# Admit Decision Workbook: [Your name, intake year] > A structured workbook for choosing between MBA admits: a decision timeline with deposit and reply deadlines, a > weighted decision matrix, a computed net-cost comparison with Excel formulas, a sensitivity > test that shows how much your weights must move before the answer changes, admit-weekend questions, and the > partner, family and visa factors that often decide the matter. It is for admitted applicants with two or more > offers, or one offer and a strong alternative (staying in the job, reapplying, a waitlist). Source basis: > multi-attribute decision analysis (Keeney and Raiffa 1976; Hammond, Keeney and Raiffa 1999), written in our own > words. Replace every [bracketed] value and every row marked *Example*. The schools in the examples are fictional > ("School A, B and C"). This is a planning aid, not admissions, legal or financial advice. ## Document control | Field | Value | |---|---| | Document ID | ADMIT-[YYYY]-001 | | Owner | [Your name] | | Offers compared | [School A, School B, School C, or "stay in current role"] | | Decision deadline (earliest binding date) | YYYY-MM-DD | | Version | [0.1] | | Status | [Draft / Final decision recorded] | | Last updated | YYYY-MM-DD | | Reviewed by | [Partner, mentor or trusted adviser] | | Classification | Personal, confidential | ## Contents 1. Purpose and decision rule 2. Deadlines, deposits and reply dates 3. The decision process 4. Criteria and weights 5. Net-cost comparison 6. Weighted decision matrix 7. Sensitivity test 8. Admit-weekend questions 9. Partner, family and visa factors 10. Gut check and pre-mortem 11. Decision record and how to decline the others 12. Quality checklist 13. Common mistakes ## 1. Purpose and decision rule Choosing between admits is a decision under uncertainty with several objectives that cannot be converted into one currency. Career outcomes, cost, fit, location and family all matter, and an option that is best on one dimension is rarely best on all of them. Multi-attribute decision analysis handles this by making three things explicit: the objectives you care about, how much each one matters to you (weights), and how well each option does on each objective (scores). The weighted total is not the answer; it is a structured starting point that shows where your preferences and the facts point, and which disagreements are worth thinking about. Write your rule here, for example: "I choose the school with the highest weighted score unless the sensitivity test shows the result depends on a weight change of less than 5 points, in which case I decide on sections 8 to 10 and record why." | Item | Your answer | |---|---| | Decision rule | [Your rule] | | Options considered (include "none of these") | [School A, School B, School C, defer, reapply, stay] | | Non-negotiable constraints (any option that fails is out) | [Example: net cash cost above USD 200,000; partner cannot work in the location] | | Who else must agree | [Partner, sponsor employer, family] | ## 2. Deadlines, deposits and reply dates Admit letters usually set a reply date and one or two non-refundable deposits, and the dates differ by school and round. Deposits and dates change every year, so the rows below are placeholders: enter the figures from your own letters. Check whether a school allows you to hold a place while you wait for another decision, and whether it expects you to withdraw elsewhere once you accept. Holding several accepted places after you have decided is unfair to waitlisted applicants and some schools treat it as a breach of their admit terms. | School | Round | Admit date | First deposit and due date | Second deposit and due date | Refundable? | Aid letter received | Reply-by date | |---|---|---|---|---|---|---|---| | School A *(Example)* | [R1] | YYYY-MM-DD | [USD amount], YYYY-MM-DD | [USD amount], YYYY-MM-DD | [No] | [Yes, YYYY-MM-DD] | YYYY-MM-DD | | School B *(Example)* | [R2] | YYYY-MM-DD | [USD amount], YYYY-MM-DD | [None] | [No] | [Pending] | YYYY-MM-DD | | School C *(Example)* | [R1] | YYYY-MM-DD | [EUR or USD amount], YYYY-MM-DD | [Amount], YYYY-MM-DD | [Partly] | [Yes] | YYYY-MM-DD | | [School] | [Round] | YYYY-MM-DD | [Amount], YYYY-MM-DD | [Amount], YYYY-MM-DD | [ ] | [ ] | YYYY-MM-DD | Relative timeline (D = your earliest binding reply date): | When | Action | |---|---| | D-6 weeks | Request every aid letter in writing; book admit-weekend travel; list open questions | | D-4 weeks | Complete sections 4 to 6 with current information | | D-3 weeks | Attend admit weekends; talk to current students and recent graduates in your target function | | D-2 weeks | If you plan a scholarship reconsideration request, send it now (mba/mba-scholarship-negotiation-letter) | | D-1 week | Run the sensitivity test, the pre-mortem and the partner conversation (sections 7, 9, 10) | | D | Accept one offer, pay the deposit, decline the others promptly and courteously | ## 3. The decision process Work through the steps in this order. The loop back from admit weekends to scoring is normal: new evidence should change scores, but it should not change the weights you fixed in section 4 unless you find a missing criterion. ```mermaid flowchart TD A["Offers and aid letters received"] --> B["Screen: non-negotiable constraints"] B -- "Fails a constraint" --> X["Drop the option, record why"] B -- "Passes" --> C["Set criteria and weights before scoring"] C --> D["Compute net cost and loan payment"] D --> E["Score each option, build weighted matrix"] E --> F{"Does a small weight change flip the ranking?"} F -- "No, clear winner" --> G["Pre-mortem and partner check"] F -- "Yes, close call" --> H["Admit weekends, targeted questions, aid reconsideration"] H --> E G --> I["Decide, pay deposit, decline the others"] ``` ## 4. Criteria and weights Good criteria are distinct, measurable or at least judgeable on a stated scale, and tied to your goals rather than to general prestige. Define what a 5 and a 1 mean for each one before scoring. A weight is the importance of the difference between the best and worst option on that criterion, not the importance of the topic in general: if all your admits have equally strong teaching, "curriculum" deserves little weight even if you care about learning, because it does not separate the options. Keeney and Raiffa (1976) set out why weights must reflect the range of each attribute; the practical procedure that asks you to rank and weight these best-to-worst "swings" is known as swing weighting (von Winterfeldt and Edwards 1986). | ID | Criterion | Weight | What a 5 means | What a 1 means | Evidence source | |---|---|---|---|---|---| | CR-1 *(Example)* | Career outcomes in target function | 25% | Strong placement in my target function and region, active club, recruiters on campus | Few graduates enter my target function | Employment report, alumni conversations | | CR-2 *(Example)* | Fit and culture | 15% | I can see myself contributing; teaching style suits me | Repeated mismatch with how I learn and work | Admit weekend, class visits | | CR-3 *(Example)* | Net economic cost | 20% | Lowest economic cost among my options | Highest economic cost | Section 5, computed | | CR-4 *(Example)* | Location, partner and family | 15% | Partner can work, family support nearby | Partner cannot work, long separation | Section 9 | | CR-5 *(Example)* | Network in target region | 10% | Dense alumni presence where I want to work | Little presence | Alumni directory, LinkedIn search | | CR-6 *(Example)* | Curriculum and teaching | 10% | Electives and teaching method fit my goals | Key electives missing | Course catalogue | | CR-7 *(Example)* | Visa and post-study work | 5% | Clear post-study work route for my nationality | Uncertain or no route | Official immigration pages, section 9 | | | **Total** | **100%** | | | | ## 5. Net-cost comparison Three numbers matter. **Net cash cost** is what you must fund: tuition, fees, living costs, health insurance and relocation, minus written scholarship and expected internship pay after tax. **Economic cost** adds the after-tax pay you give up while studying, which is usually the largest single item and the main reason a one-year programme can be much cheaper than a two-year one. **Loan burden** turns the amount you must borrow into a monthly repayment you can compare with your expected post-MBA pay. The long-run return (salary uplift, career options) belongs in mba/mba-decision-roi; this section compares the cost side of the admits you actually hold. ### 5.1 Worked example: three admits *(Example)* Assumptions, all fictional: after-tax pay forgone USD 85,000 a year; savings available USD 60,000; loan interest 8.5% a year, monthly payments over 10 years (120 months). School C is a one-year programme with no summer internship. | Row | Item (USD, whole programme) | School A *(Example)* | School B *(Example)* | School C *(Example)* | Excel (column B shown) | |---|---|---|---|---|---| | 2 | Programme length (years) | 2 | 2 | 1 | input | | 3 | Tuition | 164,000 | 158,000 | 105,000 | input | | 4 | Fees | 8,000 | 7,000 | 2,000 | input | | 5 | Living costs | 64,000 | 52,000 | 38,000 | input | | 6 | Health insurance | 10,000 | 9,000 | 3,000 | input | | 7 | Relocation and travel | 8,000 | 6,000 | 9,000 | input | | 8 | Gross cost | 254,000 | 232,000 | 157,000 | `=SUM(B3:B7)` | | 9 | Scholarship (written) | 60,000 | 0 | 25,000 | input | | 10 | Internship pay after tax | 30,000 | 36,000 | 0 | input | | 11 | **Net cash cost** | **164,000** | **196,000** | **132,000** | `=B8-B9-B10` | | 12 | After-tax pay forgone | 170,000 | 170,000 | 85,000 | `=$F$2*B2` | | 13 | **Economic cost** | **334,000** | **366,000** | **217,000** | `=B11+B12` | | 14 | Amount to borrow | 104,000 | 136,000 | 72,000 | `=MAX(B11-$F$3,0)` | | 15 | Monthly repayment | 1,289.45 | 1,686.21 | 892.70 | `=PMT($F$4/12,$F$5,-B14)` | | 16 | Total repaid over 10 years | 154,734 | 202,345 | 107,124 | `=B15*$F$5` | | 17 | Of which interest | 50,734 | 66,345 | 35,124 | `=B16-B14` | | 18 | Cost score (1 to 5) | 1.86 | 1.00 | 5.00 | `=1+4*(MAX($B$13:$D$13)-B13)/(MAX($B$13:$D$13)-MIN($B$13:$D$13))` | Input cells: F2 = 85000 (forgone pay a year), F3 = 60000 (savings), F4 = 8.5% (annual rate), F5 = 120 (months). `PMT` returns a negative number for a positive loan, so the formula passes `-B14` to show the payment as a positive amount. The cost score in row 18 rescales economic cost so that the cheapest option scores 5 and the most expensive scores 1; it feeds criterion CR-3 in section 6. What the example shows. School A's USD 60,000 scholarship makes it cheaper in cash than School B, even though School A's tuition and living costs are higher. School C is the cheapest by a wide margin, mostly because it costs one year of forgone pay instead of two, but it has no summer internship, which matters a great deal for a career switcher who needs one to test a new function. The cost table cannot see that trade-off; criterion CR-1 must. ### 5.2 Your net-cost table | Row | Item (currency: [USD]) | [School 1] | [School 2] | [School 3] | Source | |---|---|---|---|---|---| | 2 | Programme length (years) | [ ] | [ ] | [ ] | Programme page | | 3 | Tuition | [ ] | [ ] | [ ] | Official cost of attendance | | 4 | Fees | [ ] | [ ] | [ ] | Official cost of attendance | | 5 | Living costs | [ ] | [ ] | [ ] | School budget, adjusted | | 6 | Health insurance | [ ] | [ ] | [ ] | School page | | 7 | Relocation and travel | [ ] | [ ] | [ ] | Your estimate | | 9 | Scholarship (written) | [ ] | [ ] | [ ] | Aid letter dated YYYY-MM-DD | | 10 | Internship pay after tax | [ ] | [ ] | [ ] | Conservative estimate | | 11 | Net cash cost | [ ] | [ ] | [ ] | computed | | 13 | Economic cost | [ ] | [ ] | [ ] | computed | | 15 | Monthly repayment | [ ] | [ ] | [ ] | computed | Currency note: if your admits charge in different currencies, convert at one stated rate on one date, then test the result at a rate 10% higher and 10% lower. A decision that reverses on a 10% currency move is a close call. ### 5.3 Loans: dated facts, not advice As of October 2026, US federal Grad PLUS loans are no longer available to new graduate borrowers from 1 July 2026 (with a limited exception for students already enrolled and borrowing in the same programme), and an MBA student who is not a "professional" student under the new rules can borrow federal Direct Unsubsidized loans of up to USD 20,500 a year and USD 100,000 in total, within a lifetime limit of USD 257,500 (One Big Beautiful Bill Act, 2025; US Department of Education loan-limit guidance, May 2026). These rules change and depend on your status; check [studentaid.gov](https://studentaid.gov) and each school's financial aid office before you rely on them. Private and school-arranged loans, including loans for international students without a co-signer, have their own terms: compare them with personal/loan-comparison and plan repayment in mba/mba-loan-repayment-plan. This template does not tell you which loan to take. ### 5.4 Scenario check in Excel (optional) To test many scenarios without rebuilding the table, compute each school's key results in one cell each from the inputs in rows 2 to 10 and the input cells F2 to F5 (column B shown; copy to C and D): | Result | Excel (column B) | School A *(Example)* | School B *(Example)* | School C *(Example)* | |---|---|---|---|---| | Net cash cost | `=SUM(B3:B7)-B9-B10` | 164,000 | 196,000 | 132,000 | | Economic cost | `=SUM(B3:B7)-B9-B10+$F$2*B2` | 334,000 | 366,000 | 217,000 | | Monthly repayment at 7.5% | `=PMT(7.5%/12,$F$5,-MAX(SUM(B3:B7)-B9-B10-$F$3,0))` | 1,234.50 | 1,614.34 | 854.65 | | Monthly repayment at 8.5% (base) | `=PMT($F$4/12,$F$5,-MAX(SUM(B3:B7)-B9-B10-$F$3,0))` | 1,289.45 | 1,686.21 | 892.70 | | Monthly repayment at 9.5% | `=PMT(9.5%/12,$F$5,-MAX(SUM(B3:B7)-B9-B10-$F$3,0))` | 1,345.73 | 1,759.81 | 931.66 | For a full grid, list rates down a column and use Data, What-If Analysis, Data Table with F4 as the column input cell. The ranking on cost does not change across this range of rates: the interest rate moves the payment by about USD 40 to 75 a month here, far less than the differences between the schools. ## 6. Weighted decision matrix The weighted score of option $j$ is the sum over criteria of weight times score: $$ S_j = \sum_{i=1}^{m} w_i \, s_{ij}, \qquad \sum_{i=1}^{m} w_i = 1 $$ | Symbol | Meaning | |---|---| | $S_j$ | weighted score of option $j$ (between 1 and 5) | | $w_i$ | weight of criterion $i$, as a share of 100% | | $s_{ij}$ | score of option $j$ on criterion $i$, from 1 to 5 | | $m$ | number of criteria | The additive form assumes that a shortfall on one criterion can be offset by strength on another. When that is not true (a partner who cannot work in a location is not offset by a better club), treat the criterion as a constraint in section 1 instead of a weight. ### 6.1 Worked example *(Example)* | ID | Criterion | Weight | School A | School B | School C | Excel | |---|---|---|---|---|---|---| | CR-1 | Career outcomes in target function | 0.25 | 5 | 4 | 4 | input | | CR-2 | Fit and culture | 0.15 | 4 | 5 | 3 | input | | CR-3 | Net economic cost | 0.20 | 1.86 | 1.00 | 5.00 | linked to row 18 of section 5 | | CR-4 | Location, partner and family | 0.15 | 3 | 4 | 2 | input | | CR-5 | Network in target region | 0.10 | 4 | 3 | 5 | input | | CR-6 | Curriculum and teaching | 0.10 | 4 | 4 | 4 | input | | CR-7 | Visa and post-study work | 0.05 | 3 | 3 | 4 | input | | | **Weighted score** | **1.00** | **3.62** | **3.40** | **3.85** | `=SUMPRODUCT($C$22:$C$28,D22:D28)` | On the matrix sheet the weights sit in C22:C28 and the scores of School A, B and C in columns D, E and F, so School B's total is `=SUMPRODUCT($C$22:$C$28,E22:E28)`. Check that the weights sum to 1 with `=SUM($C$22:$C$28)`. Contributions to the total (weight times score) show what drives each result: School C earns 1.00 point from cost alone, against 0.37 for School A, while School A earns 1.25 from career outcomes against 1.00 for School C. School C leads by 0.23 points, mainly because of cost. ```vega-lite { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Weighted score contributions by criterion, example data", "width": 360, "height": 220, "data": { "values": [ {"school": "School A", "criterion": "Career", "points": 1.25}, {"school": "School A", "criterion": "Fit", "points": 0.6}, {"school": "School A", "criterion": "Cost", "points": 0.372}, {"school": "School A", "criterion": "Location and family", "points": 0.45}, {"school": "School A", "criterion": "Network", "points": 0.4}, {"school": "School A", "criterion": "Curriculum", "points": 0.4}, {"school": "School A", "criterion": "Visa", "points": 0.15}, {"school": "School B", "criterion": "Career", "points": 1.0}, {"school": "School B", "criterion": "Fit", "points": 0.75}, {"school": "School B", "criterion": "Cost", "points": 0.2}, {"school": "School B", "criterion": "Location and family", "points": 0.6}, {"school": "School B", "criterion": "Network", "points": 0.3}, {"school": "School B", "criterion": "Curriculum", "points": 0.4}, {"school": "School B", "criterion": "Visa", "points": 0.15}, {"school": "School C", "criterion": "Career", "points": 1.0}, {"school": "School C", "criterion": "Fit", "points": 0.45}, {"school": "School C", "criterion": "Cost", "points": 1.0}, {"school": "School C", "criterion": "Location and family", "points": 0.3}, {"school": "School C", "criterion": "Network", "points": 0.5}, {"school": "School C", "criterion": "Curriculum", "points": 0.4}, {"school": "School C", "criterion": "Visa", "points": 0.2} ] }, "mark": "bar", "encoding": { "y": {"field": "school", "type": "nominal", "title": null}, "x": {"field": "points", "type": "quantitative", "aggregate": "sum", "title": "Weighted score (1 to 5)"}, "color": {"field": "criterion", "type": "nominal", "title": "Criterion"} } } ``` ### 6.2 Your matrix | ID | Criterion | Weight | [School 1] | [School 2] | [School 3] | Evidence for scores of 1 or 5 | |---|---|---|---|---|---|---| | CR-1 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | CR-2 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | CR-3 | Net economic cost | [ ] | [computed] | [computed] | [computed] | Section 5 | | CR-4 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | CR-5 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | CR-6 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | CR-7 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | | | Weighted score | 1.00 | [ ] | [ ] | [ ] | | ## 7. Sensitivity test A weight is a judgement, and you are unlikely to be sure of it within five percentage points. The test below shifts weight from career outcomes (CR-1) to cost (CR-3) and back, keeping every other weight fixed. | Career weight | Cost weight | School A | School B | School C | Leader | |---|---|---|---|---|---| | 0.15 | 0.30 | 3.31 | 3.10 | 3.95 | School C | | 0.20 | 0.25 | 3.46 | 3.25 | 3.90 | School C | | 0.25 | 0.20 | 3.62 | 3.40 | 3.85 | School C (base) | | 0.30 | 0.15 | 3.78 | 3.55 | 3.80 | School C, narrowly | | 0.35 | 0.10 | 3.94 | 3.70 | 3.75 | School A | Schools A and C tie when the career weight is about 0.305 and the cost weight about 0.145, a shift of 5.5 points from the base weights. In Excel, put the shift in a cell (for example H1), set the career weight to `=0.25+$H$1` and the cost weight to `=0.20-$H$1`, and use Goal Seek to set the difference between the two weighted scores to zero by changing H1. In the example the decision is therefore close: a candidate who is sure that a summer internship matters for a career switch (career weight 0.30 or more) should look hard at School A, and a candidate who already works in the target function may reasonably prefer School C. Your test: | Criterion moved from | Criterion moved to | Weight shift at which the ranking flips | Is that shift within your uncertainty? | |---|---|---|---| | [CR-1] | [CR-3] | [ ] | [Yes / No] | | [CR-4] | [CR-1] | [ ] | [Yes / No] | ## 8. Admit-weekend questions Admit weekends are designed to sell, so use them to gather evidence for specific scores rather than for an overall impression. Useful questions, grouped by criterion: | Criterion | Question | Who to ask | Answer and source | |---|---|---|---| | CR-1 Career | Of the students who wanted [target function] last year, how many received offers, and how did they get them? | Second-year students in that function, career office | [ ] | | CR-1 Career | Which employers in [target function and region] recruit on campus, and which hire mainly through networking? | Career office, club leaders | [ ] | | CR-2 Fit | What does a typical week look like in the first term? How are learning teams formed? | First-year students | [ ] | | CR-2 Fit | What do students who were unhappy here say went wrong? | Several students, privately | [ ] | | CR-3 Cost | Is the scholarship renewable in year two, and on what conditions? | Financial aid office, in writing | [ ] | | CR-4 Location and family | What support exists for partners and families (partner club, housing, childcare)? | Partners club, student life office | [ ] | | CR-5 Network | How responsive are alumni in [region] to students from this school? | Alumni you contact directly | [ ] | | CR-6 Curriculum | Are the electives I need offered every year, and can first-years take them? | Course catalogue, programme office | [ ] | | CR-7 Visa | What share of international students in my situation worked in [country] after graduating, and what support does the school offer? | International student office | [ ] | If you cannot attend, ask for a virtual session, a class visit or calls with two or three current students in your target function. ## 9. Partner, family and visa factors These factors are personal and often decisive. Answer the questions with the people affected. | Question | Your answer | |---|---| | Can my partner work in each location? What would their income and career cost be? | [ ] | | How long would we live apart, if at all, and is that acceptable to both of us? | [ ] | | What do children's schooling, childcare and health care cost in each location? | [ ] | | Who in my family depends on me, and how far will I be from them? | [ ] | | Do I need a student visa for each school? What proof of funds does it require, and by when? | [ ] | | Does each country offer a post-study work route for my nationality, and how secure is it? | [ ] | | If I plan to work in a different country after the MBA, how does each school's network reach it? | [ ] | Immigration rules change often and some are contested in court. Treat every visa fact as dated, check it on the government's official immigration page, and plan the detail in mba/mba-international-applicant-plan. This workbook does not give immigration advice; ask the school's international student office or a qualified adviser. ## 10. Gut check and pre-mortem Two short exercises test the result. **Gut check.** Imagine a friend phones to say the decision has been made for you and you are going to the top-scoring school. Note your first reaction. Relief supports the result; disappointment suggests a missing criterion or a weight you set too low. Write down what the disappointment is about, add it to section 4, and re-score. **Pre-mortem.** Imagine it is two years from now and the choice turned out badly. Write the three most likely reasons for each finalist (for example: "did not get a summer internship in my target function", "partner could not find work", "debt payment forced me to take the highest-paid offer"). For each reason, write what you can do now to reduce it. Reasons you can do nothing about deserve weight in the matrix. | Option | Most likely reason it fails | What I can do about it now | |---|---|---| | School A *(Example)* | Partner cannot find work in the city | Partner starts the job search now; check the partner network | | School C *(Example)* | No internship, so the switch to [function] fails | Build experience through a pre-MBA project and in-programme consulting projects | | [Option] | [ ] | [ ] | ## 11. Decision record and how to decline the others | Field | Value | |---|---| | Decision | [School and intake] | | Date | YYYY-MM-DD | | Deposit paid | [Amount], YYYY-MM-DD, receipt [reference] | | Main reasons (three) | [1] [2] [3] | | What I give up | [The strongest feature of the option I declined] | | What would have changed the decision | [ ] | | Offers declined, with date | [School B, YYYY-MM-DD] | | Waitlists withdrawn from, with date | [School, YYYY-MM-DD] | A decline message is short and courteous: thank the admissions committee, state that you have accepted another offer, and release the place. You do not need to say where you are going, though many people do. Withdraw from any waitlist at the same time. ## 12. Quality checklist - [ ] Every deadline and deposit in section 2 is copied from the admit letter or portal, with the refund terms. - [ ] Criteria and weights were written before scoring, and the weights sum to 100%. - [ ] Each criterion separates the options; none duplicates another. - [ ] Cost figures come from official cost-of-attendance pages and written aid letters, with dates. - [ ] Economic cost includes after-tax pay forgone. - [ ] Scholarship renewal conditions for year two are known. - [ ] The sensitivity test is done and the flip point is recorded. - [ ] Every score of 1 or 5 has a line of evidence. - [ ] Partner, family and visa questions are answered with the people affected. - [ ] The pre-mortem is written for each finalist. - [ ] Loan and visa facts are dated and checked on the official page. - [ ] Other offers and waitlists are declined promptly once you accept. ## 13. Common mistakes 1. **Scoring first, weighting later.** Weights set after scoring tend to favour the option you already like. Fix the weights first. 2. **Counting brand twice.** "Prestige", "career outcomes" and "network" often measure the same thing. Merge overlapping criteria. 3. **Comparing sticker tuition.** Tuition alone ignores living costs, scholarship, internship pay and, above all, pay forgone; a one-year programme can cost much less in total. 4. **Counting promised aid.** Only written awards count; confirm whether they are renewable in year two. 5. **Ignoring the internship.** For a career switcher, the summer internship is the main way to test and enter a new function; a cost saving that removes it may not be a saving. 6. **Treating a constraint as a weight.** If an option is unacceptable on one dimension, drop it rather than letting strength elsewhere offset it. 7. **Skipping the sensitivity test.** A 0.2-point lead that disappears with a 5-point weight change is a tie, not a win. 8. **Deciding at the admit weekend.** Events are designed to impress; take a day after each before you re-score. 9. **Deciding alone.** A partner or family who will move with you should be part of section 9, not informed after the deposit is paid. 10. **Holding several places.** Keeping accepted places after you have decided blocks other applicants and may breach admit terms. 11. **Relying on undated visa or loan facts.** These rules change; check the official page and record the date. ## Sources and further reading - Keeney, R. L. and Raiffa, H. (1976). *Decisions with Multiple Objectives: Preferences and Value Tradeoffs*. Wiley. The foundation of the multi-attribute value approach used in sections 4 to 7. - von Winterfeldt, D. and Edwards, W. (1986). *Decision Analysis and Behavioral Research*. Cambridge University Press. Describes swing weighting, the weighting procedure used in section 4. - Hammond, J. S., Keeney, R. L. and Raiffa, H. (1999). *Smart Choices: A Practical Guide to Making Better Decisions*. Harvard Business School Press. An accessible guide to objectives, alternatives and trade-offs. - Kahneman, D. (2011). *Thinking, Fast and Slow*. Farrar, Straus and Giroux. Background on the biases the checklist guards against. - US Department of Education, Federal Student Aid: loan limits after 1 July 2026, [studentaid.gov](https://studentaid.gov) and the FSA Partner Connect loan-limit FAQ (May 2026). Checked 2026-10-28. - Each school's official cost-of-attendance, financial aid and admitted-student pages (check the current year). - Related templates: mba/mba-decision-roi (long-run return), mba/mba-funding-scholarship-plan, mba/mba-international-applicant-plan, mba/mba-budget-during-program, 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] | [Updated after School B admit weekend and aid letter] | ## Standard and licence note Based on the multi-attribute decision analysis of Keeney and Raiffa (1976) and the public US federal student loan rules as published by the US Department of Education (checked October 2026). The text, examples and calculations are original; the schools and figures are fictional. The template is not affiliated with or endorsed by any business school or accreditation body. Check every school's current instructions and every official page before you rely on a date or rule. Template licence: MIT.