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

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

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

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

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

Data Innovation Panel of Understanding Society Waves 7-8 (IP7 and IP8) Community Life Survey (CLS) 2014-2015 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

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

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%)

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%)

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

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

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

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 -0.078 (0.238) Divorced or separated -0.188 (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)*

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

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) -0.500 (0.163) ** 0.049 (0.422) Employment Unemployed Employed 0.355 (0.177)* 0.820 (0.344) -0.355 (0.177)* 0.465 (0.466) HH income 4th quartile 1st quartile -0.692 (0.236)** 0.003 (0.698) 0.692 (0.236)** 0.694 (0.730) 2nd quartile -0.594 (0.220)** 0.817 (0.561) 0.594 (0.220)** 1.412 (0.595)* 3rd quartile -0.557 (0.209)** 0.679 (0.553) 0.557 (0.209)** 1.235 (0.584)*

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) -0.539 (0.106)*** -0.508 (0.340) Employment Unemployed Employed 0.378 (0.136)** 0.580 (0.435) -0.378 (0.136)** 0.202 (0.446) Children in HH Yes No -0.846 (0.130)*** -0.882 (0.371)* 0.846 (0.130)*** -0.035 (0.380)

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

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

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

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