What is FACTOR Analysis?
In factor analysis, you are taking two or more variables (ordinal or scale variables) and generating a smaller number of variables that capture as much as possible of their variance. That's why it's called a data reduction technique: You may be reducing four or seven to one or two. As we have seen in the earlier classes that cluster analysis is generally used to classify a group of respondents whereas Factor analysis is a data reduction technique. Too much of data are at times difficult to interpret and as a result, factor analysis (FA) is used to reduce them. E.g. one may have 20 statements related to attitude of the consumers but it is difficult to derive sense from 20 variables. FA can be used to reduce these 20 variables to a small number of factors which would explain most of the variance in the data. FA works on Likert scale (interval). If you are dealing with 4-6 variables you need not use factor analysis - cluster can be used directly. If the number of variables is high, it is better to use factor and then cluster.
Types of Factor Analysis
· Exploratory factor Analysis
l Summarizing data by grouping correlated variables
l Investigating sets of measured variables related to theoretical constructs
l Usually done near the onset of research
· Confirmatory FA
l More advanced technique
l When factor structure is known or at least theorized
l Testing generalization of factor structure to new data, etc.
What is Conjoint Analysis?
Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.Although it has only been a mainstream research technique for the last 10 years or so, conjoint analysis has been found to be an extremely powerful of way of capturing what really drives customers to buy one product over another and what customers really value.A key benefit of conjoint analysis is the ability to produce dynamic market models that enable companies to test out what steps they would need to take to improve their market share, or how competitors’ behaviour will affect their customers.
In the 1960s and 70s, academics were looking to understand how people made decisions. If you just asked people, they tended to say what was top-of-mind, or what they thought the interviewers wanted to hear and so what people said didn’t necessarily reflect what they actually did.However, the academics noticed that almost all choices involve compromises and trade-offs as the ideal is rarely attainable (we might want a Rolex watch, but we typically have to compromise to something a little less expensive for example).In their studies, the academics found that by looking at how people made selections between a limited number of products involving different trade-offs, they were able to accurately predict which choices would be made between previously untested products.
Describing products in attributes and levels
To understand how conjoint analysis works, we need to be able to describe the products or services consistently in terms of attributes and levels in order to see what is being traded off.
· An attribute is a general feature of a product or service – say size, colour, speed, delivery time. Each attribute is then made up of specific levels. So for the attribute colour, levels might be red, green, blue and so on.
For example, we might describe a mobile telephone in general terms using the attributes, weight, battery life and price. A specific mobile phone would be described just by levels say as 80 grams, 8 hour battery costing £150.
Although this broadly describes conjoint analysis, fully understanding the impact only comes from seeing and using conjoint analysis in practice. For instance seeing dynamic market modelling in action or seeing how a conjoint analysis interview works. The dobney.com website includes an interactive conjoint demonstration showing how customers’ value can be captured.
Conjoint analysis is a sophisticated technique and there are technical issues that need to be considered. In particular, the design of attributes is a crucial step in a conjoint project as choices between poorly defined levels can render the exercise meaningless. You should also be aware that there are different flavours of conjoint analysis depending on the application. Adaptive Conjoint Analysis (ACA) is the most common, but there is also Choice-based and Full-profile Conjoint Analysis.
Author : Shahab Khan
Finance Group 5