There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations. The waterfall data shows a cohort of high school students and their progression through high school graduation, college enrollment and persistence in higher education to a second year or college completion.

This is a companion dataset to the main DART: Success After High School dataset. It contains two indicators published separately from the main dataset since the data are in a different format: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion". For all other DART: Success After High School indicators, please visit the main DART: Success After High School dataset.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system. Provides aggregated key enrolment data for college students, such as: * Fall term headcount enrolment by campus, credential pursued and level of study * Fall term headcount enrolment by program and Classification of Instructional Program * Fall term headcount enrolment by student status in Canada and country of citizenship by institution * Fall term headcount enrolment by student demographics (e.g., gender, age, first language) To protect privacy, numbers are suppressed in categories with less than 10 students. ## Related * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * School enrolment by gender * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

This dataset contains college enrollment information, by MSA, for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university within 12 months of their high school graduation. This dataset includes enrollment in two-year and four-year institutions of higher education.Click here for metadata (descriptions of the fields).

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores. Records with 5 or fewer students are suppressed (marked ‘s’). College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report. Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school. Data collected and processed by the College Board.

Report by NYS High School of the number of SUNY First Time Undergraduates entering in the Term, who reported attending that High School. The total number of First Time Undergraduates at SUNY from each High School is provided. The total is sub-divided by the SUNY Sector of the Institution the student is attending; Doctoral, Comprehensive, Technology and Community College. The report also provides a total count by NYS County, which is an aggregate of all the high schools in that County.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

This paper examines if students' college outcomes are sensitive to access to college admissions tests. I construct a dataset of every test center location and district policy in the United States linked to the universe of individual testing records and a large sample of college enrollment records. I find evidence that SAT taking is responsive to the opening or closing of a testing center at a student's own or a neighboring high school and to policies that provide free in-school administration and default registration. Newly induced takers of high academic aptitude appear likely to attend and graduate from college. (JEL H75, I23, I28)

This longitudinal data set was collected to assess the impact of college experience on students. Freshmen students entering a large midwestern university in 1962 (N=2,207) and 1963 (N=2,161) were administered a two-hour questionnaire during the orientation period. Approximately 95% of the two cohort groups answered the questionnaire. The initial freshmen questionnaire consisted of both precoded and open-ended items dealing with high school experience, anticipated college success, interests and values, and relationships with both family and peers. In the 2nd semester of their freshman year and at the end of their senior year, 450 students from each cohort group, half male and half female, were given a questionnaire which included items about university experience (including satisfaction with course work and living situation); process of decision making; relationships with faculty, family and peers; future expectations (including career goals and marital plans); the issue of career v. family (male and female perspective) and group membership while at the university. In addition, 300 new students who were seniors in 1967 were also tested to compensate for attrition of the sample over the four years. Extensive interviews were also administered to 400 students entering as freshmen in 1962 and 1963 (200 from each group), once in the second semester of their freshman year, and once in the second semester of their senior year. Approximately 1,600 participants in all were selected from these various sources to respond to the senior questionnaire. The Murray Research Archive holds all numeric file data from the study. The Murray Archive also holds a follow-up study of this data set collected from 1967-1981 (see Tangri, 00009).

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

• Socio-economic data.

• Data on habitual residence.

• Information on intercity journeys.

• Identity and sense of community.

• Personal network indicators.

• Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: <https://www.flickr.com/photos/25906481@N07/albums/72157667029974755>.

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

• Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals,

Students interested in entering university or college within three years of completing upper secondary education by gender, upper secondary programme, study structure, table content and year, broken

India Number of Students: Colleges data was reported at 26,552,301.000 Person in 2017. This records an increase from the previous number of 26,388,693.000 Person for 2016. India Number of Students: Colleges data is updated yearly, averaging 23,470,323.500 Person from Sep 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 26,552,301.000 Person in 2017 and a record low of 11,551,516.000 Person in 2010. India Number of Students: Colleges data remains active status in CEIC and is reported by Department of Higher Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

Analysis of ‘SAT (College Board) 2010 School Level Results’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/37a58362-4cfc-4c4e-a026-c83036afc11a on 26 January 2022.

--- Dataset description provided by original source is as follows ---

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores.

Records with 5 or fewer students are suppressed (marked ‘s’).

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

Data collected and processed by the College Board.

--- Original source retains full ownership of the source dataset ---

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

Provides provincial and regional level data on students who first entered a publicly funded secondary school in Ontario in Grade 9 and transitioned to postsecondary education (college, university, apprenticeship, other postsecondary education destinations) within 7 years. Other postsecondary education is determined using OSAP data to identify those students receiving OSAP who are not captured in college, university, or apprenticeship data but are attending postsecondary education (e.g., enrolled in private career colleges, in PSE outside Ontario, etc.) The information provided is based on data from the Ministry of Education, Ministry of Colleges and Universities, and the Ministry of Labour, Immigration, Training and Skills Development and is sourced from the Ontario School Information System (OnSIS), College Statistical Enrolment Report (CSER), University Statistical Enrolment Report (USER), Employment Ontario Information System – Apprenticeship (EOIS-APPR), and the Ontario Student Assistance Program (OSAP). To protect privacy, numbers are suppressed when fewer than 10 students are represented, and all values are rounded to the nearest 5. The reported number of students transitioning to postsecondary education may exhibit slight variations compared to previously published datasets for historical years. These discrepancies may arise from processing of the datasets. Source: Ontario School Information System (OnSIS): Grade 9 graduation cohorts 2010-2011 to 2015-2016 * Data includes provincially funded secondary schools. * Data excludes private schools, publicly funded hospital and provincial schools, Education and Community Partnership Program (ECPP) facilities, adult continuing education day schools, summer, and night schools. University Statistical Enrolment Report (USER) and the College Statistical Enrolment Report (CSER): Academic years 2012-2013 to 2021-2022 * Data includes full-time and part-time enrolments at Ontario's publicly assisted colleges and universities. * Data excludes college enrolment in general interest (non-credential) programs, apprenticeship training agreements and enrolment in private career colleges. Employment Ontario Information System - Apprenticeship (EOIS-APPR): Data includes all apprenticeship training agreements signed between January 2015 and December 2022 Ontario Student Assistance Program (OSAP): Academic year 2012-2013 to 2021-2022. OSAP records from publicly assisted Ontario universities and colleges excluded. Other PSE transition includes students with OSAP records at other Ontario postsecondary institutions (e.g.: private career colleges, Royal Military College) and out-of-province postsecondary institutions.

AbstractIn this study, we use data from a cohort of 4,033 Tulsa kindergarten students to investigate the relationship between pre-K enrollment and later college enrollment. Specifically, we test whether participation in the Tulsa Public Schools universal pre-K program and the Tulsa CAP Head Start program predict enrollment in two-year or four-year colleges. We use propensity score weighting with multiply imputed data sets to estimate these associations. We find that college enrollment is 12 percentage points higher for Tulsa pre-K alumni compared with children who did not attend Tulsa pre-K or Head Start. College enrollment is 7.5 percentage points higher for Head Start alumni compared to children who did not attend Head Start or Tulsa pre-K, but this difference is only marginally significant. Although Tulsa pre-K attendance is associated with two-year college enrollment among children from all racial and ethnic backgrounds, only among Black and Hispanic students does it strongly predict four-year college enrollment.

Students interested in entering university or college within three years of completing upper secondary education. Intended duration of university studies. Data up to academic year 2011/2012 by gender, upper secondary school programme, length of studies and year, broken

Analysis of ‘State University of New York (SUNY) - NYS High School Attended by First Time Undergraduate Students: Beginning Fall 2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/245345ad-21d9-43cf-b820-0cc771ae12be on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Report by NYS High School of the number of SUNY First Time Undergraduates entering in the Term, who reported attending that High School. The total number of First Time Undergraduates at SUNY from each High School is provided. The total is sub-divided by the SUNY Sector of the Institution the student is attending; Doctoral, Comprehensive, Technology and Community College. The report also provides a total count by NYS County, which is an aggregate of all the high schools in that County.

--- Original source retains full ownership of the source dataset ---

The purpose of the study: to investigate teachers' attitudes towards students' and bachelors' readiness for higher education and to identify the factors that influence the quality of readiness. Major investigated questions: respondents were asked about the school they work in and whether they like being a teacher. Relationships with administrative staff, other teachers, students, and students' parents were assessed. Working conditions at the school and the work of the administrative staff were also evaluated. Questions were asked about how the public evaluates the teaching profession. After a block of questions, respondent was asked to describe its school. It was clarified which students the respondents pay more attention to. Respondents were asked to rate their school's approach to learning this year. Respondents were also given the opportunity to name what they would like to change first in their school. It was asked if bullying was prevalent in the school. Furthermore, it was asked to rate the fact that students hire tutors or attend special classes to prepare for exams. Respondents' attitudes towards additional training with tutors or attending courses to prepare for the matriculation exams were asked. The questionnaire block was intended to clarify the importance of additional support from tutors or attending special courses for higher education. Views were sought on the proportion of students in this year's program at the respondents' school who engage in additional learning, and why students usually engage tutors or attend courses in preparation for matriculation. Respondents were asked to respond if they were a tutor for high school graduates this school year and to indicate the number of students. Respondents were asked if the preparation of high school graduates for the maturity exam has changed in the last five years. It goes on to explain what is most important for the first-year student to be ready for college matriculation. After a block of questions, students were asked to rate their school's readiness for this year's college readiness. It was clarified how many respondents paid attention to students' career orientation in school and what students' learning outcomes depended more on. It was asked whether freshers' motivation should be assessed when entering a higher education institution and how it should be assessed. Respondents also assessed the current situation in general education and higher education and commented on current and planned changes in higher education. Clarification was sought on how graduates should be admitted to the humanities program and how many state matriculation examinations should be passed in order to enter a university. The goal was to find out what the minimum score for a state matriculation exam should be in order for graduates to be admitted to a college. Finally, it asked who should receive public funding for college. Socio-demographic characteristics: age, gender, place of residence, whether respondent have completed schooling, education, how many years respondent have been working as a teacher, qualification category of teacher, when respondent upgraded qualification, number of hours worked per week, income. This survey was conducted at the initiative of the Research and Higher Education Monitoring and Analysis Centre (MOSTA). On January 1, 2019, MOSTA was reorganized into the Government Strategic Analysis Center (STRATA).