Goals and guesses as reference points: a field experiment on student performance

Goals and guesses as reference points: a field experiment on student performance Gerardo Sabater-Grande 1 ·Nikolaos Georgantzís 1,2 ·Noemí Herranz-Zarzoso 3 Accepted: 7 May 2022 ©The Author(s) 2022 Abstract In this paper, we study overconfidence and goal-setting in academic performance, with and without monetary incentives. Students enrolled in a microeconomics course were offered the possibility of setting their own target grade before taking part in the final exam. They were also asked to guess their grade immediately after they had taken the exam (“post-diction”). In general, students overestimated their performance, both at the goal-setting and at the post-diction stages. Controlling for several sources of this bias (cognitive abilities, academic record and self-reported academic confidence), we find that the use of monetary rewards mitigates the overestimation of potential achievements and eliminates overestimation of actual achievements through the improvement of actual performance. Our results suggest that monetary incentives do not cause subjects to put more effort into correct guesses but make them put more effort into academic performance. Using students’academic records to measure overall skill, we find a strong Dunning–Kruger bias which is intensified in the presence of monetary rewards. Keywords Overconfidence bias · Reference points · Self-chosen goals · Postdictions · Monetary incentives · Dunning · Kruger cognitive bias 1 Introduction As has been established by prospect theory (Kahneman &Tversky, 1979; Tversky & Kahneman, 1992), human behavior strongly depends on reference points which are used to assess whether an outcome is perceived as a gain or as a loss. Besides the &Nikolaos Georgantzís nikolaos.georga[email protected] 1 LEE and Economics Department, Universitat Jaume I, Castellón, Spain 2 CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté, Dijon, France 3 Analysis Economic Department, University of Valencia, Valencia, Spain 123 Theory and Decision https://doi.org/10.1007/s11238-022-09892-x(0123456789().,-volV)(0123456789().,-volV) reference points which may be dictated by social norms, individual reference points may relate to rational expectations (Koszegi &Rabin, 2006,2007,2009) and goals (Diecidue &Van De Ven, 2008). Using the properties of the prospect theory value function, Locke and Latham (2002) and Heath et al. (1999) suggest that goal-related reference points affect individual intrinsic motivation, improving actual performance. 1 Wu et al. (2008) present a model in which subjects’performance is improved by exogenously set goals. This finding was empirically supported in Allen et al. (2016) and Markle et al. (2018), with a sample of marathon runners. In the field of education, Meng (2019) tested the effect of grade aspiration-driven reference points on student performance. In this paper, we study 2 types of reference points, depending on the timing of elicitation: self-chosen goals, elicited ex ante, by asking students to set their own target grade in a forthcoming exam and post-dictions, elicited immediately after the exam, by asking students to forecast their grade, given their perceived performance. 2 Following Fryer and Elliot (2008), self-chosen goals are empowered and proactive, creating commitment and acceptance. Thus, as personal bests, they act as reference points by inducing effort when current performance might otherwise be insufficient (Anderson &Green, 2018). 3 When goals are associated with monetary incentives, the performance improvement is even higher. 4 Whereas goals can be considered as target-based reference points, post-dictions can be used as actual behavior-based reference points. This is so, given that “the expected value of an outcome is an easily available integrated mechanism that could be used as a (…) reference point”(Hack &von Bieberstein, 2015). Generally, the literature suggests that students’post-dictions of performance are more accurate than any type of prediction or target. 5 This is because, “whereas predictions are made prospectively and are based on what students think they know, post-dictions are made retrospectively and reflect the student’s experience of the test”(Hacker et al., 2008). In this vein, we are interested in analyzing the effectiveness of monetary incentives to encourage students to make a more thoughtful assessment of their potential and actual academic performance. We hypothesize that monetary incentives could reduce students’overestimation bias by improving their guesses. With the objective of testing this hypothesis, we conducted a randomized field experiment to elicit students’reference points, well before and immediately after the exam of a microeconomics course. In addition, we control for 2 potential factors driving students’reference points: their skill (potential or actual) and their self-reported academic confidence. Contrary to our hypothesis, we find that monetary incentives 1 Their main properties are reference point dependence, loss aversion, and diminishing sensitivity. 2 Experimental evidence suggests that goals and expectations are crucial in the formation of reference points (Hack et al., 2016). 3 In words of van Lent and Souverijn (2020), “Economic theory (…) have shown how (…) goals can be used as reference points in order to increase performance for loss averse agents or hyperbolic discounters. 4 See Goerg and Kube (2012), Dalton et al. (2015), Corgnet et al. (2015), Brookins et al. (2017) and Herranz-Zarzoso and Sabater-Grande (2018). 5 See Glenberg and Epstein (1987), Maki (1998), Winnie and Jamieson-Noel (2002), and Serra and DeMarree (2016). 123 G. Sabater-Grande et al. do not improve students’guesses but their academic performance, causing a significant decrease in students’overconfidence. 2 Literature review This paper contributes to 3 lines of research: (1) monetary incentives and academic performance, (2) monetary incentives and overconfidence and, (3) overconfidence and cognitive ability. First, we deal with the effect of monetary incentives on academic performance. The empirical evidence available 6 offers mixed conclusions on the potential of these interventions. Depending on the context, the students’age or implementation details, impacts have been generally modest or even null. As a departure point of the present study, in Herranz-Zarzoso and Sabater-Grande (2018), we conducted a randomized field experiment aiming at improving academic performance in a different course (Introductory microeconomics). In that study, we found that monetary incentives offered on the basis of self-chosen goals were effective to significantly increase students’grades. Second, we focus on overconfidence bias. It has been generally observed that a person's subjective confidence in own performance is greater than the actual performance, resulting in the well-known overconfidence phenomenon. 7 Moore and Healy (2008) distinguish among 3 types of overconfidence: over-precision, a tendency of individuals to be excessively certain about the accuracy of their beliefs: overestimation, a tendency of subjects to exaggerate their absolute actual achievement or skill; and over-placement, which occurs when people perceive their performance in a group as better than it actually is. 8 Given the 2 types of reference point elicited, we observe 2 types of overestimation: students’overestimation of their targeted academic performance, defined as the difference between the grade obtained and the goal chosen before the exam, and students’overestimation of their actual academic performance, interpreted as the difference between the actual grade and the grade forecasted immediately after the exam. Previous research has shown that students consistently overestimate their performance on academic exams, 9 especially when their grades are low. Specifically, Hacker et al. (2008)find that good students are usually more accurate, with a tendency to underestimate, while bad students usually overestimate their performance. Recently Santos-Pinto and de la Rosa (2020) reviewed empirical research on overconfidence and its effect on economic choices. Psychology evidence suggests that overconfidence depends on personal traits and environmental factors. Economic 6 In the field of education, Lavecchia et al. (2016) offer a survey about the effectiveness of monetary incentives in improving academic performance. 7 Overconfidence has been called “the most significant of the cognitive biases”by Kahneman (2011), Bazerman and Moore (2012) refer to it as “a mother of all biases”and Meloy et al. (2006) state that it “is one of the most robust findings in the decision and judgment literature”. 8 In Hoezl and Rustichini (2005) subjects estimate whether their skills are better than the median. 9 See, for example, Chen (2003) and Foster et al. (2017), where it is concluded that “even after a great deal of practice (with over 10 exams), students on average did not improve the accuracy of their predictions”. 123 Goals and guesses as reference points: a field experiment on student… evidence shows that overconfidence can persist even under monetary incentives and feedback. Third, we deal with the relationship between subjects’cognitive ability and their overconfidence. Defining overconfidence as the difference between the guess and the proportion of correct answers, 10 a positive correlation between cognitive ability and confidence is found in Wolfe and Grosch (1990) 11 and Bruine de Bruin et al. (2007). 12 On the contrary, Stanovich and West (1998) reported a negative correlation of different cognitive ability tests with subjects’overconfidence. Later, Hoppe and Kusterer (2011) found that subjects with higher cognitive reflection test (CRT) scores had a significantly more precise self-assessment of their performance. However, the effects of cognitive abilities reported in earlier studies could depend on the type of overconfidence analyzed. In this vein, Duttle (2016) showed that, although overestimation 13 is not affected by cognitive abilities (as measured by a CRT), the CRT score was associated with a significant decrease in over-placement and overprecision. Nevertheless, Bialek and Domurat (2018) showed that the relationship between cognitive abilities and overconfidence disappeared after addressing 2 critiques: (1) the CRT does not measure cognitive abilities but, rather, the analytic cognitive style, and (2) overconfidence and cognitive ability are artificially correlated since the RPM test (which served as a basis for estimating overconfidence) is also a measure of cognitive abilities. We are interested in testing for the so‐called “Dunning–Kruger effect”. 14 This well-known cognitive bias implies that when people are objectively unskilled in a given area, they tend to largely overestimate their knowledge. As Dunning (2011) states, this effect has been observed in multiple domains of skill 15 and knowledge, including academics. The empirical evidence available on the relation between skill and overconfidence is mixed, depending on the type of overconfidence analyzed and the methods used to measure the 2 variables. 16 Specifically, when the same task is used to measure overconfidence and skill, the empirical evidence obtained can be distorted by ‘regression to the mean’effects, that is, individuals with higher skill are more likely to show less overconfidence. To avoid this effect, we use different tasks to measure overconfidence and skill, evaluated through a cognitive ability test or by means of the students’academic record. 10 The problem with this overconfidence measure, as Moore and Healy (2008) suggest, is that being excessively sure of having answered correctly reflects both overestimation of your performance and excessive confidence in the precision of your knowledge. 11 Using verbal and mathematical Scholastic aptitude test (SAT) scores to measure cognitive ability and a trivial quiz where subjects report their confidence in having answered correctly. 12 Using Raven’s standard progressive matrices for nonverbal cognitive ability and form G of the NelsonDenny reading comprehension subtest for verbal ability. 13 Defined as the difference between a subject’s estimated number of correct test items in the Raven progressive matrices (RPM) test and his/her actual performance in the test. 14 Kruger and Dunning (1999)find that the more poorly participants actually performed in tests of humor, grammar, and logic grossly, the more they overestimated their own performance. 15 Park and Santos-Pinto (2010)find support for the Dunning-Kruger hypothesis in both poker and chess tournaments. 16 See Feld et al. (2017). 123 G. Sabater-Grande et al. In addition, we examine the relationship between perceived skill, measured by means of reported academic self-confidence, and overconfidence. Following Sander and Sanders (2009), we use the academic behavioral confidence scale 17 (ABC, hereafter) as a global measure of perceived academic confidence. Using a group test of general mental ability by Tandon (1971) and a self-confidence inventory by Agnihotri (1986), Dhall and Thukral (2009) investigated the relationship among intelligence, self-confidence, and academic achievement in schools in Pakistan. They found that intelligence was positively correlated with both self-confidence and academic achievement. However, Saenz et al. (2019) obtained that attendance, study habits/preparation, and/or prior performance did not offer a strong or robust explanation of students’grade predictions. The rest of the paper is structured as follows: first, we introduce the design of the experiment implemented and the hypotheses; second, we present the methods used; third, we analyze the empirical evidence collected and present our results; last, we discuss them. 3 Experimental design A randomized field experiment was conducted to analyze students’overconfidence using monetary incentives as the treatment variable. In the beginning of the semester, 154 students enrolled in a microeconomics course at a Spanish university in 2018. 18 They were offered the possibility of setting their reference points well before 19 and right after completion of the final exam. In the call, students were informed that those responding affirmatively would be immediately randomly assigned 20 to one of two groups: 1 without monetary incentives (NMI) and 1 with monetary incentives (MI). Additionally, we notified that participants would receive information about their corresponding group before they were invited to choose a goal for their final exam grade. From the 138 volunteers, 64 were randomly assigned to the NMI condition and 74 to the MI one. However, only 42 (16 females and 26 males) in the NMI treatment and 58 (26 females and 32 males) in the MI treatment decided to finally take the exam. 21 Furthermore, 16 students not responding affirmatively to our call were included in a baseline condition (non-participants group, NP group hereafter) to compare non-participants’final grades with those corresponding to the ones obtained by participating students. In doing so, we can check for potential self-selection bias. Table 1summarizes the characteristics of the 3 groups. 17 This scale encompasses cognitive judgments regarding study-related behaviors, such as confidence in independent study, attaining grades, discussing course material and questions, and academic engagement through attending formal sessions. 18 The experiment was approved in the framework of an educational innovative project promoted by the university and approved by the faculty of law and economics. 19 After a midterm exam (3 months before the final exam), self-chosen goals were elicited. Moreover, although we allowed their revision until 1 day before examination, no student submitted a new goal after their initial decisions. The midterm (final) exam represented 30% (70%) of the course grade. 20 A pure random draw using a die was used to allocate subjects individually into groups. 21 A Mann–Whitney test shows no gender differences between treatments (pvalue: 0.5029). 123 Goals and guesses as reference points: a field experiment on student… In the call, students were instructed that, if assigned to the MI group, their monetary reward (R) would depend on the chosen reference point (RP) and the grade (GR) they obtained in the exam, according to the following scoring rule: R¼RP2if GR ¼RP;R¼0if GR\RP This is a more conservative test of overconfidence bias than a grade forecast question because the reward depends on a chosen reference point instead of the obtained grade. This is so because, like in Park and Santos-Pinto (2010), 22 a risk neutral player who overestimates her performance in a given amount incurs in a larger loss (0 earnings and loss equal to the square of the RP) than if she underestimates it in the same quantity (loss equal to the square of the difference between GR and RP). Thus, the optimal reference point of a risk neutral player should be smaller than his optimal grade forecast. To counterbalance the abrupt fall in earnings from a grade below the reference point, the quadratic scoring rule chosen here gives a good incentive for choosing a higher reference point because earnings increase marginally more for higher (successful) bets. Moreover, students were instructed that only 1 of the 2 elicited reference points (participants’self-chosen goal or post-diction), chosen at random, would be used to determine their rewards. The actual average payment received from successful subjects in the experiment was €16 from a maximum potential reward of €49 (given that the top score in the final exam is 7 points). To distinguish between the 2 types of overconfidence, we define students’potential overconfidence (POC) as the selfchosen goal minus the grade obtained, and students’actual overconfidence (AOC) as the difference between the post-diction and the actual grade. In addition, participants’cognitive ability was measured by means of the abstract reasoning part of the differential aptitude test (DAT-AR for PCA, Bennett et al., 1974). Moreover, we elicited scores on the ABC scale. 23 The research by Nicholson et al. (2013) suggested that undergraduates’confidence in their ability is related to academic performance. Specifically, this study showed that students who, at the beginning of the semester, were confident about their grades also perform better in Table 1 Summary of groups Groups Number of subjects Paymentoffered in the call Willingness to participate Randomization Payment used NP 16 No payment/money No No – NMI 42 No payment/money Yes Yes No money MI 58 No payment/money Yes Yes Real money 22 They use a similar quadratic scoring rule as well as bets to incentivize poker and chess players’ forecasts of relative performance in tournaments. The exam grade performance studied here resembles more the chess rather than the poker treatment, because students have a rather good idea of their abilities and would not be expected to treat their exam grades as purely random. 23 See Sander and Sanders (2009). 123 G. Sabater-Grande et al. their end-of-semester marks. Given the type of course considered in this study, we are especially interested in two factors of the ABC scale: grades and study beliefs. Using the aforementioned experimental design, we propose four hypotheses. Because monetary incentives induce individuals’more thoughtful guesses, they should help to bring goals and expectations closer to actual academic performance. In consequence, Hypothesis 1 (H1) Introducing monetary incentives to elicit students’reference points, should soften both potential and actual overconfidence by improving guesses. In addition, it is expected that monetary incentives will lead to more effort, producing a higher academic performance. Hence, Hypothesis 2 (H2) Implementation of monetary incentives will increase actual grades. Moreover, assuming that both cognitive ability and academic record are good predictors of academic aspiration, performance and lower overconfidence, we hypothesize that: Hypothesis 3 (H3) Students with higher potential and actual skills will choose higher goals, they will achieve a better academic performance and they will show a lower level of overconfidence. Finally, since self-reported studying and grade confidence should be related to students’aspirations, Hypothesis 4 (H4) Students self-reporting a higher academic confidence should choose higher goals. Summing up, by controlling for potential driving factors, such as skill and reported academic self-confidence, our experimental design aims at analyzing whether both overconfidence and Dunning–Kruger bias can be mitigated using monetary incentives to elicit students’reference points. 4 Methods In this section, we offer detailed information on the measures used to elicit students’ cognitive abilities and self-reported academic confidence, respectively. 1. The abstract reasoning part of the differential aptitude test for personnel and career assessment. The abstract reasoning (AR) scale of the DAT used in this experiment is included in the Spanish adaptation of DAT-5 by the publisher TEA (Cordero &Corral, 2006). This test is used as a non-verbal measure of reasoning ability and involves the capacity to think logically and to perceive relationships in figures made up of abstract patterns. It is considered as a marker of fluid intelligence (Colom et al., 2007) and the component of intelligence most related to general intelligence or g factor (McGrew, 2009). The advantage of this test is that it can be administered quickly, containing 40 multiple-choice items within a 20-min time limit. 2. The academic behavioral confidence scale (Sander &Sanders, 2009). The ABC scale used in this research was the 24-statement version. These statements elicit 123 Goals and guesses as reference points: a field experiment on student… the student’s expectation of achieving good grades in assessments (grades), engaging in independent study (studying), attending lectures, tutorials and other taught sessions (attendance), and discussing material with tutors, lecturers, and peers (verbalizing). However, all analyses presented in this paper were computed only for 2 ABC subscales: grades and studying. In Sander et al. (2011), the ABC scale shows cross-cultural validity when translated into Spanish and administered to over 2 thousand Spanish psychology students. 5 Data analysis 5.1 Sample self-selection Given that our design requires students’willingness to participate in the experiment, potential self-selection problems do not affect differences between MI and NMI. Nevertheless, the baseline group allows us to test for self-selection bias by comparing prior midterm grades of participating and non-participating subjects. A Mann– Whitney test shows that differences between participant and non-participant groups are not statistically significant (pvalue: 0.1517). 5.2 Descriptive statistics and tests Table 2shows the sample split into NMI and MI, and presents descriptive statistics of: normalized data to a scale from 0 to 10 corresponding to (1) subjects’self-chosen goals, (2) post-dictions, (3) grades and (4) POC and AOC. Moreover, we display additional descriptive statistics corresponding to: (1) score in the DAT-AR test, (2) Table 2 Descriptive statistics and group comparisons of self-chosen goals, post-dictions, grades, overconfidence, DAT-AR score, studying and grades subscales of the ABC scale, and academic record N NMI N MI Mean NMI Mean MI S.D NMI S.D I M–W test/ Ttest pvalue nex-post power Self-chosen goal 42 58 6.01 5.83 0.98 0.99 0.4348 942 Post-diction 42 58 5.34 5.32 1.58 1.23 0.7625 157,344 Grade 42 58 4.04 5.26 2.23 2.41 0.0116** POC 42 58 1.97 0.58 2.16 2.40 0.0037*** AOC 42 58 1.30 0.06 2.06 2.32 0.0072*** DAT-AR score 42 58 25.19 22.74 7.62 7.71 0.1186 308 Studying confidence 42 58 14.31 15.10 2.87 2.16 0.1539 326 Grades confidence 42 58 21.62 22.03 3.81 4.02 0.5320 2866 Academic record 42 58 6.40 6.40 0.66 0.49 0.4402 – ***p<0.01, **p<0.05, *p<0.1 123 G. Sabater-Grande et al. scores in the grades and studying subscales of the ABC scale, and (3) academic record. Additionally, we present pvalues from a ttest or a Mann–Whitney (M–W) test comparing 24 NMI and MI conditions for all these variables. Moreover, to check whether the lack of significance for some differences was due to a low statistical power, we conduct an ex-post power analysis using Stata with power set at 0.80 and probability at 0.05. The last column of Table 2shows the minimum sample size to find statistically significant differences. We observe that although self-chosen goals and post-dictions are similar in the NMI and the MI groups, both types of overconfidence are significantly higher when subjects’reference points are elicited without incentives (NMI group). Specifically, a POC of almost 2 points is reduced to a quarter when subjects’self-chosen goals are elicited using money. In the same manner, an AOC of 1.3 points disappears when subjects’post-dictions are obtained under monetary incentives. It is worth mentioning that this reduction of the students’overconfidence is not caused by a difference in their expectations but, rather, by an improvement in their performance. Moreover, this reduction in both POC and AOC is not influenced by cognitive ability or academic confidence/records, since these variables do not present significant differences across groups. In Fig. 1, means are presented and statistical differences are tested through a Wilcoxon test. Specifically, in the first row, we display means of self-chosen goals, post-dictions, and grades, split between NMI and MI. For both groups, we obtain that the median of students’self-chosen goals is significantly higher than the median of their post-dictions. However, only NMI subjects obtain a median grade significantly lower than their median post-diction, since MI subjects post-diction their grade accurately. The 2nd row of Fig. 1presents the mean values of POC and AOC for both groups of subjects, showing that real monetary incentives significantly reduce both POC and AOC medians, the latter being close to 0. Figure 2displays the confidence measured using self-chosen goals and postdictions against the grade obtained in the midterm exams. The 45 ° line provides a benchmark, given that points above the line would represent overconfidence whereas points below the line would represent under-confidence. The dashed line is the minimum grade required to pass an exam. In the right panel of this figure, we can observe that most of the NMI subjects show both POC and AOC. Thus, this right panel reproduces the effect of overconfidence in general terms. This pattern is not observed for MI subjects (left panel). In fact, they show overconfidence for low grades and under-confidence for high grades, as can be expected when a regression to the mean effect is present. Now, we analyze the relationship between subjects’confidence and cognitive ability. Figure 3displays both potential and actual confidence for subjects included in both the NMI and the MI group against cognitive ability. For both groups, NMI and MI subjects, Fig. 3shows no pattern relating confidence and cognitive ability. 25 24 We use ttests for comparisons between normally distributed variables (grade, POC and AOC, and DATAR score) and the M–W test for comparisons between the remaining (non-normal) variables. 25 Spearman's coefficients show a non-significant correlation. 123 Goals and guesses as reference points: a field experiment on student… establishing causal relationships between monetary incentives and academic performance. Although this paper reveals the positive effect of monetary incentives in academic performance, policymakers might be concerned by (1) crowding-out effects (Gneezy et al., 2011) and (2) the financial resources needed. Regarding the argument that monetary incentives could crowd out students’intrinsic motivations, List et al. (2018) only found a limited temporary effect, obtaining that one year later nonincentivized tests were not negatively affected. With respect to the second concern, Herranz-Zarzoso and Sabater-Grande (2018) showed that lower cost-intensive mechanisms as a rank-order tournament scheme can be as effective as a piece-rate payment mechanism like the one adopted here. Further research is needed to explore factors like personality traits to better explain subjects’overconfidence bias, and new tools reinforcing monetary incentives to soften this overconfidence, like experience and feedback on previous academic tasks. 7 Appendix 7.1 Experimental instructions (translated from Spanish) 7.1.1 Call instructions The LEE Research Team of the University Jaume I is conducting a study to evaluate which factors contribute to good performance on this course. The team will use your responses, grades from this course and your academic records if you consent to participate. All the information will be anonymously associated with the findings of this research. You have the right to withdraw from the study at any time during the semester. If you withdraw there will be no consequences for you; your academic standing and record will not be affected. If you consent to participate in this study you will be immediately randomly assigned to one of two groups: group 1 or group 2. If you have been selected in group 1 you will be paid by your decisions but in the case of being assigned to group 2 you will not be rewarded by them. You will receive information coming soon by email about your corresponding group, before you have to make any decision. Once you have been informed, you will have to set a goal for your final exam grade in this course. Remember that the final exam is worth 70% of your final course grade (7 points is the maximum grade to be achieved). You will be allowed to revise your goal until one day before examination. All communications should occur over email. Moreover, you will be asked to guess the grade you think you will get in the final exam immediately after being completed. Only one of the two decisions, chosen at random, will be used to determine your reward if you have been randomly selected in group 1. In this case, your reward, in euros will be equal to: 123 G. Sabater-Grande et al. R¼Guess2if Grade Guess In addition, if you have declared your willingness to participate, you will be asked to perform two additional tasks (a 20-min abstract reasoning test and and a short question test). Please respond to this mail if you consent to participate in this study. 7.1.2 Instructions for group 1 participants You have been randomly selected in group 1. Thus, your decisions can be monetary rewarded. The first decision you need to make is choosing a goal for your final course grade (remember that your communication must be by email and the goal can be revised until one day before examination). Please, think carefully before setting your goal. Remember that the second decision you must take is to guess the grade you think you will get in the final exam, immediately after being completed (you will be asked to write your guess at the final page of the exam). Only one of the two decisions, chosen at random, will be used to determine your reward. For transparency, we will use one number of the Lotería Nacional raffle of May 26, 2018 for each class group (A, B, C, D, E and F). –Group A: third number of the first prize –Group B: forth number of the first prize –Group C: fifth number of the first prize –Group D: first number of the second prize –Group E: second number of the second prize –Group F: first number of the second prize For each course group, we will use your first decision to reward you in case that the raffle corresponding number is 1, 2, 3 or 4, and we will adopt your second decision to remunerate you if the raffle number is 5, 6, 7, 8 or 9. Remember that, in both decisions, you have to guess your grade over 7 and if you equal or exceed your guess, you will received a monetary payoff in euros according to the following function: R¼Guess2if Grade Guess 7.1.3 Instructions for group 2 participants You have been randomly selected in group 2. Thus, your decisions will not be monetary rewarded. The first decision you need to make is choosing a goal for your 123 Goals and guesses as reference points: a field experiment on student… final course grade (remember that your goal can be revised until one day before examination). The second decision you must take is to guess the grade you think you will get in the final exam immediately after being completed. 8 Logit transformation Since reference points and grades are limited variables taking normalized values between 0 and 10, the results obtained by OLS can suffer from heteroskedasticity which invalidates statistical inference. To avoid this shortcoming, on the one hand, the standard errors reported in OLS regressions are robust to the presence of heteroskedasticity. On the other hand, to check if the results obtained are robust, we perform a logit transformation of our dependent variables based on reference points and grades. The logit transformation of self-chosen goals (SCG), post-dictions (POST), grades (G), potential overconfidence (POC) and actual overconfidence (AOC) are given by: SCGi¼ln SCGi 10 SCGi  Table 3 OLS regressions for self-chosen goals and post-dictions elicited in the final exam splitting the sample between monetarily incentivized and non-monetarily incentivized subjects Self-chosen goals all subjects Self-chosen goals MI subjects Self-chosen goals NMI subjects Postdictions all subjects Postdictions MI subjects Postdictions NMI subjects MI –0.0782 (0.190) –0.0681 (0.269) Cognitive ability 0.0314** (0.0144) 0.0384** (0.0183) 0.0141 (0.0234) –0.0105 (0.0191) 0.00393 (0.0223) –0.0425 (0.0364) Academic record 0.477*** (0.168) 0.217 (0.255) 0.732*** (0.195) 0.676** (0.271) 0.549 (0.402) 0.801** (0.386) Grades confidence 0.0306 (0.0278) 0.0235 (0.0308) 0.0411 (0.0537) –0.00464 (0.0359) –0.0325 (0.0372) 0.0642 (0.0682) Studying confidence –0.0297 (0.0460) 0.0340 (0.0782) –0.0886 (0.0563) 0.0669 (0.0741) 0.123 (0.0890) –0.0138 (0.0947) Gender –0.194 (0.205) –0.299 (0.275) –0.0843 (0.307) –0.369 (0.279) –0.347 (0.334) –0.526 (0.560) Constant 2.088 (1.333) 2.675 (1.928) 1.382 (1.603) 0.631 (1.896) 0.739 (2.340) 0.299 (2.618) N100 58 42 100 58 42 R-squared 0.233 0.184 0.380 0.112 0.099 0.167 123 G. Sabater-Grande et al. Table 4 OLS regression for students’grades Grades all subjects Grades MI subjects Grades NMI subjects MI 1.079*** (0.376) Cognitive ability 0.0198 (0.0258) –0.0103 (0.0330) 0.0842** (0.0361) Academic record 1.673*** (0.404) 2.179*** (0.555) 0.988* (0.492) Grades confidence –0.0244 (0.0565) –0.0238 (0.0631) –0.126 (0.0931) Studying confidence 0.295*** (0.0977) 0.268* (0.147) 0.401*** (0.144) Gender –0.264 (0.423) –0.942 (0.603) 0.976** (0.479) Self-chosen goal 0.0661 (0.247) 0.0672 (0.367) 0.322 (0.316) Post-diction 0.359*** (0.126) 0.292 (0.247) 0.519*** (0.135) Constant –14.15*** (2.527) –13.50*** (3.721) –12.50*** (3.330) Observations 100 58 42 R-squared 0.439 0.386 0.581 The regression is split into monetarily incentivized and non-monetarily incentivized subjects. Robust standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1 Table 5 OLS regressions for POC and AOC splitting the sample into monetarily incentivized and nonmonetarily incentivized subjects POC POC MI subjects POC NMI subjects AOC AOC MI subjects AOC NMI subjects MI –1.122*** (0.399) –1.116*** (0.406) Cognitive ability 0.0127 (0.0287) 0.0448 (0.0353) –0.0527 (0.0392) –0.0286 (0.0293) 0.0115 (0.0360) –0.109*** (0.0372) Academic record –1.471*** (0.436) –2.138*** (0.595) –0.908 (0.578) –1.272*** (0.426) –1.805*** (0.642) –0.839* (0.477) Grades confidence 0.0558 (0.0641) 0.0557 (0.0698) 0.121 (0.109) 0.0196 (0.0667) –0.00217 (0.0712) 0.144 (0.0861) Studying confidence –0.348*** (0.107) –0.273* (0.159) –0.454*** (0.137) –0.250** (0.114) –0.180 (0.161) –0.379** (0.143) Gender 0.220 (0.463) 0.767 (0.680) –0.762 (0.569) 0.0402 (0.456) 0.710 (0.648) –1.206** (0.545) Constant 15.88*** (2.934) 15.79*** (3.822) 13.29*** (3.847) 14.41*** (2.812) 13.81*** (4.220) 12.20*** (3.193) Observations 99 57 42 99 57 42 R-squared 0.303 0.269 0.360 0.253 0.218 0.378 Robust Standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1 123 Goals and guesses as reference points: a field experiment on student… Table 6 OLS regressions for self-chosen goals and post-dictions elicited in the final exam splitting the sample between monetarily incentivized and non-monetarily incentivized subjects Self-chosen goals all subjects Self-chosen goals MI subjects Self-chosen goals NMI subjects Postdictions all subjects Postdictions MI subjects Postdictions NMI subjects MI –0.0262 (0.0906) –0.0721 (0.118) Cognitive Ability 0.0147** (0.00665) 0.0182** (0.00864) 0.00607 (0.0105) 1.89e–05 (0.00757) 0.00205 (0.00986) –0.00544 (0.0121) Academic Record 0.231*** (0.0833) 0.0857 (0.111) 0.372*** (0.0964) 0.302** (0.124) 0.249 (0.180) 0.350* (0.174) Grades Confidence 0.0117 (0.0121) 0.0104 (0.0134) 0.0143 (0.0237) –0.00408 (0.0152) –0.0127 (0.0161) 0.00962 (0.0326) Studying Confidence –0.0141 (0.0214) 0.00929 (0.0379) –0.0366 (0.0252) 0.0110 (0.0274) 0.0526 (0.0390) –0.0275 (0.0352) Gender –0.0882 (0.0904) –0.137 (0.120) –0.0356 (0.133) –0.146 (0.128) –0.156 (0.144) –0.165 (0.271) Constant –1.404** (0.625) –0.908 (0.847) –1.869** (0.757) –1.658** (0.743) –1.940* (1.045) –1.633 (0.982) N100 58 42 99 58 41 R-squared 0.233 0.171 0.417 0.122 0.103 0.179 Logit transformation implemented in the dependent variables Standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1 Table 7 OLS regression for students’grades. The regression is split into monetarily incentivized and nonmonetarily incentivized subjects Grades all subjects Grades MI subjects Grades NMI subjects MI 0.503*** (0.188) Cognitive ability 0.0115 (0.0130) –0.00439 (0.0162) 0.0418** (0.0197) Academic record 0.792*** (0.210) 0.925*** (0.284) 0.642* (0.322) Grades confidence –0.0182 (0.0286) –0.00708 (0.0291) –0.0854* (0.0500) Studying confidence 0.140*** (0.0488) 0.112 (0.0734) 0.209** (0.0770) Gender –0.0155 (0.207) –0.367 (0.296) 0.626** (0.267) Self-chosen goal 0.0838 (0.122) 0.106 (0.176) 0.125 (0.184) Post-diction 0.138* (0.0708) 0.103 (0.123) 0.234*** (0.0764) Constant –9.197*** (1.409) –8.267*** (1.908) –9.049*** (1.998) Observations 97 56 41 R-squared 0.379 0.316 0.521 Standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1. Logit transformation implemented in the dependent variables 123 G. Sabater-Grande et al. POSTi¼ln POSTi 10 POSTi  Gi¼ln Gi 10 Gi  POCi¼ln POCi 10 POCi  AOCi¼ln AOCi 10 AOCi  Once the dependent variables have been transformed, we performed OLS regressions. Tables 3,4and 5present the results in a similar way that Tables 67and 8, showing that previous results are robust. Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018096927-B-100), Generalitat Valenciana (AICO/2018/036), Universitat Jaume I (UJI-B2018-76/77), and Ministerio de Educación Cultura y Deporte (FPU14/02966). N Georgantzis received support by the Agence Nationale de la Recherche (grant ANR-18-CE26-0018). Availability of data and material Data are available and provided as supplementary information. Table 8 OLS regressions for POC and AOC splitting the sample into monetarily incentivized and nonmonetarily incentivized subjects POC POC MI subjects POC NMI subjects AOC AOC MI subjects AOC NMI subjects MI –0.247*** (0.0856) –0.237*** (0.0843) Cognitive ability 0.00361 (0.00619) 0.0103 (0.00737) –0.0101 (0.00923) –0.00576 (0.00600) –(0.00728) –0.0230*** (0.00793) Academic record –0.321*** (0.0925) –0.453*** (0.125) –0.209 (0.126) –0.270*** (0.0888) –0.377*** (0.134) –0.181* (0.101) Grades confidence 0.0113 (0.0134) 0.0110 (0.0140) 0.0249 (0.0243) 0.00423 (0.0137) –0.000607 (0.0143) 0.0310 (0.0184) Studying confidence –0.0756*** (0.0232) –0.0539 (0.0333) –0.102*** (0.0301) –0.0531** (0.0237) –0.0364 (0.0330) –0.0826** (0.0307) Gender 0.0350 (0.0980) 0.157 (0.142) –0.178 (0.122) 0.00201 (0.0952) 0.144 (0.134) –0.259** (0.116) Constant 3.460*** (0.641) 3.283*** (0.798) 3.009*** (0.864) 3.052*** (0.592) 2.865*** (0.874) 2.627*** (0.684) Observations 99 57 42 99 57 42 R-squared 0.309 0.267 0.367 0.257 0.218 0.382 Robust standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1. Logit transformation implemented in the dependent variables 123 Goals and guesses as reference points: a field experiment on student… Code availability Not applicable. Declarations Conflict of interest The first, second and third funding institutions provided support for experimental participant rewards, whereas the fourth provided salaries for NH-Z, respectively. None of the funders have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. NG is affiliated with a commercial company, Burgundy School of Business, which has only provided salary to this specific author and has not had any other involvement in the funding, the design, the scope or the publication process of this research. Furthermore, this commercial affiliation does not alter our adherence to Theory and Decision policies on sharing data and materials. Finally, no competing interests exist which could interfere with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of our research reported in this article submitted to one of the journals. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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