Running Head:  Affect on Student’s Years of Education

 

 

 

 

 

 

Affect on Student’s Years of Education: Parent’s Years of Education and Socio-Economic Status Along With Participant’s Gender and Race

Pamela R. Hudson Bailey

May 7, 2009

Partial Fulfillment of EDRS 811

Dr. Dimiter M. Dimitrov

George Mason University

 

 

 

 

 

 

 

 

 

Abstract

The affect parents have on their children has largely been researched among elementary and middle school children however as children become adults studies have diminished. The participants in this study are adults with data obtained from the 1991 US General Social Survey. Some aspects considered in this quantitative analysis are the socio-economic levels of the parents, the years of school completed by the parents jointly, and lastly, the participant’s gender or race. Each of these is considered in the affect they have on the participant’s years of education. This study is an effort to replicate partial studies from Ismail and Awang (2008), Hall, Davis, Bolen and Chia (1999) and Travis and Kohli (1995).

 

 

 

 

 

 

 

 

 

 

 

 

 

Affect on Student’s Years of Education: Parent’s Years of Education and Socio-Economic Status Along With Participant’s Gender and Race

            Educational levels to be achieved by students have been studied by many as the children’s academic performance is perceived while still in elementary to high school. The actual academic achievement obtained by the students has not been extensively studied. Recent accountability measures have been enacted to insure that all children learn necessary concepts in order to be successful in life. What might one do to help insure student success and encourage each to seek higher levels of education is the million dollar question. Some variables to be considered are the socio-economic status of the parents, the years of education of each of the parents and lastly, gender and ethnicity considerations.

Literature Review

Educational level and socio-economic status.

            Travis and Kohli (1995) conducted a study of 817 adults that were involved in the Adult Life Cycle Project. Parental backgrounds are a major factor in a young person’s life and academic choices. Two of the factors assessed were the birth order of the participants and the socio-economic status of the parents. Birth order was a concern due to parents being more able to economically afford higher education for an only child whereas decisions would need to be made when there are multiple siblings. A family’s income is the underlying factor for children to obtain higher education. Travis and Kohli found that income for families classified as wealthy, meager or poor did not have any impact on student academic levels. This did not hold true for middle class income families classified as comfortable. A strong negative impact of birth order on academic achievement showed that educational levels decreased from the only child to the first born, the last born then other birth order positions. Parent’s years of education, a second variable to be considered, also affects the academic achievement of students.

Parental educational level affects student educational level.

Children of parents having higher levels of education are more likely to have higher levels of mathematical achievement (Ismail & Awang, 2008). Ismail and Awang (2008) studied data obtained from the TIMSS 2003 analysis on Malaysian students. Students were grouped into three categories, high, medium and low, according to their mathematics score in relationship to the Malaysian average and the international average. As the parents academic levels raised so did the student’s academic levels or years of education. Hall, Davis, Bolen and Chia (1999) found that there was a positive correlation between the number of years of schooling completed by a parent and the highest level of mathematics course taken. They also posited that the higher the educational level and upper level mathematics courses taken by parents will result in an increased number of years of school and higher levels of mathematics performance taken by their children. Parent’s acknowledgment of the usefulness and importance of mathematics influenced student performance (Hall et al., 1999). Along with usefulness, Hall et al. posited that there is a negative correlation between parental educational level and negative attitudes toward mathematics. The negative parental attitude influenced student’s attitude and self-efficacy toward achievement in mathematics. Analyzing whether the father or the mother, separately, influenced student achievement was conducted by Travis and Kohli (1995).

While the parents years of education, together, impacts student academic level, analyzed separately reveals a different scenario. Father’s highest level of education did not influence their children’s academic performance but the mother’s highest level of education showed a unique statistical relationship (Travis & Kohli, 1995). Even though the father’s level of education does not directly influence the child, his occupation does make an impression (Travis & Kohli, 1995). The more prestigious the father’s occupation resulted in a higher level of academic achievement for the child. Parents, male and female, had an affect on their children’s years of education. A consideration of the children’s gender and the affect it might have on the years of education obtained needs to be assessed.   

Educational level affected by gender and race.

            The affect of gender and race on academic achievement remains controversial. Hall et al. (1999) stated that academic achievement is not as affected by a person’s gender as it is his or her race. Travis and Kohli (1995) posited that ethnicity of the student did not impact academic achievement in their study but the number of Blacks, Chicanos and other ethnicity participants were small. Hall et al. grouped students in two categories, White and Black. Their findings indicated that there was a positive correlation between White parents level of mathematics course taken and student academic achievement and a negative correlation with student mathematics anxiety. Keep in mind that one’s mathematical ability and level of courses taken correlates to a higher number of years of education (Guay, Larose, & Boivin, 2004). These findings did not hold true for Black families however parent perception of their own mathematical ability did correlate with student mathematical abilities. Gender differences were not significant for mathematical and educational achievement through middle school (Hall et al). Differences begin to surface in high school and college as women begin taking less advanced mathematics courses and perceive themselves as less confident in their mathematical abilities. When students are grouped according to academic ability, girls outperformed boys for those classified as low or medium achievers (Ismail & Awang, 2008). Gender did not significantly impact the high achievers. A parent’s perception of their children’s abilities influences the student’s attitude which in turn affects academic achievement (Hall et al.).

Research Questions   

            Previous research has involved participants mainly in elementary and middle school with only minimal research with adults. The purpose of this study is an attempt to replicate previously reported results with data provided by adults. Four research questions will be addressed in the current study. Socio-economic status will be addressed by analyzing whether the educational level of the student is dependent upon the financial position of the parents, question 1. Secondly, the parent’s years of education, jointly, will be correlated with the participant’s years of education to determine if there is a positive affect. Racial considerations will also be analyzed to determine if there is a difference among racial groupings and one’s years of education. Lastly, determining whether a student’s gender affect his or her years of education obtained will be explored to ascertain which side of the controversy the selected data set will correlate. 

Methods

            Two hundred participants were randomly chosen from the 1991 US General Social Survey existing databank with a focus on gender, race, socio-economic status (SES) of parents, and the years of education obtained by parents separately. The comparison of number of males and females with respect to ethnicity may be found in Table 1 while the mean, standard deviation and mode for participants, fathers and mothers years of school may be found in Table 2. Histograms, Figures 1-3, show the number of individuals completing specific years of schooling for the participant’s, father’s and mother’s. Participants in the study ranged in age from 18 to 99 with a mean age of 44 and a standard deviation of 17.  

Years of education for participant’s, father’s and mother’s were categorically determined based on three groups, high school or lower, college courses or degree, and graduate courses or degree. The number of years of education completed by all three groups was also recorded in the data. Race was broken down into the following groups, White, Black and Other. SES categories include poor, comfortable, and wealthy and gender was recorded as male and female.

            SPSS Statistics GradPack 17.0, released in 2008, will be used to analyze the data for each of the four questions (see Appendix for statistical printout for all questions). To determine if there is a dependency between SES of the parents and the participant’s level of education, both categorical variables, a chi-squared test will be employed. Categories used for the crosstabulation include SES of the parents (poor, comfortable, and wealthy) and the ordinal levels of education for the participant (high school or lower, college courses or degree, and graduate courses or degree). Standardized residuals were also computed to determine if there were dependencies among specific cells. The father’s and mother’s years of education as a predictor of the participant’s years of education will be correlated using a multiple linear regression with the mother’s and father’s years of school as the independent variable and the participant’s years of school as the dependent variable. The omnibus analysis of variance will be assessed to determine if there is a statistically significant correlation. A t-test will reveal the significance of the regression coefficient for each parent.

            The difference between racial groups in relationship to the participant’s years of education was explored using an analysis of variance (ANOVA) with the dependent variable as the participant’s years of education and race as the fixed factor. Descriptive statistics will also be included along with an estimation of effect size and homogeneity of variance. A Tukey Post Hoc test will be conducted to scrutinize the difference in the racial groups as they relate to their years of education. Lastly, an independent samples t-test will be employed to determine if gender predicts completed years of education of the participant. The test variable for this assessment is the years of school for the participant and the group variable is gender (1=male, 2=female).

Results

            A chi-squared test for dependency showed that there is a statistically significant association between the socio-economic status of the parents and the educational level of the [participant, χ² (4, N=200) = 9.71, p = .046. Further, the standardized residual values show that none of the cells contribute to the dependency between parent SES (poor, comfortable, and wealthy) and participant level of education (high school or lower, college courses or degree, and graduate courses or degree). See Table 3 for the count and standardized residuals for the categories. An omnibus ANOVA test showed that the prediction of the participant’s years of school from the parent’s years of school is statistically significant, F(2,197) = 8.975,

p < .001. Further, R² = .084 indicates that 8.4% of the variance in the participants years of school is explained by the variance in the parent’s years of school. The t-test for the significance of the regression coefficient shows that the mother’s regression coefficient is statistically significant

(p = .021), whereas the regression coefficient for the father’s reveals no statistical significance

(p = .074). In other words, the mother’s years of school has a significantly unique contribution to the participant’s years of school over and above the prediction provided by the father. It is worthy to note that future gathering of data for father’s years of school may not be cost effective or time worthy. The regression equation is All variables are continuous. The positive coefficient for mother’s years of school (0.157) indicates that for participants with the same number of father’s years of school that a one-unit increase in mother’s years of school is associated with an increase in the predicted participant’s years of school of 0.157. A second linear regression was run due to there being no statistical significance of the father’s years of school. The omnibus ANOVA test showed that the prediction of the participant’s years of school from the mother’s years of school is statistically significant, F(1,198) = 14.56, p < .001. Further, R² = .068 indicates that 6.8% of the variance in the participants years of school is explained by the variance in the mother’s years of school. The t-test for the significance of the regression coefficient shows that the mother’s regression coefficient is statistically significant (p < .001). The mother’s regression coefficient for years of school (p  = .000) reveals a significantly unique contribution to the participant’s years of school. Finally the regression equation, if the decision is to eliminate the father’s years of school, is  The positive coefficient for mother’s years of school (0.221) indicates that a one-unit increase in mother’s years of school is associated with an increase in the predicted participant’s years of school of 0.221.

            The ANOVA test shows that there is a statistically significant difference between racial groups (white, black and other) and participant’s years of school, F(2,197) = 6.69, p = .002, pη² = .064 (see Table 4 for additional statistical data). The descriptive statistics are provided in Table 5, also the Levene’s Test of Equality of Variances assumes that the homogeneity of variances is met, F(2,197) = 1.205, p = .302. More specifically there is a statistically significant difference between white and black participant’s years of school (p = .001). White participants years of school were greater than the black participant’s years of school by a magnitude of 0.72 to 3.49. There is no statistically significant difference between white and other’s (p = .487) and black and other’s (p = .697).

            The Leven’s Test for Equality of Variances assumes that the variances are not equal,

(p = .001) for the last question, does gender (1=males, 2=females) predict years of school. An independent samples t-test revealed that there is a statistically significant difference in the participant’s years of school obtained by males and females, t(159) = 2.413, p = .017. More specifically, male participant's years of school is greater than the female participants by a magnitude of 0.217 to 2.178.

Discussion

            Conclusions drawn.

            This study does not correlate with the findings of Travis and Kohli (1995) regarding the dependency between parent SES and the participant’s level of education. The graduate courses or degree and either comfortable or wealthy cells showed a standardized residual of 1.9 and -1.9 respectively however these values are less those required for dependency. The opposite is true for predicting participant’s years of schooling based on the parent’s years of schooling. Ismail and Awang (2008) related an increase in academic achievement with an increase in mathematical achievement with both relating positively to the participant’s years of schooling. The current study showed that the mother’s years of schooling affected the participant’s years of school. These findings compare with those of Travis and Kohli.

The third question regarding difference among racial groups on the participant’s years of school showed only one statistically significant difference, white participant’s years of schooling was greater than the black participant’s years of schooling. This was opposite of Travis and Kohli’s results that ethnicity did not impact academic achievement. Hall et al. (1999) assessed only white and black groups but based it on the parent’s level of mathematics achievement. As discussed previously, higher levels of mathematical/academic achievement positively correlate with higher levels of education achievement (Guay, Larose, & Boivin, 2004). White parent’s mathematical/academic achievement impacted student academic achievement but the same was not true for black families. Therefore we might also assume that white mothers impact their children educationally more than black mothers. The affect of differences between the racial groups was not assessed by Hall et al.

            Ismail and Awang (2008) results showing a significant relationship between gender and mathematical achievement correlate with the current findings with the understanding that increased mathematical achievement positively correlates with academic achievement. Hall et al. (1999) stated that boys and girls were academically equal in mathematics through junior high. In high school girls became less confident in the mathematical skills and began taking lower level mathematics course. Relating mathematical ability to academic achievement results in similar findings between Hall et al. and the current study, male participant’s years of school is greater than the female participants.

            Limitations and recommendations.

            Limitations of the current study include data gathered randomly in the United States including ages up to and including 99 years. An alternate approach would be to survey students after they have been out of high school for eight to ten years. This would involve more participants of the same time span and experiencing the same economic struggles and growth. The racial groups studied were very limited (white, black and other’s). The diversity of the United States population warrants further breakdown in the racial groups to be studied. Years of schooling, regardless of type of schooling, was the main topic for the current study. Those completing technical or trade schools were acknowledged as lower achieving academically however they may be successful in their careers. Breaking down the study to determine highest mathematics course taken, science course taken, etc. may also be beneficial information. Lastly, information may be gathered regarding parents perception of their child’s abilities along with how they perceive themselves academically. This may lead to an understanding between males and females and academic growth and/or course selections.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Guay, F., Larose, S., & Boivin, M. (2004). Academic self-concept and educational attainment

level: A ten-year longitudinal study. Self and Identity, 3, 53-68.

Hall, C. W., Davis, N. B., Bolen, L.M. & Chia, R. (1999). Gender and racial differences in

mathematical performance. Journal of Social Psychology, 139(6).

Ismail, N. A., & Awang, H. (2008). Assessing the effects of students’ characteristics and

attitudes on mathematics performance. Problems of Education in the 21^st Century, 9, 34-

41.

Travis, R., & Kohli, V. (1995). The birth order factor: Ordinal position, social strata, and

educational achievement. The Journal of Social Psychology, 135(4), 499-507.

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1 Gender Count for Each Racial Grouping
Gender	White	Black	Other	Total
Male	69	11	7	87
Female	79	30	4	113
Total	148	41	11	200

 

 

 

Table 2 Mean, Standard Deviation and Mode for Years of Schooling for All Groups
	M	SD	Mode
Participants	12.88	3.41	12
Fathers	12.73	4.52	12
Mothers	12.30	4.05	12

 

 

 

 

 

 

 

 

Table 3 Dependency Between Parent’s Socio-Economic Status (SES) and Participant’s Level of Education
SES of Parents		Level of Education
		High School or Lower	College Courses or Degree	Graduate Courses or Degree	Total
Poor	Count Std. Residual	38.0    .5	17.0   -.7	9.0   .1	64.0
Comfortable	Count Std. Residual	30.0   -.7	18.0   -.3	14.0  1.9	62.0
Wealthy	Count Std. Residual	42.0    .2	28.0   1.0	4.0 -1.9	74.0
Total	Count	110.0	63.0	27.0	200.0

 

Table 4 Mean and Standard Deviation for Years of Schooling for Racial Groups
Race	n		M		SD
White	148		13.37		3.312
Black	41		11.27		3.009
Other	11		12.18		4.446
Total	200		12.87		3.414
Table 5 Multiple Comparisons for Years of Schooling Among Racial Groups
Racial Groups		ΔM		SEΔM		95% CI for ΔM
White – Black		2.10		0.586		0.72		3.49
White – Other		1.19		1.038		-1.26		3.64
Black – Other		-0.91		1.128		-3.58		1.75

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure Caption

Figure 1.  The number of participant’s per year for each completed years of schooling.

Figure 2.  The number of father’s per year for each completed years of schooling.

Figure 3.  The number of mother’s per year for each completed years of schooling.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix

Question #1

Is there a dependency between socio-economic status (SES) of parents and the educational level of the participant?

Crosstabs

Case Processing Summary
	Cases
	Valid		Missing		Total
	N	Percent	N	Percent	N	Percent
SES of Parents * Level of Education	200	100.0%	0	.0%	200	100.0%

                                                               

SES of Parents * Level of Education Crosstabulation
				Level of Education
				High School or Lower	College Courses or Degree	Graduate Courses or Degree	Total
SES of Parents	Poor	Count		38	17	9	64
		Std. Residual		.5	-.7	.1
	Comfortable	Count		30	18	14	62
		Std. Residual		-.7	-.3	1.9
	Wealthy	Count		42	28	4	74
		Std. Residual		.2	1.0	-1.9
	Total	Count		110	63	27	200

 

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	9.710a	4	.046
Likelihood Ratio	10.120	4	.038
Linear-by-Linear Association	.332	1	.565
N of Valid Cases	200
0 cells (.0%) have expected count less than 5. The minimum expected count is 8.37.

Question #2

Does the father's and mother's years of education predict the participants years of education?

Regression

Variables Entered/Removed
Model			Variables Entered				Variables Removed						Method
1			Years of School Father, Years of School Mothera				.						Enter
a. All requested variables entered.
Model Summary
Model		R		R Square		Adjusted R Square				Std. Error of the Estimate
1		.289a		.084		.074				3.285
a. Predictors: (Constant), Years of School Father, Years of School Mother
ANOVAb
Model					Sum of Squares			df			Mean Square				F		Sig.
1	Regression				193.720			2			96.860				8.975		.000a
	Residual				2126.155			197			10.793
	Total				2319.875			199
a. Predictors: (Constant), Years of School Father, Years of School Mother
b. Dependent Variable: Years of School
Coefficientsa
Model									Unstandardized Coefficients					Standardized Coefficients
									B			Std. Error		Beta					T		Sig.
1	(Constant)								9.562			.816							11.723		.000
	Years of School Mother								.157			.068		.186					2.326		.021
	Years of School Father								.109			.060		.144					1.796		.074
a. Dependent Variable: Years of School

 

Question #2 Continued

Does the mother's years of education predict the participant's years of education?

Regression Variables Entered/Removedb
Model	Variables Entered	Variables Removed	Method
1	Years of School Mothera	.	Enter
a. All requested variables entered.
b. Dependent Variable: Years of School

 

 

Model Summary
Model	R	R Square		Adjusted R Square			Std. Error of the Estimate
1	.262a	.068		.064			3.304
a. Predictors: (Constant), Years of School Mother
ANOVAb
Model			Sum of Squares		df	Mean Square		F		Sig.
1	Regression		158.911		1	158.911		14.560		.000a
	Residual		2160.964		198	10.914
	Total		2319.875		199
a. Predictors: (Constant), Years of School Mother
b. Dependent Variable: Years of School

 

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients
		B	Std. Error	Beta	t	Sig.
1	(Constant)	10.163	.748		13.583	.000
	Years of School Mother	.221	.058	.262	3.816	.000
a. Dependent Variable: Years of School

 

Question #3

Is there a difference among racial groups on the participant's years of education?

 

Univariate Analysis of Variance

Between-Subjects Factors
						Value Label				N
Race of Respondent			1			White				148
			2			Black				41
			3			Other				11
Descriptive Statistics
Dependent Variable: Years of School
Race of Respondent					Mean				Std. Deviation					N
White					13.37				3.312					148
Black					11.27				3.009					41
Other					12.18				4.446					11
Total					12.87				3.414					200
Levene's Test of Equality of Error Variancesa
Dependent Variable: Years of School
F	df1			df2			Sig.
1.205	2			197			.302
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + Race
Tests of Between-Subjects Effects
Dependent Variable: Years of School
Source		Type III Sum of Squares						Df				Mean Square			F	Sig.		Partial Eta Squared
Corrected Model		147.629a						2				73.815			6.694	.002		.064
Intercept		11108.335						1				11108.335			1007.410	.000		.836
Race		147.629						2				73.815			6.694	.002		.064
Error		2172.246						197				11.027
Total		35473.000						200
Corrected Total		2319.875						199
a. R Squared = .064 (Adjusted R Squared = .054)

 

 

 

 

 

 

 

 

Post Hoc Tests

Race of Respondent

Multiple Comparisons
Years of School Tukey HSD
(I) Race of Respondent	(J) Race of Respondent
		Mean Difference (I-J)	Std. Error	Sig.	Lower Bound	Upper Bound
White	Black	2.10*	.586	.001	.72	3.49
	Other	1.19	1.038	.487	-1.26	3.64
Black	White	-2.10*	.586	.001	-3.49	-.72
	Other	-.91	1.128	.697	-3.58	1.75
Other	White	-1.19	1.038	.487	-3.64	1.26
	Black	.91	1.128	.697
Based on observed means.  The error term is Mean Square(Error) = 11.027.
*. The mean difference is significant at the .05 level.

Homogeneous Subsets

Years of School
Tukey HSDa,,b,,c
Race of Respondent		Subset
	N	1
Black	41	11.27
Other	11	12.18
White	148	13.37
Sig.		.070
Means for groups in homogeneous subsets are displayed.  Based on observed means.  The error term is Mean Square(Error) = 11.027.
a. Uses Harmonic Mean Sample Size = 24.579.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.
c. Alpha = .05.

Question #4

Does the participant's gender predict their years of education?

T-Test

Group Statistics
		Respondent's Sex		N	Mean		Std. Deviation		Std. Error Mean
Years of School		Male		87	13.55		3.821		.410
		Female		113	12.35		2.979		.280
Independent Samples Test
			Levene's Test for Equality of Variances			t-test for Equality of Means
			F		Sig.	t		Df
Years of School	Equal variances assumed		10.454		.001	2.491		198
	Equal variances not assumed					2.413		158.646

 

				t-test for Equality of Means
								95% Confidence Interval of the Difference
				Sig. (2-tailed)		Mean Difference	Std. Error Difference	Lower	Upper
Years of School	Equal variances assumed			.014		1.198	.481	.250	2.146
	Equal variances not assumed			.017		1.198	.496	.217	2.178

 

Descriptive statistics for Participant's Years of Schooling

Frequencies

Statistics
Years of School
N	Valid	200
	Missing	0
	Mean	12.88
	Median	12.00
	Mode	12
	Std. Deviation	3.414
	Range	16
	Sum	2575
Percentiles	10	9.00
	20	10.20
	30	12.00
	40	12.00
	50	12.00
	60	13.00
	70	14.00
	80	16.00
	90	18.00

 

Years of School
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	4	3	1.5	1.5	1.5
	5	1	.5	.5	2.0
	6	2	1.0	1.0	3.0
	7	5	2.5	2.5	5.5
	8	8	4.0	4.0	9.5
	9	10	5.0	5.0	14.5
	10	11	5.5	5.5	20.0
	11	15	7.5	7.5	27.5
	12	55	27.5	27.5	55.0
	13	16	8.0	8.0	63.0
	14	15	7.5	7.5	70.5
	15	15	7.5	7.5	78.0
	16	17	8.5	8.5	86.5
	17	4	2.0	2.0	88.5
	18	9	4.5	4.5	93.0
	19	3	1.5	1.5	94.5
	20	11	5.5	5.5	100.0
	Total	200	100.0	100.0

 

Descriptive statistics for Parent's socio-economic status

Frequencies

Statistics
SES of Parents
N	Valid	200
	Missing	0
	Mean	2.05
	Median	2.00
	Mode	3
	Std. Deviation	.831
	Range	2
	Sum	410
Percentiles	10	1.00
	20	1.00
	30	1.00
	40	2.00
	50	2.00
	60	2.00
	70	3.00
	80	3.00
	90	3.00

 

SES of Parents
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Poor	64	32.0	32.0	32.0
	Comfortable	62	31.0	31.0	63.0
	Wealthy	74	37.0	37.0	100.0
	Total	200	100.0	100.0

 

 

 

 

Descriptive statistics for the Father's Years of Schooling

Frequencies

Statistics
Years of School Father
N	Valid	200
	Missing	0
	Mean	12.73
	Median	13.00
	Mode	12
	Std. Deviation	4.522
	Range	20
	Sum	2545
Percentiles	10	6.00
	20	8.00
	30	12.00
	40	12.00
	50	13.00
	60	14.00
	70	16.00
	80	17.00
	90	19.00

 

Years of School Father
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	0	1	.5	.5	.5
	3	4	2.0	2.0	2.5
	4	4	2.0	2.0	4.5
	5	2	1.0	1.0	5.5
	6	14	7.0	7.0	12.5
	7	5	2.5	2.5	15.0
	8	19	9.5	9.5	24.5
	9	2	1.0	1.0	25.5
	10	3	1.5	1.5	27.0
	11	5	2.5	2.5	29.5
	12	40	20.0	20.0	49.5
	13	8	4.0	4.0	53.5
	14	17	8.5	8.5	62.0
	15	11	5.5	5.5	67.5
	16	24	12.0	12.0	79.5
	17	8	4.0	4.0	83.5
	18	12	6.0	6.0	89.5
	19	10	5.0	5.0	94.5
	20	11	5.5	5.5	100.0
	Total	200	100.0	100.0

 

 

Descriptive statistics for the Mother's Years of Schooling

Frequencies

Statistics
Years of School Mother
N	Valid	200
	Missing	0
	Mean	12.30
	Median	12.00
	Mode	12
	Std. Deviation	4.051
	Range	20
	Sum	2459
Percentiles	10	6.10
	20	9.00
	30	12.00
	40	12.00
	50	12.00
	60	13.00
	70	14.70
	80	16.00
	90	17.00

 

Years of School Mother
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	0	2	1.0	1.0	1.0
	2	2	1.0	1.0	2.0
	3	5	2.5	2.5	4.5
	4	2	1.0	1.0	5.5
	5	1	.5	.5	6.0
	6	8	4.0	4.0	10.0
	7	3	1.5	1.5	11.5
	8	14	7.0	7.0	18.5
	9	4	2.0	2.0	20.5
	10	9	4.5	4.5	25.0
	11	9	4.5	4.5	29.5
	12	53	26.5	26.5	56.0
	13	10	5.0	5.0	61.0
	14	18	9.0	9.0	70.0
	15	12	6.0	6.0	76.0
	16	22	11.0	11.0	87.0
	17	9	4.5	4.5	91.5
	18	10	5.0	5.0	96.5
	19	2	1.0	1.0	97.5
	20	5	2.5	2.5	100.0
	Total	200	100.0	100.0

 

 

Crosstab to determine counts for sex and race

Crosstabs

Case Processing Summary
	Cases
	Valid		Missing		Total
	N	Percent	N	Percent	N	Percent
Respondent's Sex * Race of Respondent	200	100.0%	0	.0%	200	100.0%

 

Respondent's Sex * Race of Respondent Crosstabulation
			Race of Respondent
			White	Black	Other	Total
Respondent's Sex	Male	Count	69	11	7	87
		Std. Residual	.6	-1.6	1.0
	Female	Count	79	30	4	113
		Std. Residual	-.5	1.4	-.9
	Total	Count	148	41	11	200

 

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	7.038a	2	.030
Likelihood Ratio	7.266	2	.026
Linear-by-Linear Association	.359	1	.549
N of Valid Cases	200
a. 1 cells (16.7%) have expected count less than 5. The minimum expected count is 4.79.

 

Descriptive statistics for participant’s age

Descriptives

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Participants Age	200	18	99	44.46	16.981
Valid N (listwise)	200

 

Frequencies

Statistics
Participants Age
N	Valid	200
	Missing	0
	Mean	44.47
	Median	40.00
	Mode	35
	Std. Deviation	16.981
	Variance	288.340
	Minimum	18
	Maximum	99
Percentiles	10	24.00
	20	30.00
	30	34.00
	40	36.00
	50	40.00
	60	46.00
	70	54.00
	80	61.00
	90	70.90

 

Participants Age
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	18	1	.5	.5	.5
	20	3	1.5	1.5	2.0
	21	4	2.0	2.0	4.0
	22	7	3.5	3.5	7.5
	23	3	1.5	1.5	9.0
	24	3	1.5	1.5	10.5
	25	4	2.0	2.0	12.5
	26	2	1.0	1.0	13.5
	27	4	2.0	2.0	15.5
	28	5	2.5	2.5	18.0
	29	2	1.0	1.0	19.0
	30	4	2.0	2.0	21.0
	31	5	2.5	2.5	23.5
	32	4	2.0	2.0	25.5
	33	8	4.0	4.0	29.5
	34	8	4.0	4.0	33.5
	35	12	6.0	6.0	39.5
	36	7	3.5	3.5	43.0
	37	5	2.5	2.5	45.5
	38	5	2.5	2.5	48.0
	39	3	1.5	1.5	49.5
	40	2	1.0	1.0	50.5
	41	4	2.0	2.0	52.5
	42	4	2.0	2.0	54.5
	43	4	2.0	2.0	56.5
	44	5	2.5	2.5	59.0
	45	1	.5	.5	59.5
	46	3	1.5	1.5	61.0
	47	5	2.5	2.5	63.5
	48	2	1.0	1.0	64.5
	49	3	1.5	1.5	66.0
	50	3	1.5	1.5	67.5
	51	1	.5	.5	68.0
	52	2	1.0	1.0	69.0
	53	1	.5	.5	69.5
	54	3	1.5	1.5	71.0
	55	1	.5	.5	71.5
	56	3	1.5	1.5	73.0
	57	3	1.5	1.5	74.5
	58	3	1.5	1.5	76.0
	59	4	2.0	2.0	78.0
	60	3	1.5	1.5	79.5
	61	4	2.0	2.0	81.5
	63	1	.5	.5	82.0
	64	4	2.0	2.0	84.0
	65	2	1.0	1.0	85.0
	66	1	.5	.5	85.5
	67	2	1.0	1.0	86.5
	68	1	.5	.5	87.0
	69	2	1.0	1.0	88.0
	70	4	2.0	2.0	90.0
	71	5	2.5	2.5	92.5
	72	2	1.0	1.0	93.5
	73	1	.5	.5	94.0
	74	1	.5	.5	94.5
	75	4	2.0	2.0	96.5
	77	2	1.0	1.0	97.5
	78	1	.5	.5	98.0
	80	1	.5	.5	98.5
	85	2	1.0	1.0	99.5
	99	1	.5	.5	100.0
	Total	200	100.0	100.0