Are visual attention and preferences telling us the same story about multiattributes

Transcription

1 Are visual attention and preferences telling us the same story about multiattributes choices? 7 th October 2015 HERU, Univ of Aberdeen Internal seminar series Nicolas KRUCIEN (Univ of Aberdeen) Mandy RYAN (Univ of Aberdeen) Frouke HERMENS (Univ of Lincoln)

2 Outline of this presentation Multi-attributes choices Discrete choice experiment Visual attention (and eye-tracking) DCE: Preferences for Chlamydia screening Effect of eye-tracking on preferences (Obj. 1) Relationship between attributes and attention (Obj. 2) Summary of key results

3 Why studying multi-attributes choices (MAC)? Cognitively challenging Common and no longer confined to supermarkets Example: NHS Choices website (Decision aid tool) Search/Hospital/LocationSearch/7/Hospitals

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5 How multi-attributes choices have been studied? Two main approaches What? : Outcome-oriented (e.g., DCE, SG, TTO, CA) How? : Process-oriented (e.g., ADDM, LCA, LBA) Choice behaviour (decision making) Decision processes (e.g., information processing) Decision outcomes (e.g., choices)

6 What is a discrete choice experiment (DCE)? Stated choices survey Measure relationship between Δ attributes and choice probabilities Method is silent about decision processes As if approach relying on a pre-specified behavioural framework: Random Utility Maximisation (RUM) Random utility (Thurstone, 1927) Attributes-based utility (Lancaster, 1966) Choices-based (economic) utility (Marschak, 1960)

7 How to measure visual attention? (Eye-tracking) Eye-tracking SMI RED 500: Head movement allowance Operating distance: cm Sampling rate: 500 Hz (every 2 ms) Spatial accuracy: 0.4⁰ (0.49 cm [at 70 cm])

8 Examples of eye-tracking

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10 DCE about preferences for Chlamydia screening (1) Design (OMEP) 16 choice tasks Initial sample 30 participants Univ of Aberdeen Online ads Table 1. List of attributes and levels to describe Chlamydia screening service Attribute Level 1 Level 2 Level 3 Level 4 Place of screening GP Clinic Family planning clinic Home Genito urinary medicine (GUM) clinic Type of screening Urine Test Perineal Swab Full pelvic Examination - Type of information and support when you are None Support of a trained health - - given screening results advisor Chance of developing pelvic Inflammatory Disease (PID) if you have Chlamydia and it is not treated 1% 5% 10% 25% Cost to you of Chlamydia Free screening

11 DCE about preferences for Chlamydia screening (2) Binary choice question: Acceptation ( I would have chlamydia screening ) Rejection ( I would not have chlamydia screening ) Task order randomisation Fixed order for the attributes

12 Modelling of multi-attributes choices Analytical sample: 6 indiv excluded because of missing values (> 33%) (n=1) or serial non-participation (n=5) 24 indiv providing 240 (62.5%) Yes and 144 (37.5%) No answers (Different from chance level: Binomial two-tailed test: P 0.05 < 0.001) Conditional logit model with cluster robust SEs for indiv Effect coding

13 Preferences for screening attributes (1) Model performance Log-Likelihood: Predictive validity: 69.3% [95% HDI: ] Min sample size (Louviere et al, 2000): Prob=50%; Accu=90%; Conf=95%; Tasks=16 N=25

14 Preferences for screening attributes (2) Median choice elasticities Risk = Cost = Type = Information = Place =

15 Are preferences influenced by eye-tracking? 3 potential biases Experimenter effect: Small or inexistent (Barmettler et al, 2011) Mode of administration: Small or inexistent (Gwaltney et al, 2008) Eye-tracking: Previous studies suggest that individuals decisions can be biased by manipulating visual attention (Pärnamets et al, 2015; Shimojo et al, 2003)

16 Comparison with paper-based DCE (1) 174 women at a family planning clinic (Aberdeen, UK) Self-completed paper-based questionnaire 89 respondents excluded because of missing values (> 33%) (n=47), serial non-participation (n=19) and age (< 18 years) (n=23) We make the two samples more alike using propensity score matching (PSM)

17 Comparison with paper-based DCE (2) List of variables used for PSM: Employment status; smoking status; age; relationship status; and method of contraception EYE sample More students (100% vs. 79%) More univ degrees (100% vs. 29%) Characteristic Bilateral Fisher p-val. Employment status Smoking status > Contraception method Relationship status Chlamydia experience PID experience Ectopic pregnancy experience > Education level < 0.001

18 Effect of eye-tracking on multi-attributes choices Paper-based DCE: 179 (47%) No and 202 (53%) Yes choices Not different from chance level: P 0.05 =0.259 Different from eye-tracking-based DCE: P 0.05 =0.011 Participants less inclined to accept screening services (53% vs. 62.4%) Comparison with eye-tracking results following Swait & Louviere (1993) procedure

19 Comparison of preferences between the two DCEs Table. Estimated preferences Attribute PAPER EYE ASC_Yes Place.1 (Family) Place.2 (GP) Place.3 (GUM) Place.4 (Home) Type.1 (Pelvic) Type.2 (Perineal) Type.3 (Urine) Info.1 (Support) Info.2 (No support) Risk Cost RED: Significant + Negative; BLUE: Significant + Positive

20 Is there a role for VA in (multi-attributes) choices? Milosavljevic et al (J of Consumer Psy, 2012) Effect of visual saliency on food choices (Snack)

21 Moving from decision outcomes to processes (1)

22 Moving from decision outcomes to processes (2) Krajbich et al (Frontiers in psychology, 2012) ADDM applied to simple purchasing decisions

23 Linear Ballistic Accumulation (LBA) model Evidence Final (Accumulation) Barrier (Threshold) Initial (Bias) 0 Decision Response Time

24 Attributes-Based Evidence accumulation in Linear way (ABEL) Modif. No latency Bias 0 Barrier = f(attrib.) We model fixation time distribution

25 Fixation time as a proxy for visual attention Initially a total of 19,692 fixations (F), further narrow down by including fixations Related to experimental tasks (F=12,832) On ROI describing the attributes levels (F=7,615) With a duration longer than 50 ms (F=7,477) Within 2 SDs around of the mean (F=7,058) From the 24 indiv used in choice modelling (F=5,806)

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27 Descriptive analysis of visual attention Multi-attributes content is comprehensively processed Visual attention is unevenly distributed (P 0.05 < 0.001)

28 ABEL: Effects of Δ multi-attributes on visual attention We first compared different model specifications: Best fitting model is double accumulator with multi-attributes barrier Attribute "Yes" "No" Cost Risk RED: Significant + Negative

29 Key outcomes- and processes-based results Attribute "Yes" "No" DCE Cost Risk RED: Significant + Negative; BLUE: Significant + Positive Framing: Negative attribute (Cost) with negative decision (Rejection); Positive attribute (Risk) with positive decision (Acceptation) Asymmetric effect Non linear preferences? Non compensatory decision: Extreme levels of important attributes (Cost; Risk) are more likely to end the accumulation process. Might reflect Min{Cost} and Max{Risk} strategies. No desirability bias?

30 More news on this project and HERU projects HERU website Nicolas Krucien webpage Nicolas Krucien twitter account