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Adversity Quotients

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PSYC 41.1
MIDTERM OUTPUT
PSYCHOLOGY STATISTICS
Adversity Quotient Of First Year To Fourth Year Students From The BS Psychology, BS Education, and BS Business Administration Students

Submitted by:
Patrisha Marie O. Fernandez
Gabrielle G. Hernandez
Ena Therese A. Labradores
Camille Joy R. Magnanao
Judy Ann E. Ugay
BS PSYCHOLOGY – 2 YE

Submitted to:
Ma’am Jhoanna Mocorro

STATEMENT OF THE PROBLEM 1. What is the profile of the respondents with regards to the following variables: 2.1 Age 2.2 Gender 2.3 Course 2.4 Year Level 2.5 Weekly Allowance 2. What is the average level of Adversity Quotient of the respondents? 3. What is the average weekly allowance of the respondents? 4. Is there a significant difference in the Adversity Quotient of the respondents according to: 5.6 Age 5.7 Gender 5.8 Course 5.9 Weekly Allowance 5. Is there a significant relationship between the Adversity Quotient of the respondents and: 6.10 Age 6.11 Year Level 6.12 Weekly Allowance

DATA CLEANING I. Missing Values

Based on the results variables table shown above, there are no missing values present in our data as well as no missing values are replaced and had a complete data ( N = 90 ).

II. Outliers

An outlier is an observation that lies an abnormal distance from other values in a random sample of population. To identify the outliers of our data, the box plot was used as a display for describing and showing the results of the data. The asterisk (*) symbol is identified as the outlier of the data. From the 5 variables we chose, all 4 variables had no outliers present while the weekly allowance data had 2 outliers present, the possibility why it had an outlier because of the population we chose. Our population was based on course but people in each course was chosen randomly and there are respondents who had more weekly allowance than the other respondents which results to a big gap and distance between the values.

III. Skewness and Kurtosis

Skewness quantifies how symmetrical the distribution is. If skewness is greater than 1.0 or less than -1.0 the skewness is far from being symmetrical. Based on the skewness of our data, the course and year level of the respondents are symmetrically distributed while the age, gender and weekly allowance of the respondents are asymmetrically distributed.

Kurtosis is a measure of whether the data are peaked or flat relative to normal distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean while data sets with low kurtosis tend to have a flat top near the mean rather than a sharper peak.
______________________________________________________________________________
Table 1.1 Frequency Distribution of the Age of the Respondents
STATISTICAL TOOL:
Frequency Distribution

Figure 1.1 Frequency Distribution of the Age of the Respondents

From the Table 1.1 and Figure 1.1, the results show that when grouped according to the age of the respondents, the highest number of population were the age bracket from 17 – 18 years old (59) and the least number of population were the age bracket from 23 – 24 years old (1). 6 % are from 15 – 16 years old, 66 % are from 17 – 18 years old, 21 % are from 19 – 20 years old, 7 % are from 21 – 22 years old and 1 % from 23 – 24 years old.
Table 1.2 Frequency Distribution of the Gender of the Respondents
STATISTICAL TOOL:
Frequency Distribution

Figure 1.2 Frequency Distribution of the Gender of the Respondents

From the Table 1.2 and Figure 1.2, we find that, when grouped according to the gender of the respondents, there are more female respondents (56) than the male respondents (34). 62 % are female while 38 % are male.

Table 1.3 Frequency Distribution of the Course of the Respondents
STATISTICAL TOOL:
Frequency Distribution

Figure 1.3 Frequency Distribution of the Course of the Respondents

From the Table 1.3 and Figure 1.3, when grouped according to the course of the respondents, 33.33 % are from Psychology, Education and Business Administration. The frequency is equal to 30 respondents per course.
Table 1.4 Frequency Distribution of the Year Level of the Respondents
STATISTICAL TOOL:
Frequency Distribution

Figure 1.4 Frequency Distribution of the Year Level of the Respondents

. In the Table 1.5 and Figure 1.5, when grouped by year level, 4.4% are first year students, 56.7%% are second year students, 12.2% are third year students, 26.7% are fourth year students.
Table 1.5 Frequency Distribution of the Weekly Allowance of the Respondents
STATISTICAL TOOL:
Frequency Distribution Figure 1.5 Frequency Distribution of the Weekly Allowance of the Respondents

From the Table 1.4 and Figure 1.4, when grouped according to the weekly allowance of the respondents, 84 % of the respondents have 100 – 1000 pesos weekly, 10 % of the respondents have 1100 – 2000 pesos weekly, 1% of the respondents have 2100-3000 pesos weekly. 4% of the respondents have 4100-5000 pesos weekly.
Table 2 Frequency Distribution of the Adversity Quotient
STATISTICAL TOOL:
Frequency Distribution

Figure 2 Frequency Distribution of Adversity Quotient

From the Table 2 and Figure 2, when grouped according to the adversity quotients, 2.2 % are low and moderately low, 51.1 % are moderate, 38.9 % are moderately high and 5.6 % are high. The average level of the respondents’ adversity quotients was the moderate adversity quotient with 46 in frequency and about 51.1 % in percentage. Therefore we can conclude, that most of the respondents deal with adversities or difficult situations in a moderate level. When the result is moderate, it indicates that you deal with adversity fairly well, however your performance can be enhanced with a higher AQ. According to Malabanan & Vinas (July 2015) for moderate adversity quotient, people delay from taking constructing action. With moderate challenges, students probably do a reasonably good job of keeping faith and forging ahead. Students assessed themselves that they can influence their adversities to a moderate extent. This means that they have moderate adversity quotient. Respondents’ coping strategies are categorized in their ability to determine their adversities, immediately coping within the situation, rebuilding confidence and comprising strong network every after adversities. BASIS & SOURCES: http://apjeas.apjmr.com/wp-content/uploads/2015/07/APJEAS-2015-2.3-11-Adversity-Quotient-and-Coping-Strategies-of-College-Students.pdf http://articles.latimes.com/1998/may/18/news/ss-51010
Table 3 Frequency Distribution of the Weekly Allowance
STATISTICAL TOOL:
Frequency Distribution
Figure 3 Frequency Distribution of the Weekly Allowance

From the Table 3 and Figure 3, when grouped according to the weekly allowance of the respondents, 84 % of the respondents have 100 – 1000 pesos weekly, 10 % of the respondents have 1100 – 2000 pesos weekly, 1% of the respondents have 2100-3000 pesos weekly. 4% of the respondents have 4100-5000 pesos weekly.
The average weekly allowance of the respondents is from the range of 100 – 1000 pesos. Therefore, we conclude, that most of the respondents has a weekly allowance of 100 – 1000 pesos. Having an allowance from the price range 100 – 1000 pesos is just normal if you are studying in Xavier University because most of the food and transportation used in school is already costly and for students mostly from an average family. 4.1 DIFFERENCE BETWEEN AGE AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESES STATEMENTS:
Null Hypothesis (Ho) – There is no significant difference between the Adversity Quotient and age of the respondents
Alternative Hypothesis (Ha) – There is significant difference between the Adversity Quotient and age of the respondents
STATISTICAL TOOL:
One-way ANOVA (F test)
RESULTS & DISCUSSIONS:

A one-way ANOVA was conducted to compare the difference of age of the participants to their Adversity Quotient. There was no significant difference of the age at the p<.05 level. [F(3, 86) = 2.119, p = 0.104]. So, we DO NOT REJECT Ho. Post hoc comparisons using the Tukey HSD test indicated the mean score for the age but there was no significant difference obtained, so there is no need to compare and analyze the Post hoc results. Taken together, these results suggest that the age of the participants really do not have a difference on their Adversity Quotient. Specifically, our results suggest that when the age of a person is higher, it does not directly mean that their Adversity Quotient is also increases.
CONCLUSION:
Given that there is no significant difference between the age of the participants and their adversity quotient, the researchers assumed that since most of the respondents are in the adolescent stage, the respondents’ response to their adversities may somehow be similar. Childhood and adolescent adversity would specifically be associated with elevated startle reflexes during the safe phases that predicted anxiety disorder onset relative to other phases of the startle protocol (i.e., baseline, context, and danger phases) that did not predict anxiety disorder onset. As young people participate in activities of their own choosing, it is important that they take place in environments that contain rules, challenges, and complexities that are inherent in the real world. For example, teenagers must face intellectual, interpersonal, and intrapersonal challenges. Away from the influence of protective parents, they must have opportunities to think critically about themselves and the world, learn to get along with peers and adults, and reflect on their progress.
BASIS & SOURCES: http://www.psychologytoday.com/blog/the-moment-youth/201106/what-teens-learn-overcoming-challenges 4.2 DIFFERENCE BETWEEN GENDER AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESES STATEMENTS:
Null Hypothesis (Ho) – There is no significant difference between the Adversity Quotient and gender of the respondents
Alternative Hypothesis (Ha) – There is significant difference between the Adversity Quotient and gender of the respondents
STATISTICAL TOOL:
Independent Sample Test (T-test)

RESULTS & DISCUSSION:

An independent-samples t-test was conducted to compare the difference between gender and adversity quotient. There was no significant difference in the scores for the male (M=3.50, SD=0.097) and female (M=3.39, SD=0.824) conditions; t (88)=0.668, p = 0.506. So we DO NOT REJECT Ho. These results suggest that the gender really do not have a difference on the respondents’ Adversity Quotient.
CONCLUSION:
Given that there is no significant difference between the gender of the participants and their adversity quotient, the researchers assumed that a person’s Adversity Quotient is independent from the person’s gender. Childhood and adolescent adversity would specifically be associated with elevated startle reflexes during the safe phases that predicted anxiety disorder onset relative to other phases of the startle protocol (i.e., baseline, context, and danger phases) that did not predict anxiety disorder onset. As young people participate in activities of their own choosing, it is important that they take place in environments that contain rules, challenges, and complexities that are inherent in the real world. For example, teenagers must face intellectual, interpersonal, and intrapersonal challenges. Away from the influence of protective parents, they must have opportunities to think critically about themselves and the world, learn to get along with peers and adults, and reflect on their progress. “Overcoming adversity is not gender-related.” (Buren, 1997)
BASIS & SOURCES: http://news.google.com/newspapers?nid=348&dat=19970926&id=TWwxAAAAIBAJ&sjid=kTMDAAAAIBAJ&pg=3917,6555991&hl=en 4.3 DIFFERENCE BETWEEN COURSE AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESES STATEMENTS:
Null Hypothesis (Ho) – There is no significant difference between the Adversity Quotient and course of the respondents
Alternative Hypothesis (Ha) – There is significant difference between the Adversity Quotient and course of the respondents

STATISTICAL TOOL:
One-way ANOVA (F Test)
RESULTS & DISCUSSION:
A one-way between ANOVA was conducted to compare the effect of the course of the respondents to their Adversity Quotient. There was no significant effect of the courses at the p<.05 level for the three courses [F(2, 87) = .243, p = 0.785]. Post hoc comparisons using the Tukey HSD test indicated the mean score for the courses but there was no significant difference obtained, so there is no need to compare and analyse the Post hoc results. So we DO NOT REJECT Ho. Taken together, these results suggest that the course of the respondents does not have an effect on their Adversity Quotient. As you can see, our results suggest that when the course of the respondent is higher, it does not directly mean that their Adversity Quotient is also higher.

CONCLUSION:
We can say in the results above that there is no significant difference between the 3 courses and Adversity Quotient. Whatever your course is, it will not affect your adversity quotient because we have different problems or hassles in life that we encounter. The 90 respondents may encounter the same problems regardless of their courses. Some of these problems may be financial stability and practicality.
According to Schinasi, Garry J.(2004)Financial stability is defined in terms of its ability to facilitate and enhance economic processes, manage risks, and absorb shocks. Moreover, financial stability is considered a continuum: changeable over time and consistent with multiple combinations of the constituent elements of finance. “ By knowing what problems students encounter, it is possible for educators to offer a course that teaches the financial skills necessary to overcome these problems. The participants were asked to indicate what financial education they would be interested in if offered. Nearly all of the students expressed an interest in learning about financial management." ( Jariah, M., A. R. Husniyah, P. Laily, and S. Britt. 2004) Practicality means the quality of being effective, useful, or suitable for a particular purpose or situation and a sensible attitude toward making decisions and plans. “Generally, you should do something that at least you’re interested in,” said College of Arts and Sciences sophomore Chelsea Waida, a computer science major. “You want to have a job when you get out [of college], but you don’t want to go into something that you absolutely hate.” Thirty-six percent of students described their intended major as a “good” fit based on interest, while 32 percent described it as a “poor” fit, the report stated. Of those who selected an intended major, more than four-fifths said they were “fairly sure” or “very sure” about their choice of major (Waida). School of Management sophomore Emily Cloutier, who is majoring in business and concentrating in law and finance, said she changed her major from psychology to business based on job availability. “When I came here, I was a psychology major, but then I realized I would have to go to grad school and get a Ph.D. before I could make a living on my own,” she said. “So I switched for the money and the security of knowing that an SMG degree would get me a job.”
Unfortunately, students must sometimes sacrifice majoring in the field that interests them to make a practical career choice, Cloutier said. “For me personally, it’s sad that you have to split between what you’re interested in and what’s going to make you money, but it’s just kind of the way the world turns now,” she said.
BASIS & SOURCES: http://www.imf.org/external/pubs/cat/longres.aspx?sk=17740.0 http://www.childfinanceinternational.org/component/mtree/kb/global-platforms/financial-education/financial-behavior-and-problems-among-university-students-need-for-financial-education http://dailyfreepress.com/2013/11/12/college-majors-chosen-based-on-practicality-not-interest-study-suggests/ 4.4 DIFFERENCE BETWEEN WEEKLY ALLOWANCE AND ADVERSITY QUOTIENTS (AQ)
HYPOTHESES STATEMENTS:
Ho (Null Hypothesis) – There is no significant relationship between the weekly allowance of the respondents and the adversity quotients.
Ha (Alternative Hypothesis) – There is significant relationship between then weekly allowance of the respondents and the adversity quotients.
STATISTICAL TOOL:
One-way ANOVA (F test)
RESULTS & DISCUSSION:

A one-way ANOVA was conducted to compare the difference of weekly allowance of the participants to their Adversity Quotient. There was no significant difference of the weekly allowance at the p<.05 level. [F(3, 86) = 1.079, p = 0.362]. So, we DO NOT REJECT Ho. Post hoc comparisons using the Tukey HSD test indicated the mean score for the weekly allowance but there was no significant difference obtained, so there is no need to compare and analyze the Post hoc results. Taken together, these results suggest that the weekly allowance of the participants really do not have a difference on their Adversity Quotient.

CONCLUSION:
Given that there is no significant difference between the weekly allowance of the participants and their adversity quotient, the researchers assumed that there is no effect of the weekly allowance to their adversity quotient. We do know that having a job is positively related to financial literacy which, in turn, is positively related to self-beneficial financial behavior. Fifty years ago, the American scholars Marshall & Magruder found that children’s knowledge of money is related directly to the extensiveness of their experience with money.
However, they did not find that children have more knowledge of money if their parents gave them an allowance. In England, Newson & Newson studied 7 year olds and found that middle class children received lower allowances than working class children but were far more likely to save some of it. The savings habit was clearly learned at home but didn’t relate to the size of the allowance. Parental involvement in, and commitment to, an allowance system appears to be essential if it is to effect the monetary beliefs and behaviours of children. In France, Lassarre found that the best allocation strategy is giving allowances paired with discussions of the family budget. They conclude that the mechanism that makes an allowance system effective is the possibility it affords for discussions about financial matters within the family. Several studies found that allowances negatively impact the desire of children to work. In the US, Mortimer, Dennehy, Chaimum and Finchstudied 1,090 ninth grade students and found no significant effects of allowances on children’s savings, but did find that students who reported receiving a regular allowance in the ninth grade were less likely than other students to view work generally as a source of intrinsic satisfaction. They warn that parents and financial counselors need to be careful about undermining the development of work values through allowance practices. We can conclude by stating that by themselves, allowances do not lead to increased savings or financial literacy.
BASIS & SOURCES: http://lewismandell.com/child_allowances_-_beneficial_or_harmful 5.1 RELATIONSHIP BETWEEN AGE AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESES STATEMENTS:
Ho (Null Hypothesis) – There is no significant relationship between the age of the respondents and the adversity quotients.
Ha (Alternative Hypothesis) – There is significant relationship between then age of the respondents and the adversity quotients.
STATISTICAL TOOL:
Correlation (Bivariate)
RESULTS & DISCUSSION:

The Correlation statistical tool was used to determine the relationship between the age and the adversity quotient. It is composed of two variables and 1 question. Many social researchers are interested in the degree of association between variables. We also want to know the existence and strength of relationship between two variables.
In the correlation box, the first row is named the Adversity Quotient and the second row was the Age. These boxes contain numbers that represent variable crossings. The top box on the right represents the crossing between the ‘Adversity Quotient’ variable and the ‘Age’ variable. The bottom box on the left also happens to represent this crossing. These are the two boxes that we are interested in. They will have the same information so we really only need to read from one. In these boxes, you will see a value for Pearson’s r, a Sig. (2-tailed) value and a number (N) value.Pearson’s r provides a precise measure of the strength and direction of the correlation.
The Pearson’s r statistic can be found in the top of each box. The Pearson’s r for the relationship between the AQ and Age variables in the result is 0.246. And as you can see the Pearson’s r is close to 1. This means that there is a strong relationship between our two variables. The Sig. (2-Tailed) value in our result is 0.019. This value is less than .05. So we REJECT Ho. Because of this, we can conclude that there is a significant correlation between amount of age and adversity quotient variables.
CONCLUSION:
The Pearson’s r for the correlation between the AQ and Age variables is 0.246. The Pearson’s r is close to 1. Therefore there is a STRONG RELATIONSHIP between the AQ and the Age. This means that changes in one variable are correlated with changes in the second variable.
Our Pearson’s r were 0.246, therefore the variables were strongly correlated. This value is positive because SPSS did not put a negative sign in front of it. The Sig.(2-tailed) is 0.019 which is less than 0.05, so we REJECT OUR Ho (NULL HYPOTHESIS), therefore it has a STATISTICALLY SIGNIFICANT RELATIONSHIP OR CORRELATION between the AQ and Age of the participants.
Based on the results, the graph (as I imagined it would be, is a horizontal line going up because it is positive) and so I can say that as one would grow older and be more exposed and experienced can respond more to what has life given them(which is the AQ). For example there’s this baby whose age is 1 year old, obviously he/she is still dependent on his/her family and parents and he/she doesn’t know how to respond to life yet and has a low AQ, but if you compare it with and 18 year old (which is the mean result of the Age variable), the greater its AQ, how he/she respond to life, more independent, more exposed and experienced.
Age and Adversity Quotient relates to one another. This means that as a person grows older, he learns more and more of life and its challenges and adversities. He is grows more mature and more responsible as well as more creative in facing the challenges. For example, he reacts more positively to changes better now that he is 18 years old compared before when he was aged 10 years younger. As he grows, so does his ability to through life’s ups and down.
5.2 RELATIONSHIP BETWEEN YEAR LEVEL AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESIS STATEMENT:
Null Hypothesis (Ho) – There is no significant relationship between the Adversity Quotient and year level of the respondents
Alternative Hypothesis (Ha) - There is a significant relationship between the Adversity Quotient and year level of the respondents
STATISTICAL TOOL:
Correlation (Bivariate)

RESULT & DISCUSSION:

Correlation (Biviariation) was used to determine the relationship between year level and Adversity Quotients. There are a total of 90 respondents. The mean for the Year level is 2.61 and its Standard Deviation is 0.932. In Avdversity Quotient, the mean is 134.41 and the Standard Deviation is 17.253. The Correlation between the Adversity Quotient and Year Level (r=0.014, p<0.001) suggest that there is no significant relationship between year leevel and Adversity Quotient. When Pearson's r is close to 0, this means that there is a weak relationship between year level and Adversity Quotients. This means that changes in one variable are not correlated with changes in the second variable. Our Sig. (2-tailed) value is 0.894. The Sig.(2-tailed) value is greater than 0.05. There is no significant correlation between year level and Adversity quotient. That means, increases or decreases in one variable do not significantly relate to increases or decreases in your second variable. So, we DO NOT REJECT Ho.
CONCLUSION:
We concluded that there is no statically significant correlation between year level and Adversity Quotient, this means that from our respondents based on their year level (1st year-4th Year) when it increases or decreases, it does not significantly relate to the increase or decrease in the adversity quotient. The senior students may have experienced more hardships or difficulties, encountered more problems and surmounted more challenges in life than the freshmen. Regardless of their life encounters have made them more resilient and thus better able to face adverse life events in comparison with their younger counterparts. But the senior students may have experienced more hardships or difficulties, encountered more problems and surmounted more challenges in life than the freshmen. Regardless of their life encounters they have made them more resilient and thus better able to face their adverse life events.
Most of the college students in this study have tendency to give up easily or are resigned to their fate when faced with. They abandon their dreams if they believe they will encounter hardship in the pursuance of such dreams for they have very restricted ability or tolerance under stress and have no self-confidence to act independently. These are the people who just stand at the foot of the mountain and watch other climbers go up the mountain (Stolz, 1997).
As a result, leaders play a significant role in building highly aligned teams who have high levels of motivation and enthusiasm. According to Koul (2007), many leaders are facing greater challenges than ever before due to increased environmental complexity and the changing nature of business organizations. The current business environment requires this innovative kind of leadership style; a style that empowers employees and raises employee performance in an effort to achieve organizational objectives and continued existence (Stoltz, 1997).
The lower the R score, the more likely the individual will tend to regard events as catastrophic. On the other hand, the higher the R score, the more the individual may limit the reach of the problem to the event at hand. A person with high R score effectively compartmentalizes or contains the reach of the adversity, thus making him feel more empowered and less overwhelmed (Stoltz, 1997).
BASIS & SOURCES:
Stoltz, Paul G. (1997) Adversity Quotient: Turning obstacles into opportunities. Canada: John Willey and
Sons, Inc. Stoltz, Paul G (2000) Adversity quotient at work: Make everyday challenge the key to your success-putting principles of AQ into action. N.Y.: HarperCollins Publishing, Inc.
Koul, L. (2007) Methodology of Educational Research (3rd ed.). New Delhi: Vikas Publishing House Pvt. Ltd.
5.3 RELATIONSHIP BETWEEN WEEKLY ALLOWANCE AND ADVERSITY QUOTIENTS (AQ)

HYPOTHESIS STATEMENT:
Null Hypothesis (Ho) – There is no significant relationship between the Adversity Quotient and weekly allowance of the respondents
Alternative Hypothesis (Ha) - There is a significant relationship between the Adversity Quotient and weekly allowance of the respondents

STATISTICAL TOOL:
Correlation (Bivariate)
RESULTS & DISCUSSIONS:

Descriptive Statistics | | Mean | Std. Deviation | N | Adversity Quotient | 134.41 | 17.253 | 90 | Weekly Allowance | 989.67 | 978.443 | 90 |

Correlations | | Adversity Quotient | Weekly Allowance | Adversity Quotient | Pearson Correlation | 1 | .033 | | Sig. (2-tailed) | | .755 | | N | 90 | 90 | Weekly Allowance | Pearson Correlation | .033 | 1 | | Sig. (2-tailed) | .755 | | | N | 90 | 90 |

In the correlation box, the first row is named the Adversity Quotient and the second row was the Weekly Allowance. The top box on the right represents the crossing between the ‘Adversity Quotient’ variable and the ‘Weekly Allowance’ variable. The bottom box on the left also happens to represent this crossing. These are the two boxes that we are interested in. They will have the same information so we really only need to read from one. In these boxes, you will see a value for Pearson’s r, a Sig. (2-tailed) value and a number (N) value.Pearson’s r provides a precise measure of the strength and direction of the correlation.
The Pearson’s r statistic can be found in the top of each box. The Pearson’s r for the relationship between the AQ and Age variables in the result is 0.033. And as you can see the Pearson’s r is close to 0. This means that there is a weak relationship between our two variables. The Sig. (2-Tailed) value in our result is 0.755. This value is greater than .05. So, we DO NOT REJECT Ho. Because of this, we can conclude that there is no significant correlation between amount of weekly allowance and adversity quotient variables.

CONCLUSION:

BASIS & SOURCES:…...

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...groups took the Scholastic Aptitude Test … the kids who had held out longer scored an average of 210 points higher.” On the last page of Time’s review, it quoted with approval the recommendation “to junk the 7 The reason why the averages of both groups were above the average of all SAT-takers is that most of the children in the experiment were the children of professors and students at Stanford University. (Goleman says they were children of faculty, graduate students “and other employees” at Stanford; but his source (Shoda et al., 1990: 980) says only faculty and students.) 8 Levin 1997: 62; cf. Herrnstein and Murray 1994: 396 -6- SAT completely. ‘Yes, it may cost a heck of a lot more money to assess someone’s EQ [emotional quotient] rather than using a machine-scored test to measure IQ,’ he says.” Here, correctly, the SAT is called an IQ test. If the reviewer had read what she wrote at the beginning of her review, she would know that the SAT is an excellent measure of EQ. In containing a blatant self-contradiction, the Time review imitated one of Emotional Intelligence’s most pervasive characteristics. For example, Goleman sometimes stated, [A]chievement as measured by grades … tells nothing about how they [students] react to the vicissitudes of life (pages 35-6). [Academic] achievement tests … [and] SATs … are based on a limited notion of intelligence, one out of touch with the true range of skills and abilities that matter for life over and above IQ (page 38).......

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The Adversity Quotient and Academic Performance Among College Students at St. Joseph’scollege, Quezon City

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