Sunday, January 26, 2020
Calculating Year-On-Year Growth of GDP
Calculating Year-On-Year Growth of GDP Introduction The model which is to be developed is real GDP in the UK. From such a series of real values, it is straightforward to calculate year-on-year growth of GDP. Selection of variables To model GDP, key factors identified by Easton (2004) include labour costs, savings ratio, taxation issues, inflation and terms of trade. However, many of these variables are not available for the required 40 year time span. The variables eventually chosen and the justification were as follows: GDP: the dependent variable, measured at 1950 prices. As GDP deflator figures were not available back to 1960, the eventual starting point of the analysis, the RPI inflation measure was used to convert the series into real prices. Exim: this variable is the sum of imports and exports, at constant 1950 prices. As a measure of trade volumes, EXIM would be expected to increase as GDP also increases. The RPI deflator was also used for this series. Total trade was plasced into one variable was to abide by the constraint of no more than four independent variables. Energy: energy consumption was calculated as production plus imports minus exports in tonnes of oil equivalent. As energy use increases, we would expect to see an increase in the proportion of GDP attributable to manufacturing.[1] Labour: this variable is the total number of days lost through disputes. We would expect this variable to have a negative coefficient, since an increase in the number of days lost will lead to a reduction of GDP. Scatter diagrammes showing the relationship between the dependent variable GDP and each of the independent variables is sown in Appendix 1. These diagrammes support each of the hypotheses outlined above. Main results The regression equation produced by EViews, once the energy variable is excluded, is as follows: GDP = -73223.22384 + 1.062678514*EXIM 0.1391051564*LABOUR + 1.565374397*POPN The adjusted R2 is equal to 0.978; or, 97.8% of the variation in GDP is accounted for by the variation in EXIM, LABOUR and POPN. Each of the coefficients of the three independent variables, EXIM, LABOUR and POPN, have t-statistics sufficiently high to reject the null hypothesis that any of the coefficients is equal to zero; in other words, each variable makes a significant contribution to the overall equation. To test the overall fit of the equation, the F value of 703 allows us similarly to reject the hypothesis that the coefficients are simultaneously all equal to zero. Dependent Variable: GDP Method: Least Squares Date: 04/15/08 Time: 09:10 Sample: 1960 2006 Included observations: 47 Variable Coefficient Std. Error t-Statistic Prob. C -73223.22 23204.60 -3.155548 0.0029 EXIM 1.062679 0.117445 9.048297 0.0000 LABOUR -0.139105 0.036951 -3.764585 0.0005 POPN 1.565374 0.443541 3.529270 0.0010 R-squared 0.980046 Mean dependent var 32813.25 Adjusted R-squared 0.978654 S.D. dependent var 10905.60 S.E. of regression 1593.331 Akaike info criterion 17.66631 Sum squared resid 1.09E+08 Schwarz criterion 17.82377 Log likelihood -411.1582 F-statistic 703.9962 Durbin-Watson stat 0.746519 Prob(F-statistic) 0.000000 The Akaike and Schwartz criteria are used principally to compare two or more models (a model with a lower value of either of these statistics is preferred). As we are analysing only one model here, we will not discuss these two further. Using tables provided by Gujarati (2004), the upper and lower limits for the DW test are: DL = 1.383 DU = 1.666 The DW statistic calculated by EViews is 0.746, which is below DL. This results leads us to infer that there is no positive autocorrelation in the model. This is an unlikely result, given that we are dealing with increasing variables over time, but we shall examine the issue of autocorrelation in detail later on. Multicollinearity Ideally, there should be little or no significant correlation between the dependent variables; if two dependent variables are perfectly correlated, then one variable is redundant and the OLS equations could not be solved. The correlation of variables table below shows that EXIM and POPN have a particularly high level of correlation (the removal of the ENERGY variable early on solved two other cases of multicollinearity). It is important, however, to point out that multicollinearity does not violate any assumptions of the OLS process and Gujarati points out the multicollinearity is a consequence of the data being observed (indeed, section 10.4 of his book is entitled â€Å"Multicollinearity; much ado about nothing?†). Correlations of Variables GDP EXIM POPN ENERGY GDP 1.000000 EXIM 0.984644 POPN 0.960960 0.957558 ENERGY 0.835053 0.836279 0.914026 LABOUR -0.380830 -0.320518 -0.259193 -0.166407 Analysis of Residuals Overview The following graph shows the relationship between actual, fitted and residual values. At first glance, the residuals appear to be reasonably well behaved; the values are not increasing over time and there several points at which the residual switches from positive to negative. A more detailed tabular version of this graph may be found at Appendix 2. Heteroscedascicity To examine the issue of heteroscedascicity more closely, we will employ White’s test. As we are using a model with only three independent variables, we may use the version of the test which uses the cross-terms between the independent variables. White Heteroskedasticity Test: F-statistic 1.174056 Probability 0.339611 Obs*R-squared 10.44066 Probability 0.316002 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 04/16/08 Time: 08:24 Sample: 1960 2006 Included observations: 47 Variable Coefficient Std. Error t-Statistic Prob. C -2.99E+09 4.06E+09 -0.735744 0.4665 EXIM -49439.98 45383.77 -1.089376 0.2830 EXIM^2 -0.175428 0.128496 -1.365249 0.1804 EXIM*LABOUR -0.049223 0.047215 -1.042532 0.3039 EXIM*POPN 0.982165 0.879151 1.117174 0.2711 LABOUR -18039.83 18496.29 -0.975322 0.3357 LABOUR^2 -0.018423 0.009986 -1.844849 0.0731 LABOUR*POPN 0.344698 0.336446 1.024526 0.3122 POPN 120773.0 157305.5 0.767761 0.4475 POPN^2 -1.217523 1.523271 -0.799282 0.4292 R-squared 0.222142 Mean dependent var 2322644. Adjusted R-squared 0.032933 S.D. dependent var 3306810. S.E. of regression 3251902. Akaike info criterion 33.01368 Sum squared resid 3.91E+14 Schwarz criterion 33.40733 Log likelihood -765.8215 F-statistic 1.174056 Durbin-Watson stat 1.306019 Prob(F-statistic) 0.339611 The 5% critical value for chi-squared with nine degrees of freedom is 16.919, whilst the computed value of White’s statistic is 10.44. We may therefore conclude that, on the basis of the White test, there is no evidence of heteroscedascicity. Autocorrelation The existence of autocorrelation exists in the model if there exists correlation between residuals. In the context of a time series, we are particularly interested to see if successive residual values are related to prior values. To determine autocorrelation, Gujarati’s rule of thumb of using between a third and a quarter of the length of the time series was used. In this particular case, a lag of 15 was selected. Date: 04/16/08 Time: 08:05 Sample: 1960 2006 Included observations: 47 Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |**** | . |**** | 1 0.494 0.494 12.234 0.000 . |*** | . |** | 2 0.423 0.237 21.409 0.000 . |*. | .*| . | 3 0.155 -0.171 22.669 0.000 . | . | .*| . | 4 0.007 -0.145 22.672 0.000 .*| . | .*| . | 5 -0.109 -0.069 23.319 0.000 **| . | .*| . | 6 -0.244 -0.160 26.674 0.000 **| . | . | . | 7 -0.194 0.037 28.845 0.000 **| . | . | . | 8 -0.202 -0.004 31.247 0.000 **| . | .*| . | 9 -0.226 -0.162 34.344 0.000 **| . | .*| . | 10 -0.269 -0.186 38.859 0.000 .*| . | . |*. | 11 -0.134 0.122 40.013 0.000 .*| . | . | . | 12 -0.079 0.047 40.428 0.000 .*| . | .*| . | 13 -0.078 -0.151 40.837 0.000 . | . | . | . | 14 0.013 0.029 40.849 0.000 . | . | . | . | 15 0.041 0.018 40.970 0.000 The results of the Q statistic indicate that the data is nonstationary; in other words, the mean and standard deviation of the data do indeed vary over time. This is not a surprising result, given growth in the UK’s economy and population since 1960. A further test available to test for autocorrelation is the Breusch-Godfrey test. The results of this test on the model are detailed below. Breusch-Godfrey Serial Correlation LM Test: F-statistic 15.53618 Probability 0.000010 Obs*R-squared 20.26299 Probability 0.000040 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 04/16/08 Time: 09:23 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 9294.879 18204.51 0.510581 0.6124 EXIM 0.047292 0.092176 0.513065 0.6107 LABOUR 0.039181 0.031072 1.260967 0.2144 POPN -0.182287 0.348222 -0.523479 0.6035 RESID(-1) 0.788084 0.154144 5.112655 0.0000 RESID(-2) -0.180226 0.160485 -1.123009 0.2680 R-squared 0.431127 Mean dependent var 0.000100 Adjusted R-squared 0.361753 S.D. dependent var 1540.499 S.E. of regression 1230.710 Akaike info criterion 17.18731 Sum squared resid 62100572 Schwarz criterion 17.42350 Log likelihood -397.9019 F-statistic 6.214475 Durbin-Watson stat 1.734584 Prob(F-statistic) 0.000225 We can observe from the results above that RESID(-1) has a high t value. In other words, we would reject the hypothesis of no first order autocorrelation. By contrast, second order autocorrelation does not appear to be present in the model. Overcoming serial correlation A method to overcome the problem of nonstationarity is to undertake a difference of the dependent variable (ie GDPyear1 – GDPyear0) An initial attempt to improve the equation by using this differencing method produced a very poor result, as can be seen below. Dependent Variable: GDPDIFF Method: Least Squares Date: 04/16/08 Time: 08:17 Sample: 1961 2006 Included observations: 46 Variable Coefficient Std. Error t-Statistic Prob. C 14037.58 12694.29 1.105818 0.2753 EXIM 0.084287 0.052601 1.602398 0.1167 ENERGY 0.011470 0.011710 0.979487 0.3331 LABOUR -0.004251 0.014304 -0.297230 0.7678 POPN -0.300942 0.265082 -1.135279 0.2629 R-squared 0.207408 Mean dependent var 816.6959 Adjusted R-squared 0.130082 S.D. dependent var 657.1886 S.E. of regression 612.9557 Akaike info criterion 15.77678 Sum squared resid 15404304 Schwarz criterion 15.97555 Log likelihood -357.8660 F-statistic 2.682255 Durbin-Watson stat 1.401626 Prob(F-statistic) 0.044754 Forecasting The forecasts for the dependent variables are based on Kirby (2008) and are presented below. The calculation of EXIM for future years was based upon growth rates for exports (47% of the 2006 total) and imports (53%) separately. The two streams were added together to produce the 1950 level GDP figure, from which year-on-year increases in GDP could be calculated. The results of the forecast are shown below. The 2008 figure was felt to be particularly unrealistic, so a sensitivity test was applied to EXIM (population growth is relatively certain in the short term and calculating a forecast of labour days lost is a particularly difficult challenge). Instead of EXIM growing by an average of 1.7% per annum during the forecast period, its growth was constrained to 0.7%. As we can see from the â€Å"GDP2†column, GDP forecast growth is significantly lower in 2008 and 2009 as a result. Critical evaluation of the econometric approach to model building and forecasting GDP is dependent on many factors, many of which were excluded from this analysis due to the unavailability of data covering forty years. Although the main regression results appear highly significant, there are many activities which should be trialled to try to improve the approach: a shorter time series with more available variables: using a short time series would enable a more intuitive set of variables to be trialled. For example, labour days lost is effectively a surrogate for productivity and cost per labour hour, but this is unavailable over 40 years; transformation of variables: a logarithmic or other transformation should be trialled to ascertain if some of the problems observed, such as autocorrelation, could be mitigated to any extent. The other, more relevant transformation is to undertake differencing of the data to remove autocorrelation; the one attempt made in this paper was particularly unsuccessful! Approximate word count, excluding all tables, charts and appendices: 1,400 Appendix 1 – Scatter diagrammes of GDP against dependent variables Appendix 2 obs Actual Fitted Residual Residual Plot 1960 17460.5 15933.8 1526.78 | . | * | 1961 17816.1 16494.5 1321.57 | . | *. | 1962 17883.8 16714.1 1169.67 | . | * . | 1963 18556.7 18153.6 403.108 | . |* . | 1964 19618.0 19117.8 500.191 | . | * . | 1965 20209.7 19558.9 650.773 | . | * . | 1966 20699.1 20272.1 426.905 | . |* . | 1967 21303.1 20973.3 329.754 | . |* . | 1968 22037.1 22395.3 -358.204 | . *| . | 1969 22518.6 22824.6 -305.982 | . *| . | 1970 23272.7 23147.8 124.912 | . * . | 1971 23729.9 23395.8 334.070 | . |* . | 1972 24806.3 22418.6 2387.67 | . | . * | 1973 26134.9 27249.5 -1114.60 | . * | . | 1974 25506.2 28880.9 -3374.64 | * . | . | 1975 25944.6 28401.8 -2457.14 | * . | . | 1976 26343.7 30306.2 -3962.47 |* . | . | 1977 26468.8 29829.1 -3360.31 | * . | . | 1978 28174.4 29922.0 -1747.61 | * | . | 1979 29232.7 27846.9 1385.71 | . | *. | 1980 28957.2 29271.0 -313.855 | . *| . | 1981 28384.0 29590.8 -1206.86 | .* | . | 1982 28626.2 29526.2 -899.933 | . * | . | 1983 29915.3 30883.9 -968.627 | . * | . | 1984 30531.7 29677.7 853.960 | . | * . | 1985 31494.3 33289.4 -1795.09 | * | . | 1986 32748.5 33293.0 -544.520 | . * | . | 1987 34609.2 34223.2 385.976 | . |* . | 1988 36842.2 34669.4 2172.76 | . | . * | 1989 37539.8 35938.6 1601.20 | . | * | 1990 37187.7 35988.5 1199.22 | . | *. | 1991 36922.2 35080.4 1841.84 | . | .* | 1992 37116.4 35793.7 1322.74 | . | *. | 1993 38357.7 38051.2 306.418 | . |* . | 1994 39696.7 39790.8
Saturday, January 18, 2020
Relationships between Satisfaction with Life Essay
 Abstract            Satisfaction with life is a concept highly valued in today’s society. In an effort to understand mechanisms behind the life satisfaction, present study investigated the relationships between social network size, optimism and conscientiousness and the outcome variable, satisfaction with life. The four variables were measured through the use of a survey. Participants were students from California State University, Fullerton. A correlational analysis of the data showed a significant positive relationship between optimism, and conscientiousness and satisfaction with life. It was also found that individuals with large social network size were more satisfied than those with small social network size. These findings imply that improving levels of optimism and conscientiousness and increasing one’s social network can insure greater life satisfaction. Relationships between Satisfaction with Life, Social Network Size, Optimism, and Conscientiousness            Satisfaction with life is most often one of the greatest concerns of an individual’s life. There is a general belief that an inability to achieve satisfaction with one’s life indicates an unsuccessful life. Because of this socially generated drive for satisfaction with life, one is made to wonder. What factors are related to the experience of life satisfaction? What variables are good predictors of life satisfaction?            One suspected variable that would act as a good predictor of satisfaction with life is social network size. A social network refers to an individual’s link or relationship with other individuals. This link can cause certain social behavior to be explained (Mitchell, 1969). Quinn, Gavigan, and Franklin (1980) defined social networks to be the social units an individual is placed in contact with. Quinn et al. (1980) studied the effects of social network interaction on life satisfaction in older adults. The findings indicated that social network interaction was not a good predictor of satisfaction with life.            Another study conducted by Bowling, Farquhar, ands Browne (1991) indicated that social network size is a poor indicator of life satisfaction. The study involved the participation of two types of individuals – those who lived in rural neighborhoods and those who lived in urban neighborhoods. Bowling et al. (1991) noted larger reported social network sizes for individuals in the urban areas as opposed to those in the rural areas. Despite this difference in reported social network sizes, life satisfaction between the two groups was not found to be different. This may, however, have been a result of difference in the levels of interaction available to individuals residing in the two areas. The insignificant findings may have been a result of the inherent differences between neighborhoods and therefore not representative of the social network size of a given individual.            Optimism is a second variable deemed to be related to feelings of satisfaction with life. A greater sense of optimism allows one to maintain an outlook on life that allows for the consideration of the world as a generally positive place. Research exploring the life satisfaction felt by retired physicians showed that greater optimism resulted in a greater satisfaction with their life. (Austrom, Perkins, Damush, and Hendrie, 2003) In retired individuals, especially, optimism may be an essential variable for achieving life satisfaction as it may also be a coping mechanism to the sudden change in lifestyle for the said individuals. The retired physicians felt that the greatest challenge going against their satisfaction with life was in the loss of their professional roles, thus, optimism might have served as a form of mediation between the two stages of the transition. Having a positive outlook on the way their lives was going allowed these physicians to better accept the end of their professional careers and to look forward to the beginning of their retired life. The probable importance of optimism as a mediator was also evidenced by the fact that in the same study by Austrom et al. (2003) it was found that optimism didn’t play as significant a role in determining life satisfaction when it came to the physician’s wives. This may have been due to the fact that they did not need to maintain a positive outlook to boost a sudden change in life roles.            Optimism and not pessimism, which involves having a negative outlook on life, is found to be a greater predictor of life satisfaction. This was specifically found by a study conducted by Chang & Sanna (2003). Thus in the present study, only the variable of optimism will be taken into consideration and not its counterpart, pessimism.            Another variable that may show a relationship with an individual’s satisfaction with life is the personality trait of conscientiousness. Conscientiousness has been investigated by many researchers in terms of how well it predicts an individual’s life satisfaction. This trait refers to an individual’s tendency to be organized, diligent and reliable in their behavior. (Chapman, Duberstein, and Lyness, 2007) Conscientiousness may have a role to play in satisfaction because conscientious individuals are able to have more mature defenses and are also able to have a quality of life that is considered by most to be above par as they are able to have more responsibility and control over their health, their social interactions, and their general well-being (Chapman et al, 2007). It may well be that the same link can be found between conscientiousness and life satisfaction. If higher levels of conscientiousness indicate higher quality of life, it may also indicate greater degree of satisfaction with life as a result of the same mechanisms.            A study by Lounsbury, Saudarga, Gibson, and Leong (2005) examined just this relationship. Through an inspection of the personality characteristics accounted for in the Big Five, it was found that conscientiousness along with extroversion, agreeableness, neuroticism, and openness to experience account for 45% of total perceived life satisfaction. Is conscientiousness, then, as a variable independent of the other personality traits in the Big Five, significantly related to satisfaction with life?            The present study aims to investigate the relationships that exist between satisfaction with life and the three variables stated above: social network size, optimism, and conscientiousness. Based on the empirical evidence provided by past literature, it is predicted that satisfaction with life will be significantly correlated to optimism and conscientiousness. A greater level of optimism and conscientiousness in an individual will indicate a greater satisfaction with life. Also, social network size is hypothesized to have no significant difference on satisfaction with life. The last hypothesis is based on the findings of past literature. However, due to the questionable nature of past studies and how these measured social network against life satisfaction, the present study’s hypothesis may turn out to be negated. It is hypothesized, then, that the variables of optimism and conscientiousness will have a significant and direct relationship with satisfaction with life while that of social network size will have no significant difference on satisfaction with life. Methodology Participants            The participants of the study totaled 91 students, 23 (25.3%) of whom were male and 68(74.7%) of whom were female. (See Table 1 in Appendix for tabulated figures) All the participants were enrolled in Research Method in Psychology classes at the California State University, Fullerton. The ethnicity break down of the participants is the following: African American – 1.1%, Asian (Pacific Islander) – 3.3%, Caucasian – 49.5%, Hispanic – 27.5%, Middle Eastern – 2.2%, Southeast Asian – 2.2%, multiethnic – 11%. 3.3% of the participants reported to having other types of ethnicity. (See Table 2 in Appendix for tabulated figures) The range in ages of the participants was from 19 years to 46 years. The mean age was 23 years old. (See Table 3 in Appendix for tabulated figures) None of the participants received incentive for their participation. There were no extra credits or monetary compensations given in exchange for their contribution to the study. Materials or Measures            Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). The PANAS measures trait negative affect or the affective well being of the participants. It is composed of a 10-item scale designed to measure typical experiences of negative affect. Participants are able to rate the extent to which they experience certain mood states such as distressed, upset, scared, and irritable. They are able to do this through the indicators of a 5-point scale (very slightly or not at all, a little, moderately, quite a bit, extremely). The participants were asked to indicate to what extent they felt each feeling or emotion listed during the past two weeks from the time of the survey.            Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen & Griffin, 1985) The SWLS is a global measure for subjective well-being and life satisfaction. Diener et al (1985) defined life satisfaction as a conscious cognitive judgment life. This entails an individual’s comparison of their own life experiences with a self-set standard. The scale is composed of 5 items and utilizes a 7-point Likert-type scale (1-strongly disagree to 7-strongly agree).  The items of the test included statements such as â€Å"The conditions of my life are excellent†and â€Å"If I could live my life over, I would change almost nothing.†Possible total scores range from 5 to 35. A resulting score ranging from 5 to 19 signifies dissatisfaction while scores between 21-31 signify satisfaction.            Life Orientation Test (LOT-R; Scheier, Carver, & Bridges, 1994) The LOT-R measures generalized optimism. The test is made up of 10 items. Participants will indicate the extent to which they agree with the 10 statements in the test through a 5-point Likert-type scale (0-strongly disagree to 4-strongly agree). The statements involved sentiments like â€Å"in uncertain times, I usually expect the best†. A participant can achieve a score from 0 to 24 with a higher score indicating greater levels of optimism.            Big Five Inventory (BFI; John, Donahue, & Kentle, 1994) The BFI was used to assess the personalities of the participants with regards to the five aspects included in the big five namely extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The BFI consists of 44 items that ask the participants to rank themselves on a 5-point Likert-type scale (1-disagree strongly to 5-agree strongly). The 44 items deal with different types of behavior related to the Big Five. For the present study, the BFI will be used to measure the variable of conscientiousness. BFI items related to conscientiousness included â€Å"perseveres until the task is finished†, â€Å"is a reliable worker†, and â€Å"does things efficiently†. Lubben Social Network Scale (LSNS-6; Lubben &Gironda, 2003) The LSNS-6 is a test of a set of questions establishing ties with relatives and ties with non-relatives. Examples of these questions include â€Å"How many relatives do you see or hear from at least once a month?†and â€Å"How many friends do you see or hear from at least once a month? The participant chooses one of the options available for each question. These answers have corresponding points. Total scores of the participants may range from 0 to 30. A higher score indicates a higher level of social network. For this research, the LSNS-6 was used to measure social network size. A high score in the LSNS-6 was taken to signify a larger social network size while a low score meant a smaller social network size. Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1989) The RSES measures global self-esteem. The test is composed of 10 items. Responses are given on a 5-point Likert-type scale (1-strongly disagree to 5-strongly agree). A sample question is â€Å"At times I feel like I am no good at all.†Subjective Happiness Scale (SHS; Lyubominsky, and Lepper , 1999) The SHS measures global subjective happiness. The test consists of four items. Responses are given on a 7-point Likert-type scale (1-7). A sample question is â€Å"Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?†Gratitude Questionnaire (GQ; McCullough, Emmons, and Tsang, 2002) The gratitude questionnaire is a self-report test measuring global gratitude. The test consists of six items. Responses are given on a 7-point Likert-type scale (1-stronlgy disagree to 7-strongly agree). A sample item is â€Å"If I had to list everything that I was grateful for, it would be a very long list.†Tendency to Forgive Scale (TTF; Brown, 2002) The TTF is a test measuring global forgiveness tendencies. The test consists of four items. Responses are given on a 7-point Likert-type scale (1-strongly disagree to 7-strongly agree). A sample item is â€Å"I tend to get over it quickly when someone hurts my feelings.†Procedure Questionnaires were handed out to all participants in their respective classrooms of Research Method in Psychology at the California State University, Fullerton. Participants were given instructions as a group and were told that participation in this study would be anonymous. It was also stated that they may voluntarily choose to participate and could withdraw at anytime. The whole session took about 15-30 minutes. Participants were provided informed consent prior to the administration of the test and were debriefed after they finished. Results The results showed that individuals’ with a smaller social network size (mean=4.10) were significantly less satisfied compared to those with life larger social network size (mean=5.05; t(89)= -3.79, p.001). There was a noted positive correlation between optimism and satisfaction with life (r = 0.543, p = 0.01). A positive correlation was also found between conscientiousness and satisfaction with life (r = 0.222, p = 0.05) Discussion            The main purpose of this research was to establish whether a relationship existed between satisfaction with life and optimism, and satisfaction with life and conscientiousness. Another purpose was to establish whether social network size made a difference to satisfaction with life. The original hypothesis of the study stated that a significant positive relationship would be found between satisfaction with life and optimism as well as between satisfaction with life and conscientiousness. It was also hypothesized that social network size would not have a significant difference on satisfaction with life. The hypotheses of the present study were based on the findings of past researches. (Quinn et al, 1980; Bowling et al, 1991; Austrom et al, 2003; Chang and Sanna, 2003; Chapman et al, 2007; Lounsbury et al, 2005) The results of the current study show that there is a significantly positive relationship between satisfaction with life and two variables it was compared against, namely, optimism and conscientiousness. Results also showed a significant difference with social network size and satisfaction with life. The initial hypotheses for optimism and conscientiousness were supported. The hypothesis regarding social network size, however, was rejected by the statistical results. The findings on optimism and conscientiousness validate past research findings. These showed that greater optimism in life contributed to greater satisfaction with life (Austrom et al., 2003). Optimism was also found to be a good predictor of life satisfaction (Chang and Sanna, 2003). Past findings established conscientiousness to be a contributing factor to life satisfaction (Lounsbury et al, 2005) as well as a variable directly related to higher quality of life ratings (Chapman et al., 2007). The findings on social network size, on the other hand, disagree with past research findings where social network interaction was not found to be related to life satisfaction (Quinn et al., 1980) and where the size of the individual’s social network was determined to be a bad predictor of life satisfaction (Bowling et al., 1991). Optimism may be able to affect life satisfaction positively due to the fact that a positive outlook on life can also cause a better assessment of past experiences not just of present circumstances. If one is able to achieve a better disposition towards life, the tendency to overlook the negativity that will detract from satisfaction felt towards life will be greater. This shows that the statistical significance of optimism (r=0.543, p=0.01) with satisfaction with life is warranted. Conscientiousness, on the other hand, was also positively correlated to (r=0.035, p=0.05) with satisfaction with life. This may be due to the fact that conscientiousness indicates a better ability to handle life experiences. Conscientiousness, as defined in the Big Five Inventory (Donahue et al., 2001), entails caution, dependability, organization and responsibility. These characteristics when applied to the everyday behavior and experiences of an individual are most likely to indicate an individual who achieves success. People who are more cautious, more dependable, more organized, and more responsible are the ones who are achievers in human society. It may be that the success and achievement linked with conscientious people is also the link that sustains their satisfaction with life. This is not to say that individuals deemed to have low conscientiousness are not likely to feel satisfaction with life. The findings only suggest that a high level of conscientiousness predicts life satisfaction to great extent. The discussion of how social network size is related to satisfaction with life should be done with care. The fact that previous research found no significant difference between social network and life satisfaction may have been due to the inadequacy of measurement with the past research. Quinn et al. (1980), for example, concentrated on the interaction that occurred in social network and not size. This meant that Quinn et al. (1980) focused on the quality of the individual’s social network and not on the quantity. Bowling et al. (1991), on the other hand, compared two different localities and this is what might have caused the inconsistencies in their findings concerning social network size and life satisfaction. Inherent characteristics of urban and rural locations could have played into action and caused the insignificant findings. For the present study, however, the significant difference between social network size and life satisfaction makes sense especially because social network size is also an indicator of an individual’s degree of social interaction as well as sources of social support; both of which are essential in an individual’s development. The findings of this study are limited because of the small sample size used. A bigger sample that is more representative of the general population should be used in future research. In addition, only a few variables concerning satisfaction with life were investigated. Future research should incorporate more variables that may affect life satisfaction into the study. The variables of social support, social interaction, and pessimism are a few of the factors that should be investigated. The significant relationship between social network size and life satisfaction should also be validated by future studies as the results in this study are not in agreement with previous works. The implications of the study are far-reaching. Establishing the relationships existing between life satisfaction, optimism, and conscientiousness allows different clinicians and practitioners in the healthcare system a chance to improve their handling of clients with low satisfaction with life. This may most likely involve older adults. Because satisfaction with life in itself is a concept that health-care workers find hard to deal with, finding other personality traits and variables that are related to it enables these workers an alternative in aiding these types of patients. Increasing optimism and improving conscientiousness in an individual can help to increase their satisfaction with life. In addition, increasing the size of the client’s social network will improve their satisfaction with life. The present study’s findings can also be expanded to teachers in the field of education. Satisfaction of their students can be increased by allowing them to feel more optimistic about their activities also by guiding them to be more conscientious in their behavior. Also, increasing opportunities for students to enlarge their social networks can also help these students improve their feelings of satisfaction with life. References Austrom, M.G., Perkins, A. J., Damush, T. M., & Hendrie, H. C. (2003). Predictors of life satisfaction in retired physicians and spouses. Social Psychiatry & Psychiatric Epidemiology, 38, 134-141 Bowling, A., Farquhar, M., & Browne, P. (1991). Life satisfaction and associations with social network and support variables in three samples of elderly people. International Journal of Geriatric Psychiatry, 6, 549-566 Brown, R. (2003). Measuring individual differences in the tendency to forgive: construct validity and links with depression. Society forPersonality and Social Psychology, 29, 759-771 Chang, E.C., & Sanna, L. J. (2003). Optimism, accumulated life stress, and psychological and physical adjustment: is it always adaptive to expect the best? Journal of Social and Clinical Psychology, 22, 97-115. Chapman, B., Duberstein, P., & Lyness, J. M. (2007). Personality traits, education, and health-related quality of life among older adult primary care patients. Journals of Gerontology: series B psychological sciences and social sciences, 62B, 343-352 Diener, E., Emmons, R., Larsen, R. J., & Griffin, S. (1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49, 71-75. John, O. P., Donahue, E. M., & Kentle, R. (1991). The â€Å"Big Five†Inventory – Versions 4a and 54.  Technical Report, Institute of Personality Assessment and Research, Berkeley, CA: University of California, Berkeley. Lounsbury, J. W., Saudarga, R. A., Gibson, L. W., & Leong, F. T. (2005). An investigation of broad and narrow personality traits in relation to general and domain specific live satisfaction of college students. Research in Higher Education,46, 707-729 Lubben, J. E., & Gironda, M. W. (2003a). Centrality of social ties to the health and well-being of older adults. In B. Berkman & L. K. Harooytan (Eds.), Social work and health care in an aging world (pp. 319-350). New York: Springer Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46, 137-155. Mancini, J. A., Quinn, W., Gavigan, M. A., & Franklin, H. (1980). Social network interaction among older adults: implications for life satisfaction. Human Relations, 33, 543-554 McCullough, M. E., Emmons, R. A., & Tsang, J. (2002). The grateful disposition: A conceptual and empirical topography. Journal of Personality and Social Psychology, 82, 112-127. Mitchell, J. C. (1969) The concept and use of social networks. In Social Networks in Urban Situations: Analysis of Personal Relationships in Central African Towns Ed. J.C. Mitchell. Manchester: Manchester University Press Rosenberg, Morris. (1989). Society and the adolescent self-image. Revised edition. Middletown, CT: Wesleyan University Press. Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the Life Orientation Test. Journal of Personality and Social Psychology, 67, 1063-1078. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54,1063-1070.
Friday, January 10, 2020
Opinion Paper: Gender Reassignment
Human Sexuality/Gender Re-Assignment This is an Opinion Paper Transgender is an umbrella term for persons whose gender identity, gender expression, or behavior does not conform to that typically associated with the sex to which they were assigned at birth. Gender identity refers to a person’s internal sense of being male, female, or something else; gender expression refers to the way a person communicates gender identity to others through behavior, clothing, hairstyles, voice, or body characteristics. â€Å"Trans†is sometimes used as shorthand for â€Å"transgender. While transgender is generally a good term to use, not everyone whose appearance or behavior is gender-nonconforming will identify as a transgender person. The ways that transgender people are talked about in popular culture, academia, and science are constantly changing, particularly as individuals’ awareness, knowledge, and openness about transgender people and their experiences grow. After you do your research and readings, in a one page opinion paper, state your position on Gender Re-Assignment. Chaz Bono brought this important and once hidden topic to the forefront. Write about your position.Be sure you add a reference page in APA format and use in-text citations when using the authors words. Writing an opinion paper for college can be a daunting task for college students. Opinion papers by nature require you to state an opinion or take a position on a specific issue and defend that position. An opinion paper is not, however, license to rant and rave about an issue without providing any evidence. This paper will involve research, organization, and planning to be effective. ? 1. Research your topic. Stating an opinion in a paper involves more than reporting what you think.You'll often hear these assignments referred to as position papers, as you must take a reasonable position based on evidence that is grounded in fact. Determine your opinion based on your research, and kn ow your topic thoroughly on both sides of the argument before you state it and attempt to provide any type of reasoned defense. ? 2. ?Create an outline for your paper consisting of the three main parts that you'll follow when crafting your document: an introduction, body, and conclusion. In your introduction, state your opinion on the topic at hand. This will set the stage for the rest of the paper.You can even hint at the reasons for your opinion, but don't give them away entirely. You'll want the reader to glean that information from the rest of your paper. The body of the outline should list the reasons for your position. The conclusion should clearly and concisely sum up your argument. ? 3. ?Construct your essay using your outline. State your opinion in your introductory paragraph and then use your list of reasons for stating your opinion in the body of your essay. The body should include three to five substantive reasons why you have taken your position.Provide the most compell ing reasons last and the least compelling reasons first. This will give your paper a crescendo effect and drive home the point of your essay. ? 4. ?Write a definitive conclusion. Your conclusion is your opportunity to restate what you've already state in your introduction and in the body of the paper. Your conclusion should reiterate the main conclusion of your essay based on the facts that you've provided. Go over each point briefly, but be careful not to simply chronicle them without tying them together. The grading criteria will be the same as in other modules.
Thursday, January 2, 2020
The Problem Of Hip Hop Music - 1721 Words
In the past 2 years alone, more than 3 dozen criminal prosecutions have had rap lyrics be presented by the prosecutor as a vital piece of evidence in the case. [Manly] In many of these cases the prosecutor will use violent lyrics created by the defendant to prove that that person is inherently dangerous and in other cases, will use the lyrics as a direct confession to having committed the crime in question. If you read into these cases you will notice a common theme between them; the defense will claim that the lyrics are freedom of speech and should be considered artistic expression, while the prosecutors will claim that the song is either a criminal threat or has too many similarities to the actual crime that it should be considered a confession. The primary basis for this debate is that hip-hop music is a misunderstood cultural practice because of the racial divide between the defendant and the criminal justice system. The precedent for using rap lyrics as evidence can be traced a s far back as 1994 in California, when they were used to prove that Francisco Calderon Mora was a member of the Southside F Troop gang. The prosecutor went on to say that since he was a member of the gang, Mora has the motive and intent to commit the killing he was tried for. [O’Connor] Since this case many prosecutors through the years have started using lyrics as critical pieces of evidence in these types of trials. Often using these pieces as evidence is unfair to the defendant because theyShow MoreRelatedThe Problem Of Hip Hop Music952 Words  | 4 PagesCultural Expression Music has inspired and touched the lives of many aspiring hip-hop artists which makes it one of the most influential characters in America culture. Hip-hop has become one of the most vital, and profitable, forces in popular culture. Rap music is an international art form and is regularly heard in advertising including radio and television. 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