PARENT-CHILD FAT INTAKE CORRELATION IN CHINA, EXPLANATION FROM
SOCIAL COGNITIVE THEORY
By
Han Chen

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Sociology-Master of Arts
2013

ABSTRACT

PARENT-CHILD FAT INTAKE CORRELATION IN CHINA, EXPLANATION FROM
SOCIAL COGNITIVE THEORY
By
Han Chen
Human development like physical growth, cognitive development, and related health
status could be shaped by numerous influential determinants from family, school, and other
institutional systems. Social cognitive theory proposed a model of triadic reciprocal
determinism including behavior, cognition and other personal factors, and environmental
influences (Bandura, 1989). This study first examined the correlation between parent-child fat
intake, and then investigated the factors associated with observed correlation using a model
derived from social cognitive theory. Data came from a household-based survey of 913
groups of father and daughters, 1240 groups of father and sons, 902 groups of mother and
daughters, 1246 groups of mother and sons in nine provinces. Respondents reported all food
consumption on a 24-hour basis for three consecutive days. Parents provided their dietary
knowledge, food preference and activity preference as social cognitive covariates. Household
income and parental education level were included in the model for socioeconomic
controlling. The parent-child correlations were strong (0.87-0.88) for fat intake. The strength
of cognitive factors that were associated with observed correlations varied in each gender
group.
Key Words: parent-child fat intake correlation, social cognitive factors, socioeconomic status,
child obesity, gender difference

ACKNOWLEDGEMENTS

I express sincere gratitude to my academic advisor, Dr. Perlstadt, other two
committee members Dr. Liu and Dr. Velie, and all other faculty who gave me precious
instruction.

iii

TABLE OF CONTENTS

LIST OF TABLES.....................................................................................................v
INTRODUCTION ....................................................................................................1
CHAPTER 1………………………..........................................................................2
Familial Dietary Correlation ……….........................................................................2
Social Cognitive Theory.............................................................................................3
CHAPTER 2………………………...........................................................................6
Hypotheses ................................................................................................................6
Data, Measures, and Statistical Method.....................................................................6
Data and Sample...............................................................................................6
Dependent Variable ..........................................................................................7
Other Covariates...............................................................................................7
Statistical Methods...........................................................................................8
Result…………..........................................................................................................9
Sample Descriptive Statistic.............................................................................9
Parent-child Fat Intake Correlation...................................................................9
Parent-child Fat Intake Correlation: Overall Analysis.....................................10
Discussion .......................................................................................................12
APPENDICES...........................................................................................................15
Appendix A ......................................................................................................16
Appendix B ......................................................................................................17
BIBLIOGRAPHY......................................................................................................25

iv

LIST OF TABLES

Table 1 Grading for Parental Diet Knowledge and Preference …….…….………….16
Table 2 Grading for Parental Activity Preference …………………..……………….17
Table 3 Demographic Characteristics of Study Sample ……………………….……18
Table 4 Correlations in Mother-son Subsample…………………………………......19
Table 5 Correlations in Mother-daughter Subsample……………………………….19
Table 6 Correlations in Father-son Subsample……………………………………...20
Table 7 Correlations in Father-daughter Subsample………………………………...20
Table 8 Correlations in four subsamples…………………………………………….21
Table 9 Regression Coefficients for variables predicting Son’s Fat Intake in
Mother-son Subsample………………………………………………………….…...21
Table 10 Regression Coefficients in for variables predicting Daughter’s Fat
Intake Mother-daughter Subsample……………………………….………………....22
Table 11 Regression Coefficients for variables predicting Son’s Fat Intake in
Father-son Subsample………………………………………………………………..23
Table 12 Regression Coefficients for variables predicting Daughter’s Fat
Intake in Father-daughter Subsample………………………………………………...24

v

INTRODUCTION
Under its Reform and Opening Up policy, China allowed several western fast food
companies like McDonalds and KFC to enter the market around 1987. Since then they have
become very popular. Numerous families were attracted by the warm and fun atmosphere
created by those restaurants. Meanwhile, more and more families cooked in a westernized
way, which is handy but high in calorie and fat. With the rapid development of
communication technology, people now prefer staying in front of computer and television to
going outside. Thus, the prevalence of obesity in China has become a problem. “Obese
family” has been very common in China: many parents and children are fat.
Children develop their dietary behavior that might track into their adulthood (Mikkila,
Rasanen, Raitakari, Pietinen, & Viikari, 2005; Wang, Bentley, Zhai, &Popkin, 2002). It is
therefore crucial to advocate healthy eating among young people to lower incidence of
chronic diseases in later life, especially obesity, hypertension, heart disease etc. Social
epidemiologists suggested public education towards parents on healthy behaviors to prevent
children from adopting bad habits. Once people develop habitual ways of thinking and
behavior, they most times would ignore the informative aspects of their environment
(Bandura, 1986). It seems that parents ought to set a good model for their children in both
thinking and behavior to effectively eradicate unhealthy habits. The objective of this study is
to examine familial fat intake correlation in China and the role of social cognitive and
socioeconomic factors in it based on a survey with large sample size.

1

CHAPTER 1
Familial Dietary Correlation
Early epidemiological studies reported familial aggregation of risk factors related to
coronary heart diseases. Since it was widely known that parents would have a great influence
on children’s dietary intake, nutritionists organized several survey studies to investigate
parent-child nutrient association. Several researchers (Laskarzewski, 1980; Perusse, 1988;
Oliveria, 1992) found weak or moderate results concerning dietary correlation in American
families. More recently, social epidemiologists in the United States (Beydoun, 2009), the
Netherlands (Feunekes, 1997), and France (Vauthier, 1996) used large representative surveys,
but they did not find strong correlations between parental and children’s dietary intake. These
recent studies used gender as the stratification factor to investigate gender difference in
dietary correlation. Results varied in four gender groups, father-son, father-daughter,
mother-son, and mother-daughter.
Overall, familial dietary correlation in United States and Europe is not as strong as
common sense would expect and children’s eating behaviors are impacted by multiple factors.
In the United States, the family merely plays a partial role in their eating behavior (Wang, et
al, 2011). Most students would have a least one meal with their classmates in school; thus
peer influence would be equally important. Furthermore, children’s desires to be independent
start to increase radically when they grow older. Peer influence on eating behavior could even
exceed impacts from family.
In China, most elementary and high schools do not provide students with lunch.
Children often purchase carbohydrate snacks with limited money, and they usually do not

2

share the food with their classmates (Wang, Xuetai, 2006). Most teenagers and young adults
still tend to be dependent on their parents. Due to different social environments, dietary
correlation in Chinese families might be stronger than in American families.
Social Cognitive Theory
In social cognitive view, human functioning is explained by a model of triadic
reciprocality in which behavior, cognitive and other personal factors, and environmental
events all operate as interacting determinants of each other (Bandura, 1986). Similarly,
children adopt eating behavior based on their food preference, parental education and control,
and access to foods in familial environment. At different ages, children develop their eating
cognition in diverse ways.
People can comprehend many events and generate new knowledge by symbolically
manipulating the information derived from personal and vicarious experiences (Bandura,
1986). Furthermore, human thought furnishes a vast store of knowledge in the form of
abstract representations of experiences (Bandura, 1986). From the time older infants have
access to various foods, they begin to absorb many pieces of information. Young children
might attempt to eat anything they thought is edible. They probably end-up with bad tastes
and warning from their parents and older relatives. The influence of numerous trials could be
partially determinative for their future behaviors: the unhappy experience will remain lasting
effects or be forgotten by the next day. However, the gradual aggregation of information from
endless trials and other people grants children the ability to understand how to eat and what to
eat. Thus, early development of children’s eating behavior is mainly the abstract
representation of their experiences in foods. In this period of time, the external factors play a

3

pivotal role in the sources of information children would get for their abstract representation.
For example, if parents are fond of fast food and mention fast food is not bad for health
repetitively, children would form one piece of information in their thought: I need fast food,
or fast food is okay.
After abilities granted by human thoughts become routinized through repeated
execution, they are performed in recurring situations without requiring prior thought guides,
unless something goes in the wrong direction (Bandura, 1986). As children grow older, they
are no longer under strict control from their parents and have access to more kinds of foods.
However, children at this time have developed a very mature abstract representation of their
previous eating experiences. For example, they have eaten fast food meals numerous times. If
one had to think before carrying out every routine activity, it would consume most of one’s
attention and create a monotonously dull inner life (Bandura, 1986). As children grow older,
they start to develop their food preference. For example, they would be very upset if their
request for fast food is refused.
Once people adopt habitual ways of thinking and behavior, they often pay little
attention to the informative aspects of their environment that call for discerning
responsiveness (Abelson, 1981; M. Bandura, Langer, & Chanowitz, 1984; Chanowitz &
Langer, 1980).

As children enter young adulthood, they may already prefer fast food to

other kind of foods. Suggestions and warnings from their parents, classmates, and teachers
might only have limited influence on their eating. Moreover, high consumption of fast food
mainly triggers chronic diseases, which are revealed in their later life.
get the self-vigilance promptly.

Children could not

It is therefore not strange that many obese children continue

4

to keep bad eating habits, even though they have been educated and even warned many times.
The cognitive regulators part in Bandura’s social cognitive theory explains the
development of children’s eating behaviors explicitly. Bandura expanded his social cognitive
theory into the area of health promotion. Health knowledge, incentive systems,
self-regulatory capabilities and socio-structural supports all influence the development of
child health-related habits (Bandura 1998). It is already known that abstract representation is
from personal experiences which are consisted of thinking and behavior. In the perspective of
health promotion, health knowledge is the abstract representation of how people think about
healthy behaviors, and self-regulatory capabilities is the abstract representation of how people
act based on their health knowledge. The objective of this study is to test whether “thinking
and behavior” are equally influential in parent-child fat intake correlation.

5

CHAPTER 2
Hypotheses
Based on the literature, this study has three hypotheses. First, familial fat intake between
parents and children is highly correlated in China. Second, aforementioned review indicated
gender difference in parent-child dietary correlation, so this study hypothesizes that mothers’
influence on child’s fat intake is more important than fathers’. Finally, Bandura’s social
cognitive theory stated that both health knowledge and behavior influence the development of
health-related habits, but it did not analyze which one is more important. This study
hypothesizes that parental dietary behavior is more influential than parental dietary beliefs on
their children’s fat intake.
Data, Measures, and Statistical Methods
Data and Sample
The China Health and Nutrition Survey (CHNS), is an international collaborative
project between the Carolina Population Center at the University of North Carolina at Chapel
Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease
Control and Prevention. The survey conducted in 2009 used a multistage, random cluster
process to draw a sample of about 4400 households with a total of 26,000 individuals in nine
provinces that vary substantially in geography, economic development, public resources, and
health indicators. The nine provinces that run northeast to southwest are, Heilongjiang,
Liaoning, Jiangsu, Shandong, Henan, Hubei, Hunan, Guizhou, and Guangxi.
All household members (6-90 years) provided individual data on dietary intake, body
composition, blood pressure, health history, and health-related behaviors. The three
6

consecutive days during which detailed household food consumption data have been
collected were randomly allocated from Monday to Sunday and are almost equally balanced
across the seven days of the week for each sampling unit. Individual dietary intake for the
same three consecutive days has been surveyed for all individuals in the household. Each
individual reported all food consumed both away and at home on a 24-hour recall basis
(China Health and Nutrition Survey, 2009).
This study used gender as the stratification factor to generate four subsamples labeled
as father-daughter, father-son, mother-daughter, and mother-son for analysis. Final analyzed
subsamples in this study included 913 groups of father and daughters, 1240 groups of father
and sons, 902 groups of mother and daughters, 1246 groups of mother and sons living in the
same household who provided detailed information about their eating behaviors.
Dependent Variable
The dependent variable in this study is children’s fat intake (g/Kcal) consumption from
aforementioned three-day 24-hour food recall. Fat intake was selected because of their
importance in development of obesity. Child fat intake was log transformed for normal
distribution assumption.
Other Covariates
To assess parental thinking and behavior in the social cognitive model, this study
developed a grading system for parental dietary knowledge, and food and activity preference.
Respondents in CHNS provided opinions on five questions concerning healthy eating such as
choosing a diet with a lot of fresh fruits and vegetables is good for one’s health. Parents were
given 0 to 2 points on each question according to their correct understanding about diet
7

(right=2, neutral=1, wrong=0). Similarly, parents in CHNS also answered their preference to
five representative foods in daily life, such as fast foods, salty snack foods, fruits, etc. Parents
were given 0 to 2 points according to preferences to either healthy or unhealthy foods (dislike
healthy food=0, neutral to healthy food=1, or like healthy food=2). Both variables were
regarded as continuous variables in this study.
Studies in nutritional epidemiology indicated that it is crucial to control body weight
and physical activity to analyze nutrient research. This study included Body Mass Index
(BMI) of both parents and children as covariates. Parents in the survey provided their
preference to three kinds of activities: walking and Tai Chi, Sports, and watching TV. Parents
were graded from 0-3 in each question according to preferences to either good or bad
activities (did not participate in good activity=0, dislike good activity=1, neutral to good
activity=2, or like good activity=3). Appendix A and B present the detailed questions for
dietary knowledge, and food and activity preference.
Other covariates of parents were smoking (how many cigarettes did you smoke per
day), drinking (how often did you drink beer or any alcoholic beverage), television hours
(how many minutes did you spend in watching TV per week), education level (years), marital
status (1=married, 0=other), household income, residence (1=urban, 0=rural). In addition,
children’s age was included in the models.
Statistical Methods
Ordinary Least Squares (OLS) regression was run to predict fat intake of both parents
and children. Covariates with insignificant correlation (p>0.05) in all four models were
deleted. Another OLS regression was used to evaluate parent-child fat intake correlation in

8

China. Assumptions for OLS regression (correct specification, strict exogeneity, no linear
dependence, spherical errors, and normality) were all examined before statistical analysis.
Results
Sample Descriptive Statistic
Table 3 Demographic Characteristics of Study Sample About Here
The basic demographic characteristics of the study sample are presented in Table 1.
Overall, fathers and mothers were very close in age (55.9 to 55.1), fat intake (3.32 to 3.35),
diet knowledge, diet preference, activity preference, television hours, residence (urban/rural
with approximately 25 percent living in urban areas), education and income. Chinese men
obviously smoke (3.5 to 1.3) and drink (0.8 to 0.3) more than Chinese women. Among
children, Log fat intake and BMI did not vary much by gender (sons 1.09, daughter 1.05).
But sons were approximately two years older than daughters. Overall, the mean age of
children in CHNS was from 18.7 to 20.8, because many adult children in China still lived
with their parents.
Parent-child Fat Intake Correlation
Table 4-8 Correlations About Here
Tables 2-6 report correlations of each covariate to fat intake for each of the parent and
child subgroups. Results for four groups show that all the covariates are significantly
correlated to at least one parental- child fat intake. Parent-child correlations in fat intake
range from 0.865 for mother-daughter groups (Table 3) to 0.872 for father-son groups (Table
4), which means those models could explain 74.8-76.0% (R-squared) variation in children’s
fat intake. In terms of strength of correlations of fat intake and other covariates, four groups
are very close. Compared to son’s BMI, daughters BMI is much more correlated to both
9

mother’s (0.073 to 0.045, Tables 3 and 2) and father’s fat intake (0.058 to 0.027, Table 5 and
4). Overall, parent-child fat intake correlation is found to be strong (between 0.865 and 0.872,
Table 6), and both father and mother play an equally important role in their children’s daily
fat consumption.
Parent-child Fat Intake Correlation: Overall Analysis
Table 9-12 Regression Coefficients About Here
Tables 7-10 report beta regression coefficients of each covariate to children’s fat intake
for the four subgroups. Overall, log coefficients of parental fat intake on child’s fat intake are
all significant (mother-son 0.330, Table 7; mother-daughter, 0.340, Table 8; father-son, 0.330,
Table 9 and father-daughter, 0.380, Table 10). Since child fat intake was log transformed, we
can calculate that as a mother consumes one more unit fat, fat intake of her son and her
daughter will be predicted to increase by e0.33, 139% and e0.34, 141% (Tables 7 and 8). In
father-son and father-daughter groups, the numbers are e0.33, 139% and e0.38 , 146% (Tables
9 and 10). The number of covariates with significant coefficient is different across four
groups: seven (marital status, smoking, drinking, television hours, and BMI of mothers, and
age and BMI of sons) in mother-son groups (Table 7), six (diet knowledge, smoking,
television hours, and BMI of mothers, and age and BMI of daughters) in mother-daughter
groups (Table 8), one (activity preference of fathers) in father-son groups (Table 9), and four
(diet knowledge, diet preference, smoking, and television hours of fathers) in father-daughter
groups (Table 10).
It thus indicates potential gender differences. Girls are more sensitive to their parents’
thinking and behavior, and mothers exert more influence on their children’s fat consumption

10

than fathers. Regression coefficients of diet knowledge (0.012, 0.021 in Table 8, 10) and
preference (-0.010, Table 10) are found to be significant in groups with daughters. When
examining the influence of parental BMI, this study found that mothers’ higher BMI is
predicted to reduce their daughter’s fat intake but increase their son’s fat intake. Parental
smoking has interesting impacts on children’s fat intake. If parents smoke more cigarettes,
daughters are less likely to have higher fat intake (-0.007, -0.003 in Table 8, 10). But the
higher frequency of maternal smoking probably causes their son to consume more fat (0.005,
Table 7). In terms of children’s demographic characteristics, their BMI and age only have
significant influence in the models with mothers. As children grow older, girls tend to
consume more fat (0.006, Table 8), but boys attempt to reduce their fat intake (-0.003, Table
7). Even with higher BMI, girls are predicted to have higher fat intake (0.011, Table 8) On the
contrary, when discerning their BMI is growing, boys tend to consume less fat (-0.009, Table
7).
When examining the influences of parental thinking and behavior, this study found the
magnitude of behavior overshadows that of thinking according to the significance of beta
regression coefficients. If parents tend to consume more fat, their children will have higher
fat intake. Moreover, girls whose father has healthier food preference, consume less fat
(-0.010 in Table 10). Better parental diet knowledge, however, triggers children’s higher fat
intake in two groups. For example, fathers with better diet knowledge are predicted to have
daughters with more fat consumption (0.021, Table 10). Girls whose mother understands
healthy diet better, are more likely to have higher fat intake (0.012, Table8). It is well known
that television is a main reason for the prevalence of obesity. Compared with children with

11

parents spending more time in television, those with parents spending less time in television
consume less fat. Other parental health-related behaviors such as smoking and drinking have
insignificant influence on children’s fat intake in most groups. In addition, parental age does
not play an important role in children’s fat intake.
Variables indicating potential health disparity are mostly tested to be insignificant. The
influence of household income and parental education is predicted to be very small. It is also
shown that whether parents live in urban or rural area makes no difference for their children’s
fat intake. When examining influence of parental marital status, this study found that sons
with a married mother are more likely to consume fat.
Discussion
To my knowledge, this study is the first attempt to examine parent-child fat intake in
China using a survey with large sample size. It is also the first evaluation of social cognitive
theory in explaining children’s fat intake in China.
This study has some main intriguing findings. First, fat-intake correlation in China is
very strong. Parental fat intake plays a pivotal role in the development of children
consumption of fat. Possibly, Chinese children are more physically and psychologically
dependent on their parents. Parents who have healthy food preference are more likely to
influence their children to consume lower fat. However, parental knowledge in healthy diet
has an inverse impact on their children’s fat intake. It is well known that “it is easier said than
done.” Social cognitive theory indicated that accumulation of abstract representation from
other’s thinking and behavior could result in routinized behavior. As for health promotion,
Bandura further strengthened the importance of health knowledge and self-regulatory

12

capabilities. In terms of eating behavior, acting is predicted to be more influential than
thinking. Thus parents in China are especially recommended to have a healthy dietary
behavior instead of “only having a healthy dietary knowledge”. This study indicates that
children are more likely to be influenced by what their parents truly eat.
Second, fat-intake correlation in China is gender sensitive. Daughters are more likely
to be influenced by extern factors. Possibly, daughters are more dependent on their parents
and spend more time with their family. On the contrary, girls are less sensitive to their own
conditions. As their BMI grows higher, daughters even consume higher fat. This study
recommends parents to pay more attention to their daughter’s dietary behaviors. In terms of
maternal and paternal influences, mothers are more likely to discern the changes of age, BMI,
etc. It is very understandable that females are normally more attentive.
Finally, healthy disparity in fat intake correlation is weak in China. Possibly, foods
with higher fat are still expensive in Chinese markets. Moreover, people with lower education
might keep traditional Chinese cook style, which is comparatively healthier than western fast
food.
This study had several strengths. First, I used a survey with large sample size to
process analysis on parent-child fat intake. Second, social cognitive theory provided an
explicit image of the development of children’s eating behaviors. Finally, this study included
gender as a moderator and other multiple covariates to test potential differences and
significance.
Despite its strengths, this study had its major limitations. First, China Nutrition and
Health Survey only included nine of thirty-four provinces in China. Although it contains the

13

main areas in China that could indicate the general image, eating behaviors of most
minorities could not be investigated. Second, selection of groups was based on the head of
household (either father or mother). It is highly possible that this study neglected those
children who lived with their grandparents or lived alone. And one parent might be linked to
several children. In China, family planning greatly reduces the incidence of that condition,
but this study did not do any analyses to adjust it. Third, I did not check potential interactive
relations due to the great volume of data analyses. Finally, this study relied on nutrient
calculation from 24-hour recall, which could not accurately indicate one’s true food
consumption. Possible recall bias and poor blinding could greatly lower the precision of
nutrient calculation.
In conclusion, this study found strong correlations in parent-child fat intake in China.
Parental diet-related knowledge was predicted to be less instructive in the development of
children’s fat intake than parental true behavior and food preference. The results also
indicated great gender difference, which suggest parents to observe their daughter’s dietary
behavior more closely. Future studies could attempt to find interaction of parental thinking
and behavior and pick up one gender group to process more detailed analysis.

14

APPENDICES

15

Appendix A
Table 1 Grading for Parental Diet Knowledge and Preference
Diet Knowledge
Choosing a diet with a lot of fresh
fruits and vegetables is good for
one’s health
Eating a lot of sugar is good for
one’s health
Eating a variety of foods is good
for one’s health
Choosing a diet high in fat is
good for one’s health
Reducing the amount of fatty
meat and animal fat in the diet is
good for one’s health
Diet Preference
Fast
food
(KFC,
pizza,
hamburgers, etc.)
Salty snack foods (potato chips,
pretzels, French fries, etc.)
Fruits
Vegetables
Soft drinks and sugared fruit
drinks

Disagree
0

Neutral
1

Agree
2

2

1

0

0

1

2

2

1

0

0

1

2

Dislike
2

Neutral
1

Like
0

2

1

0

0
0
2

1
1
1

2
2
0

16

Appendix B
Table 2 Grading for Parental Activity Preference
Diet Preference

Dislike
very
much
Tai Chi and Walking
1
Sports
(ping
pong, 1
badminton, tennis, soccer,
basketball, and Volleyball)
watching TV
2

Dislike

Neutr
al

Like

1
1

2
2

2

1

17

3
3

Like
very
much
3
3

Did not
Particip
ate
0
0

0

0

3

Table 3 Demographic Characteristics of Study Sample
Male (father and son)
Parental Characteristics
Age (year)
Fat intake (g/kcal)
BMI (kg/ )
Diet Knowledge
Food Preference
Activity Preference
Residence (1=urban,
0=rural)
Marital Status (1=married,
0=other)
Smoking (number of
cigarettes)
Drinking (frequency of
drinking)
Television (minute)
Income (Yuan)
Education (year)
Children’s Characteristics
Log Fat Intake (g/kcal)
BMI (kg/ )
Age (year)

Female (mother and
daughter)

55.9 (12.4)
3.32 (1.31)
22.2 (4.2)
8.1 (2.0)
7.9 (1.9)
7.3 (1.6)
0.24 (0.43)

55.1 (12.1)
3.35 (1.32)
21.1 (4.6)
8.1 (2.0)
8.0 (1.9)
7.3 (1.6)
0.25 (0.43)

0.94 (0.24)

0.88 (0.32)

3.5 (8.0)

1.33 (4.9)

0.8 (1.55)

0.33 (1.03)

169 (198)
33339 (41683)
17.7 (8.8)

170 (199)
32913(41288)
17.4 (9.0)

1.09 (0.48)
17.0 (3.2)
20.8 (12.1)

1.05 (0.53)
16.7 (3.1)
18.7 (11.2)

18

Table 4 Correlations in Mother-son Subsample

Log Son fat intake
Mother BMI
Mother diet knowledge
Mother food preference
Mother activity preference
Mother marital status
Mother smoking
Mother drinking
Mother TV
Mother residence
Mother income
Mother education
Son BMI
Mother age
Son age
Mother fat intake

Mother fat intake
0.871*
0.049*
0.096*
0.034*
0.034*
-0.021*
0.027*
0.018*
0.044*
0.155*
0.087*
0.120*
0.045*
-0.008*
0.040*

Log Son fat intake
0.012
0.116*
-0.030
0.042
-0.002
0.002
0.032*
0.012
0.114*
0.115*
0.051*
-0.020*
-0.020*
0.016
0.871*

(*p<0.05)

Table 5 Correlations in Mother-daughter Subsample

Log Daughter fat intake
Mother BMI
Mother diet knowledge
Mother food preference
Mother activity preference
Mother marital status
Mother smoking
Mother drinking
Mother TV
Mother residence
Mother income
Mother education
Daughter BMI
Mother age
Daughter age
Mother fat intake

Mother fat intake
0.865*
0.059*
0.010*
0.038*
0.030*
-0.025*
0.031*
0.020*
0.053*
0.158*
0.091*
0.123*
0.073*
-0.001*
0.067*

(*p<0.05)
19

Log Daughter fat intake
0.009
0.143*
-0.030
0.011
-0.022
0.043*
0.055*
0.044*
0.114*
0.160*
0.077*
-0.040*
-0.021*
0.057*
0.865*

Table 6 Correlations in Father-son Subsample

Log Son fat intake
Father BMI
Father diet knowledge
Father food preference
Father activity preference
Father marital status
Father smoking
Father drinking
Father TV
Father residence
Father income
Father education
Son BMI
Father age
Son age
Father fat intake

Father fat intake
0.872*
0.056*
0.105*
0.027*
0.051*
-0.009*
0.012*
0.029*
0.036*
0.150*
0.100*
0.123*
0.027*
0.001*
0.045*

Log Son fat intake
0.036*
0.112*
-0.023
0.096*
-0.047*
0.001
0.011*
0.004
0.101*
0.125*
0.032*
-0.016*
0
0.027
0.872*

(*p<0.05)

Table 7 Correlations in Father-daughter Subsample

Log Daughter fat intake
Father BMI
Father diet knowledge
Father food preference
Father activity preference
Father marital status
Father smoking
Father drinking
Father TV
Father residence
Father income
Father education
Daughter BMI
Father age
Daughter age
Father fat intake

Father fat intake
0.869*
0.061*
0.109*
0.031*
0.046*
-0.011*
0.012*
0.033*
0.044*
0.153*
0.104*
0.125*
0.058*
0.017*
0.063*

(*p<0.05)
20

Log Daughter fat intake
0.024
0.133*
-0.054
0.063
-0.030*
0.010
0.039*
0.015
0.115*
0.175*
0.044*
-0.040*
-0.011
0.057*
0.869*

Table 8 Correlations in four subsamples

Log Daughter fat intake
Log Son fat intake

Mother fat intake
0.865*
0.871*

Father fat intake
0.869*
0.872*

(*p<0.05)

Table 9 Regression Coefficients for variables predicting Son’s Fat Intake in Mother-son
Subsample

Mother Fat Intake
Mother Diet Knowledge
Mother Diet Preference
Mother Activity Preference
Mother Marital Status
Mother Smoking
Mother Drinking
Mother Television Hours
Mother Income
Mother Age
Mother Residence
Son Age
Mother BMI
Son BMI
Mother Education
𝐑

Log ß
0.330*
0.004
-0.003
0.008
0.078*
0.005*
-0.036*
0.0001*
0.000
0.000
0.009
-0.003*
0.010*
-0.009*
-0.000
0.759

Standard Error
0.006
0.003
0.003
0.004
0.026
0.002
0.012
0.000
0.000
0.001
0.017
0.001
0.004
0.004
0.001

(*p<0.05)

21

Table 10 Regression Coefficients in for variables predicting Daughter’s Fat Intake
Mother-daughter Subsample

Mother Fat Intake
Mother Diet Knowledge
Mother Diet Preference
Mother Activity Preference
Mother Marital Status
Mother Smoking
Mother Drinking
Mother Television Hours
Mother Income
Mother Age
Mother Residence
Daughter Age
Mother BMI
Daughter BMI
Mother Education
𝐑

Log ß
0.340*
0.012*
-0.004
0.011
-0.007
-0.007*
-0.009
0.002*
0.000
-0.002
-0.009
0.006*
-0.010*
0.011*
0.000
0.748

Standard Error
0.007
0.004
0.004
0.006
0.034
0.003
0.013
0.000
0.000
0.002
0.023
0.002
0.005
0.005
0.001

(*p<0.05)

22

Table 11 Regression Coefficients for variables predicting Son’s Fat Intake in Father-son
Subsample

Father Fat Intake
Father Diet Knowledge
Father Diet Preference
Father Activity Preference
Father Marital Status
Father Smoking
Father Drinking
Father Television Hours
Father Income
Father Age
Father Residence
Son Age
Father BMI
Son BMI
Father Education
𝐑

Log ß
0.330*
0.000
-0.004
0.011*
0.003
-0.001
0.010
0.000
0.000
0.000
0.015
-0.002
0.002
-0.001
0.001
0.760

Standard Error
0.006
0.003
0.003
0.004
0.030
0.001
0.005
0.000
0.000
0.001
0.016
0.002
0.002
0.002
0.001

(*p<0.05)

23

Table 12 Regression Coefficients for variables predicting Daughter’s Fat Intake in
Father-daughter Subsample

Fat Intake
Father Diet Knowledge
Father Diet Preference
Father Activity Preference
Father Marital Status
Father Smoking
Father Drinking
Father Television Hours
Father Income
Father Age
Father Residence
Daughter Age
Father BMI
Daughter BMI
Father Education
𝐑

Log ß
0.380*
0.021*
-0.010*
0.001
-0.053
-0.003*
0.014
0.0002*
0.000
-0.002
-0.032
0.004
0.001
0.002
0.001
0.755

Standard Error
0.008
0.005
0.005
0.006
0.060
0.001
0.008
0.000
0.000
0.002
-0.032
0.002
0.003
0.004
0.002

(*p<0.05)

24

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