123people.com and meinungsraum.at conducted a survey regarding online privacy for Data Privacy Day on January 28th 2011. 90% of the respondents feel their personal information is at risk online.
A direct mail campaign was administered to existing customers who had never purchased an IRA (Individual Retirement Annuity) as of November 2007 for an offer to purchase.
After receiving confirmation of receipt for the IRA offer, these customers were tracked until July 2008, in order to predict the likelihood of a customer purchasing an IRA.
Applied clustering techniques was utilized to distinguish different groups.
A cross-sell response model was built using logistic regression.
The data was split into a sixty-percent training dataset and a forty-percent scoring and validation set.
The rate of purchase was less than 1%; therefore, the data was oversampled by taking a random sample of the non-responders and applying an offset to the model.
Vist http://www.saraconsultingllc.com to learn more about the presenter.
Analysis of Attrition & Employee engament activity at IDEA Cellular ltdalpana96
The ppt is my summer Internship Project at Idea Cellular ltd. The project was about Analysis of attrition in last 3 years in Idea, my findings on reasons of attrion and suggesting steps to reduce the churn rate. I also saw there suggessful implementation of those steps. I also worked on employee engagement acivities at idea cellular ltd.
123people.com and meinungsraum.at conducted a survey regarding online privacy for Data Privacy Day on January 28th 2011. 90% of the respondents feel their personal information is at risk online.
A direct mail campaign was administered to existing customers who had never purchased an IRA (Individual Retirement Annuity) as of November 2007 for an offer to purchase.
After receiving confirmation of receipt for the IRA offer, these customers were tracked until July 2008, in order to predict the likelihood of a customer purchasing an IRA.
Applied clustering techniques was utilized to distinguish different groups.
A cross-sell response model was built using logistic regression.
The data was split into a sixty-percent training dataset and a forty-percent scoring and validation set.
The rate of purchase was less than 1%; therefore, the data was oversampled by taking a random sample of the non-responders and applying an offset to the model.
Vist http://www.saraconsultingllc.com to learn more about the presenter.
Analysis of Attrition & Employee engament activity at IDEA Cellular ltdalpana96
The ppt is my summer Internship Project at Idea Cellular ltd. The project was about Analysis of attrition in last 3 years in Idea, my findings on reasons of attrion and suggesting steps to reduce the churn rate. I also saw there suggessful implementation of those steps. I also worked on employee engagement acivities at idea cellular ltd.
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
Census2022: Extracting value from domestic consumption data in a postcensus eraBen Anderson
Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
10 minute 'lightning' paper presented at BEHAVE 2014, Said Business School, Oxford, 4th September 2014.
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
Paper presented at 'What makes us act green?', Research & Policy Seminar, 17th December 2013, BIS Conference Centre, London. Uses @usociety survey data to analyse household uptake of solar PV and solar thermal in the UK 2008-2011
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
Census2022: Extracting value from domestic consumption data in a postcensus eraBen Anderson
Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
10 minute 'lightning' paper presented at BEHAVE 2014, Said Business School, Oxford, 4th September 2014.
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
Paper presented at 'What makes us act green?', Research & Policy Seminar, 17th December 2013, BIS Conference Centre, London. Uses @usociety survey data to analyse household uptake of solar PV and solar thermal in the UK 2008-2011
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Non-response and attrition in a multi-method longitudinal household panel survey
1. Seriously mixed methods
Do they risk non-response and attrition?
Ben Anderson
Chimera, University of Essex
2. The Menu
• Why bother?
• The background
• The panel and its methods
• Who dropped out (and why)
• What can we learn?
www.essex.ac.uk/chimera
3. Multiple methods - why bother?
• Data Triangulation:
• Different data on the same individuals
• Different instruments and methods (qual, quant,
administrative)
• Cross-confirmation and validation
• Respondents lie, they forget and they don’t
care
• Multiple methods can unravel some of this
• Different views - different insights
• Patterns (what?) and explanations (why?)
www.essex.ac.uk/chimera
4. Other reasons
• Interaction of research modes (and
researchers!)
• Leads to insights & innovation
• Multiple methods = 'real life' methods
• Increasingly valued in policy & evaluation
research
• ‘rounded view’
www.essex.ac.uk/chimera
5. But
• Such methods may
• Increase respondent burden
• Increase fears of privacy and surveillance
• Or conversely
• Develop stronger relationships between
researchers and respondents
• Increase respondent ‘attachment’
www.essex.ac.uk/chimera
6. An example: BT’s Digital Living project
Quantitative
Phone call records
PC/Internet usage logs
Surveys
Time-use diaries
Interviews
Shadowing & Observation
Digital Ethnography Rich contextual
Qualitative picture
www.essex.ac.uk/chimera
7. GB Longitudinal Panel
Dec 1998 Dec 1999 Dec 2000
• GB surveys (2500 individuals in 999 hh)
• Call record capture (635 of 999 hh)
• Internet logs (16 of 999 hh)
• ‘Long conversations’ (37 of 999 hh)
Wave 1 Wave 2 Wave 3
• Qualified random sample (clustered)
• Wave 1 interviews = CAPI
• Wave 2 & 3 = CATI
www.essex.ac.uk/chimera
8. Wave 1 process
• Conduct face to face survey (HoL)
up to 6 months
• Leave time-use diary
• Obtain permission to collect call records
• Obtain permission to re-contact for next survey and
ethnography
• Implement call record capture
• Decide ethnographic sample frame (ICT rich/poor;
income rich/poor)
• Select households from eligible pool (requires survey data)
• Approach households for interview (via survey agency)
• Interview and arrange re-interviews/shadowing etc
• Decide logging sample frame (anyone with Win95!)
• Select households from eligible pool (requires survey data)
• Approach households
• Send disk (self-installer)
www.essex.ac.uk/chimera
9. Added complexities...
• Wave 1 bias
• 100% of households to have a telephone
• 50% to have a personal computer
• Boost sample at wave 2
• Original address file, random selection
• To maintain sample size
• CATI
• Overall a rare if not unique beast!
www.essex.ac.uk/chimera
10. Wave 2 & 3 process
• Attempt re-contact
up to 3 months
• Conduct CATI survey
• Post out time-use diary
• Check permission to collect call records
• Obtain permission to re-contact for next survey and follow-
up interviews
• Boost sample (wave 2 only)
• recruit & interview as Wave 1
www.essex.ac.uk/chimera
11. Response rates (individuals)
• Cross- sectional Undefined
Survey plus diary
Wave 1
1093 42%
Wave 2
6
649 25%
Wave 3
10
723 30%
• (unweighted) Survey only
Non-response
668 26%
273 10%
918 36%
391 15%
840 35%
321 13%
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
Interviews Diaries
• Longitudinal (Always a child)
Never
697
462 13%
697
1415 39%
Wave 1 only 511 14% 480 13%
• (unweighted) Wave 2 only 136 4% 106 3%
Wave 3 only 197 5% 214 6%
Waves 1 and 2 224 6% 172 5%
Waves 2 and 3 365 10% 68 2%
Waves 1 and 3 159 4% 138 4%
Waves 1, 2 and 3 842 23% 303 8%
www.essex.ac.uk/chimera
12. What do we want to know?
• Did the three experimental ‘treatments’
cause non-response?
• To keep it simple:
• Consider w1 to w2 and w1 to w3 effects only
• Ignore boost sample
• Focus on
– refusal and non-contact in responding households
(excludes movers)
– Non-contact (non-responding households)
– Attrition
www.essex.ac.uk/chimera
13. Pathways
W1 W2 W3
79% Interviewed in all waves
48%
W1 interviewees
61%
12%
12% ‘Non-response’ w3
‘Non-response’ w2
35% 25%
72% Attrition after w1
www.essex.ac.uk/chimera
14. Wave 1 to wave 2 effects
• Comparison of response rates
No call Yes, call Difference Difference
records records (call Difference (instrumen
Wave 2 outcome % % Total % records) (qual) tation)
Interview 39.43 48.01 43.83 8.58 -0.44 10.43
Refusal 14.06 15.36 14.72 1.3 -9.10 -10.97
No contact in a responding
hh 2.83 1.35 2.07 -1.48 1.65 -2.26
No hh response 23.96 15.36 19.56 -8.6 -11.47 -7.41
Other 19.72 19.93 19.82 0.21 19.36 10.21
N 1273 1335 52 51
Chi sq 43.26*** 16.96** 10.71*
• % of w1 interviewees
www.essex.ac.uk/chimera
15. Wave 1 to wave 3 effects
• Comparison of response rates
Difference Difference
No call Yes, call (call Difference (instrument
Wave 3 outcome records % records % Total % records) (qual) ation)
Interview 77.89 78.59 78.28 0.70 -6.42 -4.83
Refusal 5.58 6.09 5.87 0.51 -1.79 -2.53
No contact in a responding
hh 2.99 1.88 2.36 -1.11 1.71 4.49
No hh response 11.75 10.78 11.21 -0.97 8.84 1.70
Other 1.79 2.66 2.28 0.87 -2.33 1.17
Total 502 640 24 29
Chi sq 2.778 2.61 2.94
• % of w1 and w2 interviewees
www.essex.ac.uk/chimera
17. Multivariate analysis
• Logistic approach
– P(x) at t = control variables/known effects +
treatments
– Where X is
• Refusal at t (responding hh)
• Non-contact at t (responding hh)
• Non-contact at t (non-responding hh)
• Attrition
www.essex.ac.uk/chimera
18. Known effects
• Based on Lynn et al (2005)
Refuse Non-contact In HoL?
Age Elderly Elderly & Young Y
Income Lower Higher and/or employed Y
Gender Men Y
Education Less Y
Composition singles singles Y
Culture Ethnic minorities Y
Mobility High mobility High mobility N
Location Urban Urban N
• In addition:
– Technophobia (‘resonance’)
– MRS code (AB, C1, C2, D,E) as proxy for wealth
www.essex.ac.uk/chimera
19. W2 results
Variable w2 refusal w2 non-contact w2 non-contact
(ind) (hh)
Age -0.017* -0.062** -0.043***
MRS Code 0.257** 0.158 0.225**
Gender (female) -0.582** -1.428* -0.258*
Qualification 0.095 -0.208 -0.048
level
Single person -0.821 0.399
Ethnic minority -0.505 0.125
Technophobia 0.028 0.164 0.049
Call records 0.079 -0.735 -0.351
Qualitative -0.875 1.831 -0.793
Internet logging -0.678 -1.163
Constant -1.961** -1.527 -0.565
Pseudo r sq 0.044 0.128 0.08
Chi sq 38.56854 20.49012 70.00324
N 1172 881 1243
• logit, cluster (household identifier) [stata], values = b
www.essex.ac.uk/chimera
20. W3 results
w3 w3 non-contact w3 non-contact
refusal (ind) (hh)
Age -0.02 -0.034 -0.026**
MRS Code -0.06 -0.187 -0.181
Gender -0.431 0.215 -0.149
(female)
Qualification 0.052 0.14 -0.016
level
Single person -0.175 0.206
Ethnic minority 0.429 1.810** 0.459
Technophobia 0.032 -0.162 -0.039
Call records 0.445 -0.39 -0.001
Qualitative -0.125 0.782 0.936
Internet logging -0.243 1.234 0.326
Constant -1.737* -0.778 0.298
Pseudo r sq 0.022 0.106 0.035
Chi sq 12.18588 34.6003 19.14218
N 880 747 932
• logit, cluster (household identifier) [stata], values = b
www.essex.ac.uk/chimera
22. Attrition
Variable b
Age -0.052***
MRS Code 0.175*
Gender (female) -0.446***
Qualification level 0.004
Single person 0.005
Ethnic minority -0.122
Call records -0.313
Qualitative 0.055
Internet logging -1.566
Technophobia 0.064*
Region (North)
yorkshire & humberside 1.310**
east midlands 0.357
east anglia 0.866
south east (excl. london) 1.089*
south west 0.893 • Added region
west midlands 0.516
north west 0.713
wales 0.802
scotland 0.656
• Call records variable ‘nearly’
greater london 1.322** significant (p = 0.066)
Constant -0.403
Pseudo r sq 0.12
Chi sq 110.2714
N 1190
• logit, cluster (household identifier) [stata]
www.essex.ac.uk/chimera
23. Conclusions I
• Multi-method projects give you ‘better’ data
• And ‘better’ results (see elsewhere)
• But
• They are resource hungry (researcher and respondent
time/load)
• They are complex to manage and analyse
• You have to be multi-disciplinary/multi-skilled
• All the usual qual/quant bickering takes place
• All of which are good reasons to do them
www.essex.ac.uk/chimera
24. Conclusions II
• Disappointingly:
• Qualitative interviews did not help prevent non-response
or attrition
• BUT encouragingly
• None of the ‘treatments’ were associated with non-
response or attrition
• So overall we should do this more often!
www.essex.ac.uk/chimera
25. Get the data
• All 3 waves of the survey
• UK Data Archive SN = 4607
• Free to UK Data Archive subscribers for non-commercial
research
• Held at Chimera (may be in UKDA
eventually):
• Qualitative transcripts
• Call records (disclosure issues)
• Internet usage logs
www.essex.ac.uk/chimera