Volume 04 Issue 04 (2016) April 2016

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ISSN: 2347-7474

International Journal Advances in Social Science and Humanities Available online at: www.ijassh.com

RESEARCH ARTICLE

JSK Achoka

Students’ Socio-Economic Status and Enrolment in Public

Secondary Schools in Kenya

S Wakwabubi, JSK Achoka

*

_{, JO Shiundu, E Ejakait}

MasindeMuliro University of Science and Technology-Kenya.

Abstract

The purpose of this study was to find out whether the socio-economic status (SES) of students has any effect on enrolment into public secondary schools in Kenya. The research design used was co-relational. The accessible study population comprised of 16,120 students from public secondary schools. A sample of 1,450 students was drawn from the population using Probability Proportion to Size (PPS) sampling technique. Questionnaires were used to collect data from the respondents. Descriptive statistics, pairwise correlation and multinomial logistic regression were used to measure the effect of students’ socio-economic status on enrolment into public secondary schools. This study found that a high proportion of students enrolled in national secondary schools were from the high socio-economic status. However, when other variables were controlled, this effect became insignificant and the main determinants of admission into the three categories of public secondary schools in Kenya emerged as student based-variables such as the student’s age at Kenya Certificate of Primary Education (KCPE), gender and KCPE scores .It was thus concluded that student/household Socio-Economic Status may not determine admission to sub-county, county or national secondary schools. Learners from the low SES category should be encouraged to work harder towards improving their academic scores in KCPE. Also they should avoid repeating classes as this does not increases the probability of such candidates being admitted to county or national secondary schools. It is recommended that that primary schools should endeavor to improve the KCPE scores of their candidates and avoid class repetition.

Keywords: Socio-Economic status, Enrollment determinants, Equity, Secondary school, Households.

Background to the Study

Secondary school education is widely seen as one of the most promising avenue for individuals to realize better, more productive lives and as one of the primary drivers of national economic development. Citizens and the government of Kenya have invested heavily in improving the access, equity and quality of secondary school education, in an effort to realize the promise of education as well as to achieve the education-related Millennium Development Goals and Vision 2030. Key goal of education is to make sure that every student has a chance to excel, both in school and in life [1]. Increasingly, children's success in secondary school education determines their success as adults, determining whether and where they go to college, what professions they enter and how much they are paid. However there are many factors preventing education from serving this role as "the great equalizer."

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affirmative action thus enabling each sub-county to get a candidate selected to a national school. The national school quota is used to select candidates per gender and merit list per sub-county. Cut-off to each national school is automatically determined based on the last candidate to be selected to a given national school from a given sub-county in a sub-county. County schools are selected on a 40% (national), 40% (to sub-county within sub-county and inclusive home sub-county of the school) and 20% (for home/host sub-county of the school). Computerized selection follows some steps. Selection of top 1 and 2, for both genders (boys and girls) to their national school choices if an allocation has been provided for their sub-county.

In absence, the computer assigns such performing candidates schools of equivalent stature to their choices. Selection of other national schools quota based on candidates ratio from public and private. The system gives advantage to counties inclusive of the host county in filling vacancies which may arise during the selection (such as inability of certain sub-counties to produce candidates with more or equal to 280 marks) .Selection of extra-county schools which have same features as national schools with variance being the number of candidates to be picked being more within the host counties of the schools .County schools purely select students within a county, all boarding schools fall under this category .Sub-county selection is still done manually due to challenges of mapping primary schools or communities to proximity of secondary day schools.

Wachira [4], argues that availability of adequate learning resources in private primary schools in Kenya has led to better performance in KCPE than their counterpart from public primary schools. Due to high fees charged in private primary schools, majority of pupils in these schools are from high socio-economic status. For instance, Bulimo [5] found that 77.8% of pupils in private primary schools are from high socio-economic status.

Research findings indicate that performance in KCPE amoung pupils who live in the

informal neighborhoods is low compared with those of pupils who live in the formal neighborhoods. A study by the International

Finance Corporation (IFC) in the year 2002 about “The business of Education: A look at Kenya’s private private education sector showed that in the 2001 secondary schools intake , the public primary schools In Nairobi sent only 16 pupils (11.5%) to national schools while private schools sent 123 (88.5%) students to national secondary schools.

This difference suggested that there is inequality in access to secondary school education by the type of primary school attended. Yet the common view is that increases one’s income and also plays an important role in enhancing individual’s social mobility [6]. This understanding implies that there is a need for intervention measures particularly for the poor because socio-economic background of students may have effect on the category of secondary school they attend. Knowledge on socio-economic status of students in different categories of secondary schools in Kenya is a critical contribution to literature, debate and scholarship in clarifying the linkages between students’ SES and enrolment in public secondary schools in Kenya.

Statement of the Problem

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Available online at: www.ijassh.com

JSK Achoka

of secondary schools are known, little is known about the effect of socio-economic status on this admission in the Kenyan context. This study therefore sought to clarify the linkages between household/student SES and enrolment to public secondary schools in Kenya.

Purpose of Study

This study set out to examine the effect of students’ socio-economic status on enrolment in public secondary schools in Kenya.

Theoretical

and

Conceptual

Frameworks

This study was guided by the Social Justice Theory of John Rawls. The term ‘social justice’ refers to an approach to the distribution of goods and services in society. It implies ideas of mutual obligation and a certain legal and institutional monitoring of the distribution of opportunities between citizens, such that all are given a fair and equal chance to succeed in life. The theory in particular focuses on the concept of equity and specifically on the idea of justice and fairness in the distribution of goods and services such as education. The social justice theory advanced by John Rawls points out that due to lack of equity in the distribution

of essential needs, the society has to make a choice about whether to stay with the existing laws and policies or to make changes so that equity is achieved. Authors who advocates for this theory suggests that for justice to prevail, the society should change its policies and laws to raise the position of the less advantaged members of the society. This theory is relevant to this study because it is necessary to infer from it and determine factors that could possibly affect enrolment of students from certain socio-economic background into national, county, and sub-county secondary schools in Kenya. If the factors affecting enrolment of students in different categories of secondary schools are known, they could be used to adjust policies on selection of students joining various categories of secondary schools so that all of them will have equal opportunity irrespective of their socio-economic background.

This study used a conceptual framework to show the relationship between independent, dependent and control variables. The schema in figure1 shows how independent, dependent and control variables are related in this study.

Figure1: Conceptual framework showing the relationships between variables Source: Literature Reviewed(2012/13/14)

It shows that students’ socio-economic status can potentially affect the category of secondary school they are enrolled in. Moreover, enrollment in different categories of secondary schools may be influenced by other factors such as the student’s KCPE score, sex, age, type of primary school the student attended and sat KCPE from, grade repetition and place of residence during holidays among others.

Study Design, Location of the Study

and Population

The research design used in this study was co-relational. Co-relational design helped in the assessment of the degree of relationship that existed between the students’ socio-economic status and enrolment in different categories public secondary schools.

Independent Variable.

Student/household socio-economic status (SES), which is a three level categorical

Dependent Variable

Category of secondary school in which a student is enrolled, which is a three level categorical variable (Sub-county, County andNational)

Control variables

Student’s KCPE score, sex, age, type of primary school, place of residence during

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The study was carried out in Nairobi and the Western region of Kenya with the later covering the Rift valley, Nyanza and Western sub-regions. The study locations were purposively sampled because they had sufficient numbers and different categories

of national schools for drawing a representative sample.

The study targeted a population of 2962 public secondary schools with an enrolment of 985,100 students. Table 1 summarizes the study population disaggregated by region.

Table 1: The target population

Province Public secondary schools Enrolment

Nairobi 60 67700

Rift valley 1182 380400

Nyanza 1069 320200

Western 651 216800

Total 2962 985100

Source: Adapted from Ministry of Education [7]

The accessible study population comprised of

8,375 boys and 7,745 girls distributed in different categories of secondary schools as presented in Table 2.

Table 2: The Accessible study population

Category of secondary school No of boys No. of Girls Total

National 3165 2835 6000

County 3190 2850 6040

Sub-county 2020 2060 4080

Total 8,375 7,745 16,120

Note :Adapted from Ministry of Education [7].

Sampling Techniques and Sample

Size

Table 3 presents the number of sampled schools from the regions. A total of 18 public secondary schools were stratified and

randomly sampled for this study. Schools from each region were stratified by sex and category before being randomly sampled. The sample had six national schools, six county schools and six sub-county schools.

Table 3: Sample schools by province

Province National County Sub-county Total

Nairobi 2 1 1 4

Rift valley 3 2 2 7

Nyanza 1 2 2 5

Western 1 1 2

Total 6 6 6 18

Source: Field Work notes (2013)

Sample size was determined using a simplified formula for proportions as follows:

n=_{𝟏+𝐍(𝐞)}𝐍 𝟐

Where n = sample size, N is the population size and e is the level of precision. For national, county and sub-county schools, the

sample sizes were 375, 376 and 365 as calculate below.

National schools

n=_1+6000(0.05)6000 2=375 students.

County schools

n=_1+6040(0.05)6040 ₂= 376 students. Sub-county schools

N= 4080

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Israel [8] recommends that a sample often needs to be adjusted upwards by up to 30% to cater for none response. Adjusting the sample by this percentage gave 487, 488 and 475 for national, county and sub-county schools as shown in the calculation below. National schools

375 × 1.3 = 487 students, County schools

376 × 1.3 = 488 students,

Sub-county schools

365 × 1.3 = 475 students.

total of 82 students were randomly sampled from each class in a school with 21 being randomly sampled from Form 1, 21 from Form 2, 20 from Form 3 and 20 from Form 4. Table 4 presents a summary of the population and the sample size.

Table 4: Accessible population and corresponding sample sizes

Category of secondary school Population Sample size

National 6000 487

County 6040 488

Sub-county 4080 475

Total 16,120 1,450

Source: Field Work Notes (2013)

Research Instruments, Validity and

Reliability

Structured questionnaires consisting of closed ended questions were used to collect data from students. The students’ questionnaire sought information on their households’ socio-economic status and category of school in which the student had been enrolled. The questionnaires consisted of five sections which were instructions for completing the questionnaire, information regarding students, information regarding parents, information regarding housing condition and amenities, information regarding household assets, source of income and lifestyle. A total of 1450 copies of questionnaires were used for this study.

Validity tells us whether the question, item or score measures what it is supposed to measure. To enhance validity of the research instruments, departmental research scholars ascertained that the instruments developed were valid measurements of students’ SES. Moreover, it was also important to check each item for readability, clarity and comprehensiveness.

A pilot study was done using 116 (8%) of potential respondents .A test-retest method at an interval of two weeks executed. These respondents were excluded from the actual research sample. A correlation between the initial responses and the second was made.

A Pearson Product Moment Correlation formula was employed to compute the correlation coefficient in order to establish the extent to which the instruments were consistent in producing the same results. The coefficient reliability of 0.80 was achieved .This measurement was considered a high enough index of reliability. Accordingly, the instrument was used for data collection.

Data Analysis

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secondary school versus sub-county secondary school. Multinomial logistic regression also gives separate coefficient estimates for each independent variable for each category of comparison. The estimated coefficients represented the relative risk of being in the comparison category versus being in base category associated with a one unit increase in the independent variable. The multinomial logistic model took the form:

Pr(y_i= j) = exp(Xiβj)

1 + ∑jj=1exp(Xiβj)

Where i is the respondent, yi is the observed outcome, Xi the independent variables and βj

are the beta coefficients that are estimated using maximum likelihood. Once the coefficients are exponentiated they give odds ratios (OR) reported as the RRR in MLR. The beta coefficients βj are interpreted as

the increase in Relative Risk Ratio of being in category j versus the base category resulting from a one-unit increase in the ith covariate, holding the other covariates constant. In this case, βj are the increases in

relative risk of admission or enrolment in county or national secondary schools versus admission or enrolment in sub-county schools.

Socioeconomic status was calculated using Principal Components Analysis (PCA) with a list of household assets as described by Filmer and Pritchett [9]. The variables included in the PCA model were based on amenities and asset ownership of the household as reported by the students.

Findings and Discussions

To model the effect of students’ SES on enrolment into different categories of public secondary schools, the preferred statistical approach was to fit MLR models because the dependent variable was an un-ordered three-level categorical variable. But first, frequencies, percentages and descriptive statistics of the variables used in the analysis are given. Table 5 presents frequencies and percentages of selected variables used in the regression analysis. The results show that 37.99% of the students were from sub-county secondary schools, 35.51% of the students were from county secondary schools and 26.50% of students were from national secondary

schools. The table also shows that there were 473 (26.50%) from low SES, 472 (33.45%) from middle SES and 466 (33.06%) from high SES. This suggested that there was a fair distribution of students in sample from low, middle and high SES. As expected, the results also show that the number of orphans and students with single parent were fewer (20.77%) compared to students who had both parents alive (79.23%). The findings further revealed that grades 6, 7 and 8 were most affected by grade repetition with 229 sampled students responding that they repeated those grades compared with 89 who repeated at grade 4 or 5. This is expected because repetition in higher classes is probably meant to improve the final KCPE score. Moreover we found that that a higher proportion of students enrolled in public secondary school were from public primary schools at 72.5% compared to 27.5% of students from private primary schools.

Table 5: Frequencies and percentages for selected variables (n=1411)

Variable name Coding Frequency %

Current secondary school 1=Sub-county school 536 37.99

2=County school 501 35.51

3=National school 374 26.50

SES 1=Low 473 33.52

2=Middle 472 33.45

3=High 466 33.03

Male 0=Female 752 53.3

1=Male 659 46.7

Age at KCPE (years) Min. 10; Max.23

Attended private primary 0=Public 1,023 72.5

1=Private 388 27.5

Both parents alive 0=No 293 20.77

1=Yes 1,118 79.23

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Grade repetition 0=Not repeated 934 66.19

1=Repeat: Lower grades 159 11.27

2=Repeat: Grades 4 & 5 89 6.31

3=Repeat: Grades, 6, 7, 8 229 16.23

Source: Analysis of Data (2013)

The findings also revealed that the range of age at KCPE is a bit wide from 10 years old to 23 years old. This range could probably be attributed to introduction of free primary education and free day secondary education by the government of Kenya in 2003 and 2008 respectively which prompted re-enrolment in secondary schools by some students who had probably dropped out of schools for various reasons.

Table 6 presents selected descriptive statistics for selected variables. Measures of central tendency (mean and median) for selected variables were computed to summarize and give a figure which represents the whole data. Measures of dispersion (Std. Dev, Variance and Range) were computed to understand the variability or spread of distribution of variables. Skewness shows the asymmetry of the

distribution and deviation from a normal distribution of selected variables. Kurtosis shows the ‘peakeddness’ or flatness of the distribution. Age at KCPE had a mean of 14.51, median of 14, Std. Dev. of 1.13, variance of 1.87 and range of 10 to 23. The mean and median revealed that majority of students sat for KCPE at around the age of 14 years. However the Standard deviation, variance and range suggested that there was some variation in the age at KCPE. Age at KCPE was one of student based control variable used in this study for examining the effect of student SES on enrolment in public secondary schools. Out of the possible 500 KCPE score, the sample had scores ranging from 178 to 450 scores. There was some variability in the KCPE scores as suggested by a standard deviation of 56.58.

Table 6: Selected descriptive statistics for selected variables

Variable Mean Median Std. Dev. Variance Skewness Kurtosis Obs. Range Min Max

Age at KCPE 14.51 14 1.37 1.87 0.88 5.10 1442 13 10 23

Student KCPE score 335.09 337 56.58 3201.16 -0.12 2.07 1448 272 178 450

Note: Std. Dev. = Standard deviation, Obs. =Observations, Min. =Minimum, Max. =Maximum. Source: Analysis of Data (2013)

Table7.Presents Chi square test between

students’ secondary school of enrolment and type of primary school attended. Table 7: Chi square test between current secondary school of enrolment and students’ primary school of origin(n=1445)

Current secondary school Type of primary school attended

Public Private Total

Sub-county school 478 66 544

a _87.87 _12.13 ₁₀₀

b _45.7 _16.54 _37.65

County school 372 134 506

a _73.52 _26.48 ₁₀₀

b _35.56 _33.58 _35.02

National school 196 199 395

a _49.62 _50.38 ₁₀₀

b _18.74 _49.87 _27.34

Total 1,046 399 1,445

72.39 27.61 100

100 100 100

Note. a_{=row percentages;}b_{=column percentages}

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The findings reveal that a bigger proportion (87.87% for sub-county secondary schools and 73.52% for county secondary schools) of students joining sub-county and county secondary schools is from public primary schools. However, the distribution of students from public and private primary schools in the national schools is approximately the same (49.62% from public primary schools and 50.38% from private primary schools).This findings discount findings by the International Finance Corporation (IFC) in the year 2002 about “The business of Education :A look at Kenya’s private education sector which found out that in the 2001 secondary schools intake , the public primary schools In Nairobi sent only 16 pupils (11.5%) to national schools while private schools sent 123(88.5%) students to national secondary schools.

Table 8 presents a pair-wise correlation of variables used in the multinomial logistic regression. This correlation helped the researcher determine which plausible interactions (association between variables) were worth pursuing in the MLR analysis. Pair-wise correlation is advantageous because it returns results with sample sizes

and p-values if requested. Using the two-tailed significance value, the researcher was able determine whether the correlations were significant. The null hypothesis was that the correlation coefficient is zero and could be rejected at a 5% significance level. Most of the variables are significantly correlated at p<.001. The dependent variable (Current secondary school of enrolment) has a moderate positive correlation of 0.43 with the independent variable (SES). The 0.85 positive correlation between the current secondary school of enrolment and the student’s KCPE score is the strongest. Other positively correlated variables are type of primary school and current secondary school at 0.34, SES and current secondary school at 0.43, type of primary school and SES at 0.38, student KCPE score and SES at 0.43 and student KCPE score and type of primary school at 0.35. The highest negative correlation was between age at KCPE and current secondary school at - 0.53.These correlations showed the existence of relationship between the variables. Because of these relationships, the variables were fitted into a sequential MLR model to measure the magnitude of effect each of these variables on the dependent variable.

Table 8: Pair-wise Correlations Between Variables Showing Significance at 5% Level of Significance

S.No. Variable name 1 2 3 4 5 6 7 8

1 Current secondary school 1

2 SES 0.43*** 1

3 Both parents alive 0.14*** 0.16*** 1

4 Type of primary 0.34*** 0.38*** 0.09* 1

5 Male -0.06* -0.02* _-0.00 -0.02 1

6 Age at KCPE -0.53*** -0.31*** -0.12*** -0.24*** 0.18*** 1

7 Grade repetition -0.36*** -0.18*** -0.04 -0.16*** -0.03 0.42*** 1

8 Student KCPE score 0.85*** 0.43*** 0.12*** 0.35*** 0.09** -0.49*** -0.41*** 1

*p<.05, **p<.01,***p<.001 Source: Analysis of Data (2013)

Table 9 presents the MLR coefficients of the effect of student SES on their enrolment in sub-county, county or national public secondary schools. Multinomial logistic regression was preferred for fitting the regression models because the dependent variable is a three level categorical variable and it allows simultaneous comparison of more than one alternative. Sequential or ordered multinomial logistic regression was

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bi-variate association of the dependent variable (current secondary school of enrolment) and the independent variable (SES). Model 2 controlled for whether both parents of the student were alive while model 3 controlled for the type of primary school where the student sat for KCPE. Model 4 controlled for student-based variables such as sex, age at KCPE and whether that student had ever repeated any class in their primary schooling as well as the student’s KCPE score. The results are presented in Table 17.

The first regression models the Relative Risk Ratio (RRR) of enrolment in a county versus a sub-county school while the second models the RRR of enrolment in a national versus a sub-county school. In models 1, 2 and 3 for both regressions, SES significantly affects the type of secondary of enrolment. Referent

to the low SES, coming from the middle or high SES increases the relative risk of students being enrolled in county or national schools than in sub-county schools. For instance, the RRR of a student from the high SES being enrolled in a county secondary school than in a sub-county secondary school increases by 3.99 times (p<.001). This risk is much larger for enrolment in a national than in a sub-county school for the same student by 18.42 times (p<.001). This advantage held by students from the middle and high SES holds even when parental survivorship status and type of primary school such students attended are controlled for. But in model 4 when student based-variables are held constant, this strong effect of student SES becomes insignificant. In model 2, the relative risk of joining county or national secondary schools than sub-county schools increases for students whose parents are alive.

Table 9: Multinomial logistic regression coefficients of SES effects on enrolment to sub-county, county or national secondary schools [n=1411, 95% confidence interval in bracket]

County school vs Sub-county school

Model 1 Model 2 Model 3 Model 4

SES: Low (ref)

Middle 1.55** (1.17-2.05) 1.53** (1.15-2.02) 1.44* (1.08-1.91) 1.22 (.79-1.90)

High 3.99*** (2.83-5.62) 3.78*** (2.67-5.35) 3.17*** (2.21-4.54) 1.39(.82-2.40)

Both parents alive 1.40* (1.03-1.89) 1.40 (1.04-1.90) 1.65* (1.03-2.63)

Attended private primary school 1.86*** (1.32-2.62) 1.22 (.72-2.08)

Male -0.26*** (.16-.41)

Age at KCPE (years) -0.60*** (.51-.71)

Grade repetition: Not repeated (ref)

Repeated lower grades -0.75 (.43-1.31)

Repeated grade 4 or 5 1.81 (.84-3.89)

Repeated grade 6 or 7 or 8 1.03 (.59-1.78)

Source: Analysis of Data (2013)

This effect holds on in the final model even after controlling for student- variables (1.65, p=.036 for county schools and 2.07, p=.063 for national schools). In model 3, the RRR of enrolling in county and national schools

other than sub-county schools increases by 1.86 (p<.001) and 3.75 (p<.001) times respectively. This advantageous risk however becomes insignificant in the final model when student-based variables are introduced in the model.

National school vs Sub-county school

Model 1 Model 2 Model 3 Model 4

SES: Low (ref)

Middle 2.46*** (1.66-3.66) 2.46*** (1.66-3.66) 2.08*** (1.39-3.12) -0.53 (-0.24-1.17)

High 18.42***

(12.23-27.75)

18.42***

(12.23-27.75)

11.75*** (7.67-18.00) -0.82 (-0.35-1.91)

Both parents alive 1.65* (1.12-2.43) 1.63 (1.10--2.41) 2.07† (.96-4.49)

Attended private primary school 3.75*** (2.62-5.37) 1.35 (.64-2.88)

Male -0.05*** (.02-.10)

Age at KCPE (years) -0.45*** (.33-.60)

Grade repetition: Not repeated (ref)

Repeated lower grades -0.78 (.28-2.15)

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Repeated grade 6 or 7 or 8 -0.82 (.25-2.72)

Student KCPE score 1.18*** (1.16-1.20)

Pseudo R2 0.10 0.10 0.12 0.64

†p<.10, *p<.05, **p<.01,***p<.001 Source: Analysis of Data (2013)

These student-based variables are sex, age at KCPE, grade repetition, and KCPE score and are the ones that hold as determinants of enrolment into the different categories of public secondary schools. It is surprising that the relative risk of enrolling in a county and national school other than a sub-county one reduces by -0.26 (p<.001) and -0.05 (p<.001) for boys respectively. This could be attributed to lower cut-off KCPE scores for admission of girls relative to boys. It is also surprising that a one year increase in age reduces the relative risk of enrolling in a county or national school other than a sub-county one by -0.60 (p<.001) and -0.45 (p<.001) respectively. In society, the older one gets, the wiser one is supposed to be. Going by this, it would be expected that older students would be wiser and probably work a little smarter than their counterparts. These results do not support this view. Another surprising result is grade repetition whose ratios are not significant meaning that the age-old practice of asking students to repeat grades in order to improve their chances of joining “good” schools does not add up and should be discarded. As expected, a one point increase in the KCPE score increases the relative risk of enrolling in county and national schools other than in sub-county schools by 1.06 (p<.001) and 1.18 (p<.001) times respectively. This confirms the Ministry of Education [3] report that national school quota is used to select candidates per gender and merit list per sub-county.

These findings have shown that when student-based variables are controlled for in the model, the advantageous effect of the probability of enrolling in either county or national schools other than sub-county schools for students from the middle or high SES becomes insignificant. This means that students can no longer ride on their status in society and wealth to join “good” schools if the playing field is level. Irrespective of their SES, they will have to try to finish primary school early enough, when still young, and work harder in improving their KCPE scores.

These results disagree, who observed that in Sub-Sahara Africa (SSA), wealth is a significant determinant of enrolment patterns to secondary school and is generally more important than gender. These findings also disagree with Bulimo [5] on ‘Equity in Access to Secondary school by Private and Public primary schools Graduates in Kakamega South Sub-county’. Bulimo’s results suggested that the governments’ effort to enhance equity in selection and access to “good” secondary schools was still skewed in favour of the few with financial muscle and that pupils who learn in private primary schools had an upper hand in joining national schools compared with those in public schools. With a large sample coupled with robust and rigorous analysis, this study now discounts these conclusions by Bulimo [10-14].

Conclusions

The purpose of this study was to find out whether the socio-economic status of the students has any effect on enrolment into public secondary schools in Kenya. The study concludes that SES does not determine enrolment into public secondary . Instead, it is student-based variables such as the sex of the student, age at KCPE and the student’s KCPE score that are the determinants of enrolment in public secondary schools in Kenya.

Recommendations

The following are the recommendations derived from the study findings:

 That primary school should seek to improve the KCPE scores of their candidates as this increases the probability of such candidates being admitted to county or national schools.  That since SES does not determine

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Government Printers

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