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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 • 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 slang 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” allocated to own group. • NB (for qual/quant integrationists) • Q&Q elicited and stored together for contextual analysis

  6. “Quantitative” analysis(effected using SORTPAC-3 program, Coxon 1998) • Each subject’s sorting is coded in “preferred data format” • (fits most appropriate programs) • her groups are sequentially numbered (inc. singletons) • for each S, row-vector with p elements • x(j)=k object j allocated to category k • This forms the N x p Basic Data Matrix • each S’s row is subsequently converted into p x p (0,1) co-occurrence matrix

  7. Fred’s sorting of drugsPDF =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 6 • 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

  8. 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

  9. Aggregation (sum) of 68 Subjects’ (0,1) data matrices • 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 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 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

  10. 68 04 06 04 49 18 47 06 09 07 05 00 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 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 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 M1 is similarity measure hi=close 57 Asp&Pen 55 Tob&Alc same category lo=distant 0= maximum separation Alc&GHB Asp & 4 hard Well-constrained data (DCR 6.75) for 2D solution DATA: M1 (simple co-occ.)of 68 Subjects’ data

  11. TRANSFORMATION & MODEL • TRANSFORM: • Monotonic (Str->Wk); • Primary Ties vs Secondary • MODEL: Euclidean Distance • SOLUTION: • Program: MINISSA(N))

  12. 3DM1- SHEP : any ideas?

  13. 3D- M1 MINISSA

  14. 2D- M1 MINISSA … NOW LET’S DO IT!)

  15. HICLUS of 28 DRUGS: how many clusters?

  16. SSA + 3 Main Categories

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

  18. Excised 1 (=original #5)Method: Variants of Free-Sorting • Objects: • names, • picture/photo/line-drawing • non-literate & often produces different structure • objects themselves (not in this case though  ) n.b. Sorting lends itself to large number of objects, & is found enjoyable by Ss • Categories: • Fixed # categories • Ordered (& distribution) Q-sort • Non-partition (allocation to > 1 category) • Augmented Sorting • up-merge & down-divide (Bimler) • full hierarchy (Coxon)

  19. Excised 2 (= orig. #10) Burton & Aggregation • Different forms of co-occurrence measures: (Burton Measures in SORTPAC): • M1 simple frequency • M2 each entry weighted by category size from which drawn • big categories get bigger weight • M3 each entry weighted by reciprocal of category size from which drawn • small categories get bigger weight • M4 an Information theoretic measure which takes into account • size of category • probability of NON-occurrence categories • For Q-analysis, Arabie & Boorman have developed range of partition-similarity [minimum-move] measures • in DISSIM in SORTPAC

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