Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles. 1. Marketing .... SEM is used for confirmatory factor analysis (CFA). Observed ...
Marketing Statistical Methods Map Statistical Analysis Used
Description
Marketing Statistics Types Canonical Correlation Analysis Cluster Analysis Conjoint Analysis Correspondence Analysis Cross-tabulation Discriminant Analysis Factor Analysis Log-linear Analysis Meta-Analysis Multinomial Logistic Regression Path Analysis Profile Analysis Structural Equation Modeling
The statistical methods in marketing are dynamic. The data acquired for quantitative marketing research can be analyzed by almost any of the range of techniques of statistical analysis. Those are generally two types: descriptive statistics and interferential statistics. An important set of techniques is that related to statistical surveys. In any instance, an appropriate type of statistical analysis should take account of the various types of error that may arise, as outlined below. Quantitative methodology in marketing research is the application of quantitative research techniques to the field of marketing. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion.
Canonical Correlation Analysis
A canonical correlation analysis enables you to discover the linear relationship between two sets of variables, without regard to which set is the independent variables and which set is dependent variables. Canonical correlation analysis does this by a redundancy analysis, finding successive linear combinations of the variables in each of the two sets.
Cluster Analysis Two types: Hierarchical cluster analysis (HCA) Non-hierarchical cluster analysis
Cluster Analysis is a collection of ad hoc techniques for grouping entities (either observations or variables) according to a distance measure specified by the researcher. The distance measure is a pairwise proximity between observations based on all available variables. If this distance measures similarity, such as the squared correlation, then it is the "similarity proximity." If this distance measures dissimilarity, such as the Euclidean distance, then it is the "dissimilarity proximity." In HCA the observation vectors are grouped together on the basis of their natural distances. A HCA is visualized with the help of a hierarchical tree called the dendogram tree. Non-HCA involves the clusters formed do not form a definite hierarchy. This technique is suited for larger data sets as there are no distance matrix calculations.
Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles
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Statistical Analysis Used
Description
Conjoint Analysis
Conjoint analysis is any decompositional method that estimates the structure of a consumer’s preferences (e.g., estimates preference parameters such as partworths, importance weights, ideal points) given his or her overall evaluations of a set of alternatives that are prespecified in terms of levels of different attributes. Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced. Conjoint analysis also has became popular because it is far less expensive and more flexible way to address these issues compared to concept testing.
Correspondence Analysis (aka Association Analysis) (Perceptual Maps)
Correspondence analysis is appropriate when attempting to determine the proximal relationships among two or more categorical variables. Using correspondence analysis with categorical variables is analogous to using correlation analysis and principal components analysis for continuous or nearly continuous variables. They provide the research with insight as to the relationships among variables and the dimensions or eigenvectors underlying them. A key part of correspondence analysis is the multi-dimensional map produced as part of the output. This statistical analysis Relationships between brands/products and attributes. This is used to understand perceptions of different brands/products and differentiators. For example: perceptions of different brands of soda.
Cross-tabulation one sample t-test Independent sample t-test Paired samples t-test t-test z-test
A cross-tabulation is used to evaluate the relationship between two categorical (nominal or ordinal) variables. There are three types of t-tests: (a) one sample t-test to compare a single sample with a population value (univariate); (b) the independent sample t-test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different; and (c) a paired sampled test is used if the two samples have the same individuals before and after some treatment. This test compares the means of two related groups to detect whether there are any statistically significant differences between these means. A t-test is used when groups are small and when the population standard deviation is unknown. A z-test is used when the groups are large.
Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles
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Statistical Analysis Used
Description
Discriminant Analysis Two methods: Linear discriminant analysis (LDA) Fisher-LDA
Discriminant Analysis may be used for two objectives: either we want to assess the adequacy of classification, given the group memberships of the objects under study; or we wish to assign objects to one of a number of (known) groups of objects. Discriminant Analysis may thus have a descriptive or a predictive objective. In LDA we express one dependent variable as a linear combination (which best explains the data) of other features or measurements. In Fisher-LDA, purpose is to maximize separation between two distributions S which is defined by Fisher to be the ratio of the variance between the classes to the variance within the classes
Factor Analysis Principal Component Analysis (PCA) Principal Axis Factoring (aka Common Factor Analysis) (PAF)
Factor analysis is useful for exploring and understanding the internal structure of a set of variables. It describes the linkages among a set of "observable" variables in terms of "unobservable" or "underlying" constructs called factors. In PCA, you construct new variables from all the observed variables; whereas in factor analysis, you reconstruct the observed variables from two new types of underlying factors. PCA tends to be exploratory. In PAF, this method seeks the least number of factors which can account for the common variance (correlation) of a set of variables. PAF tends to be confirmatory.
Log-linear Analysis General Model Hierarchical Model
Loglinear analysis is more commonly used to evaluate multi-way contingency tables that involve three or more variables. In log-linear analysis tables are for tables that contain one-way, two-way, and higher order associations. Log-Linear provides a more "sophisticated" way of looking at crosstabulation tables. Specifically, you can test the different factors that are used in the crosstabulation (e.g., gender, region, etc.) and their interactions for statistical significance.
Meta-Analysis
Meta-Analysis involves a quantitative review of a research question and focuses on the obtained effect sizes in previous studies on the topic. In a meta-analysis one attempts to obtain all previous empirical studies pertaining to the research question, including if possible both published and unpublished work. The marketing researcher using meta-analysis seeks general conclusions while searching for methodological conditions while for and substantive variables that might measurably moderate any observed main effects.
Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles
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Statistical Analysis Used
Description
Multinomial Logistic Regression Linear Regression Logistic Regression Multiple Regression
Multinomial logistic regression is a type of probabilistic Statistical Classification model. It is used for predicting the outcome of a categorical dependent Variable based on one or more features (independent variables). Multinomial logistic regression also called as “One vs. All classification problems is an extension to the logistic regression model which is a binary predictor with 2 classes of classification. Linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X. Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes). Multiple regression is a statistical technique to understand the relationship between one dependent variable and several independent variables. The purpose is to find a linear equation that can best determine the value of dependent variable Y for different values independent variables in X.
Path Analysis
Path analysis is a straightforward extension of multiple regression. The causal connections are hypothesized by the researcher. Its aim is to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. This is best explained by considering a path diagram. There are two goals of path analysis: (a) understanding patterns of correlations among the regions; and (b) explaining as much of the regional variation as possible with the model specified. Different from statistical testing in other techniques, such as multiple regression and ANOVA, the focus in path analysis is usually on a decision about the whole model: reject, modify, or accept it?
Profile Analysis
Profile analysis is the multivariate equivalent of repeated measures or mixed ANOVA. Profile analysis is also the repeated measures extension of MANOVA where a set of DVs are commensurate (on the same scale). The common use is where a set of DVs represent the same DV measured at multiple time points. Profile analysis is most commonly used in two cases: (a) comparing the same dependent variables between groups over several time-points; and (b) when there are several measures of the same dependent variable (e.g., several different psychological tests that all measure depression).
Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles
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Statistical Analysis Used Structural Equation Modeling (SEM)
Description Structural equation models (SEMs) describe relationships between variables. They are similar to combining multiple regression and factor analysis. SEMs can include two kinds of variables: observed and latent. SEM is used for confirmatory factor analysis (CFA). Observed variables have data, like the numeric responses to a rating scale item on a questionnaire such as gender or height. The latent variables in SEMs are continuous variables and can, in theory, have an infinite number of values. First, many important marketing variables are latent. Market researchers often try to estimate latent variables with only a single observed measurement, and the reliability of this measure is usually unknown. The coefficients are then used to rank the attributes in terms of their importance. This helps managers decide where to focus their marketing efforts.
Source: Marketing Statistical Methods Map developed by Dr. D. Anthony Miles
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