Pilesorts

Eliciting judged similarities

Pile Sort Technique

• Basic idea:– On each of these cards is written the name of a thing.

Please sort the cards into piles according to how similar they are. You can use as many or as few piles as you like.

Why do we do it?

• Understand structure of the domain, via a fundamental perceptual relation: similarity

• To uncover attributes of items that people use to distinguish among them – Like componential analysis

Special Advantages

• Respondents only asked for non-quantitative judgments

• Can handle large domains (up to 200)• Respondents like it

Processing

• For each pair of items, count the proportion of respondents who put them in the same pile– Called agprox – the aggregate proximity matrix

• Assuming consensus, this is a measure of the similarity of each pair of items

Aggregate Proximity Matrix

• Item by item matrix gives the percent of respondents placing the two items in the same pile

• Typically visualize with MDS and cluster analysis

Representing Proximities

• Multidimensional scaling (MDS)– Maps items to points in Euclidean space such that

points corresponding to more similar items are placed nearer to each other in the space

• Cluster analysis• Network analysis techniques

MDS of animals domain

FROGSALAMANDER

FLAMINGOWOODTHRUSHTURKEYROBIN

BEAVER

RACCOONRABBIT

MOUSE

DOLPHIN

COYOTEDEER

MOOSEELK

BEAR

WHALE

LION

SNAKESTARFISH

HYENALEOPARDGORILLA

FOX

BABOONELEPHANT

KANGAROOANTELOPE

SQUIRRELGROUNDHOG

-Strong clustering indicates subdomains

Stress = 0.12

MDS of land animals only

Fruits & Vegetables

Things people are scared of

BEING_ALONE

BUGS

CHANGE

COMMITMENT

DEATH

DENTISTS

DISEASE

DOCTORS

DOGS

DROWNING

ENCLOSED_SPACEFAILURE

FINANCIAL_TROUBLE

FIRE

FLYING

GHOSTS

GROWING_OLDGUNS

HEIGHTS

LIGHTNING

LOSING_A_LOVED_ONE

PUBLIC_SPEAKING

RAPERATS

SCARY_MOVIES

SHARKS SICKNESSSNAKESSPIDERS

TESTS

THE_DARK

THUNDER

WATER

Things to notice …

• Can use MDS with any proximity matrix– Aggregate similarities, Direct ratings, Confusion

matrices, Correlation matrices, etc. • Typically use 1-3 dimensions (mostly 2)• Measure of fit (stress)• Simplifies complex data• Interpretation centers on

– Looking for dimensions (quantitative item attributes)– Looking for clusters (qualitative item attributes)

Occupations

Undergraduates’ view of animals domain

FROGSALAMANDER

FLAMINGOWOODTHRUSHTURKEYROBIN

BEAVER

RACCOONRABBIT

MOUSE

DOLPHIN

COYOTEDEER

MOOSEELK

BEAR

WHALE

LION

SNAKESTARFISH

HYENALEOPARDGORILLA

FOX

BABOONELEPHANT

KANGAROOANTELOPE

SQUIRRELGROUNDHOG

-Strong clustering indicates subdomains

Stress = 0.12

Biologists’ view of animal domain

FROGSALAMANDER

FLAMINGOWOODTHRUSHTURKEYROBIN

BEAVER

RACCOON

RABBIT

MOUSE

DOLPHIN

COYOTE

DEERMOOSE

ELKBEAR

WHALE

LION

SNAKE

STARFISH

HYENALEOPARD

GORILLA

FOXBABOON

ELEPHANT

KANGAROO

ANTELOPE

SQUIRREL

GROUNDHOG

Things people are scared of

BEING_ALONE

BUGS

CHANGECOMMITMENT

DEATH

DENTISTS

DISEASE

DOCTORSDOGS

DROWNING

ENCLOSED_SPACE

FAILUREFINANCIAL_TROUBLE

FIRE

FLYING

GHOSTS

GROWING_OLD

GUNS

HEIGHTS

LIGHTNING

LOSING_A_LOVED_ONE

PUBLIC_SPEAKING

RAPE

RATS

SCARY_MOVIES

SHARKS

SICKNESSSNAKESSPIDERS

TESTSTHE_DARK

THUNDER

WATER

Female respondents

Things people are scared of

BEING_ALONE

BUGS

CHANGE

COMMITMENT

DEATH

DENTISTS

DISEASE

DOCTORS

DOGS

DROWNING

ENCLOSED_SPACEFAILURE

FINANCIAL_TROUBLE

FIRE

FLYING

GHOSTS

GROWING_OLDGUNS

HEIGHTS

LIGHTNING

LOSING_A_LOVED_ONE

PUBLIC_SPEAKING

RAPERATS

SCARY_MOVIES

SHARKS SICKNESSSNAKESSPIDERS

TESTS

THE_DARK

THUNDER

WATER

Male respondents

Juxtaposed Boys & Girls

gBEING_ALONE

gBUGS

gCHANGEgCOMMITMENT

gDEATH

gDENTISTS

gDISEASE

gDOCTORSgDOGS

gDROWNING

gENCLOSED_SPACE

gFAILUREgFINANCIAL_TROUBLE

gFIRE

gFLYING

gGHOSTS

gGROWING_OLD

gGUNS

gHEIGHTS

gLIGHTNING

gLOSING_LOVED_ONE

gPUBLIC_SPEAKING

gRAPE

gRATS

gSCARY_MOVIES

gSHARKS

gSICKNESSgSNAKES

gSPIDERS

gTESTSgTHE_DARK

gTHUNDER

gWATER

bBEING_ALONE

bBUGS

bCHANGE

bCOMMITMENT

bDEATH

bDENTISTS

bDISEASE

bDOCTORS

bDOGS

bDROWNING

bENCLOSED_SPACEbFAILURE

bFINANCIAL_TROUBLE

bFIRE

bFLYINGbGHOSTS

bGROWING_OLDbGUNS

bHEIGHTS

bLIGHTNING

bLOSING_LOVED_ONE

bPUBLIC_SPEAKING

bRAPEbRATS

bSCARY_MOVIES

bSHARKSbSICKNESS

bSNAKESbSPIDERS

bTESTS

bTHE_DARK

bTHUNDER

bWATER

Few big differences

Discrepancy Analysis

* English• Japanese

Romney, Moore, Batchelder and Hsia. 2002. Statistical methods … PNAS 97(1): 518-523

MDS of similarities in respondents’ sorts

* English• Japanese

Emotion Terms

Notintense

Bad Intense

Good

Crimes

-1.16

-0.87

-0.58

-0.29

0.00

0.29

0.58

0.87

1.17

1.46

1.75

-1.16 -0.58 0.00 0.58 1.17 1.75

VANDALISM

ATTMURDER

PUBENDANGER

AUTOTHEFT

CRIMES-WEAPON

ROBBERY

BATTERY

DUI

PROSTITUTIONTORTURE

WHITECOLLAR

THEFT

NECROPHILIA

DOMESTICAB

SPEEDINGRAPEMURDER

CHILDABUSE

DRUGOFFENSES

ASSAULT

BURGLARY

ARSON

ENVIRONMENTAL

AGAINSTPERSONVIOLENT

KIDNAPPING

LARCENY

HOMEINVASION

MANSLAUGHTER

TERRORISM

people

Victims,serious

Victimlessnon-serious

things

Holidays

• Demo of Visual Anthropac pre-release version

Network analysis

• Crimes dataset• Animals• Holidays