A QUALITATIVE RESPONSE MODEL ANALYSIS ON THE DETERMINANTS
OF RISK-ATTITUDE OF STUDENTS IN THE COLLEGIATE LEVEL FOR HUMAN
RESOURCE EFFICIENCY
In partial fulfillment
of the requirements for
ECOMET2 (Advanced Econometrics)
School of Economics
De La Salle University – Manila
Submitted by :
Aguila, Karina
Chan, Kendrick
Lee, Trisha
Lim, JV Stefanie
Submitted to :
Dr. Cesar Rufino
6 April 2011
Chapter 1 – Introduction
Chapter 1
Introduction
1.1 Background of the Study
In the advent of technological prowess in the late 90s, technology replaced men for most
of what men were hired for. Unemployment rose and bargaining power in the labor market
decreased. Recruitment by the human resource managers became more stringent given the fact
that they could now afford to do so. There then emerged a great incentive for men to manipulate
required documents for recruitment to ease their way through the selection process. Background
checks such as résumé verifications, media searches, credit checks, reference checks, and
criminal history tracking were then employed by the companies to mitigate the problem. Sure
enough, such devices increased costs but it would have been more costly to hire the wrong
people for the job. On the other hand, these background checks could only reach the extent of
verifying objective stated statements. If the matter to be verified includes, for example, an
individual’s attitude, then it would be better off measured in a revealed manner than merely in a
stated manner.
A notable attitude that human resource managers could pay close attention to is an
individual’s risk attitude—whether he is risk-averse or risk-seeking, in general. Previous
literature have speculated on the relationship of risk attitudes and employment. Caves (1970) and
Edwards and Heggestad (1973), among others, building on the work of Galbraith (1978, 3rd ed.),
have suggested the hypothesis that more risk-averse individuals may tend to associate
themselves with large monopoly firms rather than with more competitive companies. If the
Chapter 1 – Introduction
correlation does exist, then it would imply that new entrants in telecommunications markets, who
are accustomed to operating in the competitive arena, may have an advantage over incumbents in
terms of their tolerance for risk. Risk attitudes then matter as to which industry a person may
choose to venture into. Thus, likewise, an applicant’s risk attitude would matter for human
resource managers.
For job recruitments, an applicant’s risk attitude cannot easily determined because it
could again be easily manipulated by an applicant. Thus, what human resource managers could
do is to find factors that affect a person’s risk attitude and utilize such information to possibly
proxy for his risk attitude. Had the quest to determine risk attitude be done by companies, then
there would be a cognitive bias on the side of the applicants for they will always seek to please
the employer. On the other, if the study would be done by researchers such as the case of this
study, incentives or disincentives for cognitive biases would not be present.
1.2 Statement of the Problem
Given that the information on an individual’s risk orientation is vital for employers and
that employees have the incentive to misrepresent themselves to become what the employer
seeks for, is there a way to go around such an impediment? If an employer were to, let us say,
instead look for the notable determinants that affect a person’s risk attitude instead of flat out
asking for the individual’s risk attitude, would it produce more accurate results? Can the
hypothesized determinants well represent an individual’s risk attitude?
1.3 Objectives
Chapter 1 – Introduction
The objectives of this study include the following:

To examine the relationship between an individual’s particular set of demographics and
his risk orientation

To determine which of the hypothesized determinants of risk attitude actually
significantly affects it

To determine the possible determinants of risk attitude and the magnitude of its effect on
an individual’s risk attitude

To aid in the improvement of human resource employment as the applicants would not be
asked of his risk attitude but of the possible factors that would affect his risk attitude
instead
1.4 Scope and Limitations
The study limits its scope to the university students of De La Salle Univeristy—Manila
who are at the undergraduate level. Since the university body is a big pool of fresh potential new
entrants into the work force, the university students, although do not represent the entire labor
force, could be reasonable subjects of the study. The determinants chosen are also only
applicable to the typical university students. For example, one of the dummy variables only ask
for whether the respondent is single or is in a relationship. It no longer asks whether the
individual is married of is widowed. Furthermore, the study is only limited to 564 observations
where the sample size of 94 respondents was asked of different situational dilemmas that
encompass the six possible avenues of risk orientation. The sample size of 94 may not as
representatively speak for the entire population of the university as compared to when the
Chapter 1 – Introduction
respondents would be at least a thousand. Given the time and the resource constraint, the
researchers employed convenience sampling.
The six different aspects of risk orientation that were integrated in the survey
questionnaire include matters on finance, recreation, social, health, career, and safety. The
selected aspects does not go into the utmost detail of risk orientation in order to arrive at a
compressed version of the survey questionnaire. In addition, the survey questionnaire, although
well thought of, did not go under scientific study. It was merely patterned from published works
in the literature. The factors chosen include for the study only include the typical possible
demographics of a college student as well and these are his academic performance, extracurricular involvements, college, gender, year level, and relationship status. Thus, if such a
study would be applied to a larger scope beyond university students, the survey material would
have to be revised.
The factors chosen are also very basic in order to easily obtain the
information needed. Other more sophisticated determinants could be used for future study.
The scope of the study is also focused on the Philippine setting and is thus not making
use of a cross-country data set. Since risk orientations of the same cultural background and
basically the same environmental factors such as that the Filipinos were once colonized for
centuries, such a limitiation would then be possible for this study since the study has been pinned
down to the domestic situation only. However, the results obtained through this study will only
be applicable for the local Filipinos and not for other nationalities.
Although the study aims to eliminate biases from the applicants through their stated
claims on their risk orientation, having the respondents answer a survey is nonetheless no less
stated. On the other hand, having third parties, such as the researchers, conduct the study
Chapter 1 – Introduction
eliminates the bias. Also, because it is only the demographics and an individual’s response to
situational dilemmas that are asked for, there is less incentive to fabricate one’s answers.
In a nutshell, having these scopes and limitations present, the final estimates of the model
that is to be obtained in this study are not to be deemed perfectly conclusive. This is because
these estimates may be subject to disturbances in the usual tendencies of Filipinos or university
students. Results should then be taken with caution.
1.5 Significance of the Study
The study on the determinants of risk-attitude of students in the collegiate level can
expedite and facilitate the recruitment process of human resource managers in such a way that
the problem that the applicants have a strong incentive to commit to self-serving bias is reduced,
if not eliminated. If the study turns out successful in such a way that there is truly a relationship
between one’s demographics and his risk orientation, then the model used in the study could also
serve as a pattern for other relevant attributes that could be of interest by firms such as an
individual’s confidence level, leadership capabilities, and the like.
Students, on the other hand, could apply the study on a reversed perspective such that if
he would be interested in a company that would require risk-seeking individuals, then he could
engage himself in activites that actually contribute to a person becoming a risk-seeking
individual. For example, if the student would be interested in a very risk venturing firm and the
study proves a strong correlation between risk propensity and a student’s involvement with the
student government, then the student could then begin considering running for a position in the
university student government.
Chapter 2 – Review of Related Literature
Chapter 2
Review of Related Literature
Ever since Daniel Kahneman and Amos Tversky proposed the prospect theory, a theory against
the axioms of expected utility theory, literature has been abundant on people’s risk preferences when it
comes to different situations or different framings. Kimberly Edwards (1995) has enumerated studies
done since the theory was proposed: Payne, Laughhunn and Crum (1984) has been able to study decisionmaking on professional managers about budget plans where results have also been consistent with the
prospect theory; Arkes and Blumer (1985) has applied the prospect theory on the irrationality of human
behavior on continuing the risk of a losing prospect when money is invested or a sunk cost has been
incurred; Gregory (1986) has noted the predictions of the prospect theory when it comes to contingent
valuation studies on changes in the environment; Chang, Nichols, and Schultz (1987) related the prospect
theory on tax evasion wherein people are usually risk-averse; Budescu and Weiss (1987) looked into the
shape of the utility function as described by the prospect theory; Lowenstein (1988) applied intertemporal choice in decision-making under risk; Fiegenbaum and Thomas (1988) has used the prospect
theory to explain the Bowman’s risk-return paradox on firms; Diamond (1988) examined the effects of
prospect theory in varying levels of probability and consequences; Qualls and Puto (1989) have related
the theory to buying that depends on organizational climate; Elliot and Archibald’s (1989) study
supported the theory through an experiment on framing; D’ Aveni (1989) used the theory in explaining
about organizational bankruptcy; Meyer and Assuncao (1990) applied the theory on purchasing quantity
decisions with risky prices; Kameda and Davis (1990) applied the theory with regards to group decisionmaking; Garland and Newport (199 1) also studied the effect of sunk costs on decision-making; Kanto,
Rosenqvist, and Suvas (1992) has used the theory on explaining risk-aversion in gambling in a racetrack.
Many other studies have been applying the theory thereafter and it has been found that the theory has
been quite consistent on predicting behavior or decision-making under risk.
Chapter 2 – Review of Related Literature
Most studies conformed to the results done by Daniel Kahneman and Amos Tversky wherein
people are risk-averse in gains & risk-seeking in losses that makes a person’s utility function an S-shape
utility function. But there are also several contentions that were found in the literature. When taking into
account the degree of losses, it would be seen that individuals would actually still be risk-averse amidst
losses when they face extremely large losses that are out of proportion of the usual level of losses as
compared to the conventional thought in the prospect theory that individuals tend to be risk-seeking when
it comes to losses (Bosch-Domènech and Silvestre, 2010). Other contentions of the prospect theory
involve with the S-shape utility function proposed by the prospect theory. A study of Levy & Levy (2002)
showed that the case is otherwise. The utility function of individuals based on gains and losses show that
they may not be risk-averse on some level of gains and they may not be risk-seeking on some level of
losses. The S-shaped utility function of the prospect theory became so due to biases by the certainty effect
and the probability distortion.
Contentions of the entire prospect theory also exist such as the study of Nwogugu (2006) wherein
it was found that the theory was proved to be wrong when it comes to the decision-making of individuals.
He reasoned out that there are many other factors that affect the decision-making of individuals that was
not taken into account by the prospect theory.
Other studies in the literature extend the prospect theory. Some consider the time these gains or
losses are received by the respondents so they discount these gains and losses in the study (Ostaszewski
and Bialaszek, 2010). Aside from the probabilistic discounting, they also considered the mixing of both
gains and losses in the options. Other studies extended the prospect theory to uncertain or vague options.
As said by Daniel Ellsberg (1961), uncertain or vague options can have subjective probabilities such that
these options can be converted into risks. These subjective probabilities though are different from each
respondent because they are based on their beliefs and values with regard to the gain or loss that they are
uncertain to receive.
Tamura (2008) has been able to apply the prospect theory on decision-making using uncertainty
wherein the probability of the gain or the loss is unknown. Along with the prospect theory, they have
Chapter 2 – Review of Related Literature
applied the Dempster–Shafer probability theory on estimating the expected value function of a decisionmaker. Budescu, et al (2002) has also studied decision-making under uncertainty wherein no probability
can be measured and options are vague. They were able to conduct two experiments and were able to
conclude these results that is against of what is predicted by the Prospect Theory: 1) people are more
concerned of the precision than the probability of outcomes, 2) people seek on the vague options when it
is regarding gains, 3) people are aversive on vague options when it comes to losses, and 4) there is no
strong modal attitude toward the uncertainty of probability of gains and losses. Aside from these, there are
many other studies dealing with uncertainty and most of them have also dealt on creating models that can
predict human behavior when there is a vague or uncertain option.
Most methods done by these studies are choice experiments or surveys that introduce to the
respondents different situations and problems where they get to choose between options that is equivalent
to the certain option in the prospect theory such as insuring earnings and options that are risky such as
venturing into a new business. On the other hand, these choice experiments and surveys suffer from
hypothetical bias wherein people answer or respond to these hypothetical situations on how they would
want themselves to look like and not exactly how they would behave if they were really faced with that
situation. Though in other cases, there isn’t enough reason or incentive for respondents to answer
dishonestly in these surveys or experiments. So the bias may not be that significant to affect the results.
But most literature has dealt with people in general when it comes to risk preferences. In our
study, we will be taking into consideration the different characteristics of each individual and look at their
differences in risk preferences given the same situations in prospect theory wherein they will be faced of
the same problem but is framed in gains and losses. The researchers would survey different students in De
La Salle University to see if their characteristics affect their risk preferences. The researchers believe that
surveys can be used since randomly answering a survey does not give any incentives for dishonesty
unlike taking a survey as required for applying for a job.
Chapter 3 – Theoretical Framework
Chapter 3
Theoretical Framework
When faced with different problems, most decision-making involve a risky option and a
certain option. And from choosing between the options, you can already tell what risk orientation
a person has. But unfortunately, you won’t be able to ask your prospective employees to decide
on a hypothetical problem since they would most definitely handle the problem in the way they
think you want them to handle it. This study then looks into possible determinants that can affect
how a person decides in such decision-making. However, there is already a theory that
generalizes behavior of people in facing these problems and such theory is called the prospect
theory.
The prospect theory is developed by Daniel Kahneman and Amos Tversky during the
1970s. It contested the famous Von Neumann and Morgenstern Expected Utility Theorem and is
now deemed more precise in predicting human behavior. His study involves a simple choice
experiment which will also be adapted in this study. In this experiment, people are faced with
two equivalent options: a certain option and a risky option.
An Asian disease affected 600 people. People can either live or die based on the program
you choose. There are two programs:
Option 1: If chosen, 200 people will be saved.
Option 2: The program has a 33% chance that all 600 people will be saved and 66%
chance that all 600 people will not be saved.
In this experiment, 72% chose option 1 which is the certain option while 28% chose the
risky option. However, it should be noted that both options are equivalent. Option 2 has a
certainty equivalent of also 200 people which is 33% multiplied to 600. However, people
Chapter 3 – Theoretical Framework
generally became risk-averse at this type of problem. Then, they are faced with the similar
problem:
An Asian disease affected 600 people. People can either live or die based on the program
you choose. There are two programs:
Option 1: If chosen, 400 people will die.
Option 2: There is a 66% chance that all 600 people will die and a 33% chance that 600
people will not die.
In this similar experiment, 78% chose option 2 which is the risky option while 22% chose
the certain option. However, such a result contested one of the axioms of the Von Neumann and
Morgenstern Expected Utility Theorem. This is because both experiments are in fact just the
same problem but only phrased differently. In the first option of the second experiment, 400
people will die out of 600 is just the same as 200 people will live out of 600 people. For the
second option, it is more obvious that they are the same since both implies a 33% chance that the
people will be saved or will not die and a 66% chance that the people will not be saved or will
die. The difference between the two is just how the problem is framed. The first experiment
shows the gain frame (focuses on who will be saved) while the second experiment shows the loss
frame (focuses on who will die). When faced with two same problems, the Von Neumann and
Morgenstern Expected Utility Theorem predicts that people have well-defined preferences and
they would be consistent on their choices but what happened is people chose to be risk-averse in
the gain frame and then became risk-seeking in the loss frame. Therefore, Kahneman and
Tversky showed that people do not have well-defined preferences. In fact, their behavior on
gains and losses can be predicted through an S-shaped Utility Function:
Chapter 3 – Theoretical Framework
Figure 1.1 A hypothetical value function
According to introspection done by Kahneman and Tversky (1983), the subjective value
of utility of consumers is a concave function for gains and a convex function for losses where the
difference of subjective value of a gain/loss between lower values are greater than the difference
of a gain/loss between higher values. This just means that the subjective utility you get from a
gain of 1 dollar is lower when you already have $94 which would become $95 than a gain of also
1 dollar when you only have $5 which would become $6.
This S-shaped function has three properties as Kahneman and Tversky explained: Firstly,
it is based on changes in wealth or gain/losses and not total levels of wealth. Secondly, it is
concave for gains and convex for losses which entails that we are risk-averse for gains and riskseeking for losses. And lastly, losses are steeper than gains; which is now popularly known as
loss aversion wherein losses are more appealing than a possible gain. In an example, one who
loses $100 loses more subjective utility than the subjective utility one gets from gaining $100.
This just shows that people tend to strongly prefer to avoid losses than acquiring gains.
Basically, Daniel Kahneman and Amos Tversky predict that people are risk-averse in
gains and risk-seeking in losses. Still, some people may behave differently so it is important to
know which factors determine such behavior. In this study, such problems as presented by
Chapter 3 – Theoretical Framework
Kahneman and Tversky will also be adapted and the researchers shall see if people do generally
follow the prospect theory.
However, it may also be important to note the axioms of Von Neumann and Morgenstern
Expected Utility Theorem since there may also be some who behaves as predicted by this
theorem. This theorem has four axioms. The first axiom, which is completeness, is the axiom
that is being contested by the prospect theory. It states that if you prefer option A over option B,
you should always prefer option A no matter what framing or no matter what situation there is.
The second axiom, which is transitivity, states that if you prefer option A over option B and
prefer option B over option C, then you must also prefer option A over option C. The third
axiom, which is continuity, states that if you prefer option A over option B and option B over
option C and if the probability of option C is one minus the probability of option A, then you
should be indifferent between option B and both option A and C. The last axiom is independence
which states that if option A is preferred over option B, then you would prefer both option A and
option C than both option B and option C.
In this study, only the completeness axiom can be related to the situation. So if they
behave as predicted by the prospect theory, then they will be against this axiom. But if they do
follow the axiom, then they have well-defined and consistent preferences. So the researchers will
check if whether their respondents would have consistent preferences and see if whether there
are also factors determining why they behave so.
Chapter 4 – Operational Framework
Chapter 4
Operational Framework
There are a lot factors that contribute to one’s inclination to being either a risk-seeking or
a risk-averse individual.
Nevertheless, for the pursuit of this study as stated earlier in
introductory chapter, the researchers seeks to translate the objective information on interviewees’
demographics and credentials into information on risk orientation of that individuals. That is,
effects of one’s demographics and credentials to one’s tendency to become either risk seeking or
risk averse is identified.
However, for us to achieve a reliable and valid outcome of the study, certain ways of
generating data need to be clearly identified and considered. First, source of secondary data was
ruled out as there is no data available concerning risk orientation of individuals that could
represent those who would eventually undergo the hiring and recruitment process. Second is the
use of survey. Although use of survey in gathering data could be inefficient in terms of the
quality of data derived, where respondents would usually make declarations different from their
actions in hopes to present the best of themselves, respondent’s display problems would not be a
problem as in the case where risk attitude is involved. That is, respondents may be aware of what
subject matter is being undertaken but they nonetheless would not know which attitude to present
since there is no answer that could really reflect politically correct/best attitude. Now, in
considering the use of experiment, it would prove to be an impractical one given the time
constraint set for this study. Further, there is also no need to have strict controls as situations are
framed differently. Thus, this leaves us with the use of survey to get respondent’s risk attitude
and generate our data.
Chapter 4 – Operational Framework
4.1
Survey
a.
SurveyDesign
To operationalize the proposed study, the researchers prepared the survey questionnaire
with six pairs of situations as patterned against the pair of situations Kahneman and Tversky
(1979) used in confirming prospect theory where the two statements in a pair of situation differs
only on how the statement is framed-either losses or gains. The researchers decided to produce
six pairs of situations to include the six types of risks that an individual could possibly encounter
in his existence- financial, social, recreational, health, security, and career. (see Appendix A) questionnaire The reason for this being is because tackling on only one type of risk could prove
to create bias with respect to the data concerned as sometimes, risk attitudes vary with different
types of risk. The respondents are presented with twelve statements or six pairs of different
situations representing six types of risks are asked to choose whether they agree or disagree with
each statement. To ensure validity of questions, statements are made in a way that for some
statements, agreeing would mean being risk seeking while in some statements, agreeing would
mean being risk-averse. Further, twelve statements are presented in a random manner such that
no two statements with same situation ( only framed differently) are numbered consecutively to
avoid respondents from observing that two situations are the same and merely framed differently
and from stating risk attitude consistently for a pair of situation merely because they notice the
trend of framing. At the beginning of questionnaire, the researchers indicated a brief phase
“answer they survey using your heart and not your mind” for respondents to not treat each
statement as complex problems and simply answer the survey questionnaire sincerely.
However, subject to the limitations of this study, the researchers would merely use these
six classifications for purposes of unbiassedness in determining the risk attitude of individuals,
Chapter 4 – Operational Framework
and not anymore know the effects of each independent variable(ie. Demographics) to risk
orientation for each specific type of risk.
Such that the researchers would treat the data
generated from each respondent as six actual observations with all the independent variable
being held constant for six observations and only risk attitude for each types of risk would vary.
Putting this in intuitively, the researchers treated the cross-sectional data to be a panel data across
entities and across six types of risks. (see Appendix _)- data..
On the otherhand, in getting the data on independent variables such as demographics and
other credentials, respondents are asked to state these facts objectively. With this, the researchers
are now able to gain insights on why such choices are made and chosen, if proven to be
significant.
b.
Data Sampling
For the purpose of this study, the researchers took students of De La Salle University as
sample representative of those who would eventually undergo the hiring and recruitment process
or of the population.
Due to various discipline instilled in to different students of different colleges, the study
used stratified-convenience sampling method such that the students of De La Salle University
would be further be classified according to its respective colleges, from which a sample can be
drawn from each college. Friends from different colleges and friends of friends were used to
primarily draw respondents. However, the method is not in entirety a stratified sampling since
there is no quota or certain school population proportion based on college size that needs to be
satisfied per strata. Thus, convenience sampling is also used in drawing respondents given the
constraints in conducting survey.
Chapter 4 – Operational Framework
Survey questionnaires were circulated primarily through the use of online survey,
www.kwiksurveys.com, and partly through manually distributed questionnaire. In total, there are
94 respondents whose information would be used to represent the population. However, as
mentioned earlier, there would be 564 observations as researchers would treat the cross-sectional
data to be a panel data across entities and across six types of risks.
4.2
Methodology
The survey is a simple and straight-forward choice-based design where attitude on risks
is determined. Nevertheless, in addition to knowing which risk attitude is more observed, the
researchers seeks to see what factors affect an individual’s risk orientation. To account for the
factors affecting risk orientation, the following models shall be used:
4.3
Model Specification
Equation (1) shows the regression model in determining the relationship between
students’ individual characteristics/credentials and their risk attitude when faced with
different types of gain situations
𝐺𝑎𝑖𝑛𝑠2 = 𝛽1 𝑋1 (𝑐𝑔𝑝𝑎) + 𝛽2 𝑋2 (𝑓𝑎𝑖𝑙𝑠𝑞) + 𝛽3 𝑋3 (𝑎𝑡ℎ𝑙𝑒𝑡𝑒) + 𝛽4 𝑋4 (𝑎𝑟𝑡𝑖𝑠𝑡) + 𝛽5 𝑋5 (𝑢𝑠𝑔)
+ 𝛽6 𝑋6 (𝑐𝑠𝑜) + 𝛽7 𝑋7 (𝑑𝑜) + 𝛽8 𝑋8 (𝑠𝑝𝑜) + 𝛽9 𝑋9 (𝑠𝑜𝑐𝑖𝑜𝑐𝑖𝑣𝑖𝑐) + 𝛽10 𝑋10 (𝑐𝑐𝑠)
+ 𝛽11 𝑋11 (𝑐𝑒𝑑) + 𝛽12 𝑋12 (𝑐𝑙𝑎) + 𝛽13 𝑋13 (𝑐𝑜𝑏) + 𝛽14 𝑋14 (𝑐𝑜𝑒) + 𝛽15 𝑋15 (𝑐𝑜𝑠)
+ 𝛽16 𝑋16 (𝑒𝑥𝑒𝑐) + 𝛽17 𝑋17 (𝑚𝑎𝑙𝑒) + 𝛽18 𝑋18 (𝑠𝑖𝑛𝑔𝑙𝑒) + 𝛽19 𝑋19 (𝑦𝑒𝑎𝑟) + 𝜇
Where Gain2=1 if risk-averse
=0 if risk-seeking
Equation (2) shows the regression model in determining the relationship between
students’ individual characteristics/credentials and their risk attitude when faced with
different types of loss situations
𝐿𝑜𝑠𝑠2 = 𝛽1 𝑋1 (𝑐𝑔𝑝𝑎) + 𝛽2 𝑋2 (𝑓𝑎𝑖𝑙𝑠𝑞) + 𝛽3 𝑋3 (𝑎𝑡ℎ𝑙𝑒𝑡𝑒) + 𝛽4 𝑋4 (𝑎𝑟𝑡𝑖𝑠𝑡) + 𝛽5 𝑋5 (𝑢𝑠𝑔)
+ 𝛽6 𝑋6 (𝑐𝑠𝑜) + 𝛽7 𝑋7 (𝑑𝑜) + 𝛽8 𝑋8 (𝑠𝑝𝑜) + 𝛽9 𝑋9 (𝑠𝑜𝑐𝑖𝑜𝑐𝑖𝑣𝑖𝑐) + 𝛽10 𝑋10 (𝑐𝑐𝑠)
+ 𝛽11 𝑋11 (𝑐𝑒𝑑) + 𝛽12 𝑋12 (𝑐𝑙𝑎) + 𝛽13 𝑋13 (𝑐𝑜𝑏) + 𝛽14 𝑋14 (𝑐𝑜𝑒) + 𝛽15 𝑋15 (𝑐𝑜𝑠)
+ 𝛽16 𝑋16 (𝑒𝑥𝑒𝑐) + 𝛽17 𝑋17 (𝑚𝑎𝑙𝑒) + 𝛽18 𝑋18 (𝑠𝑖𝑛𝑔𝑙𝑒) + 𝛽19 𝑋19 (𝑦𝑒𝑎𝑟) + 𝜇
Chapter 4 – Operational Framework
Where Loss2=1 if risk-averse
=0 if risk-seeking
Equation (3) shows the regression model in determining the relationship between
students’ individual characteristics/credentials and their tendency to adhere to Prospect
Theory
𝑃𝑟𝑜𝑠𝑝𝑒𝑐𝑡2 = 𝛽1 𝑋1 (𝑐𝑔𝑝𝑎) + 𝛽2 𝑋2 (𝑓𝑎𝑖𝑙𝑠𝑞) + 𝛽3 𝑋3 (𝑎𝑡ℎ𝑙𝑒𝑡𝑒) + 𝛽4 𝑋4 (𝑎𝑟𝑡𝑖𝑠𝑡) + 𝛽5 𝑋5 (𝑢𝑠𝑔)
+ 𝛽6 𝑋6 (𝑐𝑠𝑜) + 𝛽7 𝑋7 (𝑑𝑜) + 𝛽8 𝑋8 (𝑠𝑝𝑜) + 𝛽9 𝑋9 (𝑠𝑜𝑐𝑖𝑜𝑐𝑖𝑣𝑖𝑐) + 𝛽10 𝑋10 (𝑐𝑐𝑠)
+ 𝛽11 𝑋11 (𝑐𝑒𝑑) + 𝛽12 𝑋12 (𝑐𝑙𝑎) + 𝛽13 𝑋13 (𝑐𝑜𝑏) + 𝛽14 𝑋14 (𝑐𝑜𝑒) + 𝛽15 𝑋15 (𝑐𝑜𝑠)
+ 𝛽16 𝑋16 (𝑒𝑥𝑒𝑐) + 𝛽17 𝑋17 (𝑚𝑎𝑙𝑒) + 𝛽18 𝑋18 (𝑠𝑖𝑛𝑔𝑙𝑒) + 𝛽19 𝑋19 (𝑦𝑒𝑎𝑟) + 𝜇
Where Prospect2=1 if Gain2=1 and Loss2=0 for the same situation
=0 if otherwise (or Gain2=Loss2 or Gain2=0 and Loss2=1)
Equation (4) shows the regression model in determining the relationship between
students’ individual characteristics/credentials and their tendency to have consistent risk
attitudes regardless of framing of situations (ie. gains/losses)
𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑡2 = 𝛽1 𝑋1 (𝑐𝑔𝑝𝑎) + 𝛽2 𝑋2 (𝑓𝑎𝑖𝑙𝑠𝑞) + 𝛽3 𝑋3 (𝑎𝑡ℎ𝑙𝑒𝑡𝑒) + 𝛽4 𝑋4 (𝑎𝑟𝑡𝑖𝑠𝑡) + 𝛽5 𝑋5 (𝑢𝑠𝑔)
+ 𝛽6 𝑋6 (𝑐𝑠𝑜) + 𝛽7 𝑋7 (𝑑𝑜) + 𝛽8 𝑋8 (𝑠𝑝𝑜) + 𝛽9 𝑋9 (𝑠𝑜𝑐𝑖𝑜𝑐𝑖𝑣𝑖𝑐) + 𝛽10 𝑋10 (𝑐𝑐𝑠)
+ 𝛽11 𝑋11 (𝑐𝑒𝑑) + 𝛽12 𝑋12 (𝑐𝑙𝑎) + 𝛽13 𝑋13 (𝑐𝑜𝑏) + 𝛽14 𝑋14 (𝑐𝑜𝑒) + 𝛽15 𝑋15 (𝑐𝑜𝑠)
+ 𝛽16 𝑋16 (𝑒𝑥𝑒𝑐) + 𝛽17 𝑋17 (𝑚𝑎𝑙𝑒) + 𝛽18 𝑋18 (𝑠𝑖𝑛𝑔𝑙𝑒) + 𝛽19 𝑋19 (𝑦𝑒𝑎𝑟) + 𝜇
Where Consistent2 =1 if Gain2=Loss2 (consistent choice despite framing)
=0 if Gain2 Loss2 (or when Prospect=1 or Gain2=0 and Loss2=1)
4.4
Variables
Independent
Variable:
Description
Risk
Orientation
on Gains
“gain2”
Multinomial Variable. = 1 if risk adverse,
approaches 5 if risk seeking. Risk orientation
when it comes to gains.
Risk
Orientation
on Losses
Where Gain2
=1 if risk-averse
=0 if risk-seeking
Multinomial Variable. = 1 if risk adverse,
approaches 5 if risk seeking. Risk orientation
when it comes to losses.
Chapter 4 – Operational Framework
“loss2”
Where Loss2
=1 if risk-averse
=0 if risk-seeking
Dummy Variable. Individuals are RA when
faced with gains and RS when faced with loss
situation.
Prospect
“prospect2”
Where Prospect2
=1 if Gain2=1 and Loss2=0 for the same
situation
=0 if otherwise (or Gain2=Loss2 or Gain2=0
and Loss2=1)
Framing
“Consistent2”
Dummy Variable. Individuals are inconsistent in
their risk orientation when faced with gains or
loss situation.
Where Consistent2
=1 if Gain2=Loss2 (consistent choice despite
framing)
=0 if Gain2 Loss2 (or when Prospect=1 or
Gain2=0 and Loss2=1)
Dependent
Variable:
Cumulative
Grade Point
Average
“cgpa”
A-priori
with respect
to one’s risk
orientation
in riskG and
in riskL
where
1=risk
seeking and
0 = risk
averse
-
Intuition/Implication
The grade of the individual which ranges from 0
to 4, 4 being the highest. Grades would be used
as proxy for intelligence, such that people who
are smarter are more likely to take advantage of
an opportunity.
A-priori
with
respect to
Prospect
equation
where =1
adhere
=0
otherwise
A-priori
with
respect to
Consistent
equation
where =1
consistent
=0
framing
+
-
Chapter 4 – Operational Framework
Number of
Failures
“failsq”
+/-
Dummy Variable. =0 if none, =1 if at least one
fail, =2 if the student-respondent has more than
once failed. The effects of this variable on risk
orientation is expected to be quadratic, since as
students receive their first failure, they tend to be
risk adverse, but as failures become common,
they revert back to previous orientations
+
-
-
Dummy Variable. =0 if not an athlete, = 1 if
athlete. Students who engage in sports are more
likely to be outgoing and take chances. Thus
they are more likely to be risk seeking.
+
-
-
Dummy Variable. =1 if an artist =0 if not.
Students who engage in performing are more
likely to be outgoing and take chances. Thus
they are more likely to be risk seeking.
+
-
-
Dummy Variable. =1 if member, =0 if not
member. Those who work in an organization
under CSO and those who are USG officers are
also expected to be risk seeking. Students who
are usually involved in these are usually the ones
who have a strong sense of responsibility. And
being responsible for something usually entails
risk as to the quality of job they would perform.
+
-
-
Dummy Variable. =1 if Do paragon, =0 if not
Do paragon. Those students who work under
Discipline Office (DO) in helping plan and
implement Do's program are also expected to be
risk seeking. Students who are usually involved
in these are usually the ones who have a strong
sense of responsibility. And As the name
suggests, a do paragon would not mind arresting
even their friends for a violation done. Thus, its
nature of being risk-seeking.
+
-
-
Dummy Variable. =1 if an Spo member =0 if
not. Students who do work for University's
major publications as "Lasallian campus
journalists, graphic
designers,
yearbook
manager, creative writers" are also expected to
be risk-seeking.(www.dlsu.edu.ph) The reason
for this being is that they are usually the ones
who are bold enough to expose matters to the
entire university notwithstanding the issue
attached to the article produced.
+
-
Athlete
“athlete”
Artist “artist”
Under “cso",
Under "usg”
Do paragon
"do"
Student
Publications
Office "spo"
Chapter 4 – Operational Framework
Number of
Executive
Position held
“exec”
-
College of
Engineering
“coe”
College of
Computer
Science
-
“ccs”
College of
Education
“ced”
College of
Science
“cos”
+
College of
Liberal Arts
“cla”
-
Discrete Variable. Number of executive position
held by the individual. Students who are in
leadership position tend to be more risk seeking
due to the nature of their jobs. Also they are
constantly faced with balancing extracurricular
and their academics.
Dummy variable. =1 if COE/CCS student =0 if
not. It is assumed that being an engineering or a
computer science student is positively correlated
to one’s being risk-lover. This is so because
these students are those who are generally
innovative and are willing to accept change and
thus risks. Also, individuals who usually accept
risk more readily tend to choose entirely on the
basis of anticipated costs and benefits and these
students are more capable of weighing such.
(Nadeau, Blais, 1999)
Dummy variable. =1 if CED/COS student =0 if
not. As for students of CED, COS, they are
assumed to affirm the prospect theory and have
different risk attitude towards gains or lossesthey tend to be risk averse when faced with
gains situation and risk seeking when faced
with losses situation. Such risk averse
assumption is due to the fact that these students
usually do not weigh costs and benefits and
simply give almost as much weight to the
perceived possibility of worst outcome. With
this, they become more reluctant to take risks
(Nadeau, Blais, 1999). On the otherhand, as for
the losses, they are also expected to become risk
seeking, as predicted by the prospect theory.
Dummy variable. =1 if CLA student =0 if not.
Being a liberal arts student is also anticipated to
be contributing to one’s being a risk-lover.
Liberal arts students are usually stereotyped for
being expressive, more outgoing, and are able
to have the fortitude by nature. And so,
regardless of gains or losses framing, they are
assumed to be risk-seeking.
+
-
+
-
+
-
+
+
Chapter 4 – Operational Framework
College of
Business
“cob”
-
School of
Economics
“soe”
-
Gender
“male”
-
Dummy variable. =1 if COB student =0 if not.
Business students are normally well exposed to
computing risks and thus would most probably
have consistent risk attitude regardless of gains
or losses framing. Moreover, business students
generally have the tendency to be risk-seeking,
this is because eventually in the real world,
entrepreneurs are able to give up job security
and take specific kinds of risks related to
launching a new venture because they have
confidence that they will either succeed or be
capable of carrying on a successful career. (Ray,
1994). Successful managers are also said to be
ones who are risk-seeking. According to
Sjöberg, L., & Engelberg, E. (2009), it was
found that the students of finance had a positive
attitude to economic risk-taking and gambling
behavior, a high level of sensation seeking, a
low level of money concern.
Omitted to serve as base. Economics students
are expected to be a contributing factor to one’s
being risk seeking. This is because they are
usually the ones who are able to see a short term
plan in a long term perspective. In a study by
Thaler, R. & Benartzi, S. (1999), they have
found that people are generally risk-averse in
taking a
risky asset but when they were shown the long
term implications of taking risky assets, they
now become more willing to take the risk and
thus become risk seeking.
Dummy variable. =1 if male =0 if female. Men
are asummed to be relatively more risk-seeking
or women are more risk averse compared to
their counterparts. In a study of household
holdings of risky asset, they found that single
women exhibit relatively more risk aversion in
financial decision making than single men (
Jianakoplos, Bernasek, 1998). It is also seen that
men are more likely to have the strength of mind
to handle or endure adversity of bravery and on
the otherhand, women are seen to be more
indecisive in decision making and thus would
usually end up taking the choice with great
caution.
+
+
+
+
+
+
Chapter 4 – Operational Framework
Single
“single”
-
Year Level
“year”
-
Sociocivic
member
“sociocivic”
-
4.5
Dummy variable. =1 if single =0 if in a
relationship. “Single” variable is anticipated to
be positively correlated to one’s tendency to
become risk-seeking. While a person is still
single, he would most likely be self-oriented and
would not think of others who might be affected
of his decision (ie. A partner). Given this, he
would be willing to take risks and can afford not
to take great caution with his decision.
Conversely, if a person is in a relationship, he is
seen to have the tendency to be more risk averse.
Such is because his decision might affect his
partner and taking risks means anticipating for a
greater loss or greater gain than if one had been
risk averse
Discreet Variable. As years pass by, students are
more experience in general, and thus they would
be more risk seeking.
Dummy Variable. Students who are members of
Englicom, Rotatact, Cosca, and who are
involved in socio civic organizations are in
general more risk seeking. The reason being is
that they are able to achieve a balance between
their role as students and their being socially
responsible.
+
+
+
+
+
+
Method of Regression
As in the case where the dependent variable in an equation is a quantitative dummy
variable, one of Quantitative Response Models shall be used as estimator. Thus, it is first
necessary to define the three possible models in getting the most reliable and accurate estimation.
First, linear probability model makes use of OLS estimation where although four of the five
violations could be cured by increasing sample size, the last violation of exceeding the 0-1
interval of dependent variable and constant marginal effect of independent to dependent variable
could not be resolved without using link function. Hence, we are left with logit and probit model.
Chapter 4 – Operational Framework
To differentiate, cumulative logistic function and odd’s ratio are used in logit model, while
cumulative distribution function (CDF) and z-value are used in probit model. In qualitative
terms, logit and probit model produce similar results and thus the deciding factor to the use of
model would be the on R2 and F-value of both models to reflect goodness of fit.
4.6
Statistical Software
Stata 10 will be used in estimating regression model.
Chapter 5 – Descriptive Statistic Analysis
Chapter 5
Descriptive Statistic Analysis
5.1 Detailed Summary of Descriptive Statistics
Descriptive Statistics (Spreadsheet1)
Minimum Maximum Std.Dev. Skewness
Mean
Variable
2.17202
6.0000 1.17512
0.65009 0.000000
exec
3.35983
18.0000 2.87568
1.59680 0.000000
fail
6.28546
10.80462 0.000000 324.0000 41.10066
failsq
5.33501
1.0000 0.17608
0.03197 0.000000
athlete
1.86126
1.0000 0.36680
0.15986 0.000000
artist
1.05948
1.0000 0.44248
0.26643 0.000000
usg
1.18825
1.0000 0.43054
0.24512 0.000000
cso
9.55670
1.0000 0.10277
0.01066 0.000000
do
6.64633
1.0000 0.14456
0.02131 0.000000
spo
5.33501
1.0000 0.17608
0.03197 0.000000
sociocivic
2.75179
1.0000 0.29474
0.09591 0.000000
ccs
3.57425
1.0000 0.24487
0.06394 0.000000
ced
2.55684
1.0000 0.30884
0.10657 0.000000
cla
0.83765
1.0000 0.46178
0.30728 0.000000
cob
1.65998
1.0000 0.38550
0.18117 0.000000
coe
9.55670
1.0000 0.10277
0.01066 0.000000
cos
-0.39545
3.5000 0.43209
2.72704 1.700000
cgpa
-0.08199
1.0000 0.50003
0.52043 0.000000
male
-1.25691
1.0000 0.42404
0.76554 0.000000
single
-0.43205
5.0000 0.77203
2.83659 1.000000
year
-0.04632
1.0000 0.50031
0.51155 0.000000
loss2
-0.03919
1.0000 0.50035
0.50977 0.000000
gain2
1.30451
1.0000 0.41950
0.22735 0.000000
prospect2
-0.19685
1.0000 0.49805
0.54885 0.000000
consistent
Kurtosis
4.79783
14.19778
41.37458
26.55671
1.46949
-0.88063
-0.59017
89.64894
42.32403
26.55671
5.59220
10.81364
4.55360
-1.30298
0.75822
89.64894
-0.42571
-2.00040
-0.42168
2.06248
-2.00499
-2.00560
-0.29932
-1.96826
The table above is the summary and the overview of the descriptive statistics of the
variables which were integrated in the survey questionnaires (a more detailed discussion and
presentation will be presented later on):
The first moment of interest is the sample mean which in plain words is the expected
value of each of the variables above. From the compilation of the responses of those who were
surveyed, the data set was obtained. From the data set, the sample mean is the sum of the values
Chapter 5 – Descriptive Statistic Analysis
divided by the number of values. Thus, the divisor would be 564 as in this case. The sample
mean may represent the population mean but does not always exactly equate to the population
mean because of possible errors in the model or outliers in the data set. As in the case of most of
the variables of this study, binary variables or dummy variables have values that lie between 0
and 1. Thus, for the variables athlete, artist, usg, cso, do, spo, sociocivic, ccs, ced,cla, cob, coe,
cos, male, single, loss2, gain2, prospect2, and consistent which are dummy variables that merit a
value of one if the given trait is present, it can be said that the majority of the respondents
possesses the trait if the mean is nearer to 1 than to 0.
The maximum and minimum values literally provide the maximum and minimum
outcomes of the survey done. However, because binary variables could only either have values
of 0 or 1, then it automatically gets a maximum value of 1 and a minimum value of 0 for as long
as at least one of the respondents would give an answer that merits a 0 and another respondent
would give an answer that merits a 1.
The second moment to be observed regarding the data set obtained is its standard
deviation. The standard deviation is obtained by getting the square root of the variance which is
the average of the squared differences from the mean or the expected value. In other words, it is
the measure of how spread out the values are. It can be observed that most of the standard
deviations are minimal except for the failsq variable since the value of the variable is a squared
notation of the fail variable, thus making the values much larger than they should be. Moreover,
since the values are, again, mostly binary variables, then the standard deviation would indeed be
minimal given the nature of the variables.
Chapter 5 – Descriptive Statistic Analysis
The third moment of observation is the skewness of the data set. The value of the
skewness of the data describes the measure of asymmetry of the variable’s probability
distribution. It can only be said that the values are relatively evenly distributed if the value of the
variable’s skewness is equal to 0. If not, then it is skewed. If the value is negative or that the
distribution is negatively skewed, then it means that most of the values fall by the right side of
the distribution and less values fall by the left tail. The opposite is said if the distribution is
positively skewed. It can be observed that noneof the variables have a zero skew. None of the
variables are then evenly distributed. As for the cos and the do variable, the skewness is
relatively high. This may be the case because convenience sampling was employed by the
researchers and thus these variables are not well represented.
The last and the fourth moment of observation is the kurtosis. The kurtosis measures the
level of how peaked the variable’s distribution is. If the kurtosis is high (usually having a
measure greater than 3), the distribution is said to be a tall one and that it can be said that the
value of the distribution’s variance is primarily attributable to extreme infrequent deviations
from the mean or otherwise known as outliers. On the other hand, if the kurtosis is low, then it
means to say that the distribution is flat. For the binary variables, if the kurtosis is low, it can be
observed that it is because the distribution is more distributed as compared to those with high
kurtosis such as the most unevenly distributed variable observations in the data set which are the
cos and the do variable.
5.2 Graphs and Interpretation
5.2.1 Number of executive positions held: “exec”
Chapter 5 – Descriptive Statistic Analysis
Mean = 0.6489
Mean±SD
= (-0.5255, 1.8233)
Mean±1.96*SD
= (-1.6529, 2.9507)
Summary: exec
K-S d=.37957, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: exec
3.0
450
2.5
Expected Normal Value
400
No. of obs.
350
300
250
200
150
100
1.5
1.0
0.5
0.0
-0.5
50
0
2.0
-1
0
1
2
3
4
X <= Category Boundary
5
6
-1.0
-1
0
1
2
3
Value
4
5
6
7
4
3
2
exec
Summary Statistics:exec
Mean= 0.650089
Minimum= 0.000000
Maximum= 6.000000
Std.Dev.= 1.175118
Skew ness= 2.172022
Kurtosis= 4.797829
1
0
-1
-2
It can be seen that most of the students (who were surveyed) in the university have not
held any executive positions yet. It can be seen that although the range is from 0 to 6, the mean
of the distribution does not even reach the value of 1. Also, the fact that the distribution is
positively skewed also supports the fact that, indeed, most have never held executive positions.
The kurtosis is relatively high since it exceeds the benchmark of 3. This is because if the data set
is reviewed, it can be seen that only one individual (equivalent to 6 individuals) has held six
executive positions, thus he is considered as an outlier. It can be noted that the maximum
executive positions held is 6.
5.2.2 Number of Failures: ”failsq”
Chapter 5 – Descriptive Statistic Analysis
Mean = 10.7872
Mean±SD
= (-30.279, 51.8535)
Mean±1.96*SD
= (-69.7026, 91.277)
Summary: failsq
K-S d=.39632, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: failsq
3.0
350
Expected Normal Value
2.5
300
No. of obs.
250
200
150
100
50
0
2.0
1.5
1.0
0.5
0.0
-0.5
-50
0
50
100 150 200 250
X <= Category Boundary
300
350
-1.0
-50
0
50
100
150
Value
200
250
300
350
100
80
60
40
failsq
Summary Statistics:failsq
Mean= 10.804618
Minimum= 0.000000
Maximum=324.000000
Std.Dev.= 41.100660
Skew ness= 6.285461
Kurtosis= 41.374579
20
0
-20
-40
-60
-80
The standard deviation is noticeably high. This is primarily because this variable is a
squared notation of the number of failures that an individual has. Thus, it does not only double
the value, but rather squares it and makes it increase exponentially. The kurtosis is also as high
as that of the standard deviation; this signifies that there are outliers in the distribution. It is
positively skewed and it can be said that most Lasallians only incur few failures over their
academic stay in the university. Getting the square root of the mean, the mean of the actual
number failures of the students could be obtained.
Chapter 5 – Descriptive Statistic Analysis
5.2.3 Athlete: “athlete”
Mean = 0.0319
Mean±SD
= (-0.144, 0.2078)
Mean±1.96*SD
= (-0.3129, 0.3767)
Summary: athlete
K-S d=.54007, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: athlete
Expected Normal Value
600
No. of obs.
500
400
300
200
100
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.5
0.4
0.3
0.2
athlete
Summary Statistics:athlete
Mean= 0.031972
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.176081
Skew ness= 5.335015
Kurtosis= 26.556708
0.1
0.0
-0.1
-0.2
-0.3
-0.4
As can be seen, the mean nears zero and that the kurtosis is significantly high. Again, this
can be accounted for the fact that most of the surveyed individuals are not athletes but that there
are some outliers who are actually athletes. It is again positively skewed since most of the
respondents are not athletes.
Chapter 5 – Descriptive Statistic Analysis
5.2.4 Artist: “artist”
Mean = 0.1596
Mean±SD
= (-0.207, 0.5261)
Mean±1.96*SD
= (-0.5588, 0.878)
Summary: artist
Normal P-Plot: artist
1.6
1.4
Expected Normal Value
No. of obs.
K-S d=.50866, p<.01 ; Lilliefors p<.01
Expected Normal
550
500
450
400
350
300
250
200
150
100
50
0
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.4
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.0
0.8
0.6
0.4
artist
Summary Statistics:artist
Mean= 0.159858
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.366800
Skew ness= 1.861257
Kurtosis= 1.469487
0.2
0.0
-0.2
-0.4
-0.6
-0.8
As compared to the earlier extra-curricular activities, the artists are more well represented
as the skewness is not that high although it is still positively skewed as there are still more
respondents who are not artists. Also, notice that the mean is still a lot nearer to 0. On the other
hand, the kurtosis is low which shows a flat distribution and this reflects that outliers are not a
problem in the distribution.
Chapter 5 – Descriptive Statistic Analysis
5.2.5 University Student Government: “usg”
Mean = 0.266
Mean±SD
= (-0.1763, 0.7082)
Mean±1.96*SD
= (-0.6008, 1.1327)
Summary: usg
K-S d=.46002, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: usg
1.2
500
1.0
Expected Normal Value
450
No. of obs.
400
350
300
250
200
150
100
0.6
0.4
0.2
0.0
-0.2
50
0
0.8
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.4
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.4
1.2
1.0
0.8
0.6
usg
Summary Statistics:usg
Mean= 0.266430
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.442485
Skew ness= 1.059484
Kurtosis= -0.880634
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
The representation of those in the University Student Government is even better than that
of those under the Cultural Arts Office. However, the majority are still those who are not
involved with the student government. This time, because of the better representation for this
variable, the kurtosis becomes negative. Once again, since the majority are still those who are not
involved in the student government, then the distribution is still positively skewed.
Chapter 5 – Descriptive Statistic Analysis
5.2.6 Council of Student Organizations: “cso”
Mean = 0.2447
Mean±SD
= (-0.1856, 0.675)
Mean±1.96*SD
= (-0.5987, 1.088)
Summary: cso
K-S d=.47032, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: cso
1.4
500
1.2
Expected Normal Value
450
No. of obs.
400
350
300
250
200
150
100
0.8
0.6
0.4
0.2
0.0
-0.2
50
0
1.0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.4
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.2
1.0
0.8
0.6
0.4
cso
Summary Statistics:cso
Mean= 0.245115
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.430538
Skew ness= 1.188249
Kurtosis= -0.590174
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Even though there are a lot of organizations that fall under the Council of Student
Organizations, the mean is still nearer to 0 than to 1. This signifies that there are more students
who are not involved with CSO organizations such as their professional organizations. It is either
that students are generally inactive when it comes to extra-curricular activities or that this
variable was simply mistaken for the CSO office itself (even though the specification was
included in the survey questionnaires). The distribution is once again flat and positively skewed.
Chapter 5 – Descriptive Statistic Analysis
5.2.7 Discipline Office Paragon: “do”
Mean = 0.0106
Mean±SD
= (-0.092, 0.1133)
Mean±1.96*SD
= (-0.1906, 0.2119)
Summary: do
K-S d=.53064, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: do
Expected Normal Value
700
600
No. of obs.
500
400
300
200
100
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.25
0.20
0.15
0.10
0.05
do
Summary Statistics:do
Mean= 0.010657
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.102773
Skew ness= 9.556698
Kurtosis= 89.648938
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
The DO Paragons are so far the least represented of all. Aside from the outlier which is
indicated by the extremely high kurtosis, all of the respondents are not agents of the Discipline
Office. The mean even almost zeroes out if it had not been pulled up by the outlier.
Chapter 5 – Descriptive Statistic Analysis
5.2.8 Student Publications Office “spo”
Mean = 0.0213
Mean±SD
= (-0.1232, 0.1657)
Mean±1.96*SD
= (-0.2618, 0.3044)
Summary: spo
K-S d=.53730, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: spo
Expected Normal Value
700
600
No. of obs.
500
400
300
200
100
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.4
0.3
0.2
0.1
spo
Summary Statistics:spo
Mean= 0.021314
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.144559
Skew ness= 6.646329
Kurtosis= 42.324030
0.0
-0.1
-0.2
-0.3
The distribution and thus the explanation is similar to that of the do variable.
Chapter 5 – Descriptive Statistic Analysis
5.2.9 Sociocivics: “sociocivic”
Mean = 0.0319
Mean±SD
= (-0.144, 0.2078)
Mean±1.96*SD
= (-0.3129, 0.3767)
Summary: sociocivic
K-S d=.54007, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: sociocivic
Expected Normal Value
600
No. of obs.
500
400
300
200
100
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.5
0.4
0.3
0.2
sociocivic
Summary Statistics:sociocivic
Mean= 0.031972
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.176081
Skew ness= 5.335015
Kurtosis= 26.556708
0.1
0.0
-0.1
-0.2
-0.3
-0.4
The distribution and thus the explanation is similar to that of the do variable.
Chapter 5 – Descriptive Statistic Analysis
5.2.10 Colleges: “ccs”, “ced”, “cla”, “cob”, “coe”, “cos”
College
10%
6%
COB
34%
SOE
17%
COS
CLA
10%
COE
22%
CCS
CED
1%
Mean = 0.0957
Mean±SD
= (-0.1988, 0.3902)
Mean±1.96*SD
= (-0.4815, 0.673)
Summary: ccs
K-S d=.53166, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: ccs
1.8
600
Expected Normal Value
1.6
400
300
200
100
0
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.2
-0.2
0.0
0.8
Summary Statistics:ccs
Mean= 0.095915
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.294736
Skew ness= 2.751789
Kurtosis= 5.592196
0.6
0.4
0.2
ccs
No. of obs.
500
0.0
-0.2
-0.4
-0.6
0.2
0.4
0.6
Value
0.8
1.0
1.2
Chapter 5 – Descriptive Statistic Analysis
Mean = 0.0638
Mean±SD
= (-0.1808, 0.3085)
Mean±1.96*SD
= (-0.4157, 0.5434)
Summary: ced
K-S d=.53906, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: ced
2.0
600
Expected Normal Value
1.8
No. of obs.
500
400
300
200
100
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
-0.2
-0.2
1.0
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.6
Summary Statistics:ced
Mean= 0.063943
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.244869
Skew ness= 3.574246
Kurtosis= 10.813636
0.4
ced
0.2
0.0
-0.2
-0.4
-0.6
Mean = 0.1064
Mean±SD
= (-0.2022, 0.415)
Mean±1.96*SD
= (-0.4985, 0.7112)
Summary: cla
K-S d=.52841, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: cla
1.8
600
Expected Normal Value
1.6
400
300
200
100
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.4
-0.2
0.0
0.8
Summary Statistics:cla
Mean= 0.106572
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.308843
Skew ness= 2.556840
Kurtosis= 4.553596
0.6
0.4
0.2
cla
No. of obs.
500
0.0
-0.2
-0.4
-0.6
0.2
0.4
0.6
Value
0.8
1.0
1.2
Chapter 5 – Descriptive Statistic Analysis
Mean = 0.3085
Mean±SD
= (-0.1538, 0.7708)
Mean±1.96*SD
= (-0.5976, 1.2146)
Summary: cob
K-S d=.43983, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: cob
1.2
450
1.0
Expected Normal Value
400
No. of obs.
350
300
250
200
150
100
0.6
0.4
0.2
0.0
-0.2
-0.4
50
0
0.8
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
-0.6
-0.2
1.0
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.4
1.2
Summary Statistics:cob
Mean= 0.307282
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.461778
Skew ness= 0.837652
Kurtosis= -1.302980
1.0
0.8
cob
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Mean = 0.1809
Mean±SD
= (-0.2044, 0.5661)
Mean±1.96*SD
= (-0.5742, 0.9359)
Summary: coe
Normal P-Plot: coe
1.4
1.2
Expected Normal Value
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.4
-0.2
0.0
1.0
Summary Statistics:coe
Mean= 0.181172
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.385503
Skew ness= 1.659982
Kurtosis= 0.758221
0.8
0.6
0.4
coe
No. of obs.
K-S d=.49964, p<.01 ; Lilliefors p<.01
Expected Normal
550
500
450
400
350
300
250
200
150
100
50
0
0.2
0.0
-0.2
-0.4
-0.6
-0.8
0.2
0.4
0.6
Value
0.8
1.0
1.2
Chapter 5 – Descriptive Statistic Analysis
Mean = 0.0106
Mean±SD
= (-0.092, 0.1133)
Mean±1.96*SD
= (-0.1906, 0.2119)
Summary: cos
K-S d=.53064, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: cos
Expected Normal Value
700
600
No. of obs.
500
400
300
200
100
0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
0.25
0.20
0.15
0.10
0.05
cos
Summary Statistics:cos
Mean= 0.010657
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.102773
Skew ness= 9.556698
Kurtosis= 89.648938
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
Since one’s college is mutually exclusive in such a way if the individual belongs to this
college, then it means that he is not belong to the rest of the other colleges even if he is taking up
a double degree. Thus, this is the reason why all distributions have means or averages that are
nearer to 0 than to 1. This is also the reason why all skewness are positive. Distributions will
really tend to cluster around 0 than to 1. Since the most respondents come from the college of
business, then it affects the distribution in a way that it made it flat. On the other hand, since
students from the College of Science only represent 1% of the respondents, it is not surprising
that the distribution for the cos variable is extremely high. This is because the individual from
the college of science serves as an outlier with respect to the other respondents.
Chapter 5 – Descriptive Statistic Analysis
5.2.11 Cumulative Grade Point Average: “cgpa”
Mean = 2.727
Mean±SD
= (2.2953, 3.1587)
Mean±1.96*SD
= (1.8809, 3.5732)
Summary: cgpa
K-S d=.10178, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: cgpa
3
Expected Normal Value
300
No. of obs.
250
200
150
100
50
0
1.5
2.0
2.5
3.0
X <= Category Boundary
3.5
2
1
0
-1
-2
-3
1.6
1.8
2.0
2.2
2.4
2.6 2.8
Value
3.0
3.2
3.4
3.6
3.8
3.6
3.4
3.2
3.0
cgpa
Summary Statistics:cgpa
Mean= 2.727043
Minimum= 1.700000
Maximum= 3.500000
Std.Dev.= 0.432094
Skew ness= -0.395451
Kurtosis= -0.425708
2.8
2.6
2.4
2.2
2.0
1.8
Of the Lasallian students surveyed, the average cumulative grade point average is 2.72 of
a possible 4. This is no longer a dummy variable and so the minimum and maximum values are
already relevant. The lowest grade point average among the respondents is 1.7 while the highest
is 3.5. This time, the distribution is negatively skewed as most of the students surveyed have
relatively high grades.
Chapter 5 – Descriptive Statistic Analysis
5.2.12 Gender: “male”
Mean = 0.5213
Mean±SD
= (0.0213, 1.0213)
Mean±1.96*SD
= (-0.4587, 1.5013)
Summary: male
K-S d=.35167, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: male
0.8
350
Expected Normal Value
0.6
300
No. of obs.
250
200
150
100
50
0
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.8
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.6
1.4
1.2
1.0
0.8
male
Summary Statistics:male
Mean= 0.520426
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.500027
Skew ness= -0.081992
Kurtosis= -2.000396
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
Of all the variables, gender is the most well represented as the ratio between the male n
the female is almost 1:1. Such a balanced distribution can be attributed by the fact that the mean
is 0.52 which is more or less the median of 0 and 1, the minimum and the maximum values. This
increases the chances of the researchers in deriving a significant finding from the male variable.
Chapter 5 – Descriptive Statistic Analysis
5.2.13 Relationship Status: “single”
Mean = 0.766
Mean±SD
= (0.3422, 1.1897)
Mean±1.96*SD
= (-0.0646, 1.5966)
Summary: single
K-S d=.47538, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: single
0.4
500
0.2
Expected Normal Value
450
No. of obs.
400
350
300
250
200
150
100
-0.4
-0.6
-0.8
-1.0
-1.2
50
0
0.0
-0.2
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-1.4
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.8
1.6
1.4
1.2
1.0
single
Summary Statistics:single
Mean= 0.765542
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.424037
Skew ness= -1.256913
Kurtosis= -0.421681
0.8
0.6
0.4
0.2
0.0
-0.2
Most of the respondents are single since a value of 1 means the student is single and that
the mean is nearer to 1 than to 2. This time, the skewness is negatively skewed as compared to
the earlier variables since the trait of interest is actually more present in most of the students—
that is, most of the students are not in a relationship as of the current time.
Chapter 5 – Descriptive Statistic Analysis
5.2.14 Year Level: “year”
Mean = 2.8404
Mean±SD
= (2.0637, 3.6171)
Mean±1.96*SD
= (1.3181, 4.3628)
Summary: year
K-S d=.39199, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: year
2.5
450
2.0
Expected Normal Value
400
No. of obs.
350
300
250
200
150
100
1.0
0.5
0.0
-0.5
-1.0
-1.5
50
0
1.5
0.5
1.0
1.5
2.0 2.5 3.0 3.5 4.0
X <= Category Boundary
4.5
5.0
-2.0
0.5
1.0
1.5
2.0
2.5
3.0 3.5
Value
4.0
4.5
5.0
5.5
4.5
4.0
3.5
3.0
year
Summary Statistics:year
Mean= 2.836590
Minimum= 1.000000
Maximum= 5.000000
Std.Dev.= 0.772027
Skew ness= -0.432050
Kurtosis= 2.062482
2.5
2.0
1.5
1.0
The number of respondents on their third year are significantly higher than those in the
other levels. This could then decrease the chances for the researchers to obtain a good finding
from this variable. On the other hand, the skewness for this variable is the least among the others
as there are respondents from both the lower levels and the higher levels.
5.2.15 Risk Orientation: “loss2” and “gain2”
The following is the summary of the responses on the survey questionnaire:
If I were a freshman, I would rather take the degree program with 200 graduates where 70
students would have no job opportunity at all than the other degree program also with 200
graduates where there is a 30% probability that all 200 graduates would not have jobs.
Chapter 5 – Descriptive Statistic Analysis
A terrorist locked 24 people including you in a room with a bomb. You would rather choose to
try to intercept the bomb where there is a 50% chance that the bomb would explode and kill all
of you than take the offer of the terrorist where he would kill 12 people and let the others go.
A crazy ex-convict broke in to a supermarket and held 24
people including you as hostages. The ex-convict gave
you an ultimatum. He can either let 12 people safe or let you toss a coin where if it is heads, he
would let all of you go. You would rather choose to toss a coin.
I’d rather manage a recreational activity for a group of 5 friends where 2 of them will not have
fun than another recreational activity where there is 40% chance that 5 will not have fun.
There are two degree programs that has 200 graduates each year. You would rather take the
degree program that would offer only 140 of these graduates a stable career than the other
degree program that offers a 70% probability that all 200 graduates will have a stable career.
If I own a drug manufacturing company and 150 are in dire need of help, I would recommend a
drug where it is certain that 54 of these people will not be cured than another drug that has 36%
chance that 150 will not be cured.
You have a number of shares of a corporation you want to sell. Two people approached you and
you would rather choose to sell to the 1st person who is still unsure whether to buy your shares
or from another person but told you that there is a 15% chance that he will buy your shares
where you could realize a profit worth $10,000 than the 2nd person who is certain to buy your
shares at a profit worth $1,500.
Chapter 5 – Descriptive Statistic Analysis
If I had a retail store worth $10,000, I would rather lose an amount of $8,500 than holding onto
it and possibly have 85% chance of losing the entire capital.
I would choose to organize a sport event for a group of 5 friends where 3 will certainly enjoy,
than a sport event where there is 60% chance that all 5 will enjoy.
I would introduce a medicine for 150 epidemic-stricken people where 96 will be cured than a
medicine that has 64% chance of curing all 150.
You are invited to two parties on the same date where you wanted to boast your great outfit. You
would rather go to the 1st party where you are certain 9 of your friends whom you could boast to
would go to than the 2nd party where there is a 75% chance that it will be attended by 12 of your
other friends whom you could brag to.
You lost in a bet and had to do a consequence where you had to perform an embarrassing dance
in front of the people inside a room that you can choose. There are only two rooms and you
would rather prefer the 1st room of 12 people where there is a 25% chance that these 12 people
would allow you to dance over the 2nd room of 3 people where it is certain that they want to
witness your dance.
Chapter 5 – Descriptive Statistic Analysis
Mean = 0.5106
Mean±SD
= (0.0103, 1.011)
Mean±1.96*SD
= (-0.47, 1.4913)
Summary: loss2
K-S d=.34709, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: loss2
0.8
350
Expected Normal Value
0.6
300
No. of obs.
250
200
150
100
50
0
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
-0.8
-0.2
1.0
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.6
1.4
Summary Statistics:loss2
Mean= 0.511545
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.500311
Skew ness= -0.046317
Kurtosis= -2.004990
1.2
1.0
loss2
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
Mean = 0.5089
Mean±SD
= (0.0085, 1.0092)
Mean±1.96*SD
= (-0.4719, 1.4896)
Summary: gain2
K-S d=.34617, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: gain2
0.8
350
Expected Normal Value
0.6
300
No. of obs.
250
200
150
100
50
0
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-0.8
-0.2
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.6
1.4
1.2
1.0
0.8
gain2
Summary Statistics:gain2
Mean= 0.509769
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.500349
Skew ness= -0.039188
Kurtosis= -2.005602
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
Responses on both loss and gain perspective vary almost equally. The distribution is flat
and skewness approaching 0 since responds truly vary.
Chapter 5 – Descriptive Statistic Analysis
5.2.16 Prospect Theory: “prospect2”
Mean = 0.227
Mean±SD
= (-0.1923, 0.6462)
Mean±1.96*SD
= (-0.5947, 1.0486)
Summary: prospect2
K-S d=.47873, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: prospect2
1.4
500
1.2
Expected Normal Value
450
No. of obs.
400
350
300
250
200
150
100
0.8
0.6
0.4
0.2
0.0
-0.2
50
0
1.0
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
-0.4
-0.2
1.0
0.0
0.2
0.4
0.6
Value
0.8
1.0
1.2
1.2
1.0
Summary Statistics:prospect2
Mean= 0.227353
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.419495
Skew ness= 1.304513
Kurtosis= -0.299323
0.8
prospect2
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
5.2.17 Consistency upon Framing: “consistent”
Mean = 0.5496
Mean±SD
= (0.0517, 1.0476)
Mean±1.96*SD
= (-0.4264, 1.5257)
Summary: consistent
K-S d=.36634, p<.01 ; Lilliefors p<.01
Expected Normal
Normal P-Plot: consistent
0.8
350
Expected Normal Value
0.6
300
200
150
100
50
0
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-0.2
0.0
0.2
0.4
0.6
X <= Category Boundary
0.8
1.0
-1.0
-0.2
0.0
1.8
Summary Statistics:consistent
Mean= 0.548845
Minimum= 0.000000
Maximum= 1.000000
Std.Dev.= 0.498051
Skew ness= -0.196846
Kurtosis= -1.968256
1.6
1.4
1.2
1.0
consistent
No. of obs.
250
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
0.2
0.4
0.6
Value
0.8
1.0
1.2
Appendix A: Survey Questionnaire
Appendix A: Survey Questionnaire
1. Number of executive positions in organizations held during the whole stay in DLSU-Manila:
2. Number of subjects failed in whole DLSU stay: ____
3. Select all that applies regarding extra-curricular activities:
Athlete (DLSU-Manila Varsity)
Artist (Under any Cultural Arts Group)
Student Government (elected or appointed)
Not active in any organizations
Active in other organizations: (pls. specify) _____________
4. Name (optional):
5. College:
COB
SOE
COE
CCS
COS
CED
CLA
6. CGPA (estimated): _____
7. Gender:
Male
Female
8. Relationship Status:
Single
In a relationship
Appendix A: Survey Questionnaire
9. Year Level:
1st Year
2nd Year
3rd Year
4th Year
Terminal
10. If I were a freshman, I would rather take the degree program with 200 graduates where 70
students would have no job opportunity at all than the other degree program also with 200
graduates where there is a 30% probability that all 200 graduates would not have jobs.
Agree
Disagree
11. A terrorist locked 24 people including you in a room with a bomb. You would rather choose
to try to intercept the bomb where there is a 50% chance that the bomb would explode and kill
all of you than take the offer of the terrorist where he would kill 12 people and let the others go.
Agree
Disagree
12. A crazy ex-convict broke in to a supermarket and held 24 people including you as hostages.
The ex-convict gave you an ultimatum. He can either let 12 people safe or let you toss a coin
where if it is heads, he would let all of you go. You would rather choose to toss a coin.
Agree
Disagree
13. I’d rather manage a recreational activity for a group of 5 friends where 2 of them will not
have fun than another recreational activity where there is 40% chance that 5 will not have fun.
Agree
Appendix A: Survey Questionnaire
Disagree
14. There are two degree programs that has 200 graduates each year. You would rather take the
degree program that would offer only 140 of these graduates a stable career than the other degree
program that offers a 70% probability that all 200 graduates will have a stable career.
Agree
Disagree
15. If I own a drug manufacturing company and 150 are in dire need of help, I would recommend
a drug where it is certain that 54 of these people will not be cured than another drug that has 36%
chance that 150 will not be cured.
Agree
Disagree
16. You have a number of shares of a corporation you want to sell. Two people approached you
and you would rather choose to sell to the 1st person who is still unsure whether to buy your
shares or from another person but told you that there is a 15% chance that he will buy your
shares where you could realize a profit worth $10,000 than the 2nd person who is certain to buy
your shares at a profit worth $1,500.
Agree
Disagree
17. If I had a retail store worth $10,000, I would rather lose an amount of $8,500 than holding
onto it and possibly have 85% chance of losing the entire capital.
Agree
Disagree
18. I would choose to organize a sport event for a group of 5 friends where 3 will certainly enjoy,
than a sport event where there is 60% chance that all 5 will enjoy.
Agree
Appendix A: Survey Questionnaire
Disagree
19. I would introduce a medicine for 150 epidemic-stricken people where 96 will be cured than a
medicine that has 64% chance of curing all 150.
Agree
Disagree
20. You are invited to two parties on the same date where you wanted to boast your great outfit.
You would rather go to the 1st party where you are certain 9 of your friends whom you could
boast to would go to than the 2nd party where there is a 75% chance that it will be attended by
12 of your other friends whom you could brag to.
Agree
Disagree
21. You lost in a bet and had to do a consequence where you had to perform an embarrassing
dance in front of the people inside a room that you can choose. There are only two rooms and
you would rather prefer the 1st room of 12 people where there is a 25% chance that these 12
people would allow you to dance over the 2nd room of 3 people where it is certain that they want
to witness your dance.
Agree
Disagree
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