Predicting Naturalistic Risk-Taking Behaviors
Creators: name: Millroth, Philip
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences

description: Pre-registration of study for Master thesis – Uppsala University – study designed by Millroth. P., Magnusson, M., &amp; Nilsson, M. Introduction Why do some choose to invest in stocks while others put the money under the mattress? Why do some of us drive without seat belts, visit dangerous places, or practice extreme sports? Among psychology researchers, people's so-called risk preferences have been considered central to understanding why we differ in naturalistic risk-taking (Frey et al., 2020b; Mata et al., 2018; Brailovskaia et al., 2018). Naturalistic risk-taking is used here as a collective term for individuals' involvement in risky behaviors, what the person does in their daily life, while risk preference is about whether the person is attracted to risks or not (Mata et al., 2018). However, the extent to which risk preferences can actually predict naturalistic risk-taking is still uncertain. Some studies show that predictive validity is low when risk preferences are measured through behavioral data, so-called revealed risk preferences (RRP) (Fox &amp; Tannenbaum, 2011; Arslan et al., 2020). Risk preferences measured by self-assessments, stated risk preferences (SRP), have shown more promising results regarding predictive validity (Arslan et al., 2020), although the issue is still fairly unexplored and it is unclear how strong the predictive validity for SRP actually is. In addition, there are discussions about whether it is best to use general or domain-specific risk preference in research (Frey et al., 2020a; Highhouse et al., 2017; Weber et al., 2002). Highhouse et al. (2017) argue that both general and domain-specific risk preference can predict naturalistic risk-taking and that the type of behavior to be investigated should govern which of them should be used. In addition to risk preference, other factors have been shown to play a role in a person's involvement in naturalistic risk-taking such as age (Auerbach et al., 2012; Nicholson et al., 2005), gender (Nicholson et al., 2005), level of education (Wang et al. , 2013; De Graaf Vanwesenbeeck &amp; Meijer, 2014), income (Berhan et al., 2011; Borhan et al., 2018), anxiety (Lorian &amp; Grisham, 2010), sensation seeking (Masson et al, 2020; Fischer &amp; Smith , 2004), impulsivity (Cheng &amp; Lee, 2012) and the personality traits neuroticism and extraversion (Cooper et al., 2000). However, there is a lack of research on which factors best predicts naturalistic risk-taking. It is therefore interesting to make a comparison between these factors and risk preferences in order to gain a better understanding of who tends to engage in naturalistic risk-taking. By studying these variables at the same time as risk preferences, their respective role in predicting naturalistic risk-taking can be clarified. This study is important as previous research in the field implies that risk preferences should be a recurring and dominant predictor in the models that best explain naturalistic risk-taking, even if a comparison between several different predictors has not been made. The results of this study help to shed light on which variables can best explain naturalistic risk-taking. Perhaps the role of risk preference needs to be neutralized and other variables emphasized as central to research? Another reason to test the predictive power of these variables is that many naturalistic risk behaviors tend to affect us strongly positively or strongly negatively. In order to improve clinical interventions, which aim to help people either increase or decrease their naturalistic risk-taking, a broader understanding of which psychological mechanisms have the greatest predictive power and should be focused on is needed. Focusing on underlying mechanisms has been shown to be helpful in clinical interventions (Holmes et al., 2016). Purpose and question The aim is to investigate the potential roles that the variables risk preference, age, gender, level of education, income, sensation seeking, anxiety, impulsivity and the personality traits neuroticism and extraversion play in predicting people's naturalistic risk-taking. The purpose is specified by two issues: 1. From a probability perspective, which variables are most likely to predict naturalistic risk-taking when considering an overall range of relevant prediction models? 2. Of the most likely variables, which have the strongest predictive power? The questions are operationalized and answered through the analysis of statistical inference models (see Method: Statistical analyzes). Method Participants The population measured is people in Sweden from the age of 18. A convenience selection will be made by distributing the survey via social media in various groups on Facebook, sending out the survey to friends and acquaintances and putting up posters on bulletin boards in Uppsala and Stockholm. The selection is desired to have great variation in the variables that we will examine. Heterogeneous populations are expected to be reached by actively searching for different groups where individuals may differ in these variables. Design, Material, &amp; Procedure To answer the research questions, the study involve a cross-sectional survey design. The study's dependent variable (BV) is naturalistic risk-taking and the predictors are risk preference, age, gender, education level, income level, sensation seeking, impulsivity, trait-anxiety and the personality traits neuroticism and extraversion. Survey Design. The first questions concern age, gender, income and level of education and are answered with one item each. Then naturalistic risk-taking is measured with 19 items. Anxiety level is measured with 7 items and personality traits neuroticism and extraversion are measured with about 2 items each. Sensation seeking is measured with 8 items and impulsivity with 15 items. Finally, there are 8 questions that deal with general risk preference and 6 questions that deal with domain-specific risk preference. Initially, the questionnaire contains information about the purpose of the study, the set age limit, time required, consent, who is responsible for it and their and the supervisor's contact information. There will also be information that the participants can cancel at any time, that their answers are anonymous and will only be handled by the essay writers and that no compensation is paid. Measures Although naturalistic risk-taking is measured first, we here present the measures about risk preferences first in order to convey to the readers how preferences and naturalistic risk-taking differ. General risk preferences. The recently developed and validated General Risk Propensity Scale (Zhang et al., 2019) was used to assess general risk preference. Participants responded to the items on a scale from 1 to 5 where 1 = Strongly disagree, and 5 = Strongly agree, and an aggregated score was computed by summing across all items: 1. Taking risks makes life more fun 2. My friends would say that I’m a risk taker 3. I enjoy taking risks in most aspects of my life 4. I would take a risk even if it meant I might get hurt 5. Taking risks is an important part of my life 6. I commonly make risky decisions 7. I am a believer of taking chances 8. I am attracted, rather than scared, by risk Domain-specific risk preferences. While GRIPS clearly do not ask for frequencies of events – thus making it (in our opinion) a good measure of higher-order beliefs (i.e., preferences or attitudes), existing measures focusing on specific domains tend to be “pseudo-naturalistic” in the sense that they ask for the probability that one would engage in a behavior (e.g., DOSPERT: Blais &amp; Weber, 2006; Weber, Blais, &amp; Betz, 2002), or recalling on verbal ordinal scales how often one has engaged in any said behavior (e.g., “never, rarely, often”). We believe it is better to make a clearer distinction between measures capturing higher-order beliefs and behavior frequency. Thus we designed six items intended capture the higher-order belief elements from GRIPS in the domains highlighted by previous research: recreational risks (e.g. rock-climbing, scuba diving), health risks (e.g. smoking, poor diet, high alcohol consumption), career risks (e.g. quitting a job without another to go to), financial risks (e.g. gambling, risky investments), safety risks (e.g. fast driving, city cycling without a helmet), social risks (e.g. standing for election, publicly challenging a rule or decision). Participants responded to the items on a scale from 1 to 5 where 1 = Strongly disagree, and 5 = Strongly agree: 1. I am attracted, rather than scared, by risk in the domain of recreational activities (e.g. rock-climbing, scuba diving)I am attracted, rather than scared, by risk 2. I am attracted, rather than scared, by risk in the domain of health (by e.g. smoking, poor diet, high alcohol consumption) 3. I am attracted, rather than scared, by risk in the domain of career (e.g. quitting a job without another to go to) 4. I am attracted, rather than scared, by risk in the domain of safety (e.g. fast driving, city cycling without a helmet) 5. I am attracted, rather than scared, by risk in the domain of social relations (e.g. standing for election, publicly challenging a rule or decision) 6. I am attracted, rather than scared, by risk in the domain of finance (e.g. gambling, risky investments). Naturalistic risk-taking. Capturing naturalistic risk-taking offers a challenge. Preferably, one would like to have access to data on an individual’s (for example) financial transactions, investments, peer-reports, health-records, criminal records. However, it is unlikely that researchers would be given access to such intimate data for a large mass of individuals – and in some cases it would perhaps even be unethical or illegal. A more feasible approach is to use self-reported frequency measures: survey items which aims to pinpoint to what extent people engage in a specific behavior. For example, by asking for the number of intoxications from narcotics and alcohol in the past month (e.g., Fromme, Katz, &amp; Rivet, 1997, Frey et al., 2017). This approach has the drawback that responses will be rather noisy because individuals are forced to rely on episodic memory retrieval of frequencies. However, with a sufficient number of items the signal should be able to get through this noise. Previous studies that use frequency measures has tended to focus on behavior related to drug intake, criminal behavior, and sexual relations (e.g., Fromme et al., 1997, Frey et al., 2017), and the literature lacks a measure offering a general index of naturalistic risk taking across multiple domains. To this end, a pilot study was conducted (see “Pilot-Data” below), in order to develop items with sufficient psychometric qualities, and to assert that the relationship between risk preferences and general index was not driven by a specific domain (e.g., drug intake). Results from the pilot-study showed that it was indeed possible to generate a general index of naturalistic risk taking and that index scores could be predicted by GRIPS. The following items were derived in light of the results from the pilot-study, and an aggregated score was computed by first z-transforming data for each item and then summing across all items (below is Google Translated from Swedish): 1. How many types of supplements (eg vitamins) do you take each day to protect yourself against different types of diseases? NOTE only those that have not been prescribed by a doctor. 2. How many Swedish Crowns (SEK) do you save each month as a buffer for "worse times"? Enter the number of kronor. 3. How many different types of insurance do you have? For example, accident insurance, home insurance, income insurance, insurance on specific goods. 4. Approximately how many cigarettes have you smoked the most in one day? 5. Approximately how many SEK do you spend on different types of gambling each month? For example Triss, V75 or online. 6. Approximately many times have you been involved in any type of serious traffic incident? For example, driving into objects, colliding with the car, bicycle or scooter, being hit. 7. When the sunny season begins, what strength of the sun protection factor do you usually start with? Enter strength. 8. When the senny season begins, what sun protection factor should you perhaps start with? Enter strength. 9. Approximately how many extreme sports have you tried? For example, bungee jumping, skydiving, rock climbing. 10. Approximately many times have you experienced severe consequences due to alcohol (been arrested, severe memory gaps, hospitalization, relationship problems)? 11. Approximately how many times have you broken traffic rules in the last month? Applies to both vehicles and pedestrians. For example, driving too fast, going towards red, parked incorrectly. 12. Approximately how many times in the past year have you ignored seeking care through an emergency department or health center even though you might have sought care? 13. Approximately how many types of drug-classified drugs have you ingested (cannabis, ecstasy, cocaine, amphetamine, heroin, or synthetic variants of these)? 14. How many times in the last month have you complained to your boss / teacher? (K) 15. How many times in the last month have you revealed to an outsider something your friend said to you in confidence? 16. How many times in the last month have you postponed job or school-related tasks until the last minute? ( 17. How many times in the last month have you expressed a dissenting opinion in a group? 18. How many times in the last month have you missed a deadline? 19. How many times in the last month have you started an argument? Anxiety. The validated STAIT-5 scale (Zsido et al., 2020) were used to assess general anxiety (Cella et al., 2019). The main analyses use the raw scores summed to one aggregate score: Please respond to each item by marking 1-4 1 = Not at all 2 = Somewhat 3 = Moderately so 4 = Very much so HOW YOU GENERALLY FEEL.... 1. I feel that difficulties are piling up so that I cannot overcome them 2. I worry too much over something that really doesn't matter. 3. Some unimportant thoughts run through my mind and bothers me. 4. I take disappointments so keenly that I can't put them out of my mind 5. I get in a state of tension or turmoil as I think over my recent concerns and interests. Neuroticism and extraversion. Four items from the validated short-version of the BIG-5 inventory (Rammstedt &amp; John, 2007) were used, with participants responding on an ordinal scale; Disagree strongly Disagree a little Neither agree nor disagree Agree a little Agree strongly N1: I see myself as someone who is relaxed, handles stress well (note: this item is reversed when scored) N2: I see myself as someone who gets nervous easily E1: I see myself as someone who is reserved (note: this item is reversed when scored) E2: I see myself as someone who is outgoing, sociable Impulsitivity. BIS-15 has a four point Likert response format of rarely = 1, occasionally = 2, often = 3 and almost always = 4. Each item on BIS-15, therefore, receives a score in the range of 1–4 depending on the participant’s response. BIS-15 yields a total score for impulsivity and a separate score for each of its three dimensions. BIS-15 has been reported to have good psychometric properties (Spinella, 2007; Meule et al., 2015). Items: Motor 1. I say things without thinking. 2. I do things without thinking. 3. I act “on impulse”. 4. I act on the spur of the moment. 5. I buy things on impulse. Non-planning 1. I plan for future. 2. I plan for job security. 3. I am a careful thinker. 4. I plan tasks carefully. 5. I save money on a regular basis. 6. I concentrate easily. Attention 1. I squirm at plays or lectures. 2. I am restless at lectures or talks. 3. I do not pay attention. 4. I get easily bored when solving thought problems. Sensation seeking. The Brief Sensation Seeking Scale (BSSS: ) was used. Each of the four primary dimensions of sensation seeking is represented by two items. Responses are indicated on five-point scales labeled, “strongly disagree”, “disagree”, “neither disagree nor agree”, “agree”, and “strongly agree”. Experience seeking 1. I would like to explore strange places. 2. I would like to take off on a trip with no pre-planned routes or timetables. Boredom susceptibility 1. I get restless when I spend too much time at home. 2. I prefer friends who are excitingly unpredictable. Thrill and adventure seeking 1. I like to do frightening things. 2. I would like to try bungee jumping. Disinhibition 1. I like wild parties. 2. I would love to have new and exciting experiences, even if they are illegal. Demographics. Age, gender, education, income. Statistical Analyses NOTE: The analyses may be subject to change when writing up the manuscript for submission to a peer-review journal. The below analyses were suggested with Magnusson’s and Nilsson’s Master thesis in mind. The study could use a frequentist multiple linear regression analysis, which means that a regression line shows the relationship between the outcome variable and all predictor variables (Borg &amp; Westerlund, 2006). With this analysis, you get the proportion of explained variance for each variable and for all variables in total. Then an ANOVA is performed to test whether the multiple regression model predicts the outcome variable significantly better than a null hypothesis model (ie chance). However, there are some problems with doing this analysis. With such an approach, there is a risk that we will have problems with “over fit”, ie that our model explains more variance than what is actually true in the real population (van den Bergh et al., 2020). What could then be done is to instead select one or a few models that are tested against each other and the null hypothesis (van den Bergh et al., 2020). The problem with this is that we then assume that one of these models would be the “right one” and thus do not take into account that other models with other combinations of these variables may be possible (Hinne et al., 2020; van den Bergh et al ., 2020). Van den Bergh et al. (2020) also believe that the explained variance obtained by performing a classical linear regression is not a good measure for comparison between models, as the measure explained variance will always favor the most complex model. Doing so in any of these ways would not fulfill the purpose of the study - namely to exhaustively examine the potential roles of these variables in predicting naturalistic risk-taking when all relevant models are taken into account. To avoid these problems, Bayesian regression analyzes will instead be performed in JASP (van den Bergh et al., 2020). In this analysis package we also get access to Bayesian model averaging (BMA), which means that we do not have to choose between different models but can instead see to what degree and probability each predictor variable plays in all relevant models combined (Hoeting et al., 1999 ; van den Bergh et al., 2020; Hinne et al., 2020). The relevant models will in our case include all main effects and two-way interactions, which means that over 3 million models will be tested. BMA also has an algorithm that checks for “over fit”, which means that all our variables can be included in the analysis at the same time (van den Bergh et al., 2020). This is an advantage as the study wants to look at the extent to which the variables predict naturalistic risk-taking in the total model space, which also includes models with all variables. Another advantage of Bayesian statistics is that it quantifies each variable in probability, given the data, which makes it easy to compare the different variables with each other (Burnham &amp; Anderson, 2002; Hinne et al., 2020). To illustrate, adopt two models: M1 and M2. In a frequentist perspective, we learn that both M1 and M2 predict significantly (p = .002) for both. The significance values do not give us the opportunity to know how probable the models are in relation to each other, given data, but the only thing we know is how probable data are given the null hypothesis. With Bayes statistics, we can use Bayes factors (BFs) that can quantify what we are interested in. For example, if M1 and M2 get BF10 (the probability relative to the null hypothesis model) of 10 and 2, respectively, then we know that M1 is 5 times more likely than M2 to be the “correct” explanation (10/2 = 5). Pilot-Data for Constructing the Naturalistic Risk-Taking Index As part of an undergraduate thesis, a total of 40 items were administered in a survey to 91 participants. The items surrounded the same contents as the 19 used in the present study. Participants also responded to eight items of GRIPS. Finally, the participants were asked if there was anything problematic with the survey in order to detect if it for some items was hard to remember or if the wording of specific items could be improved. Fifteen of the items used a Yes/No response (e.g., “have you ever…” in order to determine if scenarios used for the items using open-ended responding were scale dependent (if not we expected measures to be correlated and that GRIPS should predict the dependent variable independently of response mode. An index was created on basis of the continuous items by z-transforming the responses for each item, and then summing across items. Another index was created on basis of the items with dichotomous responses by tallying (yes=1, no = 0) and then z-transforming the sum across items. Bayesian regression analyses showed that GRIPS, when controlling for gender, age, and income, predicted individuals scores on the index created from the items with continuous responses (BF=41.2). When the predictive power of each individual item was tested, it was only items concerning hard drugs and alcohol that yielded at least moderate evidence (BF&gt;3). This is not surprising: as noted under “Naturalistic risk-taking” in the Method, self-reported frequency measures will likely involve a great amount of noise due to reliance on episodic-memory retrieval. However, it is problematic if there is no evidence of predictive power when aggregating across noise-filled items. We thus tested if the strong evidence (BF&gt;10) remained also when removing the items about drugs and alcohol. The strong evidence did remain (BF=18.6, controlling for gender, age, income), showing it is possible to create a general index of naturalistic risk-taking. This index correlated with the corresponding index derived from items with dichotomous responses (r = .341, BF=28.3) which was also predictable from the GRIPS-scores (BF=52.9), suggesting that the predictive power of the naturalistic risk-index is not dependent on response-mode. Ethical considerations The Swedish Research Council (2020) describes the research ethics principles that must be taken into account in humanities and social science research. The information requirement, the consent requirement, the confidentiality requirement and the utilization requirement are the four main requirements that concretize the important things to protect the participants' integrity. In this study, the information requirement will be achieved through the introductory text in the questionnaire where the purpose, voluntariness and contact information for those responsible are stated. As participation is completely self-selected, the survey informs about consent and it is clear that it is possible to cancel at any time without explaining why the consent requirement will be achieved. In order for the confidentiality requirement to be complied with, it will be clear that the survey is anonymous and that it will not be possible to distinguish who has answered what. All data collected will only be used for research purposes, which means that the usage requirement can also be met. The survey can be perceived as time-consuming to answer, which can be problematic from an ethical point of view. Some issues may be sensitive to share. As the time required is presented at the beginning of the survey and all answers will be handled anonymously, these ethical problems are assessed as small.