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Running Head: FAMILY SIZE 1
Family Size:
A Comprehensive Study of the Factors that Influence Family Size
Matthew T. Laidlaw
Black Hills State University
Running Head: FAMILY SIZE 2
Family Size:
A Comprehensive Study of the Factors that Influence Family Size
There are many factors that aid in determining how many offspring an individual will
have. These factors include race, religion, education, income, birth place, marriage age, and
another wide range of factors. Despite the significance of these factors this paper hypothesis that
education, income, and sibling numbers most significantly influence how many offspring an
individual will have.
REVIEW OF LITERATURE
Number of Siblings versus Number of Offspring
Does the number of siblings an individual has growing up determine how many offspring
they produce in their own adult family? This topic becomes important when predicting family
size among individual groups. It also aids in explaining why developing countries produce larger
families and developed countries tend towards smaller families. Furthermore, this research could
greatly aid in determining population growth within the U.S. most specifically in areas with high
predisposition of predicted values; such as education levels and income.
Fertility:
Little modern research has been done in this field of study, however the aging research
found shows a proportional correlation between the number of siblings one has growing up and
the number of offspring that given individual has in his or her lifetime (Duncan, Freedman,
Coble, Slesinger 1965). The research states that while sibling numbers may be influential in
family size, it is possible that genetics plays an even greater role. Perhaps it is not the siblings
Running Head: FAMILY SIZE 3
that affect a given child’s view of family size, but rather a predisposed affinity to genetic
fertility.
Resource Investment:
According to many more modern researchers, income and time resources play far greater
roles in the development between birth family size and adult family size. A child born to a larger
family is predisposed to a more challenging life, as a result of fewer resources growing up.
Larger families must “split” their resources amongst more children; as such these children will
have less invested into their future. Furthermore these children are more predisposed to early
marriage and pregnancy because their limited resources create situations in which fewer other
options are available, especially for women. These low income families have less time and
resources to invest in a child’s education and extracurricular activities, as such these children are
more predisposed to activities in which unplanned pregnancy increases (Keister 2004).
Educational Achievement:
Children with fewer educational investments often strive for family oriented goals rather
than career or educational oriented goals. Large families often become a bi-product of this
disposition with family situated goals. Most specifically a women’s ability to attain educational
achievement and then career opportunities links directly with family size. A women that is able
to achieve an occupation outside the home, is less inclined to want more children. There is
simply no time for a large family (Blake 1989).
Large families create situations in which children have less opportunity at education.
Therefore these children attain lower career opportunities if any at all. This in turn translates to
less men and more specifically women in the high end income workplace. This in turn creates a
Running Head: FAMILY SIZE 4
situation in which many non-career oriented women have more children. Large families create
circumstances in which more large families are created (Blake 1989).
Contraceptive Use and Availability:
Contraceptive availability plays a key role in why some households produce large
families. Homes without readily available or accepted contraceptive use produce far more
children. These homes without contraceptive use also tend to be low income and low education.
There is often an inability to afford contraceptive as well as a lack of understanding. As such
these homes often raise children with an adult misunderstanding of contraceptives as well as an
inability to receive contraceptives readily (Forest, Frost 1996).
While religion has been mentioned many times in literature dealing with correlations of
family size, it seems that a religion’s open tolerance and viewpoint of contraceptive use seems to
relate more to family size correlation. Many religions view contraceptive as morally or
spiritually wrong and as such they discourage their members from its use. As such households
that follow these religions tend to produce larger families. This trend further contributes if the
children of these households adopt their parent’s religion (Brewster, Cooksey, Guilkey, Rindfuss
1998).
Race:
A correlation between race and family size has also been found. This however on a
national scale at least, also seems to be linked more to income and education once again.
However among low income households race does seem to have a disproportional correlation
among blacks, Hispanics, and whites. Seventy-four percent of pregnancy that occurred to women
within 150% of the federal governments established poverty line were unintended. 79% of those
Running Head: FAMILY SIZE 5
pregnancies among blacks were unintended. 63% percent of those among Hispanics were
unintended. 54% of those among whites and non-Hispanics were unintended (Forest, Frost
1996).
This disproportion among races seemed to show that contraceptive attitudes differed
among races. Those that had a positive attitude about contraceptive had lower unintended
pregnancy rates. Black and Hispanic communities had a more negative view of contraceptive use
compared to whites. It seems as a result of this view contraceptive was used less often and led to
greater pregnancy rates.
Women’s Rights:
Another important factor in determining family size is a society’s view of the role of
woman. Nations that hold woman as nothing more than home keepers and wives, tend to produce
larger families. Societies that restrict a woman’s rights have higher birth rates, because in these
nations women have little options outside of the household. As such women have little choice in
contraceptives, family size choices, sexual activity, abortion, and divorce. This trait tends to
appear in lower income/low education households, and also among developing nations (Anne
Moursund, Oystein Kravdal 2003), (Forest Frost 1996).
Evaluation:
While race, religion, and predisposed genetic fertility do have a role in understanding
why large families produce more large families; it seems that income and education play a far
larger role. It is these two later factors that lead to situations in which a lack of resource
availability creates households were children are raised without the same benefits as smaller
child rearing homes.
Running Head: FAMILY SIZE 6
The resource split homes lack the same availability to opportunity, thus have lower
income and lower education in their own adult lives. These less funded and less educated homes
tend to have a lack of availability to contraceptives, as well as a more predisposed availability to
actions that create unintended and young pregnancy. These homes in turn have a harder chance
achieving a career oriented household and instead focus more intensely on family oriented goals.
Large families produce more large families.
Hypothesis and Theory of Family Size:
Comparing What Influences Family Size
What factors contribute to the size of a family unit in the United States? Social scholars
have done little research into the variables that affect family size and growth, as well as which
factors determine an individual’s number of offspring. One such explanation for the number of
offspring an individual will have can be based upon the number of siblings one had during
childhood and adolescence. Individuals that have grown up with multiple siblings are more likely
to have multiple children. On average the more siblings one has the more offspring one
produces.
The research that is available seems to suggest that a wide variety of characteristics
determine family size. The most widely accepted theories agree that the financial wellbeing
brought about through education and social class most dominantly determine whether an
individual will produce many children. This defining characteristic further deduces that
members of minorities with traditionally lower income tend to produce more children. African
Americans and Hispanics tend to fall amongst such financially disadvantaged groups. As such
these races tend to produce more children than non-Hispanic groups (Forest, Frost 1996).
Running Head: FAMILY SIZE 7
One of the strongest theories for explaining this disparity between low income intervals
and high birth rates comes from the Resource Investment theories (Keister 2004). These theories
state that individuals born into families with multiple children are far more likely to become low
income individuals in their adult lives.
This theory is rested on the idea that families with many children must “split” time and
income amongst more household members. As a result these children do not receive the same
educational opportunities or parental supervision to achieve the requirements needed to acquire
higher education and high end job placement. Furthermore these children are more likely to
engage in unsupervised activities. These children more frequently engage in illegal activities,
become pregnant teens, and feel detached from structural family life (Keister 2004).
Along with these two theories, the correlation between contraceptive use and teen
pregnancy becomes apparent. Members of low income families are far more likely to engage in
unprotected sex. Pregnancy rates increase; as such so does the financial burden placed upon the
parents. The children are then subjected to a childhood with financial disparity and yet again
receive less educational opportunity.
Hypothesis
Family units with multiple children must “split” resources. As such children in these
families are more likely to achieve low incomes and low educational attainment. This hypothesis
will be based on the Resource Investment Theory. These children have not been given the same
financial availability or time investment as their single or low number child counterparts.
Instead of achieving career oriented lives, these children instead are more likely to
experience young marriages and teen pregnancy. Their own family size grows at a younger age
Running Head: FAMILY SIZE 8
and more children are produced from a given individual. When these children become adults
they in turn have multiple children, and the “cycle,” tends towards continuation.
We expect to find that as income decreases so too does number of children. We also
expect that as education level decreases number of children increases. And based upon the
Resource Investment Theory, we would expect that as the number of siblings increases; the level
of education as well as the amount of income decreases. Finally based on the finding of the
hypothesis above one would expect that as number of siblings increases so to does the number of
children produced.
Operationalization
In order to test this hypothesis, multiple correlations will be achieved. Whereas the
Resource Investment Theory is based upon “income splitting,” multiple correlations will be
compared. Income will be compared to the number of siblings growing up; as well as educational
achievement and the number of siblings growing up. Using these correlations, we can first find
whether or not sibling number determines whether or not an individual will be more likely to
become low income and attain low education achievement.
The GSS will be utilized and will use the questions of “How many siblings do you
have?” in order to determine sibling numbers. The GSS will ask, “How much money do you
make?” in order to determine income. It will ask, “What is the highest degree you have earned?”
in order to determine education. And finally it will ask, “How many children do you have?” in
order to determine offspring.
While conducting this correlation the number of siblings one has will be defined as the
number of identified siblings the individual includes. This may or may not include half or step
Running Head: FAMILY SIZE 9
siblings. It will be based entirely upon the individual’s identification; as to allow for individuals
to determine those siblings that have possibly affected their lives. For example, a step brother
that has grown up along an individual will have more affect upon their lives; than a brother or
sister they have never met that lives among another household. As such the individual can
determine the “nature” of a sibling.
Following these correlations, the study will then correlate the relationship between the
number of offspring and the level of income; as well as the number of offspring and educational
achievement. These correlations become important in determining whether or not low income,
low educational individuals produce more offspring than their wealthier more educated
counterparts.
During this study, offspring number will be defined as any child identified by the
respondent. This could include step children, biological children, or adopted children.
Following these correlations, the study will compare the number of siblings against the
number of offspring produced. This will allow the research to determine if the number of siblings
one has growing up aids in determining family size. This research will also allow one to
determine whether or not the Research Investment Theory is valid or not.
One would expect that because large families produce children with less educational
opportunity, that they in turn would have more children. These individuals in turn allow their
children to grow up in homes with more siblings. Resources are split and the cycle continues.
This research is not indefinite. Exceptions will occur, however one would expect that a definitive
correlation to be found.
Running Head: FAMILY SIZE 10
Findings
Frequencies
Statistics
NUMBER
OF
CHILDREN
Number
children in
thirds
NUMBER
OF
BROTHERS
AND
SISTERS
Number of
siblings in
thirds
FAMILY
INCOME IN
CONSTANT
$
INCOME IN
THIRDS
RS
HIGHEST
DEGREE
N Valid 5791 5791 5789 5789 5118 5118 5793
Missing 13 13 15 15 686 686 11
Mean 1.84 2.02 3.64 1.84 33945.00 2.04 1.52
Median 2.00 2.00 3.00 2.00 24830.00 2.00 1.00
Mode 0 2 2 1 34380 3 1
Std. Deviation 1.650 .756 3.028 .830 31680.295 .829 1.188
This table indicates that most individuals had 1.84 children or roughly 2 children. Most
individuals had 3.64 siblings or roughly 3 siblings. The average income correlated to $33,945
with a median of $24,830. The average educational level indicated was 1.52 whereas 1= High
school education and 2 = junior college, with a mean of 1 meaning that the average participant
was a high school graduate.
RS HIGHEST
DEGREE
INCOME IN
THIRDS
Number of
siblings in thirds
Number children
in thirds
N Valid 5793 5118 5789 5791
Missing 11 686 15 13
These frequencies show the total number of usable and missing variables encountered
through the GSS surveys. 5804 people were asked about their education level. 5793 answered in
such a way that their answers were usable. 11 of these people answered in such a way that their
Running Head: FAMILY SIZE 11
information could not be used. This would include answers that were unknown, invalid, or
unfinished.
These frequencies show that 5804 people were interviewed by GSS’s survey about
income. Of these participants 5118 had usable answers. 686 answered in such a way that their
answers were unusable.
These frequencies also show that 5804 people were asked about the number of siblings
that they had. 5789 had answers that were usable. 15 answered in such a way that their
information could not be used.
Finally 5804 people were asked about the number of children they had. Of these
participants 5791 had answers that were usable. 13 participants answered in such a way that their
answers could not be used
RS HIGHEST DEGREE
Frequency Percent Valid Percent
Cumulative
Percent
Valid LT HIGH SCHOOL 871 15.0 15.0 15.0
HIGH SCHOOL 3021 52.1 52.1 67.2
JUNIOR COLLEGE 400 6.9 6.9 74.1
BACHELOR 1004 17.3 17.3 91.4
GRADUATE 497 8.6 8.6 100.0
Total 5793 99.8 100.0
Missing DK 4 .1
NA 7 .1
Total 11 .2
Running Head: FAMILY SIZE 12
Total 5804 100.0
This frequency shows that the 5804 participants were classified as less than high school
degree, high school degree, junior college, bachelor degree, graduate degree, not applicable, or
missing. Of these participants 871 had less than high school degree and accounted for 15 percent
of participants. 3021 participants had only a high school degree and accounted for 52.1 percent
of applicants. 400 participants finished junior college and accounted for 6.9 percent of
applicants. 1004 had bachelor’s degrees and accounted for 17.3 percent of applicants. 497
participants had graduate degrees and accounted for 8.6 percent of applicants. 4 participants did
not know what education level they had and accounted for .1 percent of applicants. 7 applicants
were declared non-applicable, meaning that in some way they answered in such a way that the
answers were unusable. These applicants accounted for .1 percent of all applicants.
INCOME IN THIRDS
Frequency Percent Valid Percent Cumulative Percent
Valid Low 1667 28.7 32.6 32.6
Moderate 1593 27.4 31.1 63.7
High 1858 32.0 36.3 100.0
Total 5118 88.2 100.0
Missing System 686 11.8
Total 5804 100.0
Running Head: FAMILY SIZE 13
This frequency table shows that 5804 applicants were asked about their income. These
applicants were categorized as either low, moderate, high, or missing. Of these applicants 1667
were categorized as low income and they accounted for 28.7 percent of applicants. 1593
applicants were categorized as moderate income and accounted for 27.4 percent of applicants.
1858 applicants were categorized as high income and accounted for 32 percent of applicants. 686
applicants were categorized as missing answers and accounted for 11.8 percent of applicants.
Running Head: FAMILY SIZE 14
Number of siblings in thirds
Frequency Percent Valid Percent
Cumulative
Percent
Valid Zero thru 2 2533 43.6 43.8 43.8
Three or four 1653 28.5 28.6 72.3
Five or more 1603 27.6 27.7 100.0
Total 5789 99.7 100.0
Missing System 15 .3
Total 5804 100.0
In this frequency table participants were asked how many siblings they had. These
numbers were then categorized as either zero thru 2, three or four, five or more, or missing
answers. Of these participants 2533 answered zero thru 2 and accounted for 43.6 percent of
applicants. 1653 applicants answered three of four and accounted for 28.5 percent of applicants.
1603 applicants answered five or more and accounted for 27.6 percent of applicants. 15
participants were categorized as missing and accounted for .3 percent of applicants.
Running Head: FAMILY SIZE 15
Number children in thirds
Frequency Percent Valid Percent
Cumulative
Percent
Valid None 1587 27.3 27.4 27.4
One or two 2483 42.8 42.9 70.3
Three thru 8 or more 1721 29.7 29.7 100.0
Total 5791 99.8 100.0
Missing System 13 .2
Total 5804 100.0
In the final frequency table participants were asked about how many children they had.
The applicants were divided into the categories of none, one or two, three or more, or missing
answer. Of these 5804 applicants, 1587 answered none and accounted for 27.3 percent of
participants. 2483 answered one or two and accounted for 42.8 percent of applicants. 1721
answered three or more and accounted for 29.7 percent of applicants. 13 applicants were
declared missing answers and accounted for .2 percent of applicants.
Running Head: FAMILY SIZE 16
Means
Report
Number of siblings in thirds
RS HIGHEST
DEGREE
INCOME IN
THIRDS
Number children
in thirds
Zero thru 2 Mean 1.78 2.13 1.90
N 2532 2248 2532
Std. Deviation 1.234 .819 .748
Three or four Mean 1.50 2.06 2.01
N 1649 1478 1646
Std. Deviation 1.133 .827 .748
Five or more Mean 1.13 1.86 2.24
N 1598 1385 1600
Std. Deviation 1.055 .822 .726
Total Mean 1.52 2.04 2.02
N 5779 5111 5778
Std. Deviation 1.188 .829 .755
This graph indicates the mean for the number of siblings one has when compared to
highest degree achieved, income in thirds, and number of children declared in thirds. Degrees of
education where set as 0 representing less than high school degree , 1 representing a high school
degree, 2 representing a junior college degree, 3 representing a bachelor degree, and 4
representing a graduate degree.
Running Head: FAMILY SIZE 17
Individuals with zero- 2 siblings had an average mean of 1.78 for highest degree with a
standard deviation of 1.234. Individuals with 3-4 siblings had an average degree level of 1.5 with
a standard deviation of 1.133. Individuals 5 or more siblings had an average degree level of 1.13
with a standard deviation of 1.055. The total average degree level was 1.52 with a standard
deviation of 1.188.
Income was categorized as 1 representing low income, 2 representing middle income, and
3 representing high income. Individuals with zero-2 siblings had an average income of 2.13 with
a standard deviation of .819. Individuals with 3-4 siblings had an average income of 2.06 with a
standard deviation of .827. Individuals with 5 or more siblings had an average income of 1.86
and a standard deviation of .822. The total average of income was a 2.04 with a standard
deviation of .829.
When finding the mean of the number of children the categories were defined as 1
representing no children, 2 representing 1-2 children, and 3 representing 3 or more children.
Research found that individuals with zero-2 siblings had an average of 1.90 represented children
with a standard deviation of .748. Individuals with 3-4 siblings had an average of 2.01
represented children with a standard deviation of .748. Individuals with 5 or more siblings had an
average of 2.24 represented children with a standard deviation of .726. The total average number
of represented children was 2.02 with a standard deviation of .755
Crosstabs
Running Head: FAMILY SIZE 18
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Number of siblings in thirds * RS HIGHEST DEGREE
5779 99.6% 25 .4% 5804 100.0%
Number of siblings in thirds * INCOME IN THIRDS
5111 88.1% 693 11.9% 5804 100.0%
Number of siblings in thirds * Number children in thirds
5778 99.6% 26 .4% 5804 100.0%
This table simply shows us which answers were valid and usable or not. Many of the
answers may not have been completed, completely understood, or accurate; as such these
answers were declared missing.
Number of siblings in thirds * RS HIGHEST DEGREE
Running Head: FAMILY SIZE 19
RS HIGHEST DEGREE
Total
LT HIGH
SCHOOL
HIGH
SCHOOL
JUNIOR
COLLEGE BACHELOR GRADUATE
Number
of
siblings
in thirds
Zero
thru 2
Count 236 1249 189 550 308 2532% within Number of siblings in thirds
9.3% 49.3% 7.5% 21.7% 12.2% 100.0%
Three
or
four
Count 207 930 109 282 121 1649% within Number of siblings in thirds
12.6% 56.4% 6.6% 17.1% 7.3% 100.0%
Five
or
more
Count 425 836 101 169 67 1598
% within
Number
of
siblings
in thirds
26.6% 52.3% 6.3% 10.6% 4.2% 100.0%
Total Count 868 3015 399 1001 496 5779
% within Number of siblings in thirds
15.0% 52.2% 6.9% 17.3% 8.6% 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 362.759a 8 .000Likelihood Ratio 353.779 8 .000Linear-by-Linear Association 288.869 1 .000N of Valid Cases 5779
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 110.33.
Running Head: FAMILY SIZE 20
This table aids in determining which groups have a higher or lower percentage under
each category. It becomes clear that as the number of siblings increases, the number of
individuals with less than a high school degree also increases. Individuals with higher sibling
numbers fair worse in each of the higher established education levels entirely across the board.
Less graduate from college, less graduate from junior college, and less finish high school.
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 362.759a 8 .000Likelihood Ratio 353.779 8 .000Linear-by-Linear Association 288.869 1 .000N of Valid Cases 5779
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 110.33.
This Chi-Square indicates that p<=.000 as such is significant to the 99% level.
Number of siblings in thirds * INCOME IN THIRDS
INCOME IN THIRDS
TotalLow Moderate High
Number of siblings in thirds
Zero thru 2 Count 629 703 916 2248
% within Number of siblings in thirds
28.0% 31.3% 40.7% 100.0%
Three or four
Count 460 462 556 1478
% within Number of siblings in thirds
31.1% 31.3% 37.6% 100.0%
Five or more
Count 574 425 386 1385
% within Number of siblings in thirds
41.4% 30.7% 27.9% 100.0%
Total Count 1663 1590 1858 5111
% within Number of siblings in thirds
32.5% 31.1% 36.4% 100.0%
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Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 89.140a 4 .000Likelihood Ratio 89.119 4 .000Linear-by-Linear Association 81.504 1 .000N of Valid Cases 5111
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 430.86.
Number of siblings in thirds * Number children in thirds
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Crosstab
Number children in thirds
TotalNoneOne or two
Three thru 8 or more
Number of siblings in thirds
Zero thru 2
Count 854 1087 591 2532
% within Number of siblings in thirds
33.7% 42.9% 23.3% 100.0%
Three or four
Count 452 725 469 1646
% within Number of siblings in thirds
27.5% 44.0% 28.5% 100.0%
Five or more
Count 277 667 656 1600
% within Number of siblings in thirds
17.3% 41.7% 41.0% 100.0%
Total Count 1583 2479 1716 5778
% within Number of siblings in thirds
27.4% 42.9% 29.7% 100.0%
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This table clearly demonstrates that as number of siblings increases, the amount of
income decreases. 41.4 % of individuals with 5 or more siblings are categorized as low income,
whereas only 31.1 % of individuals with 3-4 siblings are categorized as low income, and only 28
% of individuals with zero-2 siblings are categorized as low income. The table also indicates that
there are far less high sibling individuals among the high or middle income brackets when
compared to low sibling individuals.
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 89.140a 4 .000Likelihood Ratio 89.119 4 .000Linear-by-Linear Association 81.504 1 .000N of Valid Cases 5111
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 430.86.
This Chi-Square test clearly shows that p<=.000 and is thus significant to the 99% level.
Running Head: FAMILY SIZE 24
This table indicates that as te number of siblings increases so too does the number of children.
Only 17.3 % of individuals with 5 or more siblings have no children, whereas 33.7% of
individuals with zero-2 siblings have no children. It becomes clear that while there are far fewer
individuals with 5 or more siblings than those with 3-4 siblings or zero-2 siblings; they account
for a disproportionate amount of high children households.
Number children in thirds
TotalNoneOne or two
Three thru 8 or more
Number of siblings in thirds
Zero thru 2
Count 854 1087 591 2532
% within Number of siblings in thirds
33.7% 42.9% 23.3% 100.0%
Three or four
Count 452 725 469 1646
% within Number of siblings in thirds
27.5% 44.0% 28.5% 100.0%
Five or more
Count 277 667 656 1600
% within Number of siblings in thirds
17.3% 41.7% 41.0% 100.0%
Total Count 1583 2479 1716 5778
% within Number of siblings in thirds
27.4% 42.9% 29.7% 100.0%
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Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 201.564a 4 .000
Likelihood Ratio 203.428 4 .000
Linear-by-Linear Association 193.767 1 .000
N of Valid Cases 5778
The Chi-Square indicates that p<=.000 and is thus significant to the 99% level.
Running Head: FAMILY SIZE 26
RegressionNumber of siblings versus the highest degree achieved
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 -.224a .050 .050 1.158
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 407.671 1 407.671 303.961 .000a
Residual 7748.087 5777 1.341
Total 8155.758 5778
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
b. Dependent Variable: RS HIGHEST DEGREE
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.B Std. Error Beta
1 (Constant) 1.842 .024 77.379 .000
NUMBER OF BROTHERS AND SISTERS
-.088 .005 -.224 -17.434 .000
a. Dependent Variable: RS HIGHEST DEGREE
Running Head: FAMILY SIZE 27
Based upon my theory that as the number of siblings increases education decreases, I ran
a regression model with siblings as the independent variable and education as the dependent
variable. The model supported my hypothesis.
Highest degree achieved= 1.842+ (-.088) (Number of identified siblings)
Where B= -.088 and a= 1.842. According to ANOVA, this OLS regression model is significantly
significant at the p<=.000 level. As the number of siblings increases, the level of degree
achieved decreases. This relationship is supported with a correlation of r= -.224 which indicates
a negative relationship that shows that as siblings increase, educational degree achieved
decreases. The relationship is fairly weak but it does show that r2= .050 or 5% of the variation in
the relationship between likelihood of a higher degree can be predicted by sibling numbers. This
regression clearly supports my hypothesis.
Number of Siblings versus Income in Constant
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 -.125a .016 .015 31447.643
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 7.976E10 1 7.976E10 80.652 .000a
Residual 5.053E12 5109 9.890E8
Total 5.132E12 5110
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
b. Dependent Variable: FAMILY INCOME IN CONSTANT $
Running Head: FAMILY SIZE 28
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 38756.183 690.980 56.089 .000
NUMBER OF BROTHERS
AND SISTERS
-1332.336 148.357 -.125 -8.981 .000
a. Dependent Variable: FAMILY INCOME IN CONSTANT $
My theory was that as siblings increase, income decreases. Siblings was chosen as the
independent variable and income was determined as the dependent variable. The linear
regression model supports my hypothesis.
Income= 38,756.183 + (-1332.336) (Number of siblings)
Where b= -1332.336 and a = 38,756.183. According to ANOVA, this OLS regression model is
significant at the p<= .000 level. An increase in siblings determines a decrease in income. This
relationship is supported with a correlation of r= -.125, which indicates that as siblings increase,
income decreases. It further indicates that r2= .015 or 1.5% of the variation is dependent upon
sibling number. Sibling number can determine up to 1.5% the likelihood of income an individual
will have.
Number of Siblings versus Number of Children
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .204a .042 .042 1.615
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
Running Head: FAMILY SIZE 29
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 656.285 1 656.285 251.520 .000a
Residual 15071.182 5776 2.609
Total 15727.467 5777
a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
b. Dependent Variable: NUMBER OF CHILDREN
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 1.437 .033 43.284 .000
NUMBER OF BROTHERS
AND SISTERS
.111 .007 .204 15.859 .000
a. Dependent Variable: NUMBER OF CHILDREN
My hypothesis was that as siblings increase, number of children increases. Sibling
number was indicated as the independent variable and child number was chosen as the dependent
variable. The linear regression model supported my hypothesis.
Number of Children= 1.437 + (.111) (Number of siblings)
Where b= 1.437 and a= .111. According to ANOVA, this OLS regression model is significant at
the p<=.000 level. So an increase in siblings corresponds to an increase in number of children.
This relationship is supported by a correlation of r=.204 which indicates a positive relationship
between an increase between the two. R2=.042 and corresponds to a 4.2 % variation between the
likelihood of siblings determining number of children.
Running Head: FAMILY SIZE 30
Discussion
The finding clearly shows that my hypothesizes are correct. The relationships are weak at
best but do show significance. The hypothesis of as siblings increase, education decreases;
found that this was the case. The relationship between the two was only accountable to a 5%
prediction, but was still valid and statistically valid.
In the hypothesis of as siblings increase, income decreases; we found similar results. The
hypothesis was true, but displayed a weak relationship. This relationship could only account for
roughly 1.5 % of the variance; however it was significant and valid.
And finally the hypothesis predicting that as siblings increase, the number of children an
individual will have increases; was also true. This relationship was also weak. It found that the
relationship accounted for 4.5 % of the variance, yet it was also significant and valid.
The theory of resource investment was supported through the research and found that a
relationship does occur. Children with many siblings make less money and attain less
educational achievement. Furthermore they in turn produce larger families that yet again attain
similar results.
Limitations of this Research
The research found was limited in that it found only a weak relationship. Had the
relationships between the hypotheses been able to show a stronger relationship, the resource
investment theory would have been better supported.
Furthermore my research should have also compared other variables. These variables
should have included the region of the country to account for living standards and the price of
living in certain areas. For example a low income family in New York City may have entirely
different resource availability to education and work when compared to a Midwestern family.
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The variables should have also included race to account for differences in society. For
example, perhaps it is less family size and more race relations and resource allocation that
accounts for family size and resource disparity.
The variables should have also included things such as contraceptive availability. This
would have accounted for whether or not it is contraceptives and their use rather than income
that explains larger family size. It would have also aided in getting a “bigger” picture” of the
complexity of the issues of family size. This variable could not be attained because the GSS did
not offer it as a question; as such I was limited in applying this variable.
Religious affiliation could have also been a useful variable for aiding in determine family
size. Perhaps certain religious groups encourage large families, rather than the hypothesis that
resource disparity determining family size.
Finally the GSS was limiting in the way many questions were asked. For example, when
determining the number of siblings one had. The GSS did not account for only biological
siblings. Instead the GSS used self identifying sibling numbers. As such certain individuals could
have skewed the data. Someone may have included step brothers or sisters, close friends or
outside family members, adopted siblings, etc. As such it becomes difficult to analyze which of
these siblings had a direct financial and social impact upon the questioned participant.
Number of children was also such a category. The GSS simply asked the participants to
identify how many children they had, rather than the number of biological children they had. As
such the participants may have included “children” that they do not actually provide monetary or
social benefit to. It would have been far more effective if the GSS had split these numbers into
categories such as biological children, adopted children, step children, etc…
Running Head: FAMILY SIZE 32