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Socio-economic status

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Background of the Study
Theoretical Framework
Conceptual Framework
Statement of the Problem
Scope and Limitations of the Study
Significance of the Study
Definitions of Terms

Foreign Literature
Local Literature
Foreign Study
Local Study

Method of Research Use
Sources of Data
Respondents of the Study
Sampling Procedure
Data Gathering Instruments
The Survey Questionnaire
Statistical Treatment of Data


A family’s socioeconomic status is based on family income, parental education level, parental occupation, and social status in the community (such as contacts within the community, group associations, and the community’s perception of the family), note Demarest, Reisner, Anderson, Humphrey, Farquhar, and Stein (1993). Families with high socioeconomic status often have more success in preparing their young children for school because they typically have access to a wide range of resources to promote and support young children’s development. They are able to provide their young children with high-quality child care, books, and toys to encourage children in various learning activities at home. Also, they have easy access to information regarding their children’s health, as well as social, emotional, and cognitive development. In addition, families with high socioeconomic status often seek out information to help them better prepare their young children for school. Crnic and Lamberty (1994) discuss the impact of socioeconomic status on children’s readiness for school: “The segregating nature of social class, ethnicity, and race may well reduce the variety of enriching experiences thought to be prerequisite for creating readiness to learn among children. Social class, ethnicity, and race entail a set of ‘contextual givens’ that dictate neighborhood, housing, and access to resources that affect enrichment or deprivation as well as the acquisition of specific value systems.” Ramey and Ramey (1994) describe the relationship of family socioeconomic status to children’s readiness for school: “Across all socioeconomic groups, parents face major challenges when it comes to providing optimal care and education for their children. For families in poverty, these challenges can be formidable. Sometimes, when basic necessities are lacking, parents must place top priority on housing, food, clothing, and health care. Educational toys, games, and books may appear to be luxuries, and parents may not have the time, energy, or knowledge to find innovative and less-expensive ways to foster young children’s development.

Even in families with above-average incomes, parents often lack the time and energy to invest fully in their children’s preparation for school, and they sometimes face a limited array of options for high-quality child care–both before their children start school and during the early school years. Kindergarten teachers throughout the country report that children are increasingly arriving at school inadequately prepared.” (p. 195) Families with low socioeconomic status often lack the financial, social, and educational supports that characterize families with high socioeconomic status. Poor families also may have inadequate or limited access to community resources that promote and support children’s development and school readiness. Parents may have inadequate skills for such activities as reading to and with their children, and they may lack information about childhood immunizations and nutrition. Zill, Collins, West, and Hausken (1995) state that “low maternal education and minority-language status are most consistently associated with fewer signs of emerging literacy and a greater number of difficulties in preschoolers.” Having inadequate resources and limited access to available resources can negatively affect families’ decisions regarding their young children’s development and learning. As a result, children from families with low socioeconomic status are at greater risk of entering kindergarten unprepared than their peers from families with median or high socioeconomic status.

Background of the Study

Theoretical Framework
The theory of cultural capital by Pierre Boudieu, (1986) who stated that education leads to social reproduction and a stratified society by honoring the cultural capital of the elite classes.

The theory of concerted cultivation by Annet Lareuae, (2003) which stated that lower income families have children who do not succeed to the level of the middle income children.

The theory of credentialism by Collins Randall 1979, which stated that public schools are socializing institutions that teach and reward middle class values of competition and achievement.

Conceptual Framework



The demographic profile of the respondents.
The socio-economic profile of the respondents.

Through the use of the following:
Gathering of data
Statistical treatment that the researchers used for the socio-economic results such as Frequency Percentage, Pearson and T-test.

The Socio-economic status of the respondents.
The effect of the socio-economic status of the respondents to their academic status. The difference between the Bachelor Degree Students and Voc. Tech Students.

Statement of the Problem
This study was undertaken to find out socio-economic status of freshman students in Polytechnic University of the Philippines Quezon City Campus. Specifically, this is sought out to answer the following:

1. What is the demographic profile of the students with regards to: 1.1. Age
1.2. Gender
1.3. Civil Status
1.4. Permanent Address
1.5. Religion
1.6. Language written and spoken
2. What is the family background of the respondents according to? 2.1. General profile of the parents
2.1.1. Mortality Rate
2.1.2. Educational Attainment
2.1.3. Occupation
2.1.4. Nature of Employment
2.1.5. Marital Relationship
2.2. Number of family members
2.2.1. Number of siblings
2.2.2. Number of siblings gainfully employed
2.2.3. Number of siblings studying
3. What is the family economic status of the respondents with regards to their? 3.1. Estimated total family income per month
3.2. Estimated total family expenses per month
3.3. Sources of Income
3.4. Nature of home occupancy
3.5. Quantity of appliance or gadgets owns of the family
3.6. Real properties owned
3.7. Vehicle owned by the family
4. What is the educational data of the students according to? 4.1. Type of high school graduated
4.2. Region of high school graduated
4.3. Fourth year high school general average
4.4. PUPCET Score
4.5. Residence while studying at PUP
4.6. Planning to work while studying
4.7. Daily mode of transportation
4.8. Weekly budget for food, transportation, cell phone load, etc. 4.9. Total weekly allowances, expenses and savings
4.10. Financial assistance in schooling
4.11. Reasons for choosing PUP as their educational institution 4.12. Choice of course
4.12.1. First Choice
4.12.2. Second Choice
4.13. Influences in choosing their course
4.14. Usually confide in when having a problem
4.15. Extra-Curricular Activities
4.15.1. Major Interest
4.15.2. Activity Interest
5. The Correlation of the Bachelor Degree Students and the Vocational Technology Students. Is there a significant difference among the BDS and VTS In terms of their selected educational variables: 5.1. Type of high school graduated

5.2. Region of high school graduated
5.3. Fourth year high school general average
5.4. PUPCET Score
6. The Correlation of the Economic Status and Academic Status of the Respondents. Is there a significant relationship between the Economic Status and Academic Status of respondents in terms of the selected economic and educational variables? 6.1. Estimated total family income per month

6.2. Estimated total family expenses per month
6.3. Sources of Income
6.4. Real properties owned
6.5. Type of high school graduated
6.6. Region of high school graduated
6.7. Fourth year high school general average
6.8. PUPCET Score

Scope and Limitation
The study of the Socio-Economic of PUP Quezon City Campus covers the freshman students of the year 2013-2014. The study deals in their social standing and condition and how they venture the society in the struggles of life.

This study will give information to the government about the SES of the students and their families in PUPQC. This study will serve as an eye opener to the government to let them see the effects of this SES to the academic status of the students and for them to do moves and action about this matter. PUPQC Administration/Faculty

This study will give facts and information to the Administration/Faculty about the SES of the students that is entering the threshold of the university. Information in this study about the SES of the students may be one of the bases of limitations of the Administration/Faculty on giving activities that may fit to the students. Parents/Guardian

This study will provide the family facts and information about their SES. This study will let them know the effects of their SES on their children so that they will strive harder to finance the studies of their children. Students/Respondents

This study is centered on the SES of the students that will be a guide for the students to know the effects of their SES on their Academic status and for them to use this information to improve themselves and study harder in spite of the situation that they are into. Future Researcher

This research will serve as guide and reference for the formulation of their future study.

The researchers


– The process of training; knowledge
– It is one of the main determinants to know the Socio-Economic Status Educational Attainment
– The highest level of education an individual has completed Expense – It is the payment of money for something; money spent on some activity. – Money that has been consumed to buy something
Freshman Students
The first year college student.
The major respondents of this study

High Socio-economic Status
– Families belong to the higher class level, elite families (Php. 154,750* monthly income) Income
The gained or received from labors investments
Source of money to satisfy the needs
Low Socio-economic Status
Families belong to the lower class level (Php. 15,916.67* monthly income) Middle Socio-economic Status
Families belong to the middle class level (Php. 50, 250* monthly income) Occupation
Work or any action in order to have a source of income
Occupancy; possession, temporary possession of every country. Poverty Threshold
The minimum level of income deemed adequate in a given country (Php. 6,277.99* as of 2011) Poverty level or line
Socio-Economic Status
An economic and sociological combined total measure of person’s work experience and of an individual’s family economic and social position relative to others, based on income, education and occupation. It measures the individuals’ or groups’ social standing

*Family Income Distribution in the Philippines (http://makuhari.wordpress.com/2011/03/07/family-income-distribution-in-the-philippines-1985-2009-essentially-the-same/)

This chapter presents the review of related literature and studies which enlightens the researcher in the course of their research.

Socioeconomic Status, Race, Gender, & Retention: Impact on Student Achievement
Since Coleman’s (1966) landmark study on Equality of Educational Opportunity, socioeconomic status has been seen as a strong predictor of student achievement. Coleman asserted that the influence of student
background was greater than anything that goes on within schools. Poverty is indeed a factor among children in the United States. Rainwater and Smeeding (1995), in their 18 nation Luxembourg Income Study, found that during the 1990s families of children in the United States had lower real income than of children in almost every other nation. Although the poverty rate for people under 18 years old dropped from 16.9% in 1999 to 16.2% in 2000 (U.S. Census Bureau, 2001), American children remained the poorest population by age group. Of these approximately 12 million children, one third live in extreme poverty in families with incomes below 50% of the poverty line. The child poverty rate in Louisiana is the second highest in the United States (Hoff, 2002) and the highest in the South at 29% (Bennett & Lu, 2000). The per capita personal income in Louisiana in 1998 was only 82% of the U.S. average, and that of the school district in this study was only 69% of the United States’ average (Center for Business & Economic Research, n.d.). This means that another large portion of children was very near the poverty level, adding to the total number of children in economic constraints at the time this study was conducted.

The issue of socioeconomic status and its relationship to student achievement is more complex than Coleman’s (1966) report first intimated. First of all, the relationship can be explored on various unit levels, from that of nations and states, districts, and schools, and on to classes and individual students. Payne and Biddle (1999) commented in their study of data obtained from the Second International Mathematics Study (SIMS) that if the United States had been represented only by its school districts with low-level poverty, the United States would have ranked second out of the 23 nations involved. If the high-poverty district scores were used, the United States would have ranked only above Nigeria and Swaziland. Findings from Binkley and Williams’ (1996) study of the International Association for the Evaluation of Educational Achievement (IEA) Reading Literacy Study supported a somewhat similar comparison between poverty and reading literacy. The low-poverty fourth-grade group in the United States faired better than any group in the 32 other countries. The high-poverty group scored much lower than the low poverty group, but never fell below the international average. Since the IEA assessment measured only a basic comprehension level, low
socioeconomic status was not as strong a detriment to U.S. students in an international reading comparison as that shown by the study of the SIMS higher level mathematics assessment.

At the national level, Chall (1996) analyzed a combination of National Assessment of Educational Progress (NAEP) reading results, Scholastic Aptitude Test scores over time, and a synthesis of research on beginning reading from 1910 to 1996. She also concluded that there are large differences between higher- and lower socioeconomic status children. The differences were smaller among younger children and increased in the higher grades.

Using the 1996 NAEP data for state-level mathematics achievement and for state level poverty and Education Week’s 1997 edition of Quality Counts for state-level funding of education, Biddle (1997) concluded that the child poverty/achievement correlation was r = .700 (p < .001) and that, together, school funding and child poverty predict 55% of the variance of state differences in mathematics achievement. The impact of child poverty was stronger at the state than the district level. Darling-Hammond (1999) also used NAEP data at the state level, this time from two years of fourth-grade mathematics results, two years of eighth-grade mathematics results, and two years of fourth-grade reading results. She also concluded that poverty was significantly and negatively correlated with student outcomes at the state level.

In a district level study of urban schools belonging to the Council of Great City Schools (2001), the results of the Stanford Achievement Test indicated that the greater the concentration of poverty in the school districts, the lower the student achievement. Of the three grades—4th, 8th, and 10th —selected to report poverty data, achievement gaps between districts of high and moderate concentrations of poverty were generally greatest in fourth grade in both reading and mathematics. Caldas (1999) compiled the results of all Louisiana 10th graders in 1990 who took the Louisiana Graduation Exit Examination to correlate both district- and school-level effects of poverty on achievement. SES accounted for 45.5% of the variation between districts and 41% of the variation among schools within districts. However, Caldas discovered that the percentage of one-parent families accounted for 96% of the variation in average school test scores among districts and for 59% of the variation among schools within districts. He further found that even if a student came from a two-parent family, the domination of a school or district by one parent families could have an overriding negative influence stronger than that of poverty or race.

In a study of West Virginia districts and schools in grades 3, 6, 9, and 11, Howley (1995) found a weaker level of correlation between SES and achievement at these levels. Additional analysis revealed that the smaller class sizes in most West Virginia schools tended to ameliorate the negative effects of poverty. The Matthew Project (Howley & Bickel, 1999) extended this study to four additional states: Ohio, Georgia, Texas, and Montana. The additional findings further supported the benefits of smaller class sizes for impoverished communities and the benefits of larger classes for more affluent communities. This was most evident at the school level.

The U.S. Department of Education conducted The Longitudinal Evaluation of School Change and Performance (LESCP) in Title I Schools (2001a) to determine the effectiveness of Title I schools. Key findings were that individual and school poverty had a clear, negative effect on student achievement and that students who attended schools with the highest percentages of poor students performed worse initially on both reading and mathematics tests. Gaps in reading remained the same from third to fifth grades, but gaps in mathematics partially closed. Teacher effects made the difference in this study and in another one by Fetler (1999) of California state high schools. Again, however, poverty had a strong relationship to achievement at the school level. Analysis of Stanford 9 scores in reading and mathematics from 2,000 fifth graders in Texas (Klein, Hamilton, McCaffrey, & Stecher, 2000) also showed a strong negative correlation at the school level. The percentage of students at a school who were in the federal free- and reduced lunch program predicted that school’s mean on the test regardless of test type, multiple choice or open-ended. Sander (2001) compared Chicago schools with those in the rest of Illinois. Again, the
low-income students had lower achievement, but Chicago grade schools were just as efficient as the others in teaching reading and mathematics after factoring out family background. Reading scores became significantly lower for impoverished students at the high school level, consistent with Chall’s (1996) findings.

The strength of the district and school level influences of socioeconomic status on academic achievement is evident in a growing movement to integrate school districts on the basis of equitable economic status rather than on racial equity. LaCrosse, Wisconsin, was the first (Kahlenberg, 1999), followed by others including San Francisco, California, and most recently, Cambridge, North Carolina (Richard, 2002).

A study of more than 6,000 fourth-grade classrooms in Texas (Lopez, 1995) revealed that low SES classrooms had significantly lower gains on the Norm-referenced Assessment Program of Texas than non-low SES classrooms. At the classroom level, however, teacher factors influenced student achievement causing greater variance. Poverty played a significant role in the print environment and experience of students in first-grade classrooms in the greater Boston area (Duke, 2000). Poor classes had books and magazines, less print on the walls and other surfaces, less exposure to and experience with extended text, and less time engaged in activities in which students had a high degree of authorship. The reverse was true of classes with more financial support.

To further illustrate the degree to which individual schools and classes can reduce the effects of poverty on student achievement, The Education Trust (Jerald, 2001) identified 4,577 schools nationwide that were in the top third of their state in reading or mathematics performance and that had at least 50% low-income or at least 50% minority students compared with other schools at their grade level. Louisiana had 96 of those schools, but none of them were in the school district in this study.

The influence of socioeconomic status at the individual level is still prevalent (Capraro, 2000) but less strong in much of the literature.
Entwisle and Alexander (1996), in a study of mother-only, mother-extended family, and two-parent families with children in first through third grades, concluded that two measures of parent expectations had a somewhat stronger influence than did the economic variables. The effects of prior achievement were stronger than poverty on junior high and high school students in a study of data obtained from the Longitudinal Study of American Youth (Brookhart, 1997). Cultural effects of a race and gender interaction for African American males among elementary school-aged children (Diamond & Onwuegbuzie, 2001) were stronger than socioeconomic status in predicting reading achievement. SES became stronger for individuals at the postsecondary level (Trusty, 2000) because more family and individual resources are necessary to attain this level. However, low prior mathematics achievement can have a strong barrier effect as well, regardless of family or individual SES.

In a meta-analysis of socioeconomic status, White (1982) concluded that the utility and wisdom of using SES in conjunction with academic achievement depended largely on the unit of analysis and the validity of the way in which it was defined. This study used operational definitions of SES. First, individual SES was defined by participation in the federal free- and reduced-lunch program to show only a weak correlation with academic achievement at this level. Secondly, enrollment in a Title I school designated school-level SES because these schools, by qualification, must have 70% or more of their student populations participating in the free- and reduced-lunch program. Race

The factor of race or ethnicity is closely associated with that of poverty as a predictor of achievement. Harkreader and Weathersby (1998) found its influence much less than economic factors, whereas Bankston and Caldas (1998) concluded that minority status was more highly related to achievement than was socioeconomic status.

Coleman’s report (1966) was the basis for the desegregation required in the civil rights acts of the 1960s. As a result, the South became the most highly integrated part of the nation with the most substantial contact
between African American and White students (Orfield, 2001). During the 1990s, there were three major Supreme Court decisions authorizing a return to segregated neighborhood schools and limiting the reach and duration of desegregation orders. These decisions took the stance that positive policies taking race into account for the purpose of creating integration were suspect and had to demonstrate both a compelling reason and prove that the goal could not be realized without considering race. The 2000 Census showed a continuing return of African Americans to the South into more racially segregated situations. However, it is still more common for African Americans to attend school with Whites in the South than in any other part of the country (Orfield, 2001). African American children (33.1%) are more likely to live in poverty than White children (13.5%). They are also more likely to have single parents, and more likely to be welfare dependent (Rector, Johnson, & Fagan, 2001). African American children are also disproportionately represented in Title I schools (Puma, 2000). Racial minority status is more likely to be correlated with lower teacher qualifications such as certification and years of experience (Darling-Hammond, 1999).

Data collected at a national level have been analyzed in a variety of ways to determine if the achievement gap between White students and racial minority students has narrowed. The National Center for Education Statistics (Jacobson, Olsen, Rice, & Sweetland, 2001) used data from several cohorts of the Chapter I Prospects Study, a study commissioned by congress to evaluate the Title I program, to determine that mathematics and reading scores of African Americans were generally lower than corresponding scores of Whites even with similar levels of prior achievement one or two grades earlier. The gap narrowed during elementary school but widened during junior high school with little change in high school. Phillips, Crouse, and Ralph (1998) used these data plus the National Education Longitudinal Survey (NELS) data and came to the same conclusion about mathematics achievement. However, they determined that race had a stronger effect on reading growth than on mathematics growth, with the biggest gap generated in elementary school.

Grissmer, Kirby, Berends, and Williamson (1994) used data from NELS together
with data from the National Longitudinal Survey of Youth and the National Assessment of Educational Progress (NAEP) test to determine that the rising test scores of minorities have resulted in a significant closing of the achievement gap between minority and nonminority youth. However, the gap that remained was still significant. Barron and Koretz (1994) maintained that the small sampling sizes for minorities used in the trend NAEP, one part of the NAEP test specifically designed to track national longitudinal achievement data, cause unreliable conclusions to be reached in racial comparisons. Standard errors for minorities were twice that of Whites. The study determined that minorities would have to have much larger gains than Whites to achieve significance. The source for recording racial data also has a strong impact on the results. Young elementary students who self-reported race were more likely to respond inconsistently with adults who reported observed racial options. Since NAEP racial data are self-reported, the smaller sampling of minority students might indeed be affected by this inconsistency, particularly at the fourth grade, the lowest grade tested.

The particular achievement test used was also an influencing factor in determining racial gap scores. Klein et al. (2000) reported that the NAEP test showed a gap that was wide to begin with and got wider with time for Texas students. During the same time period, Texas Assessment of Academic Skills showed that the gap started off somewhat smaller and then became substantially smaller over a four-year period. In a study of the Ohio state proficiency test, Dimitrov (1999) determined that the response format, open-ended versus multiple-choice, did not make much difference by ethnicity. Only the low and high ability Hispanics had negative academic relationships to their response strategies for multiple-choice items, not the extended response items one might expect with limited English proficiency. The end-of-grade tests in North Carolina (North Carolina State Department of Public Instruction, 2000) reflected a racial disparity with African Americans, Hispanics, and Native Americans performing well below Multi- Racial, Asian and White groups. However, all groups continued to improve.

The Education Trust (2001) reported a closing of the gap in basic skills in mathematics by race during the 1970s and the 1980s, but the gap remained the same or widened at higher levels of cognition. In Louisiana, in particular, African American fourth graders made more progress in mathematics from 1992 to 1996 than they did in most other states (Education Trust, 2001). However, achievement gaps remained constant between eighth grade White and African American students from 1992 to 2000 (National Education Goals Panel, 2001).

In reading, the National Center for Education Statistics (Donahue, Voelkl, Campbell, & Mazzeo, 1999) concluded that in 1998 eighth grade African American students achieved a significant gain over their NAEP scores in both 1992 and 1994, while fourth grade African American students achieved a significant gain over their 1994 results. Louisiana African American fourth graders, however, made reading gains five points less than the national average (Education Trust, 2001). At the eighth grade, the gap between Louisiana African American and White students’ reading achievement was 27 points. Besides being influential in their own right, racial and ethnic cultural influences may also combine with smaller gender effects to predict achievement. Gender

Some correlation appears to exist between gender and reading achievement. Disaggregation of the 1998 NAEP reading results by gender rather than race (Donahue et al., 1999) revealed that females outperformed males in 4th, 8th, and 12th grades, as they also did in 1992 and 1994. At the 4th-grade level, however, the males made a significant gain over their 1994 score while the females remained the same. A similar trend was noted in the North Carolina end-of-grade tests administered in grades three through eight (North Carolina State Department of Public Instruction, 2000). A similar phenomenon appears to be occurring in Great Britain as well (Salisbury & Rees, 1999).

Perhaps some of this gender difference can be explained by a national survey of reading attitudes conducted with 18,185 children across the United States in first through third grade (McKenna, Kear, & Ellsworth, 1995). Girls as a group possessed more positive attitudes than boys at all grade levels, both toward recreational and academic reading. These attitudes appeared unrelated to ability. A four-year longitudinal study of elementary school age children in Michigan (Eccles, Wigfield, Harold, & Blumenfeld, 1993) revealed that girls valued reading significantly more than boys and also saw themselves as being more competent readers than boys. Using data from the National Longitudinal Survey of Youth, Baharudin and Luster (1998) found that female children in the overall sample and in the Caucasian subsample appeared to receive more supportive care than male children. These same two groups scored significantly higher than males on reading achievement as well. Effects for gender in reading were seen as early as second grade (Entwisle & Alexander, 1996) and continued through high school (Binkley & Williams, 1996).

Gender as a predictor of mathematics achievement in Baharudin and Luster’s study (1998) of six- to eight-year olds emerged again as significant for females in general and for the African American female subgroup. On the NAEP 2000 Mathematics Assessment (U.S. Department of Education, 2001b), however, a higher percentage of boys performed at or above Proficient than girls at 4th, 8th, and 12th grades, with the older two grades being significantly higher. The gap between the average scale scores of males and females was quite small at all three grades and has fluctuated only slightly over the past 10 years. There was no significant difference by gender at the fourth-grade level. In Louisiana, neither the scale scores nor the percentage of students scoring at or above the Proficient level was significant for gender at fourth grade. At eighth grade, the difference in scale scores was not significant, but the difference in percentages scoring above the Proficient level was positively significant for males.

In an international comparison of Third International Mathematics and Science Study data in English-speaking countries, Webster, Young, and Fisher (1999) determined that in Australia and the United States very little of the student level variance was explained by gender and SES, although most of the variance was at the student level and not at the class level. The U.S. Department of Education’s (2000) analysis of that same data revealed that males outperformed females in 3 of the 25 countries at the fourth-grade level, in 8 of the 39 countries at the eighth-grade level, and in 18 of the 21 countries participating in their final year of secondary school. However, in the United States, males and females scored similarly at all three levels.

Results from an analysis of the National Educational Longitudinal Study of 1988 data (Catsambis, 1994) showed that male and female eighth graders attained similar achievement, but a larger portion of girls were placed in high-ability classes and a larger portion of boys were placed in low-ability classes. Racial/ethnic influences may have played a role in secondary mathematics course selections and judgment of academic performance. The chances of young African American women enrolling in high-ability mathematics classes were 48% greater than those of African American male students. Hispanic females reported lower participation in these classes and higher performance anxiety, while White females had the highest enrollment in high-ability classes. Females in general in this study, and also in Campbell and Beaudry’s study (1998) of the Longitudinal Study of American Youth data, revealed less confidence in their mathematical ability and greater exertion of effort in mathematics classes than males.

Mathematical ways of thinking may differ by gender according to Fennema, Carpenter, Jacobs, Franke, and Levi (1998). These researchers studied 82 children as they progressed from first through third grades. They identified gender differences in strategy use that was evident from the beginning of the study and persisted through the end. Girls tended to use more modeling or counting strategies, while boys tended to use more abstract strategies such as derived facts or invented algorithms. By the third grade, girls used significantly more standard algorithms than did the boys.

In an analysis of the Delaware Student Testing Program and the Stanford Achievement Test Series 9th Edition for students in 3rd , 5th , 8th, and 10th grades, Zhang and Manon (2000) found that males had a larger variance in mathematics scores than females. In this study, females tended to outperform males among the low-achieving students and males tended to outperform females among the high-achieving students. This higher variance for males makes them more susceptible to rewards and sanctions in many state accountability systems. Retention

Gender, race, and socioeconomic status also have a role to play in the issue of retention. McCoy and Reynolds (1998) used data from the Chicago Longitudinal Study of 1,164 low-income, mostly African American 14-year-old students who had all attended a federally funded kindergarten program. Retained children were most likely to be boys and most likely to have lower scores in reading and mathematics achievement. No national or regional agencies monitor grade retention. However, a report for the National Research Council (1999b) used information from the U.S. Census Bureau to determine that, nationally, sex differential in retention gradually increases with age from five percentage points at ages 6 to 9 to ten percentage points at ages 15 to 17. Rates of retention are racially similar at the younger ages, but by ages 15 to 17 the rate is between 40% to 50% among African Americans and Hispanics, but only 25% to 35% among Whites. Hauser (1999) estimated that at least 15%, and probably 20%, of children have been held back at some time in their lives. In 1998, 41% of teachers reported that their schools promoted students based on age, but in 2001, only 31% did so (Johnson, Duffett, Foleno, Foley & Farkas, 2001). The Louisiana Department of Education (2001) analysed its Student Information System (SIS) data from 1997-2001 in grades K-12 and found that male students were more likely to be retained than female students, and students on free lunch were twice as likely to be retained as students not receiving any food services. However, African American students receiving reduced lunch had significantly lower retention rates than those on free lunch or those not receiving any food services in 2000- 2001.

This same study (Louisiana Department of Education, 2001) revealed that the number of students retained more than tripled in fourth and eighth grades, reflecting the impact of high stakes testing on retention in the state of Louisiana. With eight states planning to base promotion in some grades on statewide assessment results by 2004 (Edwards, Chronister, & Olson, 2002), this impact is likely to increase. Cizek, Trent, Crandell, Hirsch, and Keene (2000) surveyed teachers and principals of a random stratified sample of fourth-grade students across the state of Ohio to determine if their assessment of students’ readiness for fifth grade corresponded with the results of the Ohio Proficiency Test administered at the end of fourth grade. Educator agreement was high, but varied by district in relation to the standards of the proficiency test. Since the number of students actually retained was considerably less than the number of students deemed unprepared, suggested further research includes discovering additional criteria on which to base decisions related to retention.

The results of retention were decreased academic progress and higher dropout rates. Roderick, Bryk, Jacob, Easton, and Allensworth (1999) conducted an analysis of the implementation of the first two years of the Chicago Public Schools’ intensive effort to end social promotion and raise achievement, which began in 1996. Their analysis revealed that only one fourth of retained eighth graders and one third of retained third and sixth graders in 1997 made “normal” progress to pass the test cutoff the next May. Retention was therefore better for some students in the short term. However, the average Iowa Test of Basic Skills (ITBS) score increase in the two years required to repeat a grade was 1.2 grade equivalents compared to 1.5 grade equivalents for students who had similar scores and were promoted prior to policy implementation. The performance of third graders was significantly poorer than that of sixth and eighth graders, indicating that remediation strategies may need to be different for younger children than for older children.

A later follow-up of the Chicago study (Roderick, Nagaoka, Bacon, & Easton, 2000) disclosed some additional negative results of retention. First, despite higher passing rates, retention rates have not fallen. This is due to the fact that, over the three year study, fewer students are being socially promoted as a result of the stricter guidelines for promotion. Secondly, retained students are struggling in their second time to face the promotion policy because they still do not do well in the next tested grade. Finally, nearly a third of retained eighth graders in 1997 had dropped out by the fall of 1999. However, overall dropout rates were stable. Several positive results were also discovered. Passing rates improved in all three grades, more at-risk sixth and eighth graders are raising their test scores during the school year, and more students maintained positive test
trajectories two years after promotion.

Students in the state of Texas were analyzed by Haney (2000), who found that a comparison of the cumulative total of 2.2 million students enrolled in sixth grade between the fall of 1984 and the spring of 1993 and of the cumulative total of 1.5 million graduates in the classes of 1992 and 1999 meant that during that nine year period around 700,000 children were lost or left behind before graduation. Haney attributed this to an increase in retention rates, particularly among African Americans and Hispanics, and an increase in the dropout rate. Only 50% of minority students have been progressing from ninth grade to graduation since the initiation of the Texas Assessment of Academic Skills (TAAS), again reflecting the impact of high stakes testing and accountability. With increased pressure on students to achieve comes increased pressure on teachers’ ability to teach them what they need to know and to be able to do. The MacArthur Scale of Subjective Social Status

Summary prepared by Nancy Adler and Judith Stewart in collaboration with the Psychosocial Working Group. Last revised March, 2007.
Theories of social class and social stratification suggest a variety of bases for social hierarchies. On an empirical basis, different indicators of socioeconomic status each show similar graded relationships with health despite the fact that they are only moderately interrelated. Education, income and occupation provide specific resources. In addition, they locate individuals in relevant social hierarchies where relative position may itself be a risk or protective factor. The MacArthur Network on SES & Health developed a measure of subjective social status to try to capture individuals’ sense of their place in the social ladder which takes into account standing on multiple dimensions of socioeconomic status and social position. Measurement

The MacArthur Scale of Subjective Social Status was developed to capture the common sense of social status across the SES indicators. In an easy pictorial format, it presents a “social ladder” and asks individuals to place an “X” on the rung on which they feel they stand. There are two versions of the ladder, one linked to traditional SES indicators (SES ladder) and the second linked to standing in one’s community (community ladder). The difference between these two ladders may be of particular interest in poorer communities in which individuals may not be high on the SES ladder in terms of income, occupation, or education, but may have high standing within their social groups such as a religious or local community. Insofar social standing has beneficial effects on biological processes related to health, standing on the community ladder may be as important as standing on the SES ladder. Ideally, it would be best to use both ladders (and, if so, one should have participants complete the community ladder first so responses to it aren’t keyed to the SES indicators which are described for the SES ladder). If the research is investigating traditional SES, it will be of particular importance to use the SES ladder to be able to make comparisons between objective and subjective SES. Relationship to SES

Relationship of objective indicators of SES to the SES ladder. Several studies have examined predictors of the SES ladder. We would expect there to be a significant but not perfect relationship of traditional indicators of SES (e.g., measures of education, individual and family income, occupation and wealth) and subjective social status. Ladder rankings should reflect but not be redundant with the objective indicators. The SES ladder provides a summative measure of social status. Individuals are asked to summate across those indicators and each may assign different weights to the various components of SES. In addition, people may have a deeper understanding of the meaning of their standing on a given aspect of SES. Thus just like self-rated health predicts mortality even when adjusted for all known objective risk factors (Idler and Benyamini, 1997), subjective social status may be a “value added” of the individual’s evaluation of their status and the actual implications of the objective indicators. For example, measures of educational attainment treat “high school graduation” as the same value whether one graduated from an elite prep school or an inner city high school, and “college graduation” the same for graduation from a top-ranked college or a diploma-mill. Yet the life chances of these graduates are quite different and are likely to be more sensitively captured by the ladder ranking than by the rather crude educational levels.

Whitehall II Study of British civil servants SES ladder scores are predicted by employment grade, education, household and personal income, household wealth, satisfaction with standard of living, and feelings of financial security. Childhood SES is also related to ladder ranking. Education and income are more strongly related to objective SES (as assessed by occupational grade) while a feeling of financial security is more strongly related to subjective SES as assessed by the SES ladder. Childhood SES and wealth are almost equally related to objective and subjective SES. Singh-Manoux, Marmot, Adler, (2005) suggest that the stronger association between subjective SES and the feeling of financial security in this middle-aged cohort suggests that subjective SES provides a better assessment of a person’s future prospects, opportunities, and resources than objective SES. In the US, predictors differ for White and Black participants in the Coronary Artery Risk Development in Young Adults (CARDIA). Financial security, material deprivation and education are significant predictors of ranking on the SES ladder for both white and black participants. However, household income and wealth predict subjective social status for Whites in CARDIA but not for Blacks (Adler, Singh-Manoux, Schwartz, Stewart, Matthews & Marmot, 2008). Unlike the results in Whitehall II where psychosocial factors were not related to ladder scores, in CARDIA ladder rankings were associated with optimism for both groups, and with control in Whites and mental health for Blacks.

Qualitative investigation of criteria used for SES ladder rankings. In an exploratory qualitative study done for the MacArthur Network, Snibbe, Stewart & Adler (in preparation 2007) explored the criteria people say they use to decide their position on the SES and Community ladders. In an interview, 60 participants in the CARDIA study narrated how they ranked themselves on the SES ladder. The most frequently mentioned source of subjective social status on the SES ladder was material wealth, mentioned by over 90% of participants (however about a fifth of participants also rejected materialism). The next most frequently mentioned sources of subjective social status were occupation (72%) and education (62%). Almost a quarter of participants indicated using their spirituality or ethical values, and a fifth of the sample used the extent to which they give to others and their health as considerations when determining ranking on the SES ladder. It is not surprising that respondents thought about financial, educational and occupational standing in answering
the question of where they stand on the SES ladder since these are the dimensions the scale specifically mentions.

The categories of ethics/spirituality, health and social responsibility or altruism (“giving”) which were spontaneously generated have more to do with personal qualities and a subjective sense of self-worth than objective markers of status. As indicated such factors may play a not insignificant role in subjective social status. Definition of “community” and criteria used for Community ladder rankings. In addition to the SES ladder a second ladder asks people to indicate how they stand in their communities. No criteria are given either for the nature of community or the dimensions of status to be used. The qualitative analyses revealed that the majority of participants defined community as their neighborhood (57%). Significantly more African American participants (80%) than European American participants (33.33%) used this definition. The next most popular definition of community was city or town (37%), followed by religious groups (22%), social supporters (20%), workplace (18%), family (18%), friends (12%), people who share their interests (12%), their region (12%), and, finally the nation or world (10%). Additionally, almost half of the participants generated idiosyncratic definitions of community that did not fit in any one category. There were no gender or race differences in mentions of categories other than “neighborhood.”

Unlike their responses to the SES ladder, participants relied little on their wealth (25%), education (7%), or occupation (22%) when deciding where to place themselves on the Community ladder. Instead, they most frequently mentioned the everyday ways they give to others—as volunteers, as donors, as good citizens, and as good neighbors with close to 87% mentioning participation in giving activities. A second frequently mentioned source of subjective social status was how well-liked or respected they are by others (52%), followed closely by admissions of feeling as if they do not give enough to their community (50%). (Interestingly, “being respected” was a high frequency content code for the Community ladder but not for the SES ladder.) The remaining categories, in order of frequency, were giving as a leader (37%), giving as a parent (32%), and 12% rejected materialism as contributing to Community subjective social status. Health was not mentioned as a source of subjective social status on the Community ladder as it had been on the SES ladder. Analyses of the Community ladder narratives revealed that more African American (36.67%) than European American (13.33%) participants mentioned materials / money, and conversely, more European American (13.33%) than African American (0.00%) participants mentioned education. Finally, a trend-level effect showed that more African Americans (33.33%) mentioned spirituality and ethics than did European Americans. The best predictor of the Community ladder rankings was SES ladder ranking, which alone explained 33% of the variance with two of the “giving” variables—giving as a parent and complaining of not being active enough—increasing the explained variance to 47%. The qualitative study gives some hints about answers to important questions such as how people define community and what dimensions they use in placing themselves on the Community ladder. Relationship of the SES and Community ladders. To date relatively little research has been done using the Community ladder.

In the few studies where both ladders have been used they have been found to be correlated. Goldman, Cornman and Chang (2006) in a Taiwanese sample showed that although the two ladders were highly correlated (with a Pearson correlation, equal to the Spearman rank correlation of 0.78), their sample of middle-aged and older Taiwanese residents ranked their social position within their community an average of 0.4-rung higher than they ranked their position within Taiwan. Analyses of ladder rankings from CARDIA at the Year l5 exam show the two ladders were correlated 0.54. In this group of over 3,000 participants African-American men and women ranked themselves an average of 0.92 rungs higher on the Community than SES ladders whereas European-American men and women demonstrated more similar SES and Community ratings, 0.19 and 0.34 differences respectively (personal communication J.Schwartz).

Gender, Education and Socioeconomic Status: Economic Theories, Credentialism and Beyond, JEAN MCKENZIE LEIPER
Human capital theory, market signalling theory and credentialism are explored as ways of explaining the relationship between formal education and socioeconomic status. For both men and women, years of schooling and diplomas or degrees help to ensure access to high-skill jobs which carry high socioeconomic status. The market signalling approach is relevant for men because skills, are positively associated with socioeconomic status: employers value diplomas and degrees if they indicate that employees bring high skill levels to the labour market. The market signalling approach is not confirmed for women in this study. Some economists suggest that human capital theory is limited because it assumes women make voluntary choices to limit their education and job experience in favour of family responsibilities. Credentialism, by discounting the importance of skills acquired in school, ignores the issue of gender-based power differences that are related to skills. New theories are needed to address the issues of work and family commitments for both women and men.


According to Morales (2001) in her study, variables in relations to educational aspiration of fourth year students shows that socio-economic status of the family of the respondents has significant relationship with the educational aspirations of the fourth year student respondent in both public and private high schools, while in the study, some factors in relation to the career plans of high school senior students, Consumo (1998), shows that the socio-economic status of the family does not affect so much the career plans of the senior students for their future occupation and it has significant implication in the vocational guidance programs. (Jane Sullivan, Socio-economic Status and Career Preference of High and Low Achievers Senior High School Students of Nationalized High Schools in Kidapawan City Division School Year 2001-2002)

Socio-economic Studies in PUPQC

The research of Socio-economic Status as of Academic Year 2010-2011 (Bustos, et. al, 2011), most of the family earned through salaries and wages (66.42%) followed by business with 27.86%. Most of their parent were high school undergraduate but despite of this, still it is followed by a good result that the college graduate category of the parents resulted in a great number as an encouraging findings in our research, most of their parents were
employed locally and those who work abroad were very few which upshot them to earn from Php. 7,001- Php. 14,000 as their monthly income. On the other hand, the respondent’s monthly expense only ranges Php. 7,000-below. As their final conclusion, most of the families of the freshmen students, academic year 2010-2011, belong to the poverty threshold under the middle-lower class level.

On the year 2011-2012 (Bombales, et. al,), most of their parents were employed with 54.97% which gives them as monthly income ranges Php 7,001- Php. 14,000. On the other hand, their monthly expense also ranges Php. 7,001- Php. 14,000. Most of their father was high school graduate followed by college undergraduateand on part of their mother’s educational attainment; most of them were high school graduate. As their final conclusion, majority of the respondents belong to the middle class while some they are in average class on the society. This study, The Socio-Economic Status of Freshman Students of PUPQC Academic Year 2012-2013 highlighted the three main variables that determine to Socio-Economic Status of a community; educational attainment, occupation and income.

There is no marked difference in terms of the educational attainment, employment and monthly income and expenses of the parents of the bachelors’ degree students as well as the Office Management Certificate Program students. It only shows that those who are enrolled in Office Management Technology Certificate Program are just the same in terms with their Socio-Economic Status with those who are in bachelors’ degree it’s just that they lack requirements or qualities that is required in order to attain a Bachelors’ Degree course. If we try to see if there is a marked difference between the freshman students of Academic Year 2011-2012 and the freshman students of Academic Year 2012-2013, in terms with the educational attainment of the family of the respondents, majority of this years’ freshmen father were high school graduate followed with college graduated compared to last year result high school graduate followed with college undergraduate.

And with regards to the mother’s educational attainment, last year’s and this year’s result were the same, most of them were high school graduate followed with college graduate. For the second variable, the  occupation, last year’s father employment status reached only to 39% while this year it reaches to 61%. And for the mother’s employment rate, the researchers found out this year that it decreases to only 25% compare to last year’s result reaching to 42%. And for the monthly income as well as their monthly expenses that also affect the SES of the PUP, last year’s monthly income ranges to 7,001-14,000 with 35% while the researcher found out this year that the latest monthly income of the freshman students ranges only 7,000-below with a majority percentage of 36%. And to their monthly expenses, this year, it ranges 7,000-below with 47% while last year, it also lies on the same range but with only 38%. And the major sources of family income of the respondents are salaries followed with those who have businesses.

The result of this year’s study on Socio-Economic Status clearly shows that the families of these freshman students are stable in terms of the work as well as to the flow of money in their home. Therefore, the researcher concluded that the majority of the freshmen belong to the lower level of society.

Chapter III

In this chapter, it presents the research design and procedures. The contents of discussion are the following: 1. Method of Research Use
2. Sources of Data
3. Respondents of the Study
4. Sampling Procedure
5. Data Gathering Instruments
6. The Survey Questionnaire
7. Statistical Treatment of Data

1.The Methods of Research Used
The survey method is used in this research and supplemented to check and re-check the information gathered. This is a descriptive research. Descriptive research is a fact finding methodology with adequate
interpretation. This being a descriptive research will be hopefully useful to improve the quality of education at PUP.

2.Source of Data
The data of this study are derived from varied sources such as books, periodical, manuals, defended thesis and dissertation and other sources that are most especially concerned with the relationship of SES to education. Master list and class schedule of the students were also requested at the registrar’s office of the university. Other persons where interviewed to gathered important information relative to the design of the study, such as our mentor and the researcher’s thesis adviser.

3 Respondent of the Study
The total numbers of respondents taken in this study are 531. The respondents of this study were divided according to their courses. These are the BBTE, BSENT, BSBA-MM, BSBA-HRDM, BSIT, DOMT and DICT. All are freshman students. The target population consists of freshman students and has been able to ask to answer the questionnaire to determine the Socio-economic Status.

The Frequency Distribution of the Respondents According To Their Course COURSES

4.Sampling Procedure
The proponents took the whole population (100%) Freshman Students of PUPQC as their respondents.

5.Data Gathering Procedure
The research instrument utilized questionnaire assisting of the list of prepared questions devised to gather the essential data and which was dry-run by the researchers. The questionnaire was close and open ended type, that is, it provides the possible answer.

6.The Survey Questionnaire
The questionnaire was prepared by the “OVPRD” and each was asked for helping and administering their respective advisory classes while taking the survey. Then the proponents retrieved, collected, interpreted and analysed in preparation for the making of the tables.

7.Statistical Treatment
The following descriptive and inferential statistical tools were utilized in the treatment of the data for the analysis and interpretation. Weighted mean is the most common type of average where instead of each of the data points contributing equally to the final average, some data points
contribute more than others.

Percentage this was used to compare two or more magnitudes to determine their relationship.

Where: P=Percentage
N=Number of respondents

Ranking Arbitrary Scale

Weighted average formula is used to calculate the average value of a particular set of numbers with different levels of relevance.


Where: Ό=mean
F=no. of respondents
x=no. of respondents
n=total respondents

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