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
2. 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 willhave. These factors include race, religion, education, income, birth place, marriage age, andanother wide range of factors. Despite the significance of these factors this paper hypothesis thateducation, income, and sibling numbers most significantly influence how many offspring anindividual will have.REVIEW OF LITERATURENumber of Siblings versus Number of Offspring Does the number of siblings an individual has growing up determine how many offspringthey produce in their own adult family? This topic becomes important when predicting familysize among individual groups. It also aids in explaining why developing countries produce largerfamilies and developed countries tend towards smaller families. Furthermore, this research couldgreatly aid in determining population growth within the U.S. most specifically in areas with highpredisposition of predicted values; such as education levels and income.Fertility: Little modern research has been done in this field of study, however the aging researchfound shows a proportional correlation between the number of siblings one has growing up andthe 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 infamily size, it is possible that genetics plays an even greater role. Perhaps it is not the siblings
3. Running Head: FAMILY SIZE 3that affect a given child’s view of family size, but rather a predisposed affinity to geneticfertility.Resource Investment: According to many more modern researchers, income and time resources play far greaterroles in the development between birth family size and adult family size. A child born to a largerfamily 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 willhave less invested into their future. Furthermore these children are more predisposed to earlymarriage and pregnancy because their limited resources create situations in which fewer otheroptions are available, especially for women. These low income families have less time andresources to invest in a child’s education and extracurricular activities, as such these children aremore predisposed to activities in which unplanned pregnancy increases (Keister 2004).Educational Achievement: Children with fewer educational investments often strive for family oriented goals ratherthan career or educational oriented goals. Large families often become a bi-product of thisdisposition with family situated goals. Most specifically a women’s ability to attain educationalachievement and then career opportunities links directly with family size. A women that is ableto achieve an occupation outside the home, is less inclined to want more children. There issimply 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 toless men and more specifically women in the high end income workplace. This in turn creates a
4. Running Head: FAMILY SIZE 4situation in which many non-career oriented women have more children. Large families createcircumstances in which more large families are created (Blake 1989).Contraceptive Use and Availability: Contraceptive availability plays a key role in why some households produce largefamilies. Homes without readily available or accepted contraceptive use produce far morechildren. 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 suchthese homes often raise children with an adult misunderstanding of contraceptives as well as aninability to receive contraceptives readily (Forest, Frost 1996). While religion has been mentioned many times in literature dealing with correlations offamily size, it seems that a religion’s open tolerance and viewpoint of contraceptive use seems torelate more to family size correlation. Many religions view contraceptive as morally orspiritually wrong and as such they discourage their members from its use. As such householdsthat follow these religions tend to produce larger families. This trend further contributes if thechildren of these households adopt their parent’s religion (Brewster, Cooksey, Guilkey, Rindfuss1998).Race: A correlation between race and family size has also been found. This however on anational 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 correlationamong blacks, Hispanics, and whites. Seventy-four percent of pregnancy that occurred to womenwithin 150% of the federal governments established poverty line were unintended. 79% of those
5. Running Head: FAMILY SIZE 5pregnancies among blacks were unintended. 63% percent of those among Hispanics wereunintended. 54% of those among whites and non-Hispanics were unintended (Forest, Frost1996). This disproportion among races seemed to show that contraceptive attitudes differedamong races. Those that had a positive attitude about contraceptive had lower unintendedpregnancy rates. Black and Hispanic communities had a more negative view of contraceptive usecompared to whites. It seems as a result of this view contraceptive was used less often and led togreater pregnancy rates.Women’s Rights: Another important factor in determining family size is a society’s view of the role ofwoman. Nations that hold woman as nothing more than home keepers and wives, tend to producelarger families. Societies that restrict a woman’s rights have higher birth rates, because in thesenations women have little options outside of the household. As such women have little choice incontraceptives, family size choices, sexual activity, abortion, and divorce. This trait tends toappear in lower income/low education households, and also among developing nations (AnneMoursund, Oystein Kravdal 2003), (Forest Frost 1996).Evaluation: While race, religion, and predisposed genetic fertility do have a role in understandingwhy large families produce more large families; it seems that income and education play a farlarger role. It is these two later factors that lead to situations in which a lack of resourceavailability creates households were children are raised without the same benefits as smallerchild rearing homes.
6. Running Head: FAMILY SIZE 6 The resource split homes lack the same availability to opportunity, thus have lowerincome and lower education in their own adult lives. These less funded and less educated homestend to have a lack of availability to contraceptives, as well as a more predisposed availability toactions that create unintended and young pregnancy. These homes in turn have a harder chanceachieving 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 scholarshave done little research into the variables that affect family size and growth, as well as whichfactors determine an individual’s number of offspring. One such explanation for the number ofoffspring an individual will have can be based upon the number of siblings one had duringchildhood and adolescence. Individuals that have grown up with multiple siblings are more likelyto have multiple children. On average the more siblings one has the more offspring oneproduces. The research that is available seems to suggest that a wide variety of characteristicsdetermine family size. The most widely accepted theories agree that the financial wellbeingbrought about through education and social class most dominantly determine whether anindividual will produce many children. This defining characteristic further deduces thatmembers of minorities with traditionally lower income tend to produce more children. AfricanAmericans and Hispanics tend to fall amongst such financially disadvantaged groups. As suchthese races tend to produce more children than non-Hispanic groups (Forest, Frost 1996).
7. Running Head: FAMILY SIZE 7 One of the strongest theories for explaining this disparity between low income intervalsand high birth rates comes from the Resource Investment theories (Keister 2004). These theoriesstate that individuals born into families with multiple children are far more likely to become lowincome individuals in their adult lives. This theory is rested on the idea that families with many children must “split” time andincome amongst more household members. As a result these children do not receive the sameeducational opportunities or parental supervision to achieve the requirements needed to acquirehigher education and high end job placement. Furthermore these children are more likely toengage 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 teenpregnancy becomes apparent. Members of low income families are far more likely to engage inunprotected sex. Pregnancy rates increase; as such so does the financial burden placed upon theparents. The children are then subjected to a childhood with financial disparity and yet againreceive less educational opportunity. Hypothesis Family units with multiple children must “split” resources. As such children in thesefamilies are more likely to achieve low incomes and low educational attainment. This hypothesiswill be based on the Resource Investment Theory. These children have not been given the samefinancial availability or time investment as their single or low number child counterparts. Instead of achieving career oriented lives, these children instead are more likely toexperience young marriages and teen pregnancy. Their own family size grows at a younger age
8. Running Head: FAMILY SIZE 8and more children are produced from a given individual. When these children become adultsthey 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 alsoexpect that as education level decreases number of children increases. And based upon theResource Investment Theory, we would expect that as the number of siblings increases; the levelof education as well as the amount of income decreases. Finally based on the finding of thehypothesis above one would expect that as number of siblings increases so to does the number ofchildren produced. Operationalization In order to test this hypothesis, multiple correlations will be achieved. Whereas theResource Investment Theory is based upon “income splitting,” multiple correlations will becompared. Income will be compared to the number of siblings growing up; as well as educationalachievement and the number of siblings growing up. Using these correlations, we can first findwhether or not sibling number determines whether or not an individual will be more likely tobecome low income and attain low education achievement. The GSS will be utilized and will use the questions of “How many siblings do youhave?” in order to determine sibling numbers. The GSS will ask, “How much money do youmake?” 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?” inorder to determine offspring. While conducting this correlation the number of siblings one has will be defined as thenumber of identified siblings the individual includes. This may or may not include half or step
9. Running Head: FAMILY SIZE 9siblings. It will be based entirely upon the individual’s identification; as to allow for individualsto determine those siblings that have possibly affected their lives. For example, a step brotherthat has grown up along an individual will have more affect upon their lives; than a brother orsister they have never met that lives among another household. As such the individual candetermine the “nature” of a sibling. Following these correlations, the study will then correlate the relationship between thenumber of offspring and the level of income; as well as the number of offspring and educationalachievement. These correlations become important in determining whether or not low income,low educational individuals produce more offspring than their wealthier more educatedcounterparts. During this study, offspring number will be defined as any child identified by therespondent. This could include step children, biological children, or adopted children. Following these correlations, the study will compare the number of siblings against thenumber of offspring produced. This will allow the research to determine if the number of siblingsone has growing up aids in determining family size. This research will also allow one todetermine whether or not the Research Investment Theory is valid or not. One would expect that because large families produce children with less educationalopportunity, that they in turn would have more children. These individuals in turn allow theirchildren 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 definitivecorrelation to be found.
10. Running Head: FAMILY SIZE 10 FindingsFrequencies 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 DEGREEN Valid 5791 5791 5789 5789 5118 5118 5793 Missing 13 13 15 15 686 686 11Mean 1.84 2.02 3.64 1.84 33945.00 2.04 1.52Median 2.00 2.00 3.00 2.00 24830.00 2.00 1.00Mode 0 2 2 1 34380 3 1Std. 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. Mostindividuals had 3.64 siblings or roughly 3 siblings. The average income correlated to $33,945with a median of $24,830. The average educational level indicated was 1.52 whereas 1= Highschool education and 2 = junior college, with a mean of 1 meaning that the average participantwas a high school graduate. RS HIGHEST INCOME IN Number of Number children DEGREE THIRDS siblings in thirds in thirdsN Valid 5793 5118 5789 5791 Missing 11 686 15 13 These frequencies show the total number of usable and missing variables encounteredthrough the GSS surveys. 5804 people were asked about their education level. 5793 answered insuch a way that their answers were usable. 11 of these people answered in such a way that their
11. Running Head: FAMILY SIZE 11information could not be used. This would include answers that were unknown, invalid, orunfinished. These frequencies show that 5804 people were interviewed by GSS’s survey aboutincome. Of these participants 5118 had usable answers. 686 answered in such a way that theiranswers were unusable. These frequencies also show that 5804 people were asked about the number of siblingsthat they had. 5789 had answers that were usable. 15 answered in such a way that theirinformation could not be used. Finally 5804 people were asked about the number of children they had. Of theseparticipants 5791 had answers that were usable. 13 participants answered in such a way that theiranswers could not be used RS HIGHEST DEGREE Cumulative Frequency Percent Valid Percent PercentValid 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 99.8 100.0Missing DK 4 .1 NA 7 .1
12. Running Head: FAMILY SIZE 12 Total 11 .2Total 5804 100.0 This frequency shows that the 5804 participants were classified as less than high schooldegree, high school degree, junior college, bachelor degree, graduate degree, not applicable, ormissing. Of these participants 871 had less than high school degree and accounted for 15 percentof participants. 3021 participants had only a high school degree and accounted for 52.1 percentof applicants. 400 participants finished junior college and accounted for 6.9 percent ofapplicants. 1004 had bachelor’s degrees and accounted for 17.3 percent of applicants. 497participants had graduate degrees and accounted for 8.6 percent of applicants. 4 participants didnot know what education level they had and accounted for .1 percent of applicants. 7 applicantswere declared non-applicable, meaning that in some way they answered in such a way that theanswers were unusable. These applicants accounted for .1 percent of all applicants. INCOME IN THIRDS Frequency Percent Valid Percent Cumulative PercentValid 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 88.2 100.0Missing System 686 11.8Total 5804 100.0
13. Running Head: FAMILY SIZE 13 This frequency table shows that 5804 applicants were asked about their income. Theseapplicants were categorized as either low, moderate, high, or missing. Of these applicants 1667were categorized as low income and they accounted for 28.7 percent of applicants. 1593applicants 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. 686applicants were categorized as missing answers and accounted for 11.8 percent of applicants. Number of siblings in thirds Cumulative Frequency Percent Valid Percent PercentValid 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
14. Running Head: FAMILY SIZE 14 Total 5789 99.7 100.0Missing System 15 .3Total 5804 100.0 In this frequency table participants were asked how many siblings they had. Thesenumbers were then categorized as either zero thru 2, three or four, five or more, or missinganswers. Of these participants 2533 answered zero thru 2 and accounted for 43.6 percent ofapplicants. 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. 15participants were categorized as missing and accounted for .3 percent of applicants. Number children in thirds Cumulative Frequency Percent Valid Percent PercentValid 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 99.8 100.0Missing System 13 .2
15. Running Head: FAMILY SIZE 15 Number children in thirds Cumulative Frequency Percent Valid Percent PercentValid 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 99.8 100.0Missing System 13 .2Total 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 missinganswer. Of these 5804 applicants, 1587 answered none and accounted for 27.3 percent ofparticipants. 2483 answered one or two and accounted for 42.8 percent of applicants. 1721answered three or more and accounted for 29.7 percent of applicants. 13 applicants weredeclared missing answers and accounted for .2 percent of applicants.
16. Running Head: FAMILY SIZE 16 Means Report RS HIGHEST INCOME IN Number childrenNumber of siblings in thirds DEGREE THIRDS in thirdsZero thru 2 Mean 1.78 2.13 1.90 N 2532 2248 2532 Std. Deviation 1.234 .819 .748Three or four Mean 1.50 2.06 2.01 N 1649 1478 1646 Std. Deviation 1.133 .827 .748Five or more Mean 1.13 1.86 2.24 N 1598 1385 1600 Std. Deviation 1.055 .822 .726Total 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 tohighest degree achieved, income in thirds, and number of children declared in thirds. Degrees ofeducation where set as 0 representing less than high school degree , 1 representing a high schooldegree, 2 representing a junior college degree, 3 representing a bachelor degree, and 4representing a graduate degree.
17. Running Head: FAMILY SIZE 17 Individuals with zero- 2 siblings had an average mean of 1.78 for highest degree with astandard deviation of 1.234. Individuals with 3-4 siblings had an average degree level of 1.5 witha standard deviation of 1.133. Individuals 5 or more siblings had an average degree level of 1.13with a standard deviation of 1.055. The total average degree level was 1.52 with a standarddeviation of 1.188. Income was categorized as 1 representing low income, 2 representing middle income, and3 representing high income. Individuals with zero-2 siblings had an average income of 2.13 witha standard deviation of .819. Individuals with 3-4 siblings had an average income of 2.06 with astandard deviation of .827. Individuals with 5 or more siblings had an average income of 1.86and a standard deviation of .822. The total average of income was a 2.04 with a standarddeviation of .829. When finding the mean of the number of children the categories were defined as 1representing 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 childrenwith a standard deviation of .748. Individuals with 3-4 siblings had an average of 2.01represented children with a standard deviation of .748. Individuals with 5 or more siblings had anaverage of 2.24 represented children with a standard deviation of .726. The total average numberof represented children was 2.02 with a standard deviation of .755
18. Running Head: FAMILY SIZE 18CrosstabsCase Processing Summary Cases Valid Missing Total N Percent N Percent N PercentNumber of siblings in 5779 99.6% 25 .4% 5804 100.0%thirds * RS HIGHESTDEGREENumber of siblings in 5111 88.1% 693 11.9% 5804 100.0%thirds * INCOME INTHIRDSNumber of siblings in 5778 99.6% 26 .4% 5804 100.0%thirds * Numberchildren in thirds This table simply shows us which answers were valid and usable or not. Many of theanswers may not have been completed, completely understood, or accurate; as such theseanswers were declared missing. Number of siblings in thirds * RS HIGHEST DEGREE RS HIGHEST DEGREE LT HIGH HIGH JUNIOR SCHOOL SCHOOL COLLEGE BACHELOR GRADUATE TotalNumber Zero Count 236 1249 189 550 308 2532of thru 2 % within 9.3% 49.3% 7.5% 21.7% 12.2% 100.0% Numbersiblings ofin 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 thirdsTotal Count 868 3015 399 1001 496 5779
19. Running Head: FAMILY SIZE 19 This table aids in determining which groups have a higher or lower percentage undereach category. It becomes clear that as the number of siblings increases, the number ofindividuals with less than a high school degree also increases. Individuals with higher siblingnumbers 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) aPearson Chi-Square 362.759 8 .000Likelihood Ratio 353.779 8 .000Linear-by-Linear Association 288.869 1 .000N of Valid Cases 5779a. 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 TotalNumber of siblings Zero thru 2 Count 629 703 916 2248in 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 thirdsTotal Count 1663 1590 1858 5111 % within Number of 32.5% 31.1% 36.4% 100.0% siblings in thirds
20. Running Head: FAMILY SIZE 20 This table clearly demonstrates that as number of siblings increases, the amount ofincome 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 thatthere are far less high sibling individuals among the high or middle income brackets whencompared to low sibling individuals. Chi-Square Tests Value df Asymp. Sig. (2-sided) aPearson Chi-Square 89.140 4 .000Likelihood Ratio 89.119 4 .000Linear-by-Linear Association 81.504 1 .000N of Valid Cases 5111a. 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.
21. Running Head: FAMILY SIZE 21 Number children in thirds One or Three thru None two 8 or more TotalNumber of siblings Zero thru Count 854 1087 591 2532in 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% of siblings in thirds Five or Count 277 667 656 1600 more % within Number 17.3% 41.7% 41.0% 100.0% of siblings in thirdsTotal Count 1583 2479 1716 5778 % within Number 27.4% 42.9% 29.7% 100.0% of siblings in thirdsThis 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% ofindividuals with zero-2 siblings have no children. It becomes clear that while there are far fewerindividuals with 5 or more siblings than those with 3-4 siblings or zero-2 siblings; they accountfor a disproportionate amount of high children households.
22. Running Head: FAMILY SIZE 22 Chi-Square Tests Asymp. Sig. (2- Value df sided)Pearson Chi-Square 201.564a 4 .000Likelihood Ratio 203.428 4 .000Linear-by-Linear Association 193.767 1 .000N of Valid Cases 5778 The Chi-Square indicates that p<=.000 and is thus significant to the 99% level.
23. Running Head: FAMILY SIZE 23RegressionNumber of siblings versus the highest degree achieved Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate a1 -.224 .050 .050 1.158a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS b ANOVAModel Sum of Squares df Mean Square F Sig. a1 Regression 407.671 1 407.671 303.961 .000 Residual 7748.087 5777 1.341 Total 8155.758 5778a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERSb. Dependent Variable: RS HIGHEST DEGREE a Coefficients Unstandardized Standardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) 1.842 .024 77.379 .000 NUMBER OF -.088 .005 -.224 -17.434 .000 BROTHERS AND SISTERSa. Dependent Variable: RS HIGHEST DEGREE
24. Running Head: FAMILY SIZE 24 Based upon my theory that as the number of siblings increases education decreases, I rana regression model with siblings as the independent variable and education as the dependentvariable. 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 significantlysignificant at the p<=.000 level. As the number of siblings increases, the level of degreeachieved decreases. This relationship is supported with a correlation of r= -.224 which indicatesa negative relationship that shows that as siblings increase, educational degree achieveddecreases. The relationship is fairly weak but it does show that r2= .050 or 5% of the variation inthe relationship between likelihood of a higher degree can be predicted by sibling numbers. Thisregression clearly supports my hypothesis. Number of Siblings versus Income in Constant Model Summary Adjusted R Std. Error of theModel R R Square Square Estimate a1 -.125 .016 .015 31447.643a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS b ANOVAModel Sum of Squares df Mean Square F Sig. a1 Regression 7.976E10 1 7.976E10 80.652 .000 Residual 5.053E12 5109 9.890E8 Total 5.132E12 5110a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERSb. Dependent Variable: FAMILY INCOME IN CONSTANT $
25. Running Head: FAMILY SIZE 25 a Coefficients Standardized Unstandardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) 38756.183 690.980 56.089 .000 NUMBER OF BROTHERS -1332.336 148.357 -.125 -8.981 .000 AND SISTERSa. Dependent Variable: FAMILY INCOME IN CONSTANT $ My theory was that as siblings increase, income decreases. Siblings was chosen as theindependent variable and income was determined as the dependent variable. The linearregression 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 issignificant at the p<= .000 level. An increase in siblings determines a decrease in income. Thisrelationship 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 uponsibling number. Sibling number can determine up to 1.5% the likelihood of income an individualwill have. Number of Siblings versus Number of Children Model Summary Adjusted R Std. Error of theModel R R Square Square Estimate a1 .204 .042 .042 1.615a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERS
26. Running Head: FAMILY SIZE 26 b ANOVAModel Sum of Squares df Mean Square F Sig. a1 Regression 656.285 1 656.285 251.520 .000 Residual 15071.182 5776 2.609 Total 15727.467 5777a. Predictors: (Constant), NUMBER OF BROTHERS AND SISTERSb. Dependent Variable: NUMBER OF CHILDREN a Coefficients Standardized Unstandardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) 1.437 .033 43.284 .000 NUMBER OF BROTHERS .111 .007 .204 15.859 .000 AND SISTERSa. Dependent Variable: NUMBER OF CHILDREN My hypothesis was that as siblings increase, number of children increases. Siblingnumber was indicated as the independent variable and child number was chosen as the dependentvariable. 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 atthe 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 relationshipbetween an increase between the two. R2=.042 and corresponds to a 4.2 % variation between thelikelihood of siblings determining number of children.
27. Running Head: FAMILY SIZE 27 Discussion The finding clearly shows that my hypothesizes are correct. The relationships are weak atbest 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. Thehypothesis was true, but displayed a weak relationship. This relationship could only account forroughly 1.5 % of the variance; however it was significant and valid. And finally the hypothesis predicting that as siblings increase, the number of children anindividual will have increases; was also true. This relationship was also weak. It found that therelationship 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 arelationship does occur. Children with many siblings make less money and attain lesseducational achievement. Furthermore they in turn produce larger families that yet again attainsimilar results. Limitations of this Research The research found was limited in that it found only a weak relationship. Had therelationships between the hypotheses been able to show a stronger relationship, the resourceinvestment theory would have been better supported. Furthermore my research should have also compared other variables. These variablesshould have included the region of the country to account for living standards and the price ofliving in certain areas. For example a low income family in New York City may have entirelydifferent resource availability to education and work when compared to a Midwestern family.
28. Running Head: FAMILY SIZE 28 The variables should have also included race to account for differences in society. Forexample, perhaps it is less family size and more race relations and resource allocation thataccounts for family size and resource disparity. The variables should have also included things such as contraceptive availability. Thiswould have accounted for whether or not it is contraceptives and their use rather than incomethat explains larger family size. It would have also aided in getting a “bigger” picture” of thecomplexity of the issues of family size. This variable could not be attained because the GSS didnot 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 familysize. Perhaps certain religious groups encourage large families, rather than the hypothesis thatresource disparity determining family size. Finally the GSS was limiting in the way many questions were asked. For example, whendetermining the number of siblings one had. The GSS did not account for only biologicalsiblings. Instead the GSS used self identifying sibling numbers. As such certain individuals couldhave skewed the data. Someone may have included step brothers or sisters, close friends oroutside family members, adopted siblings, etc. As such it becomes difficult to analyze which ofthese 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 toidentify how many children they had, rather than the number of biological children they had. Assuch the participants may have included “children” that they do not actually provide monetary orsocial benefit to. It would have been far more effective if the GSS had split these numbers intocategories such as biological children, adopted children, step children, etc…