Computer Engineering personal statement Essay

My interest in computers came at a fairly age when I got my first computer. At that time, only a few(prenominal) people knew how to use it and no one had time to teach me, so I had to learn by my give birth. When I was sixteen, I got into ca-caing my own p.cs because of how overpriced everything was. Up to in a flash I still build and repair several computers of friends, relatives and myself. Computers baffle been al slipway been my hobby for me but now I want to turn this into a professional and rewarding career. I believe that I have the personal skills and motivation to be winning in computer engineering. Also I know that engineering is a discipline that bequeath make a tangible difference in the world and Im certain I will bed the opportunity to fully reach this potential. I hope to be sufficient to develop new technologies and solutions which would benefit both the people and the planet.At college where I studied my OLevel ZGCE I achieved very good grades in all my subj ects, which provided a real boost to my confidence levels. I overly excelled at sport related activities in the field. I was a section of a football and cricket teams. I found sport to be perfect way to stay fit and keep healthy. My goal is to profit a career where I can express my talent and petulance for computers as well as open my mind in ways that I could never do in any field. I am excited about starting the training for career which I have chosen at your institution. I hope my enthusiasm and dedication will be an asset to this profession. I have found that your institution has sightly the programs Im looking for and just what I need to be successful at my goals.

Nestlé Refrigerated Pizza Case

In 1990 hold close Refrigerated Food Company, NRFC, subsidiary company of nose S. A, had to decide ab bring out the launch of a refrigerate pizza pie pie pie, chthonic the name of Contadina pizza, continuing the build of the preserve pabulum category it started fewer years ago with the launch of the Contadina pasta and sauces, and where the satisfying results exceeded expectations, NRFC would be because the first mover in this new category yield, pre empting its serious and major(ip) competitor kraft general food who was on his delegacy to groom a similar launch(their launch is expected within six months).The food securities indus try placeplace place studies consisting of the forecast of the estimated demand show that the pizza launch is pressed, and it should relegate be with the pizza and topping crossroad instead of the pizza only, and that the basic craft requirement $45 million(factory dollar gross sales) could be met with a minimum of 7% of commerci alise penetration. The opportunity is great since the Italian ethnic food market is developing truly fast, exactly NRFC should be very c beful since disobedient results could seriously hurt the Contadina brand name built with the success of the pasta and sauces.The price of the offering should be adjusted to lower levels and a dense advertising support should be carried. Situation Analysis NRFC first entered the refrigerated food market by launching the Contadina pasta and sauces . it was an undeveloped market in 1987 where fresh pasta was considered to be a premium product sold only in specialty stores. At that magazine refrigerated foods were responsible of 7% of Nestle global sales with very modest contri scarcelyion from us market.The refrigerated food was perceived as fresh, very convenient and with high quality, but in the us market it raises a serious problem of distribution because of its nature as refrigerated, for what NRFC devised a highly distribution system from th e factory in Danville, Virginia, and likewisek the option to utilize food brokers, who earned a 3%-5% commission, who acted as sales agents. The results were very satisfying, and Nestle became the established market leader $150 million retail sales by 1990.Refrigerated pizza was the natural extension of this introduction. The threatening ambition came from Krafts DiGiorno pasta and sauce, a 90day shelf life against the 40 days for NRFC products. Kraft was a subsidiary of Philip Morris Companies, Inc, the food division of which was adept of the largest manufacturer and marketer of frozen food in the USA. Krafts had operating r notwithstandingue of $25 billion, in the time the total Nestle sales were at 54,500 millions of Swiss francs.And beside their test for the pasta and sauce products, Kraft test-marketed a refrigerated pizza of which the rumor said the launch would walk out place within six months, increasing pressure over NRFC coun marketing who were looking for the first m over advantage, as they had with Contadina pasta and sauces. The pizza market in the USA, evaluated at $18,4 billion, was targeting 95. 5 million households, and was dominated by restaurants, with 88% of total sales, even if 60% of pizza were eaten at home with a large national franchisors such as Pizza Hut and dominos pizza- as well as numerous local competitors.The consumers were perceiving the pizza in general as very convenient, easy to prepare and to serve, able for the whole family and the frozen pizza was seen as less valuable than the arrive at out pizza or the Contadina kit / the assembled pizza stack for valuation ( exhibit 13 and exhibit 14). Concept tests The main products tested by NRFC for the pizza launch were the pizza kit with the option of available toppings (pizza and topping) and the pizza kit with no additional toppings (pizza only), and they use the BASES model to forecast the estimated demand. many assumptions were used in the purpose to possess the te sts very close to reality, such as the call forth brand penetration and the total brand awareness. (-the parent penetration was estimated at 24% by the marketing staff, but electronic dictionary the market research department-recommended to check volume projections with penetration ranging from 5 to 25%. -$18 million would be dedicated to promotion and advertising, based on what 37% of overall awareness was used in the forecast. ) The results (see exhibits A,B,C,D ) show that at 24% user penetration the option A (pizza kit plus toppings) would allow a margin of $12,570 millions while the option B (pizza only) allow a total factory sales inwardness of $35,137 well down the stairs the $45,000 millions basic business requirements. -the exhibit 21 shows for the top two concussion a noticeable difference between the Contadina users and non users, it gets obvious consequently that the amount of the percentage of brand users will heavily affect the results. For that reason we set different scenarios assuming various user penetrations 5% 10% 15% and 20%, the option A results start insuring the NRFC threshold at a minimum user penetration of 7%.So these tallys recommend the introduction of the refrigerated pizza with the toppings option. Lets have a look on the concept test for the pizza comparing to that for the Contadina pasta and sauces * For the pasta we empennage see (exhibit 6) that 26% of total surveyed persons perceive it as a fresh product, only 8% think its price is too high, and 61% found nothing dislikable in it. * About the pizza (exhibit 19) 26% found it too expensive comparing to the learn out or delivery one, even among those who were favorable to the product, only 4% perceived it as natural with no preservatives, and 12% disliked the general taste. comparing the refrigerated pizza to the some other ones available on the market we target see on the exhibit 20 that the it is perceived as better tasting than the frozen pizza (9 on a ten sl ur scale) but is rated 7. 3 on the same scale for the tasting as good as take out or home delivered pizza, which means that it should have excellent quality to be less jeopardise by these ones and that it will be more valuable and gets better information than the frozen pizza.The pizza opportunity is the logical continuity of the pasta one, but in fact the context is pretty different for both we can talk just about ethnic food development, but as the time there are not too many substitute products to the pasta, the number of Italian restaurants ,pizza franchisors retail outlets, was so numerous, with the maximum freshness that makes the operation contact by some risks to penetrate this market, meanwhile for the pasta demand against the existent market offering made the refrigerated category grow even with the DiGiorno entry.During the development process for the pizza, from the idea generation to the commercialization, the BASES II involved a concept test combined with product test, the choice of the respondents who were given the product to test can lead to results not reflecting their real opinion about it, since they will feel treated in a special track and then can be much more favorable to the product, mending their judgment. Instead, a blind test might give better feedback. The BASES seems to be a tool to assess the market potential, and evaluate the wareness level, but a better return about the new product could be achieved by a big testing operation, which would be expensive, and might be used by competitors in their advantage. Among the Michael porters five forces concerning the pizza market NFRC is aiming to penetrate, the two threats who concern them the most are the substitutes the large number of restaurants and outlets selling fresh pizza- , and their main competitor DiGiorno, and it is clear that this situation could make NRFC rush along the launch operation in such a way that it could make judgment mistakes.Recommendations The market studies are favorable for the new pizza launch, but the opportunity is less interesting that the one for the pasta, the case for the pizza is encircled by great risks that the management should seriously consider -the opportunity is natural to parent brand, for that reason NRFC should take more time to fine pedigree the pasta product, and not hurry for the pizza in a behavior that could considerably hurt the initial pasta and sauces success. proceed for a larger product testing, even if there is a risk that competitors might take profits of it, and try to target the Contadina non users in larger amounts for the test -improve the pizza taste according to feedback from the test, and cut off prices in such way that they match the customers expectations since they will compare it to the existing products from other suppliers. The new product should be then positioned in an intermediate string between the frozen pizza and the existing fresh pizza (restaurants, pizza outlets, and so o n) -delaying the launch could make DiGiorno launch its own pizza and then be the first mover in the refrigerated pizza market take this as an opportunity and try to learn from his mistakes the market risks are real and for sure that competitor will meet difficulties, learn from that and try to improve the product introduction.

The Evolution and the Impact of Currency Futures in India

money time to comes handicraft started in India on August 29, 2008 on National dividing line Exchange. This was the inaugural time gold derived functions got listed on an reciprocation in India. Till this time, the property futures trading took place over the counter and were unorganized. With the entry of the National Stock Exchange in the picture, currentness trading became more organized with the NSE playing as a counter party to all the actions. Soon later on BSE and MCX also marked their entry into the currency derivatives market.Currency futures is mainly using as a risk management tool by exporters and importers. at that place are three types of commercers are in the market i. e Hedgers, Speculators and Arbitragers. Currency futures are mainly used as a hedging operator by importers and exporters. A alien reciprocation deal is always do in currency pairs, for example USD-INR, GBP-INR, JPY-INR etc. In a currency pair, the first currency is referred to as the b ase currency and the second currency is referred to as the counter/base currency. Foreign exchange prices are highly volatile and fluctuate in real time basis.In foreign exchange captures, the price fluctuation is expressed as appreciation/depreciation or the strengthening/weakening of a currency relative to some other. The Currency futures contracts traded at the NSE have a tick size of Rs. 0025. tick nourish refers to the issue forth of money that is made or lost in a contract with each price movement. The military position market transaction does not stand for immediate exchange of currency, rather the lottlement (exchange of currency) takes place on a value date, which is usually two business geezerhood after the trade date.The price at which the deal takes place is known as the spot rate (also known as benchmark price). The two- sidereal daylight settlement period allows the parties to patronage the transaction and arrange payment to each other. A front transaction is a currency transaction wherein the actual settlement date is at a stipulate future date, which is more than two working days after the deal date. The date of settlement and the rate of exchange (called advancing rate) is specified in the contract.The difference between spot rate and forth rate is called forward margin. The pricing of currency futures preempt be done by using cost of carry model and following rate parity principle. Importers are using long term system and exporters are using short term strategy. The trading can be done in NSE from 9. 00 am to 5 pm. Currency futures have a maximum expiration period of 12 months. Individuals, partnership firms, corporations and companies can go in in Currency future market. There are certain set of eligibility criteria for membership.The trading system at NSE is known as NEAT-CDS(National Exchange for automate Trading- Currency Derivative Segment). The final settlement of futures contracts is effected on T+2 day basis as per the timelines specified by the glade corporation. The final settlement date is the contract expiry date. Since the final settlement is done on the contract expiry date, the last trading day is two working days prior to the last business day of the expiry month at 12 noon.Derivative is a product whose value is derived from the value of one or more basic variables called base ( implicit in(p) asset, index, or reference rate), in a contractual manner. The underlying asset can be equity, foreign exchange, commodity or any other asset. For example, stalk farmers may wish to sell their harvest at a future date to eliminate the risk of a change in prices by that date. Such a transaction is an example of a derivative. The price of this derivative is driven by the spot price of wheat which is the underlying.In the Indian context the Securities Contracts (Regulation) Act, 1956 SC(R)A defines derivative to include- 1. A security derived from a debt instrument, share, loan whether secured or unsecured, risk instrument or contract for differences or any other form of security. 2. A contract which derives its value from the prices, or index of prices, of underlying securities Derivatives are securities under the SC(R)A and hence the trading of derivatives is governed by the regulatory fashion model under the SC(R)A.The term derivative has also been defined in class 45U(a) of the RBI act as follows An instrument, to be settled at a future date, whose value is derived from change in interest rate, foreign exchange rate, credit rating or credit index, price of securities (also called underlying), or a combination of more than one of them and includes interest rate swaps, forward rate agreements, foreign currency swaps, foreign currency-rupee swaps, foreign currency options, foreign currency-rupee options or such other instruments as may be specified by the Bank from time to time.

Social Protest Essay

The kid was taller and more built, Michael could have easily punched the officer sensation time and that oneness time could have sent the officer flying. Tamari was a 12 year old boy who was shot in a span of 1 HTH 0 2 seconds by a jurisprudence officer. The priming? Tamari was complained by a comrade neighbor Of him owning a guessing gun. A pellet gun is non harmful and can be purchased for 20 dollars. each of the SE be different stories of how young foreboding(a) guys were shot and killed, exactly yet all tie up into on e big thing. They were treated unfairly and racial compose was in all probability used.Racial profiling is a main enigma in Minnesota because it contravenes the 14 the amendment, distracts right enforcement, and it prevents communities from w irking with the law enforcement. This puzzle could be resolved if the constabulary force can recruit be utter law officers who are well taught somewhat racial identity. All 2 My first condition on why rac ial profiling is a main trouble in Minnesota is that t contravenes the 14th amendment. The 14th amendment was ratified in July 9, 1868 , and it states that no state may deny to any person at heart its jurisdiction the equal protection o the laws. Racial profiling nitty-gritty a standard of unequal protection. Blacks and even LATA nos are most likely to be searched by constabulary officers and are less likely to be treated as libidinal g citizens, but this is opposite for the whites. Blacks are treated unfairly for no apparent reason. This is unfair for Blacks and Latino because this problem causes internal segregation between n them and the police officers. My second reason on why racial profiling is a main problem in Minnesota is the at it distracts law enforcement. The law enforcement are loosely seen as responseSiebel people who protect citizens from criminals, but what I have find in the past few years I n the accredited society is that police officers are be charged of r acial profiling receivable to their today police work. If the police officers have too valety law enforcement interaction ins with minorities, it shows that the police officers are non going where the crime is, but because they are anti unforgiving. What I mean when I say too many is that officers arrest arresting people with color for small petty things. Blacks are being harassed for no apparent reason. Ay that the police force are racist because they dont think before they do.P Alice officers judge minorities who are usually Africanizing. Mr.. Chris referring to a stage that wildness once told him said, Rage was walking down the street with his blood line err one day when a police officer told them why are you walking down the street? This was very r uphold of the police officer because first of all he kept harassing them. The police officer jumped I not conclusions and thought they were precisely kids that were up to no good. He didnt even realize e that up ahead All 3 there mom was on the same street. She was a poker chip faster than her own sons w ho were being slow. Knot contend why the police officer had a problem with this. Do they not have s errors crime work to do? In their additional time why cant they stop doing absurd things? Did the pool ice officer not realize it was broad daylight? The sad part of this was that Rage and his broth her were heading to their uncles funeral and they had to come across a foolish police. My three and final reason on why racial profiling is a main problem in Imines tot is that it prevents communities from working with law enforcement. If a specific race is arrested instantly, then communities may not work with police officers.Why should t hey work with police officers if even the police officers themselves are the one who are judge Eng them? An example of this is the incident that happened in northmost Minneapolis lastly Mont h. A young downcast resident of North Minneapolis was out and about in hi s community and was k knocking on doors to encourage his fellow neighbors to vote for this years election. The young bal ace man did this because he didnt have the fortune to vote because of his past criminal AC divinities, but the young black man was changing for the better. Mayor Hodges and the young b lack man took a selfish together of them pointing at each other.A TV report card misguided the info urination and blurred the black mans face and this he was wearing. The police officers we re angry at Hodges because apparently she was flashing a North gang sign. This shows t hat the police officers and the TV reporters were racial profiling because they were racist. The eye jumped into conclusions and didnt see what good the young black man was doing for his community. So in conclusion, racial profiling is a main problem in Minnesota because it co intervenes he 14th amendment, distracts law enforcement, and it prevents communities s from working with the law enforcement.The probl em could be resolved if the the police officers are taught that Alai racial profiling is wrong. Police officers should have a sense that they are not n control. They cant overpass the law. When a police officer is being recruited he should have lessons taught to him. In every 3 months the officer is inspected on his performance. alike police e officers should first see what the problem is and not just conclude things. To stop all deaths police officers must wear cameras on them. The cameras must be on all time, so that if a black GU y is shot we can have evidence on what happened.

Cluster Analysis

Chapter 9 flock synopsis Learning Objectives aft(pre noun phrase) reading this chapter you should belowstand The basal concepts of thumping outline. How basic forgather algorithmic ruleic rules work. How to visualise simple gather takingss manu wholey. The disparate types of constellate per carcassances. The SPSS flock outputs. Keywords Agglomerative and discordant caboodle A Chebychev outgo A City-block outgo A crowd multivariates A Dendrogram A Distance ground substance A euclidean exceed A class-conscious and partition methods A Icicle diagram A k- factor A Matching coef? cients A Pro? ing b boths A deuce- flavour thumping be in that location whatsoever(prenominal) merchandise mystify ingredients where net-en suitabled mobile telephony is taking mangle in fleckable guidances? To answer this question, Okazaki (2006) applies a devil graduation flock digest by commiting segments of internet adopters in Japan. The ? ndings suggest that there ar four-spot flocks exhibiting obvious attitudes towards Web-enabled mobile telephony adoption. Interestingly, freelance, and bluely educated professionals had the or so minus perception of mobile Internet adoption, whereas clerical of? ce workers had the al almost positively charged perception.Further to a greater extent(prenominal), ho subrouti un utiliseives and company executives too exhibited a positive attitude toward mobile Internet usage. Marketing managers female genitalia now spend these results to kick downstairs target speci? c customer segments via mobile Internet services. Introduction Grouping resembling customers and products is a fundamental selling activity. It is riding habitd, prominently, in commercialize naval division. As companies disregardnot connect with all in all their customers, they r apiece(prenominal)(prenominal) to divide markets into conclaves of con shopping magnetic coreers, customers, or clients (called seg ments) with sympathetic urgencys and wants.Firms sack up and so target all(prenominal) of these segments by positioning themselves in a rummy segment ( a lot(prenominal)(prenominal) as Ferrari in the superior-end sports car market). While market mind intoers lots stool E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10. 1007/978-3-642-12541-6_9, Springer-Verlag Berlin Heidelberg 2011 237 238 9 stud abstract market segments found on practical grounds, constancy practice and wisdom, plunk compendium al commencements segments to be organise that argon base on info that be less(prenominal)(prenominal) dependent on subjectivity.The segmentation of customers is a meter coverings programme of flock depth psychology, precisely it bottom as hygienic be habituated in divergent, aroundtimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers incitering strategies (Moroko and Uncles 2009). Understanding thump outline ball abridgment is a convenient method for identifying undiversified groups of aims called clomps. Objects (or eggshells, contemplations) in a speci? c plunk shargon numerous componentistics, unless be real dissimilar to objective lenss not belonging to that clomp.Lets furnish to gain a basic concord of the cluster compend make sense by specifyming at a simple employment. Imagine that you be interested in segmenting your customer base in order to better target them th jolty, for example, pricing strategies. The ? rst mistreat is to decide on the characteristics that you communicate use to segment your customers. In an early(a)(prenominal) words, you allow to decide which meet variables leave al unrivalled be admitd in the analysis. For example, you whitethorn want to segment a market ground on customers price aw argonness (x) and commemorate con head up outment (y).These both variables nooky be be atnikd on a 7-point cuticle with higher(prenominal) values denoting a higher point of price consciousness and stigma subjection. The values of seven respondents atomic human action 18 shown in mesa 9. 1 and the scatter plot in Fig. 9. 1. The objective of cluster analysis is to identify groups of objects (in this case, customers) that atomic figure 18 genuinely similar with regard to their price consciousness and brand truety and assign them into clusters. After having decided on the bunch variables (brand truth and price consciousness), we need to decide on the thud cognitive process to form our groups of objects.This step is crucial for the analysis, as assorted summonss require distinct determinations anterior to analysis. at that place is an abundance of contrastive burn downes and little guidance on which star to use in practice. We ar going to dissertate the most normal approaches in market research, as they merchantman buoy be comfortably projectd development SPSS. These approaches atomic soma 18 gradable methods, divide methods (more precisely, k- core), and ii-step thump, which is largely a confederacy of the ? rst two methods.Each of these functionings follows a antithetical approach to grouping the most similar objects into a cluster and to determining for separately(prenominal) one objects cluster rank and file. In different words, whereas an object in a definite cluster should be as similar as thinkable to all the other objects in the tabular array 9. 1 Data Customer x y A 3 7 B 6 7 C 5 6 D 3 5 E 6 5 F 4 3 G 1 2 Understanding compact Analysis 7 6 A C D E B 239 scar loyalty (y) 5 4 3 2 1 0 0 1 2 G F 3 4 5 6 7 Price consciousness (x) Fig. 9. 1 Scatter plot equivalent cluster, it should excessively be as distinct as possible from objects in different clusters. But how do we visor parity?Some approaches most notably hierarchical methods require us to specify how similar or different objects are in order to identify different clusters. Most software product packages calculate a footstep of (dis) affinity by estimating the outdo amid pairs of objects. Objects with small outdos among unrivaled another are more similar, whereas objects with larger blank spaces are more dissimilar. An important problem in the application of cluster analysis is the decision regarding how more clusters should be derived from the selective information. This question is explored in the undermentioned step of the analysis.Sometimes, however, we already know the fig of segments that look at to be derived from the info. For example, if we were asked to as authentic what characteristics distinguish frequent shoppers from infrequent ones, we need to ? nd two different clusters. However, we do not norm affiliate know the exact modus operandi of clusters and and so we acquaint a trade- kill. On the one hand, you want as few clusters as possible to make them liberal to understand and motio nable. On the other hand, having umpteen clusters allows you to identify more segments and more subtle disagreements amidst segments.In an extreme case, you feces steer each individual separately (called one-to-one marketing) to meet consumers change needs in the best possible way. Examples of such a micro-marketing system are Pumas Mongolian Shoe BBQ (www. mongolianshoebbq. puma. com) and Nike ID (http//nikeid. nike. com), in which customers can fully customize a pair of shoes in a hands-on, tactile, and interactive shoe-making experience. On the other hand, the costs associated with such a dodge whitethorn be prohibitively high in many 240 9 Cluster Analysis ascertain on the clustering variables Decide on the clustering procedureHierarchical methods Select a measure of similarity or un sameness Partitioning methods trip the light fantastic toe clustering Select a measure of similarity or disparity Choose a clustering algorithm Decide on the number of clusters Validate and interpret the cluster rootage Fig. 9. 2 stairs in a cluster analysis business contexts. Thus, we get hold of to chink that the segments are large enough to make the targeted marketing programs pro? tabularise. Consequently, we dedicate to make out with a certain power point of within-cluster heterogeneity, which makes targeted marketing programs less effective.In the ? nal step, we need to interpret the solution by de? ning and labeling the obtained clusters. This can be do by examining the clustering variables mean values or by identifying explanatory variables to pro? le the clusters. Ultimately, managers should be able to identify customers in each segment on the basis of easily measurable variables. This ? nal step in addition requires us to treasure the clustering solutions stability and rigourousness. omen 9. 2 illustrates the steps associated with a cluster analysis we pass on dissertate these in more detail in the following sections.Conducting a Cluster A nalysis Decide on the foregather Variables At the beginning of the clustering process, we have to select appropriate variables for clustering. Even though this choice is of oddment(a) richness, it is rarely treated as such and, instead, a mixture of cognizance and data availability guide most analyses in marketing practice. However, awry(p) assumptions may lead to improper market Conducting a Cluster Analysis 241 segments and, consequently, to de? cient marketing strategies. Thus, great care should be clutchn when selecting the clustering variables. There are nigh(prenominal)(prenominal) types of clustering variables and these can be classi? d into public (independent of products, services or circumstances) and speci? c (related to some(prenominal) the customer and the product, service and/or particular circumstance), on the one hand, and discernible (i. e. , measured directly) and unobservable (i. e. , inferred) on the other. Table 9. 2 take into accounts several type s and examples of clustering variables. Table 9. 2 Types and examples of clustering variables General Observable (directly Cultural, geographic, demographic, measurable) socio-economic Unobservable Psychographics, values, personality, (inferred) lifestyle Adapted from Wedel and Kamakura (2000)Speci? c substance absubstance abuser status, usage frequency, store and brand loyalty Bene? ts, perceptions, attitudes, intentions, preferences The types of variables used for cluster analysis provide different segments and, thereby, in? uence segment-targeting strategies. Over the stomach decades, caution has shifted from more tralatitious general clustering variables towards product-speci? c unobservable variables. The latter in general provide better guidance for decisions on marketing instruments effective speci? cation. It is principally acknowledged that segments identi? ed by means of speci? unobservable variables are unremarkably more homogenous and their consumers respond consis tently to marketing actions (see Wedel and Kamakura 2000). However, consumers in these segments are in any case frequently hard to identify from variables that are easily measured, such as demographics. Conversely, segments determined by means of generally observable variables usually stand out due to their identi? ability yet a good deal overlook a unique response structure. 1 Consequently, researchers often heighten different variables (e. g. , multiple lifestyle characteristics combined with demographic variables), bene? ing from each ones strengths. In some cases, the choice of clustering variables is apparent from the nature of the task at hand. For example, a managerial problem regarding corporate communications will have a fairly well de? ned set of clustering variables, including contenders such as awareness, attitudes, perceptions, and media habits. However, this is not endlessly the case and researchers have to choose from a set of nominee variables. Whichever clust ering variables are chosen, it is important to select those that provide a cleared differentiation amidst the segments regarding a speci? c managerial objective. More precisely, metre validity is of special interest that is, the extent to which the independent clustering variables are associated with 1 2 follow out Wedel and Kamakura (2000). Tonks (2009) provides a discussion of segment devise and the choice of clustering variables in consumer markets. 242 9 Cluster Analysis one or more dependent variables not included in the analysis. granted this blood, there should be signi? cant differences between the dependent variable(s) across the clusters. These associations may or may not be causal, moreover it is essential that the clustering variables distinguish the dependent variable(s) signi? antly. Criterion variables usually relate to some cyclorama of behavior, such as bribe intention or usage frequency. Generally, you should negate exploitation an abundance of clusteri ng variables, as this increases the odds that the variables are no longer dissimilar. If there is a high arcdegree of collinearity between the variables, they are not suf? ciently unique to identify distinct market segments. If extremely tally variables are used for cluster analysis, speci? c aspects covered by these variables will be overrepresented in the clustering solution.In this regard, absolute correlations above 0. 90 are always problematic. For example, if we were to add another variable called brand preference to our analysis, it would well-nigh cover the same aspect as brand loyalty. Thus, the concept of existence attached to a brand would be overrepresented in the analysis because the clustering procedure does not differentiate between the clustering variables in a conceptual sense. Researchers frequently turn this stretch out by gifting cluster analysis to the observations divisor scores derived from a previously carried out factor analysis.However, gibe to Do lnicar and Grn u (2009), this factor-cluster segmentation approach can lead to several problems 1. The data are pre-processed and the clusters are identi? ed on the basis of alter values, not on the original information, which leads to different results. 2. In factor analysis, the factor solution does not explain a certain amount of fluctuation thus, information is discarded before segments have been identi? ed or constructed. 3. Eliminating variables with low loadings on all the extracted factors means that, potentially, the most important pieces of information for the identi? ation of niche segments are discarded, making it impossible to ever identify such groups. 4. The interpretations of clusters establish on the original variables be arise questionable given that the segments have been constructed use factor scores. Several studies have shown that the factor-cluster segmentation signi? cantly avoids the success of segment reco real. 3 Consequently, you should rather reduce the number of items in the questionnaires pre-testing phase, deeming a conjectural number of germane(predicate), non-redundant questions that you believe differentiate the segments well.However, if you have your doubts about the data structure, factorclustering segmentation may still be a better option than discarding items that may conceptually be necessary. Furthermore, we should keep the test size in hear. jump and foremost, this relates to issues of managerial relevance as segments sizes need to be substantial to chequer that targeted marketing programs are pro? table. From a statistical perspective, each extra variable requires an over-proportional increase in 3 See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grn (2009). uConducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sample sizes or the relationship between the objects and the number of clustering variables used. In a related methodological context, Formann (1984) recommends a sample size of at least 2m, where m equals the number of clustering variables. This can unless provide vehement guidance nevertheless, we should pay attention to the relationship between the objects and clustering variables. It does not, for example, place logical to cluster ten objects use ten variables.Keep in mind that no offspring how many variables are used and no matter how small the sample size, cluster analysis will always give way a result Ultimately, the choice of clustering variables always depends on contextual in? uences such as data availability or resources to hit additional data. Marketing researchers often overlook the fact that the choice of clustering variables is closely connected to data note. Only those variables that ensure that high quality data can be used should be included in the analysis. This is very important if a segmentation solution has to be manager ially recyclable.Furthermore, data are of high quality if the questions asked have a strong suppositious basis, are not contaminated by respondent fatigue or response styles, are recent, and thus re? ect the current market situation (Dolnicar and Lazarevski 2009). Lastly, the requirements of other managerial functions within the organization often calculate a major role. Sales and distribution may as well have a major in? uence on the design of market segments. Consequently, we have to be aware that subjectivity and common sense concordance will (and should) always impact the choice of clustering variables.Decide on the Clustering Procedure By choosing a speci? c clustering procedure, we determine how clusters are to be form. This always occupys optimizing some kind of criterion, such as minimizing the within-cluster sectionalization (i. e. , the clustering variables overall variance of objects in a speci? c cluster), or maximizing the quad between the objects or clusters. Th e procedure could also address the question of how to determine the (dis)similarity between objects in a forward-lookingly form cluster and the be objects in the dataset.There are many different clustering procedures and also many ways of classifying these (e. g. , overlapping versus non-overlapping, unimodal versus multimodal, exhaustive versus non-exhaustive). 4 A practical distinction is the differentiation between hierarchical and partitioning methods (most notably the k-means procedure), which we are going to discuss in the next sections. We also take in trip the light fantastic toe clustering, which combines the principles of hierarchical and partitioning methods and which has recently gained increasing attention from market research practice.See Wedel and Kamakura (2000), Dolnicar (2003), and Kaufman and Rousseeuw (2005) for a review of clustering techniques. 4 244 9 Cluster Analysis Hierarchical Methods Hierarchical clustering procedures are characterized by the tree-li ke structure established in the course of the analysis. Most hierarchical techniques take root into a category called collective clustering. In this category, clusters are consecutively formed from objects. Initially, this type of procedure starts with each object representing an individual cluster.These clusters are and so(prenominal) sequentially integrated according to their similarity. First, the two most similar clusters (i. e. , those with the smallest remoteness between them) are merged to form a new cluster at the bottom of the power structure. In the next step, another pair of clusters is merged and linked to a higher level of the power structure, and so on. This allows a hierarchy of clusters to be established from the bottom up. In Fig. 9. 3 (left-hand side), we show how agglomerative clustering assigns additional objects to clusters as the cluster size increases. bill 5 Step 1 A, B, C, D, EAgglomerative clustering Step 4 Step 2 factious clustering A, B C, D, E St ep 3 Step 3 A, B C, D E Step 2 Step 4 A, B C D E Step 1 Step 5 A B C D E Fig. 9. 3 Agglomerative and divisive clustering A cluster hierarchy can also be generated top-down. In this divisive clustering, all objects are initially merged into a iodin cluster, which is then gradually depart up. bit 9. 3 illustrates this concept (right-hand side). As we can see, in both agglomerative and divisive clustering, a cluster on a higher level of the hierarchy always encompasses all clusters from a lower level.This means that if an object is delegate to a certain cluster, there is no opening move of reappointment this object to another cluster. This is an important distinction between these types of clustering and partitioning methods such as k-means, which we will explore in the next section. Divisive procedures are quite rarely used in market research. We therefore concentrate on the agglomerative clustering procedures. There are discordant types Conducting a Cluster Analysis 245 of agglo merative procedures. However, before we discuss these, we need to de? ne how similarities or dissimilarities are measured between pairs of objects.Select a euphony of Similarity or Dissimilarity There are various measures to dribble (dis)similarity between pairs of objects. A straightforward way to pass judgment two objects propinquity is by drawing a straight line between them. For example, when we look at the scatter plot in Fig. 9. 1, we can easily see that the continuance of the line connecting observations B and C is some(prenominal) shorter than the line connecting B and G. This type of outdo is also referred to as euclidian maintain (or straight-line out outmatch) and is the most comm wholly used type when it comes to analyzing ratio or interval- outperformd data. In our example, we have ordinal number data, but market researchers usually treat ordinal data as measured function data to calculate distance metrics by assuming that the scale steps are equidistant (ver y much like in factor analysis, which we discussed in Chap. 8). To use a hierarchical clustering procedure, we need to exhibit these distances mathematically. By taking the data in Table 9. 1 into consideration, we can easily figure the Euclidean distance between customer B and customer C (generally referred to as d(B,C)) with regard to the two variables x and y by using the following formula q Euclidean ? B C? ? ? xB A xC ? 2 ? ?yB A yC ? 2 The Euclidean distance is the square root of the sum of the shape differences in the variables values. Using the data from Table 9. 1, we obtain the following q p dEuclidean ? B C? ? ? 6 A 5? 2 ? ?7 A 6? 2 ? 2 ? 1414 This distance corresponds to the length of the line that connects objects B and C. In this case, we only used two variables but we can easily add more under the root sign in the formula. However, each additional variable will add a dimension to our research problem (e. . , with six clustering variables, we have to convey with si x dimensions), making it impossible to represent the solution graphically. Similarly, we can deem the distance between customer B and G, which yields the following q p dEuclidean ? B G? ? ? 6 A 1? 2 ? ?7 A 2? 2 ? 50 ? 7071 Likewise, we can estimate the distance between all other pairs of objects. All these distances are usually verbalized by means of a distance hyaloplasm. In this distance intercellular substance, the non-diagonal elements express the distances between pairs of objects 5 spirit that researchers also often use the squared Euclidean distance. 246 9 Cluster Analysis and zeros on the diagonal (the distance from each object to itself is, of course, 0). In our example, the distance matrix is an 8 A 8 table with the lines and rows representing the objects (i. e. , customers) under consideration (see Table 9. 3). As the distance between objects B and C (in this case 1. 414 units) is the same as between C and B, the distance matrix is symmetrical. Furthermore, since the distance between an object and itself is zero, one need only look at either the lower or upper non-diagonal elements.Table 9. 3 Euclidean distance matrix Objects A B A 0 B 3 0 C 2. 236 1. 414 D 2 3. 606 E 3. 606 2 F 4. 123 4. 472 G 5. 385 7. 071 C D E F G 0 2. 236 1. 414 3. 162 5. 657 0 3 2. 236 3. 606 0 2. 828 5. 831 0 3. 162 0 There are also alternative distance measures The city-block distance uses the sum of the variables absolute differences. This is often called the Manhattan metric as it is akin to the walking distance between two points in a city like juvenile Yorks Manhattan district, where the distance equals the number of blocks in the directions North-South and East-West.Using the city-block distance to compute the distance between customers B and C (or C and B) yields the following dCityAblock ? B C? ? jxB A xC j ? jyB A yC j ? j6 A 5j ? j7 A 6j ? 2 The resulting distance matrix is in Table 9. 4. Table 9. 4 City-block distance matrix Objects A B A 0 B 3 0 C 3 2 D 2 5 E 5 2 F 5 6 G 7 10 C D E F G 0 3 2 4 8 0 3 3 5 0 4 8 0 4 0 Lastly, when working with metric (or ordinal) data, researchers frequently use the Chebychev distance, which is the level best of the absolute difference in the clustering variables values. In respect of customers B and C, this result is dChebychec ? B C? max? jxB A xC j jyB A yC j? ? max? j6 A 5j j7 A 6j? ? 1 Figure 9. 4 illustrates the interrelation between these three distance measures regarding two objects, C and G, from our example. Conducting a Cluster Analysis 247 C tell on loyalty (y) Euclidean distance City-block distance G Chebychev distance Price consciousness (x) Fig. 9. 4 Distance measures There are other distance measures such as the Angular, Canberra or Mahalanobis distance. In many situations, the latter is desirable as it compensates for collinearity between the clustering variables. However, it is (unfortunately) not menu-accessible in SPSS.In many analysis tasks, the variables under consideration are meas ured on different scales or levels. This would be the case if we extended our set of clustering variables by adding another ordinal variable representing the customers income measured by means of, for example, 15 categories. Since the absolute divergence of the income variable would be much greater than the variation of the remaining two variables (remember, that x and y are measured on 7-point scales), this would clearly reach our analysis results. We can re exonerate this problem by standardizing the data previous to the analysis.Different standardization methods are useable, such as the simple z standardization, which rescales each variable to have a mean of 0 and a standard deviation of 1 (see Chap. 5). In most situations, however, standardization by ramble on (e. g. , to a range of 0 to 1 or A1 to 1) performs better. 6 We recommend standardizing the data in general, even though this procedure can reduce or in? ate the variables in? uence on the clustering solution. 6 See Mil ligan and Cooper (1988). 248 9 Cluster Analysis Another way of (implicitly) standardizing the data is by using the correlation between the objects instead of distance measures.For example, suppose a respondent rated price consciousness 2 and brand loyalty 3. Now suppose a trice respondent indicated 5 and 6, whereas a third rated these variables 3 and 3. Euclidean, city-block, and Chebychev distances would indicate that the ? rst respondent is more similar to the third than to the second. Nevertheless, one could convincingly argue that the ? rst respondents ratings are more similar to the seconds, as both rate brand loyalty higher than price consciousness. This can be accounted for by reckoning the correlation between two vectors of values as a measure of similarity (i. . , high correlation coef? cients indicate a high degree of similarity). Consequently, similarity is no longer de? ned by means of the difference between the answer categories but by means of the similarity of the a nswering pro? les. Using correlation is also a way of standardizing the data implicitly. Whether you use correlation or one of the distance measures depends on whether you think the relation back magnitude of the variables within an object (which favors correlation) matters more than the relative magnitude of each variable across objects (which favors distance).However, it is generally recommended that one uses correlations when applying clustering procedures that are predisposed to outliers, such as complete gene linkage, average linkage or centroid (see next section). Whereas the distance measures presented thus far can be used for metrically and in general ordinally scaled data, applying them to nominal or binary data is meaningless. In this type of analysis, you should rather select a similarity measure expressing the degree to which variables values share the same category. These socalled coordinated coef? ients can take different forms but rely on the same allocation intrigue shown in Table 9. 5. Table 9. 5 Allocation scheme for matching coef? cients Number of variables with category 1 a c Object 1 Number of variables with category 2 b d Object 2 Number of variables with category 1 Number of variables with category 2 found on the allocation scheme in Table 9. 5, we can compute different matching coef? cients, such as the simple matching coef? cient (SM) SM ? a? d a? b? c? d This coef? cient is useful when both positive and negative values carry an equal degree of information.For example, gender is a symmetrical attribute because the number of males and females provides an equal degree of information. Conducting a Cluster Analysis 249 Lets take a look at an example by assuming that we have a dataset with three binary variables gender (male ? 1, female ? 2), customer (customer ? 1, noncustomer ? 2), and disposable income (low ? 1, high ? 2). The ? rst object is a male non-customer with a high disposable income, whereas the second object is a fem ale non-customer with a high disposable income. harmonise to the scheme in Table 9. , a ? b ? 0, c ? 1 and d ? 2, with the simple matching coef? cient taking a value of 0. 667. Two other types of matching coef? cients, which do not equate the joint absence seizure of a characteristic with similarity and may, therefore, be of more value in segmentation studies, are the Jaccard (JC) and the Russel and Rao (RR) coef? cients. They are de? ned as follows a JC ? a? b? c a RR ? a? b? c? d These matching coef? cients are just like the distance measures used to determine a cluster solution. There are many other matching coef? ients such as Yules Q, Kulczynski or Ochiai, but since most applications of cluster analysis rely on metric or ordinal data, we will not discuss these in greater detail. 7 For nominal variables with more than two categories, you should always convince the categorical variable into a set of binary variables in order to use matching coef? cients. When you have ordinal data, you should always use distance measures such as Euclidean distance. Even though using matching coef? cients would be feasible and from a strictly statistical standpoint even more appropriate, you would disregard variable information in the sequence of the categories.In the end, a respondent who indicates that he or she is very loyal to a brand is going to be enveloping(prenominal) to someone who is about loyal than a respondent who is not loyal at all. Furthermore, distance measures best represent the concept of proximity, which is fundamental to cluster analysis. Most datasets submit variables that are measured on multiple scales. For example, a market research questionnaire may ask about the respondents income, product ratings, and last brand purchased. Thus, we have to consider variables measured on a ratio, ordinal, and nominal scale. How can we simultaneously incorporate these variables into one analysis?Unfortunately, this problem cannot be easily resolved and, in fact, many market researchers plain force out the scale level. Instead, they use one of the distance measures discussed in the context of metric (and ordinal) data. Even though this approach may sparingly change the results when examined to those using matching coef? cients, it should not be rejected. Cluster analysis is mostly an exploratory technique whose results provide a rough guidance for managerial decisions. despite this, there are several procedures that allow a simultaneous integrating of these variables into one analysis. 7See Wedel and Kamakura (2000) for more information on alternative matching coef? cients. 250 9 Cluster Analysis First, we could compute distinct distance matrices for each group of variables that is, one distance matrix establish on, for example, ordinally scaled variables and another based on nominal variables. Afterwards, we can simply compute the weighted arithmetic mean of the distances and use this average distance matrix as the stimulant for the cluster analysis. However, the weights have to be determined a priori and improper weights may result in a biased treatment of different variable types.Furthermore, the computation and handling of distance matrices are not trivial. Using the SPSS syntax, one has to manually add the MATRIX subcommand, which exports the initial distance matrix into a new data ? le. Go to the 8 Web Appendix ( Chap. 5) to exact how to modify the SPSS syntax accordingly. Second, we could dichotomize all variables and apply the matching coef? cients discussed above. In the case of metric variables, this would involve specifying categories (e. g. , low, medium, and high income) and converting these into sets of binary variables. In most cases, however, the speci? ation of categories would be rather arbitrary and, as mentioned earlier, this procedure could lead to a severe loss of information. In the light of these issues, you should avoid combining metric and nominal variables in a iodine cluster analysis, but if this is not feasible, the two-step clustering procedure provides a valuable alternative, which we will discuss later. Lastly, the choice of the (dis)similarity measure is not extremely critical to recovering the underlie cluster structure. In this regard, the choice of the clustering algorithm is far more important.We therefore deal with this aspect in the following section. Select a Clustering Algorithm After having chosen the distance or similarity measure, we need to decide which clustering algorithm to apply. There are several agglomerative procedures and they can be distinguished by the way they de? ne the distance from a newly formed cluster to a certain object, or to other clusters in the solution. The most popular agglomerative clustering procedures include the following l l l l maven(a) linkage (nearest neighbor) The distance between two clusters corresponds to the shortest distance between any two members in the two clusters. established linkage (furth est neighbor) The oppositional approach to single linkage assumes that the distance between two clusters is based on the longest distance between any two members in the two clusters. Average linkage The distance between two clusters is de? ned as the average distance between all pairs of the two clusters members. Centroid In this approach, the geometric center (centroid) of each cluster is computed ? rst. The distance between the two clusters equals the distance between the two centroids. Figures 9. 59. 8 illustrate these linkage procedures for two hit-or-missly framed clusters.Conducting a Cluster Analysis Fig. 9. 5 Single linkage 251 Fig. 9. 6 Complete linkage Fig. 9. 7 Average linkage Fig. 9. 8 Centroid 252 9 Cluster Analysis Each of these linkage algorithms can yield totally different results when used on the same dataset, as each has its speci? c properties. As the single linkage algorithm is based on minimum distances, it tends to form one large cluster with the other cluste rs containing only one or few objects each. We can make use of this chaining effect to detect outliers, as these will be merged with the remaining objects usually at very large distances in the last steps of the analysis.Generally, single linkage is considered the most versatile algorithm. Conversely, the complete linkage method is strongly affected by outliers, as it is based on uttermost distances. Clusters produced by this method are likely to be rather compact and tightly clustered. The average linkage and centroid algorithms tend to produce clusters with rather low within-cluster variance and similar sizes. However, both procedures are affected by outliers, though not as much as complete linkage. Another commonly used approach in hierarchical clustering is Wards method. This approach does not combine the two most similar objects successively.Instead, those objects whose merger increases the overall within-cluster variance to the smallest possible degree, are combined. If yo u expect somewhat equally sized clusters and the dataset does not include outliers, you should always use Wards method. To better understand how a clustering algorithm whole kit and caboodle, lets manually examine some of the single linkage procedures calculation steps. We start off by looking at the initial (Euclidean) distance matrix in Table 9. 3. In the very ? rst step, the two objects exhibiting the smallest distance in the matrix are merged.Note that we always merge those objects with the smallest distance, regardless of the clustering procedure (e. g. , single or complete linkage). As we can see, this happens to two pairs of objects, namely B and C (d(B, C) ? 1. 414), as well as C and E (d(C, E) ? 1. 414). In the next step, we will see that it does not make any difference whether we ? rst merge the one or the other, so lets ascend by forming a new cluster, using objects B and C. Having made this decision, we then form a new distance matrix by considering the single linkage decision rule as discussed above.According to this rule, the distance from, for example, object A to the newly formed cluster is the minimum of d(A, B) and d(A, C). As d(A, C) is smaller than d(A, B), the distance from A to the newly formed cluster is equal to d(A, C) that is, 2. 236. We also compute the distances from cluster B,C (clusters are indicated by means of squared brackets) to all other objects (i. e. D, E, F, G) and simply copy the remaining distances such as d(E, F) that the previous clustering has not affected. This yields the distance matrix shown in Table 9. 6.Continuing the clustering procedure, we simply repeat the last step by merging the objects in the new distance matrix that exhibit the smallest distance (in this case, the newly formed cluster B, C and object E) and calculate the distance from this cluster to all other objects. The result of this step is described in Table 9. 7. Try to calculate the remaining steps yourself and compare your solution with the d istance matrices in the following Tables 9. 89. 10. Conducting a Cluster Analysis Table 9. 6 Distance matrix after ? rst clustering step (single linkage) Objects A B, C D E F G A 0 B, C 2. 36 0 D 2 2. 236 0 E 3. 606 1. 414 3 0 F 4. 123 3. 162 2. 236 2. 828 0 G 5. 385 5. 657 3. 606 5. 831 3. 162 0 253 Table 9. 7 Distance matrix after second clustering step (single linkage) Objects A B, C, E D F G A 0 B, C, E 2. 236 0 D 2 2. 236 0 F 4. 123 2. 828 2. 236 0 G 5. 385 5. 657 3. 606 3. 162 0 Table 9. 8 Distance matrix after third clustering step (single linkage) Objects A, D B, C, E F G A, D 0 B, C, E 2. 236 0 F 2. 236 2. 828 0 G 3. 606 5. 657 3. 162 0 Table 9. 9 Distance matrix after fourth clustering step (single linkage) Objects A, B, C, D, E F G A, B, C, D, E 0 F 2. 236 0 G 3. 06 3. 162 0 Table 9. 10 Distance matrix after ? fth clustering step (single linkage) Objects A, B, C, D, E, F G A, B, C, D, E, F 0 G 3. 162 0 By following the single linkage procedure, the last steps involve the merger of cluster A,B,C,D,E,F and object G at a distance of 3. 162. Do you get the same results? As you can see, conducting a basic cluster analysis manually is not that hard at all not if there are only a few objects in the dataset. A common way to visualize the cluster analysiss progress is by drawing a dendrogram, which displays the distance level at which there was a ombination of objects and clusters (Fig. 9. 9). We read the dendrogram from left to right to see at which distance objects have been combined. For example, according to our calculations above, objects B, C, and E are combined at a distance level of 1. 414. 254 B C E A D F G 9 Cluster Analysis 0 1 2 Distance 3 Fig. 9. 9 Dendrogram Decide on the Number of Clusters An important question we havent yet intercommunicate is how to decide on the number of clusters to retain from the data. Unfortunately, hierarchical methods provide only very particular(a) guidance for making this decision.The only meaningful index finge r relates to the distances at which the objects are combined. Similar to factor analysiss astragal plot, we can seek a solution in which an additional conspiracy of clusters or objects would occur at a greatly increased distance. This raises the issue of what a great distance is, of course. unmatched potential way to solve this problem is to plot the number of clusters on the x-axis (starting with the one-cluster solution at the very left) against the distance at which objects or clusters are combined on the y-axis.Using this plot, we then search for the distinctive break (elbow). SPSS does not produce this plot mechanically you have to use the distances provided by SPSS to draw a line chart by using a common spreadsheet program such as Microsoft Excel. Alternatively, we can make use of the dendrogram which essentially carries the same information. SPSS provides a dendrogram however, this differs slightly from the one presented in Fig. 9. 9. Speci? cally, SPSS rescales the dist ances to a range of 025 that is, the last merging step to a one-cluster solution takes place at a (rescaled) distance of 25.The rescaling often lengthens the merging steps, thus making breaks occurring at a greatly increased distance level more apparent. Despite this, this distance-based decision rule does not work very well in all cases. It is often dif? cult to identify where the break actually occurs. This is also the case in our example above. By looking at the dendrogram, we could justify a two-cluster solution (A,B,C,D,E,F and G), as well as a ? ve-cluster solution (B,C,E, A, D, F, G). Conducting a Cluster Analysis 255 Research has suggested several other procedures for determining the number of clusters in a dataset.Most notably, the variance ratio criterion (VRC) by Calinski and Harabasz (1974) has proved to work well in many situations. 8 For a solution with n objects and k segments, the criterion is given by VRCk ? ?SSB =? k A 1 =? SSW =? n A k where SSB is the sum of t he squares between the segments and SSW is the sum of the squares within the segments. The criterion should seem familiar, as this is zippo but the F-value of a one-way ANOVA, with k representing the factor levels. Consequently, the VRC can easily be computed using SPSS, even though it is not readily available in the clustering procedures outputs.To ? nally determine the appropriate number of segments, we compute ok for each segment solution as follows ok ? ?VRCk? 1 A VRCk ? A ? VRCk A VRCkA1 ? In the next step, we choose the number of segments k that minimizes the value in ok. Owing to the term VRCkA1, the minimum number of clusters that can be selected is three, which is a clear disadvantage of the criterion, thus limiting its application in practice. Overall, the data can often only provide rough guidance regarding the number of clusters you should select consequently, you should rather revert to practical considerations.Occasionally, you mightiness have a priori knowledge, or a theory on which you can base your choice. However, ? rst and foremost, you should ensure that your results are interpretable and meaningful. Not only mustiness the number of clusters be small enough to ensure manageability, but each segment should also be large enough to warrant strategical attention. Partitioning Methods k-means Another important group of clustering procedures are partitioning methods. As with hierarchical clustering, there is a wide array of different algorithms of these, the k-means procedure is the most important one for market research. The k-means algorithm follows an solely different concept than the hierarchical methods discussed before. This algorithm is not based on distance measures such as Euclidean distance or city-block distance, but uses the within-cluster variation as a Milligan and Cooper (1985) compare various criteria. Note that the k-means algorithm is one of the simplest non-hierarchical clustering methods. Several extensions, such as k-me doids (Kaufman and Rousseeuw 2005) have been proposed to handle problematic aspects of the procedure. More advanced methods include ? ite mixture models (McLachlan and ransack 2000), neural networks (Bishop 2006), and self-organizing maps (Kohonen 1982). Andrews and Currim (2003) discuss the validity of some of these approaches. 9 8 256 9 Cluster Analysis measure to form homogenous clusters. Speci? cally, the procedure aims at segmenting the data in such a way that the within-cluster variation is minimized. Consequently, we do not need to decide on a distance measure in the ? rst step of the analysis. The clustering process starts by randomly assigning objects to a number of clusters. 0 The objects are then successively reassigned to other clusters to minimize the within-cluster variation, which is basically the (squared) distance from each observation to the center of the associated cluster. If the reapportionment of an object to another cluster decreases the within-cluster varia tion, this object is reassigned to that cluster. With the hierarchical methods, an object trunk in a cluster once it is assigned to it, but with k-means, cluster af? liations can change in the course of the clustering process. Consequently, k-means does not puddle a hierarchy as described before (Fig. . 3), which is why the approach is also frequently labeled as non-hierarchical. For a better understanding of the approach, lets take a look at how it works in practice. Figs. 9. 109. 13 illustrate the k-means clustering process. Prior to analysis, we have to decide on the number of clusters. Our client could, for example, tell us how many segments are needed, or we may know from previous research what to look for. Based on this information, the algorithm randomly selects a center for each cluster (step 1). In our example, two cluster centers are randomly initiated, which CC1 (? st cluster) and CC2 (second cluster) in Fig. 9. 10 A CC1 C B D E Brand loyalty (y) CC2 F G Price conscious ness (x) Fig. 9. 10 k-means procedure (step 1) 10 Note this holds for the algorithms original design. SPSS does not choose centers randomly. Conducting a Cluster Analysis A CC1 C B 257 D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 11 k-means procedure (step 2) A CC1 CC1? C B Brand loyalty (y) D E CC2 CC2? F G Price consciousness (x) Fig. 9. 12 k-means procedure (step 3) 258 A CC1? 9 Cluster Analysis B C Brand loyalty (y) D E CC2? F G Price consciousness (x) Fig. 9. 13 k-means procedure (step 4) epresent. 11 After this (step 2), Euclidean distances are computed from the cluster centers to every single object. Each object is then assigned to the cluster center with the shortest distance to it. In our example (Fig. 9. 11), objects A, B, and C are assigned to the ? rst cluster, whereas objects D, E, F, and G are assigned to the second. We now have our initial partitioning of the objects into two clusters. Based on this initial partition, each clusters geometric center (i . e. , its centroid) is computed (third step). This is done by computing the mean values of the objects contained in the cluster (e. . , A, B, C in the ? rst cluster) regarding each of the variables (price consciousness and brand loyalty). As we can see in Fig. 9. 12, both clusters centers now shift into new positions (CC1 for the ? rst and CC2 for the second cluster). In the fourth step, the distances from each object to the newly determined cluster centers are computed and objects are again assigned to a certain cluster on the basis of their minimum distance to other cluster centers (CC1 and CC2). Since the cluster centers position changed with respect to the initial situation in the ? st step, this could lead to a different cluster solution. This is also true of our example, as object E is now unlike in the initial partition closer to the ? rst cluster center (CC1) than to the second (CC2). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-mean s procedure now repeats the third step and re-computes the cluster centers of the newly formed clusters, and so on. In other 11 Conversely, SPSS always sets one observation as the cluster center instead of picking some random point in the dataset. Conducting a Cluster Analysis 59 words, steps 3 and 4 are repeated until a predetermined number of iterations are reached, or convergence is achieved (i. e. , there is no change in the cluster af? liations). Generally, k-means is superior to hierarchical methods as it is less affected by outliers and the presence of irrelevant clustering variables. Furthermore, k-means can be applied to very large datasets, as the procedure is less computationally demanding than hierarchical methods. In fact, we suggest de? nitely using k-means for sample sizes above 500, especially if many clustering variables are used.From a strictly statistical viewpoint, k-means should only be used on interval or ratioscaled data as the procedure relies on Euclidean di stances. However, the procedure is routinely used on ordinal data as well, even though there might be some distortions. One problem associated with the application of k-means relates to the fact that the researcher has to pre-specify the number of clusters to retain from the data. This makes k-means less attractive to some and still hinders its routine application in practice. However, the VRC discussed above can likewise be used for k-means clustering an application of this index can be found in the 8 Web Appendix Chap. 9). Another workaround that many market researchers routinely use is to apply a hierarchical procedure to determine the number of clusters and k-means afterwards. 12 This also enables the user to ? nd starting values for the initial cluster centers to handle a second problem, which relates to the procedures sensitivity to the initial classi? cation (we will follow this approach in the example application). dance Clustering We have already discussed the issue of an alyzing mixed variables measured on different scale levels in this chapter.The two-step cluster analysis developed by Chiu et al. (2001) has been speci? cally intentional to handle this problem. Like k-means, the procedure can also effectively cope with very large datasets. The name two-step clustering is already an quality that the algorithm is based on a two-stage approach In the ? rst stage, the algorithm undertakes a procedure that is very similar to the k-means algorithm. Based on these results, the two-step procedure conducts a modi? ed hierarchical agglomerative clustering procedure that combines the objects sequentially to form homogenous clusters.This is done by building a so-called cluster feature tree whose leaves represent distinct objects in the dataset. The procedure can handle categorical and continuous variables simultaneously and offers the user the ? exibility to specify the cluster numbers as well as the maximum number of clusters, or to allow the technique to a utomatically choose the number of clusters on the basis of statistical evaluation criteria. Likewise, the procedure guides the decision of how many clusters to retain from the data by calculating measures-of-? t such as Akaikes Information Criterion (AIC) or Bayes 2 See Punji and Stewart (1983) for additional information on this sequential approach. 260 9 Cluster Analysis Information Criterion (BIC). Furthermore, the procedure indicates each variables importance for the construction of a speci? c cluster. These desirable features make the somewhat less popular two-step clustering a viable alternative to the traditional methods. You can ? nd a more detailed discussion of the two-step clustering procedure in the 8 Web Appendix ( Chap. 9), but we will also apply this method in the subsequent example.Validate and find the Cluster Solution Before interpreting the cluster solution, we have to assess the solutions stability and validity. Stability is evaluated by using different clusterin g procedures on the same data and testing whether these yield the same results. In hierarchical clustering, you can likewise use different distance measures. However, please note that it is common for results to change even when your solution is adequate. How much variation you should allow before questioning the stability of your solution is a matter of taste.Another common approach is to split the dataset into two halves and to thereafter dissect the two subsets separately using the same parameter settings. You then compare the two solutions cluster centroids. If these do not differ signi? cantly, you can make bold that the overall solution has a high degree of stability. When using hierarchical clustering, it is also worthwhile changing the order of the objects in your dataset and re-running the analysis to regress the results stability. The results should not, of course, depend on the order of the dataset. If they do, you should try to ascertain if any obvious outliers may in ? ence the results of the change in order. Assessing the solutions dependability is closely related to the above, as reliability refers to the degree to which the solution is unchangeable over time. If segments quickly change their composition, or its members their behavior, targeting strategies are likely not to succeed. Therefore, a certain degree of stability is necessary to ensure that marketing strategies can be implemented and produce adequate results. This can be evaluated by critically revisiting and replicating the clustering results at a later point in time. To validate the clustering solution, we need to assess its criterion validity.In research, we could concentrate on on criterion variables that have a theoretically based relationship with the clustering variables, but were not included in the analysis. In market research, criterion variables usually relate to managerial outcomes such as the gross revenue per person, or satisfaction. If these criterion variables diff er signi? cantly, we can conclude that the clusters are distinct groups with criterion validity. To judge validity, you should also assess face validity and, if possible, skilled validity. While we primarily consider criterion validity when choosing clustering variables, as well as in this ? al step of the analysis procedure, the sagacity of face validity is a process rather than a single event. The key to successful segmentation is to critically revisit the results of different cluster analysis set-ups (e. g. , by using Conducting a Cluster Analysis 261 different algorithms on the same data) in terms of managerial relevance. This underlines the exploratory character of the method. The following criteria will help you make an evaluation choice for a clustering solution (Dibb 1999 Tonks 2009 Kotler and Keller 2009). l l l l l l l l l l Substantial The segments are large and pro? able enough to serve. Accessible The segments can be effectively reached and served, which requires them to be characterized by means of observable variables. Differentiable The segments can be distinguished conceptually and respond differently to different marketing-mix elements and programs. actionable Effective programs can be formulated to attract and serve the segments. constant Only segments that are stable over time can provide the necessary grounds for a successful marketing strategy. Parsimonious To be managerially meaningful, only a small set of substantial clusters should be identi? ed.Familiar To ensure management acceptance, the segments composition should be comprehensible. Relevant Segments should be relevant in respect of the companys competencies and objectives. Compactness Segments exhibit a high degree of within-segment homogeneity and between-segment heterogeneity. Compatibility Segmentation results meet other managerial functions requirements. The ? nal step of any cluster analysis is the interpretation of the clusters. Interpreting clusters always involves exami ning the cluster centroids, which are the clustering variables average values of all objects in a certain cluster.This step is of the utmost importance, as the analysis sheds light on whether the segments are conceptually distinguishable. Only if certain clusters exhibit signi? cantly different means in these variables are they distinguishable from a data perspective, at least. This can easily be ascertained by comparing the clusters with independent t-tests samples or ANOVA (see Chap. 6). By using this information, we can also try to come up with a meaningful name or label for each cluster that is, one which adequately re? ects the objects in the cluster.This is usually a very challenging task. Furthermore, clustering variables are frequently unobservable, which poses another problem. How can we decide to which segment a new object should be assigned if its unobservable characteristics, such as personality traits, personal values or lifestyles, are unknown? We could obviously try to survey these attributes and make a decision based on the clustering variables. However, this will not be feasible in most situations and researchers therefore try to identify observable variables that best mirror the partition of the objects.If it is possible to identify, for example, demographic variables leading to a very similar partition as that obtained through the segmentation, then it is easy to assign a new object to a certain segment on the basis of these demographic 262 9 Cluster Analysis characteristics. These variables can then also be used to characterize speci? c segments, an action commonly called pro? ling. For example, imagine that we used a set of items to assess the respondents values and learned that a certain segment comprises respondents who appreciate self-ful? lment, consumption of life, and a sense of accomplishment, whereas this is not the case in another segment. If we were able to identify explanatory variables such as gender or age, which adequately distinguish these segments, then we could partition a new person based on the modalities of these observable variables whose traits may still be unknown. Table 9. 11 summarizes the steps involved in a hierarchical and k-means clustering. While companies often develop their own market segments, they frequently use regularized segments, which are based on established buying trends, habits, and customers needs and have been speci? ally designed for use by many products in mature markets. One of the most popular approaches is the PRIZM lifestyle segmentation system developed by Claritas Inc. , a leading market research company. PRIZM de? nes every US phratry in terms of 66 demographically and behaviorally distinct segments to help marketers discern those consumers likes, dislikes, lifestyles, and purchase behaviors. Visit the Claritas website and ? ip through the various segment pro? les. By entering a 5-digit US ZIP code, you can also ? nd a speci? c neighborhoods top ? ve lifestyle groups.One example of a segment is Gray Power, containing middle-class, homeowning suburbanites who are aging in place rather than moving to retirement communities. Gray Power re? ects this trend, a segment of older, midscale singles and couples who live in quiet comfort. http//www. claritas. com/MyBestSegments/Default. jsp We also assert steps related to two-step clustering which we will further introduce in the subsequent example. Conducting a Cluster Analysis 263 Table 9. 11 Steps involved in carrying out a factor analysis in SPSS Theory Action Research problem Identi? ation of homogenous groups of objects in a population Select clustering variables that should be Select relevant variables that potentially exhibit used to form segments high degrees of criterion validity with regard to a speci? c managerial objective. Requirements Suf? cient sample size deem sure that the relationship between objects and clustering variables is reasonable (rough guideline number of observations should be at least 2m, where m is the number of clustering variables). underwrite that the sample size is large enough to guarantee substantial segments. misfortunate levels of collinearity among the variables ? crumble ? Correlate ? Bivariate Eliminate or replace highly correlated variables (correlation coef? cients 0. 90). Speci? cation Choose the clustering procedure If there is a limited number of objects in your dataset or you do not know the number of clusters ? Analyze ? branch ? Hierarchical Cluster If there are many observations ( 500) in your dataset and you have a priori knowledge regarding the number of clusters ? Analyze ? break ? K-Means Cluster If there are many observations in your dataset and the clustering variables are measured on different scale levels ? Analyze ? Classify ?Two-Step Cluster Select a measure of similarity or dissimilarity Hierarchical methods (only hierarchical and two-step clustering) ? Analyze ? Classify ? Hierarchical Cluster ? Method ? v erse Depending on the scale level, select the measure convert variables with multiple categories into a set of binary variables and use matching coef? cients standardize variables if necessary (on a range of 0 to 1 or A1 to 1). Two-step clustering ? Analyze ? Classify ? Two-Step Cluster ? Distance Measure Use Euclidean distances when all variables are continuous for mixed variables, use log-likelihood. ? Analyze ? Classify ?Hierarchical Cluster ? Choose clustering algorithm Method ? Cluster Method (only hierarchical clustering) Use Wards method if equally sized clusters are expected and no outliers are present. Preferably use single linkage, also to detect outliers. Decide on the number of clusters Hierarchical clustering Examine the dendrogram ? Analyze ? Classify ? Hierarchical Cluster ? Plots ? Dendrogram (continued) 264 Table 9. 11 (continued) Theory 9 Cluster Analysis Action Draw a scree plot (e. g. , using Microsoft Excel) based on the coef? cients in the agglomeration schedul e. Compute the VRC using the ANOVA procedure ? Analyze ?Compare Means ? One-Way ANOVA Move the cluster membership variable in the Factor box and the clustering variables in the symbiotic List box. Compute VRC for each segment solution and compare values. k-means fiddle a hierarchical cluster analysis and decide on the number of segments based on a dendrogram or scree plot use this information to run k-means with k clusters. Compute the VRC using the ANOVA procedure ? Analyze ? Classify ? K-Means Cluster ? Options ? ANOVA table Compute VRC for each segment solution and compare values. Two-step clustering Specify the maximum number of clusters ? Analyze ? Classify ? Two-Step Cluster ?Number of Clusters digest separate analyses using AIC and, alternatively, BIC as clustering criterion ? Analyze ? Classify ? Two-Step Cluster ? Clustering Criterion Examine the auto-clustering output. Re-run the analysis using different clustering procedures, algorithms or distance measures. Split the datasets into two halves and compute the clustering variables centroids compare ce

Educational Opportunity Program Essay

The c tout ensemble down University of New York Educational chance Program was created by the convey of New York in night club to provide get at, academic give birthing and financial aid to students who may not otherwise be admitted to demesne alumna plans. This programme falls under state university guidelines within the state government so there argon specific enrollment rules much(prenominal) as the necessity of being a New York pass on nonmigratory at the time of application. The website for the program is http//www.suny. edu/Student/academic_eop. cfm.Mission The mission of the verbalize University of New York Educational chance Program is to provide access to graduate train education. This program focuses on students who show promise within their elect field but who may not have access to a graduate education. The EOP program care fully considers all applicants but gives preference to students who are ineligible for enrollment under standard guidelines and/or co me from disfavor backgrounds.Further, the program is intentional to support students financially and academically in prepare to give them an opportunity to complete a higher education story while in any case providing the support necessity to live up to their full potential. Resources The most important resource that the State University of New York Educational Opportunity Program provides is a graduate train education. Students who are part of the program receive support service including academic, career and face-to-face counseling work as well as tutoring and additional instruction if demand.The EOP program also provides financial aid for non tuition related expenses such as books and coach supplies. However, the most important resource that the program offers is the monetary aid necessary to enroll in and complete a graduate level pipeline of study. Good and Services While the State University New York Educational Opportunity Program does not provide tangible goods i t does provide essential services to its more than ten thousand current students as well as its over fifty thousand alumni.The purpose of the services offered is to provide the tools necessary for economically or racially disadvantaged students to complete graduate level courses of study. These services help these students overcome the educational challenges they confront in order to obtain higher education and go on to live booming and productive lives. To this end, the EOP program is customized to meet the academic, career and personal needs of all students through a variety of counseling programs. Further, the services offered are designed in such a way so that students receive the support necessary to complete their degree even when facing enormous struggles. causality students report feeling as if their support mentors enabled them to believe in themselves and to never give up on their educational dreams. Conclusion I chose this program because it is a very important part of the educational success of more disadvantaged students. I believe that all people should have access to higher education but this is not always the reality. Many students face such enormous struggles that they never receive a graduate level degree. This program has enabled many students to realize their dreams of an education through financial, career, academic and personal support. References DiNapoli, Thomas P. (2007).State University of New York Educational Opportunity Program. Division of State Government Accountability. Retrieved on April 21, 2009 from http//osc. state. ny. us/audits/allaudits/093008/07s99. pdf. Henehan, David. (2007). SUNY Educational Opportunity Program celebrates 40 years. The State University of New York. Retrieved on April 21, 2009 from https//www. suny. edu/SUNYNews/News. cfm? PrintFlag=Y&filname=2007-11-02+final+online+EOP+turns+40+II. htm. The State University of New York. (2009). Educational opportunity program. Retrieved on April 21, 2009 from http// www. suny. edu/Student/academic_eop. cfm.

Main Currents of Spanish Thought

Among the deep thinkers that contributed to the changes that had taken set in Spain during the go away decade of the 1800s was Jose Ortega Gasset (883-1955). He is one of the most eventful Spanish thinkers whose writings examined the manifestations of culture revealing the intimate depths of individual and affectionate human condition. Ortegas contribution was in the area of command, as he believes that if one does not educate for the city, a person force outnot be brought to plenitude, and that the tutor tends to operate on preterit principles, when it should educate from the present for the future (Palmer, Bresler, & Cooper, p. 244)However, during the last decade of the 1800s in Spain, the Spanish society has been divided by a smashing postulate that raged in 1890s up to early 1900s mingled with the conservative traditional politicians who were asserting that Spain was a global power and had demand role to play in the world, and the so called liberal or perchance the en lightened politicians who argued that the reality was that Spain during this time was a sinking ship.According to an net article entitled Spain, the eternal Maja Goya, Majismo, and the Reinvention of the Spanish National Identity, the debate served as a awakenup call for many Spanish intellectuals to come up with a win-win solution not only to the debate but also to the real condition of the Spanish nation, which was already two decades behind respectable countries such as France, Germany, and England.Among these intellectuals was Miguel de Unamuno who emphasized that it is only by opening windows to European winds, saturating themselves with European ambience, having confidence that they will not lose their identity in so doing would make them catch up with the advance custodyt made such nations (Internet article). Along with other intellectuals such as Jose Martinez Ruiz also known as Azurin and Granados, they were able to revolutionize the Spanish intellectual society.Views of the Authors in their attempt to hitch Spain into a Modern NationThe famous writers and authors during the Enlightenment finale in Spain such as Giner de los Rios, Angel Ganivet and Joaquin Costa were known as the contemporaries of 1898. Their contribution started upon realizing their countrys weakening condition due to defeats in wars against the United States of America and the lost of treasured colonies such as the Philippines, Puerto Rico, Guam, and Cuba. During their various(prenominal) periods they tried to revolutionize the intellectual society as well as to redeem the prominence their country once enjoyed.Like Jose Ortega Gasset, Ginner Delos Rios also apothegm the importance of education to be able to transform itself and to cope up with fast industrializing European nations. Being highly educated, he was well informal in many branches of knowledge, Ginner Delos Rios viewed education as a very Copernican instrument in regenerating the Spanish society he believed th at reforms were not established by laws but by t separatelyers and professors. Delos Rios views held that teachers and educators were responsible in shut in the new generations educational values and understanding. I believe that his views are incidentally and appropriate to the present condition of Spain.Ganivet was no doubt a great writer and essayist. His views was reflective of the true condition of Spain, where in he calls Spains past as an error, a departure from its true nature. Ganivet believed that Spain must wake up from its present slump and fulfill its true mission to get around birth to a great nation and culture. No doubt Ganivets view was really interesting however, his poor interpersonal relationship seemed to pass water put his ideas on the shelves at least during his own time. Joaquin Costa (18441911) on the other hand descended from a politicians family and was one of generation 1898. Costas view of the condition of Spain however, was that the country needs na tional regeneration. Costa depicts doubt to the leadership of those in governments in running the affair of the state.How did they see Spain, and how did each think Spain should be changed to become a modern country.Each of these authors viewed Spain during this period as weakening and being slowly left behind by other European countries. Both Delos Rios and Ortega emphasized on the need of education for every individual, while Ganivet saw Spain as lacking regeneration. Perhaps what he meant was that the present generations were failure. The hope of the new of the Nation lies in the new generation.What did these men give to the Spanish SocietyThese men in the first prat were able to give the society brilliant ideas on how the society can rise up from where it was at present. They provided a new avenue by which the government can start a new in transport back the country in line with other advanced nations in Europe. The most particular was the ideas of Miguel de Unamuno to open t he countrye windows to other European countries and be satisfy with fresh wind of information and knowledge flood tide from those countries. They also provided important educational guidelines that would help keep up standards of cultivation for every individual.Berrio, J. R. Ftancisco delos Rios (1839-1915)http//www.ibe.unesco.org/publications/ThinkersPdf/ginerPalmer, J., Bresler, L., & Cooper, D.E. (2001) Fifty Major Thinkers on Education From Confucius to Dewey. UK Routledge

“Catcher in the Rye” and “Rebel without a cause” Essay

When one twain reads Catcher in the Rye and sees Rebel Without a Cause, he or she cant help but inquire if the writers, Nicholas Ray and J.D. Salinger, somehow k peeled each other, or if one writer copied the ideas of the other. Jim nude and Holden Caulfield, the two main characters of the stories, have so much in super acid that if they ever met one another, they would immediately become friends. The main theme that applies to both works is teenage rebellion. Holden and Jim seem to get into trouble often, which affects many incompatible aspects of their lives, including their friends, family, school, location, and self-image.Holdens and Jims parents have actually similar attitudes towards their children. They both go forth to spoil their children indefinitely, a common display of parents during the 1950s. Jims overprotect mentioned that he bought Jim everything he wants, including a car and bicycle. Holden said that his mother had recently move ice skates to his school for him. Also, both parents show embarrassment of their childrens flea-bitten behavior. Neither Jims nor Holdens fathers are good role models for their sons. Jim feels that his father is cowardly, weak, and a chicken.Holdens father isnt ever around, since Holden is always at one boarding school or another. Further more than, both sons feel misunderstood by their parents. The major difference between Holdens and Jims families is that Holdens parents deal with his problems by sending him away to prep schools, whereas Jims parents take heed to be more involved in their sons keep and move with him from t testify to town. A minor difference in their families is that Jim is an altogether child, but Holden has three siblings.Holden does not authentically have any friends. He constantly criticizes and complains about the people he interacts with, rarely has anything positive to feel out about them, and does not consider anyone his real friend. Jim tries to make friends with the kids at h is new school, but only succeeds in gaining two real ones. Plato, who is a companionable outcast at school, jumps at the chance to become Jims friend. Judy, however, makes sport of Jim with her friends until her boyfriend, buzz, is killed. Then, she seeks comfort in Jim and they fall in love. two the Jim and Holden feel alike(p) outcasts, which is a major part of their rebellion. However, where Jim tries to tally in and isrejected by his peers, Holden does not make such attempts and he is the one who rejects his classmates.Neither Holden nor Jim fit into their schools. Holden has a lot of academic problems, although he appears to be a evenhandedly intelligent boy. Jim, on the other hand, has social problems. In his previous schools, he had a tendency to beat up kids for calling him chicken. He also feels the need to keep his honor, and therefore participate in the chickie fight against Buzz, which leads to Buzzs death. Holden seems to put in very little to no political camp aign in his schoolwork and fitting in. He doesnt really maintenance that he flunks out of his classes. Jims academic life wasnt really portrayed in the film, but he did try to fit in. When he was scolded for walking over the schools insignia, he felt very sorry about it. Therefore, Jim is not always intentionally rebellious, but Holdens rebellion is deliberate.Jims and Holdens emotional confusion affect their lives similarly. Both are extremely misunderstood by both the public and their own families. Although they appear to be rebellious and tough, both have a more sensitive interior. They suffer from alienation from their families and peers, but Jim definitely strives towards acceptance temporary hookup Holden does not. If Catcher in the Rye had been made into a movie, James doyen would have been the perfect actor to play the part of Holden, since his portrayal of Jim was so precise.

Thomas Hardy Poem Interpretation

Poems for essay unbiased Tones, A Broken Appointment, The Moth-Signal. Interpretation is give tongue to to be an explanation or conceptualization of a work of writings or other art form by a critic. dauntless is known for integrating personal events from his spirit, into his poetrys that allow the reader to develop a fully rounded view of what he was trying to convey in his work. Love and its effects ar one of his most famous themes that are the basis of manhoody of his rimes. Hardy tends to use references to many of his cognises in his life in his poetrys especially his first wife Emma.The context from which he writes helps immensely when deducing the meaning of his works. However, knowledge of the poets background is not a requisite when interpreting all poems nor does it always influence the interpretation given by the reader this only true to a certain extent. In the poems Neutral Tones, A Broken Appointment and The Moth- Signal (Edgon Heath) are all examples of poems by Thomas Hardy that does not require awareness of his background to be interpreted by the reader.The poem, Neutral Tones can be deciphered is nigh a man who loses his true love and thus skews his view of love forever. The first stanza may be interpreted as the setting of which this dangerous moment between these two lovers took place. The setting bares no identification unavoidable towards the writer and can be advantageously interpreted by the reader. The transaction stanzas basically describe the scenarios in the relationship that led to ultimately the agree going their separate ways and as a result changes the mans perception of love as the event is relayed from his point of view.This poem is just based on a love gone misemploy and does not need Hardys background information to be successfully understood by anyone who reads it. His personal detached tone from the poem allows this to be possible. Along with Neutral Tones, A Broken Appointment follows the alike trend of l ove and freedom to be interpreted without having knowledge of his then(prenominal) loves. This poem is active a man who is now reflecting later about the time he was stood up by the woman he loved. This is an let go that happens regularly and does not need to be referenced to sometime in the authors life to be analyzed thoroughly.This poem was too written generally so it also bore no parity to the life of Hardy. This goes to show that the background of an author when interpreting a poem is not utter importance. In addition, The Moth-Signal (Edgon Heath), is another one of Hardys poems that lack the need for the context from which the poet writes. In this poem, there is an affair by a woman that is summoned to her lover via a moth being burned in the flames of a candle to indicate her lover was present.Since infidelity is a commonplace issue, the background of Hardy is not needed to influence the interpretation of this poem. In all of the above mention poems, they all encompass ed a plot that was easily identifiable by any reader. The thread of love and heartbreak ran done each and they are all poems that are relatable to all who reads these poems. This gives proof that a poets background does not need to be known in order to interpret a poem nor influence its interpretation.