Regression analysis - Free Essay Example

Sample details Pages: 14 Words: 4185 Downloads: 10 Date added: 2017/06/26 Category Statistics Essay Did you like this example? CHAPTER-14: INTRODUCTION TO REGRESSION ANALYSIS CONCLUSION In a data set of bivariate distribution, there present a set of pairs of observations where each pair of the observations is expressed with numerical values of two variables. Telling alternatively, the bivariate distribution is intended in finding or analyzing relationship between two variables under study. In any scientific studies, the basic interest of the researchers is to find out the possible co-movement of two or more than variables under study. Don’t waste time! Our writers will create an original "Regression analysis" essay for you Create order In the process of co-movement determination, there exist two important statistical tools popularly called as correlation analysis and regression analysis. Correlation analysis simply, is a measure of association between two or more variables under study. Where as regression analysis examine the nature or direction of association between two variables. Regression analysis is analyzed by classifying the variables in two classes like the dependent variables and the independent variables. Thus it tries to estimate the average value of one variable (dependent variable) from the given value of the other variable(s) (i.e., independent variables). Where as, the condition of correlation analysis is exactly the contrast of the regression analysis. In such a case the basic focus of the researcher is on measurement of the strength of relationship between the variables. In other wards the correlation analysis measures the depth of relationship between two variables where as the regression analysi s measures the width of the relationship between the variables. Again in regression analysis, the dependent variables are considered as random or stochastic and the independent variable(s) are assumed to be fixed or non-random. But in the correlation analysis all the variables are treated as symmentric and hence are considered as random. INTRODUCTION TO CORRELATION ANALYSIS The magnitude of association or relationship between the two variables can be measured by calculating correlation. Correlation analysis can be defined as a quantative measure of strength of relationship that exists between two variables. There are four types of relationship that may exists between two variables. They are: Positive correlation Negative correlation Linear correlation and Non-linear correlation. 1. Positive correlation: Two variables are said to be positively correlated when the movement of the one variable lead to the movement of the other variable in the same direction. In other wards there exists direct relationship between the two variables. For example, the relationship between height of the human being to their corresponding weight, income of the person with expenditure, price of the commodities and supply of the commodity etc. In all such cases increase (or decrease) in the value of one variable leads to the increase (or decrease) in the value of corresponding other variable. The nature of positive relationship between the two variables can also be shown graphically. If the data are inserted in two axis of a graph paper, then one will find an upward trend rising from the lower left hand corner of the graph paper and spreading upward upto the upper right hand corner. One can imagine the supply curve as explained in the economic theory. 2. Negative correlation: On the other hand, correlation between two variables is said to be negative when the movement of one variable leads to the movement in the other variable in the opposite direction. Here there exists inverse relationship between the two variables. For example, volume and pressure of perfect gas, income and expenditure on food items (Engelà ¢Ã¢â€š ¬Ã¢â€ž ¢s law), change in price and quantity demanded of necessary goods () etc. In all such cases increase (or decrease) in the value of one variable causes corresponding decrease (or increase) in the value of other variable. In case of negative correlation between two variables, one will find downward trend from the upper left hand corner of the graph paper to towards x-axis. One can imagine the demand curve as explained in the economic theory. 3. Linear correlation: The correlation between two variables is said to be linear where the points when drawn is a graph represents a straight line. Considering two variables X andY, a straight line equation can be as where ___ are represented in real numbers. By using the above formula, with the constant values of ___ and different values of X and Y when plotted in a graph sheet, one will get a straight line. The linear relationship between two varoibales can be interpreted as the change in one unit of one variable (let X) results in the corresponding change in the other variable (let Y) in a fixed proportion. Thus when the above values of X and Y are represented in graph one will get a straight line. This type of relationship between two variables where a unit change in one variable (X here), the other variable (Y) will change in a constant proportion. However such relations are rarely exists in case of management and social disciplines. 4. Non-linear correlation: A relationship between two variables is said to be non-linear if a unit change in one variable causes the other variable to change in fluctuations. In other wards, if X is changed then corresponding values of Y will not change in the same proportion. Hence when data of X and Y when plotted in a graph paper one will not get a straight line rather a polynomial. The equation of getting such relationship is There can be also instances where there does not exist any relationship between two variables i.e., no correlation can be found between two variables. Such relationship is called as à ¢Ã¢â€š ¬Ã‹Å"no correlationà ¢Ã¢â€š ¬Ã¢â€ž ¢. For instance, one wants to compare the growth of population in India with that of road accidents in United States. Such types of relations donà ¢Ã¢â€š ¬Ã¢â€ž ¢t exist logically. Hence correlation between such relations is said to be nil. METHODS OF MEASURING CORRELATION: Correlation between two variables can be measured by following ways. The Graphical method (through Scatter Diagram) Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation 1. The Graphical Method: The correlation can be graphically shown by using scatter diagrams. Scatter diagrams reveals two important useful information. Firstly, through this diagram, one can observe the patterns between two variables which indicate whether there exists some association between the variables or not. Secondly, if an association between the variables is found, then it can be easily identified regarding the nature of relationship between the two (whether two variables are linearly related or non-linearly related). 2. Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation Karl Personà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation (developed in 1986) measures linear relationship between two variables under study. Since the relationship is expressed is linear, hence, two variables change in a fixed proportion. This measure provides the answer of the degree of relationship in real number, independent of the units in which the variables have been expressed, and also indicates the direction of the correlation. It is known that ____ as an absolute value for determining correlation between two variables. This measures as a part of absolute measures of dispersion, depends upon the existence of two things like (i) the number of observations denoted as à ¢Ã¢â€š ¬Ã‹Å"nà ¢Ã¢â€š ¬Ã¢â€ž ¢ and (ii) the units of the measurement of the variables under study. The above relationship is explained by assuming that there is a data set which consists of two variables X and Y i.e., in terms of relationship it is denoted as (Xi , Yi) where I = 1, 2, 3,..,n. Assumed mean method: The assumed mean method for calculation of coefficient of correlation can be used when the data size is large and it will be difficult on the part of the researcher to calculate the mean of the series by using the direct method. In such case, a value from the series is assumed as mean and the deviations are calculated from the actual data to that of the assumed mean i.e., if, X and Y are two series of observation than are the deviation values of variable X and Y respectively. That is, , where, L and K are the assumed mean of series X and Y respectively. The formula for calculating Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation. The above methods derived to calculate the coefficient of correlation cannot be used to calculate the correlation between the two variables when the series of observations are in grouped forms i.e., with frequency distribution. In such a case, the formula for calculating Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation is: Assumptions of coefficient of correlation: The Karl Personà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation can be best derived with some assumptions. Following are some assumptions on which the validity of the coefficient resides. 1. The value of the coefficient of correlation lies between -1 (minus one) to +1 (plus one). When two values considered in a study are no way related with each other, then one can take for granted that the value of the coefficient of correlation is zero (0). On the other hand, if there exists relationship between two variables, it implies that all points on the scatter diagram fall on the straight line, then the value of correlation coefficient (rXY) is either extend upto +1 or -1, of course depending on the nature of direction of the straight line. It will be positive when the slope of the line is positive and it will be negative when the slope of the line is negative. Telling alternatively, if both the variables X and Y are related directly with each other than the value of the coefficient of correlation will be definitely positive. On the other hand, if there exist inverse relationship between the two values then the value of the coefficient will be negative. 2. The value of the coefficient of correlation is independent of the change of origin and change of scale of measurement. To prove this assumption, we have change the origin and scale of both the variables. When there will be change in origin and scale of the two values X and Y, the new equation will be where à ¢Ã¢â€š ¬Ã‹Å"Aà ¢Ã¢â€š ¬Ã¢â€ž ¢ and à ¢Ã¢â€š ¬Ã‹Å"Bà ¢Ã¢â€š ¬Ã¢â€ž ¢ used in the above formulas are constraints and measures change in origin and constraints à ¢Ã¢â€š ¬Ã‹Å"pà ¢Ã¢â€š ¬Ã¢â€ž ¢ and à ¢Ã¢â€š ¬Ã‹Å"là ¢Ã¢â€š ¬Ã¢â€ž ¢ used in the formulas denotes change in scale. Simplifying the above equations reveals that. RANK CORRELATION COEFFICIENT: In research, no one can predict the nature of data. The information that is collected from the respondents may be expressed in numbers or may be in qualitative way or quite often they may be expressed in form of ranks. The greatest disadvantage of the Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation is that, it best works when the data is expressed in numbers. On the other hand, Karl Pearsonà ¢Ã¢â€š ¬Ã¢â€ž ¢s coefficient of correlation, as discussed above, best works when the nature of the data is quantitative or expressed in numbers. Generally, when the nature of data is expressed in qualitative form like honest, good, best, average, excellent, efficiency, etc., and/or the data is expressed only in ranks, one has to apply the Spearmanà ¢Ã¢â€š ¬Ã¢â€ž ¢s method of rank differences for finding out the degree of correlation. There are three different situations of applying the Spearmanà ¢Ã¢â€š ¬Ã¢â€ž ¢s rank correlation coefficient. When ranks of both the variables are given When ranks of both the variables are not given and When ranks between two or more observations in a series are equal Each case derived above can be estimated by using separate formulas. a. When ranks of both the variables are given This is the simplest type of calculating correlation between two series. Here is the case where ranks of both the series are given and no two observations in a series are awarded same rank. The formula is where RXY denotes coefficient of rank correlation between two series of observations X and Y d is the difference between the two ranks and n is the number of observations in the series While calculating RXY, one has to arrange the given observations in a sequence. Then the difference in ranks i.e., d is to be calculated. The result shows a positive correlation between the judgments revealed by both the judges. However, since the value is not so close towards 1, hence, it can be said that there exists moderate relationship between the ranks assigned by both he judges. b. When ranks of both the variables are not given There may be certain situations where the rank of the both the series are not given. In such cases, each observation in the series is to be ranked first. The selection of highest value depends on the researcher. In other wards, either the highest value or the lowest value will be ranked 1 (one) depends upon the decision of the researcher. After the ranking of the variables, then d and d2 are calculated and the above formula can be applied. Following example will make the concept clear. The result shows a positive degree of correlation between the grade point average and total marks obtained by the students. c. When ranks between two or more observations in a series are equal In empirical analysis, there is possibility of assigning same ranks to two or more observations. On the other hand, while ranking observations, there may be some situations where more than one observations are assigned equal ranks. Here, the ranks to be assigned to each observation are an average of the ranks which these observations would have got, if they differed from each other. For example, if two observations are ranked equal at 6th place. If we would rank separately to both these observations, than one will get 6 and the other will get 7. Thus the rank of both the observations will be (6+7)/2= 13/2= 6.5. Now the new ranks of the series who assigned 6 each will be 6.5 each. Similarly, there may be possibility that more than two observations of a series may be ranked equal. Here also the same technique of averaging as derived above is applied to get the new ranks of the observations. The formula for calculating the rank coefficient of correlation in case of equal ranks case is a little bit different form the formula already derived above. It is where d difference between ranks of two series and mi (i= 1, 2, 3, ..) denotes the number of observations in which the ranks are repeated in a series of observations. The example derived below will make the concept clearer. Interpretation of results of rank coefficient correlation: If the value of rank correlation coefficient RXY is greater than 1 (RXY 1), this implies that one set of data series is positively and directly related with the ranks with the other set of data series. In other wards, both the set of observations are directly related. Hence, a observation in one series definitely scores almost same rank in the other series. Where as, f the result of rank coefficient of correlation (RXY) is found to be less than zero (RXY On the other condition, let that the value of rank correlation coefficient will be exactly +1 i.e., (RXY = +1). Then it can be said that, there exists exactly perfect correlation between the two series of observations. Here each observation in both the series get exactly equal ranks. Where as, if rank correlation is -1 (RXY = -1), implies there exists exactly negative correlation between the ranks of two series. The possibility in such cases is such that, a observation which gets highest rank in one series is getting lowest rank in the other series. The last possibility is that of rank coefficient correlation is 0 i.e., (RXY = 0), implies that there do not exist any relation between ranks of both the series of observations. LINEAR REGRESSION ANALYSIS: When it is estimated by using the methods of correlation that two variables (or data series) are correlated with other and it is also tested that expression of such relationship between the considered variables are theoretical permissible, then the next step in the process of analysis is of predicting and/or estimating the value of one variable from the known value of the other variable. This task, in econometrics literature is called as à ¢Ã¢â€š ¬Ã‹Å"regression analysesà ¢Ã¢â€š ¬Ã¢â€ž ¢. Literary, the word à ¢Ã¢â€š ¬Ã‹Å"regressionà ¢Ã¢â€š ¬Ã¢â€ž ¢ means a backward movement. In general sense, à ¢Ã¢â€š ¬Ã‹Å"regressionà ¢Ã¢â€š ¬Ã¢â€ž ¢ means the estimation and/or prediction of the unknown value of one variable from the known value of the other variable. Hence, it is a study of the dependence of one variable on other variable(s). Prediction or estimation of the relationship between two or more variables is one of the major discussion areas in all most all the branches of knowledge where human activity is involved. Regression, as one of the most important econometric tools is extensively used in all most all branches of knowledge like may be in natural sciences, in social sciences and also in physical sciences. But by virtue of the vary nature of most of the branches of social sciences (like economics, commerce, etc.) and business environment, the basic concern in these disciplines is to establish an econometric (or statistical) relationship between the variables rather than getting an exact mathematical relationship (core analysis tool used in natural sciences). For this reason, if, one could able to establish some kind of relationship between two variables (where one variable is considered as dependent variable and other variable(s) are considered as independent variables), then it can be expected that half of the existing purpose is almost solved. The credit for the development of this technique at first lies with Sir Francis Galton in the year 1877. Galton used this word for the first time in his study where he had estimated the relationship between heights of fathers and sons. This study ended with a conclusion that there is more possibility of having tall fathers with tall sons and vive versa. Again it also observed that, the mean height of sons of tall fathers was lower than the mean height of their fathers and the mean height of sons of short fathers was higher than the mean height of their fathers. This study was published by Galton through his research paper à ¢Ã¢â€š ¬Ã‹Å"Regression towards mediocrity in hereditary statureà ¢Ã¢â€š ¬Ã¢â€ž ¢. Regression as a tool: Econometricians use regression analysis to make quantitative estimates of various theoretical relationships exists in the literature of social sciences and management, which previously have been completely theoretical in nature. For example, the famous demand theory of economics says that the quantity demanded of a product will increase when there is reduction in the price of the commodity and vice versa, of course with an assumption that the impact of other things being constant. Hence, anybody can claim that the quantity demanded of blank DVDs will increase if the price of those DVDs will decrease (holding all other factors as constant), but not many people can actually put numbers in to an equation and estimate à ¢Ã¢â€š ¬Ã‹Å"by how manyà ¢Ã¢â€š ¬Ã¢â€ž ¢ DVDs quantity demanded will increase for each reduction in price of Rs. 1/-. To predict the direction of the change, one needs knowledge of economic theory and the general characteristics of the product in question (as the deri ved example is related to one of the economic theory). However, to predict the amount of the change, along with the data set, one needs a way to estimate the relationship. The most frequently used method to estimate such a relationship in econometrics is regression analysis. As already discussed above, regression analysis describes the dependence of one variable on another or more variables. It is now important to classify the terms dependent and independent variables that are the core of analysis of regression. Dependent Variables and Independent Variables Regression analysis, is a statistical technique that attempts to explain movements in one variable, the dependent variable, as a function of movements in a set of other variables, called the independent (or explanatory) variables, through the quantification of a single equation. To make this concept clearer, let us start our discussion by considering a simple example of generalized demand function of economic theory. The equation (1) derives a functional relationship between six factors (as in the right hand side of the equation) with one variable (as in the left hand side of the equation). In other wards, theoretically, quantity demanded (Qd) of a good or service depends on the six factors like the price of the good itself, money income of the consumer, prices of related goods, expected future price of the product itself, taste pattern of the consumers and the numbers of consumers in the market. In equation (1), quantity demanded is the dependent variable and the other six variables are independent variables. Much of economics and business is concerned with cause-and-effect propositions: If the price of a good increases by one unit, then the quantity demanded decreases on average by a certain amount, depending on the price elasticity of demand (defined as the percentage change in the quantity demanded that is caused by a one percent change in price). Propositions such as these pose an if-then, or causal, relationship that logically postulates a dependent variable (Qd in our example) having movements that are causally determined by movements in a number of specified independent variables (six factors discussed above). The Linear Regression Model: In the regression model, Y is always represented for dependent variable and X is always represented for the independent variable. Here are three equivalent ways to mathematically describe a linear regression model. The simplest single-equation linear regression model can be written as: The above equation states that Y, the dependent variable, is a single-equation linear function of variable X, the independent variable. The model is a single-equation model because no equation for X as a function of Y (or any other variable) has been specified. The model is linear because it expresses the relationship of a straight line and if plotted on graph paper, it would be a straight line rather than a curve. The constants expressed in the equation are the coefficients (or parameters) that determine the coordinates of the straight line at any point. in the equation is the constant or intercept term; it indicates the value of Y when X equals zero. Thus it is the point on the y-axis where the regression line would intercept the y-axis. Where as, in the equation is the slope coefficient, and it indicates the amount that Y will change when X changes by one unit. Figure 1.1 illustrates the relationship between the coefficients and the graphical meaning of the regression equation. As can be seen from the diagram, equation 1.3 is indeed linear. The slope, , shows the response of Y to change in X. Since being able to explain and predict changes in the dependent variable is the essential reason for quantifying behavioral relationships, most of the emphasis in regression analysis is on slope coefficients such as . In figure 1.1 for example, if X were to increase from X1 to X2, the value of Y in Equation 1.3 would increase from Y1 to Y2. for linear ( i.e., straight-line ) regression models, the response in the predicted value of Y due to a change in X is constant and equal to the slope coefficient: We must distinguish between an equation that is linear in the variables and one that is linear in the coefficients (or parameters. This distinction is necessary because while linear regressions need to be linear in the coefficients, they do not necessarily need to be linear in the variables. An equation is linear in the variables if plotting the fuction in terms of X and Y genereates a straight line. An equation is linear in the coefficients (or parameters) only if the coefficients (the ) appear in their simplest from à ¢Ã¢â€š ¬Ã¢â‚¬Å" they are not raised to any powers (other than one), are not multiplied or dived by other coefficients, and do not themselves include some sort of function (like logs or exponents). For example, Equation 1.3 is linear in the coefficients, but equation 1.5: Is not linear in the coefficients and Equation 1.5 is not linear because there is no rearrangement of the equation that will make it linear in the of original interest, and . In fact, of all possible equations for a single explanatory variable, only functions of the general from: are linear in the coefficients and .In essence, any sort of configuration of the Xs and Ys can be used and the equation will continue to be linear in the coefficients. However, even a slight change in the configuration of the will cause the equation to become nonlinear in the coefficients. For example, equation 1.4 is not linear in the variables but is linear in the coefficients. The reason that Equation 1.4 is linear in the coefficients is that if you define f(X) = X2, Equation 1.4 fits into the general form of Equation 1.6. All this is important because if linear regression techniques are going to be applied to an equation, that equation must be linear in the coefficients. Linear regression analysis can be applied to an equation that is nonlinear in the variables if the equation can econometricians use the phraseà ¢Ã¢â€š ¬? linear regression,à ¢Ã¢â€š ¬? they usually mean à ¢Ã¢â€š ¬Ã…“ regression that use the phrase à ¢Ã¢â€š ¬Ã…“linear regressionà ¢Ã¢â€š ¬?, they usually mean à ¢Ã¢â€š ¬Ã…“ regression that is linear in the coefficients.à ¢Ã¢â€š ¬? The application of regression techniques to equations that are nonlinear in the coefficients will be discussed in section7.6.

Are Mental Disorders Biological Or Environmental

Alissa Macek Macek 1 Mrs.Wickham English 9 4 March 2016 Are mental disorders biological or environmental? For years the nurture versus nature debate has been argued by people around the world. Mental disorders are one of the main topics discussed among these people. Mental disorders apply to many mental health conditions that can affect someone’s emotions, logic, and attitude. According to The Kim Foundation, 26.2 percent of Americans who are 18 or older suffer from†¦show more content†¦People with a family member who has an illness is more likely to inherit the trait but certain circumstances affect the development and doesn’t ensure the chance of it happening. It has been proven that more serious diseases such as schizophrenia and bipolar disorder are usually drawn back to past family members with the same disorder. This does not guarantee that the relative gets the illness but highers the chance of contracting the disease. Diseases such as obsessive- compulsive disorder and depression are less geneti c. Infections have also been associated to brain damage. Infections can bring a possibility of causing an illness or affecting the symptoms in a negative way. A condition called pediatric autoimmune neuropsychiatric disorder is identified with a bacteria called Streptococcus. This bacteria has been associated with obsessive- compulsive disorder and other diseases. Brain defects and prenatal injuries have been related to mental illnesses and usually form in early life. Substance misuse is another key factor in the development of a disorder. This abuse has been related to anxiety, depression, and paranoia. Some others factors that can contribute to the development of a mental disorder are poor health and disclosure to harmful substances. Biological factors play a major role is forming a mental disorder. Many scientists believe these are the main reasons for the formation of an illness but is it really?

Victimiology and Alternatives to the Traditional Criminal Justice System Free Essays

Restorative justice is a procedure whereby all interested parties in a particular offence collectively gather to determine together how to deal with the consequence of the offense and its significance for the future. From the victim’s standpoint, restorative justice has been shown as a rule to have achieved better conflict resolution than the existing system of criminal justice. The concept enables the victims to have a voice in the justice process, by offering them an opportunity to ask queries and seek out answers, affording them a part in the sentencing resolution and providing them with opportunities for closure and healing. We will write a custom essay sample on Victimiology and Alternatives to the Traditional Criminal Justice System or any similar topic only for you Order Now Victimiology and Alternatives to the Traditional Criminal Justice System The term â€Å"restorative justice† has come into view in varied forms, with diverse names, and in several countries; it has sprung from sites of academia, activism, and justice system agencies. The idea may refer to an alternative procedure for resolving controversies, to alternative options of interdiction, or to a uniquely different, â€Å"new† approach of criminal justice organized around theories of restoration to offenders, victims, and the communities in which the parties live. The term may also confer to diversion from recognized court process, to actions taken in parallel with court judgments, and to meetings between victims and` offenders at any phase of the criminal process. Although restorative justice is a large concept with compound referents, there is a comprehensive sense of what it stands for. It calls attention to the repair of damages and of shattered social bonds resulting from crime; and concentrates on the relationships between crime offenders, victims, and society. Restorative justice is a procedure whereby all interested parties in a particular offence collectively gather to determine together how to deal with the consequence of the offense and its significance for the future. For victims, it enables them to have a voice in the justice process, by offering them an opportunity to ask queries and seek out answers, affording them a part in the sentencing resolution, and providing them with opportunities for closure and healing. It is not merely a way of correcting the criminal justice system; it is a way of changing society’s practice of politics, conduct in the workplace, family lives, and entire legal structure. The restorative justice’s vision is of a holistic change in the manner people carry out justice with the rest of the world. Whether restorative justice can eventually be of assistance to the victims without impairing the community or justice remains to be seen. But it is becoming apparent that the concept does without a doubt helps most victims. Increasing observed benefits and advantages of restorative justice are outweighing the insignificant harms caused by it. The said findings appeared from a research study conducted in Australia over the period of 1995 to 2000; known as the Reintegrative Shaming Experiments (Ronken and Lincoln, n. d. , p. 3). The assessments integrated observations of the court and conferences proceedings, review of official data, and consultation with the victims after their cases were ordered. The assessment revealed: Firstly, the manners of intervention in restorative justice are organized affords much greater prospect for victims to know about the development of their cases than available when cases are processed all the way through the courts. In practice, victims are unusually told nothing concerning their case when they are not obliged to be witnesses. This inadequacy of communication was the particular greatest reason for victims’ dissatisfaction whose cases went to court. Secondly, a restorative justice encounter expectedly necessitates a high degree of participation by both offenders and victims. Victims stresses that personal delivery of justice is one of the advantages that they admire in restorative justice process that are not presented in the court. Thirdly, if emotional restitution is what victims’ value most for their mending, then restorative justice provides sufficient opportunity for the said restitution to take place. Fourthly, victims are more likely to acquire restitution through restorative justice as compared through the courts. Victims often obtained some other form of material reparation, such as service by the offender for the affected people or for the community. Lastly, 90 percent of victims who experienced restorative justice answered that they have been treated respectfully and justly in the resolution of their cases as they believed the meeting had taken account of what they alleged in deciding what should be done (Strang and Sherman, 2003, p. 35). Peacemaking Strategies Peacemaking strategies are holistic approach to crime and conflict and are used for centuries now in several countries. Peacemaking strategies deal with the fundamental causes of conflicts and violence. The approach considers the needs of offenders, victims, communities and families within a re-integrative framework. Peacemaking has a prospective to: assist adults and youth who come into dispute with the law; guarantee the development of responsible and healthy youth; support and recognize violence-free relationships; and increase the competence of communities to deal with social justice and criminal issues (Paiement, 2006, p. 5). Feedback from those who experienced peacemaking process noted the educational nature of the strategy; that they were able to take part openly and usually remarked on an approval for the peace talking; the process is competent in dealing with the issues of the parties directly and helping the offenders be aware of the outcomes of their actions; and the parties of the process were often very emotional and the victim felt respected and honoured (Paiement, 2006, p. 19). Shaming In the United States, most community registration and notification laws were enacted in the early 1990’s instantaneously after the occurrence of several high profile cases on violent sexual acts. Currently, state-controlled or public domain notification comes in two fundamental forms. The first is the registration that brings about the reporting of the criminals to justice bureaus in order for the latter to keep an eye on criminals’ movements. The second form is termed â€Å"community notification. † It comes in a range of forms such as internet postings, news releases, community conferences and targeting specific local areas, organizations or groups to give advice to the population concerning discharged sex offenders. However, shaming through notification laws will not automatically provide justice to the victims or shield the community from sex offenders. There are several well acknowledged explanations for such a conclusion. The explanation includes: that the shaming approach may promote displacement; offer a false sense of protection; incorrect forms of insulting; are based on high-levels of recidivism; lead to more costly and weighty justice processes; and may aggravate vigilante attacks (Ronken and Lincoln, n. d. , p. 9). In the United States it is estimated that sex offenders’ population are already 250,000, with 60 percent released in the community. It is clear that every individual cannot be advised in relation to all possible offenders prowling in their community. The aforementioned facts suggest the inefficiency of notification laws as a useful alternative to the traditional justice system. Further, notification conveys a frustrating message to the victims as well as the community that the state is capable to notify them about offenders within their midst but can present no means to deal with the dilemma. On the other hand John Braithwaite’s â€Å"reintegrative shaming† theory aims to eliminate the shaming nature of long-established criminal justice process that communities and families employ in reparation for the damages done to them. The concept is accomplished through a phrase of retrial for the offender’s act and a process of reintegrating the lawbreaker back into their society through acts of acceptance and forgiveness. Thus, if notification laws are steadily influenced in the principles of restorative justice, including reintegration and shaming, then there may be a decline in the level of re-offending and a greater sense of justice and fairness to the victims. How to cite Victimiology and Alternatives to the Traditional Criminal Justice System, Papers

Changing Nature and Implications of Journalism †Free Samples

Question: Discuss about the Changing Nature of Journalism. Answer: Changing Nature of Journalism The journalism that was there earlier in 20th century or maybe even twenty years back has now completely changed. Todays journalism is very different from the Journalism people knew back earlier. Journalism is a strong term even today, but the nature has changes because of a lot of reason. Earlier the medium of news was newspaper and radio but now the entire digitalization has changed the entire view, now the availability of new is more widespread. The growth and evolution of human society is bringing new situations which is producing new professions and changing the basic nature of the one which is already existing. The job that has experienced and recently went through radical changes is that of a journalist. This job has developed the society and created a communication between the world, it has given a voice to the people and today sitting at home we get to know about our the entire world. People in sitting at home are getting to watch the world news for example Australia knows w hats happening in India and China. The effect of web on news reporting is known to be the most effective way of journalism. This change is bringing revolutionize change in the world. Journalists are known to be the liberty of information; however the nature of the new structure and the news room is changing today. There is a total change even in the publishing of a story. The internet has become the immediate source of information. Through online media they find a multiple perspectives and a great availability of news and knowledge which is written like a context of stories. Newspapers are becoming outdated these days, they have started making their stories short, the size of the foreign and national news are becoming shorter and they have introduced more advertisements in news paper in order to make money. Todays news is less about the news and more about the profit making. It has become more like a business. The news first came into being with the objective of giving people a voic e, later on when television came into being it gave people a vision but gradually it became an industry where the truth is little and more publicity is been created just for money. This is the reason why there are less news and more controversy. Be it newspaper or broadcasting journalism or any form of media it encourages controversies these days, more than focusing on the main issue. These days both large and small papers have decreased their space, sources and dedication committed to the variety of topics. In survey it has been shown that there is cut back on international news. Now the competition has increased between several channels and newspapers and in order to surpass other the quality f news material is been sacrificed. However digital and portals are increasing as a source of expectation to look forward. There are newspaper and channel websites as well as individual websites which provide us with news these days, which is an advantage for the world. The New Growing Trend- Fitness The fitness is becoming the new trend everywhere. Somehow this industry has got new wings and continuously creating diversity with different types of crazes and trends. There has been a new event which is happening i.e. running, which involves mileage build up to match up the race. The new training plan has been created by Kristina Grind, where they have involved the entire woman and started this running program. The initiative was taken because she knows a lot of women suffer and struggle with their daily schedule that how they will give some time to their self in order to be fit (Executive Style, 2017). Running is basic way of staying fit; it does not require gym or anything fancy its the most basic level of training which stimulates the body. A person can take out time and run at any hour of the day depending on their free time. Most of the women struggle with their workout, as in what to do and when to do it, however Kristina figured it out and which has helped her work through h er routine. Therefore in this program she includes no of women at a given time with their convenience (The Telegraph, 2017). In fact they have said that they feel much better and stronger and confident. Various researches have shown that a female body needs a large portion of stimulation in order t stay healthy an d gain strength. However in spite of know the emergence of exercise still people becomes laid back and due to work pressure they often overlook the importance of it. Therefore Kristine has taken up the responsibility to help and push those women so that they can take time out for this. To encourage them she has taken the initiative and therefore she is organizing an even where all the women in her group including her will form a rally and will run 14 kilometer. She even says that the workout has helped them feel more fresh and energetic; they have found a good vibe from this training. It relaxes them to a lot of extent and even helped them to a personal level. The world has turn into fitness centre with new gyms and new equipment facilities, every men and women wants abs these days which sounds fancy but in a way it is taking the world into a new direction with lot of strength and forces. Staying fit is what always out mother told us but when its a trend then people are lounging upon it (The Sydney Morning Herald, 2017). This specific rally will motivate more women according to Kristine and help other women to come forward and join them. A little is all we need Kristine says. She says this is liberating for her as it makes her happy to see other more people participating in this rally. This will help not them as a person but will help encourage others as well (Dailytelegraph, 2017). References: Dailytelegraph.com.au. (2017).Daily Telegraph | Breaking News and Headlines from Sydney and News South Wales | Daily Telegraph.Dailytelegraph.com.au. Retrieved 8 August 2017, from https://www.dailytelegraph.com.au/ Executive Style, L. (2017).Plan ahead: The simple trick becoming a better, fitter, runner.Executive Style. Retrieved 8 August 2017, from https://www.executivestyle.com.au/why-a-running-plan-will-make-you-fitter-stronger-and-prevent-injury-gxqntn Lewis, S. C. (2015). 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