INTRODUCTION

     Researchers generally agree that a constellation of familial factors exert significant influence on the educational aspirations and academic achievements of children (e.g. Garg, Kauppi, Lewko, &Urjnik, 2002; Garg, Melanson& Levin, 2007; Sanchez, Reyes, & Singh, 2006; Teachman, &Paasch, 1998). Among these salient factors are parental involvement, parent‟s occupation, educational attainment, socioeconomic status, family type and family size.

      Considerable research evidence suggests that parent‟s behaviors with their children-stimulation; consistency, moderation, and responsiveness influence the children‟s cognitive and social development (Clarke-Stewart 1983.) If we can identify parental practices that are relatively successful in enhancing cognitive growth, we may be able to help more parents help their children reach their intellectual potential. This study examined parental factors affecting academic performance of grade six students in Kisumu city by exploring the following factors: parental involvement, parental socioeconomic, parent‟s level of education, family type and size. Kisumu City faces several challenges as it endeavors to achieve educational Millennium Development Goals (MDGs). It is a city with high level of poverty, high prevalence of HIV/AIDS and a rapidly growing urban population. In 2006, about half of the city inhabitants were poor, 15 percent were HIV-positive and over 60 percent lived in peri-urban informal settlements (UN-HABITAT, 2006). Other challenges faced in Kisumu include shortage of houses, lack of adequate water, poor sanitation and unemployment. Many residents derive their livelihoods from subsistence fishing, agriculture or the informal sector, known as Jua Kali, and earn between KShs. 3,000 and 4,000 [$37.50-50] per month (Ibid). The extremely high poverty rates in areas such as East Kisumu, South West Kisumu, and East and West Kolwa threaten to undermine efforts to address international commitments such as achieving the Millennium Development Goals (MDGs) by 2015. Although Free Primary Education (FPE) has contributed to increased numbers of children joining primary schools across Kisumu, escalating poverty has contributed to a high drop-out rate and low completion rates (Kosgei, 2006). Moreover, a number of major informal settlements (slums) are located in East Kisumu and Kolwa West, where there are relatively few public primary schools. As a result, some children living in these slum areas are still not benefiting from FPE. Many poor parents have been unable to meet costs and have opted to not enroll their children. The increased level of poverty makes parents unable to feed their children properly and provide education. Children whose parents cannot afford the cost of instructional materials, school uniforms, tuition fees and activity fees, tend to go to school irregularly and in the long run drop out of school (Government of Kenya, 2000). While education is considered to be a basic right and need, the delivery of primary education in Kisumu has registered deterioration and noted a sluggish implementation due to several bottlenecks: high prevalence of HIV/AIDS, parental influences and poverty have slowed or prevented progress of goals towards specific Education For All (EFA) (Ministry of Planning and Development, 2005). James Coleman‟s large scale study of the factors that influence academic achievement showed a stronger correlation between achievement and family background and environment than between 

     achievement and the quality of the school (Coleman et al. 1966). Researchers have since devoted much attention to the ways parents can foster their children‟s school achievement. The literature distinguishes between cognitive socialization-how parents influence the basic intellectual development of their children, and academic socialization-how parents influence the development of attitudes and motives that are essential for school learning (Baker and Stevenson 1986; Epstein et al. 1989; Stevenson and Baker 1987; Milne et al. 1986). In some families, the socialization of achievement operates in ways that produce a relative match between the child‟s learning skills, attitudes, and motives and the demands of the school (see Bempechat and Ginsburg 1989; Epstein 1989). In other families, the socialization of achievement operates in such a way that children have difficulty realizing their full potential, so that they fall behind in their school work and develop poor attitudes, low expectancies, and maladaptive achievement behaviors. In a study of achievement motivation in Chinese, Southeast Asian, Korean and CaucasianAmerican fifth and sixth graders, Bempechat, Mordkowitz, Wu, Morison, and Ginsburg (1989) found that regardless of ethnicity and social class, high academic achievement was associated with intense educational socialization, including close supervision of school progress. Epstein (1989) has examined home factors that contribute to academic performance. She argues that differences in children‟s motivation and learning can be partly accounted for by the degree to which the environment of the school and the home overlap. Her model of educational socialization (TAGET Structures) identifies six interrelated aspects of the home environment that are conducive to academic achievement. 1. Task structure or the variety of activities, including intellectual activities that children participate in at home. The literature suggests that preschoolers who are actively prepared for school are more ready for its formal onset, have more initially positive attitudes, and experiences less grade retention. 2. Authority structure or the degree to which children have responsibilities and participate in family decision-making. Authoritative, rather than permissive or authoritarian, parenting is associated with independent and exploratory behavior in young and older children. 3. Reward structure, or the ways in which parents recognize advances in learning. Epstain suggests that particularly when children begin formal schooling, parents are unsure of how best to reward children for intellectual progress. 4. Grouping structure, or ways in which parents influence the child‟s interactions with family members and peers. 5. Evaluation structure, or parental standards for and means of judging performance. Clear and realistic standards that are communicated warmly and constructively can foster motivation.

    6. Times structure, or the ways in which parents manage children‟s time for schoolwork and other activities. Parents that manage children‟s time effectively support the completion of both school and non-school related tasks Parental educational level is known as a factor positively related to children‟s academic achievement (Grissmer, Kirby, Berends& Williamson, 1994). The family is the main factor influencing the lives and outcomes of students (Okantey, 2008). The educational level of a parent is a powerful factor influencing children‟s academic success. It has been established that generally, the educational level of parents is greatly connected to the educational attainment of their children (Sarigiani, 1990). There were a few studies of interest for the present study that examined the independent variables of parent education on students‟ scholastic success. The study sample in Vick and Packard‟s (2008) research resided in a town with a disproportionately high percentage of families in poverty and high school dropout rate more than twice the statewide average. This negative outcome was linked to participants‟ parents who had graduated from a 4-year college at a rate of 17% or less. Suitor, Plikuhn, Gilligan, and Powers‟ (2008) study isolated the variable of mother‟s education and its impact on her children. The thirty-five women in this sample were followed for a decade to determine the longitudinal effect of their return to school and the consequences their academic achievements had on children‟s educational goals and orientations. The research found that return to school was consequential on children‟s aspirations only when mothers completed their degrees (Suitor et al., 2008). Chiu and Khoo (2005) reported 15 year-old students‟ test scores correlated significantly with mothers‟ mean years of schooling. Most of the relationship is due to differences in parental education (Teachman&Paasch, 1998, p. 712). The educational levels, as well as income of parents, are interconnected; this is because educated parents, by virtue of their educational background, possess the potential for increased income. Thus, educated parents have the capacity to build bridges out of poverty and benefit from better quality of life (Okantey, 2008). Parental education which leads to good income empowers parents to give their children a solid foundation for school and life success and enables them to build up strong partnerships between parents and schools in order to sustain achievement standards (Okantey, 2008). It has been put forward that parents of high socio-economic status have more positive attitudes towards their children‟s schooling and have high expectations for their children since they have the economic empowerment to buy the advantages that money can buy. Payne, (1998) believes that students from poverty lack cognitive strategies needed to be successful in the education system. Conger and Elder (1994) assert that families at a variety of income levels who suffer economic stress of any kind are more likely than families that are not economically stressed to experience depression, marital clashes and to be harsh with their children, which points to the fact that poverty and economic stress are associated with parent-child conflict which leads to poorer grades and weakens emotional an social growth. Ezewu and Okoye (1981) found that educated parents who most often fall into high or middle-economic class families tend to show more concern over their children‟s poor performance at school either by teaching them in those subjects in which they performed poorly or they appoint lesson teachers to further coach them. Ezewu (1990) said that children from high socio-economic status families are likely to improve on their academic achievement even if they have been performing poorly before because they can be provided with the incentive to do better.

METHOD

A causal comparative research design was employed in the study. This type of design was considered appropriate because it permits the researcher to compare two or more groups on one variable; thus identify relationship between an independent variable and a dependent variable. Kisumu, the third largest city in Kenya is the head quarter of Kisumu District as well as Nyanza Province. It is one of the fastest growing cities in Kenya. Surrounded by an agriculturally rich hinterland mainly supporting large-scale sugar industry and rice irrigation, Kisumu‟s contribution to the National economy is significant. Kisumu has a very well developed road transport connection with adjacent towns such as Kericho, Kakamega, Homa-Bay, Kisii, Siaya, and Busia and sugar belt satellite townships of Muhoroni, Awasi, Chemelil, Miwani and Nandi Hills. Its geographical position and its cosmopolitan profile places the city strategically as a competitive growth center in the emergent age of technological and economic development. Despite its rich resource base, Kisumu is still one of the poorest cities in Kenya. It is estimated that 53 percent of the population (267,310) people live below the poverty line. The welfare monitoring surveys of 1994 and 1997 indicate that poverty levels have been increasing over time (Ministry of Planning and National Development, 2005). The Kenyan Government introduced free primary education yet educational standards and performance have deteriorated significantly and food insecurity, growing urban poverty and the high prevalence of HIV/AIDS are also key concerns. The primary school population in the old Kisumu District is 109,853 with 41% attending school while the 59% do not attend. Boys‟ attendance is higher than girls, with 40.3% of pupils in school being boys and 37.8% girls (Ibid). Kisumu City covers an area of approximately 918.5 square kilometer (Km2 ). The population is approximately 535,571. It has a well established department of education with 159 primary schools (115 public schools and 44 private) and pupil population of 80,862. The management of primary and secondary schools has been in the hands of the private investors and the community since the inception of the cost-sharing policy. However, the government nowadays provides for free primary education, school materials such as textbooks, note books and writing materials. This study targeted both public and private primary schools in the city, selected pupils of grade six, and their parents. Out of the 115 schools in Kisumu City a total of 12 public schools and 8 private schools were selected using stratified sampling technique. 400 pupils of grade six and 400 parents were also selected to participate in the research. This study adopted purposive sampling. This is where the researcher decides who to include in the sample and therefore for this research, Kisumu City was chosen and only standard six pupils participated because of their level of maturation and ability to handle the research questions. Stratified Sampling was used in sampling the schools, the 9 zones were stratified into three stratus and one zone randomly sampled within the 3 stratus. Schools were then randomly sampled and out of the 20 schools that were sampled a total of 400 pupils were randomly sampled. For the schools, the sample consisted of 20 primary schools: 12 public schools and 8 private schools. All the 9 zones were stratified into 3 stratus each consisting of 3 zones. 1 zone from each cluster was then randomly chosen and schools from the particular zone also randomly chosen to give a fair chance for each school to participate in the study. The sample for the pupils consisted of 400 day scholars‟ pupils of grade six. This was arrived at using the formula (Zuluta, Nestor, Costales 2004) n = 2 1 Ne N Where n= the size of the sample N= the size of the population e= the margin of error A total of 168 pupils from strata one, 115 pupils from strata two and 117 from strata three were randomly selected to ensure that each member of the sample had an equal chance of being included in the sample. A total of 400 parents of the 400 pupils in grade six who were sampled participated in the research.

RESEARCH INSTRUMENTS

Two types of research instruments were used for both pupils and parents; a questionnaire and a document analysis. A questionnaire was used because it is convenient for gathering information from a target population and it also enhances presentation of uniform questions. The questionnaire for the parents sought information about their demographic profile and their involvement in their children„s academic performance both at home and in the school. The questionnaire for the grade six pupils was to establish their demographic profile and their parents‟ involvement in their school work both at home and school set up. A document analysis was used to determine the pupils academic performance based on end of term 1, 2009 examination which was obtained from the class teachers‟ compiled list. Reliability was ascertained by doing a pilot study in one public and one private school with not less than 30 grade six pupils who did not participate in the research but were within the research area. After receiving the completed questionnaires, reliability was further ascertained by subjecting the instruments to a reliability tests using Cronbach‟s Alpha Coefficient. If a reliability coefficient of not less than 0.60 was attained an instrument was considered reliable. The pilot study of the instrument yielded a Cronbach‟s alpha coefficient of 0.67. Data Collection Procedures Prior to the data collection process, the study area was visited to establish the schools, respondents in the schools and the parents. This was followed by pilot study. One public and one private schools that were not to participate in the study were randomly chosen. A total number of 30 pupils- 15 from the public school and 15 from the private school and their parents participated in the pilot study. All these activities were done to familiarize the researchers with the administration and sharpening of the instruments. The questionnaires of grade six pupils were administered in their respective schools after permission was granted. The questionnaires were handed to the concerned, followed by a brief explanation on how to answer each of the questions. After that the researchers consulted with the class masters/mistress in the same school on the pupils‟ academic performance based on end of term 1, 2009 examinations. At the end of the school‟s visit, the questionnaires were collected from the respondents. The questionnaires of the parents were given to the participating pupils to take to their parents and upon completion, they were to hand them to the class master/mistress and the researchers were to collect them later. All these questionnaires were coded for easy match of the pupil, parent and academic performance. Since all schools in the Municipality from class six through class eight sit a common exam every term, the results of end of term 1, 2009, were used to judge the academic performance of the pupils. Statistical Treatment of Data After receiving the completed questionnaires, the researchers inspected all of them for completeness, appropriate making of responses and suitability for coding. The open ended questions were categorized and coded together with the close ended questions. Analysis of the data was performed using the Statistical Package for Social Science (SPSS) computer program (version 15). Descriptive statistics such as frequencies, percentages, means and standard deviations and inferential statistics like T-test, and Pearson product-moment correlation coefficient were used to analyze the data. Tables and figures were used to present the analyzed data.

FINDINGS AND DISCUSSION

   This study investigated parental factors (socio-economic status, educational level, family type, family size and parental involvement) affecting grade six pupils‟ academic performance in Kisumu City.

Table-1. Correlations between selected parental factors and academic performance

 

 

 

Perf ormanc e

 

Socio-econ omic status

Mother's educ ational level

Father's educ ational level

 

Family Type

 

Family siz e

 

Parental

Involvement

Perf ormanc e                      Pearson Correlation

Sig. (2-tailed) N

1

 

378

.171**

.004

277

.228**

.000

344

.275**

.000

301

.113*

.028

377

-.007

.896

377

.247**

.000

378

Socio-economic s tatus       Pearson Correlation

Sig. (2-tailed) N

.171**

.004

277

1

 

294

.346**

.000

279

.381**

.000

241

.091

.121

293

.043

.459

294

.021

.725

294

Mother's educ ational lev el   Pearson Correlation

Sig. (2-tailed) N

.228**

.000

344

.346**

.000

279

1

 

366

.669**

.000

305

.002

.967

365

-.017

.745

365

.073

.161

366

Father's educational level   Pearson Correlation

Sig. (2-tailed) N

.275**

.000

301

.381**

.000

241

.669**

.000

305

1

 

324

.078

.161

323

-.009

.875

323

.139*

.012

324

Family Type                       Pearson Correlation

Sig. (2-tailed) N

.113*

.028

377

.091

.121

293

.002

.967

365

.078

.161

323

1

 

401

.102*

.042

400

.167**

.001

401

Family siz e                        Pearson Correlation

Sig. (2-tailed) N

-.007

.896

377

.043

.459

294

-.017

.745

365

-.009

.875

323

.102*

.042

400

1

 

401

-.073

.144

401

Parental Inv olvement         Pearson Correlation

Sig. (2-tailed) N

.247**

.000

378

.021

.725

294

.073

.161

366

.139*

.012

324

.167**

.001

401

-.073

.144

401

1

 

402

**. Correlation is s ignif icant at the 0.01 lev el (2-tailed).

*. Correlation is s ignif icant at the 0.05 lev el (2-tailed).

 Jeynes, W. H. (2002) “Examining the effects of parental absence on the academic achievement of adolescents: the challenge of controlling for family income” Journal of family and economic issues 23(2). Kosgei, K. C. (2006) “DEO Education Brief Presented to the Parliamentary Education Committee in March 2006 and to the Ministry of Education in July” McLanahan, S. S. & G. Sandefur. (1994) “Growing Up with a Single Parent: What Hurts, What Helps?” Cambridge, MA: Harvard University Press. McNeal, R. B. (2001) “Differential effects of parental involvement on cognitive and behavioral outcomes by socioeconomic status” Journal of Socio-Economics Vol.30, No.2, pp.171 Milne, A., Myers, D., Rosenthal, A., & Ginsburg, A. (1986) “Single parents, working mothers, and the educational achievement of school children” Sociology of Education Vol.59, pp.125-139. Ministry of Planning and National Development, Nairobi: Government of Kenya (2005). Okantey, George (2008) “Facilitating educational attainment and student achievement: Science bound parents program” Purdue Extension, Marion County. 6640 Intech BLVD. Ste. 120. Indianapolis, IN 46278. Payne, R. (1998). “A framework for understanding poverty” Highlands: RFT Publishing. Sanchez, S., Reyes, O., & Singh, J. (2006)“ Making it in college: The value of significant individual in the lives of Mexican American Adolescents” Journal of Hispanic Higher Education, Vol.5, pp.48-67. Sarigiani, P.A. (1990) “Review of child development Research” Chicago: University of Chicago Press. Seyfried, S. F. (1998).“Academic achievement of African American preadolescents: The influence of teacher perception” American Journal of Community Psychology, Vol.26, No.3, pp.381-402. Stevenson, D. and Baker, D. (1987) “The family-school relation and the child‟s school Performance” Child Development. Vol.58, pp.1348-1357. Suitor, J.J., Plikuhn, M., Gilligan, M., & Powers, R.S. (2008) “Unforeseen consequences of mothers‟ return to school: Children‟s educational aspirations and outcomes” Sociological Perspective, Vol.51, pp.495-513. Teachman, J. D., &Paasch, K. (1998) “The family and educational aspirations” Journal of Marriage and Family, Vol.60, pp.704-714. UN-HABITAT, (2006) “Managing the HIV/AIDS Pandemic at the Local Level: Experience from Kisumu.” U.S. Census Bureau. (1997) “Children With Single Parents – How They Fare” Census Brief. Washington, DC Vick, R.M.& Packard, B.W. (2008) “Academic success strategy use among community-active urban Hispanic adolescent” Hispanic Journal of Behavioral Sciences, Vol.30, pp.463-480. Zutula, F.M., Nestor, T.&Costales, J.R (2004) “Methods of research thesis- writing and applied Statistics” Philippines: National Bookstore.

Table 2

Table-2.Regression summarizing the findings on parental factors that significantly affect child‟s

academic performance

 

Model                    R                             R Square               Adjusted

R Square

 

Std. Error of the

Estimate

 

1                            .333a

 

2                            .413b

 

3                            .438c

 

.111

 

.171

 

.192

 

.107

 

.163

 

.181

 

65.40965

 

63.32030

 

62.65372

 

  1. a. Predictors: (Constant), Parental Involvement

 

  1. Predictors: (Constant), Parental Involvement, Father‟s educational level

 

  1. c. Predictors: (Constant), Parental Involvement, Father‟s educational level, Socio-economic status

 

Anovad

 

 

 

Model

 

Sum of

 

1

 

Regression

Squares

112891.6

df

1

Mean Square

112891.599

F

26.386

Sig.

000a

 

 

Residual

 

902747.0

 

211

 

4278.422

 

 

 

 

Total

 

1015639

 

212

 

 

 

2

Regression

173651.9

2

86825.941

21.655

.000b

 

 

Residual

 

841986.7

 

210

 

4009.460

 

 

 

 

Total

 

1015639

 

212

 

 

 

3

Regression

19511.5

3

65079.492

16.576

000c

 

 

Residual

 

820427.1

 

209

 

3925.489

 

 

 

 

Total

 

1015639

 

212

 

 

 

 

  • a. Predictors: (Constant), Parental Involvement
  • Predictors: (Constant), Parental Involvement , father‟s education level
  • Predictors: (Constant), Parental Involvement, father‟s education level and socioeconomic status
  • Dependent variable: Performance

     Parents’ Socioeconomic Status From the above table, the correlation coefficient of socioeconomic status with child performance is 0.171 with a corresponding p-value of 0.004 hence socioeconomic status statistically and significantly correlated with the child performance. The SES of a child is most commonly determined by combining parents‟ educational level, occupational status and income level (Jeynes, 2002). Studies have repeatedly found that SES affects student outcomes (Hochschild, 2003). Students who have a low SES earn lower test scores and are more likely to drop out of school (Eamon, 2005). Low SES students have been found to score about ten percent lower on the National Assessment of Educational Programs than higher SES students (Seyfried, 1998). SES has also been shown to override other educational influences such as parental involvement (McNeal, 2001). It is believed that low SES negatively affects  academic achievement because low SES prevents access to vital resources and creates additional stress at home (Eamon, 2005). The economic hardships that are caused by low SES lead to disruptions in parenting, an increasing amount of family conflicts, and an increased likelihood of depression in parents. For this reason, SES is closely tied to home environment and one could argue that SES dictates the quality of home life for children. Parents’ level of education The table also indicates the correlation coefficient of mother‟s education level with child performance as 0.228 with a corresponding p-value of 0.000, hence mother‟s level of education is statistically and significantly correlated with the child performance. The correlation coefficient of father‟s education level with child performance was 0.275 with a corresponding p-value of 0.000, hence father‟s level of education was statistically and significantly correlated with the child performance. It can be concluded that the pupils whose mothers and fathers have higher educational attainment tend to perform better. Family Type The correlation coefficient of family type with child performance is 0.113 with a corresponding pvalue of 0.028, hence family type is statistically and significantly correlated with the child performance. The children with both parents performed better compared to their counterparts from different family types. However, children from guardian and step mother and real father families performed averagely the same with a difference with their counterparts in single family type. The lowest performed category was pupils from step fathers and real mother family type. Growing up without both parents is associated with a host of poor child outcomes, such as high poverty rates and lower levels of educational attainment compared to children living with their married, biological parents. Research shows that on average, children of divorced parents are disadvantaged compared to children of married-parent families in the area of educational achievement (Jeynes, 2002). Children of divorced parents are more than twice as likely to have serious social, emotional, or psychological problems as children of intact families-25 percent versus 10 percent (Heatherington, 2002). On the other note, although death of a parent does put children at a disadvantage, children of widowed parents do the best of all categories of children of single parents. Children of widowed mothers are about half as likely to drop out of high school or have a teen birth as children of divorced or children born outside of marriage (McLanahan&Sandefur, 1994). Children of never-married mothers are at risk of experiencing negative outcomes and are among those most likely to live in poverty. Roughly 69 % of children of never-married mothers are poor, compared to 45 % of children brought up by divorced single mothers (U.S. Census Bureau, 1997). Never-married mothers are significantly younger, have lower incomes, have fewer years of education, and are twice as likely to be unemployed as divorced mothers. Regardless of the mother‟s age at birth, a child born to an unmarried mother is less likely to complete high school than a child whose mother is married (Haveman, Wolfe, & Pence, 2001). For step-families, in spite of their better economic circumstances on average, children in step-families face many of the same risks as children of never-married or divorced parents. They are more likely to have negative behavioral, health, and educational outcomes, and they tend to leave home earlier than children who live with both married biological parents. Finally, children in step-families are at increased risk for experiencing physical, emotional, and sexual abuse (Daly, & Wilson, 1985). Thus, students from families with both parents performed better as compared to the rest. About 30% of them scored average or above average while about 10% from single families also performed better as they scored average or above average. Family Size From the table above, the correlation coefficient of family size with child performance was -0.007 with a corresponding p-value of 0.896 and family size is not statistically and significantly correlated with the child performance, thus the difference observed occurred by chance. However, smaller family size has been linked with higher academic achievement (Eamon, 2005). Students with fewer siblings are likely to receive more parental attention and have more access to resources than children from large families. The additional attention and support leads to better school performance. Parental Involvement On parental involvement, the correlation coefficient with child performance was 0.247 with a corresponding p-value of 0.000, hence parental involvement is statistically and significantly correlated with the child performance. The effect of parental involvement in their children‟s school has on academic achievement is less clear (Domina, 2005). Parental involvement in school has been linked to both positive and negative influences on academic achievement (McNeal, 2001). Explanations for this discrepancy are not conclusive. It is thought that the type of involvement may make a difference and that in some cases, parents become involved after their child has already had academic difficulties (Domina, 2005). However, helping children enjoy learning and being successful in school is an important goal for parents, other family members and schools. It takes two major institutions, the home and the school, working together to successfully educate the child. Students, family members and teachers are all necessary links in a positive learning experience. Even the most caring and competent teacher needs support from parents and family members who will encourage children and teach them to value education. Parents and other family members are the most important teachers of their children. By nature, children are curious and want to learn. Parent's attitude and value about education are easily transferred to children by their actions and words. To ensure success in school, children need their parents‟ support for school and non-school activities. Studies indicate that children whose parents and/or family members share in their formal education tend to do better in school. Their involvement may be more difficult based on challenges that they face such as lack of time, knowledge of ways to be involved and poor communication between school and home. Children need and want their families to be involved in their lives. Looking to the future, employers will want employees who are willing to take on responsibility, learn new skills, and effectively communicate with their co-workers. Helping our children become successful in school today can improve their success in the world of work tomorrow. In conclusion, the selected parental factors affecting the academic performance of grade six pupils in Kisumu City are summarized on the tables presented below:

Table-2.Regression summarizing the findings on parental factors that significantly affect child‟s 

 

a. Predictors: (Constant), Parental Involvement b. Predictors: (Constant), Parental Involvement , father‟s education level c. Predictors: (Constant), Parental Involvement, father‟s education level and socioeconomic status d. Dependent variable: Performance Of the six independent variables included in the multiple regression models, three of them were identified as best predictors of academic performance. These include parental involvement, father‟s education level and socio economic status. These three variables explain 18.1% of the variation in academic performance with parental involvement alone accounting for 10.7 % of the variation. The remaining 81.9% of the variance in academic performance is explained by other variables not included in this study.

CONCLUSIONS

    Socioeconomic status statistically and significantly correlated with the child performance, suggesting that pupils whose parents have higher socio-economic status and higher educational level and who are significantly involved in their school work, and who come from both parents family type tend to perform better academically. Further, the best predictors of pupils‟ academic performance are parental involvement, father‟s educational level, and socio-economic status. It is recommended that parents improve on their educational levels through adult education programs. Pupils from low socioeconomic backgrounds should try to preserve through financial hardships and remain in school because schooling eventually has a redeeming effect on their poor flight. Social and economic policies should be put in place to enable children from parents of low economic status to have equal opportunity of advancing the cause of education of their children. Further, studies could be advanced on the discrepancy between high performing students from less educated parents and their counterparts from well-to-do families

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