In the academic year of 2020/21, about 202,334 Master's degrees were earned in business across the United States, making it the most common master's degree earned. In that same year, another 47,257 Master's degrees were earned in engineering.

The best master's degree for getting a job was considered to be Physicians Assistant with a mid-career median salary of 97,133 U.S. dollars in 2021. Salaries for nurse practitioner and computer science master's were also high.

Introduction This STEM advising outreach program was developed for undergraduate students who are contemplating future applications to PhD programs in the life sciences. The audience of ~20 students ranged in academic stage, and was composed mostly of life sciences undergraduates enrolled at Bowdoin College.

We have previously described two similar outreach events (ref. 1,2); this 90-minute combination of seminar and discussion built on that pilot program. This session at Bowdoin College was intended to complement the advising that students receive from their primary research mentors on campus. Although undergraduates at many excellent institutions have access to extensive pre-professional advising for careers in medicine, law and some other directions, the structure of advising for scientific research and the many career options that rely on PhD training is less consistent. Independent study or thesis research mentors are often a student’s primary source of advice. Career advisors have confirmed that reiteration and reinforcement of advising principles by professionals external to the school environment is helpful. Therefore, this outreach program’s content was developed with a goal of demystifying PhD programs and the benefits that they provide. The topics covered included (a) determining the key differences between programs, (b) understanding how PhD admissions works, (c) preparing an effective application, (d) proactive planning to strengthen one’s professional portfolio (internships, independent research, cultivating mentors), (e) key transferable skills that most students learn in graduate school, (f) what career streams are open to life science PhDs, and, (g) some national and institutional data on student career aspirations and outcomes (ref. 3). Methods The approach of bringing a faculty member and an administrative staff member who both have life science PhD training backgrounds was intentional. This allowed the program to portray different perspectives and experience to guide student career development, while offering credible witnesses to the types of experiences, skills and knowledge gained through PhD training. Central to the method of this outreach program is the willingness of graduate educators to meet the students on their own ground. The speakers guided students through a process of identifying national graduate programs that might best serve their individual interests and preferences. In addition to recruiting prospective applicants to Harvard Medical School (HMS) summer internships and PhD programs, the speakers made an explicit appeal to students to hone their professional portfolio proactively by discussing important skills that undergraduates need to be competitive in admissions and the career workplace including acquiring training in statistics and programming, soliciting diverse mentorship, acquiring authentic research experiences/internships, conducting thesis research, and obtaining fellowships). By reinforcing much of the anecdotal and formal advising content that is made available by faculty mentors and career counselors, our host saw the value of external experts to validate guidance.

This event built off our most recent event (ref. 2); we delivered a presentation covering the relevant topics and transitioned into an open discussion featuring a third visitor in our team. In contrast to the aforementioned previous event, the time constraint at lunch time prevented us from doing a formal panel. Our third speaker was a HMS Curriculum Fellow (ref. 4) whose career goals included teaching at a comparable institution (primarily undergraduate institution, PUI).

Students were encouraged to have lunch during the session, as the program was held at midday to avoid conflicts with other academic or extracurricular events. ResultsAs the principal goal of the session was to encourage and engage students, not to evaluate them, and the students ranged widely in stage and long-term career objectives, there were no assessment surveys of learning gains. Informally, student engagement was excellent as judged by the frequency and thoughtful nature of questions asked during the discussion phase of the session. Ad hoc student feedback directly following the event was extremely positive, as was our host’s follow up by email after the event. The success of the program was also evident by an invitation for a repeat of the program or other forms of collaboration in the future, including the possibility of reciprocal visits to HMS.DiscussionThis advising session was a continued refinement of our prototype, and thus served to prepare us for a series of similar events across a larger network of colleges. Our decision to incorporate a HMS Curriculum Fellow served three purposes: (1) to engage speaker who pursued doctoral training at three different institutions (UCLA, Tufts University, Harvard University), (2) to broaden the range of career trajectories presented as outcomes from doctoral programs, and (3) to provide networking and career development opportunities for the Curriculum Fellow.

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.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

This graph shows the list of graduate degrees with the highest mid-career salary in the U.S. in the academic year 2016/17. In 2016/17, petroleum engineering was ranked first with the mid-career salary of 172,000 U.S. dollars.

We collected data on every tenure-track (TT) faculty member in the 122 PhD-granting Political Science departments in the United States to identify which graduate programs place faculty in our discipline’s research universities. The top 20% of departments produced 75% of all faculty while the bottom 50% accounted for less than 5% of all TT faculty at a research university. Forty-nine programs did not have a single graduate placed in a TT-position at a PhD-granting department in the last 10 years, and 18 programs do not have a single graduate in a TT-position at a PhD-granting department at all. The overwhelming majority of TT faculty are at a lower or equally ranked department. The results have important implications for prospective graduate students and the future of our discipline.

In 2024, the average salary of a graduate from the Tsinghua University School of Economics and Management in China with low work experience was 198,874 U.S. dollars (once adjusted for purchasing power parity). This was the highest in the world, ahead of students from the Shanghai Institute of Finance at Shanghai Jiao Tong University and HEC Paris. Salaries of MBA graduates The salaries of business school MBA graduates worldwide stood at 120,000 U.S. dollars in 2024, while graduates who held a bachelor’s degree could expect a starting salary of 69,000 U.S. dollars. The largest university worldwide is located in Africa The university with the highest number of students in the world is found in Nigeria. Ambrose Alli University has more than 536,000 registered students. Tribhuvan University in Nepal and Payame Noor University in Iran round up the top three.

Introduction: In an age of increasingly face-to-face, blended, and online Health Professions Education, students have more selections of where they will receive a degree. For an applicant, oftentimes, the first step is to learn more about a program through its website. Websites allow programs to convey their unique voice and to share their mission and values with others, such as applicants, researchers, and academics. Additionally, as the number of Health Professions Education programs rapidly grows, websites can share the priorities of these programs. Methods: In this study, we conducted a website review of 158 Health Professions Education websites to explore their geographical distributions, missions, educational concentrations, and various programmatic components. Results: We compiled this information and synthesized pertinent aspects, such as program similarities and differences, or highlighted the omission of critical data. Conclusion: Given that websites are often the first point of contact for prospective applicants, curious collaborators, and potential faculty, the digital image of HPE programs matters. We believe our findings demonstrate opportunities for growth within institutions and assist the field in identifying the priorities of HPE programs. As programs begin to shape their websites with more intentionality, they can reflect their relative divergence/convergence compared to other programs as they see fit and, therefore, attract individuals to best match this identity. Periodic reviews of the breadth of programs, such as those undergone here, are necessary to capture diversifying goals, and serve to help advance the field of Health Professions Education as a whole. Methods Our team deduced that most HPE programs would have a website, and that this would serve as a representation of how individuals within the program choose to view themselves and hope to be viewed by others. Further, our team determined that these websites would be an efficient means of collecting programmatic information for the purposes of learning more about program growth, diversity, and values. We conducted the website review from August 2021 to April 2022 using a list of worldwide Health Professions Education programs, which was acquired from the Foundation of Advancement of International Medical Education and Research’s (FAIMER’s) website. FAIMER was chosen as the origin source of programs studied due to its use in another published study evaluating HPE programs. Each master's degree in HPE offered by a university was counted separately, allowing us to note the differences in course and time requirements across all programs. Only HPE master's programs were selected for this study. Certificate and Ph.D. programs were excluded. Next, we developed a data extraction tool. Categories were jointly identified for data collection by three of our authors (JS, SW, and HM). JS, SW, and HW worked independently through a set of three HPE programs, obtaining the data for our selected categories. Afterward, we cross-checked each other's work for verification purposes. For example, if JS obtained the information, SW or HM, who were blinded to JS’s findings, would independently find the answers to the same questions/ topics. This was performed until an agreement between pre and post-review information was above 95%. There was no discovered information that was not agreed upon after discussion. Once 100% agreement was reached with this method, the total number of HPE programs analyzed was split between JS and SW, and the raw data was obtained for the same categories. This data then underwent a review by the other two researchers to ensure high accuracy. This review consisted of information verification on individual program websites where it was originally obtained. For example, if JS found the information about a program, SW and HM (now not blinded) would both have to independently find the same information. Any identified discrepancies were rectified through discussion, and three-way agreement was mandatory for the team to move on to the next program.

The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course.

Sepcifically it contains the answers of 22 participants to the following questions:

1) Did you use the following concepts or tools since you took STA472?
Good practice for...

... spreadsheets
... file and folder organization
... version control
... dynamic reporting
... LaTeX
... presentation slide design
... oral presentations
... designing graphs
... designing tables
... structure for manuscript
... logic of a paragraph
... writing style
... writing R functions
... using unit tests
... setting up simulations
... code styling
... writing vectorized code
... writing parallelized code
... containerizing code

Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know

2) If you used the above concepts at least rarely, did the training of STA472 help you?

Good paractice for...

... spreadsheets
... file and folder organization
... version control
... dynamic reporting
... LaTeX
... presentation slide design
... oral presentations
... designing graphs
... designing tables
... structure for manuscript
... logic of a paragraph
... writing style
... writing R functions
... using unit tests
... setting up simulations
... code styling
... writing vectorized code
... writing parallelized code
... containerizing code

Answers are in the scale: Not really Somewhat Definitively I do not know I do not use this concept

The recent movement underscoring the importance of career taxonomies has helped usher in a new era of transparency in PhD career outcomes. The convergence of discipline-specific organizational movements, interdisciplinary collaborations, and federal initiatives has helped to increase PhD career outcomes tracking and reporting. Transparent and publicly available PhD career outcomes are being used by institutions to attract top applicants, as prospective graduate students are factoring in these outcomes when deciding on the program and institution in which to enroll for their PhD studies. Given the increasing trend to track PhD career outcomes, the number of institutional efforts and supporting offices for these studies have increased, as has the variety of methods being used to classify and report/visualize outcomes. This report comprehensively synthesizes existing PhD career taxonomy tools, resources, and visualization options to help catalyze and empower institutions to develop and publish their own PhD career outcomes. Similar fields between taxonomies were mapped to create a new crosswalk tool, thereby serving as an empirical review of the career outcome tracking systems available. Moreover, this work spotlights organizations, consortia, and funding agencies that are steering policy changes toward greater transparency in PhD career outcomes reporting. Such transparency not only attracts top talent to universities, but also propels research progress and technological innovation forward. Therefore, university administrators must be well-versed in government policies that may impact their PhD students. Engaging with government relations offices and establishing dialogues with policymakers are crucial steps toward staying informed about relevant legislation and advocating for more resources. For instance, much of the recent science legislation in the U.S. Congress, including the Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, significantly impacts federal agency programs influencing universities. To ensure sustained development, it is imperative to support initiatives that enhance transparency, both in terms of legislation and resources. Increased funding for programs supporting transparency will aid legislatures and institutions in staying informed and responsive. Many efforts presented in this publication have received support from federal and state governments or philantrophic sources, underscoring the need for multifaceted support to initiate and perpetuate this level of systemic change.

This paper investigates the dynamics of discrimination in Political Science PhD programs with a survey of current political science graduate students in the top 50 departments. We focus on mentorship, funding, sexual harassment, racism, homophobia, and labor exploitation: 20% of respondents report labor exploitation, 19% experienced racial discrimination, 9% report sexual harassment and 6% experienced homophobia. Discrimination is uneven across individuals: Some groups of graduate students experience widespread discrimination, especially racial discrimination, while other groups are largely unaware of these issues. We ran a survey experiment to gauge the impact of misconduct on formal reporting mechanisms and find that hearing about racial discrimination has a chilling effect on reporting. Importantly, we find that experiencing discrimination harms how satisfied students are in their programs. We find that factors linked to student vulnerability, like international status and funding, are significantly associated with harassment, and that reporting discrimination predicts more discrimination.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

Data on the top universities for Computer Science in 2025.

Context

Pakistan has a large number of public and private universities offering degrees in multiple disciplines. There are 162 universities out of which 64 are in private sector and 98 are public sector/government universities recognized by the Higher Education Commission of Pakistan (HEC).

According to HEC, Pakistani universities are producing over half a million graduates per year, which include over more than 10,000 Computer Science/IT graduates.

From year 2001 to 2015 there is a mass increase in number of enrollment in universities. The recent statistics shows that in 2015, 1,298,600 students enrolled in different levels of degree, 869,378 in Bachelors (16 years), 63,412 in Bachelors (17 years), 219,280 in Masters (16 years), 124,107 in M.Phil/MS, 14,373 in Ph.D, and 8,319 in P.G.D. However, in 2014 the number of doctoral degree awarded were 1,351 only.

Moreover, according to HEC report, in 2014-2015 there are over 10,125 fulltime Ph.D. faculty teaching in Pakistan in all disciplines. Computer Science and related disciplines are widely taught in Pakistan with over 90 universities offering this discipline with qualified faculty. According to our dataset, there are 504 PhD faculty members in Computer Science in Pakistan for 10,000 students. So we have a PhD faculty member for every 20 students on average in computer science program.

Current Student to PhD Professor Ratio in Pakistan is 130:1 (while India is going towards 10:1 in Post-Graduate and 25:1 in Undergrad education).

Here is world's Top 100 universities with Student to Staff Ratio.

Content

Dataset: The dataset contains list of computer science/IT professors from 89 different universities of Pakistan.

Variables: The dataset contains Serial No, Teacher’s Name, University Currently Teaching, Department, Province University Located, Designation, Terminal Degree, Graduated from (university for professor), Country of graduation, Year, Area of Specialization/Research Interests, and some Other Information

Acknowledgements

Data has been collected from respective university websites. Some of the universities did not mention about their faculty profiles or were unavailable (hence the limitation of this dataset). The statistics mentioned above are gathered by Higher Education Commission of Pakistan (HEC) website and other web resources.

Inspiration

Here is what I like you to do:

1. Which area of interest/expertise is in abundance in Pakistan and where we need more people?
2. How many professors we have in Data Sciences, Artificial Intelligence, or Machine Learning?
3. Which country and university hosted majority of our teachers?
4. Which research areas were most common in Pakistan?
5. How does Pakistan Student to PhD Professor Ratio compare against rest of the world, especially with USA, India and China?
6. Any visualization and patterns you can generate from this data

Let me know how I can improve this dataset and best of luck with your work

The number of graduates by institution type, program type, credential type, gender and Classification of Instructional Programs, Primary groupings (CIP_PG).

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Alongside these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH -BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labour Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Management Team, made up of two professionals from each of the three statistical organizations. The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labour) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analysed data.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war. At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected. Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.] The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample. The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated.

(b) SAMPLE DESIGN For reasons of funding, the smaller option proposed by the team was used, or Option B. Stratification of Municipalities The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame. Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed, and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure. However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry. Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:

• Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output
• The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10)
• Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews)
• Understanding of Round 2 Data entry screen (Modules 11-13)
• Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
• Control Procedures; Copying

This statistic shows the factors executive MBA alumni considered to be the most important when they initially enrolled in their degree, by the type of MBA, in 2015. In 2015, 69 percent of alumni of executive MBA degrees cited developing managerial knowledge/technical skills as a key factor in their decision.

In 2022, Canada had the highest share of adults with a university degree, at over 60 percent of those between the ages of 25 and 64. India had the smallest share of people with a university degree, at 13 percent of the adult population. University around the world Deciding which university to attend can be a difficult decision for some and in today’s world, people are not left wanting for choice. There are thousands of universities around the world, with the highest number found in India and Indonesia. When picking which school to attend, some look to university rankings, where Harvard University in the United States consistently comes in on top. Moving on up One of the major perks of attending university is that it enables people to move up in the world. Getting a good education is generally seen as a giant step along the path to success and opens up doors for future employment. Future earnings potential can be determined by which university one attends, whether by the prestige of the university or the connections that have been made there. For instance, graduates from the Stanford Graduate School of Business can expect to earn around 250,000 U.S. dollars annually.

In an impressive increase from years past, 39 percent of women in the United States had completed four years or more of college in 2022. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 36.2 percent of the U.S. male population having completed four years or more of college in 2022, up from 5.5 percent in 1940.

4- and 2-year colleges

In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree.

Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job.

Earnings after college

Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance.

All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.