|Year : 2022 | Volume
| Issue : 4 | Page : 317-324
Socio-demographic factors influencing measures of cognitive function of early adolescent students in abuja, Nigeria
Vincent Ebuka Nwatah1, Patience Abaluomo Ahmed1, Lamidi Isah Audu2, Selina Nnuaku Okolo3
1 Department of Paediatrics, National Hospital Abuja, Federal Capital Territory, Abuja, Nigeria
2 Department of Paediatrics, Barau Dikko Teaching Hospital, Kaduna State University, Kaduna, Nigeria
3 Department of Paediatrics, Jos University Teaching Hospital, University of Jos, Jos, Nigeria
|Date of Submission||01-Jun-2022|
|Date of Decision||07-Sep-2022|
|Date of Acceptance||04-Oct-2022|
|Date of Web Publication||27-Oct-2022|
Vincent Ebuka Nwatah
Department of Paediatrics, National Hospital, Federal Capital Territory, Abuja
Source of Support: None, Conflict of Interest: None
Background: The brain in the early adolescent period undergoes enhanced changes with the radical reorganisation of the neuronal network leading to improvement in cognitive capacity. A complex interplay exists between environment and genetics that influences the outcome of intellectual capability. We, therefore, aimed to evaluate the relationship between socio-demographic variables and measures of cognitive function (intelligence quotient [IQ] and academic performance) of early adolescents. Methods: The study was a descriptive cross-sectional study of early adolescents aged 10–14 years. Raven's Standard Progressive Matrices was used to assess the IQ and academic performance was assessed by obtaining the average of all the subjects' scores in the last three terms that made up an academic year. A confidence interval of 95% was assumed and a value of P < 0.05 was considered statistically significant. Results: The overall mean (standard deviation) age of the study population was 11.1 years (±1.3) with male-to-female ratio of 1:1. Female sex was associated with better academic performance with P = 0.004. The students with optimal IQ performance were more likely (61.7%) to perform above average than those with sub-optimal IQ performance (28.6%). As the mother's age increased, the likelihood of having optimal IQ performance increased 1.04 times (odds ratio [OR] = 1.04; 95 confidence interval [CI] = 1.01–1.07). Students in private schools were three times more likely to have optimal IQ performance than those from public schools (OR = 2.79; 95 CI = 1.65–4.71). Conclusion: The present study demonstrated that students' IQ performance and the female gender were associated with above-average academic performance. The predictors of optimal IQ performance found in this study were students' age, maternal age and school type.
Keywords: Academic performance, cognitive function, early adolescents, intelligence quotient, socio-demographics
|How to cite this article:|
Nwatah VE, Ahmed PA, Audu LI, Okolo SN. Socio-demographic factors influencing measures of cognitive function of early adolescent students in abuja, Nigeria. Niger Postgrad Med J 2022;29:317-24
|How to cite this URL:|
Nwatah VE, Ahmed PA, Audu LI, Okolo SN. Socio-demographic factors influencing measures of cognitive function of early adolescent students in abuja, Nigeria. Niger Postgrad Med J [serial online] 2022 [cited 2022 Dec 5];29:317-24. Available from: https://www.npmj.org/text.asp?2022/29/4/317/359757
| Introduction|| |
Cognitive changes that occur during early adolescence, such as abstract and logical thinking, can be as dramatic as the physical changes of pubertal growth. A recent neuroscientific findings reveal that during the early adolescent period, the brain undergoes enhanced electrical and physiological changes with the doubling of brain cells and radical reorganisation of the neuronal network leading to improvement in cognitive capacity. Academic achievement relates to student's cognitive capability and performance, which is multidimensional with intricate connections to various aspects of child development. Grades awarded to students at the end of an academic study show their academic ability and performance. The intelligence quotient (IQ) determines learning efficiency and cognitive task comprehension, hence is a good predictor of academic performance.,
A complex interplay exists between environment and genetics that influences the outcome of intellectual capability. This has led to an ongoing debate in academic parlance on the impact and significance of nature versus nurture in the relationship between genetics and environment on intelligence.,,, It has been argued by Neisser et al. that the impact of genetic factors on IQ of an individual is dependent on environmental factors and vice versa. Franić et al. in their study of twin intelligence stability asserted that phenotypic stability observed in their study was primarily driven by the genetic factors of which additive genetic influences were stable while the shared environment by family members contributed moderately to stability and unique individual environmental factors contributed to temporal instability. The above study by Franić et al. favours nurture over nature on their impact on intelligence. Intelligence has been reported to have a relationship with social phenomena such as educational attainment and socio-economic status (SES).
Socio-economic status was not significantly associated with the IQ of students in Akure, as reported by Ijarotimi and Ijadunola although it had a weak positive correlation. However, it has been argued that IQ and SES are interdependent of each other as IQ can predict an individual's later SES while present SES has also been noted to predict IQ. The factors relating to SES, such as household income, place of residence and fathers' and mothers' education, have been demonstrated to be significantly associated with IQ., It has also been noted that the association between SES and intelligence cannot be considered in isolation without considering the impact of other influencing factors.
Academic performance in school depends on several factors apart from intelligence, such as persistence, interest in schooling, willingness to study, motivation from peers/family/teachers and cultural factors. In general, these can be classified based on the source of influence into; student factors, home/family factors and school factors., The examples of these factors will include the following: student factors: Age, gender, self-confidence, personal motivation and substance abuse; family/home factors: parental education, parental motivation/expectations, family-SES and school factors: school environment/infrastructures, school ownership and school motivation/guidance.,,, These factors do not influence academic performance in isolation; hence, it may be difficult for investigators to prove an isolated relationship except in well-controlled environment. The factors such as insufficient infrastructure, health problems such as malnutrition, poor socio-economic status and environment affect cognitive function and academic performance with difficulty in delineating their relative impact when they coexist., Several authors have demonstrated significant associations between SES and academic performance.,,,, Socio-economic status influences academic performance at varying levels of interplay. We, therefore, aimed to evaluate the relationship between socio-demographic variables and measures of cognitive function (IQ and academic performance) of early adolescents.
| Methods|| |
Study design and area
The study was a descriptive cross-sectional study conducted in randomly selected secondary schools located in Abuja Municipal Area Council (AMAC) in the Federal Capital Territory (FCT), Nigeria. The FCT has a multi-ethnic population, and it is strategically located in the centre of the country. AMAC being the seat of the Federal Government of Nigeria, is predominantly an urban setting with few peripheral suburban and rural settlements. The study period data collection was from March to June 2018.
The study population comprised apparently healthy adolescent junior secondary school pupils aged 10–14 years.
Sample size determination
The desired sample size was calculated using the Fischer formula based on a prevalence of malnourished children with an IQ below average, in a previous study done in Akure, Nigeria (53%), normal standard deviation (SD) set at 1.96 and degree of accuracy set at 5%. Thus, n = ([1.96]2 × 0.53 × 0.47)/(0.05)2 = 382.
Assuming a 10% non-response, poor/incomplete response and loss of questionnaire, the total minimum sample size was (382 × 10/100) +382 = 420.
Participants were enrolled using a multistage random sampling technique. A total of five districts were randomly selected from AMAC out of 15 major districts. Five public schools and nine private schools were selected. Simple random sampling in each school for student selection was done.
Inclusion and exclusion criteria
Apparently, healthy students between the ages of 10 and 14 years were recruited. Students with any form of neuromuscular deficits (hemiplegia) or physical challenge involving the limbs or spine that may affect their height and subsequently, body mass index were excluded. Furthermore, students with any known chronic illness (such as sickle cell disease, asthma or diabetes) that may influence their school attendance were excluded from the study.
Ethical approval was obtained from the Health Research Ethics Committee of our institution (NHA/EC/086/2016) and the Universal Basic Education Board of the FCT (FCTUBEB/DSC/06/III) on June 2017 and November 2016, respectively. A written informed consent was obtained from the parents/guardians and assent was obtained from the students.
The questionnaire was self-administered and given to the pupils to take home to their parents for filling, and they were encouraged to return these by the following day. The researchers then immediately reviewed the returned questionnaire to ensure all questions were answered. The questionnaire obtained information on students' socio-demographic variables and sections on IQ test scores and academic performance. The socio-economic class was determined using the method described by Olusanya et al. based on the educational attainment of the mother and father's occupation. Age was determined from a personal interview and confirmed using the birth certificate and/or school records.
Determination of intelligence quotient
Raven's Standard Progressive Matrices (RSPM) was used to assess the intelligence quotient, which was group administered. RSPM is a nonverbal 60-item test that requires the use of inductive reasoning about perceptual patterns, which can be used to determine IQ. The students were given 60 min to answer the questions without interruption. The raw score of the IQ test of each student was obtained, while the IQ was then determined and graded according to a standardised scheme. Subsequently, the IQ grades were further categorised into optimal IQ performance (Grade I, II and III) and suboptimal IQ performance (Grades IV and V).
Determination of academic performance
This was done by obtaining the total average of all the subjects' scores in the last three terms that made up an academic year. Academic performance categorisation (above average, average and below average) was also employed due to wide variations in academic performance assessment across schools and even within schools with individual class peculiarities. Each student's term's average score was obtained by summing the final individual subjects' scores for all subjects and dividing the sum by the total number of subjects offered by the student. Subsequently, the average of the three terms scores was obtained for each student. The participants' academic performance was classified as follows: above-average – student's average academic score was above his/her respective class academic year average; average – student's score was at his/her respective class academic year average; below-average – student's score was below his/her respective class academic year average.
Data were analysed using the Statistical Package for the Social Science software version 21.0 (IBM Corp., Armonk, NY). Variables, such as age, sex and parental age, were presented using frequency tables and charts. Quantitative continuous variables, such as age and IQ test scores, were summarised using mean, SD and median. Categorical variables were summarised using percentages. Pearson's Chi-squared test was used to test the association between the two categorical variables. Logistic regression was used to assess the effect of socio-demographic variables on IQ in determining the predictors of optimal IQ. A confidence interval of 95% was assumed, and a P < 0.05 was considered statistically significant.
| Results|| |
Socio-demographic characteristics of study participants
The overall mean (SD) age of the study population was 11.1 years (±1.3), as shown in [Table 1]. The male-to-female ratio of the study participants was 1:1. Of the overall participants' population, Igbo tribe made up 108 (22.8%), Yoruba tribe 85 (17.9%), Hausa/Fulani 77 (16.2%) and other tribes 204 (43%). The mean (SD) parental age for fathers and mothers was 57.9 ± 7.7 and 40.5 (±7.0), respectively, while a majority of the study participants are from the upper social class (63%), as shown in [Table 1].
[Figure 1] demonstrates the distribution of participants' IQ grade distribution.
For the IQ grade categorisation, 334 (70.5%) and 140 (29.5%) had optimal IQ performance and sub-optimal IQ performance, respectively. [Figure 2] illustrates the participants' academic performance grading.
Association between students' academic performance and other variables
There was a statistically significant association between students' academic performance and sex with χ2 = 10.953 and P = 0.004 [Table 2]. Female students were more likely (59.5%) to score above average than male students (44.3%). Other socio-demographic variables had no statistically significant association with the academic performance of the students.
|Table 2: Association between general characteristics and students' academic performance|
Click here to view
The students with optimal IQ performance were more likely (61.7%) to perform above average than those with sub-optimal IQ performance (28.6%). There was a statistically significant association between IQ performance and student's academic performance with χ2 = 52.227; df = 2 and P < 0.001 [Table 3].
|Table 3: Association between students' intelligence quotient performance and academic performance|
Click here to view
Association between socio-demographic characteristics and intelligent quotient performance
There was statistically significant difference between IQ performance categories with respect to some general characteristic variables like: Age (P < 0.001); school type (P < 0.001); number of siblings (P = 0.001); father's age (P = 0.026); mother's age (P = 0.001) and SES P < 0.001 [Table 4]. Students that were 10 years old were most likely to have optimal IQ performance (84.5%), followed by those that were 11 years (78.4%), 13 years (72.9%) and 12 years (72.3%), while those that were 14 years old were least likely (42.9%). Students in private schools were more likely (86.5%) to have optimal IQ performance compared with those in public schools (54.4%). The more the number of siblings above 1–3, the more likely the participant will have sub-optimal IQ. Younger maternal ages of 20–29 and paternal ages of 30–39 were associated with the least likelihood of having optimal IQ. Students from upper SES were most likely (80.9%) to have optimal IQ performance, followed by those from middle SES (56.5%), while those from lower SES were least likely (44.2%).
|Table 4: Association between general characteristics and intelligence quotient performance|
Click here to view
Logistic regression analysis for optimal intelligent quotient
As age decreased by one unit, the likelihood of having optimal IQ performance increased 1.3 times and could be as high as two times (odds ratio [OR] = 0.77; 95 confidence interval [CI] = 0.64–0.94), as shown in [Table 5]. As the mother's age increased by one unit, the likelihood of having optimal IQ performance increased 1.04 times, and it could be as high as 1.07 times (OR = 1.04; 95 CI = 1.01–1.07). Furthermore, as shown in [Table 5], students in private schools were three times more likely to have optimal IQ performance than those from public schools, and it could be as high as five times (OR = 2.79; 95 CI = 1.65–4.71).
|Table 5: Simple and multiple logistic regression of optimal intelligence quotient performance on associated factors|
Click here to view
| Discussion|| |
In the present study, only gender and IQ were found to be associated with students' academic performance. Female students were more likely to score above-average academic performance than their male counterparts. Females outperforming their male counterparts have been documented in some studies.,, The explanation for this observation is multifactorial as male students are mostly unorganised with behavioural issues and tend to have short attention spans with a higher prevalence of attention-deficit hyperactivity disorder. Furthermore, males are generally more involved in physical activities like sports which may serve as a form of academic distraction for some.
This study also demonstrated that the students with optimal IQ were more likely to perform above average in their academic performance than those with sub-optimal IQ. The current study evinced that those with optimal IQ are more likely to have above-average academic performance. This could be attributed to the impact of intelligence on individual's capability to exploit domains of cognition, especially memory and attention, which are very essential in tasks necessary to succeed in scholastic achievement. Similarly, other studies have demonstrated this predictor effect of IQ on academic achievement.,,, However, some authors have argued that IQ was not a good determinant of academic performance., It was demonstrated by Heaven and Ciarrochi that the effect of IQ on academic performance was eliminated when factors like personality traits were controlled. This may buttress the notion that other non-intellective factors such as personality, motivation and ambition may also mediate academic performance.
These present study findings were consistent with the findings of Veas et al. in Spain, as they established that gender and intellectual ability were all essential indicators that influence academic achievement. Furthermore, Akinlana in Ogun state identified mental ability and academic optimism as two potent determinants of the academic performance of secondary school students. The study by Veas et al. and Akinlana showed that intellectual ability was central in explaining the contribution of other variables. This may infer that for other factors to favourably influence academic achievement, intellectual ability needs to be optimal. Intelligence, therefore, remains an important factor for scholastic achievement.
The current study did not find any significant association between SES and academic performance. The reason for this observation may not apparent. Conversely, Al-Mekhlafi et al. asserted that poor SES is a strong inhibitor to academic performance with demotivating influence on learning and students' study. The presence of poor SES more likely propagates weakness in academic performance with the children being forced to work and hence affects their school attendance and study. When the student's basic needs are unfulfilled, their capacity to perform academically remains suboptimal. In Enugu, Ezenwosu et al. observed that academic performance was significantly associated with SES. This finding by Ezenwosu et al. was amongst patients with sickle cell anaemia, which could have been influenced by sick role factors or by other school/social factors not controlled by these authors.
Several factors such as students' age, school type, number of siblings, maternal age and upper SES score were found to have a statistically significant association with IQ in the current study. However, after controlling for confounding variables (like the number of siblings and SES), only students' age, maternal age and school type were found to predict optimal IQ. The younger students have better odds to have optimal IQ compared to older ones, while with increasing maternal age, the higher the likelihood of optimal IQ. The reason for this observation may not be very obvious but may be due to increased propensity for the development of new neural connections with enhanced curiosity as well as less environmental or psychosocial distractions amongst the younger early adolescents. Maternal age could have influenced the optimal IQ of students as the more mature the maternal age, the more experience the mother. Furthermore, more experienced mothers tend to provide better environmental factors that will stimulate IQ enhancement. The students from private schools were three times more likely to have optimal IQ. This could be attributed to better environmental influence and greater motivation/expectation from parents as a majority of the private students are from upper SES.
Participants' age, maternal age as well as other factors such as sex and SES were found to be predictor variables for IQ by Deary et al. and Lawlor et al. which was consistent with the current study findings. Conversely, Tabriz et al. in Iran found no difference in IQ scores based on sex, parental age and ethnicity. The Iranian study, which focused on both rural and urban areas with both younger study population, as well as younger parental age, did not assess for any school factors. Ijarotimi and Ijadunola also showed that participants' age, sex and SES were not predictors of IQ. The author used a different means of assessing SES and had more than 70% of the study population having below-average or intellectual deficit IQ performance.
The impact of genetics and other environmental factors (like school infrastructures and personal and parental motivation) that could influence measures of cognition were not determined in the current study and thus may limit the application of study findings without controlling for such factors. The incorporation of these factors will definitely widen the scope of this study. However, with all the possible associated factors' incorporation, successful study completion may not be easily achievable due to limited resources.
| Conclusion|| |
The present study demonstrated that students' IQ performance and gender were associated with above-average academic performance. It was also evident that even though students' age, school type, maternal age and SES appeared to be associated with IQ, the predictors of optimal IQ performance found in this study were students' age, maternal age and school type. We thereby recommend the need for improvement of the learning environment, especially for public schools in which students were found not to have comparable optimal IQ with those in private schools.
We wish to thank the schools, students and their guardians for their patience and cooperation during the study period.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
United State Department of Education. Helping Your Child through Early Adolescence. Washington: Office of Communications and Outreach; 2005.
United Nations Children's Fund. The State of the World's Children 2011: Adolescence an Age of Opportunity. New York, USA: United Nations Children's Fund; 2011.
Ampofo E, Osei-Owusu B. Determinants of academic performance of senior high school (SHS) students in the Ahsanti Mampong municipality of Ghana. Eur J Res Reflect Educ Sci 2015;3:33-48.
Neisser U, Boodoo G, Bouchard Jr. T, Boykin AW, Brody N, Ceci SJ, et al
. Intelligence: Knowns and unknowns. Am Psychol 1996;51:77-101.
Lynn R, Vanhanen T. Intelligence: A Unifying Construct for the Social Sciences. London, UK: Ulster Institute for Social Research; 2012.
Motlagh A, Safarpour M, Hosseini M, Safarpour M, Safarpour H, Maskooni M. Intelligence Quotients and socioeconomic factors. Int Res J Appl Basic Sci 2013;7:704-8.
Franić S, Dolan CV, van Beijsterveldt CE, Hulshoff Pol HE, Bartels M, Boomsma DI. Genetic and environmental stability of intelligence in childhood and adolescence. Twin Res Hum Genet 2014;17:151-63.
Nisbett R, Aronson J, Blair C, Dickens W, Flynn J, Halpern D, et al
. New findings and theoretical developments. Intelligence 2012;67:130-59.
Polderman TJ, Gosso MF, Posthuma D, Van Beijsterveldt TC, Heutink P, Verhulst FC, et al
. A longitudinal twin study on IQ, executive functioning, and attention problems during childhood and early adolescence. Acta Neurol Belg 2006;106:191-207.
Ijarotimi OS, Ijadunola KT. Nutritional status and intelligence quotient of primary schoolchildren in Akure community of Ondo State, Nigeria. Tanzan Health Res Bull 2007;9:69-76.
Tabriz AA, Sohrabi MR, Parsay S, Abadi A, Kiapour N, Aliyari M, et al
. Relation of intelligence quotient and body mass index in preschool children: A community-based cross-sectional study. Nutr Diabetes 2015;5:e176.
Dissanayake DS, Kumarasiri PV, Nugegoda DB, Dissanayake DM. The association of iron status with educational performance and intelligence among adolescents. Ceylon Med J 2009;54:75-9.
Farooq M, Chaudhry A, Shafiq M, Berhanu G. Factors affecting students' quality of academic performance: A case of secondary school level. J Qual Technol Manag 2011;7:1-14.
Mersha Y, Bishaw A, Tegegne F. Factors affecting female students' academic achievement at Bahir Dar University. J Int Coop Educ 2013;15:135-48.
Ezenwosu OU, Emodi IJ, Ikefuna AN, Chukwu BF, Osuorah CD. Determinants of academic performance in children with sickle cell anaemia. BMC Pediatr 2013;13:189.
Al-Mekhlafi HM, Mahdy MA, Sallam AA, Ariffin WA, Al-Mekhlafi AM, Amran AA, et al
. Nutritional and socio-economic determinants of cognitive function and educational achievement of Aboriginal schoolchildren in rural Malaysia. Br J Nutr 2011;106:1100-6.
Lacour M, Tissington L. The effects of poverty on academic achievement. Educ Res Rev 2011;6:522-7.
Raychaudhuri A, Debnath M, Sen S. Factors affecting students academic performance : A case study in Agartala municipal council area. Bangladesh E J Sociol 2010;7:34-41.
Araoye MO. Sample size determination. In: Research Methodology with Statistics for Health and Social Sciences. Ilorin, Nigeria: Nathadex; 2003. p. 117-8.
Olusanya O, Okpere E, Ezimokhai M. The importance of social class in voluntary fertility control in a developing country. West Afr J Med 1985;4:205-12.
Deary I, Strand S, Smith P, Fernandes C. Intelligence and educational achievement. Intelligence 2007;35:13-21.
Dayıoglu M, Turut-Asik S. Gender differences in academic performance in a Large Public University in Turkey. Ankara, Turkey: Economic Research Center, Middle East Technical University; 2004. [Doi: 10.1353/rhe. 2002.0021].
Veas A, Gilar R, Miñano P. The influence of gender, intellectual ability, academic self-concept, self-regulation, learning strategies, popularity and parent involvement in early adolescence. Int J Inf Educ Technol 2016;6:591-7.
Ramtekkar UP, Reiersen AM, Todorov AA, Todd RD. Sex and age differences in attention-deficit/hyperactivity disorder symptoms and diagnoses: Implications for DSM-V and ICD-11. J Am Acad Child Adolesc Psychiatry 2010;49:217-28.e1.3.
Chandra R, Azimmudin S. Influence of intelligence and gender on academic achievement of secondary school students of Lucknow City. IOSR J Humanit Soc Sci 2013;17:9-14.
Moenikia M, Zahed-Babelan A. A study of simple and multiple relations between mathematics attitude, academic motivation and intelligence quotient with mathematics achievement. Procedia Soc Behav Sci 2010;2:1537-42.
Tias PA, Istamar S, Corebima AD. The contribution of intelligence quotient (IQ) on biology academic achievement of senior high school students in Medan, Indonesia. Int J Educ Policy Res Rev 2015;2:141-7.
Naderi H, Abdullah R, Aizan HT, Sharir J. Intelligence and academic achievement : An investigation of gender differences. Life Sci J 2010;7:83-7.
Heaven PC, Ciarrochi J. When IQ is not everything : Intelligence, personality and academic performance at school. Pers Individ Dif 2012;53:518-22.
Akinlana T. Academic optimism, motivation and mental ability as determinants of academic performance of secondary school students in Ogun State, Nigeria. Eur J Bus Soc Sci 2013;1:68-76.
Lawlor DA, Batty GD, Morton SM, Deary IJ, Macintyre S, Ronalds G, et al
. Early life predictors of childhood intelligence: Evidence from the Aberdeen children of the 1950s study. J Epidemiol Community Health 2005;59:656-63.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]