Topic 14 maternal education

Maternal Education & Child Care:
Two way ANOVA

Source: Babu and Sanyal (2009) 1

Child Malnutrition
• Causes:
Inadequate food, health, lack of sanitation facilities,
high fertility rates, ignorance about child-care
practices and lack of access to health services.
Poor water, sanitation and hygiene account for 16
per cent of deaths of children under 5 globally WHO
(2005).
Understanding the causes and context of
malnutrition is important in devising strategies that
generate better child health and nutritional
outcomes.

Maternal Education & Child Care 2

Importance of Mother’s Education
• Caldwell (1979): Education of women played an
important role in determining child survival even
after controlling for other socioeconomic
characteristics.
• Mother’s education can enhance child survival :
Implementation of health knowledge, an increased
capability to interact in the modern world and
greater control over health choices for her children.


Issue
• Policy and program formulation:
 How various determinants of child malnutrition
contribute independently and interact with each other in
determining the final outcomes.
 Formal education of the mother may increase the care
practices through knowledge.
 Educated mothers are also time constrained due to their
participation in labor markets and illiterate care-givers
may have time but may have indigenous (or primitive)
child survival practices.
 Understanding the interaction of such related variables
has been a policy and programmatic challenge since such
interactions are usually cultural and context specific.


Method: Two-way ANOVA
• Verify impact of maternal education and child-care on
children’s nutritional status as measured by height for age
(HAZ) and weight for height (WHZ) Z-scores, using a two-
way ANOVA approach.
• Advantage: Permits verification of “interaction
effect”, simultaneous assessment of the effects of two
(or more) independent variables on a single dependent
variable and the possible combined effects of the
independent variables on the dependent variable.
• Current Context: Effect of one factor (for
example, maternal education) depends on the level of the
second factor (child-care).


Conceptual Framework


Conceptual Framework
• Better educated women:
 Knowledgeable about health care
 If exposed to new information, assimilate this improved knowledge
into better care practices than women with lesser education; become
aware of health services (such as health center facilities and
availability of doctors) and generate additional nutritional
knowledge (such as immunization of children against diseases, taking
appropriate actions on incidence of infant diarrhea, feeding the child
during sickness and breastfeeding during early childhood).
 Such good care practices can, in turn, improve the nutritional status
of children.
 Better health care practices are especially relevant for less educated
mothers, for mothers with more dependents and for children from
households with limited resources, poor housing conditions and lack
of access to hygiene and sanitation services.


Conceptual and measurement issues on
child-care


Conceptual and measurement issues
on child-care
• Child survival, nutrition and health = f(household
food security, a healthy environment, available
health services, care provided to women and
children).
• Care = ‘the provision in the household and the
community of time, attention and support to meet
the physical, mental, and social needs of the
growing child and other household members’.


on child-care
Care (Engle et al., 1999):
1. Care for pregnant and lactating women
2. Breastfeeding and complementary feeding of
young children
3. Food preparation and food storage behaviors
4. Hygiene behaviors
5. Care for children during illness


on child-care
Resources for care:
1. Education, knowledge, and beliefs
2. Health and nutritional status of the care-giver
3. Mental health, lack of stress, and self-
confidence of the care-giver
4. Control of resources and intrahousehold
allocation
5. Workload and time constraints
6. Social support from family members and the
community.


Measurement issues
• Care = time spent (quantity of care) and the nature of the activities undertaken
(quality of care).
• ‘time spent on care’ method: Time spent in specific activities with children (such as
bathing, feeding, etc.) along with other activities of the household. Most of the
studies do not find any significant association between child-care time and nutritional
status; may not be a good indicator of nutritional status.
• ‘quality of care’ approach: Determines how specific practices lead to better nutritional
outcomes for children; classified into caregiver and psychosocial care practices –
former affects child’s nutrient intake through psychomotor capabilities (such as use of
finger foods, spoon handling ability, etc.) and appetite (Engle et al., 1999).
• Additionally, the care-giver’s ability to feed responsively may include encouraging the
child to eat, offering additional foods, responding to poor appetite and using a
positive style of interaction with the child.
• Some studies in developing countries have also found a strong association between
specific feeding behaviors (such as location of feeding, organization of feeding event)
with mothers’ educational status (Guldan et al., 1993). Psychosocial care, on the other
hand, refers to the provision of affection and warmth, responsiveness to the child
and the encouragement of autonomy and exploration (Engle et al., 1999). Culture
plays a central role in psychosocial care.


Empirical Analysis
• Interface of maternal education, child care and child
• The two-way ANOVA: Determines if there are overall
differences in weight for height Z-scores (ZWH) between
different educational levels of the mother, between
varying levels of child-care and whether there is an
interaction effect of educational level and child-care on
improving child nutritional status .
• The interaction effect can be thought of as saying that the
effect of one factor (e.g. educational level) depends on
the level of the second factor (e.g. child-care). For
example, it may be the case that higher educated women
provide better child-care than lower educated women.


Analysis
• Educational level effect:
• H0: mean weight for height Z-scores do not differ by
educational levels of the mother.
• H1: mean weight for height Z-scores differ by
educational levels of the mother.


Analysis
• Care effect:
• H0 : mean weight for height Z-scores do not differ by
care levels by the mother.
• H01: mean weight for height Z-scores differ by care
levels by the mother.


Analysis
• Interaction effect:
• H0 : there is no interaction between educational
levels and care levels.
• H1: there is an interaction between educational
levels and care levels.


Data Description
• Dependent variable: weight for height Z-
scores, which is a measure of short-term child
nutritional status.
• Define a transformation such that:
 ZWHNEW = 1 if ZWH ≥ -2 (normal Z scores) and = 0
if ZWH < -2 (low Z scores).
 Value of 1 indicates no wasting, while a value of 0
indicates wasting.


Data Description
• Education of the spouse (EDUCSPOUS):
A categorical variable, the value of which ranges
from 1 to 7.
Measures the education level of the spouse (or
mother) in number of years. (In the case of female-
headed households in the two-way ANOVA analysis,
we separate out females who are heads of the
household and thus are not the spouse of a male-
headed household.) For example, the variable
attains a value of 5 if the spouse completed
secondary education.
Higher values indicate more number of years in
schooling.

Data Description
• Child-care index (CARE):
 F(variables related to child-feeding practices (such as
breastfeeding and feeding the child during sickness) and
preventive health seeking behavior (whether the child was
immunized).
 The index ranged on a continuous scale from -1 to +1, with -1
denoting poor child-care practices and +1 denoting good care
practices.
 For age groups where a particular practice (such as
breastfeeding for children above 24 months of age and
compulsory immunization to children below 9 months) is not
likely to improve the growth of children, the component was
assigned a value of 0 implying a neutral effect. The index was
made age specific for each age group.


Table 7.1 Prevalence of stunting by
mothers’ educational level

ZHANEW

Low Normal Total

No education 41 50 91

Adult literacy 1 6 7
EDUCSPOUS
Std 1–4 14 19 33

Std 5–8 24 29 53

Total 80 104 184 =n


Prevalence of stunting by mother’s
education level
 No marked differences in the prevalence of stunting
between non-educated and educated women.
 For example, for mothers with no education, the
prevalence of stunting is 51.3 per cent relative to the
presence of normal children of 48.1 per cent.
 For some level of educational attainment of the mother
(std 5–8) which in this sample is the highest educational
attainment, the prevalence of stunting is 30 per cent
relative to no prevalence (or normal children) of only 27.9
per cent. The p value is 0.457 and, as the significance
level is greater than 0.1, the null hypothesis cannot be
rejected at the 10 per cent level. Thus, we can conclude
that there is no significant difference in prevalence of
stunting between educated and non-educated mothers.


Table 7.2 Prevalence of wasting by
mothers’ educational level
ZWHNEW

Low Normal Total

No education 15 84 99

78.90% 47.50%

Adult literacy 2 5 7

10.50% 2.80%
EDUCSPOUS
Std 1–4 0 35 35

0.00% 19.80%

Std 5–8 2 53 55

10.50% 29.90%

Total 19 177 196=n


Prevalence of wasting by mothers’
educational level
 Significant differences in the prevalence of wasting between
non-educated and educated women. For mothers with no
education, the prevalence of wasting is almost 79 per cent
compared to non-prevalence (47.5 per cent). Thus, it would
appear that short-term nutritional status is significantly
influenced by mothers’ educational level.
 As educational level increases, there is much less prevalence of
wasting. P value from the Pearson chi-square statistic is 0.006
and since it is less than 0.1, the null hypothesis that there is no
difference between wasting among uneducated and educated
mothers can be rejected.
 Educational level matters for short-term nutritional status. In
the next section, we investigate the role of mothers’ education
and child-care on weight for height Z-scores using a two-way
ANOVA approach.


Two-way ANOVA results
• Three different statistical tests:
1. Main effect of educational level of the spouse.
2. Main effect of care levels by terciles of care.
3. Interaction effects between educational level of the
spouse and care levels.


Definition of main effect
• This is the effect of one independent variable on the
dependent variable across the levels of the other
independent variable.
• Issue:
• Verify if there is a difference in the mean weight for height
Z-scores by the educational levels of the mother averaging
over the child-care levels. In other words, ignoring the
effect of child-care levels, do weight for height Z-scores
differ between educated and non-educated mothers?
• Verify if there is a difference in weight for height Z-scores
by child-care terciles ignoring the educational levels of the
mother. One way to understand the main effect is to
examine the marginal means.


Table 7.3 Effect of mothers’ education on
ZWHNEW
EDUCSPOUS Mean

No education 0.844

Adult literacy training 0.75

Std 1–4 1

Std 5–8 0.961


Effect of mothers’ education on ZWHNEW

• Mean performance on weight for height Z-
scores is greater for mothers with some
educational level relative to mothers with no
education.
• Do the marginal means differ?


Table 7.4 Effect of child-care on ZWHNEW

NCARE Mean

1 0.842

2 0.974

3 0.884


Effect of child-care on ZWHNEW
Mean weight for height Z-scores is highest for
mothers in the medium care tercile (0.974) followed
by the upper care tercile.
As care behavior improves (such as breastfeeding
children below 2 years of age), nutritional status of
children improves after controlling for educational
level of the mother.

• Issue: If the marginal means among the various
child-care terciles differ
• Ans: Calculate the sum of squares.

Variance partitioning for two-way ANOVA


Between Sum of Squares

• SSB = Squares of mothers’ educational level
SS (EDUCSPOUS) + Sum of squares of terciles
of child-care SS (NCARE) + Sum of squares of
the interaction SS (interaction).


Sum of Square of the Interaction Term


Two-way ANOVA model


Two-way ANOVA model

• The left-hand side of equation (7.5) is the total sum of
squares, while the right hand side consists of SSB and SSW.
• The first three terms on the right hand are the sum of
squares of factor A, factor B and the interaction term, while
the last expression denotes the sum of squares of the error
term or the sum of squares within.
• After dividing the relevant expressions by the degrees of
freedom, we obtain the mean square expressions.
• The corresponding F ratios are obtained by dividing the
mean square of factor A, factor B and the interaction term
(A*B) by the mean square error.


Table 7.5 ANOVA table for an a*b factorial
experiment

Source SS df MS

Factor A SS(A) (a-1) MS(A) =SS(A)/(a 1)

Factor B SS(B) (b-1) MS(B) = SS(B)/(b 1)

MS(AB) = SS(AB)/(a -1)(b -
Interaction AB SS(AB) (a-1)(b-1)
1)

Error SSW (N-ab) SSW/(N -ab)

Total (corrected) TSS (N-1)


ANOVA Results
• Main effect of mothers’ education on weight for
height Z-score: F-value = 4.085 (p-value = 0.008)
• Reject null hypothesis that mean weight for
height Z-scores do not differ by educational
levels of the mother. Thus, education of the
mother has a significant influence on weight for
height Z-scores.
• Inference: There is a significant difference in the
mean weight for height Z-scores between
educated and non-educated mothers ignoring
the impact of child-care.


ANOVA Results
• Main effect of child-care terciles on weight
for height Z-score: F-value = 3.056 (p = 0.049)
• Reject the null hypothesis that the mean
weight for height Z-scores do not differ by
care levels of the mother and thus child-care
levels have a significant impact on weight for
height Z-scores after ignoring the impact of
mothers’ education.


Table 7.6 Tests of between subject effects:
dependent variable ZWHNEW
Type III sum of
Source df Mean square F P value
squares

SSB 2.187 10 0.218 2.691 0.003

Intercept 64.330 1 64.330 798.914 0.000

EDUCSPOUS 0.987 3 0.329 4.085 0.008

NCARE 0.492 2 0.246 3.056 0.049

EDUCSPOUS*NCARE 0.708 6 0.118 1.468 0.202

SSW 14.971 185 0.081

TSS 17.158 195
R squared = 0.132 (adjusted R squared = 0.085).


Interaction effect and post-hoc tests
• Issue: There are more than two means; which means (if any) are
significantly different.
• Four educational levels of the mother and three child-care levels.
• Table 7.6: Overall interaction effect (EDUCSPOUS*NCARE) is not
significant.
• Still there may be significant differences among the means of
mothers’ educational levels and child-care, i.e., there may be
differences among combinations of education levels and child-
care terciles.
• Post-hoc tests are used when the researcher is exploring
differences among group means, otherwise the likelihood of type
1 errors increases.
• Illustration with reference to educational level of the mother and
childcare terciles.

Results
• Mothers with more education relative to mothers with no
education practice greater child-care.
• The simple post-hoc analysis compares a given pair of means. If
they are significantly different (p < 0.05), different letters are
placed next to these means to indicate that they are significantly
different.
• For the same level of child-care, higher education among mothers
(elementary schooling) improves short-term nutritional status as
measured by weight for height Z-scores.
• For a given level of education (no education or some elementary
schooling), mothers in the second child-care tercile perform better
than ones in the highest care tercile = Impact of positive child-care
practices improves short-term nutritional status for households in
the lower socioeconomic terciles compared to the upper
socioeconomic terciles.


Table 7.7 Multiple comparison test

EDUCSPOUS

No education Std 1–4
NCARE
2 0.929 a 1.00 b

3 0.735 c 1.00 d


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