Practice hunting with British telephone call records

Practice hunting with British
telephone call records
Ben Anderson, Department of Sociology
Dr Alexei Vernitski, Department of Mathematical
Sciences
Dr David Hunter, School of Computer Science and
Electronic Engineering

With data & funding support from BT plc
Read the research @ cresi.essex.ac.uk Join the conversation @ cresi.wordpress.com

Contents
• Practices - the view from here
• Practice Hunting
• Meet the data
• Contact 'practices'
• Identifying and quantifying 'practices'
• Practice patterns
• Final Thoughts


So, what are practices?
Entities
a temporally unfolding and
spatially
dispersed nexus of doings and
sayings
Schatzki, 1996

‘habits’, ‘bodily and mental routines’
Why people don’t do
‘permanent dispositions’
what they ‘should’ - Jim Skea, 2011
Reckwitz, 2002;

habituation, routine, practical
consciousness,
tacit knowledge, tradition
Performance Performance often neither fully conscious
nor reflective
Warde, 2005


Three questions that arise...
• If practices are distributed across space and time, how are they to be
defined and recognised?
• How do they develop, expand or retract in spatial and temporal scale?

• If practices emerge, persist and are reproduced and transformed over time
• How can such processes be detected and revealed, and at what scales?

• As present practices are shaped by practices and systems of practice from
the past
• How does this shaping work?
• How do present practices constitute institutions and infrastructures of
the future?

With apologies to: Researching Social Practice and Sustainability: puzzles and
challenges, SPRG Discussion Paper, 2011


Can we observe them?

Image: Anthony B. Wooldridge,
1986

“The recurrent enactment of specific
practices leaves all sorts of “marks” –
diet shows up in statistics on obesity;
heating and cooling practices have effect
on energy demand, and habits of laundry
matter for water consumption.
Identifying relevant “proxies” represents
one way to go.”
SPRG Discussion Paper
Image: Eric Shipton, 1951


'Marks' and 'proxies'...

• Tried:
• Shadowing/tracking/observation
– Small n, can ask why, investigator effects (?)
– Historical?
• Time use surveys (diaries, e.g. UK ONS 2000, MTUS)
– Big n, non response issues, can’t ask why, complex
data
– Rarely longitudinal, sometimes historical (MTUS)
• Relatively Untried:
• Expenditure Surveys
–
E-Living Time Use Diary 2001 n, proxies for % personscan’t ask why, complex
Big (weekdays), practices, reporting
data
• TechnoTraces
–
Transactions/meters/bills, proxies for practices,
complex data, difficult to access
ONS 2005 Time Use Survey Data (UK, weekdays) % of persons reporting


Meet the data I
Wave 1 - 1998 Wave 2 - 2000 Wave 3 - 2001

Undefined 6 10

Survey plus diary 1093 649 723

Survey only 668 918 840

16+ survey total 1761 1567 1563

Non-response 273 391 321

Children’s diary 163 82 73

No children’s diary 125 220 208

Child under 9 286 289 231

Total sample size 2608 2555 2406

Number of households 999
Home Online Household Panel 1998-2001 (survey + time use diary)
GB representative sample

Are those who said yes to linkage strange?

• Of the 999, c 60% said yes to call record linkage
• → (self) selection bias?

partner+kid>15
partner+kid>11
partner+kid<12
part,nokid,>55
part,nokid,<56
part,nokid,<36 Yes at wave 2
Yes at wave 1
lonepar,kid>15
No
lonepar,kid<16
un-other rel
alone over 55
alone under 56

0 20 40 60 80 100 120 140 160
N



• → (self) selection bias?
Don't know (25% said yes)

Any other company (76% said yes)

Cable TV company (55% said yes)

Mercury/Cable & Wireless (69% said yes)

BT (74% said yes)

0 100 200 300 400 500 600 700 800 900
Number of households

No Yes but calls not logged Yes and calls logged



• Logisitic (selection) regression
Said 'yes' to linkage Said 'yes' to linkage and call
records collected

Less likely to be: Less likely to be:
 Lone parents (x3)  Non-BT (!)

 Young couples (x0.2)

More likely to be: More likely to be:

 Better off

 Home owners (x0.6)
• → no effects for (lots) of other variables including social network responses


Ego network sizes
• Biological networks:
• Many nodes with few connections & few
nodes with many connections: y = a/x or y
= a/x2 (log-linear or ‘scale-free’)
• Human calling networks:
• Same principle
• C = number of contacts

• Do we find the same thing?

McCarty et al (2001) Comparing two methods for estimating network
size, Human Organization; Spring 2001; 60, 1; pg. 28


Contact groups (ego networks)

• Survey item:
• N local friends
• N non-local friends
• N local relatives
• N non-local
relatives
Summed for those contacted
at least once a year


• But

• Correlation = 0.299 (***) and 0.297 (***)


• So...
– The distributions appear to match reasonably well (but see K-S test)
– But the individual responses do not

Kolmogorov-Smirnov test = -5.528 (***) Correlation = 0.297 (***)


• But...McCarty et al (2001) Comparing two methods for estimating
network size, Human Organization; Spring 2001; 60, 1; pg. 28

Kolmogorov-Smirnov test = sig. Correlation = 0.55 (***)


• Does household type tell us
anything?
• For some, call-records
derived contact numbers
predict survey responses

Couple, children aged > 16
Couple, children aged 11-15
Couple, children aged < 12
Couple aged > 56, no children
Couple aged 36-55, no children
Couple aged < 36, no children
Lone parent, children > 15
Lone parent, children aged < 16
Unrelated persons
Alone, aged > 55
Alone, aged < 56
-1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5

Regression coefficient


Contacting your contacts
• A few people in your
network get a lot of
calls
• Most people get few
calls
• No evidence of
clumping (Dunbar)


This is a familiar curve - Pareto (scale free)

If C = total number of contacts, and f(n) = number of
contacts called n times

Then f(n) = C/(n*(n+1)) if n > 0

Contacting your contacts
• A few people in your
network get a lot of calls
• Most people get few
calls

• If C = total number of contacts • So you will call 50% of your
• And f(n) = number of contacts called n times contacts once (n = 1)
• Then f(n) = C/(n*(n+1)) if n > 0 • 16.67% of them twice (n = 2)
• 8.33% of them 3 times (n = 3)
• etc


Habits...



'Calling practices'
• Qualitative study
• Lacohee & Anderson 2000 Interacting with the telephone, Int. J. Human-Computer
Studies (2000) 53, doi:10.1006/ijhcs.2000.0439
• “Participants reported that what they knew about the lives, habits, and routines of their friends
and family had an effect on when they might make calls. They not only declared themselves to be
differentially available at different times, they were also differentially available to different people
at different times, all of which modified their calling behaviour.”
• Call types
• Duty calls: generally to family members and were made because the caller felt a
sense of duty to keep in touch
• Maintenance calls: real motivation was to maintain a friendship
• Grapevine calls: passing on news, often prompted by a call
• Batch calls: making a series of outgoing calls


Grapevine calls
 Generally between family members but could involve circles of friends

 One call (incoming) begets more (outgoing)

 “My mum rang me to tell me that my cousin had just had a baby, she was premature
and there’d been all sorts of complications and things. I asked her about it but she’d
got the news from her sister, via my aunt, and she didn’t really know what was
happening so I telephoned my other cousin to find out what had really happened.”
− (Female participant in 2001 study)


Batch calls
 Batch calls are a collection of calls made at one sitting

 Making a single call can prompt more calls to be made, even if it was not originally
intended

 Other reasons: take advantage of cheap rate, boredom, loneliness

 “When I picked up the telephone I was just going to make a quick call to my sister but
when we’d finished talking I rang three other people. It’s as though once I’ve put the
telephone down I think, who can I ring now?”
− (Female participant in 2001 study)


Call groups: the algorithm
 Two or more calls made or received in succession by the same subscriber

 O → 140 secs → <begin group> O → 20 seconds → O → 30 seconds → I → 180 seconds
<end>

 The “gap” value of 120 seconds can easily be changed

 So:

 Grapevine calls occur when the first call in a group is incoming, but the remainder are
outgoing: IOO...O but not OIO

 Batch calls occur when all the calls in a group are outgoing: OOO but not OIOOO

 Not all call groups fall into these two categories e.g. OOI

 But: beware artefacts – e.g. modems and faxes (OOOOOOOOO) !!


Group sizes...

• As you would expect...
• Many groups of a few calls, few groups of many calls


Batch calls
• Day 1 = Monday
• Day 7 = Sunday

• Clear cluster around 8 pm, goes on late
• Doesn't happen on Thursday & Friday?


Grapevine calls
• Day 1 = Monday
• Day 7 = Sunday

• Clear cluster around 7 pm, stops at 11 pm
• Less likely on Thursday & Friday?


People living alone aged < 56
• Day 1 = Monday
• Day 7 = Sunday

• Clear effect of 'work'
• Notice batch calls on Monday evening, Grapevines on Friday evening


People living alone aged > 55
• Day 1 = Monday
• Day 7 = Sunday

• More day-time calling, especially batch calls
• 10 am (especially at weekends) and then evening for grapevine
• Saturday night?!


Couple aged < 36, no children
• Day 1 = Monday
• Day 7 = Sunday

• Notice batch calls on Monday morning, Grapevines through the week


Couple young children
• Day 1 = Monday
• Day 7 = Sunday

• Notice two waves of calling – 4-5pm, 7pm
• Notice batch calls in evenings, some grapevines may be children


Couple older children
• Day 1 = Monday
• Day 7 = Sunday

• Notice again two waves of calling for grapevines – 4-5pm, 7pm
• Notice batch calls also after school


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