...Ego Defence Mechanisms Introduction Ego psychology embodies a more optimistic and growth oriented view of human functioning and potential than do the earlier theoretical formulation. It generated changes in the study and assessment process and led to an expansion and systemization Of interceptive strategies with individuals. It fostered a re-conceptualization of the clinic worker relation ship, of change mechanisms, and of the interventive process. It helped to refocus the importance of wok of with the social environment as well as work with the family and the group. Moreover, it has important implications for the design of service delivery, large-scale social programs, and social policy. DEFINITION OF DEFENCE MECHANISM Ego-defense mechanisms are learned, usually during early childhood and are considered to be maladaptive when they become the predominant means of coping with stressors. What is EGO psychology? Ego psychology comprises a related set of theoretical concepts about human behavior that focus on the origins, development, structure, and functioning of the executive arm of the personality _the ego_ and its relationship to other aspects of the personality and to the external environment. The ego is considered to be a mental structure of the personality responsible for negotiating between the internal needs of the individual and the outside world. The following seven propositions characterize ego psychology’s view of human...
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...University of Phoenix Material Week 4 Review Worksheet Psychodynamic Theories Complete the following table. |Theorists |Main tenets of theory |Unique contributions |Limitations | |Freud | | | | | | | | | | | | | | | | | | | |Jung | | | | | | | | | | | | | | | | | | | |Adler | | |...
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...Repression occurs when a person unconsciously holds back unwanted or stressful emotions to protect themselves from reliving or acknowledging an experience. A subject can repress certain thoughts in whole or partially depending on the extent of the trauma. While attending this class I have realized that since my early teens, I have been repressing emotions that stem from sexual abuse by a person who was both a family friend and our church pastor. This realization was brought to the forefront because my wife and children enjoy attending church services and participating in church related activities, while I will make up excuses to not attend. My lack of attendance at church is a frequent subject in our household and the cause of more than a few heated discussions. The thought of going to a church service makes me uneasy and I do it only after much prodding from my wife, but even then I only go for a couple of weeks before I start making excuses again. It was after one of these discussions that I decided to use the things I had read and try to understand why I don’t like going to church. That’s when I recognized the correlation between the abuse and my lack of interest in church. Now that I recognize my apprehension toward going to church services is because I’m trying to avoid the emotions from the abuse I am going to re-evaluate my views on attending church services. Who knows, maybe I’ll like going and that will make my wife happy and relieve some stress between us. I’m not...
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...Mechanism Denial: when my grandfather passed away, I was told about it while I was still in university and after hearing it I went back to the lounge and laughed with my friends as if nothing happened. Projection: I’ve been wanting to eat healthy and stop eating junk food but I still do eat junk food and every time I see my brother having junk food I lecture him about how unhealthy and bad it, same thing happens when he asks for it. Repression: once my parents were having a fight and I just put my head phones on and started watching a movie as if nothing was happening and to this day I remember doing it but not to the extent that my sister remembers it. She says that my parents almost had a divorce but I remember it as a normal fight. Regression: when my younger brother was born. I started wanting to drink from the bottle again and sleep next to my parents. Reaction Formation: In school a lot of my classmates were super religious and had a strong opinion on praying like a person should force themselves to pray so they can be good Muslims but I don’t agree with that, I do miss prayers because I feel like a person should want to pray and it’s more than just getting the “job” done but I never said anything and would agree with them. Displacement: once before a wedding I didn’t like the way I did my hair and when my sister came to help me with my makeup I started screaming at her and telling her she’s doing things wrongly and it was because of her I didn’t want to go to the...
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...Intercontinental University Unit 5 Individual Project BUSN311-1301B-10: Quantitative Methods and Analysis Instructor Leonidas Murembya April 23, 2013, Abstract This paper will be discussing regression analysis using AIU’s survey responses from the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online). Introduction Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013). Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.018314784 R Square 0.000335431 The portion of the relations explained Adjusted R Square -0.009865228 by the line 0.00033% of relation is Standard Error 1.197079687 Linear. Observations 100 ANOVA df SS MS F Significance F Regression 1 0.04712176 0.047122 0.032883 0.856477174 Residual 98 140.4339782 1.433 Total 99 140.4811 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept...
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... * Case 1 -- If you know population variances, use this and Normal table * Case 2 -- If you know only sample variances, and samples are large, use this and Normal table * Case 3 -- If you know only sample variances, and samples are small, and unknown population variances can be assumed identical, use this and t-table, with n1 + n2 - 2 df. (sp is called “pooled estimate of σ”) * We use standard error of difference to compute (actual) t Hypothesis Testing – Means Of Dependent (Paired) Samples tActual * pooled estimate of population proportion Regression and Correlation Simple Linear Regression (Only 1 independent variable, and linear relationship) Regression Coefficients Using Method Of Least Squares, we get: Standard Error Of Estimate Correlation: * Variation of y around the regression line * Variation of y around its own mean * Coefficient of Determination Direct Computation of r: Chi-Square * Make working table as follows: * List observed frequency cells, fo , in 1st column. * Compute expected frequency, fe , for each cell, and write in 2nd column. * fe = RT*CT/n where RT = row total, CT = column total, n = total no of observations in all cells of data table. * Compute (fo – fe ) for each row of working table in column 3 * Compute (fo – fe )2 for each row of working table in column 4 * Compute (fo – fe...
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...Center for Demography and Ecology University of Wisconsin-Madison The Effects of Changing Family Structures on Higher Education for Black and White American Cohorts: 1908-1969 Wendy Y. Carter CDE Working Paper No. 96-22 The Effects of Changing Family Structures on Higher Education for Black and White American Cohorts: 1908-1969 CDE Working Paper No. 96-22 Wendy Y. Carter, Ph.D. Assistant Professor of Sociology Department of Social and Behavioral Sciences Arizona State University West 4701 West Thunderbird Road P.O. Box 37100 Phoenix, AZ 85069-7100 Submission for Population Association of America 1999 Annual Meeting Abstract Social scientists have been concerned with the effect of social origins on educational attainment since the early days of the discipline. One important aspect of social origins that continues to occupy the interest of researchers and the public is the family. The issue of race has also been central to this concern. Recent demographic changes in mortality and marriage behavior have had a profound impact on the increasing proportion of children who will reach age eighteen without both biological parents. This research investigates the effects of trends in family and household structures on the educational attainment for recent black and white cohorts in the United States. We know from previous cross-sectional reports that those who grow up with both biological parents are more likely to attain higher levels of education than those...
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...Generally regression analysis is used with the regression equation. Here a linear equation is created to predict the Body fat (%) using 3 predictors or also known as independent variables which are the chest circumference, abdomen circumference and weight. From the result a line equation is formed , that is in the in form of (Y) = a + bx Where : (Y) is the average predicted value that is the variable which need to be estimated for any value of x. X is the independent variable and at each time a different predictor (independent variable) is used that is chest circumference, abdomen circumference and weight. A is the Y intercept . This is the Y value (body fat %) when x = 0 B is the slope of the regression line . This slope measures the average change in Y (the body fat %) for each unit change in x ( chest circumference, abdomen circumference and weight) The following are the result of the regressing which were attach after the result. Result 1 : Predicting Body Fat % Using chest circumference (Y) = -51.72 +0.697 X The slope of the equation is 0.697. This means that for every one unit increase in the chest circumference the body fat % will increase 0.697 unit. Result 2 : Predicting Body Fat...
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...(ranch, two floor, or tri-level), garage, basement, fireplace, age of the home, and the school district which will be our independent variables. Out of these, we have decided to pay close attention to the square feet (home size of 1900 square feet or more), number of bathrooms (3 or more), heat (gas forced), basement, and the age of the home (less than or equal to 10 years). We chose these factors because we wanted to know what kind of relationship, if any, they have with the price of the home. To figure that out, we will be doing a series of tests to discuss the price difference for the independent variables. Then we will get the probability and confidence interval for price in relation to our criteria which will lead us to simple linear regression correlations and fitted line plots. Whenever one is buying a home or selling a home, the most important element that they will take into consideration is the price of the home and how it is affected by other variables in the home. The first thing we would like to figure out is the mean price...
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...and logarithm of cap to determine sensitivities for stock-specific factors. After we got all 13 sensitivities, we did a weighted regression of the 13 sensitivities against the 1000 stock’s forward return in 60 periods, respectively. For each period, we got 13 coefficients, which were the risk premiums for each risk factor. Finally, we regressed the risk premium from our regression against the portfolio return, bench return and active return. Doing so, we get these returns’ sensitivities against risk factors and were able to determine our portfolio’s exposure to various risk factors. 2. Procedures and Analysis 2.1. Working Procedure Nearly all of the calculations in part II were done by VBA Macros. In the Project part2.xlsm, there were 6 worksheets. The Data recorded all the cross-sectional data for 60 periods, having 1000 stocks in each period. StockPriceData recorded the data for stocks in our portfolio. To run the regression, we assumed that for a particular stock, the forward return in a particular period was, rt+1=bisec10λsec10+…+bisec55λsec55+bibetaλbeta+bilnbpλlnbp+bilncpλlncp+e . The equation has no intercept, the risk premium for sector factor has already included the zero beta rate. To prevent stocks with smaller market capitalization from being overweighed by stocks with larger capitalization, we should run a weighted regression. In WeightedFactors, we calculated the z-score for “logarithm of b_p” and “logarithm of cap”. Plus beta and 10 sector factors,...
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...1. Module Name: Introductory Econometrics Code: P12205 Credits: 10 Semester: Spring 2011/12 Delivery: 16 one-hour lectures + 4 one-hour workshops Aims: The main aims of this module are: to introduce students to the principles, uses and interpretation of regression analysis most commonly employed in applied economics; to provide participants with sufficient knowledge of regression methods to critically evaluate and interpret empirical research. On completion of this module students should be able to: demonstrate understanding of the assumptions and properties underlying regression analysis and the principle of ‘least squares’; interpret and manipulate the coefficients of multiple regression and performance criteria; conduct diagnostic checking of the validity of regression equations coefficients; appreciate the problems of misspecification, multicollinearity, heteroscedasticity and autocorrelation. Content: 1. Simple Regression Analysis 2. Multiple Regression Analysis 3. Dummy Variables 4. Heteroscedasticity 5. Autocorrelation Main Textbook: Dougherty, C. (2011). Introduction to Econometrics, 4th edition, Oxford. 2. Module Name: Computational Finance Code: P12614 Credits: 10 Semester: Spring 2011/12 Programme classes: 12 1-2 hour lectures/workshops Aims: The module aims to describe and analyse the general finance topics and introduces students to implement basic computational approaches to financial problems using Microsoft Excel. It stresses...
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...Julian LaFarge Labor Force and Employment 06/07/2012 Employment, Social Capital, and Health: An Empirical Analysis The United States labor force has been undergoing massive changes in over the past 30 or so years. More people are working as the population continues to rise, but even more so people with more diverse backgrounds and demographics are entering the work force or clamoring for jobs, as how the economy is currently structured seems to predict that individuals who would like to enter the labor force won’t be able to. Sociologists White and Cunneen contend that “structural unemployment and underemployment, privatization of state services and withdrawal of income support” (White and Cunneen) inherently exist in neo-liberal ideologies, making this disparity between the employed and the unemployed seemingly unavoidable. With this increase in size and diversity of the labor force and the accompanying restrictions to entry, the effects of employment merit analysis. One could possibly make the argument that of employment's causal relationships, perhaps none are more important than employment's affect on one's health. Indeed, “occupational conditions have consequences for physical health outcomes” (Wickrama Lorenz 363). Further, “A central theoretical proposition of sociology states that social structural positions have dramatic effects on life...
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...Forecasting Methods Genius forecasting - This method is based on a combination of intuition, insight, and luck. Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts are based exclusively on intuition. Science fiction writers have sometimes described new technologies with uncanny accuracy. There are many examples where men and women have been remarkable successful at predicting the future. There are also many examples of wrong forecasts. The weakness in genius forecasting is that its impossible to recognize a good forecast until the forecast has come to pass. Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream science generally ignores this fact because the implications are simply to difficult to accept. Our current understanding of reality is not adequate to explain this phenomena. Trend extrapolation - These methods examine trends and cycles in historical data, and then use mathematical techniques to extrapolate to the future. The assumption of all these techniques is that the forces responsible for creating the past, will continue to operate in the future. This is often a valid assumption when forecasting short term horizons, but it falls short when creating medium and long term forecasts. The further out we attempt to forecast, the less certain we become of the forecast. The stability of the environment is the key factor in determining whether trend extrapolation is an appropriate forecasting...
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...– ceotenˆ2 15. profmarg – profits as % of sales We would like to investigate how CEO compensations are determined. Use lsalary as dependent variable to conduct the analysis. Include a constant, age, college, grad, comten, ceoten, lsales, profits, lmktval as independent variables. Please note that a one unit change in ln(z) can be interpreted as a 100% change in z. 1. Assess the overall goodness of fit the model. (You could imagine how “bad” this model should be given your prior belief that CEO compensations were “out of whack.”) Answer: The regression output is as follows. As can be seen, the R2 is very low, 0.355378, which means only about 35% of the sample variation in lsalary is explained by the model; so the model does not fit well. 1 Dependent Variable: LSALARY Method: Least Squares Sample: 1 177 Included observations: 177 Variable C AGE COLLEGE GRAD COMTEN CEOTEN LSALES PROFITS LMKTVAL R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient 4.781665 7.72E-05 -0.065452 -0.094189 -0.010608 0.016701 0.191536 6.41E-05 0.083520 0.355378 0.324681 0.498046 41.67239 -123.1536 11.57722 0.000000 Std. Error 0.514250 0.005289 0.233712 0.079397 0.003724 0.005746 0.040237 0.000149 0.062977 t-Statistic 9.298328...
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...Semester Project: What does it mean to be an Operations Manager in The International Environment Fedorenko Danil 21.02.2013 Introduction Many of the techniques used in Operations Management are essential for the international business consultant. These tools allow consultants to advise companies on selecting the best locations for their facilities, determining the most efficient balance of employees to comprise their global workforce, customizing products to satisfy specific local needs, maximizing the efficiency of the global supply chain, and determining the necessary capacity of local operations. In order to use these tools in a multinational context the operations manager must have a solid grasp on global issues. He or she must be familiar with the firm's overall international strategy and the reasoning that went into developing it. He or she must be must familiar with the critical success factors involved in selecting site locations, the process by which an international corporate strategy is formed, and the changes in the organization that will need to take place in order to implement it. Without a grasp of these concepts the operations manager will not be able to use the tools of operations management to help his or her firm establish a presence in new markets. How OM Techniques can help International Consultants to become better Managers The operations aspect of international consulting should start with a SWOT analysis, an examination of Strengths, Weaknesses...
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