Causal modeling: Causal modeling is aimed at advancing reasonable hypotheses about underlying causal relationships between the dependent and independent variables. Consider for example a simple linear model: y = a0 + a1 x1 + a2 x2 + e where y is the dependent variable, x1 and x2 are independent...
View Full DescriptionCentroid: The centroid of several continuous variables is the vector of means of those variables. The concept of centroid plays the same role, for example, in multiple analysis of variance (MANOVA) as the mean plays in analysis of variance (ANOVA) . Browse Other Glossary Entries
View Full DescriptionCluster Analysis: In multivariate analysis, cluster analysis refers to methods used to divide up objects into similar groups, or, more precisely, groups whose members are all close to one another on various dimensions being measured. In cluster analysis, one does not start with any apriori notion of group characteristics. The...
View Full DescriptionContingency Tables Analysis: Contingency tables analysis is a central branch of categorical data analysis , and is focused on the analysis of data represented as contingency table s. This sort of analysis includes hypothesis testing as well estimation of model parameters, e.g. applying loglinear regression methods to fit loglinear models...
View Full DescriptionCorrespondence analysis: Correspondence analysis (CA) is an approach to representing categorical data in an Euclidean space, suitable for visual analysis. CA is often used where the data (in the form of a two-way continegency table) have many rows and/or columns and are not easy to interpret by visual inspection. The...
View Full DescriptionCorrespondence Factor Analysis: Correspondence factor analysis is a synonym for Correspondence analysis . Browse Other Glossary Entries
View Full DescriptionCorrespondence mapping: Correspondence mapping is a synonym for Correspondence analysis . Browse Other Glossary Entries
View Full DescriptionDetrended Correspondence Analysis: Detrended correspondence analysis is an extension of correspondence analysis (CA) aimed at addressing a deficiency of correspondence analysis . The problem is known as the "arch effect" - a non-monotonic relationship between two sets of scores derived by CA. The basic idea is to split the first...
View Full DescriptionDiscriminant Analysis: Discriminant analysis is a method of distinguishing between classes of objects. The objects are typically represented as rows in a matrix. The values of various attributes (variables) of an object are measured (the matrix columns) and a linear classification function is developed that maximizes the ratio of between-class...
View Full DescriptionDiscriminant Factor Analysis: See Multiple discriminant analysis . Browse Other Glossary Entries
View Full DescriptionDiscriminant Function: In discriminant analysis , a discriminant function (DF) maps independent (discriminating) variables into a latent variable D. DF is usually postulated to be a linear function: D = a0 + a1 x1 + a2 x2 ... aN xN The goal of discriminant analysis is to find such...
View Full DescriptionDiscriminant Function Analysis: Discriminant function analysis is a synonym for Discriminant Analysis . Browse Other Glossary Entries
View Full DescriptionDissimilarity Matrix: The dissimilarity matrix (also called distance matrix) describes pairwise distinction between M objects. It is a square symmetrical MxM matrix with the (ij)th element equal to the value of a chosen measure of distinction between the (i)th and the (j)th object. The diagonal elements are either not considered...
View Full DescriptionDistance Matrix: Distance matrix is often used as a synonym for dissimilarity matrix . The "distance" does not necessarily means distance in space. It is a common situation when the "distance" is a subjective measure of dissimilarity. The only property the concept of "distance" implies is that its value is...
View Full DescriptionDual Scaling: Dual scaling is a synonym for Correspondence analysis . Browse Other Glossary Entries
View Full DescriptionFactor Analysis: Exploratory research on a topic may identify many variables of possible interest, so many that their sheer number can become a hindrance to effective and efficient analysis. Factor analysis is a "data reduction" technique that reduce the number of variables studied to a more limited number of underlying...
View Full DescriptionFactorial ANOVA: Factorial ANOVA (factorial analysis of variance ) is aimed at assessing the relative importance of various combinations of independent variables. Factorial ANOVA is used when there are at least two independent variables. Browse Other Glossary Entries
View Full DescriptionLatent Class Analysis (LCA): Latent class analysis is concerned with deriving information about categorical latent variable s from observed values of categorical manifest variable s. In other words, LCA deals with fitting latent class models - a subclass of the latent variable models - to the observed data. LCA is...
View Full DescriptionLatent Class Cluster Analysis: The latent class cluster analysis is a branch of the latent class analysis where the latent variable is considered as a single categorical variable taking on t possible values, corresponding to t classes. Browse Other Glossary Entries
View Full DescriptionLatent Class Factor Analysis: The latent class factor analysis is a branch of the latent class analysis where the latent variable is a vector of several categorical variables, usually dichotomous variables. Browse Other Glossary Entries
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