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Categorization and Sorting : DRUGS

Categorization and Sorting : DRUGS. A Study of folk-categorization of recreational drugs Initiated as Class Exercise in Graduate course of Methods of Systematic Data Collection University of Essex, 2001 … with subsequent replications. Stage 1: Definition & Elicitation.

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Categorization and Sorting : DRUGS

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  1. Categorization and Sorting : DRUGS • A Study of folk-categorization of recreational drugs • Initiated as Class Exercise in Graduate course of Methods of Systematic Data Collection • University of Essex, 2001 • … with subsequent replications

  2. Stage 1: Definition & Elicitation • FIRST: Method of Free-listingused to elicit drug names • “Drugs” is deliberately unspecific, and is NOT intended to be restricted to either “Ethical” (Prescribed), or to “Recreational” drugs. Part of the exercise is to determine what the subject defines as counting as “Drugs” • Free-listing is really “retrieval from memory”, and this is already clustered in recall (Bousfield 1958), so Interviewers are alerted to significance of time-gaps as category markers

  3. RANK & FREQUENCY OF MENTION OF DRUGS (FREE-LISTING) (31) 11 Cannabis 11 Cocaine 9 Heroin 8 Ecstasy 8 LSD 8 Poppers (Nitrites) 7 Glue 6 Alcohol 6 Dope 5 Aspirin 5 Cough mixture (inc expectorant and dry) 4 amphetamine 4 Morphine 4 Tobacco 3 Caffeine 3 Marijuana 3 Paracetamol 3 Prozac 3 Steroids 2 Barbiturates 2 Chocolate 2 Ibuprofen 2 Immodium 2 Insulin 2 Magic-mushrooms 2 Methadone 2 Penicillin 2 speed 2 Temazepam 2 Valium 2 Viagra Only 1 Mention Ampicillin Cimetedine Co_codamol *crack Datura Diclofenic Dopamine GHB GTN *hemp *Kaolin and Morphine Lithium Maxalon *Milk of magnesia *Nicotine Nifedapine Nutmeg Omnopon poppy seed tea Ranitadine Stemetil Thorazine Tylex *Vitamins * Possibles

  4. Drug-names (“objects”) • 28 drug-names retained (with ‘street’ synonyms) • 1. ALCOHOL 2. AMPHETAMINE 3. ASPIRIN • 4. BARBITURATES 5. CAFFEINE 6. CANNABIS • 7. CHOCOLATE 8. COCAINE 9. COUGH MXT • 10. CRACK 11. ECSTASY 12. GHB • 13. GLUE 14. HEROIN 15. IMMODIUM • 16. INSULIN 17. KETAMINE 18. LSD • 19. MAGIC-MUSHR.20. METHADONE 21.PCP • 22. PENICILLIN 23. POPPERS 24. PROZAC • 25. STEROIDS 26. TEMAZEPAM 27. TOBACCO • 28. VIAGRA

  5. Method: Free-sorting*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 handed to S; • (ID # on back) • asked to sort them in to as many or as few groups/piles as they wish in terms of similarity or “what goes with what” • encouraged to verbalise during task, and “break, re-make or re-arrange” at end until satisfied • give short name & description of each pile/group • choice of 1,2 exemplars/prototypes of all non-singleton groups • any “leftovers” each allocated to own group. • NB (for qual/quant integrationists) • Q&Q elicited and stored together for contextual analysis

  6. Fred’s sorting of the 28 drugs PDF =2 4 6 5 2 22 4 6 3 1 2 3 5 6 6 1 11 7 3 3 8 6 3 5 3 6 SO: Fred’s Groups/piles are: • 1 = ecs,ket,LSD,Mag • 2 = alc,caf,can,cho,GHB • 3 = cra,glu,PCP,pen,ste,tob • 4 = amp, coc • 5 = bar,her,tem • 6 = asp,cou,imm,ins,pro,via • 7 = methadone • 8 = poppers

  7. FRED's SORTING CONVERTED INTO SIMPLE CO-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 • 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 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 • 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 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 • 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 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 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 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 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 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 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

  8. Aggregation (sum) of 68 Subjects’ (0,1) data matrices ->M1: x(j,k) = no of Ss who put objects j & k in same group.Hence Similarity measure • 68 04 06 04 49 18 47 06 09 07 0500 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 08 • 06 04 68 20 09 03 08 00 50 00 00 02 03 02 36 49 11 00 03 21 06 57 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 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 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 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 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 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 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 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 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 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 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 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 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 36 • 02 30 11 24 01 09 01 23 10 24 22 26 10 20 12 11 68 18 1818 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 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 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 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 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 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 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 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 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 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 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 68 • 00 (ALC & GBH) lowest – no-one put into same pile: most different/distant • 57 (ASP & PEN) highest: 87% put into same pile: most similar / proximate

  9. DETAILS OF SCALING • DATA: 2W1M FSM, similarities • TRANSFORM: Weak Monotonic / Ord. • MODEL: Euclidean Distance • Program: NewMDSX MINISSA • 2D, weak Monotonicity, Primary Approach Ties ______________________________________________________ • SOLUTION: • Stress1 = 0.097 (vs Spence random 0.290); very acceptable!

  10. SHEPARD diagram -any ideas?

  11. 2D- M1 MINISSA … NOW LET’S DO IT!)

  12. HICLUS of 28 DRUGS: how many clusters?

  13. SSA + 3 Main Categories

  14. & finally … Let’s use interactive MDS (PERMAP) … • to clear up the structure • using information about • outliers • liaison points / links to strip down & INTERPRET the 2D solution • let’s do it …

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