categorization and sorting drugs
Download
Skip this Video
Download Presentation
Categorization and Sorting : DRUGS

Loading in 2 Seconds...

play fullscreen
1 / 15

Categorization and Sorting : DRUGS - PowerPoint PPT Presentation


  • 73 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Categorization and Sorting : DRUGS' - rusti


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
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
slide3

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

drug names objects
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
slide5

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
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 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
fred s sorting converted into simple co occurrence matrix via sortpac
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
slide8

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
details of scaling
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!
finally
& 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 …
ad