Importance of country’s economic system free essay sample

I not shock , as indicated by my past experience as a material controller in LG gadgets and these days as a buying part in Suez Canal Authority The Logistics has a chronicled relationship with the Economic framework extraordinarily with GDP . before 1980 coordinations didnt surpass the 10% of GDP , however this rate has been expanded yearly until 2005 , the coordinations speak to 45% of the GDP This significance not just on the Macro level piece additionally on the Micro level , for instance the private part in china perceive that as chinas work cost advantage movements to different nations , coordinations turns into a fundamental segment to energizing an economy that has been extending at somewhere in the range of 8 and 10 % every year Q2:) Due to specialized issues, Toyota had reviewed different models of their vehicles in the worldwide market. This consequently severy affected their vital and operational reasoning and arranging. From a coordinations the board point of view, portray how the coordinations capacity can add to beat Toyotas review emergency. We will compose a custom article test on Significance of country’s monetary framework or on the other hand any comparative point explicitly for you Don't WasteYour Time Recruit WRITER Just 13.90/page Answer First of all we should characterize the difficult that cause the review , TOYOTA has found in 2013 That the issue is because of water spilling out from the cooling condenser unit into the airbag control module TOYOTA review around 850,000 vehicles from the universal market To contribute the beat review TOYOTA must chip away at 2 primary concerns: 1-The inbound coordinations : this attention on the buying materials and info quality control ( IQC ) . the buying dep. Must check the provider material to guarantee that TOYOTA get best quality and best cost and should make a persistent physical examination by IQC dep. The outbound coordinations : Toyota has confronted a phenomenal arrangement of reviews lately, including various assistance activities identified with purported unintended increasing speed, The creator will straightforwardly advise proprietors in the weeks ahead and vendors will make fixes at no charge. The fix requires the utilization of sealant and another spread to keep water from spilling onto the airbag control module. this will concentrate on the administration community dispensed worldwide to persistent get an input from clients and answered to the operational dep. And furthermore to deal with the present review clients to be guarantee that there vehicles issue has been illuminated .

Ageing and Disability Policies | Country Comparison

Maturing and Disability Policies | Country Comparison Gian Nestle Sorronda Maturing and Disability Task 4 4.1 Incapacity US of America †In United States of America, they made a demonstration that would help reduce or dispense with separation in the nation for those individuals who have incapacity. This demonstration was made back on 1990 and was known as the Americans with Disability Act or ADA which disallows separation towards the incapacitated individual in any area of the network. This is from work, transportation, open settlement, correspondence and some other government exercises. The administrations of the referenced ought to be appropriate, sensible and practical for the people with incapacities to be qualified. New Zealand In New Zealand, they have framed a demonstration that would help and give benefits for the debilitated individual. The demonstration was known as The New Zealand Disability Strategy which was made last 2001. This demonstration brought up 15 destinations that would essentially address the requirements of those person who are experiencing handicap. This was made to ensure that New Zealand has a drawn out arrangement for these people who are seen by the general public as various. This demonstration additionally will clear approach to changing New Zealand’s perspective from a debilitating society to a comprehensive society. Joined Kingdom †In 2010, the United Kingdom supplanted the demonstration that was built up in 1995 and called the Disability Discrimination Act. It was changed to The Equality Act of 2010 and the objective was to address the oppression age, sex, race, religion, sex, sexual direction, common organization, marriage, and finally the inability. This demonstration was built up to ensure that the referenced gathering or part of the general public gets satisfactory assistance. The assistance ought to be reasonable enough and appropriate to ensure that it will agree to the demonstration. Similitudes and Differences: The similitudes of these two approaches are that they needed the individuals of their nation and their separate network to treat the individuals with handicap with reasonableness, equity and without segregation. This implies they have to support and permit instruction for the incapacitated individual, guarantee that their privileges are met, appropriate work, mindful on the most proficient method to get to administrations, direction for living and permit them to live with a quality life. Then again, contrasts are additionally recognizable particularly in the piece of United Kingdom wherein they incorporated the handicap along with other piece of the general public. In contrast to the United States of America and New Zealand, they have made a different demonstration and system for those impaired individual to ensure that their needs will be tended to right away. Maturing US of America †They have built up a represent the more established individuals to ensure needs are met. It is known as the Older American Act which was set up back on 1965. This was made to give every network to have administrations for the maturing individuals. This was passed because of the worry that was emerging during this time more established individuals are not accepting proper consideration in the network. The administration for the most part finances the administrations that will be given to the individual however there some not-for-profit offices too that assists for the administrations, the legislature is as yet the essential supplier. Joined Kingdom †The Equality Act of 2010 by the UK Government additionally covers the necessities and the privileges of the maturing populace by accepting satisfactory assistance from them and treat them without separation by people in general and from the general public. This spreads from their work, instruction and preparing. One person who in truth is maturing doesn’t lose their privileges to have the option to work and bolster themselves. They can keep on being utilized as much as they like or seek after any training that they might want. New Zealand †In New Zealand, there are two current act and procedure that is set up and dynamic. One was built up in 2001 and called the New Zealand Positive Aging Strategy and the other is the Health of Older People Strategy which was set up in 2002. Both of these demonstration has just a single objective which is to give an appropriate consideration and spot for the maturing individuals and carry on with a quality life regardless of their age. Both of these additionally energize the legislature or guide the administration to have offices to have the option to give the sort of administrations that these more seasoned individuals need. Beginning from a spot where an individual can age and still can have a quality life until to the administrations that are required from transport administrations, correspondence and so on. Similitudes and Differences The similitudes with these demonstration and procedure are that they are pointing one objective for the more seasoned individuals. These demonstrations are a portrayal from the administration by every nation that everybody has the privilege to get legitimate consideration as they matured and that the legislature is as yet responsible for them. These nations are very much aware also that they have to subsidize the administrations that will be given to maturing individuals. Their separate government for the most part gives the subsidizing and they likewise connected with other charitable or nearby government unit to give appropriate help, care, transportation needs, correspondence needs, versatility needs and that they won't be segregated in any piece of the general public from work to different administrations for the matured. The distinctions are predominantly how these demonstrations are built up. Both US of An and New Zealand made or set up an alternate and explicit act and techniq ue to address the maturing populace while United Kingdom made a more extensive viewpoint as maturing was incorporated to their Equality Act of 2010 which covers from sexual orientation, religion and inability. 4.2 Handicap US of America †The Americans with Disability Act of 1990 or ADA offers types of assistance such consideration in a medical clinic, instruction and open vehicle to the crippled individual from the administration. This implies administrations are being supported by the American government to give out legitimate consideration to the individuals who are influenced. This is by method of the formation of sub gathering of the legislature to especially give these considerations and furthermore from an individual or private division yet at the same time completely subsidized by the administration. Joined Kingdom †The Equality Act of 2010 by the administration exclusively addresses the required guide on the best way to give cares to the impaired individuals. This might be by the legislature or any private specialist organization. This is to ensure that cares and administrations conveyed from or by an open medical clinic, instructive establishment, not-for-profit association and neighborhood government unit are adjusted to the demonstration. This is likewise to guarantee that there is no biased demonstration anyplace from banks, shops, schools, transport administrations, taxi and open bodies. New Zealand †the New Zealand Disability Strategy of 2001 gives manage on the best way to have the option to give out appropriate consideration and administrations to a person who is experiencing incapacity. This is to ensure that administrations given to them are related and aligned by the demonstration. This is likewise one method of ensuring that DHB’s or District Health Board, General Practitioner’s or GP’s and other private and nearby organizations are giving consideration without segregation and that they are giving their consideration as per the demonstration. Likenesses and Differences The likenesses inside the demonstrations of these three nations are that every one of them can have the option to apply for those person who are influenced by mental inability and physical incapacity. These demonstrations were set up for them to have the option to have an entrance at whatever point they required assistance. Particularly when a person that has inabilities have been segregated by and in the general public. In conclusion, the fundamental objective of these demonstrations is to give decency in giving out consideration and administrations for a person who has inability. Then again, the contrast between these demonstrations is that for the United Kingdom, they consolidated 116 enactment and made into a demonstration which will offer better assistance for the debilitated. A few administrations too are freely supported and there are administrations that are not and could be sponsored by the legislature and the family. Maturing US of America †The Older American Act of 1965 was built up to give network administrations to the maturing individuals whose administrations are frequently supported by the legislature and some are given by a private office. This is to guarantee that more established individuals can in any case improve their lives according to their pay, wellbeing, lodging, business, long haul care, retirement, and network administrations. The reason for the demonstration is to give and upgrade the capacity of a maturing individual to keep up its autonomy and to have the option to remain collaborate still in their home and in their locale. The assets are given from government reserves wherein it is designated exclusively with the end goal of the demonstration. Joined Kingdom †Since the demonstration made by the UK government supplanted past laws and made into a one demonstration which made it to be simpler to get a handle on and comprehend. The demonstration just obviously expresses that giving consideration to the matured individuals ought to be without segregation. They ought to get a similar treatment as what others got too. The law especially expresses that an individual ought to get reasonable treatment when they get administrations from the legislature or some other neighborhood government unit and ought not be segregated because of their age. This likewise covers for the administrations as well as for instruction, transportation, getting to the administrations and correspondence. New Zealand †This is to guarantee that Kiwi’s or New Zealanders will get appropriate mind and have a huge life while coming to age. The technique ensured that they will have the option to secure administrations at whatever point they required from DHB’s or District Health Board, in the network and at whatever point they use transportation. The technique additionally ensured that maturing individual can in any case get administrations like wellbeing and health, monetary security,

21 Welcoming Classroom Door Ideas for Back-to-School

21 Welcoming Classroom Door Ideas for Back-to-School A positive classroom environment is an important aspect of student achievement. Start the back-to-school welcome party at the door â€" literally â€" with these colorful, clever, and creative ideas from your fellow teachers! 1. Goal Getters: A post shared by Ms. ?RteGa (@fourthgrade.fairytale) on May 11, 2017 at 4:48pm PDT Credit: @fourthgrade.fairytale   2. Adventure Begins: A post shared by Brittany Fitz (@its.heart.work) on Jul 26, 2017 at 7:11pm PDT Credit: @its.heart.work   3. Be Something This World Needs: A post shared by Alexandra Baker ?? (@learningwithmrsbaker) on Jul 18, 2017 at 1:53am PDT Credit: @learningwithmrsbaker   4. A Good Day: A post shared by Kayse Morris (@kaysemorristeachingonless) on Jul 26, 2017 at 8:09am PDT Credit: @teachingonless   5. Ready for an Incredible Year: View this post on Instagram A post shared by Mrs. Schultz (@kickin.it.in.kindergarten) on Aug 24, 2018 at 12:54pm PDT Credit: @kickin.in.in.kindergarten   6. All You Have To Do Is Fly: View this post on Instagram A post shared by The Printed Society (@theprintedsociety) on Jun 15, 2018 at 11:49am PDT Credit: @theprintedsociety   7. Come In, We're Awesome: A post shared by Shelby Lintz (@lintzin5th) on Jul 13, 2017 at 11:13am PDT Credit: @lintzin5th   8. Pixar-Inspired: A post shared by Amanda White (@ms.amandawhite) on Jul 28, 2017 at 10:06am PDT Credit: @mswhiteinthird   9. Be a Rainbow: A post shared by Teaching in the Tropics (@teachinginthetropicsblog) on Jul 12, 2017 at 2:21pm PDT Credit: @teachinginthetropicsblog   10. Choose Kind: A post shared by Kelly Mitchell (@mrskkmitchell) on Aug 2, 2017 at 7:41am PDT Credit: @mrskkmitchell   11. Have Big Dreams: A post shared by Miss Willcox (@misswillcoxs_cleverclassroom) on Jul 31, 2017 at 2:27am PDT Credit: @misswillcoxs_cleverclassroom   12. Leaders Popping Up: A post shared by Katey Champagne (@kateychampagne) on Jul 28, 2017 at 3:00pm PDT Credit: @kateychampagne   13. Shine Bright Like a Diamond: View this post on Instagram A post shared by kristen//dear john designs (@4skiski) on Aug 13, 2018 at 10:34am PDT Credit: @4skiski   14. In a Galaxy Far, Far Away... A post shared by Ms. Mandi Russell (@thegeekyteacher) on Jul 24, 2017 at 10:02pm PDT Credit: @thegeekyteacher   15. We Are Family: A post shared by CK (@thewellredlibrarian) on Jul 23, 2017 at 7:45pm PDT Credit: @thewellredlibrarian   16. The More You Know: A post shared by Miss B? (Beth?) (@_teach_love_) on May 5, 2017 at 1:40pm PDT Credit: @_teach_love_   17. Unmask the Super-Leader: A post shared by Cassandra Erwin (@cassafrass101) on Sep 11, 2016 at 10:20am PDT Credit: @cassafrass101   18. Turtle-y Awesome: A post shared by Tasha Workman (@tashaworkman) on Apr 20, 2017 at 5:25pm PDT Credit: @tashaworkman   19. Un-Bee-Lievable: A post shared by 4thgradestateofmind (@4thgradestateofmind) on Apr 3, 2017 at 12:47pm PDT Credit: @4thgradestateofmind   20. Learn a Latte: View this post on Instagram A post shared by Kaytee Deines (@1stgradedeinesty) on Aug 7, 2018 at 2:04pm PDT Credit: @1stgradedeinesty   21. The Magic Begins Here: A post shared by Staci Lamb (@theengagingstation) on Apr 19, 2017 at 5:03pm PDT Credit: @theengagingstation   We hope this gave you Back-to-School inspiration! Share your own classroom creations with us on Instagram. Connect with us! Follow TeacherVision on Facebook, Instagram, Twitter,Pinterest, and Google+.

The Goals Of The French Revolution - 1360 Words

Beginning in 1789, the French Revolution was a time of rebellion, passion, betrayal, death, violence, and perseverance. Before 1789, King Louis XVI was taxing his poorest people heavily, whilst the rich were hardly taxed at all. The socioeconomic system in place at the time, known as the ancien regime, ruthlessly upheld the status quo. When the tension in France rose to a boiling point, fiery, young rebels to rose up against the Monarchy, the Ancien Regime, and the Clergy. These revolutionaries desperately wanted change, and they had three main principles around which their movement centered: Libertà ©, Égalità ©, and Fraternità ©. Liberty is the freedom to do as one pleases so long as one’s actions do not unjustifiably harm others. Equality is being considered the same. Fraternity is a sense of brotherhood and companionship. The goals of the French Revolution were initially achieved with alacrity, however they were abandoned with relative quickness as malcontented lead ers quested for, and abused, power and authority. Liberty, a virtue promised to countless groups and individuals throughout history, was temporarily achieved during the French Revolution. Unfortunately, it was stripped away from those who needed and wanted it most rather shortly after it was obtained. In 1789, when the people of Paris stormed the Bastille, they acted as catalysts for what would evolve into the Revolution. Shortly thereafter, a group of poor, brawny, enraged parisian women stormed Versailles,Show MoreRelatedAchieving the Goals of the French955 Words   |  4 PagesThe French Revolution is known to be the bloodiest and most violent revolution humankind has experienced. The French Revolution was started with the following three goals in mind: a change in government, a change in economy, and the delivery of equality for all. An extensive effort was used to correct these problems; however, it came at the cost of people’s freedom. Authoritarian revolutionari es intended to solve France’s struggles and drove the revolution in hope to improve the lives of the peopleRead MoreThe Third Estate Prior to the French Revolution1111 Words   |  5 PagesJoseph Sieyes in 1789, right before the French revolution. This Third Estate had goals of gaining freedoms that the other â€Å"estates† had at the time. The Third Estate was defined majorly as the common people of France or the middle class, while the first estate was made up of the clergy of the church and the second estate was made up of the french nobility and monarchs. This Third Estate ultimately played a major factor in spurring on the French Revolution by igniting the passions of those tryingRead MoreFrench Revolution vs. the Haitian Revolution762 Words   |  4 PagesThe French Revolution vs. The Haitian Revolution A revolution is a shift, a turning point, a change in government. A revolution usually occurs when the majority of a nation is frustrated with the economic, political, or the social situation of their country. Two very interesting revolutions were the Haitian and the French revolutions. While the Haitian and French revolutions took place in close proximity to one another, the cause for the revolutions and results were quite different. The politicalRead MoreThe French Revolution of 1789- 1799 was a time of change for many people of France. The Revolution600 Words   |  3 PagesThe French Revolution of 1789- 1799 was a time of change for many people of France. The Revolution led to many changes in France which at the time of the Revolution, was the most powerful state in Europe. The major cause of the French Revolution was the disputes between the different types of social classes in French society. Harsh economic conditions brought high taxes and bad harvests resulted in suffering for the revolutionary women. They broke people down in Three estates: 1st was made up ofRead MoreRevolutions And The French Revolution956 Words   |  4 Pages Revolutions are a common occurrence throughout world history. With the amount of revolutions in history, there are those that get lost and those that are the most remembered or well known. One of the well known revolutions is the French Revolution which occurred in the years 1789 to 1799. Before the French Revolution, France was ruled by an absolute monarchy, this meaning that one ruler had the supreme authority and that said authority was not restricted by any written laws, legislature, or customsRead MoreFrench Revolution611 Words   |  3 PagesInfluenced Events in the French Revolution DeAnna Andrew HIS/114 April 1, 2014 Julie Thomas Influenced Events in the French Revolution What is a revolution? According to Merriam-Webster (2014), revolution is a fundamental change in political organization; especially: the overthrow or renunciation of one government or ruler and the substitution of another by the governed. Various social, political, and economic conditions led to the revolution. These conditions included dissatisfaction amongRead MoreWas Napoleon the Child of the Revolution?1319 Words   |  6 Pagesday, Was Napoleon a child of the revolution? What Napoleon achieved and gave to the world after the French Revolution had been won truly gave the world a new sight of mind. To first determine that Napoleon was in fact a child of the revolution you must first look at both sides of the argument. You must first ask yourself, what one means by the Revolution. In the history of France there was not just one Revolution, but a series of revolutions as the French struggled to create a new politicalRead MoreRiley: Republic and French Revolution Essay607 Words   |  3 PagesGraded Assignment Two Revolutions Complete and submit this assignment by the due date to receive full credit. (50 points) The French Revolution began less than two decades after the American Revolution. In many ways, the American experience was an inspiration for the citizens of France. But the people of the two countries had different situations and had different concerns, which influenced the way each revolution began, progressed, and ended. In this assignment you will write an essayRead MoreThe French Revolution And The Revolution1523 Words   |  7 PagesThe French Revolution was a time rife with violence, with many revolutionaries using extreme actions to overturn the French Monarchy and create a government based on equality and justice, rather than tyranny and despotism. This violence reached gruesome and terrible heights throughout the revolution, but was justified by the revolutionaries, who believed that their goals of total equality, the end of tyranny, and the return to a virtuous society, allowed them to use means necessary to attain theseRead MoreRevolutions Of The French Revolution1139 W ords   |  5 PagesRevolutions are often characterized under two dominant schools of thought, either the structural or the cultural viewpoint. The structural approach favors causes that are of inherent forces in the system of sovereign nations, whilst the cultural view favors individuals actions and ideas as rudimentary to revolution. Skocpol and Trimberger’s essay Revolutions: A Structural Analysis modernizes the ideas of Karl Marx and reconciles them with the modern revolutions that have occurred to form a new viewpoint;

The Black Death - Worst plague in history - Free Essay Example

Sample details Pages: 2 Words: 494 Downloads: 1 Date added: 2019/07/01 Category History Essay Level High school Tags: Black Death Essay Did you like this example? Making a grand entrance into Europe in the mid 1300s, the Black Death took almost one third of the continents population, with numbers exceeding twenty million people. The devastating epidemic sprung from a strand of the Bubonic plague that infected twelve ships docked in Messina, venturing from the Black Sea. When docked, majority of the sailors living on the boats were found to be dead, those who managed to say alive did not last much longer. The drastic change in population caused significant changes for the citizens of Europe as they entered the Late Middle Ages. Gruesome symptoms began to take over the internal operations and external appearance of the body. Symptoms spread in a fast manner, making the plague one of the most contagious diseases to ever attack the world. Once infected, it was likely to experience headaches, fevers, chills, and eventually fatigue, all normal symptoms of any day-to-day virus. After developing the basics, symptoms got a little more unique and developed into an aching of the entire body. On top of all of these, the patient would then notice swellings which consisted of hard, painful, and burning lumps on the neck, under the arms, and on inner thighs. Without much time passing, these swellings turned into large black growths that would then begin to ooze fluids, such as pus and blood, at a random rate. These extreme symptoms would soon become fatal due to the internal bleeding most were known to cause. Moments before the plague took over the body one final time, it created a unique stench that stemmed from differe nt parts of rotting body. The most common way this dreaded disease was spread throughout Fourteenth Century Europe was through a series of flea bites. Don’t waste time! Our writers will create an original "The Black Death Worst plague in history" essay for you Create order These fleas, having ingested plague infected blood from their hosts would often attempt to feed on other victims, invariably injecting some of the infected blood into its new host, resulting in the Bubonic Plague. Once infected, one could infect others through airborne pathogens in close quarters of those who remained healthy up until that point. Due to the rapid transmission rate, the few doctors that dared to come face to face with the Black Death wore a beak-like mask filled with aromatic items to fight off the deadly disease. Fourteenth Century Europe was devastated. Religious, social, and economic riots took place and left a permanent mark in European history, this including the end of feudalism in Europe. The citizens decided that tomorrow remained unpromised, so they lived each day in the moment. The population decrease brought mostly favorable results to the surviving people in Western Europe. Land was plentiful and the innovation of labor-saving technologies became the first major movement after the plague. The Black Plague will not be forgotten anytime soon. As one of the worst diseases to ever create an epidemic of this severity, the Black Death remains engraved in the history of Europe along with the troubles and upheavals that came with it.

Review Chapter 19 Free Essays

Chapter 19 Review Questions 1. D 2. B 3. We will write a custom essay sample on Review Chapter 19 or any similar topic only for you Order Now D 4. C 5. E 6. D 7. C 8. B 9. B 10. A 11. B 12. C 13. b 14. B 15. D 16. 1. G, 2. C, 3. F, I, 4. H, 5. D 17. Their location and the thinness of walls, allow for exchange of materials between the blood and interstitial fluid. 18. Elastic arteries: Location- near the heart Histology elastin and smooth muscle cells Functional adaptations they function as simple elastic tubes Muscular arteries Location deliver blood to specific body organs Histology smooth muscle and fibrous Functional adaptation active in vasoconstriction Arterioles Location leading to the capillary beds Histology thinner but also mostly smooth muscle and fibrous Functional adaptation determine the minute to minute flow into the capillary beds. 19. Blood flow =the difference in blood flow divided by resistance 20. Blood Pressure is the force per unit area exerted on a vessel wall by the contained blood expressed in mmHg. Systolic is pressure at contraction, and diastolic is pressure at rest. B) 110-140, over70-80. Short term control mediated by the nervous system and blood borne hemicals,counteracting moment to moment fluctuations in bp by altering peripheral resistance. Involves baroreceptors and to a lesser degree chemoreceptors. 21. It is fastest in the aorta and large arteries, where the cross sectional area is the highest and slower in the capillaries where the cross sectional area is least. 22. The blood flow to the skin for the purpose of nutrients is served by autoregulation in response to the need for oxygen. The flow for tem perature regulation requires neural intervention, and are controlled by temperature receptors from higher CNS centers. 23. When experiencing the Fight or flight reflex the nervous system and blood borne chemicals counteract moment to moment fluctuations in B/P and CO. The neural controls distribute blood to the organs needing it like you musculoskeletal system during running, and chemoreceptors react to increased co2 or decreased o2 levels are present, flight. 24. Tissue perfusion, blood flow through body tissues is involved in the delivery of oxygen and nutrients to and the removal of wastes from the tissue cells, as well as gas exchange in the lungs absorption of nutrients in the digestive tract and urine formation in the kidneys. The rate of blood flow to the tissues is almost exactly right to provide proper function. 1. Lipid soluble molecules such as respiratory gasses diffuse through the lipid bilayer of the endothelial cell plasma membranes. Small water soluble solutes such as amino acids and sugars pass through fluid filled intercellular capillary clefts or fenestrations. Some larger molecules such as proteins are actively transported in pinocytitic vesicles or caveolae. 25. Hepatic portal vein, Superior mesenteric vein, Splenic vein, Inferior mesenteric vein. In most circulation, the blood goes from the heart to the aorta to the arteries, to the organ or tissue, to the veins and back to the heart. In a portal circulation, the blood goes from the heart to the aorta to the arteries, to the organ or tissue, to a vein, then to another tissue or organ BEFORE it goes back to the heart. An example of this is the blood that goes through the anterior mesenteric artery to the small intestine, goes through the mesenteric vein to the hepatic portal vein, and to the liver, before it returns to the heart. This is because the blood coming from the intestines has a lot of sugar that it absorbed. The body tries to keep the level of sugar in the blood the same at all times. So, the blood goes to the liver, where it stores some of the sugar, before it goes back to the heart and through the rest of the circulation. Then, in the middle of the night, when you haven’t eaten anything in a while (and the level of sugar in your blood is low), some of that sugar that was stored in the liver is released back to the blood. How to cite Review Chapter 19, Papers

Strengths And Weaknesses Essays - Educational Psychology

Strengths And Weaknesses Everyone has strengths and weaknesses but as a child, it's hard to deal with your weaknesses. It's not until you grow up and realize that you have to use your strengths to overcome your weaknesses. Combining logic with technology can be a useful tool. For me it was the way to overcome my disability. Early on in my education I encountered learning disabilities. These learning disabilities included reading, spelling, grammar, and difficulty in time writing reports. These learning disabilities have frustrated me. I wondered Why couldn't I do this? I knew that I had the intelligence, but I just could not finish the assignments. I would then become angry. My anger sometimes led me to break pencils and throw them across my room. The basis of my learning disabilities, I discovered, was in transferring my thoughts and ideas from my head to paper. Reading was also a challenge for me in the early grades. I never had trouble understanding stories themselves; but when I had to answer questions a bout what I had read, I often had difficulty formulating an answer. Another major issue I had was the slow pace of my work. In elementary school I usually didn't finish my homework until 10 o'clock at night. Other students in my grade were finished by 7 o'clock. Although this was difficult for me to deal with, I did not let it stop me from completing my homework. Originally I felt sorry for myself. I believed that I would never overcome these problems. This led me to complain how unfair it was. Perhaps, I thought, someone did not want me to succeed. Word processing was a huge benefit to me in overcoming my learning disabilities. Before I had a desktop computer I had great difficulty writing reports. This was due to the fact that I had to type up my reports using a typewriter. After every sentence, my mother had to check my spelling. Due to this my progress was at a snail's pace. Also, throughout elementary school, and into the beginning of high school, I had illegible notes. Then, a t the beginning of junior year, I purchased a notebook computer. This aided me greatly. I could now actually take notes since I could type far faster than I could write. The computer enabled me to read the notes, which I had taken because my handwriting was often not legible. Finally I came to the realization that everyone has strengths and weaknesses and that I had to learn how to overcome my weaknesses and focus on my strengths. Logic has always been one of my strengths. I began to understand the technology, which had so helped me. Everyone has strengths and weaknesses but as a child, it's hard to deal with your weaknesses. It's not until you grow up and realize that you have to use your strengths to overcome your weaknesses. Combining logic with technology can be a useful tool. For me it was the way to overcome my disability. Early on in my education I encountered learning disabilities. These learning disabilities included reading, spelling, grammar, and difficulty in time writin g reports. These learning disabilities have frustrated me. I wondered Why couldn't I do this? I knew that I had the intelligence, but I just could not finish the assignments. I would then become angry. My anger sometimes led me to break pencils and throw them across my room. The basis of my learning disabilities, I discovered, was in transferring my thoughts and ideas from my head to paper. Reading was also a challenge for me in the early grades. I never had trouble understanding stories themselves; but when I had to answer questions about what I had read, I often had difficulty formulating an answer. Another major issue I had was the slow pace of my work. In elementary school I usually didn't finish my homework until 10 o'clock at night. Other students in my grade were finished by 7 o'clock. Although this was difficult for me to deal with, I did not let it stop me from completing my homework. Originally I felt sorry for myself. I believed that I would never overcome these problems. This led me

Auto Pact essays

Auto Pact essays In May of 2000 the World Trade Organization (WTO) ended the Canadian made Auto Pact. The Auto Pact was an agreement constructed by Pierre Trudeau in 1965 that allowed companies to bring auto parts and vehicles from anywhere in the world into Canada without any tariff, but with two conditions. One condition was a requirement of commitment to jobs in Canada, manufacturers had to produce one car in Canada for everyone they sold. They also had to maintain a Canadian Value Added (CVA), which basically means no depreciation on Canadian made machinery and that parts in assembly were also made in Canada. Automotive trade markets are among the most open in the world. They maintain no tariff barriers. This treaty kept North America happy however; the overseas companies were left with the shorter end of the stick. This caused the fall of the Auto Pact. The Auto Pact is too important to Canada, and Canada will suffer extreme losses.(reword) (give the main points) The 1965 Auto Pact is Canadas most successful trade agreement. We should be kissing his ass, says the wise yet youthful Jon Cooper as he refers to Trudeaus ingenious scheme. It is a building block of Canada. Since signing the Auto Pact, the Canadian automotive industry has enjoyed enormous growth. Production and automotive manufacturing employment have increased 200% and automotive shipments grew from $2.2 billion in 1964 to $70.5 billion in 1995. The main two focuses of Trudeaus were exports and employment. Trudeau increased the automotive sales from a once scarce Canadian industry into its top export. His other concern, creating employment doubled. Without the Auto Pact enforcing these numbers they will fall. The Canadian Auto Workers (CAW) president Buzz Hargrove said at the Caw Joint Council meeting in December 2000 that the Auto Pact was: A trading pact that has meant so much to the growth of the...

A Perfect Research Paper Introduction

A Perfect Research Paper Introduction A Perfect Research Paper Introduction Introduction is always an important part of a written assignment, as it presents thoughts to the reader and allows making up his mind whether to go on further. Moreover, it is very important if you are completing a research paper, as it may contain complex information and the topic may seem difficult to understand. In such cases, your introduction will serve as an outline and contain information about the research itself. Unfortunately, many students find it difficult to limit the whole paper to a couple paragraphs and don’t know where to start. Following our simple tips and advice will definitely help you to complete an introduction without any efforts. Give information on the topic It is not a secret that every introduction should start with an engaging and interesting overview of the topic, as the readers should know what to expect. That is why the best way to start your introduction is to state the topic. You should also need to add various matters on the subject and state your personal opinion. It will definitely be intriguing and make the audience to go on reading. The advice here is very simple: begin with a general information on the topic and then narrow it to several aspects, which you want to highlight. This section doesn’t require a deep analysis of the subject but you still shouldn’t sound shallow. If your topic is complicated and contains terms that are difficult to comprehend, try to explain it in simple words not to bore the audience. Provide a literature overview Any research paper should be based on credible and relevant sources, so you need to make a thorough research and analyze only trustful literature. Every introduction should contain a brief overview of the literature, used during your studies. In addition, you can include quotes of various authors or philosophers to back your ideas and support your point of view. Provide the background When you start your research paper, you need to provide the rationale, which can be a perfect way to show your attitude to the subject and give readers a chance to ponder on the subject. Such background allows to reveal information on the topic and prove that your research is important for the society or the field of science. Provide a catchy thesis Your thesis statement links introduction conclusions and body paragraphs, so you need to be very attentive and thoughtful, when completing this section. Remember that your thesis should contain general information on the whole work and engage your views on the subject to help the audience to understand your ideas. It can be called the business card of your whole work, so don’t forget to include general information on the subject and reflect all the important issues. In addition, remember to stay clear and engaging to catch interest of the readers. Don’t forget about the outline When you have almost completed your introduction, it is time to provide the structure of the whole work. Not every professor requires to include an outline to your introduction but you still need to be ready for such a demand. In this case, you need to write a short paragraph, which contains three or four sentences about the main goal and content of the whole research paper. If you lack ideas, you can find introduction examples online to get fresh ideas. Require assistance? If you follow all the instructions, writing a proper introduction for your research paper may not seem challenging at all. However, even the best students may face various complications and it is very difficult to choose a proper topic and deliver everything on time. That is why they often wonder how to make introduction perfect and avoid sleepless nights and lots of stress. There is a great way out, which won’t require much of your time and efforts: contacting a professional writing company. With the assistance of professional writers, you won’t have to spend long hours, looking for a proper introduction online or trying to complete it on your own. So if you ask us ‘can you complete my research paper?’ we will definitely say ‘yes’! Our skilled writers will provide your with a written from scratch content and you will always be sure that it will contain all of your requirements. Moreover, you will always meet the deadlines, avoiding stress and anxiety. If you still have doubts, just make a request and see yourself how simple and fun your student years can be!

Alternating Current and Direct Current Essay Example | Topics and Well Written Essays - 2000 words

Alternating Current and Direct Current - Essay Example The paper shed light upon several advantages and disadvantages of both DC and AC, how AC is converted into DC has also been discussed in this paper. It is important to note that DC is easier to analyze and has a plethora of other advantages but DC’s inability to convert different voltages is a big con because of which AC is used more widely then DC. However, the modern day electricity devices require DC so they come with automatic converters that instantly convert AC into DC. For transferring current over a long-distance DC is used because it is cost-effective but otherwise AC is used, another difference is that people in North America use AC and people in European counties use DC. These were some of the advantages, disadvantages and the most important differences between alternative current and direct current. Alternative current won over direct current in the 20th century when it was chosen over DC because people considered it more reliable, it had very few risks and this is why it was chosen as the standard current in North America, DC on the other hand had several major issues which were later sorted out and fixed. After fixing the major issues with DC several European countries chose it as their standard current, these days DC is being used in some of the elite European countries. It is necessary to have a circuit in order to distribute electricity. A circuit is usually made up of two wires which lead up to a device which uses electricity. In the case of alternating current, one of the two wires is positive and the other is either neutral which is also known as ground or negative, the two wires take their turns which enables sending electricity. In direct current the process works a little differently, the polarity in DC is always the same unlike in the case of AC where in polarity differs, in a DC circuit one wire is negative which means the other is sending electricity and is positive, the negative wire does not send electricity in a DC circuit. T here is no cyclic change and hence no hum in a DC, it is a fact that DC scores over AC in long-distances. In high-voltage transmission also DC is preferred because in AC a lot of electricity is lost, in DC less wastage takes place and this is why it scores over AC. AC scores over DC when it comes to converting DC into AC, it takes a lot of money to do that and it is not advisable to do so. DC should ideally be used only for long-distance transmission and AC in all other circumstances. Heating elements and light bulbs hardly care if the electricity supplied to them is DC or AC but it is very important to note that most modern day electrical gadgets require DC to work effectively. These days a transformer is used which consistently converts AC into DC for the devices to work effectively. In countries like Canada and the United States AC outlets have a voltage of 115 V and this is at 60 Hz. In European countries this is much different; most countries use 220 to 240 V which is about 50 Hz. It is also important to understand how peak voltage works, 115 V AC power usually has a peak voltage which is in the region of 163 V. Home and office electrical outlets are delivered electricity when the terminal has one hot and one neutral terminal. AC voltage is preferred by several people because a transformer is used to convert it into DC and it is very easy and a reliable source of getting direct current. A transformer consists of two inductors, the loops and turns present in these inductors are very different from each other. The number of turns usually determines the ratio of primary to secondary voltage. Electric generators have been made to produce alternating current, they can also produce DC but this can only be done by using a rectifier. A rectifier is made up of diodes which are also the most important part of a rectifier; the main use of a diode is that it allows the current to flow only in a single direction. DC power can also be converted into AC power; this can be done by using an inverter. Heating and arcing are two problems which are involved in producing AC and DC, alternating current can be generated at a much higher voltage than DC and the best part is that there will be fewer problems like heating and arcing. It is

Management of Human Resources (HRM) Essay Example | Topics and Well Written Essays - 1000 words

Management of Human Resources (HRM) - Essay Example Without such kind of mechanisms, some managers, as perfect as they are have failed in their functions, especially in initiating internal change of an organization and determining appropriate solutions to the problems existing in the HR domain. Examples arise from the U.S firms moving to operate in China and vice versa, which have resulted either in a failure in their external markets or a success to those that effectively managed change in the human resource (Jackson et al, 2012). A manager like any other human resource is a vital and valuable asset used to enhance organization performance and success. In other words, the organization â€Å"believes in the employee’s element as a strategy to drive innovation, productivity, and sharing price through workforce mobilization towards organization success† (humanresourcesexplained.com, n.d.). The HRM therefore performs various functions to ensuring proper policies and programs are developed and administered, according to the designed plan in mind, to enable effective utilization of the human resource, while steering them for individual development into higher performance and leadership positions in future. In general, HRM is a broad scope â€Å"focused on people in the organizations, and as a designed management system to enable human talents to be used efficiently and effectively in accomplishing organization goals† (vmouonline.in, n.d, p.2). HR functions involve decision making in the scope of their activities (e.g HR planning, selection, development, rewarding, appraisal, and safety among others), such that the objectives intended for the organization are met, and the HR brings up competent managers and leaders to lead the organization in various fields. Ensuring competent managers are in place The recruitment and selection function of the HR is essential to ensure the right personnel fit for an existing position or able to perform certain tasks required in leadership selected fro the benefit of the benefit of the company. In the recruitment process, HRM is responsible for attracting suitable human resources, availing the job description and candidate specification and bringing a job opening to the attention of the targeted individuals (Kyriakidou, n.d). Hence, candidate with management expertise or those satisfied can apply for the job so that organization invest in their human capital, acquire their talents and utilize them in leadership. In selection stage, modern organizations use interviews, references, and in advanced cases with the help of Biodata and Psychometric testing (Braton and Gold, 2007). In the end the HRM can acquire managers and employees with the right skills for the job, one wiling to learn new tasks and capable of adapting to the changing conditions and cultures of the organization. One of the HR function is managing and developing identified talents in organizations. The HR specialist engage in coordinating the new hire orientations, providing train ing, instruction and in house workshops, and conduct needs assessment based on the employees’ identified skill sets (Mayhew, 2013). HR can hence determine potential skills based on the pool of employees they manage and decide to select them for advanced training. Different employees have different capabilities and it is the role of HR to tap into and develop the exceptional talents, whether in leadership

Tackling Obesity at a Global Level

Tackling Obesity at a Global Level Shirley PANG Obesity has become increasingly recognized as a serious health issue and has aroused public concern. World-wide, 475 million adults and 200 million teenagers are currently struggling with obesity (World Obesity Federation, 2014). The majority are in America and Europe (Ibid). This phenomenon results from diet imbalance due to over-consumption of calories and lack of exercise. Not only are people suffering from several types of disease such as heart problems and high blood pressure (World Obesity Federation, 2014), but also are being suffering discrimination by society as abnormal. The prevalence of obesity has been rising extensively and constantly in UK over the past two decades (Public Health England, 2014). Currently, 67% of males and 57% of women suffer from obesity or are overweight (Tran, 2014). Obesity is not just destructive and antagonizing to patients’ own physical health, but also a heavy burden to the government and the whole economy. This essay will examine the ca use of obesity and drawbacks of unsatisfactory levels of obesity. The conclusion of the paper present various strategies used in Denmark and UK to reduce the levels of obesity. Firstly, wholesome foods such as organic vegetables and vitamins are getting more expensive than ever and have become relatively expensive for citizens. For example, the cost of fruit and vegetables rose markedly by 17% in 1997 and 2003 (DeFusco, 2014). Obviously, non-nutritious foods such as canned foods and processed foods are cheaper, which provides an incentive for customers to purchase them. (Harvard School of Public Health, n.d.). As a result, unwholesome food trades a bigger market than fresh food. Low-income families tend to spend more on bargain groceries, and having a higher consumption rate of junk food in households induces a high obesity rate (Yale Rudd Centre, n.d.) The different structures of price greatly impact chronic obesity, which is why governments seek to implement a fat tax to prohibit such a situation. Secondly, moving to another cause of obesity. Continuing physical activity delivers the benefit of burning calories. Without a doubt, people are contributing less and less of their time to exercise that maintains a healthy lifestyle. This globalization of this circumstance commonly occurs because of shifts in economic development and modern technology (Harvard School of Public Health, n.d.). In the United Kingdom, people are less active as result their desire to own a vehicle instead of walking or cycling (Ibid). Frequently diet or eating high calorie foods, especially sugars and fats, which gradually accumulate in the body without regular physical exertion (NHS Choices, 2014). As a consequence, there has been a modest increase in mortality. It is compelling that insufficient physical activity is a crucial explanation for the growth in obesity. Obesity is considered as a potential threat to life expectancy. Heart disease is the main cause of fatalities, killing 598,000 people annually (FOX, 2013). Heart disease is followed by cancer, which causes 575,000 deaths annually (Ibid). Cardiovascular diseases are come from overweight (Centers for Disease Control and Prevention, 2014). Officially, the expected longevity for a female is 81 years, while for a male it is 76.2 years (Centers for Disease Control and Prevention, 2014). The measure of body mass index (BMI) is a tool for assessing the appropriate level of weight (NHS Choices, 2014). Researchers investigated the effect of body mass index on life span. A BMI of 30-35 kg/m2, which is regarded as modest obesity, causes a loss of 3 years of lifetime (University of Oxford, 2009). A BMI with 40-50kg/m2, which is considered as morbid obesity, causes a decrease in lifetime of 8 to 10 years (Ibid). Thus, the greater BMI value, the higher the likelihood that people will be afflicted w ith various types of disease. It is noteworthy that obesity both maximizes the risk of death and minimizes the quantity of life. We will now consider the negative connection in between obesity and income. Obese employees have diminished job prospects and fewer opportunities for promotion. Theoretically, wage is determined by how much a worker contributes to a company (Cawley, 2011). For example, not only does obesity contribute to limited productivity, which leads to reduced company profit, but it also increases the likelihood of diseases, leading to workers frequently applying for sick leave with a higher cost for health insurance (Havard School of Public Health, 2012). Therefore, an obese worker produces less output and reduces company profit, causing that employer to avoid hiring or promoting obese workers. Food and beverages plays a vital role in declining obesity rates because of the calories that they contain a huge energy. The UK government has reported that excess calories are the result of sugar (Public Health England, 2014). Therefore, The UK department of Health declared a â€Å"Calorie reduction pledge† would be implemented. These days, 39 food companies have given support and signed an agreement agreeing to reduce sugar and calories in soft drinks (Department of Health, 2014). Coca-Cola is a large-scale manufacturer retailing a wide variety of drinks. Apart from reducing calories in carbonated drinks, Coca-Cola is also providing a detailed accounting of the calories in their drinks and providing easily understood nutrition labeling on their improved products. To illustrate, cola without sugar and calories accounted for 42 % of beverages sold (Department of Health, 2014).The producer has encouraged their retail merchants to purchase Diet Coke and Coca‑Cola Zero be cause these drinks are without calories and sugar (Coca-Cola, n.d.). Apparently, the motivation is to assist their customers to improve their lifestyle though promotion of healthy beverages. Without enforcement of legislation, advertising continuously promotes unwholesome food products. Advertising has been recognized as a highly effective medium for communication, and for selling products to particular targets, particularly aimed at obese people and children (Dehghan, Akhtar-Danesh and Merchant, 2005). Research has verified that 10% of advertisements promote confectionary or fast food chain restaurants such as KFC or McDonalds during family time (BBC News, 2014). Children and obese people are powerfully susceptible to choosing foods as a result of broadcast stations which promote a variety of food and beverage and large scale convenience food restaurants. Meanwhile, they prefer eating morsels of photo chips, and snacks with carbonated drinks instead of food with good nourishment. Therefore, the advertising legislation was officially announced starting from April 2007 by the regulator, Office of Communications. Ofcom analyzed the outcome and efficiency of the scheme by c ontrasting 2005 and 2008. They found that children watched advertisement with unhealthy foods 41% less (Department of Health, 2012). With unhealthy products sharply dismissed from advertising, children watching advertising fell by 25% (Office of Communications, 2010). These data indicated a considerable reduction of opportunities to view harmful advertising. Food advertisement is a form of marketing skill, providing a variety of information and affecting food choices and the preferences of viewers. A corporation mostly seeks to earn profits and attain turnover. Hence, rigid rules and regulation on advertising have decreased purchases of fast food. Physical activity reduces the cost of obesity and improves economic effectiveness. A study has recommended that an adult should exercise for 2.5 hours weekly while an adolescent and youngster should spend more than 7 hours per week exercising, on average. (World Health Organization, 2010). The unexpected outcome was that merely 27.1% of high school students actually allocate 60 minutes for a daily exercise (Centers for Disease Control and Prevention, 2014). The survey indicated that less than 48% of adults fulfilled the requirement(Centers for Disease Control and Prevention, 2014). The imposition of taxes is a global strategy used to reduce the prevalence of obesity by shifting food choice to away from the consumption of unhealthy and fatty foods. In fact, taxing is not only implemented to curb obesity, it is used to improve the average life experience by reducing heath disease and other serious illnesses (Kaplan, 2013). Denmark as an example, being the first European country to apply a surcharge on high saturated foods. The surcharges were applied to foods containing more than 2.3% of saturated fats, butter, meat and Danish pastries (Channel 4 News, 2011). Prices increased sharply by 14% for oils and fats, 4% for meats and 3% for milk (Petkantchin, 2013).In addition, healthier foods are taxed favorably versus junk foods which are taxed heavily, making them costly. As a result of, consumers lowered their demand for butter, cream and cheese by almost 7% (Snowdon, 2013). Not all the consumers could suffer the increase in the price, especially for those needy peop le. For public of the view, perspective of tax collecting was pessimistic although markup the price to regulated consumer purchase fatty food. The public was displeased about the increased prices due to taxation however the tax was successful at reducing the consumption of fatty foods. Bibliography BBC News, (2014).Ban TV junk food ads until 21:00, say campaigners. [Online] Available at: http://www.bbc.co.uk/news/health-26665952 [Accessed 7 Jul. 2014]. Cawley, J. (2011).The Obesity Wage Penalty. [Online] The New York Times. Available at: http://www.nytimes.com/roomfordebate/2011/11/28/should-legislation-protect-obese-people/the-obesity-wage-penalty [Accessed 29 Jul. 2014]. Coca-Cola, (n.d.).Coca-Cola Enterprises : Product Portfolio. [Online] Available at: https://www.cokecce.com/corporate-responsibility-sustainability/product-portfolio [Accessed 7 Jul. 2014]. Centers for Disease Control and Prevention, (2014).Facts about Physical Activity. [Online] Available at: http://www.cdc.gov/physicalactivity/data/facts.html [Accessed 28 Jul. 2014]. Centers for Disease Control and Prevention, (2014).Physical Activity Adolescent and School Health. [Online] Available at: http://www.cdc.gov/healthyyouth/physicalactivity/facts.htm [Accessed 28 Jul. 2014]. Centers for Disease Control and Prevention, (2014).FastStats How Healthy Are We. [Online] Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/nchs/fastats/healthy.htm [Accessed 31 Jul. 2014]. DeFusco, D. (2014).High Cost of Fruits, Vegetables Linked to Higher Body Fat in Young Children | News | School of Public Affairs | American University. [Online] American University, School of public affair. Available at: http://www.american.edu/spa/news/fruits-vegetables-children-study.cfm [Accessed 3 Jul. 2014]. Dehghan, M., Akhtar-Danesh, N. and Merchant, A. (2005). Childhood obesity, prevalence and prevention.Nutrition journal, [online] 4(1), p.4. Available at: http://www.nutritionj.com/content/4/1/24#sec3 [Accessed 27 Jun. 2014]. Department of Health, (2014).Responsibility Deal annual updates 2013/14 published today. [Online] Available at: https://responsibilitydeal.dh.gov.uk/responsibility-deal-annual-updates-201314-published-today/ [Accessed 6 Jul. 2014]. Harvard School of Public Health, (n.d.).Physical Activity. [Online] Harvard School of Public Health. Available at: http://www.hsph.harvard.edu/obesity-prevention-source/obesity-causes/physical-activity-and-obesity/#references [Accessed 28 Jun. 2014]. Harvard School of Public Health, (2012).Economic Costs. [Online] Available at: http://www.hsph.harvard.edu/obesity-prevention-source/obesity-consequences/economic/#references [Accessed 29 Jul. 2014]. Mayhew, L. (2009).The impact of obesity on life expectancy. [Online] Cass Business School. Available at: http://www.cass.city.ac.uk/news-and-events/news/2009/april2/the-impact-of-obesity-on-life-expectancy [Accessed 31 Jul. 2014]. NHS Choices, (2014).Whats your BMI? [Online] NHS Choices. Available at: http://www.nhs.uk/Livewell/loseweight/Pages/BodyMassIndex.aspx [Accessed 31 Jul. 2014]. NHS Choices, (2014).Causes of obesity. [Online] Available at: http://www.nhs.uk/Conditions/Obesity/Pages/Causes.aspx [Accessed 31 Jul. 2014]. Public Health England, (2014).About Obesity [Online] Available at: http://www.noo.org.uk/NOO_about_obesity [Accessed 25 Jun. 2014]. Tran, M. (2014).Cadbury makes anti-obesity pledge with cap on chocolate bar calories. [Online] Guardian. Available at: http://www.theguardian.com/uk-news/2014/jun/03/cadbury-anti-obesity-pledge-cap-chocolate-calories-mondelez [Accessed 7 Jul. 2014]. West Virginia Health Statistic Center, (n.d.).Section One Continued OBESITY AND MORTALITY. [Online] West Virginia Health Statistic Center. Available at: http://www.wvdhhr.org/bph/oehp/obesity/mortality.htm [Accessed 30 Jul. 2014]. Worldobesity.org, (2014).World Obesity Federation about Obesity. [Online] Available at: http://www.worldobesity.org/aboutobesity/ [Accessed 25 Jun. 2014]. World Health Organization, (2010).Global Recommendations on Physical Activity for Health. 1st ed. [eBook] World Health Organization, p.8. Available at: http://whqlibdoc.who.int/publications/2010/9789241599979_eng.pdf [Accessed 28 Jul. 2014]. Yale Rudd Centre, (n.d.).Rudd Center for Food Policy Obesity — What We Do — Economics — Topics: Food Prices. [online] Yaleruddcenter.org. Available at: http://www.yaleruddcenter.org/what_we_do.aspx?id=85 [Accessed 3 Jul. 2014]. University of Oxford, (2009).Moderate obesity takes years off life expectancy. [Online] University of Oxford. Available at: http://www.ox.ac.uk/media/news_stories/2009/090317.html [Accessed 31 Jul. 2014]. Chan, K., Prendergast, G., Grà ¸nhà ¸j, A. and Bech-Larsen, T. (2011).Danish and Chinese adolescents’ perceptions of healthy eating and attitudes toward regulatory measures. 3rd ed. Emerald Group Publishing Limited, p.219 Channel 4 News, (2011).Denmark introduces fat tax on food. [Online] Available at: http://www.channel4.com/news/denmark-introduces-fat-tax-on-food [Accessed 26 Jun. 2014]. Office of Communications, (2004).Childhood Obesity à ± Food Advertising in Context. 1st ed. [eBook] Office of Communications, p.13. Available at: http://stakeholders.ofcom.org.uk/binaries/research/tv-research/report2.pdf [Accessed 26 Jun. 2014] National obesity observatory, (no date).TV viewing and obesity in children and young people. [eBook] Available at: http://www.noo.org.uk/uploads/doc/vid_15867_TV_viewing.pdf [Accessed 27 Jun. 2014]. Department of Health, (2012).An update on the government’s approach to tackling obesity. [eBook] National Audit Office, p.25. Available at: http://www.nao.org.uk/wp-content/uploads/2012/07/tackling_obesity_update.pdf [Accessed 27 Jun. 2014]. Office of Communications, (2010).HFSS advertising restrictions. 1st ed. [eBook] Office of Communications, pp.2-3. Available at: http://stakeholders.ofcom.org.uk/binaries/research/tv-research/hfss-review-final.pdf [Accessed 28 Jun. 2014] Benson, B., Ferris, A., Eberle, M. and Huntington, C. (2008).The Economic Impact of Prevention. 1st ed. [ebook] University of Connecticut, p.i. Available at: www.publichealth.uconn.edu/assets/economicimpact_06_25_08_final.pdf [Accessed 28 Jun. 2014]. NHS, (n.d.).Obesity Causes NHS Choices. [Online] Available at: http://www.nhs.uk/Conditions/Obesity/Pages/Causes.aspx [Accessed 28 Jun. 2014]. Stanford Hospital Clinics, (n.d.).What Causes Obesity?. [Online] Available at: http://stanfordhospital.org/clinicsmedServices/COE/surgicalServices/generalSurgery/bariatricsurgery/obesity/causes.html [Accessed 1 Jul. 2014]. Harvard School of Public Health, (2013).Eating healthy vs. unhealthy diet costs about $1.50 more per day. [online] Available at: http://www.hsph.harvard.edu/news/press-releases/healthy-vs-unhealthy-diet-costs-1-50-more/ [Accessed 1 Jul. 2014]. Kaplan, K. (2013).Denmark aims to improve health with. [online] Los Angeles Times Articles. Available at: http://articles.latimes.com/2011/oct/03/news/la-heb-fat-tax-denmark-20111013 [Accessed 2 Jul. 2014]. Petkantchin, V. (2013).Nutritiontaxes:thecostsofDenmarksfattax. [ebook] Institutà ©conomiqueMolinari, p.3. Available at: http://www.institutmolinari.org/IMG/pdf/note0513_en.pdf [Accessed 2 Jul. 2014]. Snowdon, C. (2013).The Proof of the Pudding: Denmarks fat tax fiasco. 1st ed.[ebook] The Institute of Economics Affairs, p.20. Available at: http://www.nzjba.org.nz/myfiles/The_Proof_of_the_Pudding.pdf [Accessed 2 Jul. 2014]. European Commission/EACEA/Eurydice, 2013. Physical Education and Sport at School in Europe Eurydice Report. Luxembourg: Publications Office of the European Union. P.1

Cultural Globalization Essay

Indeed, the world gets smaller by the minute. Not literally though, but figuratively. It seems smaller because the people are able to interact with each other, ignoring the limitations of time and distance. In the advent of science and technology, people are able to bridge the gap of our geographical differences and unite everyone through interactions of interconnections of the modern age. Man is not anymore limited by the physical world he lives in. No one is alone anymore, as people get closer to others with the advancements in travel, telecommunications, and a lot more. This breakthrough has led to other concepts and ideas, and one of this is cultural globalization: ideas, attitudes, and values exchanged in permeable national borders leads to the interaction and interconnection of people from different cultures and different walks of life. In order to understand the concept of cultural globalization further, we need to look at its components separately. Culture and globalization are different topics with different meanings. Culture is defined as a set of shared attributes such as attitudes, values, practices, and goals of a certain group or organization. We understand that every nation of the world possesses unique culture that sets them apart from others. Within these nations, there are cultures, subcultures, and other cultures that thrive, collectively giving that certain nation a unique identity (Dyczewski, 2003). Globalization on the other hand, is said to be the growing integration of various economies and societies all over the world into one global economy or society. It was Marshall McLuhan who had the first idea regarding this concept, previously calling it a global village, wherein nations are able to connect and interact with each other, and is characterized by the instantaneous exchange of ideas all over the world. Globalization integrates the practices which were previously limited to a certain specific country or nation. Having separately defined culture and globalization, we put them together into a single concept, which is cultural globalization. It is defined as the rapid traversing of various ideas, concepts, values, attitudes and perception across different nations. This results to a sharing of ideas between these nations, and the result of this is the interconnection and interaction of each and every nation. It is said that the tools used for the proliferation of the concept of cultural globalization are the mass media and communication technologies. It was through this that it was possible to know what’s happening in other sides of the world, thus promoting awareness not only of their own nation but of others. Several authors have different takes about the topic. One author, Leon Dyczewski, offers the argument that globalization can bring about the annihilation and destruction of ethnic and national cultures, so the relationship between the two is more of a destructive rather than constructive. Basing on this, we can say that the authors means to tell us that as our own cultures become more and more permeable because of globalization, it slowly disintegrates in order to accommodate the emerging idea of cultural globalization. Another point being raised is that there is an emergence of a uniform and pluralistic world. The different components of the world that becomes open to globalization would then conform to a globalized standard of everything. It limits the people’s creativity and uniqueness, in order to achieve a globalized nation. One the other hand, globalization introduces the individuals to a wider world, other that what they got used to growing up. Cultural globalization, in the eyes of the author, is not really about the destruction of small scale nations, ethnic groups, or even the local communities. Instead, cultural globalization offers some sort of unity to the people, effectively leading them to a global society that would let them maintain cultural identity, as well as develop it from time to time. On the other hand, there are those who offer an alternative perspective of the issue. Authors Breidenbach and Zukrigl explain that cultural globalization causes cultural homogenization. Cultural globalization results to a creation of a culture that would encompass its entire component, creating the â€Å"main† culture to be adhered to. Cultural globalization simply narrows down the differences between everyone, possibly creating a much more productive environment (Breidenbach & Zukrigl, 1998). One criticism being slapped to cultural globalization is that it leans towards western ideology and other practices. It was more like adapting to the greatest influence, which in the case of cultural globalization, is the United States of America. What seemed to be an interrelated link of people all over the world became an alternative reason to be like western countries. It is also called as a McDonaldization effort by some, who clearly opposes the concept and idea. Elements of the west, just like McDonalds becomes an icon of cultural globalization, References: Breidenbach, J. , & Zukrigl, I. (1998). The Dynamics of Cultural Globalization. The myths of cultural globalization. Retrieved March 16, 2009, from http://www. inst. at/studies/collab/breidenb. htm Dyczewski, L. (2003). The National and Ethnic Cultures in a Globalizing World. Retrieved March 16, 2009, from http://www. crvp. org/conf/Istanbul/abstracts/dyczewski. htm

Philosophy Essay Samples Children Consent with Parents Obtaining Birthcontr Tips

Philosophy Essay Samples Children Consent with Parents Obtaining Birthcontr Tips Philosophy Essay Samples Children Consent with Parents Obtaining Birthcontr - Dead or Alive? In the majority of states, there are specific medical conditions and treatments for which minors do not need parental consent. Oral preventives supply no protection from sexually transmitted diseases like HIV. In the latter event, you want the subject's consent to get hold of them later. How to acquire birth control without a physician's visit. Women have a lot of choices to select from, when they would like to avoid pregnancy. Pills must be taken once per day to work. They must be taken ever day. Some pills work for sure women but don't get the job done for other ladies. Thirdly, failure to use birth control results in overpopulation, which will probably pose a danger to the peace of earth. Abortion isn't right and unjust housands of women around the world obtain abortions annually. 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Evidence Law and Audit Program Essay - 879 Words

As corporate controller for Apollo Shoes, you are tasked to find and explain any irregularities in the Apollo Shoes Case. Resource: Apollo Shoes Casebook Define the process you will use and address the following assessed classroom discussion questions: What procedures will be used to collect accounting evidence? What sampling tools and techniques will be used for the examination? How will you use analytical and inferential tools to evaluate accounting evidence? Submit your assignment to the facilitator. Note. APA formatting is not required for this assignment. Use a title and reference page where appropriate. Consider using a checklist or flowchart to outline your process. There are many irregularities that can arise†¦show more content†¦79-80). Substantive procedures for detecting irregularities in cash receipts include the following: ââ€" ª review the cash receipts journal and master file for unusual transactions; ââ€" ª trace cash receipts entries from the cash receipts journal entries to the bank statement; ââ€" ª prepare a proof of cash receipts; ââ€" ª obtain a... Although every investigation case is unique, the investigation process is similar for most cases and usually consists of four stages: Goal Setting, Planning, Investigation and Evaluation. The investigation process starts with goal setting. Goal Setting sets the expectation and identifies the complainant’s goals as well as obtainable results and acceptable outcomes. â€Å"The effectiveness of every fraud investigator, in pursuing either criminal or civil fraud investigations, is ultimately judged by how closely—and consistently—she reaches or exceeds her investigative goals.† Once the goals and expectation have been clearly defined, then an investigative plan is developed. Financial fraud investigations usually involves large amounts of data and information, an investigative plan focuses and controls the investigation and helps to manages the volumes of data gathered in an investigation. The investigation process begins with intelligence gathering, these procedures includes database searches,Show MoreRelatedFinancial Statements And On Design Internal Control Systems895 Words   |  4 Pagesprovides opinions on the reliability of the report and the effectiveness of the internal control. 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Cluster Analysis Free Essays

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. We will write a custom essay sample on Cluster Analysis or any similar topic only for you Order Now – 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. Interestingly, 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. 1007/978-3-642-12541-6_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 segmenting 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 groups 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 clusters. 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 procedure Hierarchical 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 illustrates 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 unobservable (i. e. , inferred) on the other. Table 9. 2 provides several types and examples of clustering variables. Ta ble 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 homogenous 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 variables. 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 (2 000). 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. Generally, 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 thes e 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 carried 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 segment 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). u Conducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sample sizes or the relationship between the objects and the number of clustering variables used. In a related methodological context, Formann (1984) recommends a sample size of at least 2m, where m equals the number of clustering variables. This can 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 very 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 the 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 characterized 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, E Agglomerative 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 clustering procedure, we need to express these distances mathematically. By taking the data in Table 9. 1 into consider ation, 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. Similarly, 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 5 Note that researchers also often use the squared Euclidean distance. 246 9 Cluster Analysis and zeros on the diagonal (the distance from each object to itself is, of course, 0). In our example, the distance matrix is an 8 A 8 table with the lines and rows representing the objects (i. e. , customers) under consideration (see Table 9. 3). As the distance between objects B and C (in this case 1. 414 units) is the same as between C and B, the distance matrix is symmetrical. Furthermore, since the distance between an object and itself is zero, one need only look at either the lower or upper non-diagonal elements. Table 9. 3 Euclidean distance matrix Objects A B A 0 B 3 0 C 2. 236 1. 414 D 2 3. 606 E 3. 606 2 F 4. 123 4. 472 G 5. 385 7. 071 C D E F G 0 2. 236 1. 414 3. 162 5. 657 0 3 2. 236 3. 606 0 2. 828 5. 831 0 3. 162 0 There are also alternative distance measures: The city-block distance uses the sum of the variables’ absolute differences. This is often called the Manhattan metric as it is akin to the walking distance between two points in a city like 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 metric (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 d istance 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 of 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) standardizing the data is by using the correlation between the objects instead of distance measures. For example, suppose a respondent rated price consciousness 2 and brand loyalty 3. Now suppose a 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 standardizing the data implicitly. Whether you use correlation or one of the distance measures depends on wh ether 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 t he 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. According 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 measures such as Euclidean distance. Even though using matching coef? cients would be feasible and – from a strictly statistical standpoint – even more appropriate, you would disregard variable information in the sequence of the categories. In the end, a respondent who indicates that he or she is very loyal to a brand is going to be 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 simply 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. 7 See 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 have to be determined a priori and improper weights may result in a biased treatment of different variable types. Furthermore, the computation and handling of distance matrices are not trivial. Using the SPSS syntax, one has to manually add the MATRIX subcommand, which exports the initial distance matrix into a new data ? le. Go to the 8 Web Appendix (! Chap. 5) to 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-step clustering procedure provides a valuable alternative, which we will discuss later. Lastly, the choice of the (dis)similarity measure is not extremely critical to recovering the 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 linkage assumes that the distance between two clusters is based on the longest distance between any two members in the two clusters. Average linkage: The distance between two clusters is de? ned as the average distance between all pairs of the two clusters’ members. Centroid: In this approach, the geometric center (centroid) of each cluster is computed ? rst. The distance between the two clusters equals the distance between the two centroids. Figures 9. 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 somewhat 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 linkage decision rule as discussed above. According to this rule, the distance from, for example, object A to the newly formed cluster is the minimum of d(A, B) and d(A, C). As d(A, C) is smaller than d(A, B), the distance from A to the newly formed cluster is equal to d(A, C); that is, 2. 236. We also compute the distances from cluster [B,C] (clusters are indicated by means of squared brackets) to all other objects (i. e. D, E, F, G) and simply copy the remaining distances – such as d(E, F) – that the previous clustering has not affected. This yields the distance matrix shown in Table 9. 6. Continuing the clustering procedure, we simply repeat the last step by merging the objects in the new distance matrix that exhibit the smallest distance (in this case, the newly formed cluster [B, C] and object E) and calculate the distance from this cluster to all other objects. The result of this step is described in Table 9. 7. Try to calculate the remaining steps yourself and compare your solution with the 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 procedure, the last steps involve the merger of cluster [A,B,C,D,E,F] and object G at a distance of 3. 162. Do you get the same results? As you can see, conducting a basic cluster analysis manually is not that hard at all – not if there are only a few objects in the dataset. A common way to visualize the cluster 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 calculati ons 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 this 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 Fig. 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 practical 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 non-hierarchical clusteri ng 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 cluste r 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 cluster) in Fig. 9. 10 A CC 1 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 clusters. Based on this i nitial 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 the ? rst cluster center (CC1’) than to the second (CC2’). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-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 clustering variables are used. From a strictly statistical viewpoint, k-means should only be used on interval or ratioscaled data as the procedure relies on Euclidean 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 procedure’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 categorical and continuous variables simultaneously and offers the user the ? exibility to specify the cluster numbers as well as the maximum number of clusters, or to allow the technique to 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. Y ou can ? nd a more detailed discussion of the two-step clustering procedure in the 8 Web Appendix (! Chap. 9), but we will also apply this method in the subsequent example. Validate and 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 your 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 variables, 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 charact er of the method. The following criteria will help you make an evaluation choice for a clustering solution (Dibb 1999; Tonks 2009; Kotler and Keller 2009). l l l l l l l l l l Substantial: The segments are large and pro? able enough to serve. Accessible: The segments can be effectively reached and served, which requires them to be characterized by means of observable variables. Differentiable: The segments can be distinguished conceptually and respond differently to different marketing-mix elements and programs. Actionable: Effective programs can be formulated to attract and serve the segments. 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 heterogeneity. Compatibility: Segmentation results meet other managerial functions’ requirements. The ? nal step of any cluster analysis is the interpretation of the clusters. Interpreting clusters always involves 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 segment 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. Whi le 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 marketers discern those consumers’ likes, dislikes, lifestyles, and purchase behaviors. Visit the Claritas website and ? ip through the various segment pro? les. By entering a 5-digit US ZIP code, you can also ? nd a speci? c 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 obse rvations 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 dataset and the clustering variables are measured on different scale levels: ? Analyze ? Classify ? Two-Step Cluster Select a measure of similarity or dissimilarity Hierarchical methods: (only hierarchical and two-step clustering) ? Analyze ? Classify ? Hierarchical Cluster ? Method ? 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 clusters Hierarchical clustering: Examine the dendrogram: ? Analyze ? Classify ? Hierarchical Cluster ? Plots ? Dendrogram (continued) 264 Table 9. 11 (continued) Theory 9 Cluster Analysis Action Draw a scree plot (e. g. , using Microsoft Excel) based on the coef? cients in the agglomeration 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 ? Number 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 How to cite Cluster Analysis, Essay examples