Political Culture Essay Example

Political Culture Essay â€Å"Drawing models from any one Caribbean domain and utilizing popular conclusion information from sources , how much is political culture less reliant on neighborhood culture because of late innovative turns of events. † Political culture is the specific arrangement of directions, convictions, customs, and previously established inclinations which are shared by individuals from the general public, which offer significance to their political procedure, and which give the basic suppositions that oversee their political conduct and open strategies (Lawrence 6). Then again we have nearby culture, which Karen Lawrence portrays as the history, family, governmental issues, scene, craftsmanship, music and qualities and individuals merged into a feeling of spot that feels dynamic and durable and immovably established. Nearby culture is the place and how one is associated with life and one another; it is the aggregate memory of a network. What is innovation may one inquire? Innovation is the utilization of devices or instruments for any human movement. These human exercises can be creation, stockpiling, trade, transportation or even utilization. It is the part of information that manages the creation and utilization of specialized methods and their interrelation with life society and the earth, drawing upon such subjects as mechanical engineering,â appliedâ science,â andâ pure science. Governmental issues is a zone which shows may parts of our way of life and in these cutting edge times, political culture in the Caribbean region of Jamaica has gotten subject to ongoing mechanical turns of events. We will compose a custom article test on Political Culture explicitly for you for just $16.38 $13.9/page Request now We will compose a custom paper test on Political Culture explicitly for you FOR ONLY $16.38 $13.9/page Recruit Writer We will compose a custom paper test on Political Culture explicitly for you FOR ONLY $16.38 $13.9/page Recruit Writer Globalization is a procedure of collaborations and coordination among individuals, organizations, and administrations of various countries, a procedure driven by universal exchange and venture and helped by data innovation. This procedure has consequences for the earth, on culture, on political frameworks, on financial turn of events and thriving, just as human physical prosperity in social orders the world over. Because of globalization, cash, products and assembling have gotten more portable than they used to be, and subsequently companies and organizations are permitted to fund, contribute and move any place they pick, they have a more extensive access to different nations over the world. There is a move to be decided of intensity between worldwide organizations and governments. National governments are less ready to control the exercises of versatile organizations than before, while partnerships and monetary foundations are in a superior situation to direct to national governments. They do this by, buying the monetary standards of states whose approaches they support and forsaking, or taking steps to surrender the rest. In 2003, Jamaica’s obligation remained at $4. 4billion inferable from the IMF, the obligation necessitated that 52 pennies of each dollar earned goes for premium installments. At this point Jamaica had just taken care of multiple times the measure of cash initially acquired. Cultivating and industry have been in emergency. There was a high joblessness rate and there has been relocation from the rustic regions to Kingston, individuals looking for employments and openings. There was an expansion in wrongdoing and medication exchange, which thusly have prompted visit police killings. Political viciousness and revolting has been found as of late. Dependable to a limited extent for this agitation and strife is globalization, capturing the nation in huge obligation masterminded by the International Monetary Fund (IMF), the World Bank, and other worldwide loaning offices at high financing costs. â€Å"The auxiliary alterations that were forced by the IMF had cause for an expansion in loan costs to industry and ranchers, so they could no longer contend with the surge of financed or â€Å"dumped† imported products. These approaches have driven down wages and constrained the legislature to curtail subsidizing for instruction, medicinal services, and horticultural projects which advantage poor people (Harper 2003). † Again because of globalization there was respectful turmoil and disorder in Jamaica. In 2009 there was a removal demand from the USA for Christopher â€Å"Dudus† Coke, a medication boss who appreciated mythical being status in the west Kingston fortress of Tivoli Gardens. The United States needed him for medicate dealing and firearm exchange. The then decision party, the Jamaica Labor Party would not like to surrender Mr. Coke, as he had close binds with the gathering. This incorporated a development organization called Incomparable Enterprises, where Dudus was an accomplice, was granted three government decreases by the JLP esteemed at $US 400 thousand, additionally an agreement worth US $150,000 to move materials to Riverton City landfill. Additionally the JLP was likewise answerable for crusading and foundation of the â€Å"garrison† known as Tivoli. The USA said that they would cut all binds with Jamaica if Mr. Coke was not removed. Jamaica relied upon the US for cash and imported items. Jamaica couldn't bear to lose America’s support, as so thusly, Dudus was removed. This is an away from of the impact of globalization. As indicated by Alec Ross, one of the makers of innovation strategy for Barack Obamas 2008 presidential battle, â€Å"We are well past having the option to consider online networking a trend. On the off chance that you are not open to internet based life spaces, at that point you are not receptive to the elements in the city and you penance both comprehension and force. The development of Social Media has caused for the political culture in Jamaica to be less reliant on neighborhood culture, because of this ongoing innovative progression. In 2007 insights indicated that 539. 543 for each 1,000 peopleâ in Jamaica utilized the web, in 2005 3,042,000 individuals in Jamaica were phone endorsers. Likewise in 2007 there were 2,700,000 cell phone endorsers and 444. 079 for every 1,000 individuals in Jamaica who had radios. In 2002, 70. 04% of family units in Jamaica had TVs. These numbers are rising each year, with online life getting increasingly mainstream as the years pass by. In Jamaica there are 7 neighborhood TV stations; the two fundamental nearby channels being CVM and TVJ (Gordon 89). These channels no longer spotlight on what is happening locally in the nation or show numerous privately delivered appears. The time allotments for these stations are loaded up with American based TV programs, animation and motion pictures. The nearby news likewise doesn’t center around what's going on in the nation every day, except just gives us a concise review and afterward jumps to â€Å"international news† refreshing people in general on what's going on in spots, for example, USA, Canada, Asia and South America just to give some examples. It is theorized that in Jamaica, the individuals find out about what is happening in America than they do about what's going on in their â€Å"own lawn. † In Jamaica during political race time, the ideological groups utilize the media as an approach to voice their assessments of different gatherings and furthermore articles and pictures are distributed so as to convince voters to decide in favor of a specific gathering or to convince people in general to cast a ballot when all is said in done. The media likewise assists with political socialization, this is the procedure by which individuals structure their thoughts aboutâ governmental issues and obtain political qualities, the two principle political arties in Jamaica being the Jamaica Labor Party and the People’s National Party. Internet based life presently has a tremendous influence in the political culture of Jamaica. Jamaica attempts to imitate the political culture of the United States of America that is utiliz ing web based life discussions which incorporate facebook and twitter as an approach to advance ideological groups as opposed to staying the typical methods for papers and new channels. As innovation creates, governmental issues needs to create alongside it. A case of this is seen by the formation of a facebook page called â€Å"Young Jamaica† which is a member of the Jamaica Labor Party. The reason for the page is to educate Jamaica’s youth about the JLP and to advance this gathering over the PNP. It urges youthful Jamaicans to get specified and it likewise features the disappointments of the PNP and its individuals. Another case of online life gathering is the seen by the formation of a facebook page called â€Å"Blame Bruce† which is a partner of the People’s National Party accusing past Prime Minister Bruce Golding for issues in Jamaica. There was additionally a â€Å"hashtag† on twitter called â€Å"Blame Bruce†. The Jamaican political culture is less reliant on the neighborhood culture. The latest case of the neighborhood culture being brought into governmental issues was because of the Jamaica 50th festival on August sixth 2012, Jamaica was a free country for a long time. Because of mechanical turns of events, the political culture of Jamaica is less subject to neighborhood culture. Globalization and Social media have hugy affected the political culture, as these appear to have a greater amount of an impact than the nearby culture.

Word of Mouth Referrals For Term Paper Service

Word of Mouth Referrals For Term Paper ServiceA good word of mouth will always carry the writer along with him when he is writing a term paper. It would be advisable for the student to create a referral system that can build a good image for him in his potential readers' eyes.He must think of a good way to get his term paper done quickly and professionally. The students can take advantage of an online term paper service.Students would be well advised to rely on the services of the term paper service provider to provide them with the quality and quantity of the papers that they require. They can save a lot of time if they make the service providers take care of everything.The best way to get good results out of these providers is to find one that is known to work with the same school or college that the student's word of mouth is coming from. With this, the students will have to leave out of the word of mouth component.If the students opt for term paper service that works with multipl e schools, they can get the services of many types of term papers in their hands at one time. When the students use this method of word of mouth referrals, they can benefit from the service provider using their university in their school's name.Word of mouth referral can work well if the students are willing to share their opinions about the services offered by the paper service provider. They must be willing to provide their readers with quality and informative information and words.The service provider can recognize a shortcoming and address it without sacrificing the quality of the work or mentioning it too much. When they acknowledge the shortcomings, they can improve them while improving the quality of the work itself.Students must take the time to identify all the qualities that they need from their term paper service provider. They must not choose the first one, because they might end up paying more for the service than what they bargained for.

Pascals Triangle Essays - Blaise Pascal, Combinatorics, Free Essays

Pascals Triangle Essays - Blaise Pascal, Combinatorics, Free Essays Pascals Triangle Pascals Triangle Blas Pacal was conceived in France in 1623. He was a youngster wonder and was intrigued by science. At the point when Pascal was 19 he designed the main ascertaining machine that really worked. Numerous others had attempted to do likewise however didn't succeed. One of the subjects that profoundly intrigued him was the probability of an occasion occurring (likelihood). This intrigue came to Pascal from a player who asked him to assist him with improving a supposition so he could make an informed estimate. In the coarse of his examinations he delivered a triangular example that is named after him. The example was known in any event 300 years before Pascal had find it. The Chinese were the first to find it however it was completely evolved by Pascal (Ladja , 2). Pascal's triangle is a triangluar course of action of columns. Each column aside from the first push starts and finishes with the number 1 composed corner to corner. The principal push just has one number which is 1. Starting with the subsequent column, each number is the entirety of the number composed simply above it to one side and the left. The numbers are set halfway between the quantities of the column straightforwardly above it. On the off chance that you flip 1 coin the potential outcomes are 1 heads (H) or 1 tails (T). This blend of 1 and 1 is the firs line of Pascal's Triangle. In the event that you flip the coin twice you will get a couple of various outcomes as I will appear beneath (Ladja, 3): Suppose you have the polynomial x+1, and you need to raise it to a few powers, as 1,2,3,4,5,.... In the event that you cause a graph of what you to get when you do these force raisins, you'll get something like this (Dr. Math, 3): (x+1)^0 = 1 (x+1)^1 = 1 + x (x+1)^2 = 1 + 2x + x^2 (x+1)^3 = 1 + 3x + 3x^2 + x^3 (x+1)^4 = 1 + 4x + 6x^2 + 4x^3 + x^4 (x+1)^5 = 1 + 5x + 10x^2 + 10x^3 + 5x^4 + x^5 ..... In the event that you simply take a gander at the coefficients of the polynomials that you get, you'll see Pascal's Triangle! In light of this association, the passages in Pascal's Triangle are called the binomial coefficients.There's a really basic equation for making sense of the binomial coefficients (Dr. Math, 4): n! [n:k] = k! (n-k)! 6 * 5 * 4 * 3 * 2 * 1 For instance, [6:3] = 20. 3 * 2 * 1 * 3 * 2 * 1 The triangular numbers and the Fibonacci numbers can be found in Pascal's triangle. The triangular numbers are simpler to discover: beginning with the third one on the left side go down on your right side and you get 1, 3, 6, 10, and so on (Swarthmore, 5) 1 1 1 2 1 1 3 1 1 4 6 4 1 1 5 10 5 1 1 6 15 20 15 6 1 1 7 21 35 21 7 1 The Fibonacci numbers are more diligently to find. To discover them you have to go up at an edge: you're searching for 1, 1, 1+1, 1+2, 1+3+1, 1+4+3, 1+5+6+1 (Dr. Math, 4). Something else I discovered is that on the off chance that you increase 11 x 11 you will get 121 which is the second line in Pascal's Triangle. On the off chance that you duplicate 121 x 11 you get 1331 which is the third line in the triangle (Dr. Math, 4). On the off chance that you, at that point duplicate 1331 x 11 you get 14641 which is the fourth line in Pascal's Triangle, however on the off chance that you, at that point duplicate 14641 x 11 you don't get the fifth line numbers. You get 161051. In any case, after the fifth line it doesn't work any longer (Dr. Math, 4). Another case of likelihood: Say there are four youngsters Annie, Bob, Carlos, and Danny (A, B, C, D). The educator needs to pick two of them to give out books; from multiple points of view would she be able to pick a couple (ladja, 4)? 1.A and B 2.A and C 3.A and D 4.B and C 5.B and D 6.C and D There are six different ways to settle on a decision of a couple. On the off chance that the instructor needs to send three understudies: 1.A, B, C 2.A, B, D 3.A, C, D 4.B, C, D On the off chance that the instructor needs to send a gathering of K youngsters where K may go from 0-4; from various perspectives will she pick the kids K=0 1 way (There is as it were

Why Everybody Is Talking About Samples of Contemporary Theatre Essay...The Simple Truth Revealed

Why Everybody Is Talking About Samples of Contemporary Theatre Essay...The Simple Truth Revealed How to Get Started with Samples of Contemporary Theatre Essay? FACE Contemporary Theater does not pay for the operating and front of the home expenses of the institution. Inside my opinion, the art of theater is among the most empowering facets in ones life. A slice of theater only lasts for a particular time period. The major goal of a dance performance review is to assess the caliber of the dance and recommend it to the overall public. The study of theater and performance has developed rapidly in previous decades. It can be expedient if I use my very own artistic procedure to illustrate this. The stage isn't an open (postmodern) stage, rather it acts as a digital closed space. There are significant differences between Roman theatre and modern-day entertainment scene in conditions of. Australian Contemporary theatre is a tool to deal with those thoughts that are part of Australian life. Absurdism, among the most exciting and creative movements in today's theater, is a term applied to a special type of realistic drama that has absorbed theater audiences and critics for the last three decades. Today entertainment is thought to be a luxury and people expect in order to unwind and observe the players. Also, it assists in marking the loops or gaps of earlier research. Lastly, the detail of real speech makes the scene pop. More frequently than not I work site-specifically, in other words, in direct reaction to a specific site or space, as an example the dissecting room or anatomy museum. Moreover, contrary to other modes of communication, in theater an individual cannot return and see a particular scene. Theatre has quite a strong place in our society due to its culture and history. I'd like to deal with a variety of interrelated problems that confront the modern theatre. In the debut, give an overall description of the dance performance. Look for the information regarding the performance and history of this kind of dance. The main purpose of this guide is to give students an opportunity to comprehend what's a reaction paper and to describe how to complete it with no efforts. The outcome is a mix of these processes as a consequence of dynamic interaction in our mind. Nevertheless, for anyone who'd love to talk about the problem of the Filipino contemporary theatre, the difficult and very complicated postcolonial history of the nation appears among the key challenges. Schools are institutions intended to develop student's skills in all facets of life and allow them realize their talents and capabilities. Bridget's essay is quite strong, but there continue to be a couple little things that could be made better. Chock-full of language games and grammar jokes, in addition to challenges to almost every moral standby you'll be able to imagine, the play is equally as thought-provoking because it is amusing. The second part should contain your individual thoughts about the topic. At the right time of writing the exhibition hasn't yet opened and public result of the undertaking won't be known until after the evaluation period. You can go to the official concert, an amateur performance in the road, or watch it online or inside a recording. Technology has also made it much less difficult to create props, so performances nowadays are getting more realistic with better props and much better technology. To begin with, you must observe the performance. Contemporary performance provides a more expansive array of experiences. Iphigenia's' character also needs to be pondered upon. Dance is not only a concrete phenomenon, it is a social, cultural, and historical item. Also, it is going to be useful to mention different performances generated by the choreographer (artistic director). Describe the elements of the dance and the way it contributed to the general performance.

The Effect Of Exercise On Fall Prevention Paper - 886 Words

The Effect of Exercise on Fall Prevention The widespread falls among the geriatric population reduce their quality of life and take away their functional independence. Lee et al (2013) state that falls leads to the rise in mortality rates and morbidity complications such as fractures and disabilities,1 out of 3 elderly persons in a community setting falls in a year. About 87% of all fractures in the elderly are due to falls. Several of the risk factors that are associated with falls are visual impairments, cognitive impairments, and health-related problems: arthritis, orthostatic, back pains, lack of balance-weakening muscles, previous falls, polypharmacy or psychoactive drugs (Lee et al, 2013). Some of the causes for falls†¦show more content†¦The sample group involved 280 old people who were 79-90 years old and have a history of falling especially in the last 6 months. The intervention recommended was assigned one of the four controlled groups which applied different balan ce and strength exercise. Significance of findings to the Clinical Field Exercises and physical activity have been associated with numerous health benefits including reducing incidences of falls and injuries among the elderly and the geriatric population. Appropriately exercise programs and cardiovascular fitness in older people improve strength and balance. The interventions included strengthening, endurance, balance, flexibility exercises, Tai Chi, stand up/step down procedure and walking exercises. In the first research, the findings were that program targeting balance, leg strength, and freezing gait were only effective in people with milder illnesses, as opposed to more serious disorders like Parkinson’s disease. The study was done for a period of 2 years. In the second research, the findings showed that a multi-component enhanced physical performance of the community indwelling adults but did not translate to psychological outcomes or reduced rates of falling. The 2-research studies did not estab lish a strong correlation between physical exercises and reduction to the actual falling in the old population or the geriatric population.Show MoreRelatedThe Effect Of Physiological Changes On Children Essay1531 Words   |  7 Pages â€Æ' This paper will discuss the impacts that physiological changes have on mobility in older adults. Mobility is the individual’s ability to carry out activities of daily living by moving the body or parts of the body (Nursing Program Guide, 2016) For the purpose of this paper the term older adult will be referring to individuals chronologically aged 65 and older. 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Essay on 20th Century Approaches in Early Childhood Education

Did you know there are many kinds of programs and approaches used in early childhood education? The 20th century was the start of inclusive classrooms and about the idea of education for all. Education in the early preschool years boomed with early childhood development programs. While there are many popular approaches, I find that Maria Montessori and Rudolf Steiner have the reliable strait forward and captivating approaches to early childhood education. Maria Montessori has an educational method that is in use today in public and private schools throughout the world. The basic goals of the Montessori Method are to develop the child’s independence and productivity as well as preserve the dignity of the child while focusing on the†¦show more content†¦Children will stay with the class, and teacher, for the entire cycle, shaping a stable community and meaningful bonds (Introduction to montessori, 2011). You won’t find the expected rows of school desks in these programs either. Children work at tables or on the floor, rolling out mats on which to work and define their work space. The classrooms are set up to have natural lighting, soft colors, and uncluttered spaces for activities that are focused and calm. More than 4,000 Montessori schools dot the American landscape, and thousands more can be found on 6 continents worldwide (Introduction to montessori, 2011). Rudolf Steiner developed the Waldorf education. This is the view in which the child develops through a number of basic stages as he/ she grows from childhood to adulthood. Waldorf education strives to develop in each child their innate talents and abilities through the use of curriculum which develops imagination and creativity (Early child care, 2010). In the United States there are more than 160 schools across the continent, 250 early childhood centers. Within all these centers the approach to learning in early childhood is through imitation, imagination and example. 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Engineering Sustainability

Question: Discuss about the Engineering Sustainability Hydroponics Farming Systems. Answer: Introduction The human population is ever-growing and so is the demand for food. Currently, about 40% of the total land is dedicated to produce food for the vast 7 billion human population. However, it is expected that the demand for food will jump by almost 70% by the year 2050. A pertinent question thereby arises as to where would this incremental production of food come from. One possible answer could be increasing the area under cultivation but there is very limited scope in this regard (Isaacson, 2015). Another response is by increasing the productivity of agriculture akin to the introduction of high yield varieties. However, in this regard, it is noteworthy that the yield increases in the last three to four decades has been achieved at substantial environmental costs (Gray, 2015). This is apparent from the rise in use of chemicals fertilizer, pesticides and weedicides which pose significant environmental hazards. Also, these varieties typically require assured supply of irrigation which is increasingly becoming a problem especially in the developing nations where there has been over-exploitation in this regard (Killebrew and Wolff, 2014). Clearly in wake of the lack of choices as is evident above, the most plausible course of action would be the clearing of forests to create more space for agriculture. However, this would adverse influence the delicate environmental balance which he have already disturbed to an extent. In this backdrop, an alternative farming technique known as hydroponics may provide the answer (Jensen, nd). Hydroponics refers to an innovative farming practice whereby plants are grown in nutrient rich water and not soil. As a result, hydroponics tends to open a vast block of land specially situated in deserts which are currently considered unfit for farming. Also, unlike soil borne farming, the environment in hydroponics can be scientifically controlled which results in higher yields and significantly lesser problems related to pest and weeds (Mims, 2016). Additionally, the quality (including nutritional value and appearance) of the produce can also be carefully controlled which resolves the problem of wastages whereby some of the produce does not sell due to appearance issues. Hydroponics is akin to growing plants under experimental conditions as the plants are grown in an inert media which tends to drive nutrients from the nutrient rich water which is fed to these plants. Thus, all the environmental factors such as temperature, humidity, pressure, amount of sunlight and nutrient feed can be controlled which results in attainment of desired output. Besides, with the help of grow lights, hydroponics can be practised indoors also (Brechner Both, 2014). The current case study aims to critically analyse the advantages of hydroponics vis--vis traditional agriculture in the backdrop of sustainability factors. Besides, the case study would focus on highlighting the current practices related to this new farming technique and how it can potentially provide answers to the looming food problem we are expected to witness in the future. Objective The primary objective is to compare the hydroponic system of farming with the conventional farming in wake of the various sustainability factors and efficiency. Also, the report would draw on the performance of hydroponic farming system on the various sustainability factors. Further, on basis of this, the report would also deal on integration of hydroponic system with the existing farming system for securing the wider interests of the society in the long run. In order to draw a comparison between the efficiency of hydroponic farming with the conventional farming, it is imperative to compare the three key variables i.e. yield, consumption of water along with energy consumption. Barbosa et. al. (2015) through their research compared the above parameters related to the two given farming systems for lettuce and reached the following results. In terms of yield per unit area, the hydroponic farming was able to produce 11 times the yield of lettuce produced under conventional farming. Two reasons primary are responsible for this high yield. Firstly, hydroponic farming provides the flexibility of growing a given vegetable all year long even when the outside climate may not be conducive. In case of lettuce, through hydroponic systems, annually 12 crops may be produced. Secondly, due to strict environment control, the growth of the plants is faster and consistent. The water consumption per unit yield was also 12 times lower in hydroponics system as compared to conventional farming. This may be attributed to both a higher yield of crop and also lesser loss of water from transpiration. Besides, through the use of recirculating systems, the nutrient water is used efficiently thus providing only the requisite water. This is unlike the conventional farming systems where there is relatively higher wastage and transpiration loss ad less water is absorbed by the plants root (Killebrew and Wolff, 2014). With regards to energy consumption, clearly conventional farming was found to be more efficient. This is because the energy consumption in hydroponics system is about 82 times the energy consumed in the conventional farming. Higher energy needs of hydroponics arose because of greater use of artificial lighting to ensure that more crops annually are obtained. Also, there are heating and cooling related costs for providing the optimum temperature to the plants (Siegel, 2014). It is noteworthy that despite the higher energy requirements, hydroponics does provide a credible alternative to conventional farming. Further, the energy requirements can also be lesser in regions where there is ample sunshine or through the usage of solar powered hydroponics system (Nally, 2016). Besides, greenhouses may also be used to reduce the overall energy use and reduction of environment control. However, hydroponics may not be a suitable system for various cereal crops which constitute approximately 50% of the total crop production. This is because of the wafer thin margins in these crops which do not justify the high energy costs and setup costs associated with hydroponics (Isaacson, 2016). But for vegetables, hydroponics does provide an option which is being explored and it is likely that in the future, the production of vegetable hydroponically would grow (Brechner. Both, 2014). Besides, with the growing income levels, it is expected that demand for vegetables would be significantly higher than that of cereals. Hence, a major shift to hydroponic farming system would provide ample space for cultivation of the requisite cereal crops without harming the forests (Mims, 2016). Sustainability Factors With regards to sustainability of a given factor, it is imperative to critically analyse it from the standpoint of the following five factors. Social Impact It is estimated that the social impact of hydroponic systems would be quite profound especially for the marginal communities and the farmers in the third world countries. This is primarily because these people primarily engage themselves in growing of low value crops and are dependent on nature for farming. With the increasing incidence of extreme weather not only in Australia but also globally, it is essential that dependence of weather based agriculture needs to be reduced. Thus, these farmers and poor communities with the aid of the government and NGOs could grow hydroponic vegetables not only for their own consumption but for sale. Additionally, this could also provide livelihood to communities located in regions where physical conditions are not suitable for farming (Jensen, nd). Environmental Impact Conventional farming tends to dependent on extensive use of water which is being over exploited by farmers and is rapidly becoming a cause of concern. Besides, there is also usage of chemical pesticides and weedicides which lead to contamination of food products and also pose health hazard. Also, the discharge from fields is rich in various chemicals and thus causes water pollution that has adverse impact on marine life (Killebrew and Wolff, 2014),. Hydroponics on the other hand, limits the use of water and also recollects the excess water. Further, any waste water generated is either recycled or disposed after adequate treatment so that the harm to environment is minimised. It is apparent that in the long run, hydroponic based farming systems present a superior choice in terms of environmental sustainability (Kumar and Cho, 2014). Economic Viability- It is imperative that the alternative farming system while being environmentally and socially beneficial should be economical as well. From the discussion in previous section, it is apparent that on account of higher yields and quality obtained, this farming practice is economically viable. However, this practice is suitable primarily for high value crops mostly peppers, tomatoes, green leafy vegetables such as lettuce, kale and other herbs (Siegel, 2016). The economic viability of hydroponic farming is also apparent from the fact that in 2015, the produce from this farming has already reached a figure of $ 15 billion globally (Mims, 2016). Also, in the future, it is expected that these systems would become more efficient and therefore the economic viability would improve thus resulting in greater varieties of crops grown using this technique (Gray, 2015). Capacity to Deliver In wake of our insatiable food requirements, hydroponic is apparently the future of farming. This is because it would allow those crops which cannot be grown hydroponically to occupy the hand currently under other crops. Also, hydroponic farming can be practiced in arid and semi-arid regions besides in places where climate or soil is not suitable for farming. Therefore, the incremental land requirements for hydroponics farming is not a problem as it does not involve clearing of precious little forests that still continue to remain. Also, through the usage of LED lights, it also offers the scope of indoor farming where all the parameters could be controlled just like in a laboratory. However, considering the low space requirements, even indoor farming is economically lucrative and sustainable (Isaacson, 2015). Adaptability One of the key sustainability factors for a given practice is the ability to adapt to changing environment. This is also apparent in conventional farming which through genetic engineering is coming up with new varieties that are more tolerant to extreme weather. However, hydroponics in this regard presents a more convenient and durable solution as it always presents the option to grow crops indoor through artificial lights and temperature management (Mims, 2016). Further, the space requirements in the regard are minimal since these can be grown vertically using specialised cabinets. This allow urban farmers to practice this on large scale so as to not feed themselves but produce surplus which can be marketed to recoup the initial investment (Siegel, 2016). From the above discussion, it is fair to conclude that hydroponics farming systems do provide a sustainable option to conventional farming even though this is limited to certain crops only. But going forward, it is expected that more developments would be made in these systems which would lower not only the set up costs but also the operating costs. This would essentially bring more varieties of crops under the ambit of this farming which clearly needs to complement the conventional farming going forward. Summary With the rising demand for food, the conventional farming methods are expected to come under pressure. As the availability of arable land is limited and also enhanced productivity is attained at tremendous environmental damage, a more sustainable and environmental friendly practice in the form of hydroponic farming is the need of hour. Hydroponic farming is essentially soilless farming with or without any medium. This may be practiced indoor, outdoors and also inside greenhouses. As a result, this farming system presents greater flexibility in terms of control of various physical factors. The net result is that hydroponic farming systems are more efficient to conventional farming methods both in terms of yield and lesser water consumption. However, this comes at the cost of higher energy requirements which imply that this system is essentially limited to vegetables which command a higher price as compared to cereals. Further, this innovative farming means is having a positive social impact especially for marginal communities as it provides a subsistence means. Also, since this system is less prone to pests and diseases, the usage of pesticides and weedicides is also reduced which is beneficial for the environment. Besides, this system is commercially viable and can prove to be a able support to conventional farming practices to ensure food security in the long run especially in wake of dynamic environment. References Barbosa, G.L., Gadelha, F.D., Kublik, N., Proctor, A., Reichelm, L., Weissinger, E., Wohlleb, G.M. and Halden, R. 2015, Comparison of Land, Water, and Energy Requirements of Lettuce Grown Using Hydroponicvs.Conventional Agricultural Methods, International Journal of Environmental Research and Public Health, 12(6), pp. 68796891. Brechner, M. Both, A.J. 2014,Cornell Controlled Environment Agriculture: Hydroponic Lettuce Handbook. [Online] Available from https://www.cornellcea.com/attachments/Cornell CEA Lettuce Handbook.pdf (Accessed January 30, 2017) Gray, K. 2015, HOW WE'LL GROW FOOD IN THE FUTURE, [Online] Available from https://www.popsci.com/farms-grow-up-thanks-to-technology (Accessed January 30, 2017) Isaacson, B. 2015, To feed Humankind, We need the farms of the Future Today, [Online] Available from https://europe.newsweek.com/feed-humankind-we-need-farms-future-today-335238?rm=eu (Accessed January 30, 2017) Jensen M.H. n.d., Deep flow hydroponicsPast, present and future. [Online] Available from https://pdfs.semanticscholar.org/bbdb/7892e445cd8d1e25f0441a1e79e333a5d2f9.pdf(Accessed January 30, 2017) Killebrew, K. and Wolff, H. (2014), Environmental Impacts of Agricultural Technologies, [Online] Available from https://econ.washington.edu/files/2014/06/2010-Environmental-Impacts-of-Ag-Technologies.pdf (Accessed January 30, 2017) Kumar, R.R. and Cho, J.Y. 2014, Reuse of hydroponic waste solution, Environmental Science and Pollution Research International, 21(16), pp. 9569-9577 Mims, C. 2016, Are Shipping Containers the Future of Farming?, [Online] Available from https://www.wsj.com/articles/are-shipping-containers-the-future-of-farming-1465393797 (Accessed January 30, 2017) Nally, B. 2016, Solar-Powered Hydroponics Could Be the Future of Agriculture, [Online] Available from https://www.truth-out.org/speakout/item/35885-solar-powered-hydroponics-could-be-the-future-of-agriculture (Accessed January 30, 2017) Siegel, E. 2014, Dirt-Free Farming: Will Hydroponics (Finally) Take Off?, [Online] Available from https://modernfarmer.com/2013/06/dirt-free-farming-will-hydroponics-finally-take-off/ (Accessed January 30, 2017)

Beggars and Thieves Literature Essay Sample

Beggars and Thieves Literature Essay Sample The Book Beggars and Thieves Literature Essay Example The Book Beggars and Thieves Literature Essay Example Modern world is highly competitive and people are in a constant race for enrichment and money because income affects quality of life, health, and social well-being. Even working hard does not guarantee social success and acknowledgement because some people have more opportunities and live in more prosperous environments than others. Crime thrives in poor areas and infects people with adjusting social deviations, making theft and beggary a common and trivial sign of modern highly industrialized urban cities. The problem is not new and it originates in poverty and approaches of officials to combating this evil. There are people who have hardly enough money to provide food and water for themselves and their families and there are people who possess endless bank accounts and can afford more than they want. High rates of income discrimination have become a serious threat to the world stability in the twenty first century and have led to development and thrive of street gang crime. It has become one of the most dangerous and latent forms of offence. Depending on delinquency prevention conditions, societies form psychological climate in the country and attitude to the state authorities and criminal justice. Street criminals are usually young people with antisocial behavior, formed on the background of the use of alcohol or drugs or leading a parasitic way of life. The problem originates in the crisis in economic, cultural, spiritual, and legal spheres of society and modern polarized world. The inability of social adaptation and marginalization make people get and return behind the bars again and again. Mark S. Fleisher discusses dimensions and roots of the problem in his book Beggars and Thieves: Lives of Urban Street Criminals issued in 1995. He investigates peculiarities and evils of ramshackle areas in Seattle and carries out a research based on personal experience of living in the street and in a real jail. Street Ethnography Being a social scientist, anthropologist Mark S. Fleisher (1995) tries to perceive the problem of thieves and beggars from inside and applies some modifications of traditional research methods. He wins confidence of felons and law offenders by establishment of equal and trustful relationships with them. He puts an aim to weave into the criminal social network by means of authoritative inmates (Fleisher, 1995). This way, he can perceive the problem of theft and beggary through the prism of criminals’ vision. Stylistics of the author is far from scientific writing. He adheres to numerous descriptions, reflections, and stylistic devices while depicting lively and characteristic signs of the street life. He writes in the first person singular and tries to use the same language as criminals because his first purpose is to weave into illegal social networks and be able to communicate positively with the outlaws. The primary method is an interview. Fleisher adapts questions to rules of the criminal world to make the interview relevant and lively. For example, simple questions about family background and marriage status can sound offensive because of socio-cultural non-adaptation and frustration. In cases with criminals, Fleisher has first obtained reference of some meaningful people to prove his liability. He invites people into a conversation through common dining and drinking bear. He does not conceal his intentions and tells sincerely about his purposes. Some criminals understand and some reject. However, in all cases the author underlines the necessity to be the same and non-conspicuous with his real social status and relevant social success. The essence of conditions created by Fleisher consists in sincere revelation of free and profitless ideas and evidences of those who just want to share their story and to relief their soul and conciseness. The collected database shows not only statistical information, but contains a lot of reflections and assumptions of the misdemeanants on the problem of poverty and possibility to combat it, conditions of first abuse, and acquaintance with drugs and substances. In his research, Fleisher (1995) feels sympathetic with criminals and tries to understand motives of their behavior. The author foresees possible conclusions about his empathy to the offenders. His free statements about the scope of drug traffic and consumption can lead to a thought that he has personal addiction to drugs and in such way only justifies his own weaknesses. The author does not give a direct answer about his own addictions. However, he admits that behaving like a criminal imposes certain destructive habits just by definition. He uses rude lexical units and behaves blatantly and offensively, as well as not shaving daily, as it is accepted in the middle class society. â€Å"Illegal behavior, to some degree, is required of researchers studying criminals† (Fleisher, 1995, p. 69). Fleisher sees that police officers and criminal authorities follow definite unspoken rules and support each other in providing order and its maintenance. The author started establishing connection with the criminal world through the local police. He wanted to study and analyze the street ethnography before entering jail conditions. Local cops turned out to be the most helpful and instructive guides. According to the Fleisher’s research (1995), the street is unsafe in certain areas, which are under control of criminal authorities and the police. There are representatives of various cultures and minorities, which often becomes a reason of discrimination and polarization of certain marginal groups. Thieves and beggars do not want to be homeless and vulnerable to abuse. However, they often fail to see the essence of their problem and discuss it with representatives of other ethnic group. The concept of connections seems to be crucial in establishment of relationships with criminals. Fleisher addressed the police in order to direct him in his street ethnography. He met Popcorn who became his green card into the world of drug dealers and hustlers. The man was a character whom Fleisher observed as being free and then in jail (Fleisher, 1995). Fleisher (1995) shows the connection between street crimes, ordinary thieves and beggars, and organized groups and syndicates. He explains gang signs seen on jail tattoos. By the way, he starts his interviews from the explanation of tattoo meanings and criminals are willing to discuss the topic. The scientist underlines that the international organized crime is flourishing in the United States despite strong legislation and political will to deal with this problem. The root can be in the division of responsibility between local law enforcement agencies, state, and federal authorities because they are fully exploited by criminal groups. Since the mid 60s, the US has expanded the arsenal of tools to fight organized crime and gangsters. The first law in this area became a universal law of 1968 on combating crime and ensuring safety on streets. It was passed against organized crime and included a large number of legal institutions. In particular, it established special investigation agen cies, which provided a guarantee of immunity to witnesses. They introduced a special penal provision for the punishment of participants of criminal organizations. Moreover, supporting programs for the most vulnerable and poor layers of the population were adopted and implemented. In the episode where the policemen ask Popcorn to show Fleisher how he plays his game, all men appear as allies. The episode evidently shows that the police and criminals know about each other much more than they reveal or report. â€Å"As long as a cop plays by street rules, local gang boys do not hold a grudge against him† (Fleisher, 1995, p. 30). Street gangs and bands represent branches of the organized criminal world. They prefer to involve young people and unite them in a typical way of clothing and behavior. The youth follow the general group trend in violence and appearance. It happens because in many situations streets of large and small settlements create a psychological climate for the population living there. It helps in evaluation of the criminal situation in a particular locality and region. It stipulates a social favorable or unfavorable effect on the formation of public opinion about activities not only of criminal justice, but also of the government in general. The study of regional aspects of street crime conducted by Fleisher reflects regional peculiarities and determinants of specific criminal manifestations in Seattle and other urban regions of the American gang world. In most cases, street gangs display aggression and offence and try to violate social norms. Distorted Families Mark Fleisher researches early memories of thieves and beggars in a natural trustful conversation. All interviewed criminals reveal signs of early violence experiences and abuse. The author underlines effects of parents’ negligence and evil environment on further aggression and illegal activities. He puts questions of what can and should be done for delinquents’ prevention from offensive forms of behavior. He comes to a non-consoling conclusion that it is too late to change their fate at that time of their life. In the last chapter, Fleisher (1995) underlines that thieves and beggars are much better settled in jails than in life outside the bars. They have food and shelter and have the job to be done. They have time for rest and different facilities such as ping-pong and library. Outlaws can meet and communicate with relatives if they have any. Description of a Seattle jail includes two televisions. The author shows that the convicted feel much more realized in jail tha n in the real social environment they originate from (Fleisher, 1995). Such situation shows absence of a necessity to rehabilitate and socialize in the modern highly competitive world. People do not strive to change and improve anything in their lives. They accept as a fact that their fate is to go with the flow and do not look for some opportunities or promotion in life. The government gives up, facing this challenge of criminals’ self-rejection. They do not offend the social order and do not die of hunger or cold and here the official mission is completed. Fleisher reveals this in dialogues with misdemeanants and reveals their perception that little can contribute to the improvement of their self-esteem and social rehabilitation. He sees special mission in preventing children from imprinting addictive and destructive forms of behavior and, especially, home violence. Fleisher’s main suggestion is fencing of the new generation from evil aggressive misdemeanors. He underlines that parents and local authorities should detect cases of home violence and abuse with more accuracy and attention. Causes of domestic violence are numerous. According to Fleisher, the phenomenon has a socio-cultural nature and is an integral part of stereotyped notions about the nature of family relationships perceived through early childhood impressions. It represents the only possible model of behavior. The abuse may be the result of personal life experience of individuals and, thereby, they create moral and psychological grounds to consider this type of relationship as versatile. Another group of causes is associated with childhood trauma, which is an early devastating experience that makes the individual develop his/her childs c omplexes (Fleisher, 1995). Fleisher gives examples how domestic violence results into offensive and abusive behaviors in adult life of the observed criminals. Social and psychological frustration caused by external influences exceeds limits of personal resilience of individuals, forcing them to seek compensation and revenge. The author has found a special role in the growth of violence in such mental factors as weakening control instincts, frustration, aggression, alcoholism, and psychopathology. Violence is closely associated with social stress in the family. Among many problems that can raise the level of tension and lead to violence are differences in parenting, sex deviations, pregnancy, money problems, unemployment, or the need for long-term medical care. Constant parents’ irritation can partly be explained by chronic anxiety about unresolved issues, which creates a mismatch between desires and capabilities. Non-participation in social activities and limited availability of social support increase the risk of violence. Fleisher’s studies have shown that alcohol and drugs are not the same. Even a small amount of alcohol can affect aggressiveness and lead to aggression. In cases with a drug dose, people, on the contrary, soften and differentiate. People do not display violence because they have taken drugs, but because they cannot accept them. Therefore, some cases of violence appear in an attempt to get money from relatives for buying drugs. In cases of abuse of the elderly, a major psychological factor is the role change and dependence of adult children on parents. At the same time, certain characteristics of the victim, such as constant claims and complaints, can also contribute to violence. Adolescent Survival According to Fleisher’s observations (1995), the problem of juvenile offence can originate from family and social environment of adolescents. For example, poor relationships with parents can become the main reason of aggression and offence of the youth. In families where parents experience difficulties with personal social adaptation, children tend to be neglected, stressful, and frustrated. They are apt to using drugs, alcohol, or other addictive substances as their parents do. When parents fail in establishment of friendly and trustful relationships with their children, there can appear hostility and disbelief in young hearts. The lack of moral and financial encouragement can lead to poor learning skills development and illiteracy. Children need happy, confident, and successful parents, i.e. a positive example and trust in the world. If adults in a family need corrections themselves, they should be treated and rehabilitated together with their children. They have to be taught about modern principles of non-violence and abuse avoidance by specially trained people or in specially organized groups. Problems of trust deficiency in a family and absence of support make young people vulnerable to numerous addictions of the twenty-first century such as alcohol, cigarettes, and drugs. They start neglecting social rules and norms and look for protection and self-realization by joining certain youth sub-cultures and deviant groups. It is not a secret that nowadays the youth adhere to processes of differentiation. Differentiating factors appear more visibly than integrating ones. Under conditions of a radical transformation in the society, there have appeared profound changes in its stratification. Social polarization reflects trends relating to disengagement of traditionally preserved connections and values (Fleisher, 1995). Social crisis has spawned break of principles and norms of public morality: what was good has suddenly become obsolete and devoid of practical significance. Various marginal flows have poured into subcultures of the young generation, hence ensuring total rejection of traditional cultural values. Delinquency, rejection, and denial of any rules and regulations have started flourishing on that fertile soil. When gathering in gangs, teens can despise the law and morality or show wanton vandalism. What makes them act this way? What underlies these protests? Fleisher believes that antisocial sub-cultural groups and gangs arose in response to differences in initial conditions of the native environment. A subculture is a kind of reaction of the society, especially its younger generation, to the inability to become owners of decent life, namely to get good education and job or become rich, successful, and acknowledged. Subculture has appeared as an answer to the contradiction. Young people could achieve goals, sometimes illegally, while maintaining their own self and ensuring acquisition of their unique nature. As with other categories of social science, rejection is a form of perception of social reality. In Fleisher’s view, human development is moving towards increasing freedom that not every person can adequately take advantage of (Fleisher, 1995). It can cause a number of negative experiences and mental states, which lead to alienation. As a result of exhausting struggle, young people sometimes lose themselves, social stability, trust of relatives and step into the way of crime. Attention to problems of thieves and beggars is crucial because of their uncontrollable affects on the future generation and well-being of whole human settlements, especially in large urban regions. Fleisher generates a set of circumstances that form the distinctive shape of crime in big cities. It becomes evident not only in the quantitative characteristics prevailing in the criminological situation, but also in qualitative changes of the concepts of crime in the broadest sense. The author implies that in many cases criminals adhere to unspoken rules and behavior, which are effective and relevant to the current situation of addiction and abuses. The perception of justice and crime has acquired new features in the growing generation and the signs of aggressive reactions and rejection of social norms and rules point out evident drawbacks and lacunas in the system of education and upbringing. Fleisher reveals numerous examples of cruelty and cases where the imperturbable crowd watches a violent crime on the streets of a big city without calling the police. These are symptoms of indifference and insensitivity, developing under the influence of the general structure of a modern large city. The anonymity of city streets facilitates crime and fleeing of criminals. There is a vicious circle in which, on the one hand, the fear of crime is driving people off the streets, out of parks and squares. On the other hand, the emptiness of streets and parks promotes growth of street crime, which reinforces the fear. Architecture of modern large cities, including land, streets, parks, and squares does not enhance primary informal social control, but has a rather destructive effect, which in turn increases social isolation and fear of street crime among urban residents. The book of Mark S. Fleisher (1995) raises crucial problems of the increasing amount of delinquents in modern global connections and communication. Modern thieves and beggars shift their evil habits from the street to the Internet. The lack of morals and a desire for quick and easy enrichment make them scammers and cyber crime abusers. Formation of a thief or beggar remains conditioned by those social ties to the environment from which the person originates and grows. Antisocial attitudes, aspirations, inclinations, and other negative traits of an individual are undoubtedly a product of assimilation of similar views and orientation based on the surrounding social environment. Young people are in a constant and direct communication with other people, as well as in a range of roles and relationships that they perform every day. It affects strategies of stating and achieving goals. According to Fleisher (1995), the personality of a street criminal is characterized by such features as high sensitivity in interpersonal relationships, adherence to oppressive and violent methods in various conflicts, poor knowledge of the requirements of legal and ethical standards, and alienation from the society and its values. These features form a specific typology of thieves and beggars for whom offenses are a norm and a usual way of behavior. People repeatedly commit crimes because they have not seen other forms of communication and getting what they want in their past. The inability to resist adverse circumstances leads to reinforcement of negative and offensive activities. Personal characteristics of law perpetrators make them avoid negative effects and they fail to look for possible success. Attempts of the government to change the situation have not led to any sufficient results. On the contrary, it should be noted that the offensive psychology acquired in the street criminal experience now appears in other spheres of social media activity. For half a century, the police have persecuted, accused, imprisoned, and even ruined criminals. They have a professionally organized, well-trained service for investigation of organized crime and a disclosure set of intricate conspiracy involving dangerous criminals. They spend billions of dollars on ceasing the drug trade borders, eradicating union extortionists, and checking taxes paid by speculators and gamblers. However, the organized crime continues to conduct business as usual and involves millions of new victims every year.

How exercise can boost your performance at work

How exercise can boost your performance at work We’re all familiar with the obvious benefits of exercise- regular physical activity can keep us looking and feeling fit and healthy, increase our energy levels and confidence, and help us maintain high levels of self-esteem. Staying active can also help us fight off a wealth of potentially life-threatening illnesses, including strokes, diabetes, and heart disease. What’s more, conquering the often overwhelming â€Å"exercise obstacle† and overcoming excuses, apathy, and laziness can remind us that we’re capable of bravely facing any challenge that life throws our way. In contrast, avoiding exercise and all forms of physical activity like the plague can have the reverse effect. Embracing laziness and inactivity can leave us feeling unhealthy and unfit, send our confidence and energy levels plummeting to the basement, and eat away at our self-esteem. Letting the â€Å"exercise obstacle† win the day will surely not inspire and motivate you to set and g o after major life goals.It may seem clear by now that exercising on a regular basis is in your best interest. However, if you’re still not sold, perhaps this will help seal the deal- not only does exercise help fuel and maintain a healthy body, it can actually improve your ability to think and retain information. It’s true- your mind and body are interconnected in amazing ways, and nurturing one will benefit the other.According to a recent article in The New York Times, â€Å"many scientists suspect that exercise alters the biology of the brain in ways that make it more malleable and receptive to new information, a process that scientists refer to as plasticity,† and that â€Å"†¦many past studies have shown that exercise prompts the release of multiple neurochemicals in the brain that increase the number of new brain cells and the connections between neurons†¦these effects improve the brain’s plasticity and augment the ability to learn.†What exercise doesAlthough research regarding how exercise and brainpower work together is continual and ongoing, recent studies have shown the following exciting cognitive benefits.Boosts brainpower: If you’re looking to take your brain’s ability to the next level, you can’t do much better than regular exercise. Studies have shown that exercise can actually increase the volume of key areas in your brain- how’s that for a real boost?Enhances thinking ability: Regular workouts will help kick away the dreaded â€Å"brain fog† that keeps you from thinking clearly and keep your mind and thoughts razor sharp all day long.Helps you process and remember new information more effectively: If you’re trying to acquire a new skill or task, like learning a new language or tackling a new job responsibility, combining it with regular exercise can be an unbeatable combo. New research suggests that physical activity can increase the size of the medial tempo ral and prefrontal cortex of your brain, key areas that regulate and control thinking and memory- so you’ll be able to master that new skill faster than ever before.Improves ability on cognitive tasks: Do you have an important test for work or school coming up? Or maybe you just want to perform better on puzzles and board games? Whatever your mental goals are, you better believe that exercise will help you succeed on all sorts of cognitive tasks that test your intelligence and brainpower.Keeps away the negatives: Yes, it’s true- regular exercise will help keep your mood positive and upbeat, help you achieve more restful sleep at night, and help reduce anxiety and stress- all things that can help your brain work at its absolute best.When, what, and how much?So†¦now that you know it does work, let’s explore how you can make it work for you.  This includes the when, what, and how much regarding exercise and brainpower.Although there’s some debate rega rding the type of exercise that best serves to promote brain function, according to a recent article by Harvard Medical School, â€Å"researchers found that regular aerobic exercise, the kind that gets your heart and your sweat glands pumping, appears to boost the size of the hippocampus, the brain area involved in verbal memory and learning. Resistance training, balance and muscle toning exercises did not have the same results.†Research also suggests that although you’ll receive a brain benefit regardless of when you decide to exercise, the most promising results typically occur when you do your workout before or even during a cognitive task. This suggests that we all might learn best while we’re being active, and it may have interesting consequences on how our institutions of learning are set up. Perhaps classes on treadmills or exercise bikes aren’t too far off in the future?Another big question you might be wondering about is how much exercise you sho uld do in order to receive a cognitive benefit. The same Harvard Medical School report suggests that â€Å"standard recommendations advise half an hour of moderate physical activity most days of the week, or 150 minutes a week.†So how can you incorporate exercise into your life? If you’re worried that you’re simply too busy to exercise or find the very idea of exercise daunting, a great way to take a step forward towards a regular active lifestyle is to start small. Try taking a brief yet brisk walk for 10–15 minutes each day, and gradually increase your workout in both length and intensity as time passes.Listen to your body, it will tell you when you’re ready to take on more and bigger physical challenges. Remember, every journey- no matter how long or arduous- starts with a single step, and the best way to go nowhere is to stand still and do nothing.Now that you know all about the many benefits, both physical and mental, that exercise will bring to your life, put the excuses aside and get up and get moving towards your successful future!

Homework 3 in Excel Assignment Example | Topics and Well Written Essays - 250 words

Homework 3 in Excel - Assignment Example It is not possible for the line to achieve the TAKT time of 1.2 minutes per part off line unless it is able to reduce the bottle neck time. It may require additional labor to work at the station or creating multiple lines to speed up the process. Both will assist in increasing the output to meet the TAKT time. T&D electric manufactures high-voltage switches and other equipment for electric utilities. One line that is staffed by three workers assembles a particular type of switch. Currently the threes workers have fixed assignments; each worker fastens a specific set of components on the switch and passes it downstream on a rolling conveyor. The conveyor has capacity to allow a queue to build up in front of each worker. The bottleneck is the middle station with a rate of 11 switches per hour. The raw processing time is 15 minutes. To improve efficiency of the line, management is considering cross-training the workers and implementing some form of flexible labor system. Floor-On, Ltd., operates a line that produces self-adhesive tiles. This line consists of single-machine stations and is almost balanced (i.e., station rates are nearly equal). A manufacturing engineer has estimated the bottleneck rate to be 2200 cases per 16-hour day and the raw process time to be 30 minutes. The line has averaged 1,500 cases per day, and cycle time has averaged 5 hours. d) If after process improvements the ten machines that required ten workers to run them are no longer needed. Instead five workers are now needed what would you do with the remainder of workers not working on that line anymore? (And please don’t fire them) (1

Variable Air Volume (VAV) System to achieve better Indoor Dissertation

Variable Air Volume (VAV) System to achieve better Indoor Environmental Quality for a commercial Building & energy saving - Dissertation Example The main reason for these functions like heating, ventilation, cooling, and air-conditioning is to make occupants of rooms comfortable (Awbi, 2003). VAV system is one of the HVAC applications and its main purpose is to reduce energy cost as well as maintenance cost. The use of VAV process contains two main components and the components are the room and the VAV damper. The main VAV air handling system is the embedded HVAC system which most of the times are invisible. It is this system that is responsible for delivery of conditioned air to the whole building or structure. The use of this type of system has two merits and the merits are based on the constant volume that is found within the system. This system has the fan control capacity unlike other systems like CAV and the fan control capacity enhances reduction of the used amount of energy. CHAPTER ONE 1.1 INTRODUCTION Variable Air Volume also commonly known as (VAV) system is a kind of ventilation, heating, and air-conditioning devi ces that use a supply duct of fresh air and gives out hot or cool air as the supply air. The air which is being supplied to this device most of the time has constant temperature and therefore, the resultant airflow always varies for the achievement of the fall and rise of the losses or gains characterized with thermal zone. Most of the VAV systems have two primary merits and the merits are due to the constant volume within the system. The system has a fan control capacity which is capable of reducing the amount of energy consumed through the fan. This energy which is used by the fans forms the total energy cooling requirements which are beneficial to the building (Etheridge & Sandberg, 1996). The Variable Air Volume system has a lot of dehumidification compared to the constant volume system because the constant volume system is capable of modulating the temperature of the discharge air. The attainment of the temperature for the discharged air constitutes the cooling load of the whol e system. Within this system the blowing rate for the air is greatly varied and in the case of Variable Air Volume with a single handler for air, the serving for various multiple zones can be achieved. The flow rate associated with each multiple zone when only one handler is operational is always varied. The Variable Air system has a terminal unit and this terminal unit acts as a VAV box which controls the air flow rate for the different zones. This box operates automatically with the help of the actuator. There is a direct connection between the central or local control systems with the VAV box and the control system at times is pneumatically controlled. The use of Variable Air Volume (VAV) for many years has been able to provide quality indoor air thereby improving the quality of the surrounding environment. The quality of the surrounding air greatly affects the general environmental quality and therefore, the use of this system has been preferred in many buildings that belong to private individuals as well as companies. A lot of health benefits are attributed to indoor air quality since low quality air contains microbes that are capable of affecting the occupants of the buildings (Chadderton, 2004). A lot of prevalence has been given to the VAV system compared to CV system and this prevalence is

Treatment options for blood clots Essay Example for Free

Treatment options for blood clots Essay The option in treating blood clots is a case to case basis whether the clot has developed in an artery or a vein and creates stroke or heart attack. During emergencies, thrombolytic treatments-clot busters- may possibly used to suspend the blood clot. These treatments are applied to some selected patients in accordance to an extensive protocol. In order to be efficient, they are provided no less than one hour of the heart attack or in three hours of the stroke. Thrombolytics are normally applied when there are huge clots which cause severe and life-threatening signs. These can create abrupt and unexpected bleeding. There are many invasive methods that can also be used. One of these is the catheter-directed thrombolysis. It is a method which the catheter proc\vides thrombolytic enzymes straight to the blood clot. Throughout this process, greater enzymes’ concentrations could be utilized and may possibly have lesser side effects compared when the enzymes are supplied subcutaneously no less than two days. Thrombolytic method is followed by a treatment that has anticoagulants to stop more blood clots from formation. Anticoagulants could be advised to patients that have great risk of a blood clot. Researchers are really trying very hard to different catheter-based procedures to cure stroke rapidly. One of the experimental procedures which included is the use of a tiny â€Å"corkscrew† device which rapidly reverses the injury created by a stroke. The said device is directed to where the blood clot is; where it removes the obstruction. Another procedure depends on a special laser catheter which utilizes light energy to suspend rapidly the blood clot. These latest therapies can be accessible to a patient that cannot bear clot-busting drugs (see â€Å"Treatment options for blood. † Blood Clot, HeartCenterOnline. May 9, 2006). Furthermore, when a blood clot develops in a leg vein, there is a risk that a pulmonary embolism will form. A pulmonary embolism may possibly be a life-threatening in which blood clots break away from its location and passes through the bloodstream and stay in one of the blood vessels of the lungs. To avoid and stop a pulmonary embolism, the present blood clots in the legs may cured with a mixture of heat, thrombolytics, painkilling treatments, elevation of the injured area to lessen swelling and anticoagulants. This procedure relies on the clot’s size and the seriousness of any current symptoms. When a patient does not react to anticoagulants, the physicians will decide for a process which uses a catheter having a special device named as vena cava filter. The vena cava filter is inserted in the large vein which transports blood from the legs up to the heart and lungs (vena cava). Additionally, the vena cava filter is a kind of a device which catches blood clots within the vein before the blood clots are permitted to reach the lungs. However, there are several danger related with vena cava filters which comprise failure of the filter in terms of time and blood clots created by means of it. Many researchers really work on in improving the vena cava filters which can be withdrawn right after the medication of pulmonary embolism. There are researches that substantiated and confirmed that treatment in a longer period of time along with a low-does anticoagulant drugs aids in stopping the presence of blood clots amongst those patients who have background of vein thrombosis or pulmonary embolism (see â€Å"Treatment options for blood. † Blood Clot, HeartCenterOnline. May 9, 2006). C. 2 Diagnosis methods There are several ways on how to diagnose blood clots. It starts with a medical background and physical test. When there is suspension of clot, several of examinations are utilized to substantiate the diagnosis and these include Doppler ultrasound, CAT scan and MRI: *Doppler ultrasound pertains to an imaging technology which utilizes sound or motion waves to give visual images of the structure and of several blood vessels and organs in our body (see â€Å"Diagnosis methods for blood clots†. Blood clots, HeartCenterOnline. May 9, 2006).   CAT scan refers to computer axial tomography. It is an examination which utilizes a number of x-rays which got from different sides in order to give a computerized three-dimensional image of a body shape (see â€Å"Diagnosis methods for blood clots†. Blood clots, HeartCenterOnline. May 9, 2006). MRI means magnetic resonance imaging which is a noninvasive examination that utilizes magnetic fields to give high-resolution cross- sectional or three-dimensional visual images of the aimed surface (see â€Å"Diagnosis methods for blood clots†.Blood clots, HeartCenterOnline. May 9, 2006). References 1. â€Å"Thrombus†. Wikipedia, the free encyclopedia. October 11, 2006. http://en. wikipedia. org/wiki/Blood_clot 2. â€Å"Blood Clotting†. June 4, 2005. http://users. rcn. com/jkimball. ma. ultranet/BiologyPages/C/Clotting. html 3. â€Å"Blood Clots†. HeartCenterOnline. May 9, 2006. http://heart. healthcentersonline. com/bloodclot/bloodclots. cfm 4. â€Å"The Biology of Blood Clots†. Mayo Clinic’s Online Research Magazine. 2006.http://discoverysedge. mayo. edu/vascular_biology/ 5. â€Å"How do blood clots form? March 11, 2004. http://www. innohepusa. com/corporateweb/innohepus/home. nsf/Content/Howdoclotsform. 6. â€Å"Treatment options for blood. † Blood Clot, HeartCenterOnline. May 9, 2006. http://heart. healthcentersonline. com/bloodclot/bloodclots7. cfm 7. â€Å"Diagnosis methods for blood clots†. Blood clots, HeartCenterOnline. May 9, 2006. http://heart. healthcentersonline. com/bloodclot/bloodclots6. cfm.

The Devestating Effects of Popcorn :: essays research papers

One of the biggest threats to freedom in our world today is the ever-looming possibility that someone will realize the true power of popcorn and bring world leaders to their knees. Popcorn holds a power that to this point has remained untapped. However, if placed in the hands of a terrorist organization, the true power of popcorn could be unleashed to wreak havoc on innocent men, women and children.   Ã‚  Ã‚  Ã‚  Ã‚  Millions of people each year have the luxury of eating popcorn freely. Each night in this country alone, thousands of people flock to movie theaters to enjoy a large tub of popcorn while watching a new movie. Month after month the movies on the screen continue to change, following and sometimes affecting world trends, beliefs and styles. Through it all, the popcorn remains the same. It continues to draw the movie patrons back to the theater with the bitter taste of salt mixed with a golden layer of liquid butter. Already, many around the globe have been blindly enslaved to the irresistible experience of leisurely consuming a tub of popcorn while tucked within the dark confines of a crowded picture show.   Ã‚  Ã‚  Ã‚  Ã‚  Large snack corporations have manufactured their own plans to cash in on the subconscious popcorn craze that is sweeping the globe. They perpetuate the popcorn security risk by delivering taste variations. With a light dusting of cheddar cheese or a quick dip into a caramel pool the popcorn is transformed, bringing new tastes to life that now fill the isles at every grocery store. In doing so, a whole new generation of popcorn lovers are captured who couldn’t possibly know the grave position they are placing themselves in.   Ã‚  Ã‚  Ã‚  Ã‚  And proving that every movement has a following determined to do things their own way, some have taken to popping their own popcorn in the comfort of their homes. These renegades have tried to free themselves from a herd mentality by refusing to follow the crowd to a central location to partake in their favorite snack. They have still unknowingly resigned themselves to the same fate by falling under popcorn’s powerful spell.   Ã‚  Ã‚  Ã‚  Ã‚  A terrorist organization armed with this knowledge could potentially seize control of the world’s supply of popcorn and do so without much trouble. Our world leaders are unaware or choose to ignore this threat, and refuse to protect the supply lines of popcorn with the world’s finest armies.

Cluster Analysis

Chapter 9 Cluster Analysis Learning Objectives After reading this chapter you should understand: – The basic concepts of cluster analysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs. Keywords Agglomerative and divisive clustering A Chebychev distance A City-block distance A Clustering variables A Dendrogram A Distance matrix A Euclidean distance A Hierarchical and partitioning methods A Icicle diagram A k-means A Matching coef? cients A Pro? ing clusters A Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep cluster analysis by identifying segments of Internet adopters in Japan. The ? ndings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. In terestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical of? ce workers had the most positive perception.Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target speci? c customer segments via mobile Internet services. Introduction Grouping similar customers and products is a fundamental marketing activity. It is used, prominently, in market segmentation. As companies cannot connect with all their customers, they have to divide markets into groups of consumers, customers, or clients (called segments) with similar needs and wants.Firms can then target each of these segments by positioning themselves in a unique segment (such as Ferrari in the high-end sports car market). While market researchers often form E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10. 1007/978-3-642-1 2541-6_9, # Springer-Verlag Berlin Heidelberg 2011 237 238 9 Cluster Analysis market segments based on practical grounds, industry practice and wisdom, cluster analysis allows segments to be formed that are based on data that are less dependent on subjectivity.The segmentation of customers is a standard application of cluster analysis, but it can also be used in different, sometimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers’ branding strategies (Moroko and Uncles 2009). Understanding Cluster Analysis Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a speci? c cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster.Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segment ing your customer base in order to better target them through, for example, pricing strategies. The ? rst step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y).These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 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 are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our group s of objects.This step is crucial for the analysis, as different procedures require different decisions prior to analysis. There is an abundance of different approaches and little guidance on which one to use in practice. We are going to discuss the most popular approaches in market research, as they can be easily computed using SPSS. These approaches are: hierarchical methods, partitioning methods (more precisely, k-means), and two-step clustering, which is largely a combination of the ? rst two methods.Each of these procedures follows a different approach to grouping the most similar objects into a cluster and to determining each object’s cluster membership. In other words, whereas an object in a certain cluster should be as similar as possible to all the other objects in the Table 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 Cluster Analysis 7 6 A C D E B 239 Brand 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 same cluster, it should likewise be as distinct as possible from objects in different clusters. But how do we measure similarity?Some approaches – most notably hierarchical methods – require us to specify how similar or different objects are in order to identify different clusters. Most software packages calculate a measure of (dis)similarity by estimating the distance between pairs of objects. Objects with smaller distances between one another are more similar, whereas objects with larger distances are more dissimilar. An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. This question is explored in the next step of the analysis.Sometimes, however, we already know the number of segments that have to be derived from the data. For example, if we were asked to ascertain what characteristics distinguish frequent shoppers from infrequent ones, we need to ? nd two different c lusters. However, we do not usually know the exact number of clusters and then we face a trade-off. On the one hand, you want as few clusters as possible to make them easy to understand and actionable. On the other hand, having many clusters allows you to identify more segments and more subtle differences between segments.In an extreme case, you can address each individual separately (called one-to-one marketing) to meet consumers’ varying needs in the best possible way. Examples of such a micro-marketing strategy are Puma’s 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 strategy may be prohibitively high in many 240 9 Cluster Analysis Decide on the clustering variables Decide on the clustering procedureHierarchical methods Select a measure of similarity or dissimilarity Partitioning methods Two-step clustering Select a measure of similarity or dissimilarity Choose a clustering algorithm Decide on the number of clusters Validate and interpret the cluster solution Fig. 9. 2 Steps in a cluster analysis business contexts. Thus, we have to ensure that the segments are large enough to make the targeted marketing programs pro? table. Consequently, we have to cope with a certain degree 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 done 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 also requires us to assess the clustering solution’s stability and validity. Figure 9. 2 illu strates the steps associated with a cluster analysis; we will discuss these in more detail in the following sections.Conducting a Cluster Analysis Decide on the Clustering Variables At the beginning of the clustering process, we have to select appropriate variables for clustering. Even though this choice is of utmost importance, it is rarely treated as such and, instead, a mixture of intuition and data availability guide most analyses in marketing practice. However, faulty assumptions may lead to improper market Conducting a Cluster Analysis 241 segments and, consequently, to de? cient marketing strategies. Thus, great care should be taken when selecting the clustering variables. There are several types of clustering variables and these can be classi? d into general (independent of products, services or circumstances) and speci? c (related to both the customer and the product, service and/or particular circumstance), on the one hand, and observable (i. e. , measured directly) and un observable (i. e. , inferred) on the other. Table 9. 2 provides several types 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 User 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 last decades, attention has shifted from more traditional general clustering variables towards product-speci? c unobservable variables. The latter generally provide better guidance for decisions on marketing instruments’ effective speci? cation. It is generally acknowledged that segments identi? ed by means of speci? unobservable variables are usually more h omogenous and their consumers respond consistently to marketing actions (see Wedel and Kamakura 2000). However, consumers in these segments are also 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 but often lack a unique response structure. 1 Consequently, researchers often combine 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 always the case and researchers have to choose from a set of candidate variable s. Whichever clustering variables are chosen, it is important to select those that provide a clear-cut differentiation between the segments regarding a speci? c managerial objective. More precisely, criterion validity is of special interest; that is, the extent to which the â€Å"independent† clustering variables are associated with 1 2 See Wedel and Kamakura (2000). Tonks (2009) provides a discussion of segment design and the choice of clustering variables in consumer markets. 242 9 Cluster Analysis one or more â€Å"dependent† variables not included in the analysis. Given this relationship, there should be signi? cant differences between the â€Å"dependent† variable(s) across the clusters. These associations may or may not be causal, but it is essential that the clustering variables distinguish the â€Å"dependent† variable(s) signi? antly. Criterion variables usually relate to some aspect of behavior, such as purchase intention or usage frequency. Gen erally, you should avoid using an abundance of clustering variables, as this increases the odds that the variables are no longer dissimilar. If there is a high degree of collinearity between the variables, they are not suf? ciently unique to identify distinct market segments. If highly correlated 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 virtually cover the same aspect as brand loyalty. Thus, the concept of being 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 handle this issue by applying cluster analysis to the observations’ factor scores derived from a previously car ried out factor analysis.However, according to Dolnicar and Grâ‚ ¬n 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 transformed 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 variance; 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 based on the original variables become questionable given that the segments have been constructed using factor scores. Several studies have shown that the factor-cluster segmentation signi? cantly reduces the success of segmen t recovery. 3 Consequently, you should rather reduce the number of items in the questionnaire’s pre-testing phase, retaining a reasonable number of relevant, 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 sample size in mind. First and foremost, this relates to issues of managerial relevance as segments’ sizes need to be substantial to ensure that targeted marketing programs are pro? table. From a statistical perspective, every additional variable requires an over-proportional increase in 3 See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grâ‚ ¬n (2009). uConducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sampl e 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 only provide rough guidance; nevertheless, we should pay attention to the relationship between the objects and clustering variables. It does not, for example, appear logical to cluster ten objects using ten variables.Keep in mind that no matter how many variables are used and no matter how small the sample size, cluster analysis will always render a result! Ultimately, the choice of clustering variables always depends on contextual in? uences such as data availability or resources to acquire additional data. Marketing researchers often overlook the fact that the choice of clustering variables is closely connected to data quality. Only those variables that ensure that high quality data can be used should be included in the analysis. This is v ery important if a segmentation solution has to be managerially useful.Furthermore, data are of high quality if the questions asked have a strong theoretical 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 play 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 agreement 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 formed. This always involves optimizing some kind of criterion, such as minimizing the within-cluster variance (i. e. , the clustering variables’ overall variance of objects in a speci? c cluster), or maximizing th e distance between the objects or clusters. The procedure could also address the question of how to determine the (dis)similarity between objects in a newly formed cluster and the remaining 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 introduce two-step 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 characte rized by the tree-like structure established in the course of the analysis. Most hierarchical techniques fall into a category called agglomerative 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 then sequentially merged according to their similarity. First, the two most similar clusters (i. e. , those with the smallest distance between them) are merged to form a new cluster at the bottom of the hierarchy. In the next step, another pair of clusters is merged and linked to a higher level of the hierarchy, 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. Step 5 Step 1 A, B, C, D, EAgglomerative clustering Step 4 Step 2 Divisive clustering A, B C, D, E Step 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 single cluster, which is then gradually split up. Figure 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 assigned to a certain cluster, there is no possibility of reassigning 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 various types Conducting a Cluster Analysis 245 of agglomerative procedures. However, before we discuss these, we need to de? ne how similarities or dissimilarities are measured between pairs of objects.Select a Measure of Similarity or Dissimilarity There are various measures to express (dis)similarity between pairs of objects. A straightforward way to assess two objects’ proximity 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 length of the line connecting observations B and C is much shorter than the line connecting B and G. This type of distance is also referred to as Euclidean distance (or straight-line distance) and is the most commonly used type when it comes to analyzing ratio or interval-scaled data. In our example, we have ordinal data, but market researchers usually treat ordinal data as metric data to calculate distance metrics by assuming that the scale steps are equidistant (very much like in factor analysis, which we discussed in Chap. 8). To use a hierarchical c lustering procedure, we need to express these distances mathematically. By taking the data in Table 9. 1 into consideration, we can easily compute 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 squared 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 ? 1:414 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 deal with six dimensions), making it impossible to represent the solution graphically. Si milarly, we can compute 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 ? 7:071 Likewise, we can compute the distance between all other pairs of objects. All these distances are usually expressed by means of a distance matrix. In this distance matrix, the non-diagonal elements express the distances between pairs of objects 5Note 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-di agonal 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 New York’s 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 metr ic (or ordinal) data, researchers frequently use the Chebychev distance, which is the maximum 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 Brand 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 measured on different scales or levels. This would be the case if we extended our set o f clustering variables by adding another ordinal variable representing the customers’ income measured by means of, for example, 15 categories. Since the absolute variation 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 distort our analysis results. We can resolve this problem by standardizing the data prior to the analysis.Different standardization methods are available, 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 range (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 Milligan and Cooper (1988). 248 9 Cluster Analysis Another way of (implicitly) sta ndardizing 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 second 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 respondent’s ratings are more similar to the second’s, as both rate brand loyalty higher than price consciousness. This can be accounted for by computing 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 answering pro? les. Using correlation is also a way of standardiz ing the data implicitly. Whether you use correlation or one of the distance measures depends on whether you think the relative 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 susceptible to outliers, such as complete 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 matching coef? ients can take different forms but rely on the same allocation scheme 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 Based 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 Let’s 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 female non-customer with a high disposable income. Accord ing 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 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 Yule’s 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 convert 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 me asures 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 closer to someone who is somewhat 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 contain variables that are measured on multiple scales. For example, a market research questionnaire may ask about the respondent’s 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 s imply ignore 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 slightly change the results when compared 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 integration 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 based 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 input for the cluster analysis. However, the weights hav e 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 learn 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 single cluster analysis, but if this is not feasible, the two-ste p 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 underlying 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 Single linkage (nearest neighbor): The distance between two clusters corresponds to the shortest distance between any two members in the two clusters.Complete linkage (furthest neighbor): The oppositional approach to single linka ge 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. 5–9. 8 illustrate these linkage procedures for two randomly 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 clusters containing only one or few objects each. We can make use of this â€Å"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 maximum 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 Ward’s 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 you expect s omewhat equally sized clusters and the dataset does not include outliers, you should always use Ward’s method. To better understand how a clustering algorithm works, let’s manually examine some of the single linkage procedure’s 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 let’s proceed 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 link age 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 distance matrices in the following Tables 9. 8–9. 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 proce dure, 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 analysis’s 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 haven’t yet addressed is how to decide on the number of clusters to retain from the data. Unfortunately, hierarchical methods provide only very limited guidance for making th is decision.The only meaningful indicator relates to the distances at which the objects are combined. Similar to factor analysis’s scree plot, we can seek a solution in which an additional combination of clusters or objects would occur at a greatly increased distance. This raises the issue of what a great distance is, of course. One 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 automatically – 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 F ig. 9. 9. Speci? cally, SPSS rescales the distances to a range of 0–25; 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 obvious. 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 proven 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 the squares between the segments and SSW is the sum of the squares within the segments. The criterion should seem familiar, as this is nothing 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 pr actical considerations.Occasionally, you might 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 must the number of clusters be small enough to ensure manageability, but each segment should also be large enough to warrant strategic 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 entirely 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 n on-hierarchical clustering methods. Several extensions, such as k-medoids (Kaufman and Rousseeuw 2005) have been proposed to handle problematic aspects of the procedure. More advanced methods include ? ite mixture models (McLachlan and Peel 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 reallocation of an object to another cluster decreases the within-cluster variation, this object is reassigned to that cluster. With the hierarchical methods, an object remains 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 build 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, let’s take a look at how it works in practice. Figs. 9. 10–9. 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 clu ster) in Fig. 9. 10 A CC1 C B D E Brand loyalty (y) CC2 F G Price consciousness (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 c lusters. Based on this initial partition, each cluster’s 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 located 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 t he ? rst cluster center (CC1’) than to the second (CC2’). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-means 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 clusterin g 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 distances. 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 procedureâ €™s sensitivity to the initial classi? cation (we will follow this approach in the example application). Two-Step Clustering We have already discussed the issue of analyzing 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 designed 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 indication 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 â€Å"leaves† represent distinct objects in the dataset. The procedure can handle categoric al 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 automatically 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 Akaike’s 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 variable’s 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 subseque nt example.Validate and Interpret the Cluster Solution Before interpreting the cluster solution, we have to assess the solution’s stability and validity. Stability is evaluated by using different clustering 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 analyze 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 presume 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 y our dataset and re-running the analysis to check 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 solution’s reliability is closely related to the above, as reliability refers to the degree to which the solution is stable 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 focus on criterion variables that have a theoretically based relationship with the clustering variabl es, but were not included in the analysis. In market research, criterion variables usually relate to managerial outcomes such as the sales per person, or satisfaction. If these criterion variables differ 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, expert validity. While we primarily consider criterion validity when choosing clustering variables, as well as in this ? al step of the analysis procedure, the assessment 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; Ton ks 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. Stable: 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 company’s competencies and objectives. Compactness: Segments exhibit a high degree of within-segment homogeneity and between-segment h eterogeneity. 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 examining 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 segm ent comprises respondents who appreciate self-ful? lment, enjoyment 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 standardized 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 household in terms of 66 demographically and behaviorally distinct segments to help ma rketers 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 neighborhood’s top ? ve lifestyle groups.One example of a segment is â€Å"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 introduce 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 Make 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). Ensure that the sample size is large enough to guarantee substantial segments. Low levels of collinearity among the variables ?Analyze ? 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 ? Classify ? Hierarchical Cluster If there are many observations (> 500) in your dataset and you have a priori knowledge regarding the number of clusters: ? Analyze ? Classify ? K-Means Cluster If there are many observations in your datas et 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 ? Measure 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 Ward’s 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 clu sters 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 schedule. 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 Dependent List box. Compute VRC for each segment solution and compare values. k-means: Run 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 ?Numbe r of Clusters Run 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