Brave mother Essay Example | Topics and Well Written Essays - 1250 words

Daring mother - Essay Example of the cutting edge world, the job of the mother in the lives of her kids has come to decrease, particularly with the strengthening of ladies and their taking up of jobs, which were recently held for men. The cutting edge world has realized a lot of difficulties to parenthood, and a portion of these moves will in general spin around the way that moms are either working fulltime, or that they are setting off for college to facilitate their instruction. While in past ages the odds of couples separating were incredibly thin, in the contemporary one, it has gotten ordinary, with numerous relationships self-destructing in extremely brief timeframes. At the point when this occurs, it has become the standard for moms to be given full care of their kids, which has prompted the weight of the bringing up of youngsters to turn out to be exclusively the space of the mother. This circumstance has become incredibly troublesome particularly when one thinks about that numerous moms need to adjust ei ther work or school with the bringing up of their youngsters; something which is getting very hard to accomplish particularly for those families that need a salary. Today, moms are confronted with the errand of bringing up their youngsters, yet in addition their satisfying their commitments to their managers so as to procure a pay. This raises the requirement for moms to adjust their work and family life. So as to accomplish this, numerous moms have searched for choices, which won't just empower them to work successfully, yet in addition have the option to invest energy with their youngsters. One of the manners in which that numerous moms appear to embrace is that of telecommuting. Numerous moms have decided to work from home and this has guaranteed that they can invest more energy at home working during when they would have been driving to work. This has empowered them to dispose of work quicker so when their kids get back home from school, they discover their moms allowed to invest significant energy with them. Working moms have additionally taken to utilizing one schedule for

Decision Making at the Top: The All Star Essay

Sunru Yong arranged this case exclusively as a reason for class conversation and not as a support, a wellspring of essential information, or an outline of powerful or insufficient administration. This case, however dependent on genuine occasions, is fictionalized, and any similarity to real people or elements is adventitious. There are intermittent references to genuine organizations in the portrayal. Copyright  © 2008 President and Fellows of Harvard College. To arrange duplicates or solicitation consent to replicate materials, call 1-800-545-7685, compose Harvard Business Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. This distribution may not be digitized, copied, or in any case recreated, posted, or transmitted, without the authorization of Harvard Business School. M I C H An E L B E R S U N R U Y O N G TerraCog Global Positioning Systems: Conflict and Communication on Project Aerial Emma Richardson squinted at the TerraCog GPS (Global Positioning System) model in her grasp. She zoomed in until the presentation indicated a more clear satellite photograph of the lake 200 feet before her and into which her Labrador had as of now joyfully limited. Most ends of the week, Richardson made the climb to the lake to clear her psyche and, every so often, to test new GPS models from her manager, TerraCog, Inc. Sadly, with the â€Å"Project Aerial† dispatch meeting planned for the following day, it was hard to appreciate this specific climb. Emma thought about how to get all gatherings to agree on the value point for Aerial. TerraCog had begun losing offer to a contender, Posthaste, and it was basic to get the new item to showcase. Showing up at the lake, Richardson surrendered to the desire to check her telephone and scowled as she saw two new phone messages. The main message was from Allen Roth, the chief of structure and improvement (see Exhibits 1 and 2 for an authoritative diagram and brief memoirs of key administrators): â€Å"Emma, it’s Allen. Tune in, Tony and I have been over these cost numbers on Aerial. We cut all that we could and we wound up with just a 7% or 8% decrease to cost. Tragically, I don’t think this will get us to the value point that Sales is searching for. In any case, I don’t need to advise you that we gave Sales the highlights and usefulness they needed in Aerial, so I’m not returning currently to request that my group do the unimaginable. We’ll work it out tomorrow, however I figured it best you hear it from me.† The subsequent message was from her chief, Richard Fiero, the organization president: â€Å"Emma, I needed to beware of Aerial. I heard protesting from Ed and the business group on Friday. They appeared to be disappointed with Tony Barren’s creation group. Ensure Production has become a model of togetherness. Tony should know he’s in a dangerous situation after the ongoing creation disaster on that sonar venture he’s got the chance to prevail on Aerial. We have to have Aerial on racks toward the beginning of Q3. Some board individuals are stressed, so Aerial will be close to the highest point of the plan at the executive gathering next month.† 2184 A P R I L 1 , 2 0 8 For the restrictive utilization of B. Shi This record is approved for utilize just by Bixi Shi in Organizational Behavior-Fall 2014 instructed by Elaine Wong University of California †Riverside from October 2014 to December 2014. 2184 | TerraCog Global Positioning Systems: Conflict and Communication on Project Aerial 2 BRIEFCASES | HARVARD BUSINESS SCHOOL Neither one of the messages was empowering. The Aerial gathering the following day, involvingâ the deals, structure and advancement, and creation offices, was currently destined to be argumentative. It was March 2008-just two months since Richardson had been elevated to official VP. Fiero had requested that her move TerraCog toward more noteworthy operational arrangement and expanding cross-departmental participation. Richardson had just been tried by both stock issues and quality issues, which had prompted critical strain between the U.S. central station in Chicago and the creation group in Shenzhen, China. Presently, contradiction over the proposed value point for Aerial took steps to wreck the dispatch of the model in her grasp. Organization and Industry History TerraCog was a secretly held organization represent considerable authority in top notch Global Positioning System (GPS) and angling sonar gear. Established in 1977, TerraCog got its beginning assembling very good quality sonar gear for genuine game anglers and boaters. In the late 1990s, the organization had presented its first GPS items, promoted explicitly to trackers, climbers, and campers. The board accepted that it was the company’s aptitude at deciphering retailer and client input into extraordinary item plan and usefulness that powered the development of its GPS business. Through mindful channel the board and, as Fiero put it, â€Å"a profound comprehension of what claim to fame retailers needed,† TerraCog had created solid associations with its key records. Fiero likewise accepted that TerraCog’s handle of its consumers’ inclinations and utilization had given it an edge over GPS makers whose center business was in car applications. The firm had manufactured its GPS line for the genuine outside enthusiasts’ showcase, and the items had won approvals for solidness and worth included highlights like the incorporated compass and barometric altimeter. Additionally, industry reports showed that the TerraCog GPS beat contending items on route. TerraCog’s exclusive firmware-a custom PC program implanted into equipment that â€Å"ran† capacities improved the GPS chipset’s Wide Area Augmentation Systemâ capability, which gave increasingly exact route. The organization was not in every case first to showcase. Truth be told, TerraCog had discovered it was allowed to slack in mechanical advancement with little hazard since, when the organization at last presented new items, they outperformed those of rivals in tending to client needs. Client informal suggestions had given TerraCog solid energy with its handheld GPS. In mid 2007, TerraCog arranged to enter new, underserved GPS sub-markets, including cycling and wellness applications. â€Å"Google Earthâ„ ¢ for your GPS† At the Summer 2006 Outdoor Retailer Show-the greatest public expo for merchants of open air merchandise a contender, Posthaste, had divulged a GPS model called â€Å"BirdsI† that showed satellite symbolism. The symbolism was not live, yet rather static satellite photos that had been â€Å"stitched† into a solitary view. This was a stamped enhancement for the basic, vector-based designs utilized by the remainder of the business (see Exhibit 3 for a correlation). This didn't dazzle the TerraCog group. The symbolism was fresh and had a specific visual intrigue, however TerraCog’s look into indicated that BirdsI innovation didn't offer meaningful execution improvement over the standard maps in TerraCog’s GPS framework. Besides, the TerraCog group was persuaded that Posthaste’s recipient slacked TerraCog’s item in both exactness and gathering quality. While the TerraCog group excused the Posthaste idea, various key purchasers and item commentators thought that it was an energizing development. One magazine analyst watched, â€Å"Imagine having For the elite utilization of B. Shi This record is approved for utilize just by Bixi Shi in Organizational Behavior-Fall 2014 instructed by Elaine Wong University of California †Riverside from October 2014 to December 2014. TerraCog Global Positioning Systems: Conflict and Communication on Project Aerial | 2184 HARVARD BUSINESS SCHOOL | BRIEFCASES 3 Google Earthâ„ ¢ incorporated with your GPS-it’s substantially more convincing to take a gander at a real satellite picture than to have yellow for land, blue masses for water, and dark squiggles for roads.† Based on the buzz, TerraCog’s officials discussed whether to move up to satellite symbolism. Nonetheless, they understood that adding the component to the current GPS stage expected moves up to processor speed and memory, just as new firmware. After some pondering, the organization dropped the thought as an unnecessary prevailing fashion. TerraCog’s the board stayed sure that the company’s center clients were perceiving buyers who might esteem solidness and execution over spruced up designs. In October 2006, with much exhibition, Posthaste presented BirdsI as â€Å"the just handheld GPS with satellite imagery.† BirdsI had a select dispatch at two significant national open air retailers, the two of which were key records for TerraCog. Inside two months, TerraCog’s salespeople in the field detailed amazing sell-through rates for BirdsI across the nation. While the product’s achievement shocked TerraCog, the executives credited it to the elation of the Christmas shopping season. The TerraCog group was sure that the notoriety of BirdsI wouldn’t last. Task Aerial In any case, by spring 2007 TerraCog’s salespeople were seeing expanding client interest for a GPS with satellite symbolism like BirdsI. Ed Pryor, VP of deals, started squeezing for an inversion of the choice not to build up the item. â€Å"It’s humiliating to have no solutions for our retailers when they request our variant of this,† he said. â€Å"Look at it from our point of view. We’ve changed the remuneration plan for the entire Sales group including me-so we endure a genuine shot in the event that we don’t arrive at our business targets. Clients currently need something else, and I can’t advise my reps we have noâ plans to build up the item they have to hit those targets.† in light of these rehashed demands, TerraCog’s president, Richard Fiero, altered his perspective on satellite symbolism, if just to fulfill the â€Å"gadget† intrigue of such an advancement. The activity was named Project Aerial. So as to speed advancement and evade

Test Effective in Early Detection of Heavy Drinkers

Test Effective in Early Detection of Heavy Drinkers Addiction Alcohol Use Print Early Detection of Alcohol Consumption Test Blood chemistry test effective in identifying heavy drinkers By Buddy T facebook twitter Buddy T is an anonymous writer and founding member of the Online Al-Anon Outreach Committee with decades of experience writing about alcoholism. Learn about our editorial policy Buddy T Updated on October 21, 2019 Andrew Brookes/Cultura/Getty Images More in Addiction Alcohol Use Binge Drinking Withdrawal and Relapse Children of Alcoholics Drunk Driving Addictive Behaviors Drug Use Nicotine Use Coping and Recovery An advanced blood chemistry test is available to physicians and healthcare providers. This test is twice as likely to detect heavy drinking in their patients, compared to liver enzyme tests they have traditionally used, developers of the test claim. The multi-level test is capable of determining if someone has engaged in heavy drinking anytime during the previous four to six weeks. The test will reveal if a male patient has had more than five drinks a day or if a female patient has had more than four drinks in a day. How the EDAC Test Works The Early Detection of Alcohol Consumption test is actually an algorithm of 20 blood chemistry levels. Those measurements are compared to a database of more than 1,700 heavy and light drinkers. Research has shown that the Early Detection of Alcohol Consumption test is twice as accurate as the liver enzyme test used to detect heavy drinking. In one study, 88% of the heavy drinkers and 92% of the light drinkers were correctly identified using the test. The test is even more effective with patients over 40 years of age. Early Detection is Important Research has shown that the sooner alcohol problems are addressed the better the outcomes and the less long-term damage. At the 2008 meeting of the American Association for Clinical Chemistry, physicians were encouraged to use the test to increase early intervention with heavy drinkers. Physicians can use the test as part of an early intervention, James Harasymiw, director of Alcohol Detection Services, said in an AACC news release. When patients are confronted with test results, they may be more likely to change their behavior. How Alcoholism is Treated Physicians can show patients the test results to help convince them that their drinking is causing serious damage to their organs and other biologic systems, Harasymiw said. More Accurate Than Screening Tests? There are many short alcohol screening tests that are available to screen for alcohol problems in the healthcare setting, but the results of those tests depend upon the patient answering the questions openly and honestly. Someone trying to cover up or minimize their drinking habits could easily do so with the short-answer screening tests. But the Early Detection of Alcohol Consumption test measures actual blood chemistry levels, making it more difficult for heavy drinkers to hide their consumption. How Long Does Alcohol Stay in Your System?

F. Scott Fitzgerald s The Great Gatsby - 1381 Words

Thematic Research Paper on F. Scott Fitzgerald’s The Great Gatsby F. Scott Fitzgerald’s The Great Gatsby portrays characterization corresponding with characters’ birthplaces, desires, and determination in order to devise their statuses. The narrator, Nick Carraway, is disparate from others due to the place he grew up which is exemplified when he moves to New York from the Midwest. Tom Buchanan satisfies his desire for love by having women in his life as well as his wife Daisy. Jay Gatsby and Daisy Buchanan have been fond of each other since they met many years ago and their love for one another made Gatsby determined to create a new rich and extravagant lifestyle in order to completely win over Daisy. Nick Carraway’s personality is unique in New York compared to many of the dwellers, especially those at Gatsby’s massive parties. Minnesota, in comparison to New York, is subsequently different and the character Nick Carraway demonstrates this with his demeanor. Nick recently moved into his house in West Egg and was invited to a party at Gatsby’s mansion next door. While at the party, he describes the appearances of his fellow guests. â€Å"I was immediately struck by the number of young Englishmen dotted about; all well dressed, all looking a little hungry, and all talking in low, earnest voices to solid and prosperous Americans† (Fitzgerald 41-42). Nick Carraway is, for the first time, exposed to these kinds of people. The people from New York are materialists. They live forShow MoreRelatedF. Scott Fitzgerald s The Great Gatsby974 Words   |  4 PagesPoverty in the Valley of Ashes: The Great Gatsby â€Å"This is a valley of ashes- a fantastic farm where ashes grow like wheat into ridges and hills and grotesque gardens where ashes take the forms of houses and chimneys and raising smoke and finally, with a transcendent effort, of men who move dimly and already crumbling through the powdery air† (Fitzgerald 26). In the novel, â€Å"The Great Gatsby,† the author F. Scott Fitzgerald, mainly depicted lives of the rich and their luxuries but also showed theRead MoreF. Scott Fitzgerald s The Great Gatsby1289 Words   |  6 Pages and many people realized their own version of the American Dream during this period. The American Dream is one that many people want to achieve. However, F. Scott Fitzgerald demonstrates his true feelings about the American Dream in his classic novel, The Great Gatsby. Many characters in this story, such as Daisy and Tom Buchanan, Jay Gatsby, and Jordan Baker, found riches and happiness in materialistic things and people throughout this novel. This is the stereotypical American Dream that is associatedRead MoreF. Scott Fitzgerald s The Great Gatsby944 Words   |  4 Pages423169 Prompt #4 No Comments Lim [f (x)] - 0 = ∞ ... The Limit as X Approaches Infinity Humans continually search for success. This success surfaces in forms such as fortune, fame, glory, et cetera. The American Dream encapsulates the ideals of the â€Å"New World,† bringing together not only the idea of limitless success, but also its newfound availability and encouragement for embracing the promise land. The Great Gatsby explores the American Dream and â€Å"the actual nature of this dream... the mannerRead MoreF. Scott Fitzgerald s The Great Gatsby1265 Words   |  6 PagesJay Gatsby and His Undying Love for Daisy Buchanan F. Scott Fitzgerald wrote The Great Gatsby in the midst of the roaring twenties, which was an age full of wealth, parties, and romance. Young people living in the 1920s were centered around wanting to find love so Fitzgerald, along with many other authors during this time period, focused his writing in The Great Gatsby on relationships and affection. Jay Gatsby, one of the main characters in the novel, is a very mysterious man, but there is oneRead MoreF. Scott Fitzgerald s The Great Gatsby1845 Words   |  8 Pages â€Å"You don’t write to say something, you write because you have something to say.† F. Scott Fitzgerald was one of the most remarkable writers of all time during the Jazz Age. He started to reach an accomplishment of success with This Side of Paradise and accomplished it with The Great Gatsby. F. Scott Fitzgerald’s novels take place back in the early 1900’s; he attempts to communicate knowledge to the elocutionist, in a sophisticated, but humorous way, that making it big is not uncomplicated. FurthermoreRead MoreF. Scott Fitzgerald s The Great Gatsby1720 Words   |  7 Pagesdriving force of evolution in humanity. It allows the aspiration of being able to do astonishing things, and proffers them prosperity in life. The Great Gatsby by F. Scott Fitzgerald delves into the American Dream and it’s demise. Fitzgerald focuses on the character of Jay Gatsby to materialize the false image that the American Dream created in the 1920’s. Gatsby is th e protagonist of the novel, and is famous for throwing massive parties regardless of the secret life that he lives. The narrator, Nick CarrawayRead MoreF. Scott Fitzgerald s The Great Gatsby1202 Words   |  5 PagesJay Gatsby and F. Scott Fitzgerald F. Scott Fitzgerald is an acclaimed American author, popularly recognized for his novel The Great Gatsby. In addition to his literary work, Fitzgerald is noted for his unstable personal life. Originally coming from a low-income background, he could not marry the woman that he first loved. Even when he met another woman, he had to acquire wealth to marry her; this drove him to publish his first novel. He married her shortly after. However, a couple years after, heRead MoreF. Scott Fitzgerald s The Great Gatsby1258 Words   |  6 Pages What Killed Gatsby? Love or Greed? To certain people, Gatsby’s death was a cruel and surprising conclusion to The Great Gatsby by F. Scott Fitzgerald. But there is still some mystery around the cause of Gatsby’s death. Upon meeting Gatsby for the first time, one can tell that he has an obsession centered around Daisy Buchanan, his old love, and was dead set on getting her back. Gatsby’s obsession with repeating the past is responsible for his death and Gatsby’s greed put him in a grave. FurtherRead MoreF. Scott Fitzgerald s The Great Gatsby1761 Words   |  8 Pagescould be the main focus of people who are going out on their own to create a family. However, F. Scott Fitzgerald took a different route in his most famous novel. Fitzgerald uses his book, The Great Gatsby, to show how the idea of the American Dream is slowly dying in the society he created. Although the American Dream was prevalent during the time The Great Gatsby took place in, F. Scott Fitzgerald went against the social norm of believing in this idea and revolved his novel around the idea ofRead MoreF. Scott Fitzgerald s The Great Gatsby1894 Words   |  8 Pageslife. In the case of F. Scott Fitzgerald, this statement could not be truer. In fact, much of Fitzgerald’s most famous work feature plots that closely parallel events from his life (Lathbury 10). For example, his novel This Side of Paradise includes a young man who is rejected by the love of his life on the grounds of his social status. Zelda similarly rejected Fitzgerald for his social status at first. In comparison, it is not surprising that Fitzgerald’s story The Great Gatsby takes place in the

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

Curriculum Guide in Science free essay sample

It integrates science and technology in the civic, personal, social, economic, and the values and ethical aspects of life. The science curriculum promotes a strong link between science and technology, including indigenous technology, keeping our country’s cultural uniqueness and peculiarities intact. Whether or not students pursue careers that involve science and technology, the K to 12 science curriculum will provide students with a repertoire of competencies important in the world of work and in a knowledge-based society. The K to 12 science curriculum envisions the development of scientifically, technologically, and environmentally literate and productive members of society who manifest skills as a critical problem solvers, responsible stewards of nature, innovative and creative citizens, informed decision makers, and effective communicators. This curriculum is designed around the three domains of learning science: understanding and applying scientific knowledge in local setting as well as global, context whenever possible, performing scientific processes and skills, and developing and demonstrating scientific attitudes and values. The acquisition of these domains is facilitated using the following approaches: multi/interdisciplinary approach, science–technology society approach, contextual learning, problem/issue-based learning, and inquiry-based approach. The approaches are based on sound educational pedagogy namely: constructivism, social cognition learning model, learning style theory, and Gestalt psychology. Science content and science processes are intertwined in the K to 12 curriculum. Without the content, learners will have difficulty utilizing science process skills since these processes are best learned in context. Organizing the curriculum around situations and problems that challenge and arouse students’ curiosity motivates them to learn and appreciate science as relevant and useful. Rather than relying solely on textbooks, varied hands-on, minds-on, and hearts-on activities will be used to develop students’ interest and let them become active learners. As a whole, the K to 12 science curriculum is learner-centered and inquiry-based, emphasizing the use of evidence in constructing explanations. Concepts and skills in Life Sciences, Physics, Chemistry, and Earth Sciences are presented with increasing levels of complexity from one grade level to another (spiral progression), thus paving the way to deeper understanding of a few concepts. These concepts and skills are integrated rather than disciplinebased, stressing the connections across science topics and other disciplines as well as applications of concepts and thinking skills to real life. K to 12 Curriculum Guide Science – version as of January 31, 2012 2 K TO 12 SCIENCE Developing and Demonstrating Scientific Attitudes and Values The Conceptual Framework of Science Education K to 12 Curriculum Guide Science – version as of January 31, 2012 3 K TO 12 SCIENCE CORE LEARNING AREA STANDARD: (SCIENCE FOR THE ENTIRE K TO 12) The learner demonstrates understanding of basic science concepts, applies science process skills, and exhibits scientific attitudes and values to solve problems critically, innovate beneficial products, protect the environment and conserve resources, enhance the integrity and wellness of people, and make informed and unbiased decisions about social issues that involve science and technology. This understanding will lead to learner’s manifestation of respect for life and the environment, bearing in mind that Earth is our ONLY HOME. KEY STAGE STANDARDS: (STANDARD FOR SCIENCE LEARNING AREA FOR K-3, 4-6, 7-10 AND 11-12) K–3 At the end of Grade 3, the learners should have acquired healthful habits and developed curiosity about self and their environment using basic process skills of observing, communicating, comparing, classifying, measuring, inferring and predicting. This curiosity will help learners value science as an important tool in helping them continue to explore their natural and physical environment. –6 At the end of Grade 6, the learners should have developed the essential skills of scientific inquiry – designing simple investigations, using appropriate procedure, materials and tools to gather evidence, observing patterns, determining relationships,drawing conclusions based on evidence, and communicating ideas in varied ways to make mean ing of the observations and/or changes that occur in the environment. The content and skills learned will be applied to maintain good health, ensure the protection and improvement of the environment, and practice safety measures. – 10 At the end of Grade 10, the learner should have developed scientific, technological and environmental literacy so that they will not be isolated from the society where they live, will not be overwhelmed by change, and can make rational choices on issues confronting them. Having been exposed to scientific investigations related to real-life, they should recognize that the central feature of an investigation is that if one variable is changed (while controlling all others), the effect of the change on another variable is measured. The context of the investigation can be problems at the local or national level to allow them to communicate with students in other parts of the Philippines or even from other countries using appropriate technology. 11-12 At the end of Grade 12, the learner should have gained skills in obtaining scientific and technological information from varied sources about global issues that have impact on the country. They should have acquired attitudes that will allow them to innovate and/or create products useful to the community or country. They should be able to process information to get relevant data for a problem at hand. In addition, learners should have made plans related to their interests and expertise, considering the needs of their community and the country — to pursue either employment, entrepreneurship, or higher education. K to 12 Curriculum Guide Science – version as of January 31, 2012 4 K TO 12 SCIENCE Grade/Level K Grade Level Standards Concepts and skills in the kindergarten curriculum are taught thematically so that it is difficult to identify specific science ideas. At the end of Grade 1, learners will use their senses to locate and describe the parts of their body and tell the shape, color, texture, taste, and size of things around them. They will differentiate sounds produced by animals, vehicles cars, and musical instruments. They will illustrate how things move. They will describe similarities and differences, given two things. They will use appropriate terms or vocabulary to describe these features. They will collect, sort, count, draw, take things apart, or make something out of the things. They will practice health habits (e. . , washing hands properly, choosing nutritious food) and help clean or pack away their toys. They will ask questions. They will give simple answer/ descriptions to probing questions. At the end of Grade 2, learners will use their senses to describe more than two objects and using more than two properties. They can sort things in different ways and give a reason for doing so. They will describe the kind of weather or certain events in the home or school and express how these are affecting them. They will tell why some things around them are important. They will decide if what they do is safe or dangerous. They will give suggestions on how to prevent accidents at home (not playing with matches or sharp objects). They will switch off light when not in use or conserve water when taking a bath or brushing teeth. They will help take care of pets or of plants. They will tell short stories about what they do, what they have seen, or what they feel. At the end of Grade 3, learners will describe the functions of the different parts of the body and things that make up their surroundings rocks and soil, plants and animals, the Sun, Moon and stars. They will also learn that things may be solid, liquid or gas while others may give off light, heat and sound. They will also observe changes in the conditions of their surroundings. These will lead learners to become more curious about their surroundings, appreciate nature, and practice health and safety measures. After investigating, learners will identify materials that do not decay and use this knowledge to help minimize waste at home, school, and in the community. They will also investigate changes in the properties of materials when these are subjected to different conditions. The learners will describe the internal parts of the body and their functions in order to practice ways to maintain good health. They will classify plants and animals according to where they live and observe interactions among living things and their environment. They will infer that plants and animals have traits that help them survive in their environment. Learners will investigate which type of soil is best for certain plants and infer the importance of water in daily activities. They will learn about what makes up weather and apply their knowledge of weather conditions in making decisions for the day. Learners will also infer the importance of the Sun to life on Earth. Learners will investigate the effects of push or pull on the size, shape, and movement of an object Grade 1 Grade 2 Grade 3 Grade 4 K to 12 Curriculum Guide Science – version as of January 31, 2012 5 K TO 12 SCIENCE Grade/Level Grade Level Standards After investigating, learners will decide whether materials are safe and useful based on their properties. They will also infer that new materials may form when there are changes in properties. Learners will develop healthful and hygienic practices related to the reproductive system after describing changes that accompany puberty. They will compare different modes of reproduction among plant and animal groups and conduct an investigation on pollination. They will also make decisions about the preservation of estuaries and intertidal zones. Grade 5 Learners will recognize that different materials react differently with heat, light, and sound. They will relate these abilities of materials to their specific uses. Learners will describe the changes that earth materials undergo. They will learn about the effects of typhoons and make emergency plans with their families in preparation for typhoons. They will also observe patterns in the natural events by observing the appearance of the Moon Learners will understand how the different organ systems work together. They will classify plants based on reproductive structures and animals based on the presence or lack of backbone. They will design and conduct an investigation on plant propagation. They will also learn about larger ecosystems such as rainforests, coral reefs, and mangrove swamps. Learners will recognize that when mixed together, materials do not form new ones thus these materials may be recovered using different separation techniques. Learners will also prepare useful mixtures such as food, drinks and herbal medicines. Grade 6 Learners will describe what happens during earthquakes and volcanic eruptions and demonstrate what to do when they occur. They will infer that the weather follows a pattern in the course of a year. They will learn about the solar system, with emphasis on the motions of the Earth as prerequisite to the study of seasons in another grade level. Learners will infer that friction and gravity affect how people and objects move. They will also discover that heat, light, sound, electricity, and motion studied earlier are forms of energy and these undergo transformation. Learners will recognize the system of classification of matter through semi-guided investigations but emphasizing fair testing. Grade 7 Learners will describe what makes up the Philippines as a whole and the resources found in the archipelago. They will explain the occurrence of breezes, monsoons, and ITCZ and how these weather systems affect people. Using concepts in the previous grade, learners will demonstrate why the seasons change and how eclipses occur. Learners will describe the motion of objects in terms of distance and speed and represent this in tables, graphs, charts, and equations. K to 12 Curriculum Guide Science – version as of January 31, 2012 6 K TO 12 SCIENCE Grade/Level Grade Level Standards They will also investigate how various forms of energy travel through different media. After studying how organ systems work together in plants and animals in the lower grades, learners will now observe very small organisms and structures using a microscope. They will understand that living things are organized into different levels: cells, tissues, organs, organ systems, and organisms. These organisms comprise populations and communities which interact with nonliving things in ecosystems. Learners will now recognize reproduction as a process of cell division resulting in growth of organisms. They will also deal deeper into the process of digestion studied in the lower grades giving emphasis on proper nutrition for overall wellness. This will lead them to participate in activities that will protect and conserve economically important species used for food. Learners will explain the behavior of matter in terms of the particles it is made of. They will also recognize that ingredients in food and medical products are made up of these particles and are absorbed by the body in the form of ions. Grade 8 Learners will explain how active faults generate earthquakes and how tropical cyclones originate from warm ocean waters. They will also learn about the other members of the solar system. Learners will investigate the effects of some factors on the motion of an object based on the Laws of Motion. They will also differentiate the concept of work as used in science and in layman’s language. They will also learn about factors that affect the transfer of energy such as the molecular structure of the medium and temperature difference. After learning about the digestive system, learners will now expand their knowledge to a deeper understanding of the respiratory and circulatory systems to promote overall health. They will also learn about some technologies that will introduce desired traits in economically important plants and animals. Learners will explain how new materials are formed when atoms are rearranged. They will also recognize that a wide variety of useful compounds may arise from such rearrangements. Learners will identify volcanoes in the community or region and distinguish between active and inactive ones. They will also explain how energy from volcanoes may be tapped for human use. Learners will also learn about climatic phenomena that occur on a global scale. They will also explain why certain constellations can be seen only at certain times of the year. Learners will predict the outcomes of interactions among objects in real life applying the laws of conservation of energy and momentum. Grade 9 K to 12 Curriculum Guide Science – version as of January 31, 2012 7 K TO 12 SCIENCE Grade/Level Grade Level Standards Learners will now complete the study of the entire organism with their deeper study of the excretory and reproductive systems. They will also explain in greater detail how genetic information is passed from parents to offspring and how diversity of species increases the probability of adaptation and survival in changing environments. Learners will recognize the importance of controlling the conditions under which a phenomenon or reaction occurs. They will also recognize that cells and tissues of the human body are made up of water, a few kinds of ions, and biomolecules. These biomolecules may also be found in the food they eat. Learners will show that volcanoes and earthquakes occur in the same places in the world and that these are related to plate boundaries. Learners will also demonstrate ways to ensure safety and reduce damage during earthquakes, tsunamis, and volcanic eruptions. Learners will investigate factors that affect the balance and stability of an object to enable them to practice appropriate positions and movements to achieve efficiency and safety such as in sports and dancing. They will also analyze situations where energy is harnessed for human use whereby heat is released affecting the physical and biological components of the environment. Grade 11 Grade 12 To be completed after the meeting with G11-12 TWG To be completed after the meeting with G11-12 TWG Grade 10 K to 12 Curriculum Guide Science – version as of January 31, 2012 8 K TO 12 SCIENCE Content Characteristics of Living Things GRADE 3 Living Things and Their Environment FIRST QUARTER/FIRST GRADING PERIOD Content Standards Performance Standards The learner†¦ ? demonstrates understanding that living things breathe, eat, grow, move, reproduce, and react to light, touch, and temperature. These characteristics distinguish them from nonliving thing The learner†¦ ? tell whether a thing is living or nonliving given different samples. Learning Competencies The learner†¦ ? compares characteristics of a living and a nonliving thing Parts and Functions of Living Things ? Humans ? emonstrates understanding of the external parts of the body, their functions, and healthful practices to take care of the human body ? ? practices healthful habits and proper care of the sense organs and other external parts of the body ? ? ? ? labels the external parts of the human body describes the parts of the human body and their functions identifies the sense organs: parts and fun ctions describes how the sense organs work communicates proper ways and healthful practices to protect the sense organs and other external parts of the body makes a chart on proper ways of protecting these external parts.