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Categorization and Sorting Categorization and Sorting : DRUGS: DRUGS
• A Study of folk-categorization of A Study of folk-categorization of recreational drugsrecreational drugs
• Initiated as Class Exercise in Initiated as Class Exercise in Graduate course of Methods of Graduate course of Methods of Systematic Data CollectionSystematic Data Collection
• University of Essex, 2001University of Essex, 2001• … … with subsequent replicationswith subsequent replications
Stage 1: Definition & Stage 1: Definition & ElicitationElicitation
• FIRST: Method of FIRST: Method of Free-listing used to elicit used to elicit drug names drug names
• ““Drugs” is deliberately unspecific, and is NOT Drugs” is deliberately unspecific, and is NOT intended to be restricted to either “Ethical” intended to be restricted to either “Ethical” (Prescribed), or to “Recreational” drugs. Part of the (Prescribed), or to “Recreational” drugs. Part of the exercise is to determine what exercise is to determine what the subject the subject defines as defines as counting as “Drugs”counting as “Drugs”
– Free-listing Free-listing is really “retrieval from memory”, is really “retrieval from memory”, and this is already clustered in recall (Bousfield and this is already clustered in recall (Bousfield 1958), so Interviewers1958), so Interviewers are alerted to significance are alerted to significance of time-gaps as category markersof time-gaps as category markers
RANK & FREQUENCY OF MENTION OF DRUGS (FREE-LISTING)
(31)11 Cannabis 11 Cocaine9 Heroin 8 Ecstasy8 LSD8 Poppers (Nitrites)7 Glue6 Alcohol6 Dope5 Aspirin5 Cough mixture (inc expectorant and dry)4 amphetamine 4 Morphine 4 Tobacco3 Caffeine 3 Marijuana3 Paracetamol3 Prozac 3 Steroids 2 Barbiturates2 Chocolate2 Ibuprofen2 Immodium2 Insulin2 Magic-mushrooms 2 Methadone2 Penicillin2 speed 2 Temazepam2 Valium2 Viagra
Only 1 MentionAmpicillinCimetedineCo_codamol*crackDatura DiclofenicDopamineGHB GTN*hemp*Kaolin and MorphineLithiumMaxalon*Milk of magnesia*NicotineNifedapineNutmegOmnoponpoppy seed teaRanitadineStemetilThorazineTylex*Vitamins
* Possibles
Drug-names (“objects”)Drug-names (“objects”)
• 28 drug-names retained (with ‘street’ 28 drug-names retained (with ‘street’ synonyms)synonyms)
• 1. ALCOHOL1. ALCOHOL 2. AMPHETAMINE2. AMPHETAMINE 3. ASPIRIN 3. ASPIRIN• 4. BARBITURATES4. BARBITURATES 5. CAFFEINE5. CAFFEINE 6. CANNABIS 6. CANNABIS • 7. CHOCOLATE7. CHOCOLATE 8. COCAINE8. COCAINE 9. COUGH MXT 9. COUGH MXT• 10. CRACK10. CRACK 11. ECSTASY 11. ECSTASY 12. GHB12. GHB• 13. GLUE13. GLUE 14. HEROIN 15. IMMODIUM14. HEROIN 15. IMMODIUM• 16. INSULIN16. INSULIN 17. KETAMINE17. KETAMINE 18. LSD 18. LSD• 19. MAGIC-MUSHR.20. METHADONE19. MAGIC-MUSHR.20. METHADONE 21.PCP21.PCP• 22. PENICILLIN22. PENICILLIN 23. POPPERS23. POPPERS 24. PROZAC 24. PROZAC • 25. STEROIDS 25. STEROIDS 26. TEMAZEPAM26. TEMAZEPAM 27. TOBACCO27. TOBACCO• 28. VIAGRA28. VIAGRA
Method: Free-sorting*Method: Free-sorting*Coxon, A.P.M. (1999) Coxon, A.P.M. (1999) Sorting Data: Collection and Analysis, Newbury Pk, Ca: Sage Publications (Quantitative Applications in the Social Sciences, 07-127)
• Randomised set of cards with drug-name & synonymns Randomised set of cards with drug-name & synonymns handed to S; handed to S; – (ID # on back)(ID # on back)
• asked to sort them in to asked to sort them in to as many or as fewas many or as few groups/piles groups/piles as they wish in terms of similarity or “what goes with as they wish in terms of similarity or “what goes with what”what”
• encouraged to verbalise during task, and “break, re-encouraged to verbalise during task, and “break, re-make or re-arrange” at end until satisfiedmake or re-arrange” at end until satisfied
• give short name & description of each pile/groupgive short name & description of each pile/group• choice of 1,2 exemplars/prototypes of all non-singleton choice of 1,2 exemplars/prototypes of all non-singleton
groupsgroups• any “leftovers” each allocated to own group.any “leftovers” each allocated to own group.
– NB (for qual/quant integrationists)NB (for qual/quant integrationists)• Q&Q elicited and stored together for contextual analysisQ&Q elicited and stored together for contextual analysis
Fred’s sorting of the 28 drugs PDF =Fred’s sorting of the 28 drugs PDF =
2 4 6 5 2 2 2 4 6 3 1 2 3 5 6 6 1 1 1 7 3 3 8 6 3 5 3 62 4 6 5 2 2 2 4 6 3 1 2 3 5 6 6 1 1 1 7 3 3 8 6 3 5 3 6
SO: Fred’s Groups/piles are:SO: Fred’s Groups/piles are:• 1 = ecs,ket,LSD,Mag1 = ecs,ket,LSD,Mag• 2 = alc,caf,can,cho,GHB2 = alc,caf,can,cho,GHB• 3 = cra,glu,PCP,pen,ste,tob3 = cra,glu,PCP,pen,ste,tob• 4 = amp, coc4 = amp, coc• 5 = bar,her,tem5 = bar,her,tem• 6 = asp,cou,imm,ins,pro,via6 = asp,cou,imm,ins,pro,via
• 7 = methadone7 = methadone• 8 = poppers8 = poppers
FRED's SORTING CONVERTED INTO SIMPLE CO-FRED's SORTING CONVERTED INTO SIMPLE CO-OCCURRENCE MATRIX (VIA SORTPAC)OCCURRENCE MATRIX (VIA SORTPAC)
• 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 • 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 • 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 • 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 • 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 • 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 • 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 • 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 • 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 • 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1
Aggregation (sum) of 68 Subjects’ (0,1) data matricesAggregation (sum) of 68 Subjects’ (0,1) data matrices -> ->M1: x(j,k) = no of Ss who put objects j & k in same group.M1: x(j,k) = no of Ss who put objects j & k in same group.Hence Similarity measureHence Similarity measure• 68 04 06 04 49 18 47 06 09 07 0568 04 06 04 49 18 47 06 09 07 05 0000 13 06 06 08 02 04 09 04 02 06 03 05 03 04 55 05 13 06 06 08 02 04 09 04 02 06 03 05 03 04 55 05• 04 68 04 22 02 24 02 37 04 32 45 18 14 27 04 07 30 31 25 17 24 04 28 07 22 12 04 0804 68 04 22 02 24 02 37 04 32 45 18 14 27 04 07 30 31 25 17 24 04 28 07 22 12 04 08• 06 04 68 20 09 03 08 00 50 00 00 02 03 02 36 49 11 00 03 21 0606 04 68 20 09 03 08 00 50 00 00 02 03 02 36 49 11 00 03 21 06 57 57 03 29 21 13 08 37 03 29 21 13 08 37• 04 22 20 68 03 11 02 21 19 21 18 16 13 21 14 18 24 20 15 28 30 19 21 19 21 27 03 1804 22 20 68 03 11 02 21 19 21 18 16 13 21 14 18 24 20 15 28 30 19 21 19 21 27 03 18• 49 02 09 03 68 08 58 05 09 04 06 03 07 02 06 06 01 02 05 03 02 07 03 07 06 02 52 0749 02 09 03 68 08 58 05 09 04 06 03 07 02 06 06 01 02 05 03 02 07 03 07 06 02 52 07• 18 24 03 11 08 68 08 27 06 19 25 06 23 23 03 04 09 29 39 09 12 03 15 05 11 05 13 0418 24 03 11 08 68 08 27 06 19 25 06 23 23 03 04 09 29 39 09 12 03 15 05 11 05 13 04• 47 02 08 02 58 08 68 03 09 03 03 01 06 01 06 06 01 02 04 02 01 07 01 06 04 02 50 0647 02 08 02 58 08 68 03 09 03 03 01 06 01 06 06 01 02 04 02 01 07 01 06 04 02 50 06• 06 37 00 21 05 27 03 68 00 46 38 20 23 51 00 03 23 38 19 20 23 00 26 02 11 10 05 0106 37 00 21 05 27 03 68 00 46 38 20 23 51 00 03 23 38 19 20 23 00 26 02 11 10 05 01• 09 04 50 19 09 06 09 00 68 01 01 03 06 03 35 40 10 01 03 20 07 47 05 26 18 13 08 2809 04 50 19 09 06 09 00 68 01 01 03 06 03 35 40 10 01 03 20 07 47 05 26 18 13 08 28• 07 32 00 21 04 19 03 46 01 68 38 20 24 47 00 02 24 44 21 18 25 00 25 03 10 11 04 0107 32 00 21 04 19 03 46 01 68 38 20 24 47 00 02 24 44 21 18 25 00 25 03 10 11 04 01• 05 45 00 18 06 25 03 38 01 38 68 17 20 30 00 02 22 43 31 14 23 00 30 03 13 10 03 0405 45 00 18 06 25 03 38 01 38 68 17 20 30 00 02 22 43 31 14 23 00 30 03 13 10 03 04• 00 18 02 16 03 06 01 20 03 20 17 68 11 18 09 04 26 15 12 12 29 01 28 05 09 16 02 0600 18 02 16 03 06 01 20 03 20 17 68 11 18 09 04 26 15 12 12 29 01 28 05 09 16 02 06• 13 14 03 13 07 23 06 23 06 24 20 11 68 23 04 04 10 26 18 11 15 03 14 07 10 11 07 0713 14 03 13 07 23 06 23 06 24 20 11 68 23 04 04 10 26 18 11 15 03 14 07 10 11 07 07• 06 27 02 21 02 23 01 51 03 47 30 18 23 68 02 01 20 42 23 21 22 02 21 05 14 14 03 0306 27 02 21 02 23 01 51 03 47 30 18 23 68 02 01 20 42 23 21 22 02 21 05 14 14 03 03• 06 04 36 14 06 03 06 00 35 00 00 09 04 02 68 31 12 00 04 16 11 36 08 24 11 18 06 2106 04 36 14 06 03 06 00 35 00 00 09 04 02 68 31 12 00 04 16 11 36 08 24 11 18 06 21• 08 07 49 18 06 04 06 03 40 02 02 04 04 01 31 68 11 00 00 25 04 56 05 29 19 15 08 3608 07 49 18 06 04 06 03 40 02 02 04 04 01 31 68 11 00 00 25 04 56 05 29 19 15 08 36• 02 30 11 24 01 09 01 23 10 24 22 26 10 20 12 11 68 18 18 18 35 09 30 06 17 20 02 1102 30 11 24 01 09 01 23 10 24 22 26 10 20 12 11 68 18 18 18 35 09 30 06 17 20 02 11• 04 31 00 20 02 29 02 38 01 44 43 15 26 42 00 00 18 68 32 15 23 00 22 02 06 11 02 0304 31 00 20 02 29 02 38 01 44 43 15 26 42 00 00 18 68 32 15 23 00 22 02 06 11 02 03• 09 25 03 15 05 39 04 19 03 21 31 12 18 23 04 00 18 32 68 06 20 03 21 06 14 10 07 0609 25 03 15 05 39 04 19 03 21 31 12 18 23 04 00 18 32 68 06 20 03 21 06 14 10 07 06• 04 17 21 28 03 09 02 20 20 18 14 12 11 21 16 25 18 15 06 68 15 22 13 25 21 19 03 2104 17 21 28 03 09 02 20 20 18 14 12 11 21 16 25 18 15 06 68 15 22 13 25 21 19 03 21• 02 24 06 30 02 12 01 23 07 25 23 29 15 22 11 04 35 23 20 15 68 05 34 09 11 28 02 0602 24 06 30 02 12 01 23 07 25 23 29 15 22 11 04 35 23 20 15 68 05 34 09 11 28 02 06• 06 04 57 19 07 03 07 00 47 00 00 01 03 02 36 56 09 00 03 22 05 68 03 35 24 18 06 4006 04 57 19 07 03 07 00 47 00 00 01 03 02 36 56 09 00 03 22 05 68 03 35 24 18 06 40• 03 28 03 21 03 15 01 26 05 25 30 28 14 21 08 05 30 22 21 13 34 03 68 07 13 18 03 0603 28 03 21 03 15 01 26 05 25 30 28 14 21 08 05 30 22 21 13 34 03 68 07 13 18 03 06• 05 07 29 19 07 05 06 02 26 03 03 05 07 05 24 29 06 02 06 25 09 35 07 68 25 22 04 3405 07 29 19 07 05 06 02 26 03 03 05 07 05 24 29 06 02 06 25 09 35 07 68 25 22 04 34• 03 22 21 21 06 11 04 11 18 10 13 09 10 14 11 19 17 06 14 21 11 24 13 25 68 16 07 3503 22 21 21 06 11 04 11 18 10 13 09 10 14 11 19 17 06 14 21 11 24 13 25 68 16 07 35• 04 12 13 27 02 05 02 10 13 11 10 16 11 14 18 15 20 11 10 19 28 18 18 22 16 68 03 1504 12 13 27 02 05 02 10 13 11 10 16 11 14 18 15 20 11 10 19 28 18 18 22 16 68 03 15• 55 04 08 03 52 13 50 05 08 04 03 02 07 03 06 08 02 02 07 03 02 06 03 04 07 03 68 0655 04 08 03 52 13 50 05 08 04 03 02 07 03 06 08 02 02 07 03 02 06 03 04 07 03 68 06• 05 08 37 18 07 04 06 01 28 01 04 06 07 03 21 36 11 03 06 21 06 40 06 34 35 15 06 6805 08 37 18 07 04 06 01 28 01 04 06 07 03 21 36 11 03 06 21 06 40 06 34 35 15 06 68
• 00 00 (ALC & GBH) lowest – no-one put into same pile: most (ALC & GBH) lowest – no-one put into same pile: most different/distantdifferent/distant
• 57 57 (ASP & PEN) highest: 87% put into same pile: most similar / (ASP & PEN) highest: 87% put into same pile: most similar / proximateproximate
DETAILS OF SCALINGDETAILS OF SCALING
• DATA: DATA: 2W1M FSM, similarities2W1M FSM, similarities
• TRANSFORM: TRANSFORM: Weak Monotonic / Ord. Weak Monotonic / Ord.
• MODEL: MODEL: Euclidean DistanceEuclidean Distance
• Program: Program: NewMDSX MINISSANewMDSX MINISSA– 2D, weak Monotonicity, Primary Approach Ties2D, weak Monotonicity, Primary Approach Ties
____________________________________________________________________________________________________________
• SOLUTION:SOLUTION:– Stress1 = 0.097 (vs Spence random 0.290); Stress1 = 0.097 (vs Spence random 0.290); very very
acceptable!acceptable!
SHEPARDSHEPARD diagram - diagram -any ideas?any ideas?
2D- M1 MINISSA 2D- M1 MINISSA … NOW LET’S DO IT!)… NOW LET’S DO IT!)
HICLUS of 28 DRUGS: HICLUS of 28 DRUGS: how how many clusters?many clusters?
SSA + 3 Main CategoriesSSA + 3 Main Categories
0 10 20 30 40 50 60
Proximities
0.00
0.25
0.50
0.75
1.00
1.25
1.50
Tra
nsf
orm
ed P
roxi
mit
ies
Case Number
SRC_1
Transformation: matrix conditional, ordinal (ties kept tied).
SRC_1
Transformation Plot
-0.75 -0.50 -0.25 0.00 0.25 0.50 0.75
Dimension 1
-0.5
0.0
0.5
Dim
en
sio
n 2
ALCOHOL
AMPHETAMINEASPIRIN
BARBITURATES
CAFFEINE
CANNABIS
COCAINECOUGHMIXTURE
ECSTASY
GHB
GLUE
HEROIN IMMODIUM
INSULIN
KETAMINE
LSD
MAGICMUSHROOMS
METHADONEPCP
PENICILLINPOPPERS
PROZAC
TEMAZEPAM
VIAGRA
Common Space
Object Points
0 10 20 30 40 50 60
Proximities
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Tra
nsf
orm
ed P
roxi
mit
ies
Case Number
SRC_1
Transformation: matrix conditional, spline (degree 1, interior knots 0).
SRC_1
Transformation Plot
-0.5 0.0 0.5
Dimension 1
-0.9
-0.6
-0.3
0.0
0.3
0.6
Dim
en
sio
n 2
ALCOHOL
AMPHETAMINEASPIRIN
BARBITURATES
CANNABIS
CHOCOLATE
COCAINE
COUGHMIXTURE
CRACK
GHB
GLUE
IMMODIUMKETAMINE
LSD
MAGICMUSHROOMS
METHADONEPCPPENICILLIN
POPPERSSTEROIDS
TEMAZEPAM
Common Space
Object Points
0 10 20 30 40 50 60
Proximities
0.0
0.5
1.0
1.5
Tra
ns
form
ed
Pro
xim
itie
s
Case Number
SRC_1
Transformation: matrix conditional, spline (degree 2, interior knots 1).
SRC_1
Transformation Plot
& finally …& finally …
Let’s use interactive MDS (PERMAP) …Let’s use interactive MDS (PERMAP) …– to clear up the structureto clear up the structure– using information aboutusing information about
• outliersoutliers• liaison points / linksliaison points / links
to strip down & INTERPRET the 2D to strip down & INTERPRET the 2D solutionsolution
– let’s do it let’s do it ……
