1. What do we know about mixed-device online surveys and mobile device use in the UK?
Olga Maslovskaya, Gabriele Durrant, Peter WF Smith
14 March, 2017, NTTS conference, Brussels

2. Motivation
Internet used daily by 82% of adults, smartphones are owned by 71% of adults in the UK (ONS 2016)
Mixed-mode designs for cost savings (e.g., Understanding Society)
Surveys moving to online designs with different devices as options for respondents
UK 2021 Census – plan to collect 75% of household responses through online data collection
Can no longer be expected that all participants would use PCs or laptops in online surveys
No research on characteristics of people in mixed-device online surveys in the UK

3. Research Question
What are the characteristics of people choosing to use different devices in online survey completion in the UK?

4. Literature
Netherlands: de Bruijne and Wijnant 2014, Toepoel and Lugtig 2014
Germany: Bosnjak et al. 2013
US: Peterson 2012
Spain, Portugal, Argentina, Brazil, Chili, Colombia and Mexico: Revilla et al. 2016
UK: no studies yet

5. Previous Findings Toepoel & Lugtig ‘14 Bosnjak et al. ‘13
de Bruijne & Wijnant ‘14
Bosnjak et al. ‘13
Revilla et al. ‘16
Peterson ‘12
Age Y
Y: B, Ch, C, S
Gender N
Y: S, P
Education
Ethnicity
Employment status
HH size/ composition
Y: (P, A)
Children in HH
HH income
Urban/rural

6. Data Innovation Panel of Understanding Society Waves 7-8 (IP7 and IP8)
Community Life Survey (CLS)
European Social Survey (ESS) – Round 6 – not publicly available but descriptive analysis conducted by the ESS
1958 National Child Development Survey (NCDS)
Second Longitudinal Survey of Young People in England (LSYPE2) Wave 4 – not publically available yet but descriptive analysis was conducted by Kantar Public

7. Methods Descriptive analysis (proportions and Chi-square tests)
Binary Logistic Regression
Multinomial Logistic Regression

8. Device Used Variable 1 Main variable 1 Survey PC/laptop Mobile devices
Main variable 1
Survey
PC/laptop
Mobile devices
IP7
621 (81.6%)
140 (18.4%)
IP8
2030 (90.3%)
217 (9.7%)
ESS6
540 (91.7%)
49 (8.3%)
CLS 14-15
1606 (72.5%)
609 (27.5%)
NCDS
5056 (86.5%)
790 (13.5%)
LSYPE2
1737 (60.6%)
1128 (39.4%)

9. Device Used Variable 2 Main variable 2 Survey PC/laptop Tablet
Main variable 2
Survey
PC/laptop
Tablet
Mobile phone
IP7
IP8
2030 (90.3%)
184 (8.2%)
33 (1.5%)
ESS6
CLS 14-15
1606 (72.5%)
567 (25.6%)
42 (1.9%)
NCDS
LSYPE2
1737 (60.6%)
485 (16.9%)
643 (22.4%)

10. Results: Descriptive Analysis - Availability and Significance of Variables
ESS
CLS
NCDS
LSYP2
Age Y* Y
N/A
Pension age
Gender
Marital status
Ethnicity*
Religion
Education
Employment status
Accommodation
Tenure

11. Results: Descriptive Analysis 2 - Availability and Significance of Variables
ESS
CLS
NCDS
LSYPE2
HH income Y Y*
N/A
Number of cars
Urban/ rural
GOR
Country of residence
HH size
Children in HH
General health
Internet use
Frequency of Internet use

12. Summary of Descriptive Results
Younger – mobile devices, older – PCs and laptops – consistent with all other studies
Female – mobile devices – consistent with other studies
Employed – mobile devices, unemployed – PCs and laptops – consistent with other studies
Higher income – mobile phones – consistent with other studies
Larger households – mobile devices – consistent with Revilla et al. (2016) but not with Toepoel and Lugtig (2014)
Children in household – mobile devices – consistent with other studies
Education not significant – consistent with other studies

13. Modelling Results IP7 (N=695): Binary Logistic Regression (tablet=1)
Variable
Category
Coefficient (SE)
Gender
Male
Female
0.540 (0.207)**
Marital Status
Single
Married
(0.238)
Divorced or separated
(0.376)
Widowed
1.367 (0.461)**
Tenure
Owned outright or with mortgage
Rented from local authority or housing association
0.423 (0.325)
Rented privately
0.841 (0.339)*

14. Modelling Results NCDS (N=5,844): Binary Logistic Regression - predicted probabilities of using tablets
Significant effects:
Gender
Frequency of Internet use
Gender*Frequency of Internet use

15. Modelling Results IP8 (N=2,214): Multinomial Logistic Regression
Variable
Category
Tablet
Phone
PC/laptop
Gender
Male
Female
0.500 (0.163)**
0.549 (0.395)
(0.163) **
0.049 (0.422)
Employment
Unemployed
Employed
0.355 (0.177)*
0.820 (0.344)
(0.177)*
0.465 (0.466)
HH income
4th quartile
1st quartile
(0.236)**
0.003 (0.698)
0.692 (0.236)**
0.694 (0.730)
2nd quartile
(0.220)**
0.817 (0.561)
0.594 (0.220)**
1.412 (0.595)*
3rd quartile
(0.209)**
0.679 (0.553)
0.557 (0.209)**
1.235 (0.584)*

16. Modelling Results CLS (N=2,125): Multinomial Logistic Regression
Variable
Category
Tablet
Phone
PC/laptop
Gender
Male
Female
0.539 (0.106)***
0.031 (0.332)
(0.106)***
(0.340)
Employment
Unemployed
Employed
0.378 (0.136)**
0.580 (0.435)
(0.136)**
0.202 (0.446)
Children in HH
Yes No
(0.130)***
(0.371)*
0.846 (0.130)***
(0.380)

17. Modelling Results CLS – continued
Variable
Category
Tablet
Phone
PC/laptop
Age
70+
16-19
(0.320)
1.384 (1.270)
0.278 (0.320)
1.662 (1.292)
20-29
(0.249)
2.266 (1.096)*
0.424 (0.249)
2.691 (1.113)*
30-39
0.048 (0.243)
1.553 (1.150)
(0.243)
1.505 (1.164)
40-49
0.142 (0.235)
0.412 (1.215)
(0.235)
0.270 (1.227)
50-59
(0.224)
0.714 (1.178)
0.008 (0.224)
0.722 (1.190)
60-69
0.155 (0.200)
0.659 (1.169)
(0.200)
0.503 (1.179)

18. Conclusions
This is the first study to look at characteristics of respondents in different device group in mixed-device online surveys in the UK
Significant variables in different surveys: age of respondent, gender, marital status, employment status, religion, household composition/size, children in household, household income, number of cars, and frequency of Internet use
Results are useful for targeting of certain groups more efficiently for survey participation, might help increase response rates and reduce costs
Results are instrumental in better understanding of trends in different device use in preparation for the UK 2021 Census

19. Limitations
Sample sizes are too small for mobile device use in some surveys (respondents were discouraged to use mobile devices or mobile devices were blocked)
Data is not publically available for the analysis for some surveys

20. Future Work
Would be useful to repeat analysis on larger datasets which will soon become available (e.g., Understanding Society Wave 8)
Data quality issues by mode and by device used within online mode of data collection should be addressed