This one word (1)

Running Head: FAMILY SIZE 1

Family Size:

A Comprehensive Study of the Factors that Influence Family Size

Matthew T. Laidlaw

Black Hills State University


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


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


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


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.


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).


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


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


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.


Findings

Frequencies

Statistics

NUMBER
OF FAMILY
NUMBER Number BROTHERS Number of INCOME IN RS
OF children in AND siblings in CONSTANT INCOME IN HIGHEST
CHILDREN thirds SISTERS thirds $ THIRDS 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 INCOME IN Number of Number children

DEGREE THIRDS siblings in thirds 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


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

Cumulative

Frequency Percent Valid Percent 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

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


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.

Number of siblings in thirds

Cumulative


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.

Number children in thirds

Cumulative


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




Cumulative


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


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.


Means

Report

RS HIGHEST INCOME IN Number children

Number of siblings in thirds DEGREE THIRDS 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


Five or more Mean 1.13 1.86 2.24

N 1598 1385 1600


Total Mean 1.52 2.04 2.02

N 5779 5111 5778


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.


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
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Number of siblings in 5779 99.6% 25 .4% 5804 100.0%
thirds * RS HIGHEST
DEGREE
Number of siblings in 5111 88.1% 693 11.9% 5804 100.0%
thirds * INCOME IN
THIRDS
Number of siblings in 5778 99.6% 26 .4% 5804 100.0%
thirds * Number
children in thirds

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

RS HIGHEST DEGREE

LT HIGH HIGH JUNIOR
SCHOOL SCHOOL COLLEGE BACHELOR GRADUATE Total
Number Zero Count 236 1249 189 550 308 2532
of thru 2 % within 9.3% 49.3% 7.5% 21.7% 12.2% 100.0%
Number
siblings of
in thirds siblings
in thirds

Three Count 207 930 109 282 121 1649
or % within 12.6% 56.4% 6.6% 17.1% 7.3% 100.0%
Number
four of
siblings
in thirds
Five Count 425 836 101 169 67 1598
or % within 26.6% 52.3% 6.3% 10.6% 4.2% 100.0%
more Number
of
siblings
in thirds
Total Count 868 3015 399 1001 496 5779


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)
a
Pearson Chi-Square 362.759 8 .000
Likelihood Ratio 353.779 8 .000
Linear-by-Linear Association 288.869 1 .000
N 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
Low Moderate High Total
Number of siblings Zero thru 2 Count 629 703 916 2248
in thirds % within Number of 28.0% 31.3% 40.7% 100.0%
siblings in thirds
Three or Count 460 462 556 1478
four % within Number of 31.1% 31.3% 37.6% 100.0%
siblings in thirds
Five or Count 574 425 386 1385
more % within Number of 41.4% 30.7% 27.9% 100.0%
siblings in thirds
Total Count 1663 1590 1858 5111
% within Number of 32.5% 31.1% 36.4% 100.0%
siblings in thirds


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)
a
Pearson Chi-Square 89.140 4 .000
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.


One or Three thru
None two 8 or more Total
Number of siblings Zero thru Count 854 1087 591 2532
in thirds 2
% within Number 33.7% 42.9% 23.3% 100.0%
of siblings in thirds
Three or Count 452 725 469 1646
four % within Number 27.5% 44.0% 28.5% 100.0%
Five or Count 277 667 656 1600
more % within Number 17.3% 41.7% 41.0% 100.0%
Total Count 1583 2479 1716 5778

% within Number 27.4% 42.9% 29.7% 100.0%

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.


Chi-Square Tests

Asymp. Sig. (2-
Value df sided)
Pearson Chi-Square 201.564a 4 .000




The Chi-Square indicates that p<=.000 and is thus significant to the 99% level.


Regression
Number of siblings versus the highest degree achieved
Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
a
1 -.224 .050 .050 1.158

a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS

b
ANOVA

Model Sum of Squares df Mean Square F Sig.
a
1 Regression 407.671 1 407.671 303.961 .000

Residual 7748.087 5777 1.341

Total 8155.758 5778


b. Dependent Variable: RS HIGHEST DEGREE

a
Coefficients

Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 1.842 .024 77.379 .000

NUMBER OF -.088 .005 -.224 -17.434 .000
BROTHERS AND
SISTERS
a. Dependent Variable: RS HIGHEST DEGREE


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

Adjusted R Std. Error of the
Model R R Square Square Estimate
a
1 -.125 .016 .015 31447.643


b
ANOVA

a
1 Regression 7.976E10 1 7.976E10 80.652 .000

Residual 5.053E12 5109 9.890E8

Total 5.132E12 5110

b. Dependent Variable: FAMILY INCOME IN CONSTANT $


a
Coefficients

Standardized
Unstandardized Coefficients Coefficients


1 (Constant) 38756.183 690.980 56.089 .000

NUMBER OF BROTHERS -1332.336 148.357 -.125 -8.981 .000
AND SISTERS

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

Adjusted R Std. Error of the
Model R R Square Square Estimate
a
1 .204 .042 .042 1.615



b
ANOVA

a
1 Regression 656.285 1 656.285 251.520 .000

Residual 15071.182 5776 2.609

Total 15727.467 5777

b. Dependent Variable: NUMBER OF CHILDREN

a
Coefficients

Standardized
Unstandardized Coefficients Coefficients


1 (Constant) 1.437 .033 43.284 .000

NUMBER OF BROTHERS .111 .007 .204 15.859 .000
AND SISTERS

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.


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.


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…

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