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Spirituality and Trauma

In: Philosophy and Psychology

Submitted By lewis613
Words 2415
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The Benefits of Spirituality in providing Emotional Care to Trauma Survivors
Nigel E. Lewis
California Southern University

Abstract
Spirituality is widely utilized across cultures as means of dealing with emotional trauma such as tragedy and disasters. Ironically, in academic circles recognition of the value of spirituality as a coping mechanism has been slow. However, in the current post September 11 environment, greater attention has been given to the value of spiritual care in the treatment of victims of emotional trauma particularly in disaster mental health studies. In this paper the value of spirituality in providing emotional care to survivors of trauma is analyzed.

Why is the value of spirituality in emotional care important to me? Conventional wisdom has held to the notion that boys are better at math than girls. Perhaps, this age old belief was responsible for Mattel the company that produced “Teen talk Barbie” in 1992 to create a Barbie doll that had among its stored phrases, one phrase that said, “Math class is tough!”. This phrase was stored with 270 other girl talk phrases such as “I love shopping” and “Will we have enough clothes!”. The result was that Mattel was soundly criticized and quickly changed the doll’s offending phrase a few months later. In spite of Mattel’s effort to correct a message that verbalized an inherent cultural bias, one wonders if the is any accuracy to the stereotype that associate math being difficult with the female gender and whether math is less difficult for males. Bryner (2007) indicated that women are still outnumbered by men in math science and engineering fields. Tachibana (2010) posited that as much as 90 percent of the engineers in the United States are male. Mathematics is considered a gateway such to the hard sciences. Consequently, proficiency in math is linked to science and mathematics careers. In light of such, Tachibana’s (2010) question, ‘I math more difficult for girls than for boys?” is a relevant concern. A similar observation reported by Ginther and Kahn (2006) indicated that women are underrepresented in academic positions at research universities, especially in science and mathematics. A recent review found that women in science, engineering, and technology are less likely to obtain tenure (29% of women compared to 58% of men in full-time, ranked academic positions at 4-year colleges) and are less likely to achieve the rank of full professor (23% of women compared to 50% of men). In 2003, women accounted for 30 percent of the doctorate degrees in science and nearly 9 percent of those awarded in engineering, according to a National Science Foundation report. (Bryner, 2007). The idea of females being less proficient than males in math performance remains a contentious issue and a source of great debate. This gender preference in math performance is also an extremely sensitive issue. A statement made by Lawrence Summers while he served as president of Harvard University indicates an example of the sensitivity of this issue. On January 14, 2005, In a keynote speech at a conference on diversity, Summers hypothesized that the shortage of women in certain disciplines could be explained by innate differences in mathematical ability (Mackenzie, 2009). Although, the backlash from his statement eventually caused Summers to resign from his president, his address merely echoed some research into whether there were genetic or biological reasons that caused males to outperform females in math or whether male outperformance of females in math was myth or reality. For example, Studies (Baron-Cohen, 2003; Halpern, 2000; and Kimura, 1999) explored whether hormones gave one sex a cognitive advantage in math and science disciplines. Halpern, Benbow, Geary, Gur, Hyde & Gernbacher (2007) study indicated that males are more variable on most measures of quantitative and visuo-spatial ability, which necessarily results in more males at both high- and low-ability extremes; Successful careers in math and science require many types of cognitive abilities. Females tend to excel in verbal abilities, with large differences between females and males found when assessments include writing samples. Such studies have led some to conclude that female excellence in verbal skills corresponds to a deficit on the math-oriented cognitive abilities. However, recent studies such as Mertz & Kane (2011) that included national and international data found that gender difference was not attributable to biological cause. Not only did Mertz and Kane (2011) study find no evidence to support a biological basis for poorer math performance among girls and women but it attributed the cause of poorer math performance among females to sociocultural factors. Mertzer & Kane (2011) suggest that inadequate nurture, not nature, is the primary reason many fewer females than males are identified as excelling in mathematics at the high and highest levels.
High Scholl math performance Fleson and Tredeau (1991) found that although girls show better test grades in all subjects than boys in grades 5 – 12, their grades in mathematics are lower than their grades in other subjects. In addition, Felson and Tredeau (1991) indicated that boys do better on the SAT even though girls are at least as well prepared in coursework. Similarly, Sanders (2008) found that boys score better on than girls on the standardized test SAT. Recent studies that examined gender difference in math performance at the high school level had mixed results with boys having advantage on some measures and girls on others. For example, Voyer and Voyer (2014) meta-analytical study tested 369 samples from 308 studies involving participants from 30 countries found that boys typically scored higher in math in achievement tests while girls scored higher in classroom grades. This has contributed to the public's notion that boys truly are better at math. Such a notion, however, has some inherent bias as Voyer and Voyer (2014) indicated that while boys typically scored higher in math and science at the high school level, girls scored better in all-round classroom subject grades. In fact, the study indicated that girls have been performing better than boys all-round in all subjects for several decades. Another study (Spelke, 2005) indicated that when the cognitive abilities of males and females from birth to maturity are studied, the evidence does not support the claim that men have greater intrinsic aptitude for mathematics and science. On the other hand Stoet & Geary, (2012) found that men and women score similarly in most areas of mathematics, but a gap favoring men is consistently found at the high end of performance.
Research Question Since recent studies such as Voyer and Voyer (2014) meta-analytical study which found that males score better than females at high school level conflicts similar research, for example, Stoet & Geary, (2012) that found no difference in math performance between males and females, it remains of interest what research continue to find as studies are replicated. Although the magnitude of the gender difference has declined over the years in popular opinions, debating on gender differences in problem solving in elementary or middle school, differences favoring men emerged in high school and college. Gender differences in mathematics performance might be small. However to a statistician a small change might be significantly important. In this paper, the researcher (investigator) would like to know if gender is a predictor of math performance at the eighth grade level in New York City. The study will use the New York City Department of Education (NYC DOE, 2014) eighth grade standardized test scores data for analysis.

Question

How strongly does gender affect performance in eighth grade mathematics in New York City?

Hypotheses

H0: There is no significant impact of gender on performance in eighth grade mathematics in New York City.

H1: There is a significant impact of gender on performance in eighth grade mathematics in New York City.

The researcher will use statistical calculation software for data analysis.

General Method

For the purpose of this investigation the data used is the New York City Department of Education (NYC DOE, 2014) eighth grade standardized test scores data for analysis.

Data Analysis

Table 1

|2013 Aggregate Exam Results by Gender |
|Gender |# Tested |# Passed |% Pass |
|Female |33637 |9047 |13.02 |
|Male |35829 |8843 |12.73 |
|Total |69466 |17890 |25.75 |

[pic]

Figure 1. Bar Chart Comparing Passing Results by Gender

From Table 1 and Figure 1, above, in aggregate, it was evident that in the 2013 eighth grade math achievement test, female students outperformed male students by 0.29%. Across all the five boroughs of the New York City, from Table 2, female students outperformed male students in the 2013 eighth grade math achievement test, by 1.3% in The Bronx, 2.6% in Brooklyn, 2.0% in Manhattan, 2.7% in Queens, and 1.0% in Staten Island, that is by 1.92% in average.

Similarly, from Figure 2 and Table 4, on 2014 eighth grade achievement test, across the five boroughs, female students outperformed male students by 1.83%, with1.2 % in Bronx, 2.6% in Brooklyn, 3.9% in Manhattan, 0.8% in Queens, and 2.1% in Staten Island.

Although there was no research to ascertain the factors affecting the identified performance gap, the remarks above suggested that, at the eighth grade level, female students might perform better in math than male students in New York City. This assertion might fall within the category of sweeping generalization fallacy. Hence, a statistical analysis is important and necessary to arrive at a valid conclusion.

Table 2

|Number of Students Tested and Passed in 2013 by Borough and Gender Table |
| |Female |Male |
|Borough |
|Borough |X Values (Female) |Y Values (Males) |
|Bronx |285 |281 |
|Brooklyn |299 |294 |
|Manhattan |302 |298 |
|Queens |307 |304 |
|Staten Island |303 |301 |

Table 4

|2013 Computation Table for Pearson Coefficient |
|X |
|Gender |# Tested |# Passed |% Pass |
|Female |28158 |6759 |24.0 |
|Male |30942 |6861 |22.17 |
|Total |59100 |13620 |23.05 |

[pic]

Figure 2. 2014 Performance by Gender

Table 5

|Number of Students Tested and Passed in 2014 by Borough Table |
|Borough |Female |Male |
| |
|Borough |X Values (Female) |Y Values (Males) |
|Bronx |283 |280 |
|Brooklyn |297 |293 |
|Manhattan |298 |294 |
|Queens |302 |301 |
|Staten Island |301 |301 |

Computation of Pearson Coefficient

Table 7

|2014 Computation Table for Pearson Coefficient |
|X |
| |X |Y |
|N |10 |10 |
|Mean |297.7 |304.6 |
|Variance |60.2333 |767.3778 |
|Std. Dev. |7.761 |27.7016 |
|Std. Err. |2.4542 |8.76 |
|R |r2 |Slope |Y |Std. Err. of |
| | | |Intercept |Estimate |
|-0.361 |0.131 |-1.29 |688.5731 |27.3966 |
|T |df |
|-1.096 |8 |
| |One-tailed |0.1525 |
|P | | |
| |Two-tailed |0.3049 |

Table 10

|Confidence Intervals of rho Table |
| |Lower Limit |Upper Limit |
|0.95 |-0.807 |0.347 |
|0.99 |-0.874 |0.533 |

Conclusion

The study indicated that female eight grade students performed better in mathematics in the 2013 and 2014 within the five boroughs of New York City schools (Bronx, Brooklyn, Manhattan, Queens and Staten Island). The correlation was weak in the amalgamation of the combined two years data but stronger when the data was considered separately. A further detailed study is needed to arrive at a valid generalizability.

References
Buchner, A., Faul, F., & Erdfelder, E. (n.d.). G*Power. Retrieved from http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/download-and-register/Dokumente/GPower3-BRM-Paper.pdf

Bryner, J. (2007). Why men dominate math and science fields. LiveScience, Accessed online from: http://www.livescience.com/1927-men-dominate-math-science-fields.html
Tachibana, C. (2010). Probing question: Are boys really better at math than girls?. Penn State News. Retrieved online from: http://news.psu.edu/story/141274/2010/05/18/research/probing-question-are-boys-really- better-math-girls
Felson, R. B & Tredeau, L (1991). Gender difference in mathematics performance. Social Psychology Quarterly, 54 (2), 113 – 126.
Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A (2007). The science of sex difference in science and mathematics. Psycological Science in the Public Interest, 8 (1), 1 – 35. Accessed Online from: https://my.vanderbilt.edu/smpy/files/2013/02/ScienceSexDifferences.pdf.
Mackenzie, D. (2009). What Larry Summers said and didn’t say. Swarthmore College Bulletin,

Retrieved March 22, 2015 from http://bulletin.swarthmore.edu/bulletin-issue-

archive/index.html%3Fp=145.html?p=145.

Mertz, J. (2009). Why women remain underrepresented in mathematics at the highest level.

ASCB Newsletter, Retrieved from: http://www.ascb.org/files/0908wicb.pdf.

Mertz, J. E. & Kane, J. M (2011). Debunking myths about gender and mathematics performance. Notice of the American Mathematical Society, In Science Daily 10.1090/noti790

NYC DOE. (2014). Eighth grade achievement tests results. Retrieved from

| |http://schools.nyc.gov/accountability/data/testresults/elaandmathtestresults | | | | | |

Sanders, R. (2008). Girls and boys math performance now equal. UC Berkley News. Accessed

Online from: http://www.berkeley.edu/news/media/releases/2008/07/24_math.shtml.

Spelke, E. S (2005). Sex difference in intrinsic aptitudes for mathematics and science. American

Psychologist, American Psychological Association 0003-066X/05/$12.00 Vol. 60, No. 9,

950 –958 DOI: 10.1037/0003-066X.60.9.950

Stoet, G. & Geary, D. C (2012). Can stereotype threat explain the gender gap in mathematics

performance and achievement? Review of General Psychology, 16(1), 93 – 102.

Retrieved from: http://dx.doi.org/10.1037/a0026617

Summers LH. (2005). Remarks at NBER Conference on Diversifying the Science & Engineering

Workforce. 14 January 2005. www.president.harvard.edu/speeches/

summers_2005/nber.php.

Voyer. D. & Voyer, S. D. (2014). Gender difference in scholastic Achievement: A meta-analysis.

Psychological Bulletin, Accessed online on March 24, 2015 from:

http://www.apa.org/pubs/journals/releases/bul-a0036620.pdf

Appendix
Result Details and Calculation
X Values
∑ = 2977
Mean = 297.7
∑(X - Mx)2 = SSx = 542.1
Y Values
∑ = 3046
Mean = 304.6
∑(Y - My)2 = SSy = 6906.4
X and Y Combined
N = 10
∑(X - Mx)(Y - My) = -699.2
R Calculation r = ∑((X - My)(Y - Mx)) / √((SSx)(SSy)) r = -699.2 / √((542.1)(6906.4)) = -0.3614 r = -0.3614

Key
X: X Values
Y: Y Values
Mx: Mean of X Values
My: Mean of Y Values
X - Mx & Y - My: Deviation scores
(X - Mx)2 & (Y - My)2: Deviation Squared
(X - Mx)(Y - My): Product of Deviation Scores

Table 8

|Combined Data Table |
| |Data Cells |
|Pairs |X |Y |Residuals |
| |285 |381 |60.02 |
| |299 |293 |-9.923 |
| |302 |298 |-1.054 |
| |307 |304 |11.395 |
| |303 |301 |3.236 |
| |283 |280 |-43.56 |
| |297 |293 |-12.503 |
| |298 |294 |-10.213 |
| |302 |301 |1.946 |
| |301 |301 |0.656 |

Data Summary

| |(X = |2977 | |(X2i = |886795 |
| |(Y = |3046 | |(Y2i |934718 |
| |(XY = |906095 |
| | | |
| | | |

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