Ondo State, Nigeria. Journal of Education, 1, 11-23.
Onocha, C.O. 1985. Pattern of Relationship between Home and School Factors and Pupils’ Learning
Outcomes in Bendel Primary Science Project. Journal of Science Teachers Association of Nigeria
(STAN). 23(1), 56-63.
Rani, S. 1998. A Study of Public School Children’s Psychological Development in Relation to Home
Environment. J. Indian Ed. 27(1), 16-23.
Robert, B. J. 1987. Reaching Out: Some Issues and Dilemmas in Expanding Science Education.
Journal of Research in Science Teaching, 25(1), 133 – 146.
Rothenberg, P. B. and Varga, P. E. 1981. The Relationship between Age of Mother and Child’s Health
and Development. A.M.J. Public Health, 71(1), 810 – 817.
Setidisho, A. W. 2001. The Validity of Beliefs About Factors Related to Mathematics Achievement. A
paper Presented at the Annual Meeting of the American Educational Research Association,
Chicago, U.S.A.
Sharma, K.R. 2004. Effect of Early Home Environment on the Mental Development of Down Syndrome
Infants. A J. Mental Deficiency. 85(1), 39-44.
Simon, R. L. 2004. Families. Journal of Marriage and the Family, 83(2), 594-606.
Sogbetan, A. A. 1981. Teachers and Students Opinion about the Causes of Poor Academic
Performance in Secondary Schools. Unpublished M.Ed. Project. University of Ibadan.
Steingberg, L. 1993. Adolescence. New York: McGraw-Hill.
Taiwo, H. G. 1993. Family Environment and Educational Attainment of Some School Children in
Western Nigeria. Journal of the Science Teachers Association of Nigeria, 46 (2), 107-116.
Teese, R. 2004. Staying on at School: Improving Student Retention in Australia. Centre for Post
Compulsory Education and Life Long Learning, University of Melbourne.
Wadsworth, J., Taylor, B. and Osborn, A. 1984. Teenage Mothering: Child Development at Five
Years. Journal of Child Psycho – Psychiatry, 25, 305 – 313.
William, H. N. and Chelser, T. K. 2005. Family Environment Factors and Parents Aspirations on
Academic Achievement of Student. Journal of Research on Adolescence, 14(1), 162 – 175.
William, L. O. and Decoufle, P. 1999. Is Maternal Age a Risk Factor for Mental Retardation Among
Children? Journal of AMJ Epidemiol, 149(2), 814 – 823.
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