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ch._19.docxBusiness Research Methods, Ch. 19
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Chapter 19: Cluster Analysis The thread has 1 unread message.
created by Jynx Gresser
Last updated Mar 07, 2015, 11:34 PM
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· Comment on Mar 07, 2015, 11:34 PM
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posted by Jynx Gresser at Mar 07, 2015, 11:34 PM
Last updated Mar 07, 2015, 11:34 PM
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According to Cooper and Schindler (2011), cluster analysis is "a set of interdependence techniques for grouping similar objects or people" (p. 550). This method is often utilized in the fields of medicine, biology, and marketing (Cooper & Schindler, 2011). Within the field of marketing, one can divide customers into groups based on buying behaviors as well as age, lifestyle, and financial characteristics (Cooper & Schindler, 2011). Cluster analysis is often compared to a factor analysis, but differs in the ways that correlations are treated; they are similarity measures rather than control variables on a linear model (Cooper & Schindler, 2011). There are five basic steps in the application of cluster analysis and they include selection of the sample to be clustered, definition of the variables on which to measure objects, events, or people, computation of similarities through correlations, selection of mutually exclusive clusters, and cluster comparison and validation (Cooper & Schindler, 2011). The biggest takeaway from this analysis method is clustering similar groups together to provide a heightened awareness of links between data amongst different demographic variables. A dendogram provides a visual representation on how to categorize clusters and understand their differences. I look forward to utilizing this type of analysis when I begin my marketing classes that involves product development and how it effects buying behavior. Does anyone in class utilize this method and have some insight into how it is applied?
Reference
Cooper, D. R., & Schindler, P. S. (2011). Business Research Methods (11th ed.). New York, NY: McGraw's/Irwin. Retrieved from the University of Phoenix eBook Collection database.
Jynx Gresser
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SEM The thread has 3 unread messages.
created by ARACHEAL VENTRESS
Last updated Mar 07, 2015, 10:03 PM
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· Comment on Mar 06, 2015, 1:25 PM
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posted by ARACHEAL VENTRESS at Mar 06, 2015, 1:25 PM
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Structural equation modeling (SEM) implies a structure for the covariances between observed variables, and accordingly it is sometimes called covariance structure modeling. More commonly, researchers refer to structural equation models as LISREL (linear structural relations) models--the name of the first and most widely cited SEM computer program. SEM is a powerful alternative to other multivariate techniques, which are limited to representing only a single relationship between the dependent and independent variables. The major advantages of SEM are (1) that multiple and interrelated dependence relationships can be estimated simultaneously and (2) that it can represent unobserved concepts, or latent variables, in these relationships and account for measurement error in the estimation process.
Reference
Cooper, D. R., & Schindler, P. S. (2011). Business Research Methods (11th ed.). New York, NY: McGraw's/Irwin. Retrieved from the University of Phoenix eBook Collection database.
· Comment on Mar 07, 2015, 8:36 PM
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posted by PATRICIA MARCUS at Mar 07, 2015, 8:36 PM
Last updated Mar 07, 2015, 8:36 PM
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Great post Arachael. SEM is a flexible and extensive method for testing theory. It is a statistical modeling modeling technique to establish relationships among variables. It is best developed on the basis of substantive theory. Statistical estimates of these hypothesized covariance indicates within a margin of error how well the models fit with days. Structural equation models subsume factor analysis, regression, and path analysis. Integration of that types of analysis is an important advancement because it helps make possibleempirical specification of the linkage between imperfectly measured variables and rhetorical constructs of interest. A key feature of SEM is that observational variables are seen as a representation of a small number of constructs that can't be directly measures but only inferred from the observed measured variables. .
· Comment on Mar 07, 2015, 10:03 PM
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posted by MaDonna Keys at Mar 07, 2015, 10:03 PM
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According to stat soft its states that the SEM or the Structural Equation Modeling is a very powerful analysis technique because it includes a few different versions of specialized number of methods. The methods are used in special analysis cases. There are basics that are included in application structural equation modeling.
There are six major applications that are included in structural equation modeling those are casual modeling, confirmatory factor analysis, second order factor analysis, regression models,covariance structure model, and correlation structure model.
Casual modeling and or path analysis which is a type of modeling that focuses on hypothesizes among variables that are latent and manifested, tested and the models of the casual models with a linear equation system.
Confirmatory factor or analysis focuses on intercorrelations and their testing and the factor analysis of specific hypotheses and their structure of factors when tested.
Second order factor analysis which involves the analyzation of the common factors through a correlation matrix to provide a second order of factors through the analysis .
Regression models is weights in regression weights are constrained to equal each other based upon numerical values extending the linear regression analysis of stats.
Covariance structural models which includes the hypothesizing that a covariance matrix has a particular form. This can be tested on variables with all equal variances.
Correlation structural models this also uses hypothesizing the correlation matrix has a specific structure or form and an example of this includes circumplex.
Retrieved from Structural Equation Modeling accessed March 8, 2015
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Multivariate Analysis The thread has 2 unread messages.
created by STEPHANIE RECTOR
Last updated Mar 07, 2015, 8:25 PM
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· Comment on Mar 05, 2015, 10:26 AM
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posted by STEPHANIE RECTOR at Mar 05, 2015, 10:26 AM
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Many businesses today rely on multiple independent and multiple dependent variables because of how complex consumer preferences are. According to our readings,multivariate analysis are "those statistical techniques which focus upon, and bring out in bold relief, the structure of simultaneous relationships among three or more phenomena" (Cooper, 2011). Dependence and interdependency are the two types of multivariate techniques and selecting the correct one is of high importance. You would utilize the dependency technique if the criterion and predictor variables are clear in the research question. Cooper also states, "Alternatively, if the variables are interrelated without designating some as dependent and others independent, then inter- dependence of the variables is assumed. Factor analysis, cluster analysis, and multidimensional scaling are examples of interdependency techniques" (Cooper, 2011). Multivariate analysis can be complicated because of the inclusion of physics-based analysis. These help calculate how variables influence hierarchical "systems-of-systems."
Reference:
Cooper, D. R., & Schindler, P. S. (2011). Business Research Methods (11th ed.). New York, NY: McGraw's/Irwin. Retrieved from the University of Phoenix eBook Collection database.
· Comment on Mar 05, 2015, 6:34 PM
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posted by LOUIS DAILY at Mar 05, 2015, 6:34 PM
Last updated Mar 05, 2015, 6:34 PM
Stephanie,
Yes, when we have more than one dependent variables, we can use multivariate techniques to analyze the design.
thanks
Lou
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Conjoint Analysis The thread has 3 unread messages.
created by KIM DUNLAP
Last updated Mar 07, 2015, 8:57 PM
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· Comment on Mar 06, 2015, 6:26 PM
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posted by KIM DUNLAP at Mar 06, 2015, 6:26 PM
Last updated Mar 06, 2015, 6:26 PM
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Conjoint Analysis is one that seems to be used often in the marketing aspect of business. It is a method or tool that allows us to the relative importance of combinations of attributes and rank them in order of importance. So in other words, on almost any product, you can assign different attributes such as cost, brand, color options, size, price, options available. You could then assign combinations different levels and go out and research the public. By doing this you can rank the importance of the different combinations to your customers and market those qualities that matter most to your customer base.
Statistical software that assists the researcher in analyzing conjoint data is helpful.
· Comment on Mar 07, 2015, 5:20 PM
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posted by JUDEENE WALKER at Mar 07, 2015, 5:20 PM
Last updated Mar 07, 2015, 5:20 PM
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Conjoint analysis is a popular marketing research technique that marketers used to determine what features a new product should have and how it should be priced. The main steps involved in using conjoint analysis include determination of the salient attributes for the given product from the points of view of the consumers.
In simple terms conjoint analysis is an advanced market research technique that gets to "the root of the matter" of how people make decisions and what they really value in products and services. Using this technique one has to present people with choices then analyze the data.
Benefit of using conjoint analysis is to evaluate product/service attributes in a way that no other method can. Conjoint analysis provides the ability to use results to develop market simulation models that can be used well in to the future. In this ever changing competitive market it is important to use CA as it allows changes to be incorporated in to a simulation model showing predictions of how buyers will respond to different changes.
· Comment on Mar 07, 2015, 8:57 PM
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posted by PATRICIA MARCUS at Mar 07, 2015, 8:57 PM
Last updated Mar 07, 2015, 8:57 PM
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Great post Judeene..From what I gather from conjoint analysis and I am in agreement with you is that it presents people with choices and then it allows them to analyze those choices. It allows businesses to work or and quantify the hidden rules people use to make traps between different gratis or component pays off the offer. The principle behind vonjoint analysis is that it stays by breaking down every thing about a product or service and then test the combination of them to find out what the customer prefer. .Vonjoint analysis can be relatively compress because it twos an understanding of how to use and create. attributes and levels.Even then a vonjoint analysis don't always fit. So depending on the product or services itis possible that certain approaches are not always suitable and other methods are needed.
