The Effects of Signature Placement on Ones Capability to Lie for their Own Benefit

Ramya Kumar1 and Tooba Alwani1

1Science Department, Great Neck South High School, Great Neck, NY 11020, U.S.A. Both authors contributed equally to this workCharacter count: 30,190Running Title: Signature Placement Affects Lying Involvement

AbstractThe problem of lying is very prevalent in our country especially regarding tax return forms and fraud. But studies have shown that attention to internal honesty tends to decrease lying.Additionally, signing ones name is one way to activate that attention to oneself. Therefore signing before as compared to signing after will activate attention to oneself, promoting internal honesty, when reporting information. To test this hypothesis, a study was conducted using different signature locations to see its effects on how much a person lies with a given benefit. This was done by separating the subjects into conditions with different signature locations. Subjects were asked to report numbers through a random number generator. They believed that no one would be able to access the numbers they actually received. Subjects were given a prize incentive in order for them to lie on the numbers they reported. A correlation was found between prize incentive and lying and also between some of the signature conditions. Results mostly showed that signing leads to less lying than not signing at all.

Keywords: Internal honesty/ Moral disengagement/ Unethical behavior/ Signature/ Self-Identity

IntroductionThere are many types of problems in our country that are caused by dishonest behaviors. Dishonest behavior can be anything that deals with lying, cheating, or stealing. This paper specifically looks at lying during a self- reporting task such as filling in a tax return form, college application, curriculum vitae, etc. Misrepresentation on any of these tasks is also considered to be fraud. Fraud usually involves deception in order to gain a benefit for oneself or a loss for another (The Association of Certified Fraud Examiners 2008). Many attempts have been made to try and measure the true prevalence of fraud in our country, but most major frauds go undetected or end up not being reported (Fraud Advisory Panel 2006). Even though there isnt enough statistics to measure this, many companies say that fraud is a very prevalent and serious issue. According to the Global Economic Crime Survey (2007), 43% of international businesses were victims of fraud during the last two years. Some countries have also lost billions of dollars due to insurance frauds (Fraud Advisory Panel 2006). Depaulo et al. (2008) found that people lied in 20 to 31 percent of their social interactions, including partaking in fraud. Nowadays colleges and workplaces can easily detect lying on their forms. But financial fraud is still very difficult to detect. In order to prevent this problem, many self-reporting forms require a signature at the end of its completion (Lemert 1989). According to Mazar & Amir (2008), a way to reduce dishonesty in our society is by bringing peoples attention to their internal honesty. Internal honesty is defined as integrity to ones self (Shu, et al. 2012). Many documents require signatures rather than printing ones name because it shows commitment to what the document suggests (Kam et al. 2001; Kanpp, Crystal, and Prince 2003; Mnookin 2001). Written signatures also show evidence of ones actions and obligations during law cases (Weinberg 2003). Signing also plays a huge influence on a consumers behaviors (Kettle & Hauble 2011) due to individuals associating their signatures with their personality and character traits (Briggs 1980; King and Koehler 2000). A study done by Kettle and Hauble (2011) tested this by asking runners to sign their name on a blank piece of paper before they go and buy running shoes. He found that the runners who signed were more interested in buying the shoes than those who didnt sign before buying the shoes. This shows that signing does influence behavior, such as internal honesty, through an activation of self- identity (Kettle & Haubl 2011). Self-identity is ones attention to themselves in moral values, character traits, physical attributes, and abilities (Shu, et al. 2012). A study conducted by Bandura (1996) found that signing ones name also decreases moral disengagement, which is a process humans use to rational their unethical behavior. Signing causes this rationale to decrease because then one is in sync with his or her morals rather than excuses. The study tested this by having subjects sign a piece of paper and fill in letters that could fit to make a word in the following: __ __ R A L, __ I __ __ __ E, E __ __ __ C __ __. The subjects that were able to fill in letters that made morally related words such as moral, virtue, and ethical were more in sync with their morals. The subjects who signed a piece of paper before reporting were the ones who were more likely to think these words than those who did not sign beforehand. Overall, the effects of signing ones name will greatly reduce dishonest behavior on self-reporting tasks. But no one has really looked into the effects of the signatures location. Like previously mentioned, most forms have this signature location at the end, but what if it were at the beginning. The purpose of our study was to see if signing at the beginning or signing at the end reduces lying, or dishonest behavior, on self- reporting tasks. We hypothesized that signing ones name before a given task could promote more internal honesty than signing at the end. Signing at the beginning could cause people to remind themselves more of their morals when they are claiming information compared to signing after. To evaluate this hypothesis, we created a situation in which subjects would be spilt into different signature location conditions and given a task in which they could lie freely with no consequences and gain prizes. This paper also discusses how we quantified and observed lying within the task.

ResultsThe first thing we wanted to analyze was the amount of people who lied (did not lie) in each signing condition (Figure 2A). For this analysis, the degree of lying by the subjects did not matter, as long as some misrepresentation in numbers were found between the subjects final sheet and their assigned generator. Each signing condition had a sample size of 22 subjects (66 subjects in total). In the signing before condition, 5 people lied and 17 did not. In the signing after condition, 10 people lied while 12 did not. And finally in the no signing condition, 13 people lied and 9 did not. The ANOVA p-value amongst all these conditions was 0.0602368. We also analyzed the specific p-values between each signing condition. First, signing before and no signing had a p-value of 0.0586752 when being compared. Signing before and signing after had a p-value of 0.19862355. Finally, signing after and no signing had a p-value of 0.7997.The next two analyses looked specifically at the degree of lying done by the subjects in each condition. Only the subjects who were in the lied category in the previous analysis were being compared. The total sample size in the next two analyses is 28 subjects (signing before: n=5, signing after: n=10, no signing: n=13). The first analysis of the two, was to see the total sum difference between the subjects reported numbers and their assigned generators numbers (Figure 2B). In the signing before condition, the average sum difference was 7.4 with a standard deviation of 2.70185. In the signing after condition, the average sum difference total was 18.4444 with a standard deviation of 4.21637. And finally in the no signing condition, the average sum difference was 33.38462 with a standard deviation of 10.74351. The ANOVA P-value amongst all these conditions was 0.0000026. We also wanted to compare each condition to one another. Signing before and no signing had a p-value of 0.0000045Signing before and signing after had a p-value of 0.040443. Finally, signing after and no signing had a p-value of 0.0004392. The second degree of lying analysis, was to see how many times the subjects changed their numbers on their final sheet in each condition (Figure 2C). In our graph this is known as lying frequency. The sample size in each condition was the same as the previous analysis. In the signing before condition, the average times the subjects lied was 1.2 with a standard deviation of 0.447214. In the signing after condition, the average times the subject lied was 2.88889 with a standard deviation of 0.927961. And finally in the no signing condition, the average times the subject lied was 5.384615 with a standard deviation of 2.10311. The ANOVA P-value amongst all these conditions was 0.0000026. We also wanted to compare each condition to one another. Signing before and no signing had a p-value of 0.0000932. Signing before and signing after had a p-value of 0.1125642. Finally, signing after and no signing had a p-value of 0.0037564.The last analysis was to compare the questions we asked on the final sheet to the degree a person would lie (specifically lying frequency). Two of the questions we asked had no correlation to the lying frequency, as well as gender and grade. One question we asked however, did have a correlation to the lying frequency. The next analysis shows the prize incentive ranking question being compared to lying frequency (Figure 2D). The prize incentive ranking was determined by their response to the following question: On a scale of 1 -7, how badly do you want to win this prize? The number reported became the prize incentive rank. Each prize incentive ranking response had its own sample size (1- n=2, 2- n=7, 3- n=6, 4- n=10, 5- n=7, 6- n=12, 7- n=23). In prize incentive ranks 1, 2, and 3, the average lying frequency amongst these ranks was 0. But in order to do exponential regression, the values had to be changed to 0.00001. In prize incentive rank 4 however, the average lying frequency was 0.272727 with a standard error of 0.10447. In the prize incentive rank 5, the average lying frequency was 0.75 with a standard error of 0.134519. The prize incentive rank 6 had an average lying frequency of 2.692308 with a standard error of 0.268264. And finally, the rank 7 prize incentive had an average lying frequency of 3.16 with a standard error of 0.320902. The R2 value was 0.81949 (R-value being 0.90526).

Discussion Based on the data there is significant difference between signing and no signing in the lying frequency and sum difference analysis. This indicates that signing created the subjects to lie the least, while the subjects in the no signing condition, lied the most. In terms of the number of people that lied in each condition, there was not a significant difference, but it could be lowered with a larger sample size. There was no significant difference in lying frequency between signing before and signing after, but it can be very close with a larger sample size. This means that the lying done in the signing before and signing after conditions were very similar to one another. However, there is a significant difference in sum difference between signing before and signing after. This indicates that signing before will lead people to lie less in amount than signing after. There is also correlation between the prize incentive and how much each student lied. The more the students valued the prize, the more he or she was willing to lie for the prizes. This indicates that the prize did motivate students to lie showing that the prize set up induced lying. There however wasn't a significant correlation between competitiveness and lying. Therefore indicating that the competitiveness was not a variable in determining how much students lied. We also did not find any correlations between gender and grade compared to lying. Overall our data seems to add to previous studies. It indicates that signing indeed does activate your morals (Shu, et al. 2012). Our data shows that signing not only influences lying but could also influences the degree of lying based to the placement of the signature if a larger sample size is present. We encountered several problems in this study. First was a way to quantify lying, which we were able to do through the use of a random number generator. Additionally, we needed to find a way to allow students to lie freely. We needed to ensure that they felt we would not know the truth about the results they were getting. We went through several different methods to do this. The first method was using a program called Hypercam. Hypercam lets the user record the display his or her own computer screen. We were planning to receive the actual numbers received from the subjects by recording their screens and looking through the video then receiving the subjects actual numbers from the generator. The generator for this method was a true random number generator and not fixed, so it was not controllable, meaning that the sum and numbers could naturally be higher for others and the subjects wouldnt be as motivated to lie. A huge problem with this screen recording method was that the video files were extremely difficult to receive because they would have to be saved in a special folder. It was also difficult to activate Hypercam in a hidden manner so that the subjects would not know their screens were being recorded. The process to screen record, save the files, and locate them were very time consuming and conspicuous to the subjects. The biggest problem with this method was that it was considered to be an invasion of personal privacy, so it was not approved by the board or school. The next method we tested was to program a generator in which random numbers were given, but the numbers were much lower than the maximum value. The generator also could record the numbers given off by the generator and put them into a history section, which was accessible if the H key on the keyboard was pressed. We tested this method on a few people to see how they interacted with the task and the generator. We saw that people got very suspicious when they were generating the numbers and only receiving very low numbers as compared to their maximum value. They also got suspicious when we told them to leave their computers on once they finished the study. We needed the computers on, so we could access their actual numbers received from the generator. Because the students eventually caught on about the programmed generator, most werent motivated to lie because they knew that we could retrieve the numbers they were actually receiving. Not only was that a problem, but another was that the sum of all the numbers were not controlled by us, so people would not lie if they were happy with the sum they received. In the end, we ended up programming another generator with fixed number values. This controlled the sum of the numbers that the subjects received and avoided all the suspicion coming from the subjects tested. Our final major problem was maintaining confidentiality. Although we only used subject numbers, we still had signatures. With the previous method we tested dealing with recorded history involved generator, our survey sheets had the signature sheet attached to the number generating task. This meant that identity was set up in a way that could be associated with the numbers the subjects put down. This wasnt much of a problem in the no signing and signing before conditions because the actual signing section wasnt attached to the final reported number sheet that we collected for the raffle and data analysis. However, the signing after condition asked for the signature right underneath the subjects responses and numbers. This was a lack of confidentiality because the numbers reported were associated with the subject. Therefore, we made sure each signature was on a separate sheet and that all sheets were collected and thrown away immediately after the study. We determined the condition of the subject by their subject number. In the future, if we increased our sample size we would definitely find a correlation between signing after and signing before in lying frequency and sum difference. Also, we would want to introduce different things that affect lying. For example, we could look at how honor codes promote honesty, and how if rewriting it is more impactful than just signing it or reading it. We could also compare signing and reading the honor codes before as compared to after, which is similar to signature placement. We might also want to incorporate the moral disengagement test into our study to see when signing exactly activates your morals. We could possibly test this by having students sign a sheet of paper before and after the moral disengagement tested done by Bandura (2008). Going back to our study, we could also test the people who were highly motivated to win a prize (people who put down a 6 or 7 on the prize incentive scale) and spilt them up into the 3 signing placement conditions (again, no signing, signing before, and signing after) to see if their lying value changes.In the end, we hypothesized that signature placement would influence how much a person lied because of how signing makes you more in-sync with your morals. We created a situation in which students would have the motivation to lie and analyzed the data based on the how much and how many times each student lied. A significant difference was found among all three conditions. But specifically was between the signing conditions verses the no signing condition. A major thing that this studys conclusion could impact is exams. For example, New York State requires regent exams for high school students. These exams usually have a signature sheet, but administrators give it with the test. This means that it could be filled out by the students whenever during the exam period. This study shows that it is better to sign the sheet first before giving the exam to promote honest behavior. Any type of document, or anything that shows commitment to what the form is saying should require a signature before the actual information is provided to get the most truthful response.

Materials and Method The purpose of our methods was to create a situation where subjects can lie through the use of a random number generator that they believed was not fixed. We wanted to use this number generator situation so that lying could easily be quantified and used in statistics. Along with this, we incorporated the three signature conditions of signing before, signing after, or no signing involved during the generator task. Finally, we needed to ensure that subjects had the motivation to lie, so a prize incentive was offered.

Recreated a random number generator A fixed random number generator was created through scratch.mit.edu.com (Figure 1A). This website contained a type of programming software that dealt with giving sprites (images) certain functions. Each sprite can be assigned a function that can change the sprites movement and appearance. They could also be assigned changeable number variables. There were 5 main sprites and 3 number variables used to create this generator (Figure 1B). The 3 variables were the minimum value number, the maximum value number, and the generate value number. 4 of the sprites used in the generator were buttons that controlled the maximum and minimum value number variable. The last sprite used was a generate button that generated numbers between the minimum number value and maximum number value. The number generated between the two values appears to be random to whoever that used the generator, but it was actually fixed. The fixed quality to this generator was that we preordered a set of numbers that program would use as the generate value number instead of actually randomly generating numbers between the minimum and maximum value. The lists we created had 10 fixed numbers in which none were 10 or higher. This means that the subject had no chance of getting a 10. We were able to accomplish this by layering many of the same generate button sprites on top of each other. Each sprite layer had a different number assigned to it; when one generate sprite was clicked, the number corresponding with the sprite would show up in the variable box and another sprite layered underneath the previous would show up on the screen. This new sprite layer would have a different number assigned to it, and that number would show up if the new sprite layer was clicked on. We created 7 different versions of this generator using the same sprites and functions but each had a different set of fixed numbers (Table I). The different versions were created to prevent suspicion on the randomness of the generators. For each version of the generator, the numbers that the subject received were known to us due to each version having its own set of fixed numbers. Also, the sum of the set of numbers were relatively the same for all the generators we created. The generators could be accessed online through a provided URL (web address), and each versions URL differed only slightly. Scratch provides a unique link with every project you create on their website. The differing URLs allowed us to know which generator version we gave the subject. The purpose of this generator was to create a situation where subjects believed that only they knew the numbers because they were random. Finally, the task dealt with numbers so that the data could be quantified and measured in a simple and understandable way. Without numbers, lying would remain qualitative and hard to analyze therefore would have produced not so accurate results.

Human participants Before any subject was allowed to participate, they had to have a signed human consent form. The consent forms were handed out to those subjects interested, and only those who brought them back signed with a parent or guardian signature could participated in the study. Subjects were not allowed to participate first and then bring the consent form after. This consent form regulation was enforced due to the fact that this study involved lying. Lying tended to be a controversial topic especially when implying high school students to lie. In this situation, parent consent was vital. We collected a total sample size of 66 subjects (35 being female, 31 being male). The subjects received came from all grades, but were mostly 9th and 10th graders (26 9th graders, 30 10th graders, 7 11th graders, and 3 12th graders). The subjects received were from mostly Science Research and Spanish classes. The subjects were not recruited based off of ethnicity, religion, or economic status. Only grade and gender was to be reported from the students. The URLs that contained the different generators were distributed evenly among all the subjects (about 10 of each URL were given in the experiment).

Number generator task proceduresThe overall procedure was set up in a way that gave the subjects the most opportunity to lie. Subjects were first handed a direction sheet which indicated that they had to report random numbers between the digits 1 10, through the use of a random number generator (Figure 3A). The directions sheet gave instructions on how to use to random number generator. Subjects had to set the minimum to one and the maximum to 10 and then click generate to receive their numbers. The first 10 numbers they received from the generator were the numbers they were to write down. The person with the highest reported sum would be raffled for a $25 gift card and the person that reported the most amount of 10s would be raffled for a food party. Subjects were also not allowed to ask questions regarding the raffling process. The raffling process was put into place as the incentive for people to lie. The directions sheet also indicated that the subjects would be receiving two sheets on which they were to report their numbers on. The first sheet was simply for their personal reference and the second sheet was the one that would be collected and considered for the raffle (Figure 3B). This gave subjects another opportunity to lie if they were unsure of what they were doing the first time. This sheet also asked for subject number, grade, and gender. This finalized sheet also asked the subjects three questions in order to be able to observe any other correlation. The questions asked were the following: How many honors classes do you take? On a scale of 1-7 how competitive do you think you are? On a scale of 1-7 how badly do you want to win this prize? The last prize incentive question was particularly important because a correlation needed to be found between lying and prize incentive. Without this correlation we would be unable to know if the prize actually induced lying.

Different methodology among each signing conditionEach condition was presented in the study a slightly different way. Signing before was first given a separate signature sheet that asked subjects to confirm their participation in the study. The signature was then collected and thrown away later on in order to maintain confidentiality. After this, they were handed the first sheet with directions, which were then explained to them. This direction sheet included the space to report their numbers the first time. The sheet also had a pre-written subject number. This subject number allowed us to know what URL the subject had received so that we could access their actual numbers, and it also served as a way of identifying the winner. They were allowed to keep the first sheet. When they were done, a second sheet was handed to them which had space to report the same numbers again or different ones if they chose to lie and change them. The subjects were to report the same subject number on this sheet as well. For signing at the end the process was very similar. Instead of being given the signature sheet at the beginning, subjects were given the directions sheet first. This directions sheet was slightly different. Before any directions were presented, it was written and told that subjects would be signing at the end of this study to confirm their participation. The rest of the procedures dealing with the random number generating task were the same as the signing before condition. After subjects had handed in their second sheet they were given the signature sheet which told them to sign to confirm their participation in this study. The signature sheet was then collected and later on thrown away.For the no signing condition the procedures dealing with the number generator task were exactly the same as the previously mentioned conditions, except there was no mentioning of signing in the directions and no signature sheet was presented. The subjects simply reported the numbers twice and answered the questions that followed. Once subjects finished all the sheets in any condition, we collected the finalized sheet and simply wrote the condition the subject was in so that their data may be used later on. The prize incentive raffles were based off the reported numbers on the final sheet. The raffle was conducted anonymously by someone else.

Quantitative measurement for lyingOnce all data had been collected, we used the subject number to pull up the URL and generator that the particular subject received. We then checked the numbers they reported against what they actually received from the random number generator. Several different measures of lying were looked at when we examined both sets of numbers for statistics. The first thing we wanted to look at was how many times the subject lied on the final sheet (lying frequency). This was done by comparing the reported numbers on the final sheet and the actual numbers from the generator and seeing how many numbers were different between the two sets. The next thing we looked at was how far off the numbers were between the reported numbers and the generated numbers. To do this, we first added up the numbers on the subjects final sheet. We compared this sum to the sum from the generators numbers. We subtracted the sums from each other. This value was known as sum difference. We then used PSPP to do statistics on the data we obtained. ANOVA was used to see the overall significance among the three conditions and Tukey Posthoc was used to find specific significant values between two conditions. We also used exponential regression to compare lying frequency to the questions we asked the subjects.

ReferencesBandura A, Barbaranelli C, Caprara, G, Pastorelli C (1996) Mechanisms of moral disengagement in the exercise of moral agency. J Pers Soc Psychol 71: 364374Kettle KL, Hubl G (2011) The signature effect: signing influences consumption-related behavior by priming self-identity. J Consum Res 38: 74489Mazar N, Amir O, Ariely D (2008) The dishonesty of honest people: a theory of self-concept maintenance. J Mark Res 45: 633-644Shu LL, Gino F, Bazerman MH (2011) Dishonest deed, clear conscience: when cheating leads to moral disengagement and motivated forgetting. Pers Soc Psychol Bull 37: 330-349

Figure LegendsFigure 1: Random number generatorA This is the visual display of the generator. Once subjects entered their provided URL into a web browser, this generator set up was displayed. The minimum was automatically set to 1, but the maximum had to be changed to 10 manually by the subjects. The orange boxes displayed the changeable number variables (maximum number, minimum number, and the generate number). The maximum and minimum numbers could be manually changed with the plus and minus buttons right next to the variable. The generate number was changed when the subjects clicked on the blue generate button.B The buttons on the number generator each had their own functions. The sprites shown represent the clickable buttons on the generator that dealt with changing the number variables. The functions section referred to what would happen if the sprites above were clicked. The functions were easily built using code blocks. The blocks had their own colors to represent the different functions. The orange blocks referred to changing the number variables mentioned above. The purple blocks caused the sprites to change their appearance. In this case, when the sprites were clicked, the buttons would be dim for 0.1 seconds. This duration of appearance change was set up with the yellow blocks. Finally, the blue blocks represent a specific movement/ location of a sprite. The generate button has this specific location because we needed to layer many generate buttons on top of each other.

Figure 2: Comparing lying among the conditionsA This graph shows the amount of subjects that lied/ didnt lie in each condition. The x- axis refers to the signing conditions such as signing before the generator task, signing after, and no signing. The maroon color refers to the people who lied, and the green represents those who didnt lie. The y- axis is the frequency of subjects that lied. The degree of lying did not matter for this graph. What was considered lying was some different number was detected on their final sheet. In each condition, n=22. B For this graph, only the subjects that were in the maroon columns in the previous graph were compared in each condition. That means that the columns had varying sample sizes depending on how many people lied in each condition ( signing before n=5, signing after n=10, no signing n=13). This graph compares the total sum difference among all the conditions. Again the x-axis refers to the signing conditions, and the Y-axis is the sum difference between their reported numbers and actual numbers. The bars represent the sum difference average in each condition. The error bars in each condition refers to the standard deviation. C Like the previous graph only the subjects that were in the maroon columns in the first graph were compared in each condition. The same sample size in the sum difference graph was used in this one. This graph compares the lying frequency among all the conditions. The x-axis refers to the signing conditions, and the Y-axis is the lying frequency. Lying frequency was determined by the number of times different numbers were found on the final sheet. The bars represent the lying frequency average in each condition. And similar to the previous graph, the error bars in each condition refers to the standard deviation. D This graph compares the lying frequency number mentioned in the previous graph and the subjects ranking of the offered prize incentive. The prize incentive ranking was determined by their response to the following question: On a scale of 1 -7, how badly do you want to win this prize? The number reported became the prize incentive rank. The red dots on the graph show the average lying frequency for each prize incentive rank group. The sample size in each group varied with the amount of people who put it down as a response (1 n=2, 2 n=7, 3 n=6, 4 n=10, 5 n=7, 6- n=12, 7- n=23).The error bars on each of the points represents the standard error. The dashed green line shows the line of best fit.

Figure 3: Number generator task sheetsA- This figure shows the first sheet (directions sheet) the subjects were given dealing with the number generator task. It contains the generators URL which copied into a web browser. It also contained the directions on how to use this generator, and what the subjects will be doing with it. This sheet could eventually be kept by the subjects or thrown away. B- This figure shows the second sheet that was given after the first sheet was completed. It asked the subjects to rerecord their numbers and answer questions after. A place was provided to rewrite the subjects provided subject number, grade, and gender. This sheet was collected and used for measuring lying.

Figure 1A

B

Table I: 7 versions of the number generator: Their URL, number set, and total sum.

This table shows the different information pertaining to each of the 7 generators. The generators are very similar to each other in that they all look exactly alike in terms of visual display. What differs them is the URL and the numbers the subjects will be receiving. The URL codes above is what was plugged into the following link: www.scratch.mit.edu/projects/URL/#fullscreen The URL is what sends the subjects to different generators from one another. We also used this URL to find out the actual numbers the subjects had received. The number set column refers to the number order set up on the generator. When the subjects hit the generate button, these were the first 10 numbers they were getting. The first number all the subjects should have received was 1. This is because the first number to be generator was set to the generators minimum value (which was preset to 1). It was set up like this, just in case someone didnt set up their maximum and minimum values correctly. The total sum column represents the sum of all the numbers in one generator version, which was relatively the same among all the generators.

Figure 2

1

Figure 3A#________ Grade________ Gender__________Open up an internet browser and type the link provided below. http://scratch.mit.edu/projects/18789896/#fullscreen

Make sure the minimum number is set to 1, and the maximum is set to 10.

To use the random number generator, just hit the generate button.

You will be generating 10 numbers which will be written down in the box to the left. BSubject Number: ________ Grade: ________ Gender: _________DIRECTIONS: Please report your final numbers here. You may keep the previous sheet we have given you once you are done transferring the numbers. We will only be collecting this sheet.

DIRECTIONS: Please answer the questions below to the best of your ability. 1. How many honors classes are you taking currently (self-select, AP, honors, advanced math courses)?_______________Not competitive at all1

2

3Average competitiveness4

5

6Very competitive7

2. On a scale of 1- 7, rank how competitive you think you are.3. On a scale of 1- 7, how badly do you want to win one of these prizes?Not at all

1

2

3Average incentive4

5

6Very much

7

Thanks For participating in our study!