views of elective cosmetic surgery a survey of ucb …hhuang/stat152/ecs-write-up.pdf · views of...

Views of Elective Cosmetic SurgeryA Survey of UCB Undergraduates

Toby Dylan [email protected]

Denis [email protected]

Jon-Paul [email protected]

Nicholas [email protected]

December 16, 2005

Contents

1 Purpose 2

2 Design 2

3 Methodology 3

4 Analysis 4

5 Problems 6

6 Conclusion 7

A Appendix: Sampling Plan 9

B Appendix: Questionnaire Design 16

C Appendix: Data Collection 24

D Appendix: Analysis 33

1

1 PURPOSE 2

1 Purpose

This study aims to determine how undergraduate students from the University of California, Berke-ley (UCB) view elective cosmetic surgery, which we define as “surgery that is both voluntary andappearance altering.” We also seek to determine to what extent their views are dependent on vari-ables such as major, age, gender, ethnicity, friendships, and self-confidence. Lastly, our surveylooks into what types of elective cosmetic surgery people would have done on them if it were free,safe, and confidential.

Traditionally, elective cosmetic surgery has been looked at negatively because of media stereo-types, strong religious beliefs, and poor public education on the subject. In an endeavor to evaluatethe validity of these claims, this study also attempts to serve as an indicator of whether or notelective cosmetic surgery is now more socially accepted among the college educated youth of theUnited States.

The general methodology of this project consists of constructing and implementing a samplingdesign that produces a representative sample of undergraduates from UCB and and questionnairethat can gauge their diverse opinions. To make the survey sampling scheme as free from selectionbias as possible, we attempted to avoid samples of convenience (since the easier to sample studentswill not be representative of the harder to sample students - for example, nonresponsive studentsare more likely to have more negative opinions on the subject), not purposefully select a group ofstudents that is believed to be representative of all undergraduate students (but may be biased), andfully deal with non-respondents.

After the data was obtained, standard analysis techniques were used to try to elucidate signif-icant correlates to a person’s opinion about elective cosmetic surgery. To that end, we adopted amethod of regression analysis and significance testing that is straightforward to explain, simpleto implement, and conducive to revealing which candidate variables are strongly correlated withopinion about elective cosmetic surgery.

Becuase of careful planning and execution of the sampling design, we encountered only oneprincipal problem. In the data collection, nonresponse was significant, but surmountable. In thedata analysis, there were only a few significant correlates identified, but rather than identifyingthis as a problem, we interpreted this result as an implication that UCB students generally have ahomogenous opinion about elective cosmetic surgery.

2 Design

The sampling design we chose was stratified random selection of departments, then each depart-ment was divided into lower and upper division strata. It was decided to make the probability ofeach department being selected proportional to the number of its declared undergraduate students.

For ease of surveying, a sample size of 1 class per division was adopted, bringing the totalnumber of classes to survey per department to 2. The stratification of lower and upper divisionclasses was attempted in order to provide evidence to answer the question of if age had an influenceon the students’ views. This surveying scheme was chosen over others because the departmentswould not overlap, and it would give a high level of diversity of views.

3 METHODOLOGY 3

A sample size of15 departments out of78 were selected, and two classes from each weresampled, giving a total primary sample size of30. Each of the classes that agreed to participatewere given a survey to their discussion section that included thirteen questions involving eitherpersonal questions (age, gender, major, classes) or questions involving views on elective cosmeticsurgery (do you know anyone that has had the surgery, or what do you think about someone thathad told you they had the surgery done on them). If the class did not have a discussion section,the survey would be given to the lecture instead. For any non-responses, an alternative class wasselected that matched department, division, class size and time of day. Unfortunately, some alter-native classes were also non-respondents and by the end of the surveying process, six classes weredubbed non-respondents.

Alternatives to the sampling design above were considered, but were ultimately ruled out infavor of our design’s ease, coverage, and analytic properties. One option considered was griddingthe entire Berkeley campus and randomly select portions of the grid, but this gave rise to problemssuch as time of day should the surveys be given, or the fact that certain portions would be muchmore populated than others. Another proposal was to go many places on campus where it wasbelieve undergraduate students congregated and survey them, but this would equate too many non-respondents and it would create bias because it is a judgment sample. After deliberating the use ofother sampling designs, the first mentioned design was selected.

3 Methodology

Data analysis mostly includes calculation of the averages and proportions for the survey questions.Variances for these statistics are computed in the process. Variance of a statistic indicates howmuch it is expected to differ from its mean on average. It is difficult to calculate variance in thiscase; therefore, a method known as bootstrapping was used. This method implements taking ofseveral subsamples of the survey results and calculating how much they differ among themselvesand the original results. Thus, variance can be found through a complex calculation process. Thisallows calculation of confidence intervals for the above statistics. Such an interval indicates theexpected range of the given statistic if this survey were taken multiple times. In particular, a 95%confidence interval indicates that the statistic for, on average, 95 out of 100 surveys, will fall in theinterval.

Furthermore, regression analysis was performed to see how a given answer is dependant on anumber of factors, such as the department or gender of the respondent. In regression analysis, alinear model is built with the factors acting as the variables with unknown coefficients. The factorsare represented by dummy variables. For example, the gender variable is 1 for males and 0 forfemales. The factors coefficients and their variances are estimated, and corresponding confidenceintervals are constructed. Thus, by a technique known as t-test, a certain hypothesis about the valueof a coefficient can be validated. Since we are interested in whether a given factor is significant, thehypothesis is that a given factor’s coefficient is zero. In essence, if the corresponding confidenceinterval is close to zero, that factor is unimportant.

Throughout this estimation process, the concept of weights is employed. The weight assigned

4 ANALYSIS 4

to a respondent’s answers is the number of people in the student body that this respondent rep-resents. For example, if we sampled 2 people out of 200, each person would have a weight of100. These weights have been initially determined by the number of students at Berkeley and thenumber of classes to be surveyed, assuming that the class size is about the same and the number ofclasses in each department is proportional to the number of people in the relevant major.

Since some people missed class during the time of the survey, additional weights are requiredto have the sampled students represent some of the non-respondent, but similar, students. In thiscase, we define the weight as

wi =Number Enrolled in Class

Number Participants in Class

Since some entire classes were non-respondent, similar classes have been selected from thosealready sampled and new weights were assigned to these selected classes. In this case, we definethe weight as

wi =Number Enrolled in Observed Class + Number Enrolled in Unobserved, Comparable Class

Number Participants in Observed Class

A complicated combination of methods is used to account for missing answers if a person didnot fill in some of the questions. Essentially, similar students are chosen for the respondent whohad missing values and one of their values is used to fill in nonresponse.

4 Analysis

To reveal any relationships between views on elective cosmetic surgery and the independent vari-ables from the questionnaire (Appendix B), we used the method linear modeling to analyze thedata. This was done by looking at how specific questions on the survey triggered other questionsto trend a certain way. A more detailed and pedantic treatment of the data analysis can be found inAppendix C, but a presentation of the more significant results follows.

One interesting result was that if a student had a friend or significant other who had the surgerydone, they were significantly more accepting of elective cosmetic surgery. Around 61% of sur-veyed students knew someone that has had elective cosmetic surgery and overall 64% of the sur-veyed students agreed that elective cosmetic surgery is sometimes an acceptable option (Figure 1).

Somewhat surprisingly, neither major nor gender seemed to have much of a role on the views ofcosmetic surgery. Also, students did not seem to show any preference for whom cosmetic surgerywould be most socially acceptable, whether it be a family member or not (Figure 2).

One question on the survey proposed a fictional character, Anna, who told you on the first dayof class that she had a breast augmentation surgery. You could have chosen one or more optionsfor how you would describe Anna: confident, secure, blunt, vain, insecure, or superficial. 56% ofstudents said she was insecure.

4 ANALYSIS 5

Comsmetic Surgery Approval Levels

Percantage of Respondents

Almost Always

Rarely

Sometimes

10 20 30 40 50 60

Figure 1: Percents of extent of approval of elective cosmetic surgery, estimated using complexsampling weights.

Comsmetic Surgery Acquaintances

Percantage of Respondents

Acquaintace

Family Member

Friend

I

5 10 15 20 25 30

Figure 2: Percents of approval of elective cosmetic surgery for friends and relatives. Note that the“I” bar refers to the opinions of the people that we surveyed about themselves.

5 PROBLEMS 6

5 Problems

Carrying out this survey was a delicate endeavor through most of its stages. Several problemsarose, both in actually creating the survey as well as in the logistics of coordinating and motivatinglarge bodies of students. Within each stage of the survey, new problems surfaced for which thatstage’s group had to produce an answer. Inevitably, there were concessions to be made aboutdesign and the collection of data, and nonresponse errors to be corrected in its analysis.

In several cases our anticipation of statistical significance between the thoughts of differentgenders or majors were left unfulfilled. This is not to say our findings were insignificant. Thelack of a difference of opinion (in many, but not all, cases) between genders and majors of ourtarget audience is just as worthy a finding as if there were a difference. In a sense, we designedthe survey with something of a preconceived notion that there would be a difference in such areas,and the conclusion that the difference is not statistically significant is evidence against our initialintuitions.

However, we find it a task worth undertaking to list the problems that we had at each stage sothat the backing of our survey results might be better understood. Also, any attempt to reproducea survey like ours in the future might benefit from contingencies against those problems we faced.

In first outlining the method of sampling, we considered several options for trying to gatheringa sample that was representative of the entire undergraduate student body at UC Berkeley. Theseideas included such methods as dividing the campus into a mapped grid and and sampling froma random assortment of these sections, or sampling from heavily trafficked student ”hang-outs”such as cafes and on-campus recreational or dining facilities. But both of these methods provedto be both hard to implement and biased towards particular types of students. Some students arenot on campus as often as others, do not make use of the on-campus facilities, and many do notfrequent any local cafes. We chose instead to sample from clusters from classes listed on theUC Berkeley Undergraduate Online Schedule of Classes. This generated the problem of studentsstudying abroad, or otherwise studying in a manner that does not include them in any of the classeson the Online Schedule of Classes. Further, some students may be enrolled in more courses thanothers, and thus be more likely to be sampled. Finally, on the day we sample any given class, anylack of attendance would generate nonresponse for which we would have to correct. We concludedthat the students excluded from our sampling method were overwhelmingly in the minority and thatdisregarding their exclusion would not terribly harm our findings. Students not present on the dayof sampling was left as a problem for Data Collection.

The Data Collection group already had its own problems, including but not limited to: the lo-gistics of organizing those students responsible for administering the survey, obtaining (and beingrejected, at times) the permission of, and otherwise communicating with, those running the courses,and attempting to minimize entire cluster nonresponse (such as from a sampled class whose pro-fessor rejects the survey). Replacement classes were used in some cases where rescheduling wasnot an option, and this was obviously not ideal to preserving the integrity of the data. In totalsix sampled classes did not provide data (for a multitude of reasons), and in these cases there hadto be adjustments. Creating a template for the data entry also provided some small problems, asmany non-numeric responses had to be recorded in a uniform manner. With some conferring with

6 CONCLUSION 7

the Data Analysis group, this problem was alleviated and the project proceeded on to the analysisstage.

The Data Analysis group made some adjustments for nonresponse through weighting, but didnot encounter any significant new problems of their own to stop them from performing the analysis.The results, however, were less than spectacular. Many variables which we thought might becorrelated turned out to disappoint, leaving us with very few conclusions about any differences inopinion between genders, majors, or upper/lower division. In a sense, this made any difference wedid find stronger, but in general led us to a less exciting conclusion. As mentioned above, the lackof a difference may well be a significant result, but it was nothing like what we expected whenwe began this survey. While we may have been left disappointed, perhaps we learned that ourassumptions and predictions (which led us to test for differences in opinion across genders andmajors, and correlation between responses) were misguided and need to be reevaluated.

6 Conclusion

The findings of our survey were numerous, comprised of the independent results from each ques-tion and the relation of responses to factors such as gender, division, or major. Also, correlationbetween a particular response to a question and that respondent’s previous answers was a topic ofinterest. In most cases, our findings were marked with a lack of significance between the types ofrespondents. This lends credence to the notion that sentiments toward elective cosmetic surgery,in many cases, do not depend on things such as gender or major. We did find some noteworthydifferences between those more and less familiar with cosmetic surgery, and a startling amountof similarity in the opinions of males and females. The most deviate responses by major werethose from the Biological Sciences, whose feelings often separated themselves from the rest of themajors. What follows is a short remark on what conclusions we have drawn from the results.

As mentioned above, in most every section of interest (outside of self-rating one’s appearance)gender could not be used to differentiate student attitudes toward elective cosmetic surgery. ¿Fromthe first question, gender did not effect the answer of the general acceptability of cosmetic surgery.The responses from male and female students differed by so little, that no conclusion could bedrawn about which supports or opposes cosmetic surgery to a greater degree.

Next, we found that the majority of respondents knew someone who has had elective cosmeticsurgery. This is evidence that most students are directly familiar with elective cosmetic surgery,and that their opinions on the subject are likely more informed and immediate to them than if theyonly knew of cosmetic surgery through media representation or other third-party information.

On the matter of a close person getting cosmetic surgery, we found that Engineering studentsare significantly opposed to the idea of it being a female in the immediate family (mother, sister).We suspect that while it was not found to be statistically significant for other colleges, it is likelythese two female relations are those, of any family, which students are opposed to getting electivecosmetic surgery. On the whole, students are more opposed to their significant others receivingcosmetic surgery than their friends.

This might seem like a simple observation, but the motivation for not wanting them to have

6 CONCLUSION 8

the surgery is of important note. One may speculate that students do not wish to see unnecessarychange in someone of whom they are already fond, but perhaps it is something else. After all, ofthose advantages listed for having elective cosmetic surgery, the surgery’s help in finding a partnerwas significantly rated the highest. It is this view of cosmetic surgery (done as an attempt to betterfind a partner) which might led to respondents being more strongly opposed to their significantothers getting elective cosmetic surgery than they would oppose their friends doing so. Also,perhaps this is an explanation for why Engineering students and others are opposed to their femalefamily members having elective cosmetic surgery (they might be opposed to thoughts of thesefamily members bettering their chances to find or keep a partner).

When it came to rating oneself, men rated themselves significantly higher, on average, thanwomen. This is unfortunately resonant with the sexist stereotype that, relative to men, womenhave more awareness, concern, and pessimism toward their appearance. A related hypothesis thatour observations support is the claim that men are perhaps too positive, and compliment themselvesas a matter of pride or dignity which might not reflect a truthful self-evaluation.

Finally, in one of our more significant conclusions, we found that if the student was one thatknew a friend or family member that had elective cosmetic surgery, they themselves are morelikely to get have a surgery. Familiarity and proximity to cosmetic surgery seems to diminishany reservations a student might have toward getting such a surgery themselves and potentiallydispel some of the negative stigma that many students associate with elective cosmetic surgery (asevidenced by their judgments of our fictitious patient Anna).

The findings of our survey suggest that elective cosmetic surgery is still an issue which manystudents look upon in a negative light. The slight majority describe Anna negatively answered thatthey would not get such surgeries themselves, and were either generally or strongly opposed to anyimmediate relation, friend, or significant other having cosmetic surgery.

The majority that opposed elective cosmetic surgery was in most cases small, and not statis-tically significant. Therefore, we conclude that, resonant with traditional views, the practice ofelective cosmetic surgery is still thought of conservatively in the minds of the average student.

However, there is also a rather large minority which is accepting of the practice. These studentshave or would have it done themselves, or at the least would not be opposed to people they knowhaving such surgeries.

The stability of these proportions over time remains an unanswered question for further re-search.

A APPENDIX: SAMPLING PLAN 9

A Appendix: Sampling Plan

PLASTIC SURGERY: The Undergraduate’s Opinion Frank Cheung, Michael Chang, Dan Nguyen, Vickie Sun, Wing Tam (Alice), Cheuk Wong, Gary Wong Sampling Plan In this study, our goal is to find out UC Berkeley undergraduate students’ opinions on plastic surgery. In the past, the subject of plastic surgery has been considered a taboo because it was frowned upon by religious groups. People like us may wonder, “Should we change what God has blessed us with?” In our new age, the media has bombarded people and especially teenagers with shows that glorify movie stars and pop stars that are skinny and beautiful. There are now shows such as The Swan, I Want a Famous Face, and Dr. 90210 that shows the process of plastic surgery and how their life has changed around because of plastic surgery. What we wish to know is how this changes people’s perception of plastic surgery. Is it more acceptable now that movie stars and common people do it? Maybe people are more willing to admit they have done plastic surgery than before. We also want to see if age and major are factors affecting students’ opinions. This is why we should consider stratify or divide our population into different groups according to their age or major. Our target population is the undergraduate students at UC Berkeley. In order to make our sample as representative to our targeted population as possible, our sample population should be UC Berkeley undergraduates who are enrolled in the classes listed on the “Online Schedule of Classes” website. There are some students who may not be counted in our sample population because that student is studying abroad, enrolled in co-op programs or graduate level courses. However, this is such a small proportion relative to our target population; so overall, the sample that we pick from our sample population should be representative. The sampling frame consists of a list of undergraduate-level classes that UC Berkeley offers for the current semester. First a class is picked from the list and then we will sample every student in that particular class. Some students may not be accounted for if they are absent the day we conduct the survey. Our next step is to decide on a sample size, denoted by n, which is the number of undergraduates we need to survey we need to survey in order to make inferences about the population. Using the equation of sample size estimation under simple random sampling, an estimated sample size can be


obtained given a specified precision and error. Because this is a large population, we can use a proportion of 0.5 to attain the maximal value of the variance.

2 2

/ 2o 2 where n

(1 )

o

o

n z sn

n eN

α= =+

S2 = (0.5)(1-0.5) N = 22,000 Z �/2= 1.96 e = .03

Our sample size is 1,018 students using a marginal error of 0.03, a 95% confidence level, and a population size of 22,000 undergraduate students. After finding a practical sample size, we then have to come out with a way to sample these 1,018 students to make it as representative as possible. Ideally, a perfect sampling plan is to get a list containing all undergraduates’ contact information from the Administration Office and perform a simple random sample. This will allow everyone to have an equal chance of 1/N, where N is the total number of undergraduate students in UC Berkeley, to be chosen into our sample. However, since every student’s contact information is confidential, we cannot obtain such a list. One method of sampling that we discussed was getting a cluster sample at the most popular undergraduate hangout locations in Berkeley. This entails thinking up location and times that undergraduate students may be at their free time. This method presented several problems. The first main problem we faced was the broadness of the definition of “hangout locations.” We listed many places on and off campus where we believed undergraduates congregated. These places included the Golden Bear Café, Free Speech Café, Café Milano, Café Strata, Sproul Plaza, Student Store, and etcetera. We felt it would be too difficult to generate a complete list of places, and even if we did, getting permission from local business to approach their customers for a quick survey would also be problematic. Also finding an appropriate time period to interview people at different locations is hard since if we selected a restaurant, then we must go during its lunch hours or dinner hours, which may vary from student to student. Within a particular restaurant, not all customers eating there are undergraduate students. If we picked Recreational Sports Facility as one of our location, the best time to


go would be after 3 pm, but not everyone will be willing to spare their time while working out. Because of all these obstacles we would face if we were to conduct this type of survey sample, we decided against it. Another method of sampling that was discussed by the group was to divide the campus up in the equal sections or grids, such as the campus map online. We will take a SRS of those grids and then survey the students in those areas that are picked through the SRS. Although this method did ensure that we would be surveying primarily undergraduate students because almost all students take classes on campus, it did pose several other problems. First, different portions of the campus are populated differently; some areas have a higher population of students, like Sproul Plaza, than others. It would be difficult to rank each section by density of students. Also, we would have to take into account the time of day we took the survey. For example, students generally have classes from 9am-5pm Monday through Thursday. We would have to allocate certain times to take the survey in order to catch the right amount of students, but even this might be hard because students probably won’t want to stop and take a survey on the way to class. We would probably see a large non-response bias here. The third method that we considered was taking a simple random sample of a list of breadth courses required/offered by the College of Letters and Sciences and combining it with the humanities list from College of Engineering, and then survey all students in selected classes. The benefit of doing so is that since every undergraduate student is required to fulfill his/her elective requirement, we can get people from all different majors within a single class. However, one of the problems is that these classes usually contain more freshmen and sophomores than juniors and seniors. Also, not every student has equal chance of being picked, especially for transfer students from other community colleges or universities. Transfer students tend to finish their breadth or elective requirement before being admitted to UCB. Finally, we decided on conducting a complex survey involving proportional on departments, stratifying upper and lower divisions, and simple random sampling on classes. Since departments do not overlap between different schools/colleges, we first gathered a list of all the departments at UC Berkeley from Office of Planning and Analysis website (http://calprofilesplus.vcbf.berkeley.edu/majors/majorsClg.cfm). Some examples of schools/colleges include College of Letters and Sciences,


College of Chemistry, Haas Business School, and etcetera. We decided against sampling directly from majors because some majors are so similar that they only differ by few classes, such as Pure Mathematics and Applied Mathematics. Through separation via departments, we are grouping together similar majors. Different departments can also have similarities, say English and Literature, but since we do not know enough about each department and the unique education each has to offer, we are just going to assume that the Berkeley officials who decided on different departments in the first place saw enough of a difference to separate them. Now we must consider the differences between the sizes of each department. Obviously some departments have more undergraduate students because they are more popular or simply because some departments, like Mathematics, offer multiple majors. Therefore, we are going to weigh each department according to the proportion of undergraduates declared within that department, which would mean bigger departments would have a higher probability of being selected than smaller departments. Departments, groups of similar majors, may also be an important aspect that shapes students’ opinions. For example, a student from Mass Communication tends to think differently from a Religious Studies student. We also realize not all undergraduates are declared, but we are going to assume that the undergraduates who are not declared are going to fall into the same proportional trend as those who are currently declared. This is assuming that one year, we do not get half a freshmen class wanting to join the Statistics department. After weighing each department according to its size, we will take an SRS of thirty departments out of total seventy-eight departments at UCB, so that we can obtain a more representative opinion. Here is the list of 30 departments that we have generated: Business Lan Arch Env

Plan Spanish Portuguese

Economics Peace and Conflict Studies

Bioengineering Art History Integrative Biology

Ethnic Studies Pol Econ Indust Soc

Civil and Enviro Engineering

English Molec and Cell Bio

History Cog Sci

El Eng and Comp Sci

French LS Computer Science

Political Science

Interdisc Studies


IEOR Italian Studies LS Legal Studies

Sociology Nutritional Sci Tox

Materials Sci Eng

South SE Asian Studies

Physics Asian Studies Mechanical Eng

(This list is generated by weighting each department using R. We first obtain a list of all departments with corresponding number of students in each department. We subtract the total number of undergraduates by the number of undeclared students (22,225-8,040). Then we assign each department an interval that depends on their department size. For example, since Business is the first department on the list, we will assign it 1-527, the next department is Chemical Engineering with 528-846, and etcetera until each department has been assigned an interval. Then we will use R to generate randomly 30 numbers between all the intervals to get the table above) We decided on thirty departments because our sample size is approximately 1,018 undergraduates and we are assuming that there is on average about seventeen undergraduates per class. That means that we need to sample two classes from each of the thirty departments and taking into consideration that one lower division and one upper division class are needed per department. We will sample approximately 34 people from each department, 17 from a lower division class in that department and 17 from an upper division class in that department. We divide each department into lower division and upper division classes because we want to see if age affects attitude towards plastic surgery. The maturity of a freshman may be very different from a graduating senior, and this difference may be a factor on people’s opinions on our topic. Separation between lower and upper divisions will also account for people who are undeclared because even undeclared undergraduates must take classes within a department before declaring. To reach our sample population, we will distribute our surveys to the discussion sessions if discussions are offered. If not, we will sample the labs. If the course does not offer either discussion or lab, we will sample the lecture. Sampling a discussion session, lab, or lecture is most efficient and representative to our targeted population because almost all Berkeley students attend at least some classes, and therefore, every Berkeley undergrads is included in our sample population. Once we have generated our list of thirty departments by weighting each department, we can start contacting graduate student instructors and professors. If the professor


decides that we cannot survey from his/her class though, we may need to sample another class that is similar to the one we were not able to sample. Students have different opinions regarding plastic surgery depending on their ages and the departments they belong to. Thus, the combination of stratifying similar departments into one stratum, using proportional sampling bases on departmental size to select the thirty departments to sample, and applying the cluster sample technique by dividing lower and upper division will gives us the most representative sample where students’ opinions in different departments and age groups are included.


B APPENDIX: QUESTIONNAIRE DESIGN 16

B Appendix: Questionnaire Design

ELECTIVE COSMETIC SURGERY:

QUESTIONNAIRE DESIGN

Statistics 152: Survey Sampling Professor Huang

November 4th, 2005

Group 2

Fanzi Mao Stephanie Yee

Judieann Tadeo Christopher Chow

Tiffany Fung Lo Shan Li

Johnathan Anastacio

15486139 15783416 16648897 16024800 15856456 15977508


Objectives of the Questionnaire: The objective of the questionnaire is to obtain diverse opinions and thoughts on elective cosmetic surgery from a representative sample of Berkeley students. There were several concepts of interest regarding plastic surgery that we were interested in studying. First of all, we wanted to learn more about the stigma associated with plastic surgery. In particular, we want to figure out the extent of plastic surgery stigma, and whether or not it varies with age, type of surgery, and relationship to the person receiving surgery. Furthermore, we were interested in the overall acceptance of plastic surgery in principle, and whether or not acceptance changes based on the level of unhappiness or discontent of the person receiving surgery. Choice of Diction - Elective Cosmetic Surgery: Since there are many preconceived associations and perceptions attached to the term “plastic surgery”, we decided to use the term “elective cosmetic surgery” instead. This choice of diction conveys the idea that the surgery is 1) voluntary 2) cosmetic in nature, and not for medical purposes. Our final definition of elective cosmetic surgery also includes a list of specific types of operations to help the subject acknowledge that there are many different kinds of surgery. The final definition for elective cosmetic surgery appears at the very beginning of our survey and is stated as follows:

Definition of Elective Cosmetic Surgery:

Elective cosmetic surgery is performed when a person wants to change his or her physical appearance. These surgeries can be performed on the body or the face. They include operations such as liposuction, tummy tucks, face lifts, “nose jobs,” breast implants, collagen injections and laser skin resurfacing. Elective cosmetic surgery does not include plastic surgery that is done for medical purposes to restore form or function to a body part that is abnormal or disfigured due to trauma, cancer, birth defects, or surgery.


Questions: Question 1: Which statement do you agree with more?

In principle, elective cosmetic surgery is rarely / sometimes / almost always an acceptable option to improving physical appearance

The purpose of this question is to gauge the degree of approval of elective cosmetic surgery. The reason why we chose these three specific words is to provide clear segments in the surveyed population. We refrained from using extreme options (i.e. “never” and “always”) to reflect our belief that most people would not fall into these extreme categories. In other words, there are very few people that think it is never acceptable or always acceptable to choose elective cosmetic surgery to improve his/her physical appearance; most people would choose some level between these two extremes. Since we believe everyone has an opinion on elective plastic surgery, we did not include an option of “N/A”. The removal of this option also forces people to start thinking about their personal feelings on elective cosmetic surgery and eliminates non-response bias from this survey. Question 2: Do you know anyone who has ever had an elective cosmetic surgery procedure? A. Yes. Please circle all that apply: I have had elective Family Member Friend Acquaintance Cosmetic surgery B. No. The purpose of this question is to see how common it is for someone to have elective cosmetic surgery in the population’s community of relationships. Since it is possible that the respondent themselves has had plastic surgery before, the “I have had elective cosmetic surgery” option is included as well. This reflects our belief that it would strongly influence their acceptance of the operation. The rest of the answer options are other categories of individuals in a person’s social network who could potentially undergo elective cosmetic surgery. The different options: “Family Member”, “Friend”, and “Acquaintance” allow us to compare and quantify how the respondent’s answers to other survey questions are related to whether or not they know someone who has had plastic surgery, and if so, how strong or close the relationship. The “No” option is to determine if the individual does not know anyone who has had the operation. Once again, we did not include a non-response option. Question 3:


Imagine that on the first day of class you meet a girl named Anna and you two discuss what each of you did during the summer. Anna explains that she has breast augmentation surgery. Please circle one or more words you would use to describe Anna: Confident Secure Blunt Vain Insecure Superficial The purpose of this question is to gauge an individual’s first impression of another person who has had the operation. We chose a female in the situation because it is more common for females to undergo the operation. We chose to use breast augmentation in the situation because it is the classic example of an operation to improve one’s physical appearance and attractiveness. The reason why we chose the following options was because they are the most common motivations to go ahead with the surgery. The respondents in our initial guinea pig survey suggested some of these options. We are allowing the individual to choose one or more descriptions because generally, people will have multiple opinions on motivations. We selected a full spectrum of motivations including both positive and negative impressions. Question 4: Imagine either now or some time in the future, someone close to you wants to get elective cosmetic surgery. For each of the following, would you support their getting the surgery? Please indicate your choice for each.

Strongly Oppose

Generally Oppose

Generally Support

Strongly Support

Does not Apply

Mother 1 2 3 4 NA Father 1 2 3 4 NA Sister 1 2 3 4 NA

Brother 1 2 3 4 NA Significant Other 1 2 3 4 NA

Female Best Friend 1 2 3 4 NA Male Best Friend 1 2 3 4 NA

The purpose of this question is to gain a deeper understanding of how the individual’s opinion changes as the relational degree of closeness changes. This question also takes gender and age into consideration as we have listed both family members and significant friendships. For later analysis, these breakdowns can be aggregated to determine overall opposition or support based on relationship strength and gender. The options that we chose reflect a certain degree of support or opposition with no neutral opinion. We have also included “N/A” option to eliminate nonresponse and because some of the relationships are not applicable to certain individuals. If there was no “N/A” option, there would be no way to differentiate between a blank answer caused by nonresponse and a blank answer caused simply because the respondent does not have a brother, sister or someone of a particular relationship type. The numerical scale allows us to quantify opinions. Question 5:


Please indicate whether you agree or disagree with the following statements. All things being equal, someone who has elective cosmetic surgery done: Has a profession advantage over others Agree Disagree Is more sociable Agree Disagree Has an easier time attracting a partner Agree Disagree The purpose of this question is to find out whether people think it is advantageous to have the operation. We used the phrase “professional advantage” because we wanted to know if they think having a cosmetic surgery will increase chances in getting an ideal career. The word “sociable” in the second choice can show if people believe that someone who had a cosmetic surgery will have a greater personal network. The last choice shows whether having a cosmetic surgery leads to more opportunities in finding a partner. Question 6: How would you rate your own overall appearance? Extremely Average Extremely Unattractive Attractive 0 1 2 3 4 5 6 7 8 9 10 This question is designed to see whether people are satisfied with their own physical appearance. Once again, the numerical rating allows us to quantify individual opinions. We designed this question because we wanted to see if there is a correlation between the ratings of the individuals of their own appearance and the degree of approval of elective cosmetic surgery. Question 7: Imagine that you could have any cosmetic surgery procedure done safely and for free – and that no one would know unless you told them. Please indicate which of the following procedures, if any, you would have done. Select all that apply.

A. Breast augmentation G. Hair transplant/implant B. Liposuction H. Face Lift C. Tummy Tuck I. Eyelid surgery D. Laser skin resurfacing/dermabrasion J. Breast reduction E. Nose reshaping K. Other: _____________ F. Collagen injection L. None.

The previous questions regarding elective cosmetic surgery were not focused on the survey taker; rather it was targeted towards his/her friends, family and acquaintances. This question on the other hand, concentrates on the actual person taking the survey. The survey taker’s opinion of elective cosmetic surgery may drastically vary when it is in relation to themselves. In addition, his or her opinions may fluctuate based on a variety of elective cosmetic surgeries. Danger, high cost, and disapproval are the major reasons that most people would not choose elective cosmetic surgery. Therefore, we specifically


remove these reasons in order to focus on the ideal case where these negative factors are eliminated and only the respondent’s personal opinion and rationale remain. If elective cosmetic surgery is free, safe, and no one would know, then many people might change their mind. Questions 8-13: Question 8: What classes are you taking?

Department (e.g., Math) Course Number (e.g., 53)

Question 9: What is your major(s)? _____________________________________ Question 10: What is your gender? Male Female Question 11: How old are you? __________ Question 12: What is your ethnicity? African American American Indian Caucasian Hispanic Asian American/ Other: ____________ Pacific Islander Question 13: What year are you?

First Second Third Fourth Fifth Graduate

The last questions, numbers 8 through 13, are questions designed to learn more about the characteristics of the subject. These questions are included to gather information that may later be valuable in analyzing the survey results. Question 8 asks about what classes the subject is taking. Question 9 asks what year the student is. Question 10 asks about gender. Question 11 asks for age. Question 12 asks about your ethnicity. Question 13 asks for year. Order of the Questions:


We started the survey with the definition of “Elective Cosmetic Surgery” in order to clarify the procedures under discussion. Then we dive right in and begin the questionnaire asking about initial acceptability of the procedure. We then determine whether the individual has had the operation in order to see if that causes bias. Our third question progresses into initial impressions and stigmas the individual has of people who have had the operation. The fourth question considers how different relationships affect the acceptability of the operation. We then shift gears and move on to the perceived advantages of the operation. Next we start examining the individual asking them about how they feel about their own looks and what they would consider having their operation on. This is the final real stage in our questionnaire that elicits their true desires with improving their physical appearance. We finish the questionnaire by gathering basic information about their courses, gender, age, ethnicity, and year. We gather this information at the end so that the individual can focus on answering the most pertinent questions first. Guinea Pig Survey: The finalized questionnaire was developed based on the comments and results of the guinea pig survey, where the initial questionnaire draft was used. Nine people took the guinea pig survey, five males and four females. It took an average time of 3.05 minutes to complete. Some of the results are summarized below in the following table.

Percent Undergraduate 89 Asian American/Pacific Islander 89 Never had plastic surgery 89 Knew someone that has had elective cosmetic surgery

78

As shown above, most of the survey takers were Asian American/Pacific Islander undergraduates. Additionally most of them knew at least one family member, friend, or acquaintance that has had elective cosmetic surgery, though most of them had never had this form of surgery themselves. It appears that most (89%) of the survey takers would support their best friend (male and female) if they chose to have elective cosmetic surgery, whereas there was opposition (38%) regarding a significant other and strong opposition regarding a parent (22%). Some of the survey takers in the guinea pig survey had specific comments regarding the initial questionnaire draft. One of the comments was a request for a precise definition of “elective cosmetic surgery,” hence the definition was placed at the very beginning of the finalized questionnaire. Furthermore there were many comments pertaining to the question on the topic of how the survey taker feels about family members and friends having elective cosmetic surgery. They said their responses would not only vary depending on the person having the surgery, but what type of surgery.


C APPENDIX: DATA COLLECTION 24

C Appendix: Data Collection

Data Collection Group

Jennifer Cheng Louis Ho Jennifer Huang Yen Hua Lung Juan Sui Guan Wang Lujie Wang

Phase I: Sampling Classes and Retrieving Contact Information

As the name of our group implies, we are responsible for every aspect of data collection.Ultimately though, every effort afforded to each aspect of collection starts and ends withone goal in mind: gorgeously robust data.

Yet despite our lofty goals, we also recognized the reality of the situation and our ownlimitations which led to our first initiative involving the modification of the sample size.Originally, the Sampling Plan group presented a design that included a sample of 30departments from which sub-samples consisting of one lower division and one upperdivision class were to be made from each department. This would have resulted in a samplesize of 60 classes. We then decided that such a large survey would prove to be tooexpensive. The time cost alone would make this scenario economically unfeasibleconsidering the required pace of the project; therefore, we decided to decrease the numberof departments sampled by half to 15. The method by which we used to accomplish thiswas to perform an SRS via generating 15 random numbers from a department index. Oncewe had our desired sample, each of the chosen department's class lists was retrieved fromthe online schedule of classes database and subsequently read into R as a data frame.Code was then written to randomly select a Discussion or Lab from within each divisionaccording to an SRS manner. But for the departments that did not have discussion sectionsor labs, we substituted those with Lectures instead. Once finished, we compiled the list ofsampled classes.

Our next task was to begin retrieving class and instructor information. Class informationwould eventually be needed for directing the survey conductors which is self explanatory.The reason we wanted to obtain the contact information for the instructors was so that wecould courteously ask for permission to survey a class. Simply, this would be the polite thingto do and it would also give us the opportunity to try to gain as many positive responses aspossible by allowing Professor Huang to request permission.

For most of the classes, the GSI's name was not listed on the class schedule website.Occasionally there were a few classes to contrary, but even then in no case was the contactinformation given on any of the online class lists. Much more effort was needed to retrievesuch information. As such we looked on department websites, searched extensively onlineutilizing both Google and Berkeley's online directory, as well as visited the departmentsdirectly in person. So many different avenues were sought, many times attempting two orthree search methods for a single class because information was so surprisingly elusive.For example, there were frequent instances in which departments did not provide us anyinformation for GSIs at all. In another example, some of the classes were taught by

Elective Cosmetic Surgery


instructors from other departments, and so visiting two departments was necessary.

All of the trouble mentioned beforehand was for retrieving contact information for aninstructor whose name we had. There were many GSIs whose names were not listed on theclass schedule website and hence we started with even less. In order to deal with theseclasses, we did two things. First, we went to the department offices in person to ask for thecontact information of these instructors which again, just as in the previous scenario,information was also not consistently made available on the department's website or attheir office. Second, we either went to section or to class to ask the instructor or theProfessor for the information. Except for the rejection from the Peace and Conflict Studiesprofessor, we were able to obtain all the instructors' contact information after completingthese steps.

Phase II: Organization

With a mass of new information, organization quickly became a key priority. Taking stock ofall relevant information for all thirty classes, we inputted everything into an excel file, thatwas henceforth known as the “master list”. This would make information lookup moreconvenient. A small portion of the file is shown below.

Department Level Lecture-Section

ClassSize

Time(section)

Location(section)

time(lecture)

location(lecture) GSI Email/Telephone(gsi)

Meche(Eng) lower 45-102 33 T8-12

230hearstmin

MWF12-1P

100LEWIS

RonaldGronsky [email protected]

Meche upper 101-101 28 M1-2 101moffitt

MWF9-10A 150GSPP

REICH-WEISER,C

[email protected]

business lower 10-102 39 M9-10 C135CHEIT

MWF1-2P

WHEELERAUD

PAMIDIMARRI,P

[email protected]

business upper 107-101 35 F1-2 C135cheit M2-4P F295

HAAS KIL,L [email protected]

Organizing our group would be much more complicated. Seven people is quite a sizeablegroup. Coordinating every member, each with their own schedules, outside responsibilities,and expertise would require a tremendous amount of timing management. If one groupmember had a midterm or would be out of town due to job interviews, shuffling of workwould prove to be very difficult and risky if they may not be able to finish their task on time.Hence, the first thing we did was to make sure communication would go smoothly byconfirming our email contacts since the original email list contained accounts no longer inuse by some members. Next, we planned to meet regularly to decide how we would tacklethe collection tasks.

However, this scheme proved to be very inefficient due to the project’s inherent nature ofcontingencies in combination with our own imperfect communication capabilities andlimited personnel availability. Much later on the collection process, when the events ofsurveying and other activities were less turbulent, each member was assigned an area tohandle which in general concluded with some success.


Lastly, the only other group factor not already considered was working with EricaChristenson, the originator of survey. Working with her and keeping her updated would bebest achieved by a single person who would be keeping tabs on all the various aspects of thecollecting process and as well as would be seeing her on a frequent basis such as in class.Hence, our group leader acted as our liaison.

Phase III: Asking for Permission

Starting as early as possible, we met and discussed how we would ask the instructors forpermission to survey in their courses. Initially, we came up with three different emailmessages to be sent to the most relevant authority whose contact information could befound: section instructors, head GSI's, and Professors (in that order). However, uponconsulting with Professor Huang, we decided to make an greater effort to obtain thecontact information of section instructors by visiting departments, going to the class, andother such methods already mentioned earlier. All of this was an attempt to place all of thework on ourselves rather than on the professors and head GSI's in hopes of recieving moreresponses as well as cutting down on bias by having one standard email message be sentby Professor Huang for contact.

A group email account, [email protected], was also created to handle all the receipts ofemail correspondence between Professor Huang and the other instructors so that sortingout all the information with regards to permission, problems, and directions would be moremanageable and accessible to all members of our group.

Phase IV: Coordinating Students

Initially, our plan to mobilize our classmates for surveying was quite simplistic:

1. Notify the students that each were required to sign up forone class to survey, two if they wish to survey in with a partner

2. Notify the students that a sign-up sheet would be passedaround in the coming lecture.

3. Hand out the sign-up sheet during lecture, and then for thosewho could not or did not make it lecture, just circulate the signup sheet around during the next discussion period.

However much to our dismay, we learned that real life rarely pans out to be as simplistic asour expectations or in this case, our fantasies. The only step that went according to planwas that during the wait for the initial confirmations from GSIs and Professors, a sign upsheet was created from the master list. It would of course provide to the students theentire range of relevant information and more including location of classes, times, andspecific instructions from the Professor or GSI. However, passing a sign up sheet aroundduring lecture and discussion proved to be an unviable option, since some students chosenot to attend lecture or discussion. Many others wanted to postpone signing up, retracttheir sign-ups, check their class location and time, and other such activities which would not


have been feasible to accommodate under our original plan.

On top of this accessibility issue, many students, especially for those who did not attendclass were frequently lost at various points throughout the surveying process: "At what timedo I go survey a class?", "Where can I sign up?", "Where are the surveys?", and etc. Totackle this logistics issue, our first step was to expand the purposes of our group email andtry to provide very quick responses for all questions. However, this could only serve as atemporary solution as the emails became quite frequent and some of the inquiries were ofdire importance (i.e. "I'm suppose to survey in ten minutes but couldn't find thequestionnaires") and as such required a response quickness that would be dangerous toattempt repeatedly over the duration of our responsibilities.

As such, our more permanent solution to these logistical challenges was to create a datacollection group website. This would allow the students to gain direct access to all theinformation they needed in a more convenient manner. Moreover, this way we could ensurestudents received the most up to date and accurate information in addition to improving theefficient use of our own time by minimizing redundant responses to identical questionsasked to us in email or in person.

(Thewebsite:www.geocities.com/echoboi/index2.html)


(Websitetrafficstatistics)

Eventually, the site would grow to include much more such as updates to our progress,directions on what to do, uploaded PDF files of the questionnaire and survey instruction, anonline copy of the sign up sheet, and etc. The website was not perfect though as due to timeconstraints, a simple geocities host was selected which forcibly incorporated unwantedadvertisements and also due to our webmaster's web database knowledge being ratherrusty, we were restricted to a paper sign-up sheet that was posted on Professor Huang'sdoor instead of implementing an online sign-up capability.

There were still additional problems that could only be handled by old-fashioned readiness toact. For example, one student called in the day before she was suppose to survey andinformed us she could not make it due to an interview the following the day. Websites,emails, and presentations can only do so much in situations like these. Most of theseemergency situations in which we could not assume to find substitutes fast enough, weresolved them ourselves.

Phase V: Non-Response

Non-response became a primary concern as soon as we received our first rejection, andultimately, this became one of our biggest issues. Non-response includes Professors andGSIs who did not respond to email requests, Professors and GSIs who refused to allow us toconduct the surveys, and students who did not attend the class the day we conducted thesurvey. Having not learned how to deal with non-response yet, we felt this to be too difficult


to attempt solo so we approached Professor Huang for assistance. She advised us that theone method for dealing with non-response was to find a similar replacement. However, thisproved to be a daunting task as we realized we could not fully define “similar” in our situationgiven the information available to us. In the ideal world, we would be able to find anotherclass with the exact same position and intensity of opinions, but since that wasunobservable, we developed a set of criteria to find similar replacements according tovariables that we could observe and had reason to believe was of possible relevance. Thecriterion was primarily based on four variables.

Still unsure if we were appropriately and satisfactorily dealing with non-response, weconsulted with two graduate students regarding the practicality and validity of our method.We were advised by one of the graduate students, Melinda, that an alternative solution, onein which she would pursue herself, would be to randomly select another class from withinthe same department and division category. This made sense and seemed valid since thisis based on our original sampling method. This would be especially appealing if we werecertain that none of the observable variables would correlate with the opinions of the class.Conversing with Professor Huang for what may be the fourth time, there did not seem to beany very strong and compelling reasons to necessarily favor one option over another. Assuch, we stuck with our original replacements using the above matching criterion.

Replacements were still only utilized as a last, last resort. We attempted to preventnon-response right off the bat by asking that Professor Huang send the emails to theinstructors rather than send it ourselves. Afterwards, to deal with what appeared to be aninitial status of non-response, we relied on our ability to accommodate the needs andpreferences of the instructors to persuade them to give us permission. Hence, someclasses were surveyed at more convenient times on the same day, other days, or thefollowing weeks which was definitely worth the effort. Still after that, for those with noresponses, we asked students to visit the class at the start of session and requestpermission on the spot. Most of the instructors were kind enough to allow us to survey,which greatly reduced non-response as well. For those that still rejected us, we attemptedto ask the students directly after class and by other methods to do a survey, hoping that

Department:

Division:

ClassSize:

Timeofday:

Astudentresearcherwhohasworkedwithstudentpopulationssuggestedthatstudentswhoareenrolledinmorningclassesdifferincertainareasfromthosewhoareenrolledinlateclasses.Henoticedadifferenceinopinionwithregardstothetopicofauthority.Thismayormaynotberelevanttous,butdecidedtoerronthesideofcaution.

Self-explanatorytooasupperorlowerdivisionclassesfromadepartmenttendtobemoresimilartotheirrespectivedivision

Nocompellingreasontoassumeclasssizewouldcorrelatewithopinionsonelectivecosmeticsurgery,butthereisnoabsoluteevidencearguingitwouldn’tandalsothevariablewaseasytomatch

Self-explanatoryasclassesfromthesamedepartmenttendtobesimilar


they could at least serve as representatives of their class. Finally, replacements classeswere found if all else failed and the process repeated again.

At the end of the entire process there were a total ofsix classes listed to the right that we were unable toobtain data for reasons including: being able to defytwo rounds of replacements, had Instructors whowere so adamantly opposed to our cause would yell atus to get out of his Office hours for asking, had limitedreplacement options (ie. only 1 lower division sectionavailable which was also rejected), or had thestructure changed to something incompatible suchas empty office hours.

Phase VI: Data Input

The data input phase began before the end of the survey phase. We decided to start it earlybecause we knew data input require a lengthy amount of time to finish, and with the natureof the survey phase being quite spread out in conjunction with the time table of the DataAnalysis group, the motto was “the sooner the better”.

The first thing we did was to coordinate with the data analysis group and asked them tocreate a template which we could use to input the data. This is to make it so that they couldhave the data in a format that would be easy and convenient for them to manipulate andanalyze. However, we ran into a few problems with our first attempt at using the templatebecause it lacked clarification in certain areas like not specifying the input for somenon-numeric questions, so we worked with the data analysis group and gave them feedbackon how to improve the clarity of the template. We thought this was very important sincemany students outside of our group would also be working on the data input and therefore itwould be in the best interest of all parties that the instructions on the template be crystalclear. Right after, we also cleaned up some of the data that had been entered before thecreation of the second version of the template to make everything consistent.

Now that we possessed a complete template and several surveys ready for entry, only onelast step of preparation remained: organizing the workloads. Since some students did notparticipate in the surveying process, they were suppose to serve as an extra source of manpower to help us in entering data. After consulting with Professor Huang, we decided thateach of them would enter two classes onto the template. Organizing the workload amongour group and the other students in itself should not have been too difficult were it not forthree particular circumstances. The first one being that getting in contact with the otherstudents proved to be very slow and unreliable. The second being that we were trying veryhard to accommodate the schedule of the Data analysis group. Lastly, many students weresimply not handing the finished surveys back to us in a timely fashion. These threecircumstances worked against us with such incredible synergy that we were not allowed toplan anything with reliability.

For a long example, we would email and call various people to help us enter the data by

1 HistoryofArt Upper

2 NutritionalScience Upper

3 PeaceandConflicts Lower

4 PEIS Upper

5 CognitiveScience Lower

6 MaterialsScience Upper


Friday so that the data collection group could get it before their meeting time. However, wecould not make any definite assignments until people replied back to us which took days (twodays being the mode), which effectively diminished the possible utilization of the extra help.Taking it upon ourselves to enter data, we entered the few surveys we had in our possession,but were forced to wait for the rest of the surveys to come in. When Friday finally arrived,we received surveys just hours before the analysis group's meeting.

Also, there was an increasing number of email and phone correspondence between us andthe data analysis group as we tried to keep each other updated on files we obtained,deadlines, and feasibilities estimates of different components. Initially, a Yahoo GroupMessage board with a file database was created to help ease communication and logisticsmanagement between our groups, but several members claimed they did not know how touse it. A Data Input webpage was put online instead to share announcements and files.

The situation became more manageable as time went by. More surveys were being turnedin, more replacement classes were finished with surveying, and we were able to assign jobsto students when we accumulate a sizeable set for the Data Analysis group to work off offirst. At the time of writing this report, all surveys that were conducted have been returned.All the data inputs that are done by the members of this group have been turned in to thedata analysis group. At the time of writing this report, we are still confirming several jobsassigned to other students to make sure the data analysis group has recieved data.

LH Designs | 2005


D APPENDIX: ANALYSIS 33

D Appendix: Analysis

Data Analysis Report3 December 2005Professor Huang

Statistics 152: Sample Survey

By:Rose CendakAmy Cheng

Rebecca GraffKevin Li

Tammy LeeKunal MehtaDan Nguyen


WEIGHTS & NONRESPONSE

We generated the survey such that all students had an equal probability of beingselected under a particular set of assumptions:

i) All classes are of equal sizeii) Number of classes in each department is correlated with the number of peoplein each major

In terms of sampling we did not have to weight the responses because all students had anequal probability of selection. For data analysis, however, we were required to includeweights due to the effects of nonresponse.

Classes that were selected to be part of the sample but that did not participate inthe survey contributed to nonresponse at the cluster level. Students that did not show upto class the day that it was surveyed contributed to nonresponse within each cluster.

In order to deal with this nonresponse, we generated a method of classification forselected classes that did not participate. Such classes were grouped with observed classesin the same division level in what we felt were comparable departments:

Sampled Class Refusing Participation Comparable Class

Upper Division Art History Upper Division History

Upper Division Material Science Upper Division Mechanical Engineering

Upper Division Nutritional Science Upper Division Integrative Biology

Lower Division Peace and ConflictStudies

Lower Division Political Science

Upper Division PEIS Upper Division Political Science

Lower Division Cognitive Science Lower Division Computer Science

Had all selected classes been observed, we would have used the following weights:

Number Enrolled in ClassNumber Participants in Class

Because only some classes participated, however, we adjusted the weights for the classesthat were observed:

Number Enrolled in Observed Class + Number Enrolled in Unobserved Comparable Class (if applicable)Number Participants in Observed Class

For item nonrepsonse, we utilized a combination of random and nearest-neighborhot-deck imputation. The item nonrespondent was first catagorized by gender and classenrollment and within the given gender and class category, a random response wasimputed from all the responses. However, we chose to randomly assign a gender to those


respondents that did not select a sex because the overall probability of selection of malesand females was equal

ESTIMATING VARIANCES

Since the sample design was complicated, a closed form calculation of the pointestimates was difficult to obtain. As a result, we tried to estimate the variances bycreating a sample distribution using a bootstrap method. This included sampling withreplacement of our data set, calculating the estimate using the weights of our sampledesign, and repeating the process for numerous times for each estimate. From this weobtained a distribution of the estimates for which we calculated the variances and created95% confidence intervals.

REGRESSION MODELS

To examine if gender, major, or the class was an upper or lower division sectionhad any association with how the students answered any particular question, we usedregression analysis where significance test was done at a 5% level. The gender variablewas a 1 for males and a 0 for females. The majors were also represented by dummyvariables; all ten of them were modeled. This did not result in linear dependence becausestudents who were undeclared were also considered in the analysis; they had zeros for allof the variables representing major. Below are the dummy variables for the majors:

~ Cluster 1: Arts & Humanities~ Cluster 2: Biological Sciences~ Cluster 3: Physical Sciences~ Cluster 4: Social Sciences~ Cluster 5: Interdisciplinary Studies~ Cluster 6: Business~ Cluster 7: College of Chemistry~ Cluster 8: Engineering~ Cluster 9: Environmental Design~ Cluster 10: College of Natural Resources

A potential problem with our analysis is our method of dealing with doublemajors. Representing double majors from different departments in our model requiredhaving ones multiplied by two of the major coefficients; effectively damping the effect ofboth major variables and giving artificially low significances. We chose to do this in lieuof choosing to assign each student to one major.

We also considered some cross question regressions. Specifically, we looked athow Q6, Q2, and Q5 affected the other questions. We found that questions were notsignificantly dependent on Q6 and Q5, but Q2 significantly was able to regress on someof the questions, which will be discussed in the next section.


QUESTION ANALYSIS

Below is analysis of each question where both the results of estimated proportionsand linear models will be discussed. All tables of estimates, confidence intervals, andregression analysis can be seen in the appendix section.

Question 1:Which statement do you agree with more? In principle, elective cosmetic surgery is rarely /sometimes / almost always an acceptable option to improving physical appearance.

The purpose of this question was to explore the general feelings of undergraduatestudents toward elective cosmetic surgery. The proportions can be seen in the frequencytable section of the appendix, which shows that neither the gender of a student nor upperor lower division status of a class sampled greatly affect the percentages in each responsecategory. We also note that a majority of students (around 64%) answered that cosmeticsurgery is sometimes an acceptable option and that the second highest proportion was forcosmetic surgery being rarely an acceptable option. This could lead us to conclude thatthe population is somewhat conservative when it comes to the topic of plastic surgery.

Question 2:Do you know anyone who has ever had an elective cosmetic surgery procedure? If Yes. Pleasecircle all that apply: I have had elective; Family Member; Friend; Acquaintance

For question two, we sought to discover if respondents themselves or someone intheir community of relationships has had cosmetic surgery because it was believed that arespondent's answer to this question would affect their answers to other questions. Wefound that a majority (about 61%) of respondents knows someone who has had anelective cosmetic surgery procedure, and of these respondents, most shared that a friendor acquaintance had had a procedure done. Our 95% confidence intervals are summarizedin the table section of the appendix.

Again, we hoped to find out if respondents’ answers to this question might insome way affect their answers to other questions so we performed cross questionregressions of question two with questions three and seven. We found that the fewstudents who stated they themselves had cosmetic surgery rated Anna of question threemore positively than other students. We also found that if a family member or friend of astudent had had cosmetic surgery, the student was more likely to respond that they would,in fact, consider cosmetic surgery as described in question seven. Further discussion ofthese findings and the regression results can be found in the sections for those questions.

Question 3:


Imagine that on the first day of class you meet a gir l named Anna and you two discuss whateach of you did during the summer. Anna explains that she had breast augmentation surgery.Please circle one or more words you would use to describe Anna: Confident, Secure, Blunt,Vain, Insecure, or Superficial.

The purpose of question three is to explore respondents’ first impressions ofanother person who has had a common cosmetic surgery operation. From the totals tablewe can see that neither the gender of a student nor upper or lower division status of aclass sampled greatly affect the percentages in each response category, other than thepercentages for students who responded that they would describe Anna as superficial inupper division versus lower division sampled classes. We observe that the quality thatwould most often be used to describe Anna is insecure (56%).

We later ran regressions to see if a respondent’s major, gender, or the lower orupper division status of their class had any association with how they might describeAnna. We found that students in Arts and Humanities were more likely to say that Annais vain, but other than that, gender and major category were not good predictors of howrespondents would describe Anna. Also, it should be noted that in order to avoid omittedvariable bias, we did not eliminate variables with non-significant coefficients which ledto the model being over fitted.

As described earlier, we hoped to discover if students’ responses to question twowere in any way related to their selected descriptions of Anna. In order to do this usinglogistical regression, we categorized the adjectives as either positive (confident andsecure) or negative (blunt, vain, insecure, and superficial). We found through use ofresponses from question two as explanatory variables that students who had had cosmeticsurgery were more likely to describe Anna positively. This also removed omittedvariable bias; once we had separated out the effect of responses to question two we foundthat students from Biological Sciences, the College of Engineering, and the College ofEnvironmental Design were more likely to describe Anna negatively.

Question 4:Imagine either now or some time in the future, someone close to you wants to get electivecosmetic surgery. For each of the following, would you support their getting the surgery? Pleaseindicate your choice for each.

Surprisingly, we found no association between a student’s gender and the way hefelt about someone close to them getting elective plastic surgery. In all of our regressions,the coefficient for gender did not prove to be significant at the 5% level.

For the most part, we also found no significant association with a student’s major.The exception was engineering students. When modeling how student’s viewed theirmother receiving cosmetic surgery, the coefficient was significant at the 5% level. Thiswas also the case with student’s views of the idea of their sister getting plastic surgery. Inboth cases, the coefficient for engineering was negative, indicating that, in general,engineers are more opposed to the idea of their mother or sister getting cosmetic surgerythan other students.

In all the regressions, the intercept was highly significant. This is because thedependent variable only had four discreet values, 1, 2, 3, and 4. So, no matter the


variables, the model had to always predict a positive, non-zero, value. This, incombination with the fact that the explanatory variables were usually not significant,resulted in the need for a positive, non-zero coefficient. This resulted in the highsignificance level.

We also attempted to determine whose elective surgery students would be mostopposed to. To obtain a testable statistic, we recoded the question into a logic variablesuch that, if a respondent answered question 4 with 1 or 2, he was coded as beingopposed (oppose=1) or otherwise coded as being approved (oppose=0), and we comparedthe percentage of students who opposed to the individual getting plastic surgery using atwo sample t-test. We tested to see if students were more opposed to their mother gettingsurgery than their father getting surgery. We also tested to see if they were more opposedto their parents getting surgery than their siblings, more opposed to their family membersthan other close people, and more opposed to friends than significant others. All testswere conducted at the 5% level.

The only t-test that resulted in a significant result was the one with friends andsignificant others. Students were found to be more opposed to their significant othersreceiving elective cosmetic surgery than they were to their friends receiving it. This resultwas significant at the 5% level.

Question 5:Please indicate whether you agree or disagree with the following statements. All things being

equal, someone who has elective cosmetic surgery done:

Has a professional advantage over others

Is more sociable

Has an easier time attracting a partner

Question 5 was aimed to find out if students felt that people who have hadcosmetic surgery have professional advantages, are more sociable, and/or have an easiertime attracting a partner. Again, we used regression analysis to see if gender, major, orclass division were associated with any viewpoints. This time, we used logisticalregressions. If a student agreed with a statement, than it was a “1,” if not, it was a “0.”

For all three regressions, the intercepts were significant and gender was not. Also,some majors did prove to be significant. For the question about whether someone whohad had cosmetic surgery done had a professional advantage, the coefficient for a majorin the biological sciences was significant at the 5% level. People in the biologicalsciences were more likely to feel that people with cosmetic surgery had a professionaladvantage. The same was the case for people in the social sciences. Our model showed astronger effect of being in the biological sciences than in the social sciences, although itis possible that the difference may be attributed to chance variation. We did not test this.

For the question about people with cosmetic surgery being more sociable, thecoefficient for a major in the biological sciences was again shown to be significant at the5% level. Students in the biological sciences tended to agree with the statement more thanother students. None of the other coefficients were significant.


The question about cosmetic surgery resulting in an easier time attracting apartner had markedly different results than the previous two. Gender’s coefficient had ap-value of .0761, a value much closer to significance than the values in the other models.However, it was not significant at the predetermined testing level. This time, thecoefficient for biological sciences was not significant. Being in the college of chemistry,however, had a notable association with someone’s views on this question; chemistrystudents were much more likely to feel that people with elective cosmetic surgery had aneasier time attracting a partner.

The confidence intervals show that there is a significant difference between theproportion of students who believe that cosmetic surgery makes it easier to find a partnerand those who believe it gives a professional advantage or makes one more sociable.More students believe it makes it easier to find a partner.

Question 6:

How would you rate your own overall appearance?

We used a regression method to analyze the responses from question 6. Question6 asked people to rate themselves from a scale of one to ten. Our regression resultsshowed us that men generally rated themselves higher than women, biological sciencesmajors tended to rate themselves lower than the rest of the clusters, and theinterdisciplinary studies majors tended to rate themselves higher than the rest of theclusters. Our confidence interval for the average rating per student is [6.129, 6.436].

Question 7:Imagine that you could have any cosmetic surgery procedure done safely and for free – and thatno one would know unless you told them. Please indicate which of the following procedures, ifany, you would have done. Select all that apply.In question 7, we did a regression analysis with question 2. Question 7 asked peoplewhat sort of elective cosmetic surgery they would choose if they could get one withoutpeople knowing. We regressed question 7 by recoding it such that, if they circled anyprocedures from A to K, they would get plastic surgery (q7.yes=1) and, if they circled L,they would not get plastic surgery (q7.yes=0). Question 2 asked people if they knewanyone who had elective cosmetic surgery. We found out that if people knew a familymember or a friend who had plastic surgery, they are more likely to get plastic surgerythemselves than if they didn’ t know anyone who had gotten it. Also, women tended to bemore willing to get elective cosmetic surgery more than men. And out of all the clustermajors, the biological sciences majors tended to be more willing to get elective cosmeticsurgery than the rest of the majors. About 65% of people selected “None” when askedwhat type of cosmetic surgery they wanted, and skin resurfacing was the most popularchoice of the people who would get elective cosmetic surgery.


APPENDIX

I. Proportions and Confidence Intervals

Question 1

Female Male Lower UpperRarely 31% 30% 31% 33% 29%

Sometimes 64% 68% 60% 64% 64%Almost Always 5% 2% 8% 3% 7%

TotalGender Division Level

Total Std. Dev.Rarely 30.87% 0.0243 26.10% 35.64%

Sometimes 64.11% 0.0248 59.25% 68.96%Almost Always 5.00% 0.0117 2.71% 7.29%

95% Confidence Interval

Question 2

TotalYes 61%No 39%

Yes Respondents

Friend 30%Acquaintance 28%

Family 19%I 2%

Total Std. Dev.Proportion Yes 61.49% 0.0451 52.65% 70.33%

Friend 29.83% 0.0433 21.35% 38.30%Acquaintance 27.55% 0.0409 19.54% 35.55%

Family 19.41% 0.0344 12.68% 26.15%I 1.71% 0.0124 -0.72% 4.15%


Question 3

Female Male Lower UpperInsecure 56% 57% 56% 58% 55%

Blunt 38% 36% 41% 42% 35%Superficial 24% 26% 23% 19% 30%

Vain 16% 18% 14% 14% 17%Secure 12% 11% 13% 14% 11%

Confident 7% 6% 8% 7% 7%


Total Std. Dev.Insecure 56.43% 0.0439 47.82% 65.04%

Blunt 38.24% 0.0437 29.68% 46.80%Superficial 24.50% 0.0395 16.75% 32.24%

Vain 15.76% 0.0347 8.97% 22.56%Secure 12.48% 0.0307 6.47% 18.50%

Confident 7.27% 0.0244 2.50% 12.05%



Question 4:Mother

Female Male Lower UpperStrongly Oppose 35% 36% 34% 34% 35%Generally Oppose 35% 35% 34% 33% 36%Generally Support 24% 22% 26% 25% 23%Strongly Support 3% 4% 2% 3% 3%Average 1.95 1.94 1.96 1.96 1.94

Father


Sister


Brother


Gender

Gender Division Level

Division LevelGender


Division LevelTotal

Total

Total

Total

Q4 (Averages)Total Std. Dev.

Mother 1.951 0.047 1.859 2.043Father 1.828 0.042 1.747 1.910Sister 1.960 0.051 1.860 2.060

Brother 1.877 0.050 1.780 1.974Significant Other 1.855 0.044 1.770 1.941

Female Best Friend 2.142 0.046 2.051 2.233Male Best Friend 2.050 0.045 1.963 2.137


Question 5:

Female Male Lower UpperHas a professional advantage 31% 29% 33% 31% 31%

Is more sociable 24% 21% 27% 24% 24%Has an easier time attracting a partner 50% 44% 56% 50% 50%


Significant Other


Female Best Friend


Male Best Friend


Total

Total

Total


Gender


Division Level


Total Std. Dev.Prof Advantage? 31.24% 0.025 26.32% 36.16%More sociable? 24.01% 0.023 19.59% 28.43%Easier Partner? 50.23% 0.027 44.93% 55.53%


Question 6:

Male Female Lower UpperMean 6.28 6.12 6.44 6.13 6.44


Total Std. Dev.SelfRating 6.283 0.078 6.129 6.436


Question 7:

Female Male Lower UpperL. None. 65% 49% 22% 37% 34%

D. Lazer skin resurfacing/dermabrasion 13% 19% 7% 13% 13%B. Liposuction 10% 16% 4% 11% 9%

E. Nose reshaping 9% 11% 7% 6% 11%A. Breast augmentation 8% 11% 5% 8% 8%

I. Eyelid surgery 4% 5% 2% 2% 5%K. Other 3% 1% 4% 3% 3%

F. Collagen injection 2% 3% 2% 1% 4%G. Hair transplants/implant 2% 1% 3% 1% 2%

J. Breast reduction 2% 3% 0% 2% 1%H. Face lift 1% 1% 2% 2% 0%

Gender DivisionTotal

Total Std. Dev.None 64.68% 0.026 59.54% 69.81%Skin 12.93% 0.018 9.40% 16.47%

Liposuction 10.32% 0.016 7.21% 13.42%Tummy Tuck 10.00% 0.016 6.85% 13.16%

Nose Reshaping 8.71% 0.014 5.97% 11.46%Breast Aug 8.07% 0.015 5.07% 11.08%

Eyelid surgery 3.68% 0.010 1.81% 5.55%Other 2.86% 0.010 0.95% 4.78%

Collagen Injection 2.37% 0.008 0.82% 3.92%Hair im/transplant 1.91% 0.007 0.52% 3.29%

Breast reduction 1.58% 0.006 0.39% 2.77%Face Lift 1.44% 0.007 0.08% 2.81%


Demographics:


TotalFemale 50%

Male 50% Total

Mean 21.0

TotalAsian American/Pacific Islander 39%

Caucasian 38%Hispanic 7%

Other 7%African American 4%Did Not Respond 4%American Indian 0%

Total1 16%2 23%3 27%4 22%5 6%

Grad 5%

I I . Regressions

Question 3:Confident

Est im ate Std. Error t value Pr(> | t | )( I ntercept ) 0.08962 0.03918 2.287 0.0228 *

gender 0.03463 0.02919 1.186 0.2363lower 0.01181 0.02871 0.411 0.6812

Art s/ Hum anit ies - 0.09397 0.05488 -1.712 0.0877 .Bio Science - 0.04987 0.04263 -1.17 0.2428

Physical Science 0.03974 0.05574 0.713 0.4764Social Science - 0.03133 0.03758 -0.834 0.405

Interdis - 0.08275 0.05247 -1.577 0.1156Business 0.03806 0.0515 0.739 0.4604

Chem ist ry - 0.10772 0.09416 -1.144 0.2534engineer ing - 0.06738 0.04845 -1.391 0.1651

Env ior . Design 0.15971 0.13594 1.175 0.2408Natural. Res. 0.22823 0.15741 1.45 0.148

SecureEst im ate Std. Error t value Pr(> | t | )

( I ntercept ) 0.144596 0.049329 2.931 0.00359 * *gender 0.037182 0.036752 1.012 0.31236

lower -0.009609 0.03615 -0.266 0.79055Art s/ Hum anit ies -0.106418 0.069093 -1.54 0.12439

Bio Science -0.041774 0.053673 -0.778 0.4369Physical Science -0.084884 0.070182 -1.209 0.22727

Social Science -0.012086 0.047307 -0.255 0.79851Interdis 0.028366 0.066056 0.429 0.66787

Business 0.040185 0.064845 0.62 0.53584Chem ist ry 0.07057 0.118553 0.595 0.55204

engineer ing -0.093102 0.060997 -1.526 0.12781Env ior . Design 0.12659 0.171152 0.74 0.46

Natural. Res. 0.179546 0.198184 0.906 0.36557


VainEst im ate Std. Error t value Pr(> | t | )

( I ntercept ) 0.112802 0.054349 2.076 0.0386 *gender -0.017937 0.040493 -0.443 0.6581

lower 0.013829 0.03983 0.347 0.7286Art s/ Hum anit ies 0.165228 0.076125 2.17 0.0306 *

Bio Science 0.000865 0.059136 0.015 0.9883Physical Science 0.087556 0.077325 1.132 0.2583

Social Science 0.10553 0.052122 2.025 0.0436 *Interdis -0.003045 0.072779 -0.042 0.9666

Business -0.056059 0.071445 -0.785 0.4332Chem ist ry -0.003395 0.130619 -0.026 0.9793

engineer ing 0.046144 0.067206 0.687 0.4928Env ior . Design -0.130226 0.188571 -0.691 0.4903

Natural. Res. 0.2219 0.218355 1.016 0.3102 Blunt

Est im ate Std. Error t value Pr(> | t | )( Intercept ) 0.38932 0.07162 5.436 1.01E-07 * * *

gender 0.0361 0.05336 0.677 0.499lower -0.07847 0.05249 -1.495 0.136

Arts/ Hum anit ies 0.06843 0.10032 0.682 0.496Bio Science 0.01634 0.07793 0.21 0.834

Physical Science 0.05854 0.1019 0.574 0.566Social Science 0.04567 0.06869 0.665 0.506

Interdis 0.05294 0.09591 0.552 0.581Business - 0.0514 0.09415 -0.546 0.585

Chem ist ry 0.08669 0.17213 0.504 0.615engineering - 0.0987 0.08856 -1.114 0.266

Env ior. Design -0.06482 0.2485 -0.261 0.794Natural. Res. 0.29147 0.28775 1.013 0.312

I nsecureEst im ate Std. Error t value Pr(> | t | )

( Intercept ) 0.378765 0.073485 5.154 4.21E-07 * * *gender 0.044826 0.05475 0.819 0.413

lower 0.023664 0.053854 0.439 0.661Arts/ Hum anit ies 0.184437 0.102929 1.792 0.074 .

Bio Science 0.077462 0.079957 0.969 0.333Physical Science - 0.037268 0.104551 -0.356 0.722

Social Science 0.007297 0.070474 0.104 0.918Interdis - 0.013866 0.098405 -0.141 0.888

Business -0.06443 0.0966 -0.667 0.505Chem ist ry 0.037591 0.17661 0.213 0.832

engineering 0.048871 0.090869 0.538 0.591Env ior. Design 0.080063 0.254967 0.314 0.754

Natural. Res. - 0.068262 0.295238 -0.231 0.817


SuperficialEst im ate St d. Error t value Pr( > | t | )

( I nt ercept ) 0.2338 0.06383 3.663 0.000287 * * *gender -0.02239 0.04756 -0.471 0.638139

lower 0.08459 0.04678 1.808 0.071409 .Art s/ Hum anit ies 0.04682 0.08941 0.524 0.600822

Bio Science 0.05457 0.06946 0.786 0.432557Physical Science -0.01851 0.09082 -0.204 0.838578

Social Science -0.03622 0.06122 -0.592 0.554479Interdis -0.04032 0.08548 -0.472 0.637436

Business -0.11247 0.08391 -1.34 0.180978Chem ist ry -0.16222 0.15342 -1.057 0.291035

engineering -0.03135 0.07894 -0.397 0.691457Envior. Design 0.21619 0.22148 0.976 0.329671

Nat ural. Res. 0.0788 0.25647 0.307 0.758839

Q3 Good AdjectivesEstimate Std. Error t value Pr(>|t|)

(Intercept) 0.02659 0.02627 1.012 0.3121q2a.i 0.13934 0.06572 2.12 0.0347 *

q2a.family 0.03286 0.02244 1.464 0.1441q2a.friend 0.02718 0.0194 1.401 0.1621q2a.acqu -0.01985 0.01978 -1.003 0.3165

gender 0.03017 0.01819 1.658 0.0981 .lower 0.01888 0.01788 1.056 0.2917

Arts/Humanities -0.03398 0.03426 -0.992 0.322Bio Science -0.05401 0.02654 -2.035 0.0426 *

Physical Science 0.01506 0.03502 0.43 0.6674Social Science -0.03915 0.0234 -1.673 0.0952 .

Interdis -0.02925 0.03286 -0.89 0.3739Business 0.01703 0.03211 0.53 0.5962

Chemistry -0.05751 0.05858 -0.982 0.3269engineering -0.06631 0.03036 -2.184 0.0296 *

Envior. Design 0.2047 0.08478 2.414 0.0163 *Natural. Res. -0.12279 0.10056 -1.221 0.2228

Question 4:Mother

Est im ate Std. Error t value Pr(> | t | )( Intercept) 1.97027 0.13056 15.09 < 2e-16 * * *

gender 0.05816 0.0967 0.601 0.5479lower 0.0629 0.09568 0.657 0.5114

Arts/ Humanit ies -0.09268 0.18198 -0.509 0.6109Bio Science -0.02114 0.14067 -0.15 0.8806

Physical Science 0.04658 0.18698 0.249 0.8034Social Science -0.02269 0.12496 -0.182 0.856

Interdis 0.03264 0.17439 0.187 0.8516Business 0.02354 0.17295 0.136 0.8918

Chem ist ry -0.35793 0.30759 -1.164 0.2454engineering -0.37012 0.16095 -2.3 0.0221 *

Envior. Design -0.18945 0.4434 -0.427 0.6695Natural. Res. 0.32272 0.51358 0.628 0.5302


FatherEst im ate Std. Error t value Pr(> | t | )

( Intercept) 1.76598 0.12474 14.157 < 2e-16 * * *gender 0.10212 0.09259 1.103 0.2708

lower 0.12061 0.09125 1.322 0.1871Arts/ Humanit ies 0.11386 0.17357 0.656 0.5123

Bio Science -0.07379 0.13382 -0.551 0.5817Physical Science 0.15579 0.17616 0.884 0.3771

Social Science -0.04799 0.11913 -0.403 0.6873Interdis -0.01479 0.16569 -0.089 0.9289

Business 0.07724 0.16248 0.475 0.6348Chem ist ry -0.20037 0.29344 -0.683 0.4952

engineering -0.30537 0.15547 -1.964 0.0503 .Envior. Design -0.05563 0.4229 -0.132 0.8954

Natural. Res. 0.4931 0.4898 1.007 0.3148

Sist erEst im ate Std. Error t value Pr(> | t | )

( I ntercept ) 2.141727 0.137119 15.619 < 2e-16 * * *gender 0.099497 0.10265 0.969 0.3332

lower -0.15773 0.101462 -1.555 0.1211Arts/ Hum anit ies -0.00269 0.192563 -0.014 0.9889

Bio Science -0.04875 0.14839 -0.328 0.7428Physical Science -0.0384 0.19834 -0.194 0.8466

Social Science -0.20743 0.133035 -1.559 0.12I nterdis -0.27074 0.190516 -1.421 0.1563


engineering -0.41298 0.168233 -2.455 0.0147 *Envior. Design 0.401256 0.505339 0.794 0.4278

Natural. Res. 0.43714 0.621114 0.704 0.4821

BrotherEst im ate Std. Error t value Pr(> | t | )


lower -0.02137 0.10055 -0.213 0.832Arts/ Hum anit ies 0.20685 0.1918 1.078 0.282

Bio Science -0.08617 0.14903 -0.578 0.564Physical Science 0.0712 0.19277 0.369 0.712



engineering -0.24202 0.17029 -1.421 0.156Envior. Design 0.17695 0.43065 0.411 0.681

Natural. Res. 0.0819 0.60823 0.135 0.893


Significant OtherEst im ate Std. Error t value Pr(> | t | )


lower -0.03861 0.09402 -0.411 0.682Arts/ Hum anit ies -0.04815 0.1777 -0.271 0.787

Bio Science 0.045 0.13947 0.323 0.747Physical Science -0.01217 0.19144 -0.064 0.949



engineering -0.1586 0.16027 -0.99 0.323Envior. Design -0.31666 0.43193 -0.733 0.464

Natural. Res. 0.11942 0.50016 0.239 0.811 Fem ale Best Fr iend

Est im ate Std. Error t value Pr(> | t | )( I ntercept) 2.1509 0.124176 17.321 < 2e-16 * * *

gender 0.021048 0.091807 0.229 0.819lower - 0.02765 0.090622 -0.305 0.76

Art s/ Hum anit ies - 0.15369 0.174188 -0.882 0.378Bio Science 0.077252 0.134135 0.576 0.565

Physical Science 0.003362 0.176795 0.019 0.985Social Science 0.003736 0.119148 0.031 0.975

Interdis - 0.02017 0.164488 -0.123 0.902Business 0.240055 0.164177 1.462 0.145

Chem ist ry - 0.25367 0.29471 -0.861 0.39engineering - 0.18196 0.15422 -1.18 0.239

Env ior. Design 0.410065 0.424873 0.965 0.335Natural. Res. 0.517968 0.492124 1.053 0.293

M ale Best FriendEst im ate Std. Error t value Pr(> | t | )

( I ntercept) 2.008848 0.122847 16.352 < 2e-16 * * *gender 0.033492 0.090673 0.369 0.712

lower 0.006954 0.089625 0.078 0.938Art s/ Hum anit ies 0.051814 0.171788 0.302 0.763


Social Science 0.020833 0.11798 0.177 0.86Interdis - 0.00265 0.162218 -0.016 0.987

Business 0.215325 0.161887 1.33 0.184Chem ist ry - 0.13499 0.290492 -0.465 0.642

engineering - 0.17282 0.153559 -1.125 0.261Env ior. Design 0.52077 0.418621 1.244 0.214

Natural. Res. 0.644337 0.484827 1.329 0.185

Question 5:


Has a professional advantage over othersEst imate Std. Error t value Pr(> | t | )

( I ntercept) 0.175062 0.066792 2.621 0.00914 * *gender 0.04844 0.049764 0.973 0.33101

lower 0.006084 0.048949 0.124 0.90115Arts/ Humanit ies -0.083201 0.093554 -0.889 0.37442

Bio Science 0.188395 0.072675 2.592 0.00992 * *Physical Science -0.040607 0.095029 -0.427 0.66941

Social Science 0.151481 0.064055 2.365 0.01857 *Interdis 0.08177 0.089442 0.914 0.36121

Business -0.022335 0.087802 -0.254 0.79935Chemist ry 0.249856 0.160524 1.557 0.12047

engineering 0.107583 0.082592 1.303 0.19355Envior. Design 0.03137 0.231744 0.135 0.8924

Natural. Res. 0.140097 0.268347 0.522 0.60194

Is more sociableEst im ate Std. Error t value Pr(> | t | )

( I ntercept ) 0.136697 0.063279 2.16 0.0314 *gender 0.068633 0.047146 1.456 0.1463

lower 0.020808 0.046374 0.449 0.6539Art s/ Hum anit ies - 0.041947 0.088633 - 0.473 0.6363

Bio Science 0.157928 0.068852 2.294 0.0224 *Physical Science 0.104694 0.09003 1.163 0.2457

Social Science 0.050525 0.060686 0.833 0.4056Interdis 0.014738 0.084737 0.174 0.862

Business - 0.006209 0.083183 - 0.075 0.9405Chem ist ry 0.162198 0.15208 1.067 0.2869

engineering 0.083688 0.078248 1.07 0.2855Envior. Design 0.059616 0.219554 0.272 0.7861

Natural. Res. 0.166822 0.254231 0.656 0.5121

Has an easier time attracting a par tnerEst im ate Std. Error t value Pr(> | t | )

( I ntercept ) 0.392092 0.075388 5.201 3.33E- 07 * * *gender 0.099906 0.056168 1.779 0.0761 .

lower 0.002738 0.055248 0.05 0.9605Art s/ Hum anit ies - 0.065509 0.105594 - 0.62 0.5354


Social Science 0.006164 0.072299 0.085 0.9321Interdis - 0.029933 0.100953 - 0.297 0.767

Business - 0.004835 0.099101 - 0.049 0.9611Chem ist ry 0.461973 0.181183 2.55 0.0112 *

engineering 0.062349 0.093222 0.669 0.504Envior. Design - 0.18471 0.261569 - 0.706 0.4805

Natural. Res. 0.240359 0.302883 0.794 0.428

Question 6:


Self Rat ing of At tract ivenessEst im ate Std. Error t value Pr( > | t | )

( I nt ercept ) 6.26431 0.21901 28.603 < 2e- 16 * * *gender 0.4551 0.16317 2.789 0.00557 * *

lower -0.19237 0.1605 - 1.199 0.23149Art s/ Hum anit ies 0.30663 0.30676 1 0.31819

Bio Science -0.65363 0.2383 - 2.743 0.00639 * *Physical Science -0.13725 0.3116 - 0.44 0.65985

Social Science - 0.1843 0.21004 - 0.877 0.38081Interdis 0.61073 0.29328 2.082 0.03801 *

Business 0.25926 0.2879 0.901 0.36845Chem ist ry -0.08691 0.52635 - 0.165 0.86894

engineering -0.46373 0.27082 - 1.712 0.0877 .Envior. Design 0.38213 0.75988 0.503 0.61536

Natural. Res. 0.64811 0.8799 0.737 0.46186


Question 7:Would you get elective plastic if no one knows (Yes or No)

Estimate Std. Error t value Pr(>|t|)(Intercept) 0.39188 0.07034 5.571 5.00E-08 ***

q2a.i 0.05374 0.17599 0.305 0.76027q2a.family 0.16915 0.0601 2.815 0.00516 **q2a.friend 0.1665 0.05195 3.205 0.00147 **q2a.acqu 0.09582 0.05298 1.809 0.07137 .

gender -0.26624 0.04872 -5.465 8.73E-08 ***lower -0.05904 0.04787 -1.233 0.21829

Arts/Humanities 0.01547 0.09175 0.169 0.86618Bio Science 0.15496 0.07106 2.181 0.02987 *

Physical Science 0.17854 0.09376 1.904 0.0577 .Social Science -0.06529 0.06266 -1.042 0.29815

Interdis -0.11649 0.08799 -1.324 0.18637Business 0.06397 0.08598 0.744 0.45733

Chemistry -0.06232 0.15687 -0.397 0.69139engineering -0.04215 0.0813 -0.519 0.60442

Envior. Design -0.35375 0.22703 -1.558 0.12008Natural. Res. 0.1096 0.26927 0.407 0.68425

I I I . T test result from question 4

data: c(q4.mbest, q4.fbest) and c(q4.sig) t = -2.7823, df = 737.932, p-value = 0.005536alternative hypothesis: true difference in meansis not equal to 0 95 percent confidence interval: -0.13632295 -0.02353022 sample estimates:mean of x mean of y 0.6808803 0.7608069

data: c(q4.mother, q4.father, q4.brother,q4.sister) and c(q4.mbest, q4.fbest, q4.sig) t = 1.7806, df = 2259.825, p-value = 0.0751alternative hypothesis: true difference in meansis not equal to 0 95 percent confidence interval: -0.003317898 0.068825739 sample estimates:mean of x mean of y 0.7394578 0.7067039

data: q4.mother and q4.father t = -1.8544, df = 711.154, p-value = 0.06409alternative hypothesis: true difference in meansis not equal to 0 95 percent confidence interval: -0.124847281 0.003560653 sample estimates:mean of x mean of y 0.7103064 0.7709497

data: c(q4.mother, q4.father) and c(q4.brother,q4.sister) t = 0.1013, df = 1291.506, p-value = 0.9193alternative hypothesis: true difference in meansis not equal to 0 95 percent confidence interval: -0.04500598 0.04990912 sample estimates:mean of x mean of y 0.7405858 0.7381342


views of elective cosmetic surgery a survey of ucb …hhuang/stat152/ecs-write-up.pdf · views of...

Documents