A comparison between multivariate and bivariate analysis used in marketing research

• Author: Constantin, C.
• Position: Dept. of Economic Sciences and Business Administration, Transilvania University of Brasov
• Pages: 119-126

Bulletin of the Transilvania University of Braşov • Vol. 5 (54) •No. 1 - 2012

Series V: Economic Sciences

A COMPARISON BETWEEN

MULTIVARIATE AND BIVARIATE

ANALYSIS USED IN

MARKETING RESEARCH

Cristinel CONSTANTIN1

Abstract: This paper is about an instrumental research conducted in order to

compare the information given by two multivariate data analysis in comparison

with the usual bivariate analysis. The outcomes of the research reveal that

sometimes the multivariate methods use more information from a certain

variable, but sometimes they use only a part of the information considered the

most important for certain associations. For this reason, a researcher should

use both categories of data analysis in order to obtain entirely useful

information.

Key words: multivariate analysis, Discriminant analysis, Homogeneity analysis.

1 Dept. of Economic Sciences and Business Administration, Transilvania University of Braşov.

1. Introduction

The quality and the quantity of the

information obtained from marketing

research are very important for the

decision makers of a company. For this

reason, the data collected are analysed with

various methods meant to obtain the

information needed. But sometimes the

researchers prefer to use certain methods

according to their knowledge or try to

overrate the importance of multivariate

methods. Our research aim is to compare

some multivariate and bivariate methods

starting from the hypothesis that these

methods should be complementary in data

analysis.

2. Literature review

Multivariate analysis is considered as all

statistical methods that simultaneously

analyse multiple measurements on each

individual or object under investigation. It

deals with multiple combinations of

variables, which are put into practice by

using various multivariable methods [1].

These variables may be correlated with

each other, and their statistical dependence

is often taken into account when analysing

such data. Response variables are often

described as random variables, being often

described by their joint probability

distribution [2].

The impressive development of

information technology allowed the

scientists’ access to multivariate analysis,

which needs a large amount of data

processing and very complex algorithms.

But using multivariate analysis has become

a strong need for decision makers taking

into consideration the complexity of

markets and consumer behaviours. Some

authors consider that any problem that is

not analysed on a multivariate basis is

treated superficially and the multivariate