Multidimensional scaling (MDS) is a set of related statistical techniques often used in data visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination.
There are several steps in conducting Multidimensional (MDS) scaling research:
1. Formulating the problem - What brands do you want to compare? How many brands do you want to compare? What purpose is the study to be used for?
2. Obtaining Input Data - Respondents are asked a series of questions. For each product pair they are asked to rate similarity. There are two other approaches such as Perception data: direct approach. There is the “Perception data: derived approach” in which products are decomposed into attributes which are rated on a semantic differential scale. The other is the “Preference data approach” in which respondents are asked their preference rather than similarity.
3. Running the MDS statistical program - Software for running the procedure is available in most of the better statistical applications programs. Often there is a choice between Metric MDS (which deals with interval or ratio level data), and Non metric MDS (which deals with ordinal data). The researchers must decide on the number of dimensions they want the computer to create. The more dimensions, the better the statistical fit.
4. Mapping the results and defining the dimensions - the more difficult it is to interpret the results. The statistical program (or a related module) will map the results. The map will plot each product (usually in two dimensional spaces). The proximity of products to each other indicates either how similar they are or how preferred they are, depending on which approach was used. The dimensions must be labeled by the researcher. This requires subjective judgment and is often very challenging.
5. Test the results for reliability and Validity – The results found out should be tested properly for the reliability and validity.
There are several steps in conducting Multidimensional (MDS) scaling research:
1. Formulating the problem - What brands do you want to compare? How many brands do you want to compare? What purpose is the study to be used for?
2. Obtaining Input Data - Respondents are asked a series of questions. For each product pair they are asked to rate similarity. There are two other approaches such as Perception data: direct approach. There is the “Perception data: derived approach” in which products are decomposed into attributes which are rated on a semantic differential scale. The other is the “Preference data approach” in which respondents are asked their preference rather than similarity.
3. Running the MDS statistical program - Software for running the procedure is available in most of the better statistical applications programs. Often there is a choice between Metric MDS (which deals with interval or ratio level data), and Non metric MDS (which deals with ordinal data). The researchers must decide on the number of dimensions they want the computer to create. The more dimensions, the better the statistical fit.
4. Mapping the results and defining the dimensions - the more difficult it is to interpret the results. The statistical program (or a related module) will map the results. The map will plot each product (usually in two dimensional spaces). The proximity of products to each other indicates either how similar they are or how preferred they are, depending on which approach was used. The dimensions must be labeled by the researcher. This requires subjective judgment and is often very challenging.
5. Test the results for reliability and Validity – The results found out should be tested properly for the reliability and validity.
No comments:
Post a Comment