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Height for age z score and cognitive function are associated with Academic performance among school children aged 8–11 years old

  • Demewoz Haile1Email author,
  • Dabere Nigatu2,
  • Ketema Gashaw2 and
  • Habtamu Demelash3
Archives of Public HealthThe official journal of the Belgian Public Health Association201674:17

https://doi.org/10.1186/s13690-016-0129-9

Received: 17 November 2015

Accepted: 1 March 2016

Published: 2 May 2016

Abstract

Background

Academic achievement of school age children can be affected by several factors such as nutritional status, demographics, and socioeconomic factors. Though evidence about the magnitude of malnutrition is well established in Ethiopia, there is a paucity of evidence about the association of nutritional status with academic performance among the nation’s school age children. Hence, this study aimed to determine how nutritional status and cognitive function are associated with academic performance of school children in Goba town, South East Ethiopia.

Methods

An institution based cross-sectional study was conducted among 131 school age students from primary schools in Goba town enrolled during the 2013/2014 academic year. The nutritional status of students was assessed by anthropometric measurement, while the cognitive assessment was measured by the Kaufman Assessment Battery for Children (KABC-II) and Ravens colored progressive matrices (Raven’s CPM) tests. The academic performance of the school children was measured by collecting the preceding semester academic result from the school record. Descriptive statistics, bivariate and multivariable linear regression were used in the statistical analysis.

Results

This study found a statistically significant positive association between all cognitive test scores and average academic performance except for number recall (p = 0.12) and hand movements (p = 0.08). The correlation between all cognitive test scores and mathematics score was found positive and statistically significant (p < 0.05). In the multivariable linear regression model, better wealth index was significantly associated with higher mathematics score (ß = 0.63; 95 % CI: 0.12–0.74). Similarly a unit change in height for age z score resulted in 2.11 unit change in mathematics score (ß = 2.11; 95 % CI: 0.002–4.21). A single unit change of wealth index resulted 0.53 unit changes in average score of all academic subjects among school age children (ß = 0.53; 95 % CI: 0.11–0.95). A single unit change of age resulted 3.23 unit change in average score of all academic subjects among school age children (ß = 3.23; 95 % CI: 1.20–5.27).

Conclusion

Nutritional status (height for age Z score) and wealth could be modifiable factors to improve academic performance of school age children. Moreover, interventions to improve nutrition for mothers and children may be an important contributor to academic success and national economic growth in Ethiopia. Further study with strong design and large sample size is needed.

Background

Academic achievement of school age children can be affected by several factors such as nutritional status, demographics, and socio-economic factors [1, 2]. No nation can afford to waste its greatest national resource: the intellectual power of its people. In many poor countries where malnutrition is widespread, it is considered a problem that negatively affects the ability of children to learn and causes them to perform at a lower level in school [3]. Studies have shown that food insecurity is associated with academic performance of school age children [4, 5].

An intervention study showed that free school breakfast programs resulted in significant improvements in attendance, mathematics grades, behavior and decreases in hunger [6]. Another study showed that improvement in academic performance would be achieved once proper nutrition is achieved [7]. Children who were malnourished in early life are more likely to have lower school attendance and to score poorly in cognitive tests during school age as compared to well-nourished children [8]. Many studies done in different parts of the world showed that poor growth characteristics are associated with poor academic performance. A prospective cohort study from Canada revealed that though overweight does not increase risk for poor educational outcomes, being underweight increase risk for poorer cognitive outcomes [9]. A study from Southeast Asia showed that all poor growth characteristics (HAZ, WAZ and BAZ) and non-verbal IQ scores were associated negatively [10]. A study from Southern Ethiopia also found that better HAZ and WAZ were positively correlated with cognitive performance tests [11].

Ethiopia is one of the sub-Saharan African countries largely affected by child malnutrition [12]. Studies conducted in different parts of Ethiopia have shown that under nutrition is common among school age children. A study from northwest Ethiopia revealed that 32.3 % of school age children were undernourished (27.1 % underweight and 11.2 % stunted) [13]. Another study from Gondar showed that the prevalence of stunting among school-aged children was 42.7 % in rural areas and 29.2 % in urban areas, while the corresponding figures for thinness were 21.6 and 20.8 % [14]. A study from eastern Ethiopia showed prevalence of stunting among school-aged children was 8.9 % of which 2 % had severe stunting [15]. A 2014 study in Addis Ababa, the nation’s capital found about 31 % of children were undernourished (19.6 % stunted, 15.9 % underweight) [16]. This implies that child malnutrition is still a public health problem in both urban and rural areas.

The future of a nation entirely depends on the welfare of its younger generation. Ethiopia is currently transitioning from the end of its first national growth and transformation plan to the beginning of its second growth and transformation plan, of which its success will largely depend on the academic performance of its school children and their future contributions. In 2012/2013 the net enrolment rate in Primary Level (1–8) was 85.9 %. Similarly, the net enrollment rate at Primary Level in the nation’s largest and most populous region, Oromiya region, was 83.9 %. The national repetition rate among primary school students was found to be 7.9 % while the repetition rate of Oromiya region was 9 %. The dropout rate for Oromiya was 18.8 % while the national was 15.7 % [17]. Therefore proper attention should be given to this group of children because their performance will impact the socioeconomic development of the entire country.

Though evidence about the magnitude of malnutrition is well established in Ethiopia, there is a paucity of evidence regarding how nutritional status is associated with academic performance among the nation’s school age children. Hence, this study is aimed to determine the association of nutritional status and cognitive performance with academic performance of school children in Goba town, South East Ethiopia.

Methods

Study area and period

An institutional based cross-sectional study design was employed in Goba town, Bale zone, Oromiya region, Southeast Ethiopia. The study was conducted from May to June 2014 among grade three elementary school students in Goba town. At the time the town had one preparatory school, two high schools, ten Kindergartens and 13 elementary schools, of which 7 were private and 6 were public [18]. The town schools also served the rural residents who live around the town. From these elementary schools, the study selected grade three students from randomly selected schools, in order to minimize inter-grade variability between different levels of grades. All of the student participants were of age 8 to 11 years old and had been enrolled in the school in 2013/2014 academic year.

Sample size and sampling procedure

The sample size (n) was determined by using G power statistical software version 3.1.5 by assuming the effect size of 0.24, the level of significance (α), 0.05 (Zα/2 = 1.96) and the power (1 − β = 0.80) [19]. The study was conducted on 131 grade three school age children who were enrolled in elementary schools in Goba town. Initially the eligible schools were stratified into public schools and private schools by assuming socioeconomic difference between public and private school students. Two public and three private primary schools were then selected randomly. The total number and list of students were obtained from each selected school. To determine the number of students included in the study from each school, we used proportional allocation based on the number of eligible students from each school. For schools which have more than one section of grade three students, the same procedure was followed. Finally students were selected using a simple random sampling technique (computer generated random number). In any case when a randomly selected student was absent on three consecutive data collection days; he/she was substituted by the next randomly selected student from the same class room.

Measurements

Socio-demographic characteristics were collected using structured questionnaire adopted from the Ethiopian demographic and health survey questionnaire [12]. Dietary intake was assessed using qualitative 24 h dietary recall method in a joint interview with both parent and child. From the qualitative 24 h dietary recall, dietary diversity for each student was calculated based on the WHO eight food groups [20]. The cognitive function of the students was assessed using selected tests from the Kaufman Assessment Battery for Children (KABC-II) and Raven’s Colored Progressive Matrices (RCPM) [21, 22]. The KABC II used Number Recall, Word Order and Hand Movement for measuring sequential processing (short term memory). In KABC II, simultaneous processing is measured by Rover and Triangles while planning (fluid reasoning) is assessed by using Pattern Reasoning. From KABC-II tests, core subsets for planning and conceptual thinking, were not administered because previous studies done in similar settings indicated that, most of the pictures in the subsets were not familiar to Ethiopian children [11, 23]. Tests for measuring learning and knowledge were not used for this study because they were not used previously in similar cultural settings. Raven’s Colored Progressive Matrices (RCPM) made up of three sets of twelve problems which measures the ability to solve problems and reasoning by analogy has been used extensively as a culturally fair test of intelligence [21]. Each of the cognitive tests (selected tests from KABC-II and RCPM) were administered by different well trained data collectors in separate class rooms with a quiet and distraction free environment. One data collector administered only one cognitive test for all study subjects to reduce inter individual differences. Anthropometric measurements (height and weight) were taken for all children included in the study. Body weight was recorded to the nearest 0.1 kg using the UNICEF SECA weighing scale. Instruments were checked daily against a standard weight for accuracy. Calibration of the indicator against zero reading was checked before weighing every child. Children were weighed with light clothing and without shoes. Height was measured to the nearest 0.1 cm using the Shorr measuring board without shoes at Frankfort plane standing position. The age of children in completed years was obtained from school and confirmed from their parents. The academic performance of the school children was measured by collecting the preceding semester academic result from the school record. We used the students’ mathematics and overall average semester results to measure academic performance.

Data entry, processing and analysis

Data were checked for completeness, cleaned, coded and entered into SPSS version 20 and WHO AnthroPlus Version 1.0.4 for analysis. The nutritional status of height-for-age, weight-for-age, and weight-for-height were calculated from measurements using WHO AnthroPlus and compared with reference data according to the WHO 2006 population. Children below negative 2 standard deviation (−2SD) according to the WHO median for weight-for-age, height-for-age and weight-for-height were considered under-weight, stunted or wasted, respectively. Normal was defined as Z-score greater than or equal to -2SD. A descriptive analysis was conducted to obtain summary statistics (frequencies, means and standard deviations). Pearson correlation was used to check the relationship between nutritional status and academic performance. The association between academic performance and cognitive performance was also measured by Pearson correlation. Those variables which were found statistically significant at P value less than 0.25 in the bivariate analysis were entered into the multivariable linear regression model to identify the independent predictors of academic performance. This cutoff point prevented removing variables that would potentially have an effect during multivariable analysis [24]. Variables with p-value less than 0.05 in the final multivariable model were accepted as statistically significant and declared as associated factors.

Ethical consideration

The ethical clearance was obtained from the institutional review board of Madawalabu University. A letter of permission was also obtained from the Goba town education office. Informed written consent was obtained from all parents (mothers/caregivers) after explanation of the study objectives, study period and measurement procedures. The willingness of the school age children was also asked in addition to parents consent. The students and parents were assured that the information they provided would be kept confidential.

Results

Socio-demographic characteristics

The majority of the study participants were Orthodox (97,74.8 %) by religion and Oromo by ethic group (66, 51.1 %). Ninety five (74.8 %) and 122 (93.1 %) of the school age children were from families who were currently on marriage and residing in urban areas, respectively. Nearly one third of (42,32.8 %) the children were from households headed by husbands. Fifty four (47.0 %) of the school age children had a father who attended formal education up to the level of high school. Nearly two thirds (84, 64.9 %) of the school age children were living with both their fathers and mothers. Nineteen (14.5 %) of school age children were living with their grandparents. Eighty four (63.4 %) of the school age children were male by sex. The mean (±SD) age of the school age children was 10.02 (±0.86) years (Table 1).
Table 1

Socio-demographic and economic characteristics of respondents in Goba Town, South east Ethiopia, 2014

Variables

 

Frequency

Percent

Religion

Orthodox

97

74.8

Muslim

27

20.6

Protestant

6

4.6

Ethnic group

Oromo

66

51.1

Amhara

60

45.8

Othersb

4

3.1

Marital status

Married

95

74.8

Divorced

6

4.6

Separated

14

10.7

Widowed

13

9.9

Head of the household

Husband

42

32.8

Wife

17

13.0

Both

54

41.2

Othersc

17

13

Place of residence

Urban

122

93.1

Rural

9

6.9

Educational status of the father

Illiterate

12

10.3

Primary (1–8)

24

20.5

Secondary school (9–12)

54

47.0

Tertiary (>12)

26

22.2

Educational status of the mother

Illiterate

13

9.9

Primary (1–8)

43

32.8

Secondary (9–12)

51

38.9

Tertiary (>12)

17

13.0

Wealth index

Poor

46

36.8

Medium

35

28.0

High

44

35.2

With whom the child lives

Father only

4

3.1

Mother only

19

14.5

With both

84

64.9

Grand parents

19

14.5

Othersa

4

3.1

Sex

Male

83

63.4

Female

48

36.6

School type

Private schools

67

51.1

Governmental schools

64

48.9

Family size

≤3

32

24.6

4–5

61

46.9

≥6

37

28.5

aAunts, sister, religious brothers, any non relatives bGurage, Tigre Wolayita cgrandparents, relatives, aunts

There was a statistically significant positive association between all cognitive test scores and average academic performance except for number recall (p = 0.12) and hand movements (p = 0.08). This study found that there was a statistically significant positive correlation between all cognitive test scores and mathematics score (p < 0.05) (Table 2).
Table 2

Correlation between cognitive function test and academic performance among school aged children in Goba Town, South east Ethiopia, May 2014

Cognitive test scores

Academic performance

Average semester result

Mathematics

Number Recall score

r

0.14

0.19*

p-value

0.12

0.03

N

131

130

Rovers score

r

0.22*

0.22*

P-value

.013

0.01

N

131

130

Hand Movement score

r

0.16

0.20*

P-value

0.08

0.03

N

131

130

Pattern score

r

0.24**

0.27**

P-value

0.005

0.002

N

131

130

Word Order score

r

0.23**

0.19*

p-value

0.008

0.028

N

131

130

Triangles test score

r

0.33**

0.29**

p-value

0.001

0.001

N

131

130

Ravens CPM test score

r

0.38**

0.38**

p-value

0.001

<0.001

N

129

128

*Statistically significant at p <0.05, **Statistically significant at p<0.01

As showed in Table 3, there was a statistically significant positive correlation between height for age Z score (HAZ) and mathematics score among school aged children (p = 0.026). However, both weight for age Z score (WAZ),and body mass index for age Z score (BAZ) had no statistically significant association with academic performance (average semester result and mathematics score) (p > 0.05).
Table 3

Correlation between anthropometric Z scores and academic Achievement among school aged children in Goba Town, South east Ethiopia, May 2014

Anthropometric z scores

Academic performance

Average semester score

Mathematics score

HAZa

r

0.103

0.197a

p-value

0.249

0.026

N

128

127

WAZb

r

0.061

0.099

P-value

0.497

0.270

N

127

126

BAZc

r

−0.029

−0.061

p-value

0.743

0.496

N

127

126

aHeight for Age Z score, bWeight for Age Z score, cBody mass for age Z score

Variables including residence, maternal education, paternal education, diet diversity, meal frequency, breakfast habit, iodized salt consumption, sex of the child, occupation, attendance of preschool program and family size were not significantly associated with academic performance (P > 0.25). Hence those variables were excluded from the multivariable linear regression model.

In the bivariate analysis only height for age Z score and wealth index were significantly associated with mathematics score, qualifying as candidate variables for the multivariable linear regression model. Age and wealth index were positively associated with average score of all academic subjects (courses) of the preceding semester in the multivariable model.

In the multivariable linear regression model better wealth index was significantly associated with better mathematics score (ß = 0.63; 95 % CI: 0.12–0.74). A single unit change of wealth index resulted in 0.63 unit change in mathematics score. Height for age Z score was found positively associated with mathematics score in the multivariable model. A single unit change in height for age Z score resulted in 2.11 unit change in mathematics score (ß = 2.11; 95 % CI: 0.002–4.21). A single unit change in wealth index resulted in 0.53 unit change in average score of all academic subjects among school age children (ß = 0.53; 95 % CI: 0.11–0.95). Older children scored significantly higher in average semester result. A single unit change of age results 3.23 unit change in average score of all academic subjects among school age children (ß = 3.23; 95 % CI: 1.20–5.27).

Discussion

This study found that WAZ and BAZ were not significantly associated with average semester results or mathematics score among school age children, age 8–11 years. Similarly a study from Malaysia found no association between poor nutritional status and school performance among a sample of 7 to 8 years old primary school children [25]. However, in contrast to the finding of this study, studies have shown that higher WAZ is positively associated with academic performance [26, 27]. High WAZ score is associated with better cognitive performance [11]. The small sample size of this study possibly a reason for absence of association between WAZ and academic performance. Additionally, this study was conducted among school age children attending urban schools, where prevalence of underweight is tends to be lower than in rural schools. Therefore the effect of underweight on academic performance in a study population where malnutrition prevalence is low might not reflect the effect at a population level. The absence of association between BAZ and academic performance might be explained by the fact that BAZ indicates acute nutritional status and do not interfere with cognitive functioning.

This study found that good HAZ score was significantly associated with higher mathematics scores. Similar finding came from a study conducted in Sri Lanka which showed that higher HAZ score is associated with better academic score [27]. Height-for-age reflects the accumulation of nutritional deprivation throughout the years, which may consequently affect educational achievement of children [28]. A study from Uganda revealed that HAZ had statistically significant positive associations with learning achievement in English (language) and mathematics among grade 4 children [29]. A study among grade 4 children in Sri Lanka showed a significant positive association between height-for-age and examination scores [30]. Stunted children have shown more anxiety, depression and lower self-esteem than their non stunted counter parts [31]. Similarly, protein malnutrition (and low energy availability in general) may have negative effects on brain development. Chronic protein energy malnutrition (stunting) affects the ongoing development of higher cognitive processes during childhood years [32, 33]. Stunted children have impaired behavioral development in early life [34] and have poorer cognitive ability [35] than non-stunted children. Brain functions such as cognition, memory and locomotors skills are affected by under nutrition [36, 37]. However, the associations between poor linear growth and impaired neurodevelopment are not well understood [36]. Cognitive functioning might mediate the association between stunting and academic performance. This study found that all the cognitive test scores were significantly associated with the mathematics scores. Except number recall and hand movement, all other cognitive tests were correlated with average semester results.

This study found that higher wealth index is associated with better mathematics score of school age children. Many consistent findings were reported from different part of the world. Food security, which is correlated with wealth index has predicted impaired academic performance in mathematics for girls and boys [4]. A meta analysis study showed that there is a strong correlation between socio economic status and academic performance [38]. Poor socioeconomic condition of the family is one of the determinants for poor school performance among children [39]. Primary school children in Malaysia from the lower income group had significantly poorer academic performance [2]. The association between socio economic status and academic achievement widens with increasing age. Socio‐economic gaps in the early school years has long lasting consequences. Particularly, as children in low socio economic status get older, their situation tends to worsen [40]. The association between socioeconomic status and academic achievement is complex and included several factors such as nutrition, school environment, home living environment, material support etc. Socio economic status might affect educational performance due to its influence on affordability of quality of residence, access and affordability of Information Communication facilities and services, and library materials which deal with academic matters [41]. Low socioeconomic status is associated with chronic stress which has long term negative consequences on brain development [42]. Furthermore, low socioeconomic individuals have high levels of stress hormones such as cortisol and catecholamines [43].

This study implies that academic performance is affected by chronic malnutrition and wealth. The education sector, which largely determines the welfare of a country, can be improved by investing in nutrition, particularly on chronic malnutrition. Stunting begins in utero and continues for at least the first 2 years of postnatal life; the period from conception to a child’s second birthday (the first one thousand days) has therefore been identified as the most critical window of opportunity for interventions. There are proven interventions to prevent stunting such as exclusive breastfeeding, complementary feeding, infection prevention, sanitation and hygiene. Nutrition interventions should also be strengthened in schools to improve children’s academic performance. Improving the socioeconomic status of a community has profitable gains in terms of students’ academic performance and development of the nation.

This study had some limitations. The first limitation was its small sample size and biased inclusion of children mostly from urban residency. This study used only anthropomorphic measurements and did not assess the micronutrients status of study participants. Furthermore, there may have been differences in the evaluation system for students’ academic performance among the public and private schools. Finally, physical performance capacity and motor skills were not measured in this study.

Conclusion

Nutritional status and wealth could be additional modifiable factors to improve academic performance of the children. Moreover, interventions to improve nutrition for mothers and children may be an important contributor to academic success and national economic growth in Ethiopia Further study with a strong design and large sample size is important.

Declarations

Acknowledgment

We would like to acknowledge Madawalabu University for financing this study. We are also grateful to thank Lianna Tabar, WEEMA International, Brookline, MA, USA for her professional language editing and reviewing the manuscript.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Public Health, College of Medicine and Health Sciences, Bahir Dar University
(2)
Department of Nursing College of Medicine and Health Sciences, Madawalabu University
(3)
Department of Public Health, College of Medicine and Health Sciences, Debre Tabor University

References

  1. Zalilah M, Bond J, Johnson N. Nutritional status of primary school children from low income households in Kuala Lumpur. Malays J Nutr. 2000;6:17–32.Google Scholar
  2. Anuar M, Lim C, Low W, Harun F. Effects of nutritional status on academic performance of Malaysian primary school children. Asia Pac J Public Health. 2005;17:81–7.View ArticleGoogle Scholar
  3. Galal O, Hullet J. The relationship between nutrition and children’s educational performance: A focus on the United Arab Emirates. Br J Nutr. 2003;25:11–20.Google Scholar
  4. Jyoti D, Frongillo E, Jones S. Food insecurity affects school children’s academic performance, weight gain, and social skills. J Nutr. 2005;135:2831–9.PubMedGoogle Scholar
  5. Frongillo E, Jyoti DF, Jonesy S. Food stamp program participation is associated with better academic learning among school children. J Nutr. 2006;136:1077–80.PubMedGoogle Scholar
  6. Kleinman R, Hall S, Green H, Korzec-Ramirez D, Patton K, Pagano M, et al. Diet, breakfast, and academic performance in children. Ann Nutr Metab. 2002;46(1):24–30.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Rausch R. Nutrition and Academic Performance in School-Age Children The Relation to Obesity and Food Insufficiency. J Nutr Food Sci. 2013;3:190.View ArticleGoogle Scholar
  8. Alaimo K, Olson C, Frongillo E. Food insufficiency and American school-aged children’s cognitive, academic, and psychosocial development. Paediatrics. 2001;108:44–53.View ArticleGoogle Scholar
  9. Bisset S, Foumier M, Pagani L, Janosz M. Predicting academic and cognitive outcomes from weight status trajectories during childhood. Int J Obes (Lond). 2013;37(1):154–9.View ArticleGoogle Scholar
  10. Sandjaja PBK, Rojroonwasinkul N, Le Nyugen BK, Budiman B, Ng LO, et al. Relationship between anthropometric indicators and cognitive performance in Southeast Asian school-aged children. Br J Nutr. 2013;110(3):S57–64.View ArticlePubMedGoogle Scholar
  11. Alemtsehay B, Stoecker B, Kennedy T, Hubbs-Tait L, Thomas D, Abebe Y, et al. Nutritional status and cognitive performance of mother-child pairs in sidama zone, southern Ethiopia. Matern Child Nutr. 2013;9(2):274–84.View ArticleGoogle Scholar
  12. Central Statistical Agency (CSA) Ethiopia. Demographic and Health Survey 2011. Addis Ababa, Ethiopia and Calverton, Maryland, USA: CSA and ORC Macro; 2011.Google Scholar
  13. Alelign T, Degarege A, Erko B. Prevalence and factors associated with undernutrition and anaemia among school children in Durbete Town, northwest Ethiopia. Arch Public Health. 2015;73(1):34.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Herrador Z, Sordo L, Gadisa E, Moreno J, Nieto J, Benito A, et al. Cross-Sectional Study of Malnutrition and Associated Factors among School Aged Children in Rural and Urban Settings of Fogera and Libo Kemkem Districts, Ethiopia. PLoS ONE. 2014;9(9):e105880.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Mesfin F, Berhane Y, Worku A. Prevalence and associated factors of stunting among primary school children in Eastern Ethiopia. Nutr Diet Suppl. 2015;7:61–8.View ArticleGoogle Scholar
  16. Degarege D, Degarege A, Animut A. Undernutrition and associated risk factors among school age children in Addis Ababa, Ethiopia. BMC Public Health. 2015;15:375.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Ethiopian Ministry of Education. Education statistics Annual Abstract: Available at http://www.moe.gov.et/English/Resources/Documents/eab05.pdf.2012/2013.
  18. Goba woreda education office. Goba woreda education office annual report. 2013.Google Scholar
  19. Faul F, Erdfelder E, Lang A, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research method: Available at http://www.gpower.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPower3-BRM-Paper.pdf. 2007.
  20. WHO U, IFPRI, UC Davis, USAID, FANTA,.Indicators for assessing infant and young child feeding practices. Geneva, Switherland World Health Organization (http://apps.who.int/iris/bitstream/10665/43895/1/9789241596664_eng.pdf) date accessd May 25, 2011). 2008.
  21. Raven J. The Raven’s Progressive Matrices: change and stability over culture and time. Cogn Psychol. 2000;41:1–48.View ArticlePubMedGoogle Scholar
  22. Kaufman A, Kaufman N. Kaufman assessment battery for children. 2nd ed. Circle Pines: Pearson (AGS); 2004.Google Scholar
  23. Girma M, Loha E, Stoecker B. Iodine deficiency, anthropometric status cognitive function of school age children in Hawassa town, Southern Ethiopia. Msc.thesis submitted to Human Nutrition Graduate Program, College of Agriculture School of Graduate Studies Hawassa University: 2009.Google Scholar
  24. Peter C, Jack V. Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol. 2004;57:1138–46.View ArticleGoogle Scholar
  25. Ong LC, Chandran V, Lim YY, Chen AH, Poh BK. Factors associated with poor academic achievement among urban primary school children in Malaysia. Singapore Med J. 2010;51(3):247–52.PubMedGoogle Scholar
  26. HamidJan JM, AmalMitra K, Hasmiza H, Pim CD, Ng LO, WanManan WM. Effect of gender and nutritional status on academic achievement and cognitive function among primary school children in a rural district in Malaysia. Malays J Nutr. 2011;17(2):189–200.Google Scholar
  27. Sarma MSG, Wijesinghe DGNG, Sivananthawerl T. The Effects of Nutritional Status on Educational Performance of Primary School Children in the Plantation Sector in Nuwara Eliya Educational Zone. Trop Agric Res. 2013;24(3):203–14.View ArticleGoogle Scholar
  28. Shariff ZM, Bond JT, Johnson NE. Nutrition and educational achievement of urban primary school children in Malaysia. Asia Pac J Clin Nutr. 2000;9(4):264–73.View ArticlePubMedGoogle Scholar
  29. Acham H, Kikafunda JK, Oluka S, Malde MK, Tylleskar T. Height, weight, body mass index and learning achievement in Kumi district, East of Uganda. Sci Res Essay. 2008;3(1):1–8.Google Scholar
  30. Aturupane H, Glewwe P, Wisniewski S. The impact of School Quality, Socio-Economic Factors and Child Health on Students’ Academic Performance: Evidence from Sri Lankan Primary Schools.Available at http://siteresources.worldbank.org/INTSOUTHASIA/Resources/ImpactOfSchoolQuality_July2007.pdf.
  31. Walker S, Chang S, Powell C, Simonoff ES, Grantham-McGregor S. Early childhood stunting is associated with poor psychological functioning in late adolescence and effects are reduced by psychosocial stimulation. J Nutr. 2007;137:2464–9.PubMedGoogle Scholar
  32. Kar B, Rao SL, Chandramouli BA. Cognitive development in children with chronic protein energy malnutrition. Behav Brain Funct. 2008;4:31.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Crookston BT, Dearden KA, Alder SC, Porucznik CA, Stanford JB, Merrill RM, et al. Impact of early and concurrent stunting on cognition. Matern Child Nutr. 2011;7(4):397–409.View ArticlePubMedGoogle Scholar
  34. Lasky RE, Klein RE, Yarbrough C, Engle PL, Lechtig A, Martorell R. The relationship between physical growth and infant behavioral development in rural Guatemala. Child Dev. 1981;52:219–26.View ArticlePubMedGoogle Scholar
  35. Casale D, Desmond C, Richter L. The association between stunting and psychosocial development among preschool children a study using the South African Birth to Twenty cohort data. Child Care Health Dev. 2014;40(6):759–914.View ArticleGoogle Scholar
  36. Levitsky DA, Strupp BJ. Malnutrition and the brain: changing concepts, changing concerns. J Nutr. 1995;125:2212–20.Google Scholar
  37. Ranade SC, Rose A, Rao M, Gallego J, Gressens P, Mani S. Different types of nutritional deficiencies affect different domains of spatial memory function checked in a radial armmaze. Neuroscience. 2008;152:859–66.View ArticlePubMedGoogle Scholar
  38. Sirin SR. Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Rev Educ Res. 2005;75(3):417–53.View ArticleGoogle Scholar
  39. Kim H, Frongillo E, Han S, Oh S, Kim W, Jang Y, et al. Academic performance of Korean children is associated with dietary behaviours and physical status. Asia Pac J Clin Nutr. 2003;12(2):186–92.PubMedGoogle Scholar
  40. Daniel H. Caro Socioeconomic Status and Academic Achievement Trajectories from Childhood to Adolescence. Can J Educ. 2009;32(3):558–90.Google Scholar
  41. Okioga CK. The impact of students’ socio-economic background on academic performance in Universities, a case of students in Kisii University College. Am Int J Soc Sci. 2013;2(2):38-45.Google Scholar
  42. Turner RJ, Avison WR. Status variations in stress exposure: implications for the interpretation of research on race, socioeconomic status, and gender. J Health Soc Behav. 2003;44(4):488–505.View ArticlePubMedGoogle Scholar
  43. Cohen S, Doyle WJ, Baum A. Socioeconimic status is associted with stress hormones. Psychosom Med. 2006;68(3):414–20.View ArticlePubMedGoogle Scholar

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