Perceptual Mapping is a data analysis tool that is used to represent the varying relationships that exist between one or many marketplace competitors.The mapping attempts to measure the factors that consumers use in making their purchase decisions. Using a perceptual map has become a popular tool for data analysts to use when briefing upper management on complex issues because it can represent complex relationships graphically.
Now that you have some idea on what exactly this wonderful tool is all about let me make your life simple by giving a 4 step process on how to use perceptual maps
Step1 : Select a product or service and pick three or more companies that produce the material or provide the service
Step 2: Take a piece of paper and draw one vertical line that splits the paper vertically and another line drawn horizontally which also splits the paper so that they visually look like a 'plus sign'. Now there are software like Permap to do the same, but anyways lets continue:
Step 3: Create two questions about the product or service to ask consumers. The question should directly ask questions about how company a's product compares to company b's to company c's. A minimum of six people should be asked the questions. The vertical axis of the paper drawn on in step two will represent one of the two questions and the horizontal axis the other question. This represents the perceptual map.
Step 4:
Plot the answers to the two questions on the perceptual map labeling the company's name where the consumer's answer to the question best fits the perceptual map questions.
For example, if you want to compare Ford and Cheverolet, a question might be, do you find Ford or Chevrolet vehicles more sporty? The top of the vertical axis would be labeled sporty and the bottom labeled less sporty. You would label the top of the axis Ford with the number of answers that were that manufacturer and put the number of Chevrolet answers in the applicable spot on the perceptual map.
That way you can put characteristics in each of the 4 quads and based on that you can come up with a decision on your product and beat the thinking of the competitors.
Many companies do perceptual mapping. Ive had the chance to interact with some companies and I have learnt from them that it is a very valuable tool.
4.2.11
Finance Business Intelligence – From Data to Information to Competitive Advantage.
Today's competitive and challenging economic environment places further emphasis on real-time information for decision-making purposes. Without relevant, reliable information provided in a timely manner, decision-makers are forced into suboptimal decisions based on assumptions and gut instinct. This lack of fact-based decision making places the company at risk with one or more constituencies that interact with the company on a daily basis – the implications of which are, in aggregate, substantial and negative to the enterprise.
The time for business intelligence (BI) is now, as it can be leveraged as a competitive weapon to improve efficiencies and better manage position in the value chain. If implemented as a comprehensive solution, BI will reduce the time spent on low- to non-value-added activities across the enterprise, as well as enhance the organization's ability to make profit-enhancing decisions.
Benefit targets and categories include:
- 20 to 30 percent reduction in period-end closing activities through application automation and process redesign. Reconciliation activities can be reduced by as much as 50 to 60 percent as well.
- 30 to 40 percent reduction in report generation activities (which we have seen account for as much as 30 percent of the total finance organization). Report sets are rationalized and standardized, and the user communities are enabled to access and produce their analyses without ongoing finance/information technology (IT) support.
- 50 to 60 percent reduction in budgeting, planning and forecasting activities across the enterprise. The multiple process streams involving manual spreadsheets, reconciliation, rekeying and e-mails can be substantially reduced and create the opportunity to collapse the entire planning cycle.
- Revenue enhancement, including the information to improve gross-to-net management, tracking of sales float from sales programs, customer penetration and trending.
- Cost management by delivering accuracy in reporting major areas of spend to support benchmarking and/or other cost reduction initiatives.
- Profitability management by viewing contribution at the customer, channel and SKU (stock-keeping unit) levels, thereby supporting the creation of profit plans to influence margins.
- Avoidance of misrepresentations in external reporting and related market cap impacts, as well as the ability to build investor confidence through more fact-based support of financial performance.
The Business Intelligence Pyramid
We view business intelligence through an informational/technical "pyramid" containing the following elements: legacy systems, enterprise resource planning (ERP) solutions, data warehouse/marts, analytic applications and enterprise information system (EIS)/key performance indicator (KPI) portal (see Figure 1).
Figure 1: Business Intelligence Pyramid
As you work your way up the pyramid, the level of information correspondingly becomes more summarized and the views of information become more strategic.
Legacy Systems
The underlying legacy systems are used to support core processes and information capture (e.g., work management, time capture, etc.). These systems may require replacement over time, but are primarily viewed as a constant in the framework.
ERP Solutions
ERP software packages drive transaction processing efficiency and consistency. A company may have multiple packages, instances and/or configurations depending upon its operating model. Organizations have begun the shift from "implement" to "optimize" in this area, increasing enterprise-wide consistency in process and data definition.
Data Warehouse/Data Marts
This layer provides for the efficient extraction of data from multiple sources, the translation of disparate data into a common, rational data model and the loading of data into the analytic applications. The data structures in this layer are optimal for extracting, transforming and loading large volumes of data values from the legacy and ERP systems. The resulting data is ready to be analyzed by power users within the organizations or further manipulated within the analytic application layer.
The "bypass" concept within the pyramid represents an important decision for any BI implementation effort. In the interest of quick solution development, the data warehousing/marts layer is often overlooked as an unnecessary step. Our experience has proven, however, that consideration must be given to this decision up front to avoid the creation of a suboptimal or even unusable BI application. Furthermore, there are significant cost and timing ramifications associated with inserting an ex post facto data warehouse layer within a BI framework.
Analytic Applications
Analytic applications are purpose-built solutions meant to satisfy a specific analytic/decision-making set of requirements. These applications could be viewed as a series of workbenches designed to provide a given group within the enterprise the tools necessary to do their jobs. Specifically within the finance function, these include financial reporting, financial planning and financial analysis (see Figure 2).
Figure 2: Analytic Applications within the Finance Function
Financial Reporting
This component could be considered the required blocking and tackling owned by the finance organization. It includes core period-end activities, legal entity/SEC (Securities and Exchange Commission), tax and other regulatory reporting requirements. The opportunities here are to aggressively automate the topside adjustments and related activities, as well as standardize/automate the data submission, reconciliation and core reporting processes. By so doing, period- end windows will collapse, lower value-added activities will be eliminated and finance will have more time to truly review the numbers as well as ensure information quality.
Financial Planning
Financial planning covers the entire planning process across the enterprise, which finance typically owns from a submission, preparation and reporting perspective. In many organizations, the planning cycle has become an exercise in futility. The budget window can span eight-plus months and is plagued by sandbagging and stretch target setting. Forecasts become a manual nightmare that may consume as much as 25 percent of the finance organization's capacity each month, yet don't provide valid predictability to future earnings per share (EPS) or other finance metrics.
Financial Analysis
This is the component that enables business partnering with the organization, supporting customer, product and channel profitability analysis, predictive EVA/SVA value reporting and process-based performance and benchmarking. These analytics provide finance with multiple views of the business and create the environment to convert operational performance to financial results.
This analytic capability, however, can only be attained if finance organizational capacity is created through efficiency gains and automation in the other two components.
EIS/KPI Portal
The EIS/KPI portal is the decision-maker's desktop view and window to the various information sets and underlying analytics. This provides the enterprise with a single means of access to the information and related reporting. This means that the user can navigate through various analytics, ERP solutions and even underlying legacy systems through the same portal, thereby eliminating the need to toggle and determine what data is where within the framework.
Historically, internal Web portal efforts have often been plagued by content focused on employee self-service regarding individual human resources benefit plans, as a vehicle for mass communications, or at best, an executive-level dashboard. Today, they have evolved into a far more effective enabler for a given user to navigate the internal technological/informational landscape and complete various types of analysis.
Factors to Ensure Success
Following are key steps that can mean the difference between the success and failure of the BI endeavor.
- Start with the end in mind.
Lack of a clear, comprehensive BI strategy dramatically increases the implementation team's risk of failure. Dedicate anywhere from 6 to 12 weeks to complete the following strategic/vision-oriented components of a BI solution prior to detailed design and implementation.
- Establish a clear set of conceptual requirements across multiple constituencies/functions within the organization.
- Overtly apply the organization's strategic objectives to the business intelligence solution and communicate with key stakeholders.
- Develop a detailed, resource-loaded and phased implementation plan.
- Establish a solid business case and present same to executive management.
- Assess the technical landscape, develop the technical road map, establish the short list of vendor products and develop the selection criteria process.
- Identify the primary areas of incongruity that will require heavy analysis and decision making during the design phase (e.g., specific information standards).
- Develop a strategy to manage the personal and organizational changes that will follow during implementation.
Without these steps, any solution risks falling into the trap of being labeled either an accounting project or another system implementation.
- Prioritize and incrementalize.
The implementation requirements surrounding BI solutions are inherently different from their ERP counterparts in that the implementations are far less time- and resource-consuming. This creates the ability for an organization to rapidly provide solutions for various organizational communities. However, this rapid deployment must be executed in a way that ties the core data elements of the organization together in an efficient manner.
Once the overall BI vision for the company has been articulated, the plan of attack should be comprised of component implementations that are prioritized by the degree of information pain being experienced within different areas of the organization. Implementations are recommended to hold a 90-day benchmark and be limited in scope to a manageable array of information pain (the amount of pain addressed within each 90-day scope should increase as the team becomes more experienced with the tools and processes). The benefits of an incremental approach to BI include:
- Focusing on specific component information pains allows the implementation team to adequately address the complex functional and technological pieces of each implementation. When the entire organization is approached at once, the complexity of the components becomes unmanageable and the work unproductive.
- 90-day windows provide the team with a means to demonstrate the value being created to the various constituencies on an incremental basis.
- Momentum is maintained for the enterprise-wide deployment through a series of successes.
- Monitor and publish success.
Implementation teams can fall prey to viewing a phased solution as complete once the application set is live and the data is converted. The team must come back to the original business case, check performance to expectations and publish examples that have created value to the organization. These might include:
- Making a profit-enhancing decision during a major customer negotiation based upon the improved information.
- Supporting the analysis of a capital request.
- What-if analysis decisions regarding where to make and what to make.
- SKU rationalization/life-cycle profitability.
- Predictive economic analysis using the improved tools and information.
- Pure timeliness/capacity improvements in major areas such as financial closing, management reporting and budgeting.
While many companies have implemented some form of a BI solution, few have established organization-wide awareness of these capabilities, benefits and/or potential for future value- creation that these applications possess. By first confirming and then communicating these wins to the organization on at least a quarterly basis, the BI solution will become further indoctrinated into the organization and convert stakeholders from dreading to demanding additional BI applications for their respective areas.
Maximize the Return
In summary, remember the following:
- Track your progress. Start with the end in mind. Don't let your organization fall prey to viewing BI as a software installation.
- Begin smart by leveraging existing research, best practices, industry trends and diagnostics. Recognize and leverage the fact that you are not the first to travel the BI path.
- Focus on a complete solution, but in a way that continually drives 90-day results. Assemble a strong cross-functional team that will produce a solid business case and build the trust and commitment from the organization overall.
Today, the technology has caught up. It won't be the software that prevents a company from maximizing the return on a BI solution, but rather the content, processes and people necessary to make the solution work.
Link for this: http://www.information-management.com/issues/20031201/7733-1.html
Submitted by;
Kichawele Diwani Msuya
MBA-Finance (2009-2011)
Roll No. 12157.
3.2.11
factor analysis
On 25th January we had our last workshop on Business intelligence. We dealt with two topics discriminant analysis and factor analysis.
Discriminant analysis is a technique for classifying a set of observations into predefined classes. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. the technique constructs a set of linear functions of the predictors, known as discriminant functions, such that
L = b1x1 + b2x2 + ? + bnxn + c , where the b's are discriminant coefficients, the x's are the input variables or predictors and c is a constant.
These discriminant functions are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the ith discriminant function has the highest value.
Factor analysis is a data reduction technique that tries to reduce a list of attributes or other measures to their essence; that is, a smaller set of “factors”that capture the paterns seen in the data. Marketers and researchers who study a product, service, or industry professionally sometimes perceive many more distinctions within their category than do their consumers. This can lead to questionnaires containing attribute lists that consumers see as somewhat or largely synonymous. Factor analysis tells you how many different core factors that consumers perceived out of the list of attributes thay rated.
The main benefits of factor analysis are that the analyst can focus their attention on the unique core elements instead of the redundant attributes, and as a data ‘pre-processor’for regression models.
Submitted by,
Pragya Mishra
12153
SIBM Bangalore
Discriminant analysis is a technique for classifying a set of observations into predefined classes. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. the technique constructs a set of linear functions of the predictors, known as discriminant functions, such that
L = b1x1 + b2x2 + ? + bnxn + c , where the b's are discriminant coefficients, the x's are the input variables or predictors and c is a constant.
These discriminant functions are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the ith discriminant function has the highest value.
Factor analysis is a data reduction technique that tries to reduce a list of attributes or other measures to their essence; that is, a smaller set of “factors”that capture the paterns seen in the data. Marketers and researchers who study a product, service, or industry professionally sometimes perceive many more distinctions within their category than do their consumers. This can lead to questionnaires containing attribute lists that consumers see as somewhat or largely synonymous. Factor analysis tells you how many different core factors that consumers perceived out of the list of attributes thay rated.
The main benefits of factor analysis are that the analyst can focus their attention on the unique core elements instead of the redundant attributes, and as a data ‘pre-processor’for regression models.
Submitted by,
Pragya Mishra
12153
SIBM Bangalore
Cluster analysis Usage in various fields....... By Ajay Amarnath A
It is used mainly for software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost. The integration a neural network method with cluster analysis to estimate development cost.
Clustering is an economic development model signifying growth of similar kinds of industries at one geographical location. Locating near other similar firms provides numerous competitive advantages, including sharing a common labor pool, enhancing close working relationships between firms, reducing transaction costs and travel times between customers and suppliers, and enhancing the spread of technology through firms in the region.
As a cluster in a region takes root and expands, synergies often develop between firms and institutions, spurring additional growth and innovation. The existence of demand centre and concentration of Service Providers around the cluster also contributes to the growth of the cluster in terms of number of units. Other stakeholders like consultants, equipment manufacturers, Government Agencies etc also get concentrated in the cluster.
A cluster analysis was then performed to identify aspects of low, medium, and high risk projects. An examination of risk dimensions across the levels revealed that even low risk projects have a high level of complexity risk. For high risk projects, the risks associated with requirements, planning and control and the organization become more obvious. The influence of project scope, sourcing practices, and strategic orientation on project risk dimensions was also examined. Results suggested that project scope affects all dimensions of risk, whereas sourcing practices and strategic orientation had a more limited impact.
In marketing, cluster analysis is used for segmenting the market and determining target markets. Product positioning and New Product Development Selecting test markets the basic procedure. Formulate the problem - select the variables that you wish to apply the clustering technique.
How to solve human machine interface problem...... By KATHIRESHAN R
I have doubts regarding the human machine interface of the tool. i.e.
while we are evaluating the null hypothesis we are checking the significance value in the chi-square table and come to a conclusion whether to accept or reject the hypothesis. But the problem is, the chi-square value remains same and the way we define our hypothesis plays an important role. It can clearly be explained by 2 scenarios.
Scenario 1:
Null Hypothesis: Suppose there is no relationship between males who are educated are early married.
We found the significance value to be 0.05 from the chi-square table and hence we come to a conclusion that there is a relationship between males who are educated and early marriage.
Scenario 2:
Null Hypothesis: Suppose if we define there is relationships between male who are educated are early married.
Since the significance value which we obtained from the chi-square table is not changed then it implies that there is no relationships between male who are educated are early married.
These two scenarios contradict each other.
How is this possible and how to solve the human machine interface problem?And how to overcome the assumption made by the software? Is there a better way of defining the hypothesis in the tool?
28.1.11
Discriminant Analysis: By Vaibhav Khaparde
Discriminant Analysis (DA) undertakes the same task as multiple linear regressions by predicting an outcome. However, multiple linear regressions is limited to cases where the dependent variable on the Y axis is an interval variable so that the combination of predictors will, through the regression equation, produce estimated mean population numerical Y values for given values of weighted combinations of X values. But many interesting variables are categorical, such as political party voting intention, migrant/non-migrant status, making a profit or not, holding a particular credit card, owning, renting or paying a mortgage for a house, employed/unemployed, satisfied versus dissatisfied employees, which customers are likely to buy a product or not buy, what distinguishes Stellar Bean clients from
Gloria Beans clients whether a person is a credit risk or not, etc.
DA is used when:
The dependent is categorical with the predictor at interval level such as age, income, attitudes, perceptions, and years of education, although dummy variables can be used as predictors as in multiple regression. Logistic regression can be of any level of measurement. There are more than two DV categories, unlike logistic regression, which is limited to a dichotomous dependent variable.
The major underlying assumptions of DA are:
The observations are a random sample;
Each predictor variable is normally distributed;
Each of the allocations for the dependent categories in the initial classification are correctly classified;
There must be at least two groups or categories, with each case belonging to only one group so that the groups are mutually exclusive and collectively exhaustive (all cases can be placed in a group);
Each group or category must be well defined, clearly differentiated from any other group(s) and natural. Putting a median split on an attitude scale is not a natural way to form groups. Partitioning quantitative variables is only justifiable if there are easily identifiable gaps at the points of division;
For instance, three groups taking three available levels of amounts of housing loan; the groups or categories should be defined before collecting the data;
The attribute(s) used to separate the groups should discriminate quite clearly between the groups so that group or category overlap is clearly non-existent or minimal;
Group sizes of the dependent should not be grossly different and should be at least five times the number of independent variables.
There are several purposes of DA:
To investigate differences between groups on the basis of the attributes of the cases, indicating which attributes contribute most to group separation. The descriptive technique successively identifies the linear combination of attributes known as canonical discriminant functions (equations) which contribute maximally to group separation.
Predictive DA addresses the question of how to assign new cases to groups. The DA function uses a person’s scores on the predictor variables to predict the category to which the individual belongs.
To determine the most parsimonious way to distinguish between groups.
To classify cases into groups. Statistical significance tests using chi square enable you to see how well the function separates the groups.
To test theory whether cases are classified as predicted.
Methods implemented in this area are Multiple Discriminant Analysis, Fisher's Linear Discriminant Analysis, and K-Nearest Neighbors’ Discriminant Analysis.
Multiple Discriminant Analysis
(MDA) is also termed Discriminant Factor Analysis and Canonical Discriminant Analysis. It adopts a similar perspective to PCA: the rows of the data matrix to be examined constitute points in a multidimensional space, as also do the group mean vectors. Discriminating axes are determined in this space, in such a way that optimal separation of the predefined groups is attained. As with PCA, the problem becomes mathematically the eigen reduction of a real, symmetric matrix. The eigen values represent the discriminating power of the associated eigenvectors. The nYgroups lie in a space of dimension at most nY - 1. This will be the number of discriminant axes or factors obtainable in the most common practical case when n > m > nY(where n is the number of rows, and m the number of columns of the input data matrix).
Linear Discriminant Analysis
is the 2-group case of MDA. It optimally separates two groups, using the Mahalanobis metric or generalized distance. It also gives the same linear separating decision surface as Bayesian maximum likelihood discrimination in the case of equal class covariance matrices.
K-NNs Discriminant Analysis
: Non-parametric (distribution-free) methods dispense with the need for assumptions regarding the probability density function. They have become very popular especially in the image processing area. The K-NNs method assigns an object of unknown affiliation to the group to which the majority of its K nearest neighbors’ belongs.
Submitted By:
Vaibhav Khaparde
12054
Marketing
Factor Analysis : Introduction
Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables.
There are basically two types of factor analysis: exploratory and confirmatory..
1>Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses.
2>Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing responses in a predicted way.
Both types of factor analyses are based on the Common Factor Model, illustrated in figure 1.1. This model proposes that each observed response (measure 1 through measure 5) is influenced partially by underlying common factors (factor 1 and factor 2) and partially by underlying unique factors (E1 through E5). The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others. This is the same basic model as is used for LISREL analyses.
Factor analyses are performed by examining the pattern of correlations between the observed measures. Measures that are highly correlated (either positively or negatively) are likely influenced by the same factors, while those that are relatively uncorrelated are likely influenced by different factors.
The primary objectives of an EFA are to determine
1. The number of common factors influencing a set of measures.
2. The strength of the relationship between each factor and each observed measure.
Some common uses of EFA are to
Ø Identify the nature of the constructs underlying responses in a speci¯c content area.
Ø Determine what sets of items \hang together" in a questionnaire.
Ø Demonstrate the dimensionality of a measurement scale. Researchers often wish to develop scales that respond to a single characteristic.
Ø Determine what features are most important when classifying a group of items.
Ø Generate \factor scores" representing values of the underlying constructs for use in other analyses.
Miscellaneous notes on EFA:
1>To have acceptable reliability in your parameter estimates it is best to have data from at least 10 subjects for every measured variable in the model. This number should be increased if you expect that the influence of the common factors is relatively weak. You should also have measurements from at least three variables for every factor that you want to include in your model.
2> You should endeavour to have a wide variety of measurements for your EFA. The more accurately that your selection of measurements properly represents the population" of measurements that could be taken, the more generality you will have in your findings.
3> EFA can be performed in SAS using proc factor. Principal component analysis can be performed in SAS using proc princomp, while it can be performed in SPSS using the Analyze/Data reduction/Factor analysis menu selection. EFA cannot actually be performed in SPSS (despite the name of menu item used to perform PCA).
confirmatory factor analysis
Confirmatory factor analysis (CFA) is a special form of factor analysis. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor).
CFA is commonly used in social research. CFA is frequently used when developing a test, such as a personality test, intelligence test, or survey. CFA is also frequently used as a first step to assess the proposed measurement model in a structural equation model. Many of the rules of interpretation regarding assessment of model fit and model modification in structural equation modeling apply equally to CFA. CFA is distinguished from structural equation modeling by the fact that in CFA, there are no directed arrows between latent factors.[clarification needed] In the context of SEM ,the CFA often is called 'the measurement model', while the relations between the latent variables (with directed arrows) are called 'the structural model'
Factor analysis titus raju 12053
Factor analysis
Factor analysis attempts to discover the nature of the constructs influencing a set of responses.
Both types of factor analyses are based on the Common Factor Model where model proposes that each observed response (measure 1 through measure 5) is influenced partially by underlying common factors (factor 1 and factor 2) and partially by underlying unique factors (E1 through E5). The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others.
Factor analysis helps you find these undetermined variables by looking at the variables you have actually collected.
An important element of the factor analysis output is the standardized factor score coefficients (Output-Table), which gives location of each product on each factor.
The vectors are obtained based on the amount of correlation the original attitudes possess with the factor scores (represented as factors). The direction of the vectors indicates the factor with which each attribute is associated, and the length of the vector indicates the strength of association. Thus, on the left map the “filling” attribute has little association with any factor, whereas on the right map the “filling” attribute is strongly associated with “refreshing” factor.Although a factor is not observable like the other original variables, it is still a variable. One output of most factor analysis programs is the values for each factor for all respondents. These values are termed factor scores and are shown above for three factors that were found to underlie the five input variables. Thus, each beverage has a factor score on each factor, in addition to the beverage’s rating on the original eight attributes. In subsequent stages while doing perceptual mapping this factor scores are used to position beverages in the perceptual map.
My learning from class:
Factor analysis is basically what is done when you have a large number of variables to work with. Such a large number of variables makes it very difficult to organize data, and analyze it. Thus, we can use factor analysis, to reduce the number of variables so that it becomes much easier to work.
Factor analysis uses correlation between variables to see which ones are related, and can be eliminated, without causing significant impact on the output of analysis.
For this purpose, we take a default eigen value >1 and use verimax rotation so that the first few variables have the maximum effect.
Using Rotated Component Matrix, we can very well determine which variables are to be combined into factors, and keeping a limit on the eigen values to be accepted, we can determine what percentage of the data is contained in the factors we are choosing. According to this, we can increase or decrease the number of factors to simplify our calculations.
We can look at the correlation between the variables in scatter plot, in which visually clustered patterns can be made out and combined.
The remaining factors can be combined to give more meaning to the analysis.
Regards
Titus Raju
Factor analysis attempts to discover the nature of the constructs influencing a set of responses.
Both types of factor analyses are based on the Common Factor Model where model proposes that each observed response (measure 1 through measure 5) is influenced partially by underlying common factors (factor 1 and factor 2) and partially by underlying unique factors (E1 through E5). The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others.
Factor analysis helps you find these undetermined variables by looking at the variables you have actually collected.
An important element of the factor analysis output is the standardized factor score coefficients (Output-Table), which gives location of each product on each factor.
The vectors are obtained based on the amount of correlation the original attitudes possess with the factor scores (represented as factors). The direction of the vectors indicates the factor with which each attribute is associated, and the length of the vector indicates the strength of association. Thus, on the left map the “filling” attribute has little association with any factor, whereas on the right map the “filling” attribute is strongly associated with “refreshing” factor.Although a factor is not observable like the other original variables, it is still a variable. One output of most factor analysis programs is the values for each factor for all respondents. These values are termed factor scores and are shown above for three factors that were found to underlie the five input variables. Thus, each beverage has a factor score on each factor, in addition to the beverage’s rating on the original eight attributes. In subsequent stages while doing perceptual mapping this factor scores are used to position beverages in the perceptual map.
My learning from class:
Factor analysis is basically what is done when you have a large number of variables to work with. Such a large number of variables makes it very difficult to organize data, and analyze it. Thus, we can use factor analysis, to reduce the number of variables so that it becomes much easier to work.
Factor analysis uses correlation between variables to see which ones are related, and can be eliminated, without causing significant impact on the output of analysis.
For this purpose, we take a default eigen value >1 and use verimax rotation so that the first few variables have the maximum effect.
Using Rotated Component Matrix, we can very well determine which variables are to be combined into factors, and keeping a limit on the eigen values to be accepted, we can determine what percentage of the data is contained in the factors we are choosing. According to this, we can increase or decrease the number of factors to simplify our calculations.
We can look at the correlation between the variables in scatter plot, in which visually clustered patterns can be made out and combined.
The remaining factors can be combined to give more meaning to the analysis.
Regards
Titus Raju
27.1.11
factor analysis
Understanding of factor analysis describes us a method for investigating whether a number of variables of interest are linearly related to a smaller number of unobservable factors. It determines the strength of relationship between each factor and each observed measure. By the exercises to comprehend factor analysis we derive that it is a statistical tool to account for variability in a set of measured items in terms of a smaller numbers factors. There are several ways to conduct factor analysis and the choice of method depends on many things. We have the options pertaining to the retention of factors. The choice of either selexting factors with eigen values greater than a user- specified value or retaining a fixed number of factors. By looking at the scree plot and the Eigen values over 1 will lead us to retain the same number of factors then continue with analysis.
The objective of the factor analysis is to objectively detect natural groupings of variables. It also aims to extract quantitative information from large matrices using objective statistical criteria. It reduces the redundant data in the list.
The software uses the sample size from which communalities after extraction should probably be above 0.5. The system then finds a factor solution to a set of variables. When the first factor solution does not reveal the hypothesized structure of the loadings, it is customary to apply rotation in an effort to find another set of loadings that fit the observations equally well but can be more easily interpreted. The most widely used of these is the varimax criterion. It seeks the rotated loadings that maximize the variance of the squared loadings for each factor; the goal is to make some of these loadings as large as possible, and the rest as small as absolute value. It encourages the detection of factors each of which is related to few variables. It discourages the detection of factors influencing all variables.
The interpretability of factors can be improved through rotation. Rotation maximizes the loading of each variable on one of the extracted factors whilst minimizing the loading on all other factors. Rotation works through changing the absolute values of the variables whilst keeping their differential values constant.
The scores factor allows one to save factor scores for each subject in the data editor. It reates new column for each factor extracted and then places the factor score for each subject within that column. These scores can then be used for further analysis, or simply to identify groups of subjects who score highly on particular factors. Another feature provided by SPSS is options which helps use to list variables by size.
To analysis the output in SPSS software, the R matrix shows the Pearson correlation coefficient between all pairs of questions whereas the bottom half contains the one tailed significance of these coefficients. We can use this matrix to check the pattern of relations. The significance values details provides the confidence level or the reliability of the data. If any data of significance value more than 0.9 is found, then one should be aware that a problem could arise because of singularity in the data which demands for the check of determinants of the correlation matrix and if necessary eliminate one of the two variables causing the problem. Generally, the data is considered based on the null hypothesis proven right or wrong. Based on the acceptance or rejection of null hypothesis, further analysis is done on the information obtained.
SPSS lists the Eigen values associated with each linear component before extraction, after extraction and after rotation. The eigen values associated with each factor represent the variance explained by that particular linear component and it also displays the eigen values in terms of the percentage of variance explained.
Another output table displays the communalities before and after extraction. The principal component analysis works on the initial assumption that all variance are common, therefore before extraction he communalities are 1. Another way to look at these is in terms of the proportion of variance explained by the underlying factors. The output also explains the component matrix before rotation. This matrix contains loadings of each variable onto each factor.
Another important output is the rotated component matrix which provides with loads of information. It is the matrix that contains same information as the component matrix except that it is calculated after rotation. Before rotation most variables loaded highly onto first factor and the remaining factors didn’t really get a look in.
Therefore, the detailed analysis report explains that SPSS as a tool is every exploratory and is it should be used to guide the researcher to make various decisions. The conclusions derived out of the SPSS analysis can help the leaders to take strategic decisions and build strategies to achieve them.
Regards,
Nisha
Live...let live!
The objective of the factor analysis is to objectively detect natural groupings of variables. It also aims to extract quantitative information from large matrices using objective statistical criteria. It reduces the redundant data in the list.
The software uses the sample size from which communalities after extraction should probably be above 0.5. The system then finds a factor solution to a set of variables. When the first factor solution does not reveal the hypothesized structure of the loadings, it is customary to apply rotation in an effort to find another set of loadings that fit the observations equally well but can be more easily interpreted. The most widely used of these is the varimax criterion. It seeks the rotated loadings that maximize the variance of the squared loadings for each factor; the goal is to make some of these loadings as large as possible, and the rest as small as absolute value. It encourages the detection of factors each of which is related to few variables. It discourages the detection of factors influencing all variables.
The interpretability of factors can be improved through rotation. Rotation maximizes the loading of each variable on one of the extracted factors whilst minimizing the loading on all other factors. Rotation works through changing the absolute values of the variables whilst keeping their differential values constant.
The scores factor allows one to save factor scores for each subject in the data editor. It reates new column for each factor extracted and then places the factor score for each subject within that column. These scores can then be used for further analysis, or simply to identify groups of subjects who score highly on particular factors. Another feature provided by SPSS is options which helps use to list variables by size.
To analysis the output in SPSS software, the R matrix shows the Pearson correlation coefficient between all pairs of questions whereas the bottom half contains the one tailed significance of these coefficients. We can use this matrix to check the pattern of relations. The significance values details provides the confidence level or the reliability of the data. If any data of significance value more than 0.9 is found, then one should be aware that a problem could arise because of singularity in the data which demands for the check of determinants of the correlation matrix and if necessary eliminate one of the two variables causing the problem. Generally, the data is considered based on the null hypothesis proven right or wrong. Based on the acceptance or rejection of null hypothesis, further analysis is done on the information obtained.
SPSS lists the Eigen values associated with each linear component before extraction, after extraction and after rotation. The eigen values associated with each factor represent the variance explained by that particular linear component and it also displays the eigen values in terms of the percentage of variance explained.
Another output table displays the communalities before and after extraction. The principal component analysis works on the initial assumption that all variance are common, therefore before extraction he communalities are 1. Another way to look at these is in terms of the proportion of variance explained by the underlying factors. The output also explains the component matrix before rotation. This matrix contains loadings of each variable onto each factor.
Another important output is the rotated component matrix which provides with loads of information. It is the matrix that contains same information as the component matrix except that it is calculated after rotation. Before rotation most variables loaded highly onto first factor and the remaining factors didn’t really get a look in.
Therefore, the detailed analysis report explains that SPSS as a tool is every exploratory and is it should be used to guide the researcher to make various decisions. The conclusions derived out of the SPSS analysis can help the leaders to take strategic decisions and build strategies to achieve them.
Regards,
Nisha
Live...let live!
Applications of Multivariate Analysis (CLUSTER Analysis) in International Tourism Research: The Marketing Strategy Perspective of NTOs
Source - Applications of Multivariate Analysis in International Tourism Research: The Marketing Strategy Perspective of NTOs by Satish Chandra & Dennis Menezes
• In recent times International tourism has increased exponentially. With this growth the industry has become significantly more competitive, and the marketing role of National Tourism Organizations (NTOs) has taken on added significance. Correspondingly, research related to the marketing aspects of international tourism has increased.
• The paper focuses on:
– 1. identifying and describing the key components of marketing strategy that must be addressed by NTOs, and
– 2. identifying and describing the multivariate statistical techniques most relevant to research that relates to enhancing the marketing strategies of NTOs along with citing some of the recent related research.
Multivariate Techniques used for achieving the Marketing Strategies.
Refer Above Diagram. Prior to addressing these tasks, a SWOT analysis should be completed.
• Cluster Analysis (In Baseline/Post Hoc Segmentation): In Baseline/Post Hoc Segmentation tourists are classified into clusters on the basis of their appropriate attribute similarities.
• Baseline segmentation involves analyzing a large cross sectional sample of tourists where data has been collected on a variety of variables, such as psychological, life style, demographic, and other variables of interest. The preferred mode of analyzing this large set of data is Cluster analysis. In the baseline segmentation approach using Cluster analysis, the segments are produced analytically.
• Cluster analysis classifies the subjects into clusters, so that each subject is very similar to other subjects in that cluster with respect to selected criterion variables. The clusters formed exhibit high within cluster homogeneity and high between cluster heterogeneity. Thus, when good classification is achieved, subjects within clusters will be close together when plotted geometrically, but different clusters will be far apart. (Refer Figure 3 above).
• In the context of segmenting tourism markets, Cluster analysis can be used to identify different clusters of tourists that exist within a larger group or market of tourists. As a result, Cluster analysis may be used to develop a taxonomy of different types of tourist segments and thereby gain a better understanding of the composition of the larger population of tourists. The within cluster similarity of the tourists is typically determined using an inter subject Euclidean distance measured on two variables.
Conclusion
• International tourist arrivals increased from approximately 25 million in 1950 to 625 million in 1998, an increase of 2,500 percent. A WTO survey of NTOs and leading experts in tourism envision the following :
– (1) international tourism arrivals by 2020 to be 1.6 billion, with spending in excess of 2 trillion U.S. dollars,
– (2) the percent of the traveling population involved in international travel increasing from 3.5 percent in 1998 to 7 percent by 2020,
– (3), Europe continuing to be the largest international tourism region, although by 2020 its market share being significantly eroded,
– (4) by 2020 China being the largest receiver of international tourists,
– (5) among the various international tourism market segments, eco-tourism, cultural tourism, theme based tourism, adventure tourism, and the cruise market growing in importance, and
– (6) tourism as a sector growing at a faster rate than the global economy.
• These predictions by the WTO suggest that the international tourism market will continue to expand at a rapid rate and become increasingly competitive. In this environment, the use of effective and efficient marketing strategies (based on appropriate usage of Multivariate Techniques) by NTOs as well as other international tourism organizations will therefore become increasingly important.
Submitted by;
Sumit Acharya
Roll - 12108
Finance Batch
SIBM Bangalore
Subscribe to:
Posts (Atom)