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Every research method, explained.

6,620 methods across 190 fields — what each one is, how it works, when to use it, and the source it comes from.

🎯 Decision Making1/56

TOPSIS

Technique for Order of Preference by Similarity to Ideal Solution

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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Browse the library

190 fields, 6,620 methods

From decision science to spatial analysis — every entry is structured the same way, so you always know where to look.

AHP-BOCRAPLOCOARASAROMANARTASIBALANCED-SPOTISBest Worst Method with SortingBF-COPRASBF-EDASBF-MARCOSBF-TOPSISBF-VIKORBF-WASPASBWM-ZNUMBER-GAMEBy-Production Technology DEACF-EDASCF-MARCOSCF-TOPSISCF-VIKORCOBRACOCOSOCODASCOMETCOMPROMISE-PROGRAMMINGCONSENSUS-REACHINGCOPRASCRADISCRITERIA-REMOVALCRITIC-MCROSS-VALIDATIONCUBIC-EDASCUBIC-TOPSISCUBIC-VIKORCUBIC-WASPASD-TOPSISDEA-SBMDEA-SUPEREFFDHF-COPRASDHF-EDASDHF-TODIMDHF-TOPSISDHF-VIKORDNMAEDASELECTRE-IIELECTRE-IIIELECTRE-IVEpsilon-Based Measure DEAERVDEVAMIXFDOSMFF-ARASFF-COCOSOFF-CODASFF-COPRASFF-EDASFF-GRAFF-MABACFF-MARCOSFF-MOORAFF-SAWFF-TODIMFF-TOPSISFF-VIKORFF-WASPASFF-WPMFMEAFUCAFUZZY-ARASFUZZY-AROMANFUZZY-COCOSOFUZZY-CODASFUZZY-COPRASFUZZY-DNMAFUZZY-EDASFUZZY-FMEA-DEAFUZZY-INFORMATION-AXIOMFUZZY-MABACFUZZY-MARCOSFUZZY-MAUTFUZZY-MOORAFUZZY-MULTIMOORAFUZZY-OCRAFUZZY-PSIFUZZY-RAFSIFUZZY-RAWECFUZZY-ROVFUZZY-SAWFUZZY-SPOTISFUZZY-TODIMFUZZY-TOPSISFUZZY-TOPSIS-CHEN2000FUZZY-VIKORFUZZY-WASPASFUZZY-WISPFUZZY-WPMGOAL-PROGRAMMINGGRAGREY-ARASGREY-CODASGREY-COPRASGREY-EDASGREY-GRAGREY-MABACGREY-MARCOSGREY-MOORAGREY-PROJECTIONGREY-SAWGREY-TODIMGREY-TOPSISGREY-VIKORGREY-WASPASHELLWIGHF-ARASHF-CODASHF-COPRASHF-DFTHF-EDASHF-GRAHF-MABACHF-MARCOSHF-MOORAHF-SAWHF-TODIMHF-TOPSISHF-VIKORHF-WASPASHFL-CODASHFL-MABACIF-ARASIF-COCOSOIF-CODASIF-COPRASIF-DFTIF-EDASIF-GRAIF-MABACIF-MARCOSIF-MAUTIF-MOORAIF-MULTIMOORAIF-SAWIF-TODIMIF-TOPSISIF-VIKORIF-WASPASIF-WPMINTERVAL-GRAIR-CODASIV-ARASIV-CODASIV-COPRASIV-EDASIV-MARCOSIV-MOORAIV-PROJECTIONIV-SAWIV-TODIMIV-TOPSISIV-VIKORIV-WASPASIVIF-ARASIVIF-COPRASIVIF-MABACIVIF-TODIMIVIF-VIKORIVN-MULTIMOORAKEMIRAL2T-CODASL2T-COPRASL2T-EDASL2T-MULTIMOORAL2T-SAWL2T-TODIML2T-TOPSISL2T-VIKORLexicographic Best Worst MethodLexicographic Goal ProgrammingLINMAPLMAWLOCAL-OWALOCAL-WLCLOPMLPF-EDASMABACMACONTMAIRCAMARAMARCOSMAUTMEREC-GMHF-TOPSISMONTE-CARLO-SIMULATIONMOORAMOOSRAMPF-HF-TOPSISMPF-TOPSIS-LINGMULTIMOORAN-ARASN-AROMANN-COCOSON-CODASN-COPRASN-DNMAN-EDASN-GRAN-MABACN-MARCOSN-MOORAN-MULTIMOORAN-PSIN-RAFSIN-RAWECN-SPOTISN-TODIMN-TOPSISN-VIKORN-WASPASN-WISPN-WPMNAIADENon-linear Best Worst MethodNSS-CODASOCRAOrdinal Priority ApproachORESTEOWAP-ARASP-AROMANP-COCOSOP-CODASP-COPRASP-DNMAP-EDASP-GRAP-MABACP-MARCOSP-MAUTP-MOORAP-MULTIMOORAP-OCRAP-PSIP-RAFSIP-RAWECP-ROVP-SAWP-SPOTISP-TODIMP-TOPSISP-VIKORP-WASPASP-WISPP-WPMPAMPF-ARASPF-COCOSOPF-CODASPF-COPRASPF-EDASPF-GRAPF-MABACPF-MARCOSPF-MOORAPF-SAWPF-TODIMPF-TOPSISPF-VIKORPF-WASPASPF-WPMPHF-COPRASPHF-EDASPHF-TOPSISPHF-VIKORPHFS-EHVARPHFS-HVARPIF-ARASPIF-ARTASIPIF-CODASPIF-COPRASPIF-EDASPIF-MARCOSPIF-MOORAPIF-SAWPIF-TODIMPIF-TOPSISPIF-VIKORPIF-WASPASPIVPL-EDASPL-MABACPL-MARCOSPL-MULTIMOORAPL-TODIMPL-TOPSISPL-VIKORPROB-ARASPROBIDPROMETHEE-GAIAPROMETHEE-IPROMETHEE-VPROMETHEE-VIPROSA-CPROXIMITY-WLCPSIQR-ARASQR-COCOSOQR-CODASQR-COPRASQR-EDASQR-GRAQR-MABACQR-MARCOSQR-MOORAQR-SAWQR-TODIMQR-TOPSISQR-VIKORQR-WASPASQR-WPMQUALIFLEXRAFSIRAMRANK-REVERSALRAPSRAWECREGIMERIMROUGH-ARASROUGH-COPRASROUGH-DRSAROUGH-EDASROUGH-MABACROUGH-MARCOSROUGH-MOORAROUGH-SAWROUGH-TODIMROUGH-TOPSISROUGH-VIKORROUGH-WASPASROVSAWSECASENSITIVITY-ANALYSISSF-ARASSF-COCOSOSF-CODASSF-COPRASSF-EDASSF-GRASF-MABACSF-MARCOSSF-MOORASF-SAWSF-TODIMSF-TOPSISSF-VIKORSF-WASPASSF-WPMSIMUSSMAASMAA2SMARTSNHF-TOPSISSOWASPOTISSPROBIDSTOCHASTIC-UTAStratified Best Worst MethodSWARA IITAXONOMYTIFN-CODASTODIMTOPSISTVN-MULTIMOORAUTAUTASTARVIKORVIKOR-SMAAWASPASWEBIRAWEDBAWEIGHT-SENSITIVITYWEIGHTED-VOTINGWINGSWISPWPMWSMZ-COPRASZ-EDASZ-MARCOSZ-PROMETHEEZ-TOPSISZ-VIKORZ-WASPASZF-CRADISELECTREELECTRE-IEXPROM-IEXPROM-IIFF-PROMETHEEFUZZY-ELECTRE-IFUZZY-ELECTRE-IIFUZZY-ELECTRE-IIIFUZZY-PROMETHEEGREY-PROMETHEEHF-ELECTRE-IHF-ELECTRE-IIHF-QUALIFLEXHFL-PROMETHEEIF-PROMETHEEMPF-ELECTRE-IMPF-ELECTRE-IIMPF-ELECTRE-IIIMPF-ELECTRE-IVMPF-PROMETHEEN-PROMETHEEP-PROMETHEEPAMSSEM-IPAMSSEM-IIPF-ELECTRE-IIPF-PROMETHEEPROMETHEEPROMETHEE-IIPROMETHEE-IIIQR-PROMETHEESF-PROMETHEESIRAHPANPB-WENSLOBWMBWM-BAYESIANCIMASDANPDELPHIDEMATELDIBRF-LMAWFUCOMFUCOM-FFUZZY-AHPFUZZY-BWMFUZZY-DELPHIFUZZY-SIWECHF-AHPHFL-AHPLBWAMACBETHPIF-CIMASPIPRECIAREVISED-SIMOSROC-WEIGHTSIWECSMART-WEIGHTSWARASWINGAVERAGE-RANKINGBORDABRAUERS-DOMINANCECHOQUET-INTEGRALCONDORCETCOOK-SEIFORDCOPELANDDODGSONKEMENY-YOUNGMEDIAN-RANKINGMPF-DOMBI-WANANSONPIF-DOMBIRATSCHULZEWAMCCSDCILOSCRITICENTROPYFAREGINI-WEIGHTIDOCRIWIF-ENTROPYLODECILOPCOWMERECMPSINMDPCA-WEIGHTSD-WEIGHTWENSLODEADEA-BCCDEA-CROSSEFFDEA-DYNAMIC-NETWORKDEA-ENVDEA-NETWORKDEA-NETWORK-SBMDEA-RAMHFEAHFGPEHFPEHFPEAAHPSORTDRSAELECTRE-TRIELECTRE-TRI-BELECTRE-TRI-CFLOWSORTIF-CODAS-SORTIVIF-CODAS-SORTTOPSIS-SORTUTADISCFZN-MARCOSDIST-CHEBYSHEVDIST-EUCLIDEANDIST-HAMMINGDIST-MANHATTANDIST-MINKOWSKIMAHALANOBIS-DISTANCESFZN-CRADISSFZN-MARCOSBONFERRONI-MEANData-Driven MCDAHERONIAN-MEANMaclaurin Symmetric Mean OperatorPOWER-MEANWGMWHMDEFUZZ-ALPHA-CUTDEFUZZ-BISECTORDEFUZZ-CENTROIDDEFUZZ-CENTROID-GAUSSIANDEFUZZ-MOMDEFUZZ-SCORE-IFNDEFUZZ-TYPE-REDUCTIONLINEAR-MAX-NORMALIZATIONLINEAR-SUM-NORMALIZATIONLOGARITHMIC-NORMALIZATIONMIN-MAX-NORMALIZATIONNORM-VECTORVECTOR-NORMALIZATIONZ-SCORE-NORMALIZATIONBradley-Terry ModelElo RatingPlackett-Luce ModelRank AggregationTrueSkillSAPEVO-MSFZN-CRITICZ-AHPZ-BWMZF-LMAWTNORM-EINSTEINTNORM-FRANKTNORM-HAMACHERTNORM-SCHWEIZER-SKLARAHSPRHFLPR-PRIORITYCanberra DistanceCosine DistanceHF-MAXSCORE-PORTHF-TRADEOFF-PORTJensen-Shannon DivergenceKullback-Leibler DivergenceBN-ELECTRE-IBN-TOPSISBray-Curtis DissimilarityCFZN-WASPASDynamic Time WarpingGower DistanceHaversine DistanceHellinger DistanceLevenshtein DistancePIF-CIMAS-ARTASISorensen-Dice Coefficient

What every page gives you

Built like a method monograph

Each entry follows the same rigorous structure — so a method you've never seen reads as clearly as one you know by heart.

1
Definition & tagsA one-sentence definition plus family, data type and assumptions at a glance.
2
How it worksStep-by-step formulation, formulas transcribed faithfully from the seminal paper.
3
When to use / avoidConcrete fit cases and the alternatives to reach for when it doesn't fit.
4
Worked exampleA small, real decision matrix run end-to-end with the resulting ranking.
5
SourceFull citation with DOI or ISBN — every claim traceable to its origin.

Why researchers trust it

Reference-grade, not blog-grade

Grounded in sources

Every method traces back to the paper or book that introduced it — cited with DOI or ISBN, never paraphrased from memory.

Faithful formulas

Equations are transcribed directly from seminal works and audited against the original notation, step by step.

Built to grow

An expanding catalogue across 190 fields, structured for multilingual delivery and continuous review.

From learning to doing

Read the method, then run it on your data.

Every page links to a companion tool in the ScholarGate ecosystem — so the moment a method clicks, you can apply it without leaving the idea behind.

DDecisionMind
Run MCDM rankings on your own decision matrix.
SStatWise
Fit & test statistical models in the browser.
ScholarGate — every research method, explained