The use of multivariate data can enable you to analyze multiple, complex sets of data while examining the relationships between several variables simultaneously. For PhD scholars who are unaware of how multivariate data output is correlated and interpreted, it is best to seek professional help to ensure that their analytical knowledge or research efforts are not found lacking.
Our expert team of statisticians and data analysts use proven multivariate techniques including cluster analysis, discriminant analysis, factor analysis, regression analysis, correspondence analysis and structure equation modelling to help you obtain and analyze complicated, variable data sets with ease and efficiency. Ultimately, we can help you analyze and present your research data accurately and efficiently.
Request a QuoteWe employ various methods of classifying separate groups of related cases with data being compiled by representing similar cases in the dataset.
We make use of factor analysis to explain the variability between examined sets of dependent and independent variables.
We utilize SEM as a statistical technique to test and estimate relations between variable data sets using a combination of qualitative assumptions.
Multivariate data analysis, which involves the simultaneous observation and analysis of more than two outcome variables, can be a very complex procedure that requires a considerable amount of time and effort. While it does prove to be very beneficial for PhD scholars and their research work, the inherent complexity and involved time commitment can become a huge deterrent for students who want to pursue the path of multivariate analysis, or MVA, for their research work.
As part of our expansive multivariate data analysis services, here is what our team can do for you.
As part of our MVA services, we perform a Principal Components Analysis (PCS) to obtain a summary of a table, and identify dominant patterns in the data.
With Discriminant Analysis, our expert team helps you analyze groups in a table, learn how these groups differ, and what is the significance of it for your research.
We help you find and define relationships between columns in a data table, and demonstrate how to use one set of variables to predict another set.
We use many techniques to model and investigate several variables when the focus is on a dependent variable’s relationship with independent ones.
We use the CA technique to enable the visual discovery and interpretation of interaction between the modalities of two categorical variables.
For certain academic projects that call for it, such as ones related to marketing research, we also offer Chi-square Automatic Interaction Detector (CHAID) analysis.
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