Eleven Multivariate Analysis Techniques:
Key Tools In Your Marketing Research Survival Kit
Situation 1: A harried executive walks into your office with a stack of printouts. She says, Youre the marketing
research whiztell me how many of this new red widget we are going to sell next year. Oh, yeah, we dont know what price we can get for it either.
Situation 2: Another harried executive (they all seem to be that way) calls you into his office and shows you three proposed
advertising campaigns for next year. He asks, Which one should I use? They all look pretty good to me.
Situation 3: During the annual budget meeting, the sales manager wants to know why two of his main competitors are gaining share.
Do they have better widgets? Do their products appeal to different types of customers? What is going on in the market?
All of these situations are real, and they happen every day across corporate America. Fortunately, all of these questions are ones to which solid,
quantifiable answers can be provided.
An astute marketing researcher quickly develops a plan of action to address the situation. The researcher realizes that each question requires a specific type
of analysis, and reaches into the analysis tool bag for. . .
Over the past 20 years, the dramatic increase in desktop computing power has resulted in a corresponding increase in the availability of computation
intensive statistical software. Programs like SAS and SPSS, once restricted to mainframe utilization, are now readily available in Windows-based, menu-driven
packages. The marketing research analyst now has access to a much broader array of sophisticated techniques with which to explore the data. The challenge
becomes knowing which technique to select, and clearly understanding their strengths and weaknesses. As my father once said to me, If you only have
a hammer, then every problem starts to look like a nail.
The purpose of this white paper is to provide an executive understanding of 11 multivariate analysis techniques, resulting in an understanding of the
appropriate uses for each of the techniques. This is not a discussion of the underlying statistics of each technique; it is a field guide to understanding
the types of research questions that can be formulated and the capabilities and limitations of each technique in answering those questions.
In order to understand multivariate analysis, it is important to understand some of the terminology. A variate is a weighted combination of variables. The
purpose of the analysis is to find the best combination of weights. Nonmetric data refers to data that are either qualitative or categorical in nature.
Metric data refers to data that are quantitative, and interval or ratio in nature.
Initial StepData Quality
Before launching into an analysis technique, it is important to have a clear understanding of the form and quality of the data. The form
of the data refers to whether the data are nonmetric or metric. The quality of the data refers to how normally distributed the data are. The first few techniques
discussed are sensitive to the linearity, normality, and equal variance assumptions of the data. Examinations of distribution, skewness, and kurtosis are helpful
in examining distribution. Also, it is important to understand the magnitude of missing values in observations and to determine whether to ignore them or
impute values to the missing observations. Another data quality measure is outliers, and it is important to determine whether the outliers should be removed. If
they are kept, they may cause a distortion to the data; if they are eliminated, they may help with the assumptions of normality. The key is to attempt to understand
what the outliers represent.
Multiple Regression Analysis
Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or
more metric independent variables. The technique relies upon determining the linear relationship with the lowest sum of squared variances; therefore,
assumptions of normality, linearity, and equal variance are carefully observed. The beta coefficients (weights) are the marginal impacts of each variable, and
the size of the weight can be interpreted directly. Multiple regression is often used as a forecasting tool.
Logistic Regression Analysis
Sometimes referred to as choice models, this technique is a variation of multiple regression that allows for the prediction of an event. It
is allowable to utilize nonmetric (typically binary) dependent variables, as the objective is to arrive at a probabilistic assessment of a binary choice.
The independent variables can be either discrete or continuous. A contingency table is produced, which shows the classification of observations as to whether
the observed and predicted events match. The sum of events that were predicted to occur which actually did occur and the events that were predicted not to
occur which actually did not occur, divided by the total number of events, is a measure of the effectiveness of the model. This tool helps predict the choices
consumers might make when presented with alternatives.
The purpose of discriminant analysis is to correctly classify observations or people into homogeneous groups. The independent variables must be metric and
must have a high degree of normality. Discriminant analysis builds a linear discriminant function, which can then be used to classify the observations. The
overall fit is assessed by looking at the degree to which the group means differ (Wilkes Lambda or D2) and how well the model classifies. To determine
which variables have the most impact on the discriminant function, it is possible to look at partial F values. The higher the partial F, the more impact
that variable has on the discriminant function. This tool helps categorize people, like buyers and nonbuyers.
Multivariate Analysis of Variance (MANOVA)
This technique examines the relationship between several categorical independent variables and two or more metric dependent variables. Whereas
analysis of variance (ANOVA) assesses the differences between groups (by using T tests for two means and F tests between three or more means), MANOVA examines the
dependence relationship between a set of dependent measures across a set of groups. Typically this analysis is used in experimental design, and usually a
hypothesized relationship between dependent measures is used. This technique is slightly different in that the independent variables are categorical and the
dependent variable is metric. Sample size is an issue, with 15-20 observations needed per cell. However, too many observations per cell (over 30) and the
technique loses its practical significance. Cell sizes should be roughly equal, with the largest cell having less than 1.5 times the observations of the
smallest cell. That is because, in this technique, normality of the dependent variables is important. The model fit is determined by examining mean vector
equivalents across groups. If there is a significant difference in the means, the null hypothesis can be rejected and treatment differences can be
When there are many variables in a research design, it is often helpful to reduce the variables to a smaller set of factors. This is an independence
technique, in which there is no dependent variable. Rather, the researcher is looking for the underlying structure of the data matrix. Ideally, the
independent variables are normal and continuous, with at least three to five variables loading onto a factor. The sample size should be over 50 observations, with
over five observations per variable. Multicollinearity is generally preferred between the variables, as the correlations are key to data reduction.
Kaisers Measure of Statistical Adequacy (MSA) is a measure of the degree to which every variable can be predicted by all other variables. An overall MSA
of .80 or higher is very good, with a measure of under .50 deemed poor.
There are two main factor analysis methods: common factor analysis, which extracts factors based on the variance shared by the factors, and principal
component analysis, which extracts factors based on the total variance of the factors. Common factor analysis is used to look for the latent (underlying)
factors, whereas principal component analysis is used to find the fewest number of variables that explain the most variance. The first factor extracted
explains the most variance. Typically, factors are extracted as long as the eigenvalues are greater than 1.0 or the Scree test visually indicates how many
factors to extract. The factor loadings are the correlations between the factor and the variables. Typically a factor loading of .4 or higher is required to
attribute a specific variable to a factor. An orthogonal rotation assumes no correlation between the factors, whereas an oblique rotation is used when some
relationship is believed to exist.
The purpose of cluster analysis is to reduce a large data set to meaningful subgroups of individuals or objects. The division is accomplished on the basis
of similarity of the objects across a set of specified characteristics. Outliers are a problem with this technique, often caused by too many irrelevant
variables. The sample should be representative of the population, and it is desirable to have uncorrelated factors. There are three main clustering
methods: hierarchical, which is a treelike process appropriate for smaller data sets; nonhierarchical, which requires specification of the number of clusters a
priori; and a combination of both. There are four main rules for developing clusters: the clusters should be different, they should be reachable, they
should be measurable, and the clusters should be profitable (big enough to matter). This is a great tool for market segmentation.
Multidimensional Scaling (MDS)
The purpose of MDS is to transform consumer judgments of similarity into distances represented in multidimensional space. This is a decompositional
approach that uses perceptual mapping to present the dimensions. As an exploratory technique, it is useful in examining unrecognized dimensions about
products and in uncovering comparative evaluations of products when the basis for comparison is unknown. Typically there must be at least four times as many
objects being evaluated as dimensions. It is possible to evaluate the objects with nonmetric preference rankings or metric similarities (paired comparison)
ratings. Kruskals Stress measure is a badness of fit measure; a stress percentage of 0 indicates a perfect fit, and over 20% is a poor fit.
The dimensions can be interpreted either subjectively by letting the respondents identify the dimensions or objectively by the researcher.
This technique provides for dimensional reduction of object ratings on a set of attributes, resulting in a perceptual map of the ratings. However, unlike MDS,
both independent variables and dependent variables are examined at the same time. This technique is more similar in nature to factor analysis. It is a
compositional technique, and is useful when there are many attributes and many companies. It is most often used in assessing the effectiveness of advertising
campaigns. It is also used when the attributes are too similar for factor analysis to be meaningful. The main structural approach is the development of a
contingency (crosstab) table. This means that the form of the variables should be nonmetric. The model can be assessed by examining the Chi-square value for
the model. Correspondence analysis is difficult to interpret, as the dimensions are a combination of independent and dependent variables.
Conjoint analysis is often referred to as trade-off analysis, since it allows for the evaluation of objects and the various levels of the
attributes to be examined. It is both a compositional technique and a dependence technique, in that a level of preference for a combination of
attributes and levels is developed. A part-worth, or utility, is calculated for each level of each attribute, and combinations of attributes at specific levels
are summed to develop the overall preference for the attribute at each level. Models can be built that identify the ideal levels and combinations of
attributes for products and services.
The most flexible of the multivariate techniques, canonical correlation simultaneously correlates several independent variables and several dependent
variables. This powerful technique utilizes metric independent variables, unlike MANOVA, such as sales, satisfaction levels, and usage levels. It can
also utilize nonmetric categorical variables. This technique has the fewest restrictions of any of the multivariate techniques, so the results should be
interpreted with caution due to the relaxed assumptions. Often, the dependent variables are related, and the independent variables are related, so finding a
relationship is difficult without a technique like canonical correlation.
Structural Equation Modeling
Unlike the other multivariate techniques discussed, structural equation modeling (SEM) examines multiple relationships between sets of variables
simultaneously. This represents a family of techniques, including LISREL, latent variable analysis, and confirmatory factor analysis. SEM can incorporate
latent variables, which either are not or cannot be measured directly into the analysis. For example, intelligence levels can only be inferred, with direct
measurement of variables like test scores, level of education, grade point average, and other related measures. These tools are often used to evaluate
many scaled attributes or to build summated scales.
Each of the multivariate techniques described above has a specific type of research question for which it is best suited. Each technique also has certain
strengths and weaknesses that should be clearly understood by the analyst before attempting to interpret the results of the technique. Current statistical packages
(SAS, SPSS, S-Plus, and others) make it increasingly easy to run a procedure, but the results can be disastrously misinterpreted without adequate care.
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