# MBA Programme Comparison: [Your name, shortlist of programmes] > A structured comparison of MBA programme formats and schools: the five common formats (two-year full-time, > one-year full-time, part-time, executive and online), how to read accreditation (AACSB, EQUIS, AMBA), curriculum > and teaching method, AI in the curriculum, how to read a school's employment report, total cost, location and visa > questions, and a weighted scoring matrix with a sensitivity check. It is for anyone choosing where and how to do > an MBA, before building a school list with mba/mba-school-list-strategy. > Source basis: the public accreditation descriptions of AACSB (Global Standards for Business Education, 2026), EFMD > (EQUIS) and AMBA, the CSEA Standards for Reporting Employment Statistics, and multi-criteria decision practice, > checked in October 2026 and written in our own words. Replace every [bracketed] value and every row marked *Example*. > This is a planning aid, not admissions, legal or financial advice. No school endorses or is affiliated with this > template; School A, B and C in the examples are invented. ## Document control | Field | Value | |---|---| | Document ID | [CMP-2026-01] | | Applicant | [Your name] | | Decision | [Which format and which schools to apply to / which admit to accept] | | Programmes compared | [School A two-year MBA; School B one-year MBA; School C part-time MBA] | | Version | [0.1] | | Status | [Draft / Weights fixed / Scored / Decided] | | Last updated | YYYY-MM-DD | | Reviewed by | [Mentor or coach, for feedback only] | | Classification | Personal, private | Contents: 1. Purpose and when to use it; 2. Programme formats; 3. Accreditation; 4. Curriculum and teaching method; 5. AI in the curriculum; 6. Reading an employment report; 7. Cost, location and visa; 8. Weighted scoring matrix; 9. Sensitivity check; 10. Process; 11. Quality checklist; 12. Common mistakes; 13. Workbook; Sources and further reading; Revision history; Standard and licence note. ## 1. Purpose and when to use it Choosing an MBA programme combines a format decision (how many years, full-time or while working) with a school decision (which community, curriculum and recruiting market). Rankings compress dozens of factors into one number with weights chosen by a publisher, and those weights rarely match yours. A personal comparison makes the weights explicit, ties each score to evidence, and shows how sensitive the result is to your assumptions. Use this template twice. **Before applying** (about 9 to 12 months before the first deadline), it turns a long list of programmes into the shortlist that feeds mba/mba-school-list-strategy. **After admits arrive**, re-score the schools that admitted you with the new facts (scholarship offers, conversations at admit weekends) and carry the result into mba/mba-admit-decision. The second pass is usually the more important one, because the cost column changes with every scholarship and the evidence is better. The template does not tell you which programme is best. It gives you a structure in which the answer follows from your goals, your constraints and the evidence you collected. ## 2. Programme formats The same degree title covers very different products. The table describes typical patterns; each school defines its own format, so check the programme page. | Format | Typical length and timing | Who it suits | Strengths | Trade-offs | |---|---|---|---|---| | Two-year full-time | About 21 months with a summer internship between years | Career switchers (industry, function or country), early-career applicants | Internship to test a new career, full recruiting cycle, deepest immersion and network, room for electives and exchange | Two years of forgone earnings, highest total cost, longest time out of the workforce | | One-year full-time | 10 to 16 months, often with no or a short internship | Applicants with a clear goal, career enhancers, older candidates | Half the time out of work, lower opportunity cost, intense cohort | Little or no internship, compressed recruiting that often starts before you arrive, less elective choice | | Part-time (evening or weekend) | 2.5 to 4 years while working | Applicants staying with their employer or in their city | Salary continues, employer sponsorship possible, immediate application at work | Long timeline, limited on-campus recruiting access in some schools, fatigue | | Executive (EMBA) | About 18 to 24 months, modular (weekends or week-long blocks) | Senior professionals, often 10 or more years of experience | Peer group of senior managers, employer sponsorship common, stays in role | High fees, little career-services focus on switching, heavy travel for modular formats | | Online or hybrid | 2 to 4 years, flexible | Applicants who cannot relocate or attend at fixed times | Location independence, often lower fees | Weaker informal network unless residencies are built in, recruiting access varies widely | Questions that usually decide the format: 1. Do I need an internship to switch careers? If yes, the two-year full-time format is the strongest fit. 2. Can I afford to stop working, after scholarships and loans? See mba/mba-decision-roi and mba/mba-funding-scholarship-plan. 3. Will my employer sponsor me, and on what conditions (bond, return period)? See mba/emba-sponsorship-proposal. 4. Do I need to move country, and will the visa rules let me work after graduating? See mba/mba-international-applicant-plan. 5. How many years of experience do I have? Executive programmes and some one-year programmes expect more. ## 3. Accreditation Three international bodies matter for MBA programmes. They differ in what they accredit. As of October 2026 (check each body's official page before you rely on it): | Body | Level | What it reviews | What to check | |---|---|---|---| | **AACSB** (Association to Advance Collegiate Schools of Business) | Institution (the business school or unit) | Under the Global Standards for Business Education, ratified by the membership at the April 2026 annual meetings together with the 2026 Accounting Standards, the standards are grouped into three sections: Strategic Management, Learner Success and Pathways to Impact. This 2026 edition follows the 2020 business standards; check AACSB for when a school's review moves to the new edition. | Accredited status in the AACSB directory, the date of the next review, whether the programme you want sits inside the accredited unit | | **EQUIS** (EFMD Quality Improvement System) | Institution | The whole school across areas such as governance and strategy, programmes, students, faculty, research, executive education, internationalisation, ethics, responsibility and sustainability, and connections with practice | Accreditation term (the longest term is five years), the EQUIS list on the EFMD site | | **AMBA** (Association of MBAs) | Programme | Specific MBA, DBA and master's programmes: curriculum, teaching and learning, career development, interaction with students, alumni and employers | Whether the exact programme (for example the executive MBA, not only the full-time MBA) is on the AMBA list | Schools that hold all three are often called "triple accredited". That label signals that independent reviewers checked governance, faculty, learning assurance and programme quality. It does not say anything about recruiting outcomes for your target industry, class culture or cost, and several excellent US programmes choose not to seek EQUIS or AMBA because AACSB is the norm in their market. When accreditation matters most: - **Unfamiliar or newer programmes**, particularly online ones: accreditation is a minimum check on quality. - **Employer sponsorship and public-sector jobs**: some employers and governments require an accredited degree. - **Further study** (a doctorate) or **academic careers**: some institutions check accreditation. - **Recognition in your home country**: degree recognition rules are national and change. Check the relevant authority; this template does not cover country-specific rules. ## 4. Curriculum and teaching method Most leading programmes share a first-year core (financial and managerial accounting, finance, microeconomics, data analysis and decision making, marketing, operations, strategy, leadership and organisational behaviour, negotiation and ethics; see mba/mba-core-curriculum-map). They differ in how they teach and how much choice you get. | Dimension | Options | What to ask | |---|---|---| | Teaching method | Case method (most class time spent discussing cases), mixed (cases plus lectures, problem sets and simulations), lecture-led with projects | What share of core sessions are case discussions? How is participation graded? | | Core flexibility | Fixed core in sections, core with waivers or menus, largely flexible | Can I waive courses I already know? Can I take electives in the first year? | | Section and cohort | Fixed section of 60 to 90 students for the core, rotating teams, no sections | How big is my section and learning team? How are teams formed? | | Experiential learning | Field projects with companies, global immersions, labs, venture studios | Is it required? Who are the client organisations? See mba/mba-field-project-sow | | Specialisation | Majors, concentrations, certificates, joint degrees | Is my target area a major? How many electives does it offer each year? | | International exposure | Exchanges, global trips, multiple campuses | Which partner schools? Does an exchange clash with recruiting? See mba/mba-exchange-semester-plan | **Case method versus mixed.** In a case-method classroom you prepare a 10 to 30 page business situation the night before and argue a recommendation in a discussion led by the professor; a large share of the grade often comes from participation. It trains judgement under ambiguity, speaking in a group and fast reading (see mba/mba-case-reading-guide and mba/mba-case-prep-sheet). Mixed programmes add lectures and problem sets that build technical tools more systematically, which some learners prefer in finance and analytics. Neither is better in general; the question is which you learn from, and whether you will enjoy being cold-called. Sit in on a class of each kind before you decide (mba/mba-school-research-log). ## 5. AI in the curriculum Programmes are adding AI in three ways: a **required AI course** in the core, **AI content inside existing core courses** (forecasting in operations, AI in marketing analytics, governance in ethics), and **AI electives, majors or labs**. Dated examples, as of October 2026: - Yale SOM announced a required AI-focused course in its MBA core from the 2026-27 academic year, with AI content added to existing core courses (Yale SOM news, 2026, checked 2026-10-28). - Other leading programmes offer AI majors, concentrations or many AI-related electives rather than a required core course. The mix changes each year, so read the current curriculum page rather than press coverage. Questions to ask each school, and to score in section 8: 1. Is there a required course, and what does it build: understanding (how models work and fail), use (prompting, building with tools), strategy (where AI creates value) or governance (risk, regulation, ethics)? 2. Do core courses use AI tools in assignments, and what is the academic integrity policy for AI use? 3. Which electives and labs exist, and how many seats do they have? 4. Do recruiters for your target roles expect AI skills, and does the career centre say how the school prepares you? The library's own course on this topic is mba/mba-ai-for-managers-guide. ## 6. Reading an employment report Many schools publish employment reports that follow the **CSEA Standards for Reporting Employment Statistics** (CSEA, formerly the MBA Career Services and Employer Alliance, often written MBA CSEA). The standards define who counts as seeking a job, what counts as an accepted offer, and how salaries are reported, so that reports from different schools can be compared. Revised standards for full-time MBA, part-time MBA and specialty master's programmes apply from the graduating class of 2026 (CSEA, checked 2026-10-28). A report that follows the standards usually says so in a footnote or with a badge. If a report does not, compare its definitions with care. **Terms to find in every report:** | Term | What it means | Why it matters | |---|---|---| | Total graduates | Everyone in the graduating class | The starting denominator | | Not seeking employment | Sponsored students returning to their employer, students starting a company, continuing education, postponing the search | Excluded from the job-offer rate; a large share changes the meaning of the rate | | No recent information | Graduates whose status the school does not know | High numbers make every rate less reliable | | Seeking employment | Total minus not seeking minus no information | The denominator of the offer and acceptance rates | | Accepted offers at graduation and within three months | Share of job seekers who accepted an offer by each point | The headline outcome; the three-month figure is the usual comparison | | Salary reported | Share of accepted offers with salary data, and the median base salary and signing bonus | A median from a small or self-selected share is weak evidence | | Industry and function tables | Where graduates went, with counts | Shows whether your target employers actually hire there | | Source of job | School-facilitated versus graduate-initiated | Tells you how much the career centre helped | **Worked example A** (School A, fictional, all numbers computed in `calc/mba-program-comparison.py`): | Item *Example* | Value | Excel | |---|---|---| | Total graduates | 420 | input | | Not seeking employment | 63 | input | | No recent information | 17 | input | | Seeking employment | 340 | `=B2-B3-B4` | | Known status (knowledge rate) | 96.0% | `=(B2-B4)/B2` | | Accepted an offer by graduation | 266, so 78.2% of seekers | `=266/B5` | | Accepted an offer within three months | 312, so 91.8% of seekers | `=312/B5` | | Same 312, as a share of all graduates | 74.3% | `=312/B2` | | Accepted offers with salary data | 280, so 89.7% of accepted | `=280/312` | Two lessons follow. First, "91.8% employed" and "74.3% of graduates accepted a job within three months" are both true for the same class; the denominator decides the headline. Second, small changes in classification move the rate: if 20 more graduates were counted as postponing the search (not seeking), the three-month rate would rise to 97.5% (`=312/(340-20)`) with no extra jobs. Compare schools on the same definition and read the not-seeking line. Read the industry tables for **your** target: a school with a 91% overall rate may place only a handful of graduates in your industry or region. Count employers that hired three or more graduates in your target function, and check the number of graduates behind each salary figure. ## 7. Cost, location and visa **Total economic cost** is direct cost (tuition, fees, living costs, health insurance, travel, laptop and course materials) plus the after-tax salary you give up while not working. Worked example B is fictional and rounded (USD); replace it with each school's current cost of attendance page. | Item *Example* | School A, two-year full-time | School B, one-year full-time | School C, part-time three years | Excel | |---|---|---|---|---| | Tuition per year | 80,000 | 95,000 | 45,000 | input | | Years | 2 | 1 | 3 | input | | Living cost per month, months | 3,000 for 21 | 3,200 for 11 | 0 (already paid while working) | input | | Fees, insurance, other | 8,000 | 6,000 | 7,000 | input | | **Direct cost** | **231,000** | **136,200** | **142,000** | `=B2*B3+B4*B5+B6` | | Forgone after-tax salary | 150,000 (two years at 75,000) | 75,000 (one year) | 0 | `=75000*years out` | | **Total economic cost** | **381,000** | **211,200** | **142,000** | `=B7+B8` | The cheapest option on paper is not automatically the best value. The two-year programme costs the most but includes an internship, which is the main route for career switchers; the part-time programme keeps your salary but may give limited access to on-campus recruiting. Carry these totals into mba/mba-decision-roi, which adds the expected salary path and a payback analysis, and into mba/mba-funding-scholarship-plan for the financing questions. **Location** shapes recruiting (employers recruit most heavily near campus and in the school's home market), network (where alumni live), cost of living, and partner or family life. Score it for your target market, not in general. **Visa and work rights** after graduation decide whether you can take the job you studied for. Rules on student visas, post-study work and employer sponsorship differ by country and change often (in the United States, for example, the H-1B rules and fees were in litigation in 2026). Do not score from memory: write the dated fact and the official source for each country in mba/mba-international-applicant-plan, and check it again before you decide. ## 8. Weighted scoring matrix Fix the weights (summing to 100) before you score. Write anchors so a 4 means the same thing for every school, for example for career outcomes: 5 = at least 10 hires a year in my target function and region, 3 = some hires, needs off-campus search, 1 = almost none. Each school's total is a weighted average on the 1 to 5 scale: $$S_j = \frac{\sum_{i=1}^{n} w_i \, s_{ij}}{\sum_{i=1}^{n} w_i}$$ | Symbol | Meaning | |---|---| | $S_j$ | Total score of school $j$ (1 to 5) | | $w_i$ | Weight of criterion $i$ (weights sum to 100) | | $s_{ij}$ | Score of school $j$ on criterion $i$ (1 to 5) | | $n$ | Number of criteria | **Worked example C** (fictional applicant Amara Okafor, a supply-chain analyst aiming for operations strategy consulting; scores from her research log): | Criterion *Example* | Weight | School A | School B | School C | Evidence | |---|---|---|---|---|---| | Career outcomes in target role | 25 | 5 | 4 | 3 | Employment reports, alumni calls | | Curriculum and teaching method | 15 | 4 | 4 | 3 | Class visits, curriculum pages | | AI and data in the curriculum | 10 | 3 | 5 | 3 | Curriculum pages, student conversations | | Network and location | 10 | 5 | 3 | 4 | Alumni in target city | | Total cost (higher score = lower cost) | 15 | 2 | 4 | 5 | Section 7 | | Format and time out of work | 5 | 3 | 4 | 5 | Section 2 | | Visa and work rights | 10 | 3 | 4 | 5 | International plan, dated | | Culture and fit | 10 | 4 | 3 | 4 | Info sessions, class visit | | **Weighted total** (`=SUMPRODUCT($B$2:$B$9,C2:C9)/SUM($B$2:$B$9)`) | **100** | **3.80** | **3.90** | **3.80** | | Weighted contributions (weight times score divided by 100), from the calculation script: ```vega-lite { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Weighted score contributions by criterion for three fictional schools", "width": 360, "height": 160, "data": { "values": [ {"school": "School A", "criterion": "Career", "contribution": 1.25}, {"school": "School A", "criterion": "Curriculum", "contribution": 0.6}, {"school": "School A", "criterion": "AI and data", "contribution": 0.3}, {"school": "School A", "criterion": "Network", "contribution": 0.5}, {"school": "School A", "criterion": "Cost", "contribution": 0.3}, {"school": "School A", "criterion": "Format", "contribution": 0.15}, {"school": "School A", "criterion": "Visa", "contribution": 0.3}, {"school": "School A", "criterion": "Culture", "contribution": 0.4}, {"school": "School B", "criterion": "Career", "contribution": 1.0}, {"school": "School B", "criterion": "Curriculum", "contribution": 0.6}, {"school": "School B", "criterion": "AI and data", "contribution": 0.5}, {"school": "School B", "criterion": "Network", "contribution": 0.3}, {"school": "School B", "criterion": "Cost", "contribution": 0.6}, {"school": "School B", "criterion": "Format", "contribution": 0.2}, {"school": "School B", "criterion": "Visa", "contribution": 0.4}, {"school": "School B", "criterion": "Culture", "contribution": 0.3}, {"school": "School C", "criterion": "Career", "contribution": 0.75}, {"school": "School C", "criterion": "Curriculum", "contribution": 0.45}, {"school": "School C", "criterion": "AI and data", "contribution": 0.3}, {"school": "School C", "criterion": "Network", "contribution": 0.4}, {"school": "School C", "criterion": "Cost", "contribution": 0.75}, {"school": "School C", "criterion": "Format", "contribution": 0.25}, {"school": "School C", "criterion": "Visa", "contribution": 0.5}, {"school": "School C", "criterion": "Culture", "contribution": 0.4} ] }, "mark": "bar", "encoding": { "y": {"field": "school", "type": "nominal", "title": null}, "x": {"field": "contribution", "type": "quantitative", "aggregate": "sum", "title": "Weighted score (1 to 5)"}, "color": {"field": "criterion", "type": "nominal", "title": "Criterion"} } } ``` ## 9. Sensitivity check The example totals are 3.80, 3.90 and 3.80: a near tie. Re-run the matrix with two other plausible weight profiles (computed in `calc/mba-program-comparison.py`): | Weight profile *Example* | Career | Cost | Other criteria | School A | School B | School C | |---|---|---|---|---|---|---| | Base | 25 | 15 | as above | 3.80 | 3.90 | 3.80 | | Cost-focused | 20 | 25 | curriculum 10, others unchanged | 3.55 | 3.90 | 4.00 | | Career-heavy | 35 | 5 | others unchanged | 4.10 | 3.90 | 3.60 | School B is never last and moves least, while A and C swap places depending on whether career outcomes or cost dominate. For Amara the decision therefore hinges on one question: how much is the stronger recruiting pipeline at School A worth relative to its higher cost? That is a question for mba/mba-decision-roi and for conversations with alumni in her target role, not for more scoring. Write that conclusion down; it is the real output of the matrix. ## 10. Process ```mermaid flowchart TD A["Clarify goals and constraints"] --> B["Choose format(s): section 2"] B --> C["Long list of accredited programmes: section 3"] C --> D["Fix criteria, anchors and weights"] D --> E["Collect evidence: reports, curriculum, conversations"] E --> F["Score each school with sources"] F --> G{"Ranking stable under sensitivity check?"} G -- "Yes" --> H["Shortlist for mba-school-list-strategy"] G -- "No" --> I["Identify the deciding criterion and collect more evidence"] I --> F H --> J["Re-score after admits and scholarships"] ``` Relative timeline (adjust to your first deadline, T): | When | Step | |---|---| | T-12 to T-10 months | Format decision, long list, weights fixed | | T-10 to T-6 months | Evidence collection: reports, info sessions, class visits (mba/mba-school-research-log) | | T-6 to T-4 months | Scoring and sensitivity check; school list built (mba/mba-school-list-strategy) | | After decisions | Re-score admits with scholarship offers; decide with mba/mba-admit-decision | ## 11. Quality checklist - [ ] Format chosen for a stated reason (internship need, cost, sponsorship, location). - [ ] Every programme on the list is accredited, and the exact programme is covered. - [ ] Weights fixed and dated before any school was scored. - [ ] Each score has a written anchor and a named evidence source. - [ ] Employment data compared on the same definitions, with not-seeking and no-information lines read. - [ ] Costs on one basis: total, one currency, forgone salary included. - [ ] Visa and post-study work facts dated, with official links. - [ ] Sensitivity check run with at least two alternative weight profiles. - [ ] The deciding question, if the result is close, is written down. ## 12. Common mistakes 1. **Starting from a ranking.** Publisher weights are not your weights; use rankings as one input at most. 2. **Mixing formats in one salary comparison.** A one-year programme's graduates often have more experience and different goals; compare like with like. 3. **Choosing weights after scoring.** The favourite always wins when the weights follow the scores. 4. **Reading only the headline employment rate.** The denominator, the not-seeking share and the knowledge rate change its meaning. 5. **Ignoring industry counts.** A strong overall report says little if only a few graduates entered your field. 6. **Counting tuition only.** Living costs and forgone salary are often larger than tuition. 7. **Treating accreditation as quality ranking.** It is a floor that all leading programmes clear. 8. **Assuming visa rules.** They change, sometimes within a cycle; record dated facts from official pages. 9. **Scoring culture from marketing.** Culture scores need conversations and visits, not brochures. 10. **Breaking a tie with false precision.** A 0.1 difference on a 5-point scale is noise; find the deciding criterion. ## 13. Workbook 1. My post-MBA goal in one sentence, and the role, industry and region it implies: [ ] 2. Do I need an internship to get there? Why: [ ] 3. My hard constraints (budget ceiling, location, family, visa, employer): [ ] 4. Format I choose and the two I rule out, with reasons: [ ] 5. My criteria, anchors and weights (dated before scoring): [ ] 6. For each school, the three facts from its employment report that matter most to me: [ ] 7. What I learnt about teaching method and AI in the curriculum from a class visit or student conversation: [ ] 8. Total economic cost per school, after any scholarship: [ ] 9. Result of the sensitivity check, and the deciding question if it is close: [ ] 10. What would change my mind: [ ] | School | Format | Accreditation checked (date) | Report standard | 3-month accepted rate | Hires in my target | Total cost | Weighted score | |---|---|---|---|---|---|---|---| | School A *Example* | Two-year full-time | AACSB, EQUIS (YYYY-MM-DD) | CSEA | 91.8% | [n] | 381,000 | 3.80 | | [School] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | [School] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | ## Sources and further reading - AACSB, "AACSB Members Ratify Global Standards for Business Education" (news, 20 April 2026, checked 2026-10-28): https://www.aacsb.edu/media-center/news/2026/04/global-standards-ratification - AACSB, Global Standards for Business Education (checked 2026-10-28): https://www.aacsb.edu/educators/aacsb-global-standards - EFMD Global, EQUIS institutional accreditation (checked 2026-10-28): https://www.efmdglobal.org/accreditations/business-schools/equis/ - Association of MBAs, accreditation (checked 2026-10-28): https://www.associationofmbas.com/business-schools/accreditation/ - CSEA, Standards for Reporting Employment Statistics: https://www.cseaglobal.org/standards, and the "2025 Standards Revisions" flyer (revised standards in effect from the graduating class of 2026, checked 2026-10-28): https://cdn.ymaws.com/cseaglobal.org/resource/collection/8D13C135-96C8-4E07-AB26-7998008F950C/Standards_Flyer_2025_Revisions.pdf - Yale SOM, "Yale SOM introduces new AI-focused required course and expanded core content" (26 May 2026, checked 2026-10-28): https://som.yale.edu/story/2026/yale-som-introduces-new-ai-focused-required-course-and-expanded-core-content - Keeney, R. L. and Raiffa, H. (1976). *Decisions with Multiple Objectives: Preferences and Value Tradeoffs*. Wiley. (The theory behind weighted multi-attribute scoring.) - Related templates in this library: mba/mba-decision-roi, mba/mba-school-research-log, mba/mba-school-list-strategy, mba/mba-international-applicant-plan, career/offer-comparison. ## Revision history | Version | Date | Author | Change | |---|---|---|---| | 0.1 | YYYY-MM-DD | [Name] | First version from template | | [0.2] | YYYY-MM-DD | [Name] | [Re-scored after admits and scholarship offers] | ## Standard and licence note Based on the public descriptions of AACSB, EQUIS and AMBA accreditation, the CSEA Standards for Reporting Employment Statistics and standard multi-criteria decision practice, checked on the official pages in October 2026. The text, criteria, worked examples and fictional schools are original; no school's material is reproduced, and this template is not affiliated with or endorsed by any school or accrediting body. Standards, curricula, costs and visa rules change: check each official page before you rely on it. Template licence: MIT.