What are "interdependence techniques" in the analysis of data? What type of measurement is possible with this technique, and what are different methods that are used in marketing research?

What will be an ideal response?

Interdependence techniques show the interaction between variables and are comprised of factor analysis, cluster analysis, and multidimensional scaling (MDS). Factor analysis can be used to transform large amounts of data into manageable units. Specialized statistical programs can reduce or group the data by distilling out certain meaningful factors. These factors are selected by the program on the basis of attitudes and perceptions that can be closely aligned from a multitude of survey responses.

Factor analysis is useful in psychographic segmentation studies. It can also be used to create perceptual maps.

Cluster analysis allows the researcher to group variables into clusters that maximize within-group similarities and between-group differences. Cluster analysis shares some characteristics of factor analysis in that it does not classify variables as dependent or independent. It can also be used in psychographic segmentation. It is well suited to global marketing research because similarities and differences can be established between local, national, and regional markets of the world. Cluster analysis can also be used to perform benefit segmentation and identify new product opportunities.
MDS is another technique for creating perceptual maps. When the researcher is using MDS, the respondent is given the task of comparing products or brands, one pair at a time, and judging them in terms of similarity. The researcher then infers the dimensions that underlie the judgments. MDS is particularly useful when there are many alternatives from which to choose and when consumers may have difficulty verbalizing their perceptions. To create a well-defined spatial map, a minimum of eight products or brands should be used.