Cell Migration And Cell Cytoskeleton Biology Essay

Cell Migration And Cell Cytoskeleton Biology Essay The cytoskeleton of a cell plays a vital role in controlling the movements of the cell. 3 types of fibres form this structure; microtubules, microfilaments and intermediate filaments. Microtubules are large hollow fibres, microfilaments are the smallest at only 6nm in diameter (1) and intermediate filaments are in between. Microtubules are formed from the subunits ÃŽ ± tubulin and ÃŽ ² tubulin, which form dimers. Each tubulin dimer contains 1 ÃŽ ± tubulin subunit and 1 ÃŽ ² tubulin subunit. These then polymerise to form long protofilaments, which join together into sheets. Once a sheet of 13 protofilaments has been formed, they then fold over to produce a hollow microtubule, which continues to elongate from the +ve end where only ÃŽ ² tubulin subunits are showing. Microfilaments are made of a protein subunit called actin. There are two types; G actin and F actin. G actin is turned in to F actin by the hydrolysis of ATP, forming a fibrous filament. The actin filament continues to elongate from the -ve barbed end. There are several types of Intermediate filaments, each made of different proteins depending on the function of the cell in which they are contained. Type I Keratins acidic and Type II Keratins basic are found in epithelial cells and in the hair and nails. Nuclear lamins are found in the nuclear lamina and type III vimentin/desmin/peripherin are found in muscle cells, some neurones and stem cells (2). The monomers form dimers by coiling. Tetramers are then formed from dimers arranged in staggered formation with opposite domains together. These then arrange together forming strong coiled filaments. Many cells move by crawling over surfaces (3). Of the three fibrous structures, explained previously that make up a cell, the microfilaments play the largest part in the movement and migration of a cell. This means that actin is the vital component for cell movement. A fibroblast crawls by a leading protrusion, called a lamellipodium, which is where most of the cells actin is located. The actin is extremely flexible due its twisted, linked structure. The filaments clump / bundle together near the cell membrane, particularly at the protrusions such as the microvilli in an intestine lining epithelial cell. The actin penetrates in to the cytoplasm, where they become cross linked in to a three dimensional meshwork, governing the shape and mechanical properties of the plasma membrane and the cell surface. (4) . This means that the actin provides structural support, as well as movement potential for the cell. Filopodia are very similar to lamellipodia. They also protrude out from the mai n body of the cells like lamellipodia, and act like fingers for the cell to feel the environment around it and also to aid the detection of where the cell is supposed to migrate to. Filopodia have almost the exact same structure as lemellipodia, however have smaller protrusions and therefore contain less actin. They are about 0.1 Â µm wide and between 5 10 Â µm long, and each contains a loose bundle of 10 20 actin filaments, orientated with their positive ends pointing outward'(5), exactly like the orientation of the actin filaments in lamellipodia. However, filopodia are present around the whole circumference of the cell, not just protruding from the lamellipodium, which creates an even larger surface area for the cell to detect its surroundings. The protrusions grow by the growth of the actin filaments, where dimers are added to the positive terminals of the filaments. Although dimers are removed from both terminals, particularly the negative end, this is outweighed by the mor e rapid addition of actin at the positive end. The growth is a very quick process, which therefore allows the cells to move around the body at a fairly fast pace. Once the lamellipodium is protruded from the main structural shape of the cell, the newly produced bottom section of the cell then adheres to the surface it is moving along, hence pulling the rest of the cell along with it. At the same time, contraction occurs at the rear of the cell, then draws the body of the cell forward, in the direction the lamellipodium is protruding, in a process called traction. (6) Cell migration is extremely important to an organisms survival. It is essential for the movement of cells in the immune system, an example of which is a macrophage, which finds destructive cells and eats them. This is because it is vital for cells to be able to travel to the site of an infection in order to fight it and clear it up or to communicate with other cells, to let them know that there is a problem, which they can then sort out. Cells that can do this are called fibroblasts, which migrate through connective tissue, remodelling them where necessary and helping to rebuild damaged structures. (7). If this was not possible, the organism would be in serious trouble and could die or be badly damaged by very superficial wounds and mild diseases which are overlooked as non-serious in humans due to cells being able to migrate. The cell migration mechanism is vital to a macrophage, as its job is to move around the body, detecting and destroying harmful cells. If movement was not possi ble, the macrophage would only be able to detect destructive cells that were situated in the immediate vicinity, which means to successfully remove all harmful pathogens would be impossible. This would mean the organism would have very little protection against disease. Cells are also able to migrate by means other than by lamellipodium protrusions. Cilia are one example, as well as a male humans sperm, which moves by a tail like structure called a flagellum. The sperm is able to beat the flagellum, which is designed to move the entire cell, and instead of generating a current, they propagate regular waves along their length that propel and drive the cell through liquid (8). Unlike most other migrating cells in the human body as described above, the main component for movement in flagella is tubulin. Microtubules span the whole length of the flagellum in an axoneme, which contains two central microtubules that are surrounded by an outer ring of nine pairs of microtubules. (9). The movement is enabled by molecules of ciliary dyenin that form bridges between neighbouring microtubules around the circumference of the axoneme. (10) The end tail of one molecule attaches to a microtubule, while its other end, the head of the molecule attaches to another m icrotubule. This promotes a sliding mechanism similar to that of actin in the migration of cells with lamellipodium protrusions. Bacteria and cilia also have flagella, made of flagellin and dynein. The bacterial flagellum has a similar structure to a microtubule in the way that it is a hollow, tube like shape. Ciliary beating can either propel single cells through a fluid or can move fluid over the surface of a group of cells in a tissue (11). The second is apparent in the human respiratory system, where ciliated respiratory epithelium cells in the trachea prevent any foreign, potentially harmful particles such as dust and bacteria in the air from entering the bronchioles and lungs. They do this by acting like tiny hairs and by beating the saliva containing the harmful particles back up the trachea to exit the nasal cavities by coughing. If the cilia are unable to beat, it causes problems such as Kartageners syndrome or primary ciliary dyskinesia. Although this syndrome is extremely rare, it is a genetic disorder, meaning it is hereditary. Due to the respiratory system having little to no defence against dust and pathogens which enter the nasal cavities and then travel down the trachea and bronchi, harmful particles may enter the lungs. This causes infection and disease of the lungs, such as pneumonia or bronchitis. In conclusion, the presence of cell migration mechanisms in organisms as small as bacteria to the large, multi cellular organisms such as humans is extremely vital to their individual survival. Without such an important ability, cells would not be able to detect or fight disease, from minor superficial impediments to very serious illnesses. Reproduction in humans would not be possible and bacteria would find it extremely difficult to invade host cells for reproduction. Without cell migration, the whole human immune system would not be able to function correctly. Although the movement of cells is quite complex, it is only the beginning of a massive sequence of mechanisms in which cells can communicate with each other to orchestrate the correct workings of the human body.

College Enrollment Essay

Income inequality has been increasing for the past 20 years. A substantial part of the increase in income differences can be explained by changes in the return to education. In dollar terms, 1973 college graduates earned 45 percent more than high school graduates; by 1994 they earned 65 percent more, based on real average hourly wages for college and high school graduates (Baumol and Blinder, 1997). The increasing income disparities between groups of differing educational attainment raises concern that access to postsecondary education (PSE) may not be as widespread as desired. President Clinton urged for the goal of universal college access in his 1997 State of the Union address, â€Å"We must make the thirteenth and fourteenth years of education—at least two years of college—just as universal in America by the 21st century as a high school education is today, and we must open the doors of college to all Americans. † Using data from the National Education Longitudinal Study of 1988 (NELS) and the National Postsecondary Student Aid Study (NPSAS), this study examines access to postsecondary education by individuals in different income and test score groups. While many studies have found a statistically significant effect of income on college enrollment,1 less attention has been paid to the effect of family income after controlling for student achievement. This study specifically addresses this issue. We also explore differences in the decision of whether or not to attend PSE or in the type of PSE attended. We are interested in whether students are substituting less expensive alternatives (such as public or 2-year institutions) for high cost institutions, or whether they are not attending PSE at all. However, we do not examine selectivity of institutions attended. Another goal of this study is to determine which factors, including high school experiences, are especially important in determining college enrollment patterns. Hossler and Maple (1993) find that information on individual background factors allows them to predict, with a high degree of accuracy, which ninth-graders will go to college. The emphasis in our study is on how 1 See, for example, Leslie and Brinkman (1987), Savoca (1990), Schwartz (1986), and Mortenson and Wu (1990). SECTION I. INTRODUCTION 1 MATHTECH, INC. early indicators, such as expectations and course-taking behavior in the eighth grade, are related to college attendance six years later. 2 Last, we explore whether financial aid availability is a critical factor in determining PSE attendance. The combined effects of shifting federal support from grants to loans, and college tuition increasing at a rate faster than inflation are expected to have a large impact on enrollment patterns for low income youth. This report examines knowledge of and attitudes toward financial aid, and the relationship between such factors and PSE attendance. We also examine the effect of financial aid receipt on PSE attendance. In summary, the main research questions addressed in this report are: 1. 2. 3. 4. What percentage of students attend PSE, and what types of PSE do they attend? How are income and test score related to who goes to college? What factors, including high school experiences, are especially important in determining college enrollment patterns? Is financial aid availability a critical factor for determining PSE attendance? The rest of the report proceeds as follows. Section II describes the literature on individual and institutional factors that affect PSE attendance. Section III provides an overview of the data used in this report. It describes the NELS data, the NPSAS data, samples and weights used in the study, and correction of standard errors for sampling techniques. Section IV examines who goes to college. The section highlights the main answers to the first two research questions posed above, in a univariate or multivariate framework. Section V examines factors related to PSE attendance. Section VI explores the importance of financial aid, including knowledge of financial aid, financial aid applications, and the relationship between being offered financial aid and PSE attendance. Last, we include a bibliography of cited references. The executive summary (at the beginning of the report) highlights our findings and provides policy implications. An NCES study, not yet released, has focused on the â€Å"pipeline to higher education† using the NELS data (NCES, 1997). SECTION I. INTRODUCTION 2 2 MATHTECH, INC. One subset of analysis for this study is the group of low income, high test score students. Low income, high test score students may have the potential to benefit greatly from PSE attendance and, therefore, we want to identify factors or constraints, particularly financial ones, that might limit the students’ educational opportunities beyond high school. SECTION I. INTRODUCTION 3 MATHTECH, INC. II. LITERATURE REVIEW Much of the research on college enrollment patterns is founded upon the â€Å"human capital† model Gary Becker advanced. According to this theory, one decides to enroll in college as an investment in future earning power. Individuals calculate the value of attending college by comparing costs (direct and indirect) with expected income gains, and they make the decision that will maximize their utility over the long term. To understand enrollment behavior according to this model, it is necessary to look at such factors as tuition levels, student financial aid, average wages for high school graduates, and the difference in lifetime earnings between high school and college graduates. Economists and others agree, however, that non-monetary factors also play a major part in the college enrollment decision. Sociologists’ models of status attainment have suggested a number of background variables that join with economic factors to influence college plans. These include both personal traits (e. g. , academic ability) and interpersonal factors, such as the level of encouragement a student receives from parents and teachers. Hossler and Maple (1993) suggest that individual decisions on enrollment can be broken down into three stages: predisposition, search, and choice. According to their research, students who will ultimately attend college can be differentiated from those who will not as early as the ninth grade. Within the econometric and sociological models outlined above, the factors affecting enrollment in college can be divided into two general types: those specific to individual students, such as academic achievement and parental education levels, and those specific to educational or vocational alternatives, such as college tuition, financial aid, and unemployment levels. Students’ enrollment decisions can be viewed as jointly determined by their individual characteristics and the institutional or societal conditions that prevail. We first review individual traits that affect college enrollment, and then institutional determinants. SECTION II. LITERATURE REVIEW 4 MATHTECH, INC. A. INDIVIDUAL FACTORS THAT AFFECT COLLEGE ENROLLMENT Several studies have used data from the National Longitudinal Study of the High School Class of 1972 (NLS72), the National Longitudinal Survey of Labor Market Experience, Youth Cohort (NLSY), and the High School and Beyond Survey (HSB) to examine the factors affecting college enrollments. Manski and Wise (1983), Rouse (1994), and a number of others have used the variables included in these data sets to estimate multinomial logit models of enrollment decisions. Among the researchers, there seems to be considerable agreement regarding the individual traits that help to determine enrollment. These traits are discussed below. Manski and Wise (1983) presented a key point, namely that the enrollment process begins with the student’s decision to apply to college. This is much more important than the decisions made by college admissions personnel, since most would-be college students are likely to be admitted to some postsecondary institution of average quality. Jackson (1988) reports that in 1972, more than 97 percent of college applicants were admitted to at least one of their top three choices. The factors of greatest interest, then, are those that cause the student to seek to enroll. Both Manski and Wise (1983) and Rouse (1994) find that individual traits such as achievement levels, high school class rank, and parental education levels are of primary importance in determining the likelihood of a student’s applying to college. They state that higher family income levels increase the probability of application as well, but to a lesser extent. Manski and Wise also cite a â€Å" ‘peer’ or high school quality effect,† such that the larger the share of a high school senior’s classmates who attend 4-year schools, the more likely he or she will be to do the same. A recent NCES report (1997) describes the relationship among six risk factors (such as changing schools two or more times) and PSE attendance rates. St. John and Noell (1989) and St. John (1990) draw similar conclusions from the NLS72 and HSB data sets. St. John and Noell state that certain â€Å"social background variables† appear to make college enrollment more likely. These include higher test scores, higher grades, higher SECTION II. LITERATURE REVIEW 5 MATHTECH, INC. maternal education levels,3 and family income, as cited by Manski and Wise and Rouse. Other key variables include participation in an academic track during high school and â€Å"high postsecondary aspirations,† as measured by students’ reporting of the highest level of schooling they expect to achieve. Hossler and Maple (1993) find that parental education levels have a stronger effect on enrollment plans than student ability or income level. Other background factors that researchers have found to be significant include the level of parental encouragement (Hossler, Braxton, and Coopersmith, 1989) and students’ own expectations about the college decision (Borus and Carpenter, 1984). Jackson (1988) concludes that test scores, grades, taking part in a college preparatory program, and attending a school with many college-going peers are the student attributes most important for college enrollment. Kohn, Manski, and Mundel (1976) report that parents’ education level has a positive effect on a student’s likelihood of enrollment, but state that this effect decreases as family income rises. A number of researchers have examined the effects of family income levels on college enrollment. Manski (1992:16) concludes that there are â€Å"persistent patterns of stratification of college enrollments by income. † Both Manski (1992) and Kane (1995) present census data for multi-year periods that show, for ascending income levels, a steadily increasing percentage of 18to 19-year-old dependent family members enrolled in college. Using the same data source, Clotfelter (1991) and Mortenson and Wu (1990) cite positive income effects for the 18- to 24year-old group as well. Hauser (1993) finds large family income effects on college enrollment for White and Hispanic families, but he finds no such effects for Black families. 3 St. John and Noell do not include paternal education levels as a variable in their study. Manski and Wise and Rouse consider maternal and paternal education levels as separate variables, but present their conclusions in terms of parental education levels as a whole. Most of the studies reviewed here do not distinguish between mother’s and father’s education levels. One exception is the study by Kohn, Manski and Mundel (1976). This study estimates a model using subsamples of the SCOPE survey from two different states. While one group shows that the father’s education level has a greater effect on the likelihood of college attendance than does the mother’s, the other group shows the mother’s education level as having a greater effect. SECTION II. LITERATURE REVIEW 6 MATHTECH, INC. B. INSTITUTIONAL FACTORS THAT AFFECT COLLEGE ENROLLMENT. In addition to the factors that operate at an individual level, researchers have found a variety of institutional factors, or factors pertaining to educational and vocational alternatives, that affect college enrollment levels. Manski and Wise (1983) include among these factors tuition level, â€Å"quality of school† (as measured by the average combined SAT score of incoming freshmen), and the availability of government and institutional financial aid. Rouse (1994) examines the factor of proximity by estimating changes in enrollments that would result from decreasing the average  distance to the nearest 2-year college. She also considers the effects of tuition levels and financial aid availability, as well unemployment rates, which serve as a measure of competing opportunities available to high school seniors. Tuition levels are another institutional factor with a significant effect on college enrollment. Leslie and Brinkman (1987) review 25 studies on this subject, and find a general consensus that a $100 increase in tuition nationwide, in 1982–1983 academic year dollars, would result in a 6 percent decline in the college participation rate for the 18- to 24-year-old group. Savoca (1990) makes the point that high tuition levels may lessen postsecondary enrollments in the aggregate by discouraging some students from ever applying to college. The effects of tuition levels are moderated in many cases by the effects of financial aid. McPherson and Schapiro (1991) state that the variable of interest should be net cost, or tuition less financial aid. At the initial stages of the enrollment decision, however, students often lack information on their eligibility for financial aid and the amount of aid they would be likely to receive. Researchers have differing views regarding the effects of financial aid on enrollment at different types of institutions. Reyes (1994) finds that increases in financial aid positively affect both 2-year and 4-year college enrollment rates, based on information from the NLSY and HSB. Manski and Wise (1983), using the NLS72, conclude that financial aid affects students’ decisions to attend 2-year institutions, as opposed to not going to college at all. However, this study finds that enrollments at 4-year schools have little sensitivity to the availability of financial aid. Manski and Wise do not consider the effect of financial aid on the student’s choice between a 2-year and a 4-year institution. SECTION II. LITERATURE REVIEW 7 MATHTECH, INC. Other researchers have compared the effects of decreasing tuition with the effects of increasing financial aid. Manski and Wise (1983) find that for those attending 2-year schools, an additional dollar of financial aid would be worth more than a one dollar reduction in tuition. St. John (1990:172) also finds that â€Å"college applicants†¦ [are] more responsive to changes in student aid than to changes in tuition,† except for those in the upper income group. Kane (1995), however, argues that while financial aid increases may be more equitable because they are means tested, they are not as effective as decreases in tuition. This is a consequence of the complexity of the financial aid application process and the unwillingness of low income families to borrow to finance a college education. When studying the effect of tuition and financial aid on PSE enrollment, the group to be especially concerned about is low income students. Leslie and Brinkman (1987) and Savoca (1990) find that tuition levels affect enrollment decisions for low income students much more than for middle and upper income groups. By the same token, the availability of financial aid is a much more crucial factor for those at lower income levels. Orfield (1992) notes that the maximum Pell grant is less than one-fifth of the tuition at an elite university. Such a gap between aid and costs, he contends, may steer many low income students toward lower cost schools. Hearn’s 1991 study supports this hypothesis. He finds that when academic ability, achievement, and other factors are controlled for, lower income students are especially likely to choose institutions of lower selectivity. Schwartz (1985) finds that low income students are affected differently by publicly provided financial aid and aid supplied by institutions. He states that public grants tend to promote greater equity among income groups in college enrollment. Private grants, however, are often awarded on the basis of academic ability, and they tend to favor students who could afford to go to college without them. Clotfelter (1991) expresses the same concern about the effects of institutional aid. Manski and Wise (1983) note that even public aid is not always awarded where the need is greatest. They state that in 1979, 59 percent of Basic Educational Opportunity Grants were awarded to students who would probably have gone to college in the absence of such aid. Table 1 summarizes the data sources used in the studies mentioned here. SECTION II. LITERATURE REVIEW 8 MATHTECH, INC. Table 1 MAIN DATA SOURCES FOR WORKS CITED IN LITERATURE REVIEW STUDY Borus, Michael E.and Carpenter, Susan A. , â€Å"Factors Associated with College Attendance of High-School Seniors† (1984) Clotfelter, Charles T. , â€Å"Demand for Undergraduate Education† (1991) Hauser, Robert M. , â€Å"Trends in College Entry among Whites, Blacks, and Hispanics† (1993) Hearn, James C. , â€Å"Academic and Nonacademic Influences on the College Destinations of 1980 High School Graduates† (1991) Hossler, Don, Braxton, John and Coopersmith, Georgia, â€Å"Understanding College Choice† (1989). Hossler, Don and Maple, Sue, â€Å"Being Undecided about Postsecondary Education† (1993) Jackson, Gregory A., â€Å"Did College Choice Change during the Seventies? † (1988) Kane, Thomas, â€Å"Rising Public College Tuition and College Entry: How Well Do Public Subsidies Promote Access to College? † (1995) Kohn, Meir G. , Manski, Charles F. , and Mundel, David S. , â€Å"An Empirical Investigation of Factors which Influence College-going Behavior† (1976) Leslie, Larry L. , and Brinkman, Paul T. , â€Å"Student Price Response in Higher Education† (1987) Manski, Charles F. , and Wise, David A. , College Choice in America (1983) Manski, Charles F. , â€Å"Income and Higher Education† (1992) McPherson, Michael S., and Schapiro, Morton Owen, â€Å"Does Student Aid Affect College Enrollment? New Evidence on a Persistent Controversy† (1991) Mortenson, Thomas G. , and Wu, Zhijun, â€Å"High School Graduation and College Participation of Young Adults by Family Income Backgrounds 1970 to 1989† (1990) National Center for Education Statistics. â€Å"Confronting the Odds: Students At Risk and the Pipeline to Higher Education† (1997). MAIN DATA SOURCES 1979 and 1980 National Longitudinal Surveys of Labor Market Experience, Youth Cohort (NLSY) Review of studies done by others, with data from Current Population Survey (CPS) and High. School and Beyond (HSB) CPS HSB, Higher Education Research Institute (HERI) data Review of studies done by others Cluster sample of 5,000 Indiana ninth graders National Longitudinal Study of the High School Class of 1972 (NLS72), HSB NLSY, HSB, CPS School to College: Opportunities for Postsecondary Education (SCOPE) Survey Meta-analysis of studies done by others NLS72 NLS72, HSB, CPS Cooperative Institutional Research Program (CIRP) data, CPS HSB, Current Population Report, CPS NELS SECTION II. LITERATURE REVIEW. 9 MATHTECH, INC. STUDY Orfield, Gary, â€Å"Money, Equity, and College Access† (1992) Reyes, Suzanne, â€Å"The College Enrollment Decision: The Role of the Guaranteed Student Loan† (1994) Rouse, Cecilia Elena, â€Å"What to Do after High School: The Two-Year versus Four-Year College Enrollment Decision† (1994) St. John, Edward P. , and Noell, Jay, â€Å"The Effects of Student Financial Aid on Access to Higher Education: An Analysis of Progress with Special Consideration of Minority Enrollment† (1989) St. John, Edward P., â€Å"Price Response in Enrollment Decisions: An Analysis of the High School and Beyond Sophomore Cohort† (1990) Savoca, Elizabeth, â€Å"Another Look at the Demand for Higher Education: Measuring the Price Sensitivity of the Decision to Apply to College† (1990) Schwartz, J. Brad, â€Å"Student Financial Aid and the College Enrollment Decision: The Effects of Public and Private Grants and Interest Subsidies† (1985) Schwartz, J. Brad, â€Å"Wealth Neutrality in Higher Education: The Effects of Student Grants† (1986) MAIN DATA SOURCES Review of history of federal student financial aid programs NLSY, HSB NLSY, HSB, CPS. NLS72, HSB HSB NLS72 HSB, CPS HSB, CPS SECTION II. LITERATURE REVIEW 10 MATHTECH, INC. III. DATA A. NELS DATA While a number of studies have used data from the National Longitudinal Survey, Youth Cohort (NLSY), the National Longitudinal Study of the High School Class of 1972 (NLS72), and the High School and Beyond Survey (HSB) to examine the factors affecting college enrollments, this work effort is among the first to use NELS to analyze these types of issues. In 1988, NELS initially surveyed over 24,000 public and private school eighth graders throughout the United States. The nationally representative eighth grade cohort was tested in four subjects (mathematics, reading, science, and social studies). Two teachers of each student (representing two of the four subjects) were also surveyed, as was an administrator from each school. On average, each of the 1,052 participating schools was represented by 24 students and five teachers. Parents were also surveyed, providing researchers with detailed information on family background variables. Since 1988, the initial eighth grade cohort has been re-surveyed three times (and has been â€Å"freshened† with new sample members). The first follow-up of NELS (spring, 1990), included the same components as the base year study, with the exception of the parent survey, which was not implemented in the 1990 round. It also included a component on early dropouts (those who left school between the end of eighth grade and the end of 10th grade). The second follow-up (spring, 1992), repeated all components of the first follow-up study and also included a parent questionnaire. However, this time only one teacher of each student (either a mathematics or a science teacher) was asked to complete a teacher questionnaire. High school transcript data were also collected for these students. A subsample of the NELS:88 second follow-up sample was again followed-up in the spring of 1994, when most sample members had been out of high school for 2 years. In all, 14,915 students were surveyed, most through computer-assisted telephone interviewing. Major content areas for the third follow-up questionnaire were: education histories; work experience histories; work-related training; family formation; opinions and other experiences; occurrence or SECTION III. DATA 11 MATHTECH, INC. non-occurrence of significant life events; and income. Data collection for this wave began on February 4, and ended on August 13, 1994. At the time the data were collected, most of the respondents were 2 years out of high school. Table 2 summarizes the components of the different waves of the surveys. Table 2 OVERVIEW OF NELS NELS Components Grades included Cohort Base Year Spring term 1988 grade 8 students: questionnaire, tests questionnaire questionnaire two teachers per student (taken from reading, mathematics, science, or social studies) First Follow-up Spring term 1990 modal grade = sophomore students, dropouts: questionnaire, tests none questionnaire two teachers per student (taken from reading, mathematics, science, or social studies) Second Follow-up Spring term 1992 modal grade = senior students, dropouts: questionnaire, tests, H. S.  transcripts questionnaire questionnaire one teacher per student (taken from mathematics or science). Third Follow-up Spring 1994 H. S. + 2 years all individuals: questionnaire none none none Parents Principals Teachers B. NPSAS DATA Because the NELS database does not contain detailed information on financial aid, the National Postsecondary Student Aid Study (NPSAS) database is used to supplement our study with additional financial aid information. This database is used to predict financial aid for the respondents in NELS based on demographic and other characteristics that are available in both databases. NPSAS is constructed specifically to provide information on financing of postsecondary education, so it is a good candidate for this use. This database surveys a nationally representative sample of undergraduate, graduate, and first-professional students attending less than 2-year, 2-year, 4-year, and doctoral granting institutions. Both students who receive and those who do not receive financial aid are surveyed. SECTION III. DATA 12 MATHTECH, INC. The 1993 NPSAS study collected information on more than 78,000 undergraduate and graduate students at about 1,100 institutions. To be eligible, students must have been enrolled between May 1, 1992 and April 30, 1993 at a postsecondary institution in the United States or Puerto Rico. The students had to be enrolled in courses for credit, and in a program of 3 months or longer. Also eligible for inclusion were students who received a bachelor’s degree between July 1, 1992 and June 30, 1993. Students who were enrolled in a GED program or who were also enrolled in high school were not included. C. SAMPLE AND WEIGHTS Of the 14,915 respondents in the third NELS follow-up, 13,120 are represented in all four waves of the NELS data. The remaining 1,795 respondents are either first follow-up â€Å"freshened† students,4 second follow-up freshened students,5 base-year ineligibles,6 or base-year eligible students who declined to participate in one or more of the survey waves, but who did participate in the third survey wave. The breakdown of these 1,795 respondents is as follows: 501 first follow-up freshened students, 102 second follow-up freshened students, 271 base-year ineligibles, and 921 base-year eligibles with missing survey waves. To take advantage of the longitudinal nature of the NELS data and to be consistent across models and issues in the report, we focus our work on the sample of 13,120 respondents represented in all four waves of the NELS data. Consequently, the weight used in our analyses, (â€Å"F3PNLWT†) applies to sample members who completed questionnaires in all four rounds of NELS:88. As a result, the longitudinal analyses that we conduct, and the estimates that are produced in this study can only be used to make projections to the population of spring 1988 eighth graders. In the descriptive tables, all percentages are weighted using F3PNLWT, including the analyses with the high school transcript data. Those who were tenth graders in 1990 but were not in the base-year sampling frame, either because they were not in the country or because they were not in the eighth grade in the spring term of 1988. Those who were 12th graders in 1992 but were not in either the base year or first follow-up sampling frames, either because they were not in the country or because they were not in the eighth (10th) grade in the spring term of 1988 (1990). 6 5 4 Students excluded in 1988 due to linguistic, mental, or physical obstacles to participation. 13 SECTION III. DATA MATHTECH, INC. This sample includes dropouts, since the purpose of this study is to examine the overall question of what characteristics of eighth graders in 1988 are related to PSE attendance. We focus on early indicators, such as educational expectations and course-taking behavior in the eighth grade, and not on the â€Å"pipeline† of high school experiences that a dropout would lack access to. However, the dropouts were not asked the same set of survey questions as the other respondents, and, therefore, some of the analysis does not include dropouts. For each of our tables or figures, we note whether or not the dropouts are included in the analysis. D. CORRECTED STANDARD ERRORS Because NELS data are collected through a multi-stage sampling scheme, calculation of standard errors through standard methods can understate these errors. The sampling technique used in NELS is a selection of schools, and then within schools, a selection of students. With this sampling method, the observations of different students may not be independent from one another. Stataâ„ ¢, the statistical software used for analysis in this report, corrects the standard errors for these sampling techniques. Except for multinomial logit models, for which this correction is not available, survey correction techniques are used, and we note whenever the corrections are not used. However, we have found that such corrections do not have a large effect on our results, and therefore, we present all results with confidence. E. VARIABLE DEFINITIONS. The appendix to this study contains definitions of the key variables used in our analysis. For each key variable, we describe how we constructed the variable and we list the names of the NELS variables used in the construction. SECTION III. DATA 14 MATHTECH, INC. IV. WHO GOES TO COLLEGE? A. WHAT PROPORTION OF STUDENTS ATTEND COLLEGE, AND WHAT TYPE OF COLLEGE DID THEY ATTEND? We begin our analysis by examining the demographics of postsecondary school choice and discussing our main findings regarding college attendance rates and types of postsecondary education (PSE) attended. As shown in Table 3, a majority of 1988 eighth graders attend some type of PSE by 1994. Overall, 62. 7 percent of the respondents attend PSE. (Note that in all of the tables in this report, all percentages are weighted. ) Students are most likely to attend a 4-year public or a less than 4-year public school. Approximately 24 percent of the students attend each of these types of schools. Next most common are 4-year private schools. Just over 11 percent of the respondents attend 4-year private schools. Only 4 percent of the respondents attend less than 4-year private schools. Thirty-seven percent of the respondents do not attend any type of PSE. Women are slightly more likely than men to attend PSE. While 60 percent of men attend PSE, 65 percent of women attend. Women are more likely than men to attend 4-year private schools and less than 4-year private schools. Native Americans, Blacks, and Hispanics are least likely to attend PSE and Asians and Pacific Islanders are most likely to attend PSE. Hispanics are most likely to attend less than 4year private schools. Students whose parents have higher education levels are much more likely to attend PSE. While only 33 percent of students whose parents have less than a high school education attend PSE, 90 percent of students whose parents have an advanced degree attend PSE. SECTION IV. WHO GOES TO COLLEGE? 15 MATHTECH, INC. Table 3 DEMOGRAPHICS OF POSTSECONDARY SCHOOL CHOICE1 No PSE 4-Year Public 4-Year Private.

Transfer College Essay Samples Engineering Major - the Story

Transfer College Essay Samples Engineering Major - the Story Why Almost Everything You've Learned About Transfer College Essay Samples Engineering Major Is Wrong Apart from having an exceptional SAT score, you would have to develop the very best college essay. Discuss what you could offer. A brief sentence is utilized to make the emotional resolution of the admission essay. Transfer College Essay Samples Engineering Major and Transfer College Essay Samples Engineering Major - The Perfect Combination To discover what has to be finished, you ought to take a look at a terrific deal of other essay examples. There isn't a prompt to direct you, and that means you must ask yourself the questions that will get at the center of the story you would like to tell. Last, the detail of real speech makes the scene pop. There are a couple things you can do in your essay to be noticed and get accepted. 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Away in a Manger in Spanish

Here is a Spanish version of Away in a Manger, a popular childrens Christmas song. Dont understand the words? Give your Spanish a boost with the grammar and vocabulary guide that follows. The song was originally written in English, and it isnt well known in Spanish-speaking countries. The author is unknown. Jesà ºs en pesebre Jesà ºs en pesebre, sin cuna, nacià ³;Su tierna cabeza en heno durmià ³.Los astros, brillando, prestaban su luzal nià ±o dormido, pequeà ±o Jesà ºs. Los bueyes bramaron y à ©l despertà ³,mas Cristo fue bueno y nunca llorà ³.Te amo, oh Cristo, y mà ­rame, sà ­,aquà ­ en mi cuna, pensando en ti. Te pido, Jesà ºs, que me guardes a mà ­,amà ¡ndome siempre, como te amo a ti.A todos los nià ±os da tu bendicià ³n,y haznos mà ¡s dignos de tu gran mansià ³n. English Translation of Spanish Lyrics Jesus in a manger, without a crib, was born;His tender heard slept on the hay.The sparkling  stars shed their lightOn the baby asleep, little Jesus. The oxen bellowed and he awoke,But Christ was good and never cried.I love you, O Christ, and look at me, yes,Here in my crib, thinking about you. I ask you, Jesus, to  keep watch over me,Loving me always, as I love you.Give your blessing to all the children,And make us more worthy of your great mansion. Vocabulary and Grammar Notes Pesebre: As you can guess by the title of the song, this is the word for manger, a type of box from which farm animals eat. Because of its use in connection with the Christmas story, pesebre can also refer to a representation of the birth of Jesus, much like the English creche or French crà ¨che. Nacià ³:  Nacer translates the phrase to be born. Sin: Sin is a common Spanish preposition meaning without and is the opposite of con. Cuna: A crib or other small bed made specifically for a child or baby. Tierna: This word is often translated as tender and is frequently used, as here, as an adjective of affection. By being placed before the noun it refers to, tierna here helps convey an emotional meaning. Thus tierna coming before a noun might indicate tenderness in the sense of being gentle, while after a noun it is more likely to refer to a physical quality. Heno: Hay. Astro: Estrella is more frequently used for star than is astro. Brillando: This is the present participle of brillar, which can mean to glitter or sparkle. In standard Spanish, present participles function as adverbs, so brillando should be seen as an adverb modifying prestaban rather than as an adjective modifying astros. Prestaban: The verb prestar most often means to loan or to lend. However, it is often used, as here, to refer to providing or giving. Dormido: This is the past participle of dormir, meaning to sleep. Buey: Ox. Bramaron: Bramar refers to the groaning sound of an animal. Despertà ³: This is the third-person singular preterite (a past tense) of despertar, which means to awake. Mas: Without the accent, mas typically means but. The word isnt used much in everyday speech, where pero is generally preferred. It should not be confused with mà ¡s, pronounced the same way, which usually means more. Sà ­: Sà ­ most often means yes. As can the English word, sà ­ can also be used as a way of affirming or emphasizing what has been said. Oh: Oh here is the equivalent of the English oh here, but it was a wider range of meanings in Spanish, where it can convey happiness, pain, joy, and other feelings. It is more common in writing than in speech. Mà ­rame: The verb mirar can mean simply to look. In this context, however, it also carries the meaning of to watch over. Mà ­rame is a combination of two words, mira (watch over) and me (me). In Spanish it is common to attach object pronouns to the end of certain verb forms—commands, gerunds (see amà ¡ndome below), and infinitives. Pensando en: In Spanish, the phrase for to think about is pensar en. Me guardes a mà ­: This is a redundancy. In everyday speech, me guardes (watch over me) would be sufficient. Although in speech the addition of the grammatically unnecessary a mà ­ might be done for reasons of emphasis, here it is used to help provide the right number of syllables for the music. Amà ¡ndome: This is a combination of two words, amando (loving) and me (me). Da: In this context, da is the imperative (command) form of dar (to give) used when speaking to a friend or family member. A todos los nià ±os da tu bendicià ³n: The standard word ordering would place a todos los nià ±os after the verb. Spanish is more flexible with word order than English, however, so this sort of sentence structure isnt unusual,   Haznos: Another combination of two words, haz (the imperative form of hacer, to make, used when speaking to a friend or family member), and nos (us). Mansià ³n: Usually a dwelling place, but sometimes specifically  a mansion. In this context, tu gran mansià ³n figuratively refers to heaven.