Cluster Analysis

Chapter 9 Cluster Analysis Learning Objectives After reading this chapter you should understand: – The basic concepts of cluster analysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs. Keywords Agglomerative and divisive clustering A Chebychev distance A City-block distance A Clustering variables A Dendrogram A Distance matrix A Euclidean distance A Hierarchical and partitioning methods A Icicle diagram A k-means A Matching coef? cients A Pro? ing clusters A Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep cluster analysis by identifying segments of Internet adopters in Japan. The ? ndings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. In terestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical of? ce workers had the most positive perception.Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target speci? c customer segments via mobile Internet services. Introduction Grouping similar customers and products is a fundamental marketing activity. It is used, prominently, in market segmentation. As companies cannot connect with all their customers, they have to divide markets into groups of consumers, customers, or clients (called segments) with similar needs and wants.Firms can then target each of these segments by positioning themselves in a unique segment (such as Ferrari in the high-end sports car market). While market researchers often form E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10. 1007/978-3-642-1 2541-6_9, # Springer-Verlag Berlin Heidelberg 2011 237 238 9 Cluster Analysis market segments based on practical grounds, industry practice and wisdom, cluster analysis allows segments to be formed that are based on data that are less dependent on subjectivity.The segmentation of customers is a standard application of cluster analysis, but it can also be used in different, sometimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers’ branding strategies (Moroko and Uncles 2009). Understanding Cluster Analysis Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a speci? c cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster.Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segment ing your customer base in order to better target them through, for example, pricing strategies. The ? rst step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y).These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 9. 1 and the scatter plot in Fig. 9. 1. The objective of cluster analysis is to identify groups of objects (in this case, customers) that are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our group s of objects.This step is crucial for the analysis, as different procedures require different decisions prior to analysis. There is an abundance of different approaches and little guidance on which one to use in practice. We are going to discuss the most popular approaches in market research, as they can be easily computed using SPSS. These approaches are: hierarchical methods, partitioning methods (more precisely, k-means), and two-step clustering, which is largely a combination of the ? rst two methods.Each of these procedures follows a different approach to grouping the most similar objects into a cluster and to determining each object’s cluster membership. In other words, whereas an object in a certain cluster should be as similar as possible to all the other objects in the Table 9. 1 Data Customer x y A 3 7 B 6 7 C 5 6 D 3 5 E 6 5 F 4 3 G 1 2 Understanding Cluster Analysis 7 6 A C D E B 239 Brand loyalty (y) 5 4 3 2 1 0 0 1 2 G F 3 4 5 6 7 Price consciousness (x) Fig. 9. 1 Scatter plot same cluster, it should likewise be as distinct as possible from objects in different clusters. But how do we measure similarity?Some approaches – most notably hierarchical methods – require us to specify how similar or different objects are in order to identify different clusters. Most software packages calculate a measure of (dis)similarity by estimating the distance between pairs of objects. Objects with smaller distances between one another are more similar, whereas objects with larger distances are more dissimilar. An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. This question is explored in the next step of the analysis.Sometimes, however, we already know the number of segments that have to be derived from the data. For example, if we were asked to ascertain what characteristics distinguish frequent shoppers from infrequent ones, we need to ? nd two different c lusters. However, we do not usually know the exact number of clusters and then we face a trade-off. On the one hand, you want as few clusters as possible to make them easy to understand and actionable. On the other hand, having many clusters allows you to identify more segments and more subtle differences between segments.In an extreme case, you can address each individual separately (called one-to-one marketing) to meet consumers’ varying needs in the best possible way. Examples of such a micro-marketing strategy are Puma’s Mongolian Shoe BBQ (www. mongolianshoebbq. puma. com) and Nike ID (http://nikeid. nike. com), in which customers can fully customize a pair of shoes in a hands-on, tactile, and interactive shoe-making experience. On the other hand, the costs associated with such a strategy may be prohibitively high in many 240 9 Cluster Analysis Decide on the clustering variables Decide on the clustering procedureHierarchical methods Select a measure of similarity or dissimilarity Partitioning methods Two-step clustering Select a measure of similarity or dissimilarity Choose a clustering algorithm Decide on the number of clusters Validate and interpret the cluster solution Fig. 9. 2 Steps in a cluster analysis business contexts. Thus, we have to ensure that the segments are large enough to make the targeted marketing programs pro? table. Consequently, we have to cope with a certain degree of within-cluster heterogeneity, which makes targeted marketing programs less effective.In the ? nal step, we need to interpret the solution by de? ning and labeling the obtained clusters. This can be done by examining the clustering variables’ mean values or by identifying explanatory variables to pro? le the clusters. Ultimately, managers should be able to identify customers in each segment on the basis of easily measurable variables. This ? nal step also requires us to assess the clustering solution’s stability and validity. Figure 9. 2 illu strates the steps associated with a cluster analysis; we will discuss these in more detail in the following sections.Conducting a Cluster Analysis Decide on the Clustering Variables At the beginning of the clustering process, we have to select appropriate variables for clustering. Even though this choice is of utmost importance, it is rarely treated as such and, instead, a mixture of intuition and data availability guide most analyses in marketing practice. However, faulty assumptions may lead to improper market Conducting a Cluster Analysis 241 segments and, consequently, to de? cient marketing strategies. Thus, great care should be taken when selecting the clustering variables. There are several types of clustering variables and these can be classi? d into general (independent of products, services or circumstances) and speci? c (related to both the customer and the product, service and/or particular circumstance), on the one hand, and observable (i. e. , measured directly) and un observable (i. e. , inferred) on the other. Table 9. 2 provides several types and examples of clustering variables. Table 9. 2 Types and examples of clustering variables General Observable (directly Cultural, geographic, demographic, measurable) socio-economic Unobservable Psychographics, values, personality, (inferred) lifestyle Adapted from Wedel and Kamakura (2000)Speci? c User status, usage frequency, store and brand loyalty Bene? ts, perceptions, attitudes, intentions, preferences The types of variables used for cluster analysis provide different segments and, thereby, in? uence segment-targeting strategies. Over the last decades, attention has shifted from more traditional general clustering variables towards product-speci? c unobservable variables. The latter generally provide better guidance for decisions on marketing instruments’ effective speci? cation. It is generally acknowledged that segments identi? ed by means of speci? unobservable variables are usually more h omogenous and their consumers respond consistently to marketing actions (see Wedel and Kamakura 2000). However, consumers in these segments are also frequently hard to identify from variables that are easily measured, such as demographics. Conversely, segments determined by means of generally observable variables usually stand out due to their identi? ability but often lack a unique response structure. 1 Consequently, researchers often combine different variables (e. g. , multiple lifestyle characteristics combined with demographic variables), bene? ing from each ones strengths. In some cases, the choice of clustering variables is apparent from the nature of the task at hand. For example, a managerial problem regarding corporate communications will have a fairly well de? ned set of clustering variables, including contenders such as awareness, attitudes, perceptions, and media habits. However, this is not always the case and researchers have to choose from a set of candidate variable s. Whichever clustering variables are chosen, it is important to select those that provide a clear-cut differentiation between the segments regarding a speci? c managerial objective. More precisely, criterion validity is of special interest; that is, the extent to which the â€Å"independent† clustering variables are associated with 1 2 See Wedel and Kamakura (2000). Tonks (2009) provides a discussion of segment design and the choice of clustering variables in consumer markets. 242 9 Cluster Analysis one or more â€Å"dependent† variables not included in the analysis. Given this relationship, there should be signi? cant differences between the â€Å"dependent† variable(s) across the clusters. These associations may or may not be causal, but it is essential that the clustering variables distinguish the â€Å"dependent† variable(s) signi? antly. Criterion variables usually relate to some aspect of behavior, such as purchase intention or usage frequency. Gen erally, you should avoid using an abundance of clustering variables, as this increases the odds that the variables are no longer dissimilar. If there is a high degree of collinearity between the variables, they are not suf? ciently unique to identify distinct market segments. If highly correlated variables are used for cluster analysis, speci? c aspects covered by these variables will be overrepresented in the clustering solution.In this regard, absolute correlations above 0. 90 are always problematic. For example, if we were to add another variable called brand preference to our analysis, it would virtually cover the same aspect as brand loyalty. Thus, the concept of being attached to a brand would be overrepresented in the analysis because the clustering procedure does not differentiate between the clustering variables in a conceptual sense. Researchers frequently handle this issue by applying cluster analysis to the observations’ factor scores derived from a previously car ried out factor analysis.However, according to Dolnicar and Grâ‚ ¬n u (2009), this factor-cluster segmentation approach can lead to several problems: 1. The data are pre-processed and the clusters are identi? ed on the basis of transformed values, not on the original information, which leads to different results. 2. In factor analysis, the factor solution does not explain a certain amount of variance; thus, information is discarded before segments have been identi? ed or constructed. 3. Eliminating variables with low loadings on all the extracted factors means that, potentially, the most important pieces of information for the identi? ation of niche segments are discarded, making it impossible to ever identify such groups. 4. The interpretations of clusters based on the original variables become questionable given that the segments have been constructed using factor scores. Several studies have shown that the factor-cluster segmentation signi? cantly reduces the success of segmen t recovery. 3 Consequently, you should rather reduce the number of items in the questionnaire’s pre-testing phase, retaining a reasonable number of relevant, non-redundant questions that you believe differentiate the segments well.However, if you have your doubts about the data structure, factorclustering segmentation may still be a better option than discarding items that may conceptually be necessary. Furthermore, we should keep the sample size in mind. First and foremost, this relates to issues of managerial relevance as segments’ sizes need to be substantial to ensure that targeted marketing programs are pro? table. From a statistical perspective, every additional variable requires an over-proportional increase in 3 See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grâ‚ ¬n (2009). uConducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sampl e sizes or the relationship between the objects and the number of clustering variables used. In a related methodological context, Formann (1984) recommends a sample size of at least 2m, where m equals the number of clustering variables. This can only provide rough guidance; nevertheless, we should pay attention to the relationship between the objects and clustering variables. It does not, for example, appear logical to cluster ten objects using ten variables.Keep in mind that no matter how many variables are used and no matter how small the sample size, cluster analysis will always render a result! Ultimately, the choice of clustering variables always depends on contextual in? uences such as data availability or resources to acquire additional data. Marketing researchers often overlook the fact that the choice of clustering variables is closely connected to data quality. Only those variables that ensure that high quality data can be used should be included in the analysis. This is v ery important if a segmentation solution has to be managerially useful.Furthermore, data are of high quality if the questions asked have a strong theoretical basis, are not contaminated by respondent fatigue or response styles, are recent, and thus re? ect the current market situation (Dolnicar and Lazarevski 2009). Lastly, the requirements of other managerial functions within the organization often play a major role. Sales and distribution may as well have a major in? uence on the design of market segments. Consequently, we have to be aware that subjectivity and common sense agreement will (and should) always impact the choice of clustering variables.Decide on the Clustering Procedure By choosing a speci? c clustering procedure, we determine how clusters are to be formed. This always involves optimizing some kind of criterion, such as minimizing the within-cluster variance (i. e. , the clustering variables’ overall variance of objects in a speci? c cluster), or maximizing th e distance between the objects or clusters. The procedure could also address the question of how to determine the (dis)similarity between objects in a newly formed cluster and the remaining objects in the dataset.There are many different clustering procedures and also many ways of classifying these (e. g. , overlapping versus non-overlapping, unimodal versus multimodal, exhaustive versus non-exhaustive). 4 A practical distinction is the differentiation between hierarchical and partitioning methods (most notably the k-means procedure), which we are going to discuss in the next sections. We also introduce two-step clustering, which combines the principles of hierarchical and partitioning methods and which has recently gained increasing attention from market research practice.See Wedel and Kamakura (2000), Dolnicar (2003), and Kaufman and Rousseeuw (2005) for a review of clustering techniques. 4 244 9 Cluster Analysis Hierarchical Methods Hierarchical clustering procedures are characte rized by the tree-like structure established in the course of the analysis. Most hierarchical techniques fall into a category called agglomerative clustering. In this category, clusters are consecutively formed from objects. Initially, this type of procedure starts with each object representing an individual cluster.These clusters are then sequentially merged according to their similarity. First, the two most similar clusters (i. e. , those with the smallest distance between them) are merged to form a new cluster at the bottom of the hierarchy. In the next step, another pair of clusters is merged and linked to a higher level of the hierarchy, and so on. This allows a hierarchy of clusters to be established from the bottom up. In Fig. 9. 3 (left-hand side), we show how agglomerative clustering assigns additional objects to clusters as the cluster size increases. Step 5 Step 1 A, B, C, D, EAgglomerative clustering Step 4 Step 2 Divisive clustering A, B C, D, E Step 3 Step 3 A, B C, D E Step 2 Step 4 A, B C D E Step 1 Step 5 A B C D E Fig. 9. 3 Agglomerative and divisive clustering A cluster hierarchy can also be generated top-down. In this divisive clustering, all objects are initially merged into a single cluster, which is then gradually split up. Figure 9. 3 illustrates this concept (right-hand side). As we can see, in both agglomerative and divisive clustering, a cluster on a higher level of the hierarchy always encompasses all clusters from a lower level.This means that if an object is assigned to a certain cluster, there is no possibility of reassigning this object to another cluster. This is an important distinction between these types of clustering and partitioning methods such as k-means, which we will explore in the next section. Divisive procedures are quite rarely used in market research. We therefore concentrate on the agglomerative clustering procedures. There are various types Conducting a Cluster Analysis 245 of agglomerative procedures. However, before we discuss these, we need to de? ne how similarities or dissimilarities are measured between pairs of objects.Select a Measure of Similarity or Dissimilarity There are various measures to express (dis)similarity between pairs of objects. A straightforward way to assess two objects’ proximity is by drawing a straight line between them. For example, when we look at the scatter plot in Fig. 9. 1, we can easily see that the length of the line connecting observations B and C is much shorter than the line connecting B and G. This type of distance is also referred to as Euclidean distance (or straight-line distance) and is the most commonly used type when it comes to analyzing ratio or interval-scaled data. In our example, we have ordinal data, but market researchers usually treat ordinal data as metric data to calculate distance metrics by assuming that the scale steps are equidistant (very much like in factor analysis, which we discussed in Chap. 8). To use a hierarchical c lustering procedure, we need to express these distances mathematically. By taking the data in Table 9. 1 into consideration, we can easily compute the Euclidean distance between customer B and customer C (generally referred to as d(B,C)) with regard to the two variables x and y by using the following formula: q Euclidean ? B; C? ? ? xB A xC ? 2 ? ?yB A yC ? 2 The Euclidean distance is the square root of the sum of the squared differences in the variables’ values. Using the data from Table 9. 1, we obtain the following: q p dEuclidean ? B; C? ? ? 6 A 5? 2 ? ?7 A 6? 2 ? 2 ? 1:414 This distance corresponds to the length of the line that connects objects B and C. In this case, we only used two variables but we can easily add more under the root sign in the formula. However, each additional variable will add a dimension to our research problem (e. . , with six clustering variables, we have to deal with six dimensions), making it impossible to represent the solution graphically. Si milarly, we can compute the distance between customer B and G, which yields the following: q p dEuclidean ? B; G? ? ? 6 A 1? 2 ? ?7 A 2? 2 ? 50 ? 7:071 Likewise, we can compute the distance between all other pairs of objects. All these distances are usually expressed by means of a distance matrix. In this distance matrix, the non-diagonal elements express the distances between pairs of objects 5Note that researchers also often use the squared Euclidean distance. 246 9 Cluster Analysis and zeros on the diagonal (the distance from each object to itself is, of course, 0). In our example, the distance matrix is an 8 A 8 table with the lines and rows representing the objects (i. e. , customers) under consideration (see Table 9. 3). As the distance between objects B and C (in this case 1. 414 units) is the same as between C and B, the distance matrix is symmetrical. Furthermore, since the distance between an object and itself is zero, one need only look at either the lower or upper non-di agonal elements.Table 9. 3 Euclidean distance matrix Objects A B A 0 B 3 0 C 2. 236 1. 414 D 2 3. 606 E 3. 606 2 F 4. 123 4. 472 G 5. 385 7. 071 C D E F G 0 2. 236 1. 414 3. 162 5. 657 0 3 2. 236 3. 606 0 2. 828 5. 831 0 3. 162 0 There are also alternative distance measures: The city-block distance uses the sum of the variables’ absolute differences. This is often called the Manhattan metric as it is akin to the walking distance between two points in a city like New York’s Manhattan district, where the distance equals the number of blocks in the directions North-South and East-West.Using the city-block distance to compute the distance between customers B and C (or C and B) yields the following: dCityAblock ? B; C? ? jxB A xC j ? jyB A yC j ? j6 A 5j ? j7 A 6j ? 2 The resulting distance matrix is in Table 9. 4. Table 9. 4 City-block distance matrix Objects A B A 0 B 3 0 C 3 2 D 2 5 E 5 2 F 5 6 G 7 10 C D E F G 0 3 2 4 8 0 3 3 5 0 4 8 0 4 0 Lastly, when working with metr ic (or ordinal) data, researchers frequently use the Chebychev distance, which is the maximum of the absolute difference in the clustering variables’ values. In respect of customers B and C, this result is: dChebychec ? B; C? max? jxB A xC j; jyB A yC j? ? max? j6 A 5j; j7 A 6j? ? 1 Figure 9. 4 illustrates the interrelation between these three distance measures regarding two objects, C and G, from our example. Conducting a Cluster Analysis 247 C Brand loyalty (y) Euclidean distance City-block distance G Chebychev distance Price consciousness (x) Fig. 9. 4 Distance measures There are other distance measures such as the Angular, Canberra or Mahalanobis distance. In many situations, the latter is desirable as it compensates for collinearity between the clustering variables. However, it is (unfortunately) not menu-accessible in SPSS.In many analysis tasks, the variables under consideration are measured on different scales or levels. This would be the case if we extended our set o f clustering variables by adding another ordinal variable representing the customers’ income measured by means of, for example, 15 categories. Since the absolute variation of the income variable would be much greater than the variation of the remaining two variables (remember, that x and y are measured on 7-point scales), this would clearly distort our analysis results. We can resolve this problem by standardizing the data prior to the analysis.Different standardization methods are available, such as the simple z standardization, which rescales each variable to have a mean of 0 and a standard deviation of 1 (see Chap. 5). In most situations, however, standardization by range (e. g. , to a range of 0 to 1 or A1 to 1) performs better. 6 We recommend standardizing the data in general, even though this procedure can reduce or in? ate the variables’ in? uence on the clustering solution. 6 See Milligan and Cooper (1988). 248 9 Cluster Analysis Another way of (implicitly) sta ndardizing the data is by using the correlation between the objects instead of distance measures.For example, suppose a respondent rated price consciousness 2 and brand loyalty 3. Now suppose a second respondent indicated 5 and 6, whereas a third rated these variables 3 and 3. Euclidean, city-block, and Chebychev distances would indicate that the ? rst respondent is more similar to the third than to the second. Nevertheless, one could convincingly argue that the ? rst respondent’s ratings are more similar to the second’s, as both rate brand loyalty higher than price consciousness. This can be accounted for by computing the correlation between two vectors of values as a measure of similarity (i. . , high correlation coef? cients indicate a high degree of similarity). Consequently, similarity is no longer de? ned by means of the difference between the answer categories but by means of the similarity of the answering pro? les. Using correlation is also a way of standardiz ing the data implicitly. Whether you use correlation or one of the distance measures depends on whether you think the relative magnitude of the variables within an object (which favors correlation) matters more than the relative magnitude of each variable across objects (which favors distance).However, it is generally recommended that one uses correlations when applying clustering procedures that are susceptible to outliers, such as complete linkage, average linkage or centroid (see next section). Whereas the distance measures presented thus far can be used for metrically and – in general – ordinally scaled data, applying them to nominal or binary data is meaningless. In this type of analysis, you should rather select a similarity measure expressing the degree to which variables’ values share the same category. These socalled matching coef? ients can take different forms but rely on the same allocation scheme shown in Table 9. 5. Table 9. 5 Allocation scheme for matching coef? cients Number of variables with category 1 a c Object 1 Number of variables with category 2 b d Object 2 Number of variables with category 1 Number of variables with category 2 Based on the allocation scheme in Table 9. 5, we can compute different matching coef? cients, such as the simple matching coef? cient (SM): SM ? a? d a? b? c? d This coef? cient is useful when both positive and negative values carry an equal degree of information.For example, gender is a symmetrical attribute because the number of males and females provides an equal degree of information. Conducting a Cluster Analysis 249 Let’s take a look at an example by assuming that we have a dataset with three binary variables: gender (male ? 1, female ? 2), customer (customer ? 1, noncustomer ? 2), and disposable income (low ? 1, high ? 2). The ? rst object is a male non-customer with a high disposable income, whereas the second object is a female non-customer with a high disposable income. Accord ing to the scheme in Table 9. , a ? b ? 0, c ? 1 and d ? 2, with the simple matching coef? cient taking a value of 0. 667. Two other types of matching coef? cients, which do not equate the joint absence of a characteristic with similarity and may, therefore, be of more value in segmentation studies, are the Jaccard (JC) and the Russel and Rao (RR) coef? cients. They are de? ned as follows: a JC ? a? b? c a RR ? a? b? c? d These matching coef? cients are – just like the distance measures – used to determine a cluster solution. There are many other matching coef? ients such as Yule’s Q, Kulczynski or Ochiai, but since most applications of cluster analysis rely on metric or ordinal data, we will not discuss these in greater detail. 7 For nominal variables with more than two categories, you should always convert the categorical variable into a set of binary variables in order to use matching coef? cients. When you have ordinal data, you should always use distance me asures such as Euclidean distance. Even though using matching coef? cients would be feasible and – from a strictly statistical standpoint – even more appropriate, you would disregard variable information in the sequence of the categories.In the end, a respondent who indicates that he or she is very loyal to a brand is going to be closer to someone who is somewhat loyal than a respondent who is not loyal at all. Furthermore, distance measures best represent the concept of proximity, which is fundamental to cluster analysis. Most datasets contain variables that are measured on multiple scales. For example, a market research questionnaire may ask about the respondent’s income, product ratings, and last brand purchased. Thus, we have to consider variables measured on a ratio, ordinal, and nominal scale. How can we simultaneously incorporate these variables into one analysis?Unfortunately, this problem cannot be easily resolved and, in fact, many market researchers s imply ignore the scale level. Instead, they use one of the distance measures discussed in the context of metric (and ordinal) data. Even though this approach may slightly change the results when compared to those using matching coef? cients, it should not be rejected. Cluster analysis is mostly an exploratory technique whose results provide a rough guidance for managerial decisions. Despite this, there are several procedures that allow a simultaneous integration of these variables into one analysis. 7See Wedel and Kamakura (2000) for more information on alternative matching coef? cients. 250 9 Cluster Analysis First, we could compute distinct distance matrices for each group of variables; that is, one distance matrix based on, for example, ordinally scaled variables and another based on nominal variables. Afterwards, we can simply compute the weighted arithmetic mean of the distances and use this average distance matrix as the input for the cluster analysis. However, the weights hav e to be determined a priori and improper weights may result in a biased treatment of different variable types.Furthermore, the computation and handling of distance matrices are not trivial. Using the SPSS syntax, one has to manually add the MATRIX subcommand, which exports the initial distance matrix into a new data ? le. Go to the 8 Web Appendix (! Chap. 5) to learn how to modify the SPSS syntax accordingly. Second, we could dichotomize all variables and apply the matching coef? cients discussed above. In the case of metric variables, this would involve specifying categories (e. g. , low, medium, and high income) and converting these into sets of binary variables. In most cases, however, the speci? ation of categories would be rather arbitrary and, as mentioned earlier, this procedure could lead to a severe loss of information. In the light of these issues, you should avoid combining metric and nominal variables in a single cluster analysis, but if this is not feasible, the two-ste p clustering procedure provides a valuable alternative, which we will discuss later. Lastly, the choice of the (dis)similarity measure is not extremely critical to recovering the underlying cluster structure. In this regard, the choice of the clustering algorithm is far more important.We therefore deal with this aspect in the following section. Select a Clustering Algorithm After having chosen the distance or similarity measure, we need to decide which clustering algorithm to apply. There are several agglomerative procedures and they can be distinguished by the way they de? ne the distance from a newly formed cluster to a certain object, or to other clusters in the solution. The most popular agglomerative clustering procedures include the following: l l l l Single linkage (nearest neighbor): The distance between two clusters corresponds to the shortest distance between any two members in the two clusters.Complete linkage (furthest neighbor): The oppositional approach to single linka ge assumes that the distance between two clusters is based on the longest distance between any two members in the two clusters. Average linkage: The distance between two clusters is de? ned as the average distance between all pairs of the two clusters’ members. Centroid: In this approach, the geometric center (centroid) of each cluster is computed ? rst. The distance between the two clusters equals the distance between the two centroids. Figures 9. 5–9. 8 illustrate these linkage procedures for two randomly framed clusters.Conducting a Cluster Analysis Fig. 9. 5 Single linkage 251 Fig. 9. 6 Complete linkage Fig. 9. 7 Average linkage Fig. 9. 8 Centroid 252 9 Cluster Analysis Each of these linkage algorithms can yield totally different results when used on the same dataset, as each has its speci? c properties. As the single linkage algorithm is based on minimum distances, it tends to form one large cluster with the other clusters containing only one or few objects each. We can make use of this â€Å"chaining effect† to detect outliers, as these will be merged with the remaining objects – usually at very large distances – in the last steps of the analysis.Generally, single linkage is considered the most versatile algorithm. Conversely, the complete linkage method is strongly affected by outliers, as it is based on maximum distances. Clusters produced by this method are likely to be rather compact and tightly clustered. The average linkage and centroid algorithms tend to produce clusters with rather low within-cluster variance and similar sizes. However, both procedures are affected by outliers, though not as much as complete linkage. Another commonly used approach in hierarchical clustering is Ward’s method. This approach does not combine the two most similar objects successively.Instead, those objects whose merger increases the overall within-cluster variance to the smallest possible degree, are combined. If you expect s omewhat equally sized clusters and the dataset does not include outliers, you should always use Ward’s method. To better understand how a clustering algorithm works, let’s manually examine some of the single linkage procedure’s calculation steps. We start off by looking at the initial (Euclidean) distance matrix in Table 9. 3. In the very ? rst step, the two objects exhibiting the smallest distance in the matrix are merged.Note that we always merge those objects with the smallest distance, regardless of the clustering procedure (e. g. , single or complete linkage). As we can see, this happens to two pairs of objects, namely B and C (d(B, C) ? 1. 414), as well as C and E (d(C, E) ? 1. 414). In the next step, we will see that it does not make any difference whether we ? rst merge the one or the other, so let’s proceed by forming a new cluster, using objects B and C. Having made this decision, we then form a new distance matrix by considering the single link age decision rule as discussed above.According to this rule, the distance from, for example, object A to the newly formed cluster is the minimum of d(A, B) and d(A, C). As d(A, C) is smaller than d(A, B), the distance from A to the newly formed cluster is equal to d(A, C); that is, 2. 236. We also compute the distances from cluster [B,C] (clusters are indicated by means of squared brackets) to all other objects (i. e. D, E, F, G) and simply copy the remaining distances – such as d(E, F) – that the previous clustering has not affected. This yields the distance matrix shown in Table 9. 6.Continuing the clustering procedure, we simply repeat the last step by merging the objects in the new distance matrix that exhibit the smallest distance (in this case, the newly formed cluster [B, C] and object E) and calculate the distance from this cluster to all other objects. The result of this step is described in Table 9. 7. Try to calculate the remaining steps yourself and compare your solution with the distance matrices in the following Tables 9. 8–9. 10. Conducting a Cluster Analysis Table 9. 6 Distance matrix after ? rst clustering step (single linkage) Objects A B, C D E F G A 0 B, C 2. 36 0 D 2 2. 236 0 E 3. 606 1. 414 3 0 F 4. 123 3. 162 2. 236 2. 828 0 G 5. 385 5. 657 3. 606 5. 831 3. 162 0 253 Table 9. 7 Distance matrix after second clustering step (single linkage) Objects A B, C, E D F G A 0 B, C, E 2. 236 0 D 2 2. 236 0 F 4. 123 2. 828 2. 236 0 G 5. 385 5. 657 3. 606 3. 162 0 Table 9. 8 Distance matrix after third clustering step (single linkage) Objects A, D B, C, E F G A, D 0 B, C, E 2. 236 0 F 2. 236 2. 828 0 G 3. 606 5. 657 3. 162 0 Table 9. 9 Distance matrix after fourth clustering step (single linkage) Objects A, B, C, D, E F G A, B, C, D, E 0 F 2. 236 0 G 3. 06 3. 162 0 Table 9. 10 Distance matrix after ? fth clustering step (single linkage) Objects A, B, C, D, E, F G A, B, C, D, E, F 0 G 3. 162 0 By following the single linkage proce dure, the last steps involve the merger of cluster [A,B,C,D,E,F] and object G at a distance of 3. 162. Do you get the same results? As you can see, conducting a basic cluster analysis manually is not that hard at all – not if there are only a few objects in the dataset. A common way to visualize the cluster analysis’s progress is by drawing a dendrogram, which displays the distance level at which there was a ombination of objects and clusters (Fig. 9. 9). We read the dendrogram from left to right to see at which distance objects have been combined. For example, according to our calculations above, objects B, C, and E are combined at a distance level of 1. 414. 254 B C E A D F G 9 Cluster Analysis 0 1 2 Distance 3 Fig. 9. 9 Dendrogram Decide on the Number of Clusters An important question we haven’t yet addressed is how to decide on the number of clusters to retain from the data. Unfortunately, hierarchical methods provide only very limited guidance for making th is decision.The only meaningful indicator relates to the distances at which the objects are combined. Similar to factor analysis’s scree plot, we can seek a solution in which an additional combination of clusters or objects would occur at a greatly increased distance. This raises the issue of what a great distance is, of course. One potential way to solve this problem is to plot the number of clusters on the x-axis (starting with the one-cluster solution at the very left) against the distance at which objects or clusters are combined on the y-axis.Using this plot, we then search for the distinctive break (elbow). SPSS does not produce this plot automatically – you have to use the distances provided by SPSS to draw a line chart by using a common spreadsheet program such as Microsoft Excel. Alternatively, we can make use of the dendrogram which essentially carries the same information. SPSS provides a dendrogram; however, this differs slightly from the one presented in F ig. 9. 9. Speci? cally, SPSS rescales the distances to a range of 0–25; that is, the last merging step to a one-cluster solution takes place at a (rescaled) distance of 25.The rescaling often lengthens the merging steps, thus making breaks occurring at a greatly increased distance level more obvious. Despite this, this distance-based decision rule does not work very well in all cases. It is often dif? cult to identify where the break actually occurs. This is also the case in our example above. By looking at the dendrogram, we could justify a two-cluster solution ([A,B,C,D,E,F] and [G]), as well as a ? ve-cluster solution ([B,C,E], [A], [D], [F], [G]). Conducting a Cluster Analysis 255 Research has suggested several other procedures for determining the number of clusters in a dataset.Most notably, the variance ratio criterion (VRC) by Calinski and Harabasz (1974) has proven to work well in many situations. 8 For a solution with n objects and k segments, the criterion is given by: VRCk ? ?SSB =? k A 1 =? SSW =? n A k ; where SSB is the sum of the squares between the segments and SSW is the sum of the squares within the segments. The criterion should seem familiar, as this is nothing but the F-value of a one-way ANOVA, with k representing the factor levels. Consequently, the VRC can easily be computed using SPSS, even though it is not readily available in the clustering procedures’ outputs.To ? nally determine the appropriate number of segments, we compute ok for each segment solution as follows: ok ? ?VRCk? 1 A VRCk ? A ? VRCk A VRCkA1 ? : In the next step, we choose the number of segments k that minimizes the value in ok. Owing to the term VRCkA1, the minimum number of clusters that can be selected is three, which is a clear disadvantage of the criterion, thus limiting its application in practice. Overall, the data can often only provide rough guidance regarding the number of clusters you should select; consequently, you should rather revert to pr actical considerations.Occasionally, you might have a priori knowledge, or a theory on which you can base your choice. However, ? rst and foremost, you should ensure that your results are interpretable and meaningful. Not only must the number of clusters be small enough to ensure manageability, but each segment should also be large enough to warrant strategic attention. Partitioning Methods: k-means Another important group of clustering procedures are partitioning methods. As with hierarchical clustering, there is a wide array of different algorithms; of these, the k-means procedure is the most important one for market research. The k-means algorithm follows an entirely different concept than the hierarchical methods discussed before. This algorithm is not based on distance measures such as Euclidean distance or city-block distance, but uses the within-cluster variation as a Milligan and Cooper (1985) compare various criteria. Note that the k-means algorithm is one of the simplest n on-hierarchical clustering methods. Several extensions, such as k-medoids (Kaufman and Rousseeuw 2005) have been proposed to handle problematic aspects of the procedure. More advanced methods include ? ite mixture models (McLachlan and Peel 2000), neural networks (Bishop 2006), and self-organizing maps (Kohonen 1982). Andrews and Currim (2003) discuss the validity of some of these approaches. 9 8 256 9 Cluster Analysis measure to form homogenous clusters. Speci? cally, the procedure aims at segmenting the data in such a way that the within-cluster variation is minimized. Consequently, we do not need to decide on a distance measure in the ? rst step of the analysis. The clustering process starts by randomly assigning objects to a number of clusters. 0 The objects are then successively reassigned to other clusters to minimize the within-cluster variation, which is basically the (squared) distance from each observation to the center of the associated cluster. If the reallocation of an object to another cluster decreases the within-cluster variation, this object is reassigned to that cluster. With the hierarchical methods, an object remains in a cluster once it is assigned to it, but with k-means, cluster af? liations can change in the course of the clustering process. Consequently, k-means does not build a hierarchy as described before (Fig. . 3), which is why the approach is also frequently labeled as non-hierarchical. For a better understanding of the approach, let’s take a look at how it works in practice. Figs. 9. 10–9. 13 illustrate the k-means clustering process. Prior to analysis, we have to decide on the number of clusters. Our client could, for example, tell us how many segments are needed, or we may know from previous research what to look for. Based on this information, the algorithm randomly selects a center for each cluster (step 1). In our example, two cluster centers are randomly initiated, which CC1 (? st cluster) and CC2 (second clu ster) in Fig. 9. 10 A CC1 C B D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 10 k-means procedure (step 1) 10 Note this holds for the algorithms original design. SPSS does not choose centers randomly. Conducting a Cluster Analysis A CC1 C B 257 D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 11 k-means procedure (step 2) A CC1 CC1? C B Brand loyalty (y) D E CC2 CC2? F G Price consciousness (x) Fig. 9. 12 k-means procedure (step 3) 258 A CC1? 9 Cluster Analysis B C Brand loyalty (y) D E CC2? F G Price consciousness (x) Fig. 9. 13 k-means procedure (step 4) epresent. 11 After this (step 2), Euclidean distances are computed from the cluster centers to every single object. Each object is then assigned to the cluster center with the shortest distance to it. In our example (Fig. 9. 11), objects A, B, and C are assigned to the ? rst cluster, whereas objects D, E, F, and G are assigned to the second. We now have our initial partitioning of the objects into two c lusters. Based on this initial partition, each cluster’s geometric center (i. e. , its centroid) is computed (third step). This is done by computing the mean values of the objects contained in the cluster (e. . , A, B, C in the ? rst cluster) regarding each of the variables (price consciousness and brand loyalty). As we can see in Fig. 9. 12, both clusters’ centers now shift into new positions (CC1’ for the ? rst and CC2’ for the second cluster). In the fourth step, the distances from each object to the newly located cluster centers are computed and objects are again assigned to a certain cluster on the basis of their minimum distance to other cluster centers (CC1’ and CC2’). Since the cluster centers’ position changed with respect to the initial situation in the ? st step, this could lead to a different cluster solution. This is also true of our example, as object E is now – unlike in the initial partition – closer to t he ? rst cluster center (CC1’) than to the second (CC2’). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-means procedure now repeats the third step and re-computes the cluster centers of the newly formed clusters, and so on. In other 11 Conversely, SPSS always sets one observation as the cluster center instead of picking some random point in the dataset. Conducting a Cluster Analysis 59 words, steps 3 and 4 are repeated until a predetermined number of iterations are reached, or convergence is achieved (i. e. , there is no change in the cluster af? liations). Generally, k-means is superior to hierarchical methods as it is less affected by outliers and the presence of irrelevant clustering variables. Furthermore, k-means can be applied to very large datasets, as the procedure is less computationally demanding than hierarchical methods. In fact, we suggest de? nitely using k-means for sample sizes above 500, especially if many clusterin g variables are used.From a strictly statistical viewpoint, k-means should only be used on interval or ratioscaled data as the procedure relies on Euclidean distances. However, the procedure is routinely used on ordinal data as well, even though there might be some distortions. One problem associated with the application of k-means relates to the fact that the researcher has to pre-specify the number of clusters to retain from the data. This makes k-means less attractive to some and still hinders its routine application in practice. However, the VRC discussed above can likewise be used for k-means clustering an application of this index can be found in the 8 Web Appendix ! Chap. 9). Another workaround that many market researchers routinely use is to apply a hierarchical procedure to determine the number of clusters and k-means afterwards. 12 This also enables the user to ? nd starting values for the initial cluster centers to handle a second problem, which relates to the procedureâ €™s sensitivity to the initial classi? cation (we will follow this approach in the example application). Two-Step Clustering We have already discussed the issue of analyzing mixed variables measured on different scale levels in this chapter.The two-step cluster analysis developed by Chiu et al. (2001) has been speci? cally designed to handle this problem. Like k-means, the procedure can also effectively cope with very large datasets. The name two-step clustering is already an indication that the algorithm is based on a two-stage approach: In the ? rst stage, the algorithm undertakes a procedure that is very similar to the k-means algorithm. Based on these results, the two-step procedure conducts a modi? ed hierarchical agglomerative clustering procedure that combines the objects sequentially to form homogenous clusters.This is done by building a so-called cluster feature tree whose â€Å"leaves† represent distinct objects in the dataset. The procedure can handle categoric al and continuous variables simultaneously and offers the user the ? exibility to specify the cluster numbers as well as the maximum number of clusters, or to allow the technique to automatically choose the number of clusters on the basis of statistical evaluation criteria. Likewise, the procedure guides the decision of how many clusters to retain from the data by calculating measures-of-? t such as Akaike’s Information Criterion (AIC) or Bayes 2 See Punji and Stewart (1983) for additional information on this sequential approach. 260 9 Cluster Analysis Information Criterion (BIC). Furthermore, the procedure indicates each variable’s importance for the construction of a speci? c cluster. These desirable features make the somewhat less popular two-step clustering a viable alternative to the traditional methods. You can ? nd a more detailed discussion of the two-step clustering procedure in the 8 Web Appendix (! Chap. 9), but we will also apply this method in the subseque nt example.Validate and Interpret the Cluster Solution Before interpreting the cluster solution, we have to assess the solution’s stability and validity. Stability is evaluated by using different clustering procedures on the same data and testing whether these yield the same results. In hierarchical clustering, you can likewise use different distance measures. However, please note that it is common for results to change even when your solution is adequate. How much variation you should allow before questioning the stability of your solution is a matter of taste.Another common approach is to split the dataset into two halves and to thereafter analyze the two subsets separately using the same parameter settings. You then compare the two solutions’ cluster centroids. If these do not differ signi? cantly, you can presume that the overall solution has a high degree of stability. When using hierarchical clustering, it is also worthwhile changing the order of the objects in y our dataset and re-running the analysis to check the results’ stability. The results should not, of course, depend on the order of the dataset. If they do, you should try to ascertain if any obvious outliers may in? ence the results of the change in order. Assessing the solution’s reliability is closely related to the above, as reliability refers to the degree to which the solution is stable over time. If segments quickly change their composition, or its members their behavior, targeting strategies are likely not to succeed. Therefore, a certain degree of stability is necessary to ensure that marketing strategies can be implemented and produce adequate results. This can be evaluated by critically revisiting and replicating the clustering results at a later point in time. To validate the clustering solution, we need to assess its criterion validity.In research, we could focus on criterion variables that have a theoretically based relationship with the clustering variabl es, but were not included in the analysis. In market research, criterion variables usually relate to managerial outcomes such as the sales per person, or satisfaction. If these criterion variables differ signi? cantly, we can conclude that the clusters are distinct groups with criterion validity. To judge validity, you should also assess face validity and, if possible, expert validity. While we primarily consider criterion validity when choosing clustering variables, as well as in this ? al step of the analysis procedure, the assessment of face validity is a process rather than a single event. The key to successful segmentation is to critically revisit the results of different cluster analysis set-ups (e. g. , by using Conducting a Cluster Analysis 261 different algorithms on the same data) in terms of managerial relevance. This underlines the exploratory character of the method. The following criteria will help you make an evaluation choice for a clustering solution (Dibb 1999; Ton ks 2009; Kotler and Keller 2009). l l l l l l l l l l Substantial: The segments are large and pro? able enough to serve. Accessible: The segments can be effectively reached and served, which requires them to be characterized by means of observable variables. Differentiable: The segments can be distinguished conceptually and respond differently to different marketing-mix elements and programs. Actionable: Effective programs can be formulated to attract and serve the segments. Stable: Only segments that are stable over time can provide the necessary grounds for a successful marketing strategy. Parsimonious: To be managerially meaningful, only a small set of substantial clusters should be identi? ed.Familiar: To ensure management acceptance, the segments composition should be comprehensible. Relevant: Segments should be relevant in respect of the company’s competencies and objectives. Compactness: Segments exhibit a high degree of within-segment homogeneity and between-segment h eterogeneity. Compatibility: Segmentation results meet other managerial functions’ requirements. The ? nal step of any cluster analysis is the interpretation of the clusters. Interpreting clusters always involves examining the cluster centroids, which are the clustering variables’ average values of all objects in a certain cluster.This step is of the utmost importance, as the analysis sheds light on whether the segments are conceptually distinguishable. Only if certain clusters exhibit signi? cantly different means in these variables are they distinguishable – from a data perspective, at least. This can easily be ascertained by comparing the clusters with independent t-tests samples or ANOVA (see Chap. 6). By using this information, we can also try to come up with a meaningful name or label for each cluster; that is, one which adequately re? ects the objects in the cluster.This is usually a very challenging task. Furthermore, clustering variables are frequently unobservable, which poses another problem. How can we decide to which segment a new object should be assigned if its unobservable characteristics, such as personality traits, personal values or lifestyles, are unknown? We could obviously try to survey these attributes and make a decision based on the clustering variables. However, this will not be feasible in most situations and researchers therefore try to identify observable variables that best mirror the partition of the objects.If it is possible to identify, for example, demographic variables leading to a very similar partition as that obtained through the segmentation, then it is easy to assign a new object to a certain segment on the basis of these demographic 262 9 Cluster Analysis characteristics. These variables can then also be used to characterize speci? c segments, an action commonly called pro? ling. For example, imagine that we used a set of items to assess the respondents’ values and learned that a certain segm ent comprises respondents who appreciate self-ful? lment, enjoyment of life, and a sense of accomplishment, whereas this is not the case in another segment. If we were able to identify explanatory variables such as gender or age, which adequately distinguish these segments, then we could partition a new person based on the modalities of these observable variables whose traits may still be unknown. Table 9. 11 summarizes the steps involved in a hierarchical and k-means clustering. While companies often develop their own market segments, they frequently use standardized segments, which are based on established buying trends, habits, and customers’ needs and have been speci? ally designed for use by many products in mature markets. One of the most popular approaches is the PRIZM lifestyle segmentation system developed by Claritas Inc. , a leading market research company. PRIZM de? nes every US household in terms of 66 demographically and behaviorally distinct segments to help ma rketers discern those consumers’ likes, dislikes, lifestyles, and purchase behaviors. Visit the Claritas website and ? ip through the various segment pro? les. By entering a 5-digit US ZIP code, you can also ? nd a speci? c neighborhood’s top ? ve lifestyle groups.One example of a segment is â€Å"Gray Power,† containing middle-class, homeowning suburbanites who are aging in place rather than moving to retirement communities. Gray Power re? ects this trend, a segment of older, midscale singles and couples who live in quiet comfort. http://www. claritas. com/MyBestSegments/Default. jsp We also introduce steps related to two-step clustering which we will further introduce in the subsequent example. Conducting a Cluster Analysis 263 Table 9. 11 Steps involved in carrying out a factor analysis in SPSS Theory Action Research problem Identi? ation of homogenous groups of objects in a population Select clustering variables that should be Select relevant variables that potentially exhibit used to form segments high degrees of criterion validity with regard to a speci? c managerial objective. Requirements Suf? cient sample size Make sure that the relationship between objects and clustering variables is reasonable (rough guideline: number of observations should be at least 2m, where m is the number of clustering variables). Ensure that the sample size is large enough to guarantee substantial segments. Low levels of collinearity among the variables ?Analyze ? Correlate ? Bivariate Eliminate or replace highly correlated variables (correlation coef? cients > 0. 90). Speci? cation Choose the clustering procedure If there is a limited number of objects in your dataset or you do not know the number of clusters: ? Analyze ? Classify ? Hierarchical Cluster If there are many observations (> 500) in your dataset and you have a priori knowledge regarding the number of clusters: ? Analyze ? Classify ? K-Means Cluster If there are many observations in your datas et and the clustering variables are measured on different scale levels: ? Analyze ? Classify ?Two-Step Cluster Select a measure of similarity or dissimilarity Hierarchical methods: (only hierarchical and two-step clustering) ? Analyze ? Classify ? Hierarchical Cluster ? Method ? Measure Depending on the scale level, select the measure; convert variables with multiple categories into a set of binary variables and use matching coef? cients; standardize variables if necessary (on a range of 0 to 1 or A1 to 1). Two-step clustering: ? Analyze ? Classify ? Two-Step Cluster ? Distance Measure Use Euclidean distances when all variables are continuous; for mixed variables, use log-likelihood. ? Analyze ? Classify ?Hierarchical Cluster ? Choose clustering algorithm Method ? Cluster Method (only hierarchical clustering) Use Ward’s method if equally sized clusters are expected and no outliers are present. Preferably use single linkage, also to detect outliers. Decide on the number of clu sters Hierarchical clustering: Examine the dendrogram: ? Analyze ? Classify ? Hierarchical Cluster ? Plots ? Dendrogram (continued) 264 Table 9. 11 (continued) Theory 9 Cluster Analysis Action Draw a scree plot (e. g. , using Microsoft Excel) based on the coef? cients in the agglomeration schedule. Compute the VRC using the ANOVA procedure: ? Analyze ?Compare Means ? One-Way ANOVA Move the cluster membership variable in the Factor box and the clustering variables in the Dependent List box. Compute VRC for each segment solution and compare values. k-means: Run a hierarchical cluster analysis and decide on the number of segments based on a dendrogram or scree plot; use this information to run k-means with k clusters. Compute the VRC using the ANOVA procedure: ? Analyze ? Classify ? K-Means Cluster ? Options ? ANOVA table; Compute VRC for each segment solution and compare values. Two-step clustering: Specify the maximum number of clusters: ? Analyze ? Classify ? Two-Step Cluster ?Numbe r of Clusters Run separate analyses using AIC and, alternatively, BIC as clustering criterion: ? Analyze ? Classify ? Two-Step Cluster ? Clustering Criterion Examine the auto-clustering output. Re-run the analysis using different clustering procedures, algorithms or distance measures. Split the datasets into two halves and compute the clustering variables’ centroids; compare ce

Essay about Its Time to End School Inequality - 1141 Words

The right to an adequate education is a freedom every American child should have; however, that is not the case. Standardized testing reveals that students living in an economically stable neighborhood are more mentally developed than students living in poverty stricken communities. The problem with the educational system is not schools need to close and children need to be relocated to another one, it is inequality within the educational system continues to widen due to the expansion of the economic gap. One cannot fix issues of the broken system by closing public schools and endorsing charter school proliferation. One must first start with the economic situation of each school to ensure all students, teachers, and schools†¦show more content†¦Poverty stricken neighborhoods are underfunded, therefore, do not have all the materials on hand to teach its students. When I was in school, I had a book for every class so I may take it home and complete my homework. Some of Chicago ’s Elementary Schools does not have books until mid-year, closer to the end of the year. The teacher did not fail the student nor did the school did not fail the student. The blame belongs to the education system which failed the student, teacher, school, and community. It is impossible for the teacher to teach the student without access to material. It is impossible for the student to learn the material without access to it. However, the student, teacher, school, and community is penalized for students not doing well on standardized tests as Mr. Emmanuel decides to shut the school’s doors for good as he opens up the Charter Schools. At one of Chicago’s newest Noble Charter Schools, my daughter only had a History and Spanish book. Math, Biology, Health, English, and Literature books were only accessible at school during the class. Homework was given; however, she did not have a book to review examples or explanations when she did not remember. All she had was her notes, which lacked much of the material needed for comprehension. She became frustrated as the coursework was not hard, but not able to grab a book to do homework caused her to lose sleep as sheShow MoreRelatedGender Equality719 Words   |  3 Pages What is gender equality inequality? An easy question to answer really. Gender equality means that both men women have the same opportunities rights, but gender inequality is different. Gender inequality is when men women don’t have the same opportunities or rights. Back in Ancient Egypt/Greek 1960s society, there was gender inequality. Ancient Egypt Greek society, women were just seen a s object, made to be slaves. They were only used for cooking, cleaning, farming, etc. In the 1960sRead MoreAmerica s Classist Education System994 Words   |  4 Pagesexplains how the schools are based on a class system and higher-class areas have better recourses and more classes offered. Higher-class societies have better equipment, new technology, modern facilities, better books, and better teachers. Not only are the school’s educational facilities better the extracurricular activities are also better. For example sports facilities, training facilities, band, art, all because these lower class schools cant afford what these lower class schools can. Students inRead MoreInequality in Social Classes1668 Words   |  7 Pagesclasses, and more importantly inequality. Unfortunately, living in classless society falls under the category of a utopia, as long as greed exists in the heart of society, inequality and the division between classes is not going to end. As Mary Douglas o nce stated, â€Å"Inequality can have a bad downside, but equality, for its part, sure does get in the way of coordination.† If in today’s society, social classes is more important than race and inequality is breeding more inequality, what effects is elevatingRead MoreRace, Culture, And Socioeconomic Status1169 Words   |  5 Pagesgoing to school each day was just a routine and the end goal was to graduate high school and go onto college, always thinking that it was that easy for all others. Not until now have I discovered that it is not that easy and that there are many obstacles in the way of a good education. The Separate but Equal video that was watched in class was one that I had never seen before and allowed me to in a way to empathize with the people who lived through all of those struggles. Though many times we hearRead MoreFactors of Social Inequality723 Words   |  3 PagesSCHOOL:CENTRAL UNIVERSITY SCHOOL NAME: Tamakloe Amewusika INDEX NO: HRM/11/01/3027 STATE AND DISCUSS FIVE FACTORS OF INEQUALITY IN OUR SOCIETY. Gender refers to socially constructed roles and responsibilities of women and men. The difference in roles and responsibilities among women and men stems from our families, societies and culture. The concept of gender includes our expectations about the characteristics, attitudes and behaviours of women and men, and is vital in facilitatingRead MoreThe Inequalities of the Social Class in the United States and How to Improve It1315 Words   |  6 Pagescategorizing† is an issue that must be addressed and people must be made aware, because it seems as if it’s not going anywhere anytime soon so the least we can do is try and make it fair as possible. Being defined unknowingly by a class system is one thing, but having no say so or fair opportunity at being placed in the best â€Å"class system† as those more fortunate than others needs to change because it’s not like we all came out of our mothers womb starting the race of life at the same starting and ad vantageRead MoreThe Inequality Of Power By Mary Louise Pratt, A Woman Of The Elite Mla Academy Essay1338 Words   |  6 PagesDoes the power struggle still exist in the 21st Century and are teacher’s encouraging the inequality of power instead of addressing issues at school? (Indent)In the past, countries like South Africa experienced a power struggle called the Apartheid. White South Africans were seen as the Superior race and were allowed privileges that other races were denied. Being born and raised in South Africa after Apartheid, where it is now called a Democracy. The power struggle is still just as ripe and evidentRead MoreIncome Inequality: It’s Effect on African-American Single Mothers and Their Children1629 Words   |  7 PagesIncome Inequality: It’s Effect on African-American Single Mothers and Their Children. I believe that it’s not fair for single mothers to get paid less, when some of them are the back bones of this country. Currently the minimum wage, in the United States, is set to 8$ per hour. Women in general are only paid 77%, so it is appropriate to assume that through mathematics, women get a wage as high as $6.16. African-American women only get paid 64% of every dollar a man makes. If the minimum wage isRead MoreMr. Letter : A Fair Share Of Troublemakers Essay1393 Words   |  6 PagesMr. Bobbitt, like many teachers, has his fair share of troublemakers. One young white boy in particular has a difficult time listening and creates disruptions in class. When I arrive at the school, I bring the first graders down to music with Mr. Mac. It is here where I see the disciplinary domain emerge. Whenever this young boy starts acting up Mr. Mac will ask him to sit out in the hall and take a quick break. The boy refuses, however, and will sit with this head down. Then, as soon as Mr. MacRead MoreThe Social Institution Of Education1283 Words   |  6 PagesAlthough steps have been taken to afford individuals the opportunity to obtain an education, there still lies an inequality and therefore, a social problem is created. Using the sociological perspectives of functionalist, conflict, feminist, and interactionist, we can see how each perspective views how this inequality becomes prevalent and how social, as well as economic inequalities of society are inherited through education. Functionalist define a social problem by looking for the functions and

Death Of The Great Things She Has Done - 1138 Words

It has been only 4 months since Rose went into another dimension; it was also the last time The Doctor saw her. Doctor remembers the day quite clearly, he remembers her getting sucked into the portal, he remembers her screams for help; he remembered everything. He saw her for the last time in that other dimension of which she could not escape. He remembers the wind and the sand underneath his feet as he stood waiting for Rose and her family of the dimension to appear. He remembers her crying and he remembers the last hug. That hug was warm, almost like being engulfed in the sun, and it was comforting. As much as he didn’t want to go without Rose-his Rose, he knew he had to in order to keep the world safe. He will always remember her and†¦show more content†¦The drawing showed him in his 9th regeneration; the form he was before this one. Looking at this drawing reminded him of when he regenerated into 10 and Rose helped him to heal. Rose was on his mind once again, and he started to feel sadder than before. He truly did miss her and there was no doubt that he didn’t. Doctor just wanted to forget about this day and move on so, he tried to fall asleep. It was now 4 in the morning and the Tardis was silent. Doctor was still awake focusing on the ceiling; he was beginning to feel drowsy now. 5 AM read the alarm clock in bright red text, still, The Doctor is awake. It took him half an hour more to finally fall asleep. He started dreaming about the day that Rose was sucked into the portal and the last day he saw her. This dream was rather happy for him because he gets to see Rose as the way he remembers her. He dreamt of all the good times they had together. Even though he he can no longer visit Rose, Doctor feels like she’s actually there with him in the dream. The strange part is, that Rose seems to be mentioning things that The Doctor does not even know about. Some of these messages are things only Mickey or Jack would know about. Eve n weirder, these messages seem to be of things that are presently happen in this dimension. Doctor was beginning to roll around in his bed so much that it woke Jack up. Jack came running into his room assuming that something must be attacking Doctor. When jack stopped and surveyed the room, he sawShow MoreRelatedTuesdays With Morrie : Words Of Wisdom1334 Words   |  6 Pagesbelieves that everyone can live a great life no matter how many days they may have left. No one knows the days they exactly have left in their life. In life people should make the best of it and not worry about how much time they may have left to live, they should focus on making things better for themselves and others. They should also impact and do the right thing and be a great example in others lives. Morrie teaches people to love life through accepting death, valuing money less and forgivingRead MoreNeonatal Nursing1392 Words   |  6 Pagesespecially for those particular nurses who choose to work in the neonatal intensive care unit. The neonatal intensive care unit, or NICU, is where the infants suffering from potentially fatal diseases/disorders are held. NICU nurses struggle with life and death situations each and everyday, which is sure to be accompanied by specific emotions such as moral distress. In the words of researcher Kain (2006), â€Å"moral distress is defined as uncomfortable, painful emotions that arise when institutional constraintsRead MoreEssay about Importance of Heroes to Society1485 Words   |  6 Pagesdictionary defines a hero in the following ways: a) a mythological or legendary figure often of divine descent endowed with great strength or ability b) an illustrious warrior c) a man admired for his achievements and noble qualities d) one that shows great courage. Most of society considers a hero to be someone that saves another person’s life. The person doesn’t necessarily have great strength or ability. In mythology, a hero is a role model wi th extreme courage that does something to change or improveRead MoreJulius Caesars Responsibility for His Own Death in William Shakespeares Play870 Words   |  4 PagesJulius Caesars Responsibility for His Own Death in William Shakespeares Play William Shakespeares Julius Caesar is a tale of a very ambitious roman who is betrayed by his nearest and dearest, not to mention most trusted, friends. Caesar, a famous military general had great hopes of one day becoming sole ruler of Rome,- but was prevented from doing so by his own death . Caesar was a great man,- brave and noble,- having all the virtues of a hero,- but most terribleRead More Comparing Do Not Go Gentle into That Good Night and After a Time823 Words   |  4 Pages nbsp; Dylan Thomas Do Not Go Gentle into That Good Night and Catherine Davis After a Time demand comparison: Davis poem was written in deliberate response to Thomas. Davis assumes the readers familiarity with Do Not Go Gentle, which she uses to articulate her contrasting ideas. After a Time, although it is a literary work in its own right, might even be thought of as serious parody--perhaps the greatest compliment one writer can pay another. Do Not Go Gentle in That Good NightRead MoreKenneth Branaghs Hamlet1093 Words   |  5 PagesKenneth Branagh’s 1996 adaptation of Hamlet is a great way to enjoy the popular Shakespeare play. While I found the film to be quite lengthy, I thoroughly enjoyed seeing a film version of the story I only knew a little bit about from reading an excerpt of Hamlet in high school. I think Kenneth Branagh did an excellent job in making the screen play into a movie. Everything in every scene couldn’t have been done more perfectly. Better yet is the cast, with actors like Kenneth Branagh himself, asRead MoreDeath : How The Perspective Of People Changes When They Are On The Edge Of Life1173 Words   |  5 PagesDeath: How the perspective of people changes when they are on the edge of life. Death is the point that marks the end of a human’s life. When confronting the death, passively or actively, people usually have a different viewpoint from before and that assertion is proved in Being Mortal: Medicine and What Matters in the End by Atul Gawande, Wit by Margaret Edson and the last pages of The Stitches by David Small. On Being Mortal: Medicine and What Matters in the End by Atul Gawande, the author tellsRead MoreAnalysis Of The Book Thief 1307 Words   |  6 PagesHaving Deaths perspective gives the reader insight about the characters thoughts and feeling. Death starts off the novel introducing himself and Liesel Meminger, who he refers to as â€Å"the book thief†. He then starts the story where he first comes across Liesel at a railway with her mother, where she witnesses her brothers death. While this happens, World War II and the Holocaust are taking place. Liesel gets adopted by Rosa and Hans Hubermann. Liesel starts to enjoy her new life in Molching with herRead MoreWitches in Macbeth by William Shakespeare Essay730 Words   |  3 Pagesto violent individual and this leads to his death. The prophecies that were told by the witches were one of the factors that contributed to the degeneration of his character. Typically, a tragic hero is a figure of high stature, often of noble background. In the beginning of the play, Macbeth and Banquo are returning from the battle between the Norwegians and Scottish. They have just won the war for Duncan. Thereby he is established as person of great stature. The manner in the leader of the countryRead MoreLiterary Analysis of Macbeth Essay1310 Words   |  6 PagesLiterary Analysis of Macbeth Having a lust for power can cause a loss in many things. It’s as if you’re in a win-lose situation. In this case, the play Macbeth written by Shakespeare has scholars sayings, â€Å"The lust for power by Macbeth and Lady Macbeth led to a loss of humanity.† With that said, I totally agree with their view. Both Lady Macbeth and Macbeth went out of their way to even killing King Duncan and burdening the murder on his guardsmen. Even though that’s a common human act, you

Julius Caesar Political Propaganda - 1120 Words

Caleb Holman Miss Tanner British Literature 1 21 November 2014 Julius Caesar Used as Political Propaganda William Shakespeare was born in 1564, only a little while after the start of Queen Elizabeth I’s reign. As such he lived in a time of civil unrest later in his life because of the ruler being a woman, being childless and not naming an heir to the throne. Therefore Shakespeare used his tragedy Julius Caesar and the Roman politics in the play in order to reflect those of his day. Namely that even the government needs the support of its people, that advice given to political leaders should be taken into consideration, the consequences of rebellion, and the need for an heir. William Shakespeare first looks to show what may happen if the Queen should die without naming an heir to the throne. â€Å"Conceivably, England would revert, upon her death, to the kind of civil chaos through which it had suffered in the fiftieth century† (Maus 1550). Recognizing this Shakespeare uses his plays, particularly Julius Caesar, as a way to explore the possible outcomes of her death. Yet censorship in renaissance England made direct commentary on the political situation very difficult (Greenblatt 1550). Thus Shakespeare used Julius Caesar in order to provide that commentary. It is clear in his play Julius Caesar that Shakespeare believes that a republic is an idealistic, yet very hard to sustain, form of government. Hadfield comments on this saying â€Å"the republic was always an ideal that was inShow MoreRelatedJulius Caesar : A Reflection Of Politics934 Words   |  4 Pagesoverthrown, this cycle of human interaction is what not only binds Julius Caesar to today’s politics but to history in general.Throughout this assignment I will be speaking on how Julius Caesar is reflective of politics today. The first reason I believe that the story of Julius Caesar can be a reflection of todays politics is because of the personalities that we see in power or attempting to obtain it. Shakespeare used symbolism in Caesar, Anthony, Brutus, and Cassius to create a spectrum of charactersRead MoreReview Of The Bacchae, The Aeneid, And Book 15 Of Metamorphoses Essay1518 Words   |  7 Pages Within the societies of ancient Greece and Rome, there was a plethora of regimes, Caesars, and empires at the helm of everyday life. The political sphere encountered in the daily routines of ancient Greeks and Romans gave influence to the multitude of literary works produced in these societies. The works of Euripides, Virgil, and Ovid gave a vehicle for these writers to infuse commentary about the politics of the day. Politics is how groups of people organize making decisions that affect the individualsRead MoreThe Manipulative Tactics Of Hitler And Mark Anthony1414 Words   |  6 Pageshistory, notori ous world leaders have risen using manipulation. Hitler used manipulation to gain favor of citizens in Germany. Like Hitler, Mark Anthony manipulated Brutus to kill Julius Caesar on his way to the top. Hitler’s manipulation of the German people parallels that of Mark Anthony in Shakespeare’s Julius Caesar. Manipulation is the influence cast among the victim that aims to change their views and attitudes (Schultz). The goal of the manipulator is to socially influence the victim. TheRead MoreArt and Literature in Augustan Rome1252 Words   |  6 PagesLiterature in Augustan Rome The beginning of this time period comes with the death of Julius Caesar and the rise to power of his nephew, Octavius. He was in the Second Triumvirate that was formed to maintain order in Rome. Octavian, Marcus Lepidus, and Marc Antony ruled formally unlike the informal triumvirate of Julius Caesar. The triumvirate set out to execute members of the conspiracy against Julius Caesar. In 42 BC, Brutus and Cassius were finally defeated. In the following years the triumvirateRead MoreOctavian and Marc Antony- The Duel of Words and Deeds1091 Words   |  4 PagesOctavian and Marc Antony- The Duel of Words and Deeds Following the Julius Caesars death at the hands of the Senate, Octavian and Marc Antony propelled themselves to the pinnacle of Roman power. First joining together during the Second Triumvirate, these men represented the true power players of Roman politics. As their alliance fractured, both Romans resorted to propaganda to gain an edge over the other. Through insulting the other and polishing their own image, both Antony and Octavian lookedRead MoreAnalysis Of Julius Caesar s The Gallic War 1070 Words   |  5 PagesCritique Essay In this critique of Julius Caesar’s book, The Gallic War, I will be discussing the purpose and accuracy (or in this case, inaccuracy) over his adventures and military campaign against the Gallic tribes. There is a constant debate between historians: The Myth of Certainty. History is all about interpretation and finding truth out of subjectivity. History can often be lost in time as the firsthand accounts will eventually fade out. Even if firsthand accounts remains intact, it is notRead MoreJulius Caesar: Who Was He Really? Essay1172 Words   |  5 Pages Who was Julius Caesar? Julius Caesar accomplished many things in his day, which most would consider unbelievable. He has been considered a tyrant or dictator, and some believe he was one of the world’s greatest politician. In this paper we will compare the textbook and documentation that was written around 44 B.C.E the time of his death. The documents are considered to be â€Å"primary sources†, because of the timeframe in which they were written. To get a grasp on whom, Julius Caesar really was, weRead MoreAugustus- Absolute Power By Any Means Necessary914 Words   |  4 Pages With the death of Julius Caesar, Augustus became the leader of Julius great conquests, resources, and soldiers. Immense power was his to seize. However, the power came at a cost. At the forefront of his attention, Julius killers were still loose, sewing seeds of violent oppression to this authority. Combined with this constant fear of revolt, the propaganda of Marc Antony and others further challenged his right to rule. Therefore, for Augustus to command the absolute power bestowed upon him,Read MoreAnalyzing Julius Caesars Motives1685 Words   |  7 PagesFrom a young age, Julius Caesar was introduced to the politics of Rome through his family’s connection to Marius. Growing through his adolescence in both the proscription period of Marius and the dictatorship of Sulla, Caesar gained a lesson in extra constitutional advancement in the early career of Gaius Pompeius Magnus. Both Marius and Sulla distinguished themselves in the Social War, and both wanted command of the war a gainst Mithridates, which was initially given to Sulla; but when Sulla leftRead More Augustus Caesar: The Greatest Ruler in the Ancient World Essay1367 Words   |  6 Pagesancient world, some men were born into greatness while others dedicated their life to becoming great. Roman Emperor Augustus Caesar was part of the latter due to his achievements that set the foundation for an empire devastated by civil war. Despite the turmoil of the Roman Empire after the assassination of his adoptive father, Julius Caesar, he led Rome to social, political and economic prosperity and stability. His military tactics marked the beginning of a dynasty that saw a massive expansion of

Tourist Destination

Questions: 1. Analyze issues that affects the popularity of tourist destinations.2. Discuss the potential for responsible tourism to enhance the host community. Answers: 1. Analyze issues that affects the popularity of tourist destinations The cause and effect in the relationship of tourism is difficult and complex to identify. The issues on tourist destinations differ according to the place. The issues can differ according to the place; it can be the affect of development issue or environmental issue. It can also impact the culture and communities of the organization. Nepal has taken as a tourist spot so analyze the issues associated with it. Nepal, which was once the most attractive tourist spot, is affected because of reasons like lack of infrastructure development, environmental problem, and lack of publicity and safety facilities for the tourist. Further the country gets involved in internal conflict (Briassoulis and Van der Straaten 2013). Lack of infrastructure development- Lack of infrastructure contributed to affect the country to a considerable amount. Nepal has infrastructure issues like transportation and communication problem. Electricity is also a major issue for the country. Failure to match the demand of the tourist along with the electricity problems adversely affects the tourists of Nepal. Nepal is a landlocked country with China from the North and India from the south. The total road network and density are low and only 435 of the population of their country can access to all-weather roads. Moreover the capital of the country Kathmandu is considered as one of the seismically unstable Himalayas. The tourist of the country faces lack of drinking water within the economy. Environmental issue- Nepal has various environmental problems like loss of forests, forest degradation, soil erosion, air and water pollution and unmanaged solid waste. Trekking is the most attractive tourist activity of Nepal. The growth of it curbed because of the environmental problems like landslides. The earthquake of 2015 has affected the tourist to a considerable amount. Further the forest land declined from 30% to 22% in relation with the total area. The air and water is the most significant environmental problems in Nepal. The tourist mostly prefers Nepal because of participating in the activities like rock climbing, Bungee Jumping but people preferring o shift o other countries like India because of its major environmental problems (Holden 2016). Lack of publicity and safety- publicity makes a country more attractive and enables to gather tourists. However, the lack of it can affect negativity to the tourists. Bangkok and Malaysia is an example of it. The publicity attracts more tourists from all over the world (Tarlow 2014). However, Nepal does not incur much cost in publicity. It enables the tourist to visit other places as most of them gets ignore about the activities and scenetic beauty of the country. Safety is another concern for all tourists. Besides landslides and earthquake the country is affected by theft in the tourist sites and in hotel rooms. Internal conflict- Internal conflicts in a country makes people threaten for visiting the country. The feeling of security is most important for a tourist (Sharpley and Telfer 2014). However Maoists intervention in Nepal threatens the tourist to a considerable amount. The publications in various journals and articles hold the government responsible for not managing the crisis of the tourism. The internal conflict gave birth to loss in the tourism sectors of Nepal. 2. Discuss the potential for responsible tourism to enhance the host community Nepal has been taken as an example of tourist destination to better understanding about the responsible tourism. The part enhances the responsible tourism focusing on Nepal. Responsible tourism is the principles of the social and the economic justices and respect towards the environment and its culture. It helps to recognize the centrality of the host community and develop a sustainable and responsible tourism. It helps to create a positive interaction between the tourist industry, local communities and travelers (Sharpley 2014). It helps the operators to grow their business providing benefits in terms of social and economic to local communities and enables to respect the environment. The main form of responsible tourism is to address the environmental and the social concern through its policies and practice. The trek and expeditions of Nepal contributes in the responsibly tourism. Nepal has place porter welfare the top priorities. Nepal complies with the International Porter Protect ion Guidelines (Mihalic 2014). Further a member of Kathmandu Environmental Education project is a non-profit organization works for ecological and cultural prosperity of Nepal. The country further embraces the responsible tourism through encouraging the tourist to buy local products. The country further maintains the environmental and cultural ecology so that it does not get hampered. The flood in the eastern Nepal the country enables to take initiatives and distribute cloths, necessary items and tents to the victims. Responsible tourism helps to initiate benefits of tourism to local people and the places. It enables to make the place better to live and for the people to visit. The Cape Town declaration recognizes the Responsible Tourism through its variety forms. It characterized to minimize the negative impacts of economic, social and environmental. It imitates to enable economic benefits for the local people and enhance the host community. It also includes the local people in the decision that affect their life and changes in positive manner (Leslie 2012). It encourages to preserve the natural and cultural heritage of the country and maintain diversify of the world. It provides enjoyable experience for the tourists through its connections with the local people and understanding of the local culture, social and environmental issue. It further concentrates to give access for the people with disabilities and disadvantaged. It contributes culturally sensitive and engaging respect between the touris ts and the hosts to initiate pride and confidence. The benefits can initiate from the responsible tourism in economic benefits, soci-cultural benefits and environmental benefits. Economic Benefit- It enables to provide jobs in terms of tour guides or hotel housekeeping. Indirect employment can also initiate through agriculture, food, production and retail sectors (Sharpley 2014). Social Benefits- It can bring sense of pride and identity to the community especially in the rural and mountains areas of under developed countries. It also helps to preserve the traditions which might be risk in some countries if wouldnt preserve (Raviv et al. 2013). Environmental benefits- It provides financial support to secure their ecosystem and manage the natural resource. It enables to add values to the local business. Reference List: Briassoulis, H. and Van der Straaten, J. eds., 2013.Tourism and the environment: regional, economic, cultural and policy issues(Vol. 6). Springer Science Business Media. Holden, A., 2016.Environment and tourism. Routledge. Tarlow, P., 2014.Tourism security: strategies for effectively managing travel risk and safety. Elsevier. Sharpley, R. and Telfer, D.J. eds., 2014.Tourism and development: concepts and issues(Vol. 63). Channel View Publications. Sharpley, R., 2014. Teaching responsible tourism.The Routledge handbook of tourism and hospitality education, pp.171-184. Mihalic, T., 2014. Sustainable-responsible tourism discourseTowards responsustabletourism.Journal of Cleaner Production,30, p.1e10. Leslie, D. ed., 2012.Responsible tourism: Concepts, theory and practice. CABI. Raviv, C., Becken, S. and Hughey, K.F., 2013. Exploring values, drivers, and barriers as antecedents of implementing responsible tourism.Journal of Hospitality Tourism Research, p.1096348013491607.

Critical Thinking and Interpersonal Decision Making

Question: Discuss about the Critical Thinking and Interpersonal Decision Making. Answer: Introduction Decision making refers to the channels through which logical choices are sought and the best alternative reached. During this process all information to do with the available options has to be considered. Through critical thinking, all the known data and information surrounding the alternatives are expounded on and a more benefiting option sought. The negatives and positives of each alternative. Projecting a forecast is one way through which predictions in favor or against an option can be reached. The primary objective behind video dairy in decision making is study design and rapport development among the participants. It assures trust and excellence moderation through the process. The mix of activities and conversations ensures a deeper process coverage. Video dairies have a strong eminent way in giving details on how a process has developed. They detail on every personal view and opinion of the participants. The also deeply detail on the steps and processes taken to reach a decision. one is able to achieve an in depth understanding of a concept from video dairies. Through the use of video dairies in the decision making process opens a channel through which the ideas of every individual can be sought and be understood. The rich data sets derived from video blogs plays a major role in critical thinking and decision making purposes. Decision making and critical thinking The decision making processes should always be done on a step by step basis. In this manner, the decisions reached are always thoughtful and deliberate. The solution selected is essentially more specific to the eminent problem. Decision making and critical thinking need to follow a process as illustrated: Identifying subject matter The initial step is to get a deep understanding of the issue to be addressed. Knowing the details about it ensures a deeper development of a decision for it. Among the questions to ask at this level include: What the issue is. When it started. What causes it. What the solution need to address. The scope of cover. Information collection on the subject matter This process entails collection and documentation of all relevant information relating to the issue. The information should be sought both internally and externally. Essentially conducting a self-assessment reveals much of the data needed here. A research on the issue also provides additional insight to the scope of the issue to be decided on. Identifying alternatives and solutions after gaining in detail knowledge of the issue, one develops a better insight on the kind of solutions to be used to address the issue. All the possible alternatives need to be documented. The alternatives can be identified through imagination and by research. Assess the options Conducting a SWOT analysis for each option at this step ensures a deeper understanding of all the alternatives. The evaluation should be focused on addressing the questions raised in the first step. have a theoretical prediction on how each alternative shall be effective in addressing the issue. Once done, prioritize on the alternatives starting with the most effective option. Select and implement the best from the alternatives At this point a decision need to be made on the best alternative with all considerations in place. The alternative can then be implemented in to the process. Decision review After the implementation, there is need to continuously assess the effectiveness of the chosen alternative. The alternative need to exhaustively address all the issues raised in step 1. This ensures the issue is sufficiently addressed. Video diary in decision making and critical thinking Video diaries make a strong way through which users can make documentations and keep track on processes and decisions. The videos may as well be referred to as self-ethnography. The videos are recorded as a response to issues and concerns over time. The interactions in the activities result in a development of better understanding and relationship among the participants. From the video dairies one is usually able to ascertain the authenticity and actual attitude of the respondents and as such be aware of the attitude. The behavior comes natural as opposed to other channels through which this could have been recorded. They form a strong basis through which decisions can be made. When well detailed, the video dairies carry all the content that is discussed in a forum or process. Impact of video diaries in decision making and critical thinking. Develops a narrative journey to a decision or thinking Listening a well-documented video dairy will always take the audience through a deeply articulated steps and process through which a decision can be reached. They take the audience along in all the processes. The videos guide the audience, while providing every little details, through the channels used to reach a decision. From the tone and attitude, an audience is able to relate with the topic of discussion. To realize success, the videos need to be spontaneous. It ensures no detail in the decision making process is not left behind. Detailed in information dissemination Video dairies document every little detail through a process. There are minimal chances of information loss. When doing a critical thinking, all these details are needed to be included. Addressing them implies that the decision reached is all inclusive and considerate. They ensure a detailed narrative development Decision making and critical thinking have to be done in a particular manner. The development has to be in a definite direction. Concept development from a previous video is highly aided. It ensures the process is continuous. Challenges of loss of track in records are effectively addressed. The story line is definite ensuring minimal data loss and deeply sought process for a solution to be reached. Easier concept sharing While developing the video dairies, it is easy to incorporate details such as charts and previous results. Through this, keeping track of a process is ensured. An audience is able to instantly keep up to speed in all the elements within the subject matter. In a collaborative environment, the participants are able to gain knowledge on ongoing processes faster. Learning points From the course I have developed a stronger understanding on the proper channels through which a decision is made. I have strengthened my understanding on critical thinking and the need to keep track on all the steps and procedures passed through before a solution is reached. I have developed skills on proper and effective development of video dairies for managerial uses. I now have a strong intuition on powerful documentation of processes and steps necessary for sensitive steps. From this knowledge, I shall be in a better position to lead team members in making strong supported critical thinking and decision making in my career. I shall be able to make powerful insight on the modalities and channels for effective decision making. I shall also be able to ensure information flow and share among peers and management through video dairies to ensure efficient processes. Do data mining and manipulation for managerial decision making. Analyze managerial issues and ascertain inferences and theories surrounding them. I am also able to do evaluations on reasoning and judge self-opinion. Conclusion Decision making and critical thinking require strong interpersonal skills. They make critical managerial roles which need sensitivity while addressing. All the steps taken to reach a decision need to be sufficiently documented. Each participant need to exercise the art of listening and contributing. With effective video dairies, developing content for the decision making purposes get highly enhanced. Decisions are reached through a high developed content. The dairies are available for making revisions and revisits. The dairies are also available for a later stage revisit and recap. Sharing of ideas and concept among the decision making participants is also highly enhanced