Research Objectives
Several examinations and studies have been carried out to test for the factors that affect students’ performance in school in various subjects. In resent research carried out by Florin, T. A., Shults, J., & Stettler, N. (2011); Van et al (2011) in their results they found out that no correlation existed between the BMI and the school performance of the students in most subjects except in physics where dismal performance was showed by obese students compared to students who had normal body weight. This study will deal particularly with association of the body mass index (BMI) of the students to their performance in mathematics. Mathematics is one of the subjects that involve numerical calculations and manipulations where most of the students pose to have challenge in Kilpatrick (2014). Scientists had brought the formula for the calculations of the BMI and their various ranges showing different health risks to human beings’ health where those with BMI of (18.5 – 24.9) are healthy, BMI of (25 – 29.9) are overweight and finally BMI of 30 and above are obese Van et al (2015). Technicality involved in mathematics subject was what drew attention of conducting this research. Intelligence of the students in most cases had been tested using their scores in various subjects’ examinations where students with higher performance in all the subjects were perceived as the most intelligent as those with lower performance in the same subjects were perceived less intelligent Victoroff, K. Z., & Boyatzis, R. E. (2013).
Research objectives
 To determine the difference in male and female students performance in mathematics.
 To determine whether there is relationship between BMI and students’ mathematics performance.
This study was conducted to determine the association between the BMI of the students to their mathematics performance in the school.
Critically addressing the stated problem will be of help to find if there was any association between the BMI of the students and their performance in mathematics subject.
 Do male students perform better than the female students in mathematics?
 Is there relationship between BMI of the students and the students’ performance in mathematics?
Hypothesis
H_{0}: There is no mean difference between male and female students’ performance in mathematics
H_{1}: There is mean difference between male and female students’ performance in mathematics
H_{0}: There is no correlation between BMI of the students and their mathematics performance
H_{1}: There is correlation between BMI of the students and their mathematics performance
The above research questions were chosen to help build on the general research question thus meeting the specific and general objectives of the study.
Various components in the study can be chosen and combined by researchers in a logical and comprehensible way to ensure for proper address of research problems through research design Creswell, J. W., & Creswell, J. D. (2017). Ambiguity in the effectiveness of research are reduced by research design Venable, J., PriesHeje, J., & Baskerville, R. (2016). Descriptive statistics and Pearson correlation and the independent ttest was be used in this study to meet the purpose for this study.
Hypothesis
This is a group of objects or elements focused on by the topic under investigation and the sample is a fraction of the population i.e. a subset of the population Batsis et al (2013). In this case, the targeted group was mathematics students and their population in the school was 256. From that population, a sample size of 50 students were sampled across the classes. A sample size is the number of choice of the researcher from the available objects or elements under study population Kühberger, A., Fritz, A., & Scherndl, T. (2014).
There are various research tools that a researcher can engage in the data collection process such as questionnaires, surveys and interviews Xu, M. A., & Storr, G. B. (2012). In this research, the researcher used the questionnaire. Questions with multiple choices where the participant is required to choose from are referred to as closed questions Dolnicar (2013). Major disadvantage of open ended questions was that they resulted to bulky data which were as well not easy to analyze Choy (2014).
Questionnaires were distributed to the participants under supervision of the researcher. This ensured clarity in the questions to avoid having some of the questions not responded to or responded to inappropriately thus leading to missing data. 55 questionnaires were printed and distributed to the participants out of which only 50 were completed.
The resulted data from questionnaires were presented for analysis. The data was entered into SPSS (version 20) for analysis. Independent Ttest was used in the test of hypothesis to answer the question of the mean difference between male and female students’ performance in mathematics. Also, Pearson correlation was used to test for the correlation between BMI and mathematics performance of the students and also to test for the association between weight of a student and their ages. Frequency tables were used in the representation of data in an easier understandable and interpretable way.
Table 1: Sex of the respondents 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Male 
25 
50.0 
50.0 
50.0 

Female 
25 
50.0 
50.0 
100.0 

Total 
50 
100.0 
100.0 
Equal number of male students to that id the female students (i.e. 25 males and 25 females) participated in this study as shown in the table above.
Table 2: Descriptive Statistics 

N 
Minimum 
Maximum 
Mean 
Std. Deviation 

Age of respondents 
50 
14 
21 
17.28 
1.738 
Height of respondents 
50 
1.28 
1.80 
1.5077 
.12917 
Weight of respondents 
50 
45.00 
78.00 
61.0000 
9.13839 
Mathematics score of the respondents 
50 
30.00 
92.00 
60.6600 
15.76694 
BMI 
50 
18.83 
33.61 
26.9254 
3.25390 
Valid N (listwise) 
50 
The minimum age was 14 years while the maximum age was 21 years. The mean and standard deviations was 17.28 and 1.738 years respectively. Minimum=1.28m and maximum=1.80m. Mean and SD for respondents’ height was 1.5077 and 0.12917m respectively. The minimum and maximum weight was 45kg and 78kg respectively, the mean=61.0kg and SD=9.1389kg. Mathematics had the minimum of 30% and maximum of 92%, mean and SD for mathematics scores was 60.66% and 15.76694% respectively. The body mass index (BMI) had the minimum=18.83kgm^{2} and a maximum=33.61kgm^{2} with mean= 26.9254kgm^{2} and SD=3.2539kgm^{2}.
Descriptive Statistics
Research question 1
To answer this research question, independent ttest was used to test the hypothesis under investigation Male and female were independent variables in this question whereas mathematics was dependent variable. This question investigated the effect of sex of a person to their performance in mathematics. This helped in drawing inferences to the tested hypothesis. Hypothesis
H_{0}: There is no mean difference between male and female students’ performance in mathematics
H_{1}: There is mean difference between male and female students’ performance in mathematics
Table 3: Group Statistics 

Sex 
N 
Mean 
Std. Deviation 
Std. Error Mean 

Mathematics 
Male 
25 
63.6800 
17.37748 
3.47550 
Female 
25 
57.6400 
13.65918 
2.73184 
Table 4: Independent Samples Test 

Levene’s Test for Equality of Variances 
ttest for Equality of Means 

F 
Sig. 
t 
df 
Sig. (2tailed) 
Mean Difference 
Std. Error Difference 
95% Confidence Interval of the Difference 

Lower 
Upper 

Mathematics 
Equal variances assumed 
2.323 
.134 
1.366 
48 
.178 
6.04000 
4.42063 
2.84828 
14.92828 
Equal variances not assumed 
1.366 
45.463 
.179 
6.04000 
4.42063 
2.86111 
14.94111 
No significant difference was observed between mathematics means performance for male and female participants, t(48) = 1.366, p > .05. The male mathematics mean=63.68 and SD=17.37748 compared to female participants’ mathematics mean=57.64 and SD=13.65918 (table 3). Pvalue was greater than level of significance value (.178>.05) we failed to reject the null hypothesis and concluded that there was no mean difference between male and female students’ performance in mathematics.
Research question 2
In response to this research question, Pearson correlation statistic test was conducted to test the hypothesis under investigation. In the question, BMI was independent variable whereas mathematics performance was dependent variable. The investigated the effect of BMI on students’ performance in mathematics.
Hypothesis
H_{0}: There is no correlation between BMI of the students and their mathematics performance
H_{1}: There is correlation between BMI of the students and their mathematics performance
Table 5: Descriptive Statistics for BMI and mathematics 

Mean 
Std. Deviation 
N 

Mathematics 
60.6600 
15.76694 
50 
BMI 
26.9254 
3.25390 
50 
The mean for mathematics scores of the respondents was 60.66 and standard deviation of 15.76694 while the mean for BMI of the respondents was 26.9254 and standard deviation of 3.2539.
Table6: Correlation between mathematics and BMI 

Mathematics 
BMI 

Mathematics 
Pearson Correlation 
1 
.567^{**} 
Sig. (2tailed) 
.000 

N 
50 
50 

BMI 
Pearson Correlation 
.567^{**} 
1 
Sig. (2tailed) 
.000 

N 
50 
50 

**. Correlation is significant at the 0.01 level (2tailed). 
Significant negative correlation was observed to be existing between BMI and mathematics performance, r = 0.567, p < 0.05. Since the pvalue was less than the level of significance, we reject the null hypothesis and we can therefore conclude that there was correlation between BMI of the students and their mathematics performance. The type of correlation that existed between the two variables was negative since the Pearson correlation value was (r = 0.567).
The results showed that 50% of the respondents were male while the remaining 50% were female. This was done with the aim of reducing the disparity that would arise in case less number of either sexes was chosen. The youngest respondent sampled was 14 years while the oldest was 21 years, these were determined from the minimum and maximum values of ages (table 2). The mean age was 17.28 years. From this therefore, it was an evident that most of the respondents were teenagers. The least weight was 45kg with the mean=61.0kg. Minimum mathematics score was 30% while highest score was 92%, class average=60.66%. From class mean score, it can be seen that general students’ performance was above average On the same, BMI formed the interest variable where the lowest range of BMI recorded was 18.83 which fell in the healthy bracket (i.e. 18.5 to 24.9) and the highest BMI recorded from the respondents was 33.61 which fell in the obese bracket i.e. over 30 Sturm, R., & Hattori, A. (2013). The mean BMI=26.9254, this showed that most of the respondents were in the overweight bracket (i.e. 25 to 29.9) as in conferment with the BMI overweight scale that overweight people have the BMI of 25 to 29.9 Wadden et al (2013).
Research Question 1
Hypothesis tested in the first research question was concerning whether there was significant mean difference between male and female respondents in mathematics performance, independent ttest showed that there was no significant difference, pvalue (.178) was greater than the significant level value (0.05) thus the null hypothesis was not rejected.
Last research question, the hypothesis tested using Pearson correlation coefficient (r) showed that there was significant negative correlation between BMI and mathematics performance. The increase in BMI of the students was resulting to lower scores on mathematics performance. This result differs with previously conducted research by Florin, T. A., Shults, J., & Stettler, N. (2011) that no correlation existed between BMI and school performance of the students in most of the subjects except in physics. In this study, test showed that BMI had negative correlation with the students’ performance in mathematics.
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