We are able to offer you unmatched Internet survey experience with the most advanced data collection methods in the industry.

Our experience in the healthcare industry combined with our advanced technology has helped us succeed when confronted with difficult research situations.

Our fast, focused in-depth analysis

Statistical methods for handling of respondent data, frequencies, and cross tabulations.

Statistical significance (t and z-tests and confidence intervals)

Uni - variate and Multui - variate analysis including but not limited to regression,discriminant,analysis of variance (ANOVA), factor, cluster, multidimensional scaling (MDS) and latent class analysis

Correlation analysis, expressed by correlation coefficients, measures the degree of linear relationship between two variables.
The emphasis is on predicting one variable from the other and is typically used for linear (i.e., straight line) relationships.
If the two variables of interest have a non-linear relationship such as a parabolic relationship, the correlation coefficient (r) will fail to detect a relationship when one is actually present.

Regression analysis measures the strength of a relationship between a variable such as overall customer satisfaction & one or more explaining variables such as product quality and price.
Correlation results in a predictive equation with a correlation coefficient.If the relationship is strong (expressed by the Rsquare value).
It can be used to predict values of one variable given other variables have known values. How will the overall product attributes score change if side effects are decreased & longer duration of therapy.

Factor analysis aims to describe a large number of variables or questions by only using a reduced set of underlying variables called factors.
It explains a pattern of similarity between observed variables by grouping them.
 

Cluster analysis reveals natural groupings within a large group of observations. Cluster analysis segments the survey sample, i.e. respondents or companies, into a small number of groups.
Respondents whose answers are very similar should fall into the same clusters while respondents with very different answers should be in a different cluster. Ideally, the cases in each group should have very similar profile towards specific characteristics( e.g. Attitudin- al or behavioral questions),While the profiles of respondents belonging to different clusters should very dissimilar.

Correspondence analysis is a technique which allows rows and columns of a data matrix. (e.g. average satisfaction scores for several products)to be displayed as points in a two-dimensional space or map.
Brand maps are often used to illustrate customer's images of the market by placing prod- ucts & attributes together on a map. This allows close interpretation of company percepti- ons with a variety of product and service attributes simultaneously.
Brands are most strongly associated with the attributes that are closest to them on the map. If products are placed close to each other, it means they have a similar image or profile in the market. The relative association of brands with an attribute can be determined by drawing a perpendicular line from the attribute vector line (=line from the origin to the attribute point) to each of the brands. The distance between the brand and the attribute is the distance between the attribute location and where the perpendicular line crosses the attribute vector line.
The center of the map (the cross on the map), represents the overall mean of each attribute, and is the center around which the brands are dispersed. The more a brand tends to lie in a similar direction away from the centre as an attribute, the more a brand is associated with that attribute. This also means that brands and attributes near the centre of the maps are not differentiating. The length of an attribute vector represents the extent to which the brands differ on that attribute.

CHAID (Chi Squared Automatic Interaction Detection) is used to build a predictive model, based on a classification system that segments groups of respondents that share similar towards a specific response variable and maximizes the ability to predict the values of the response variable.
The first predictor category (on which the sample will be split) is the predictor that is associated the most with the response variable. i.e. it gives the most differentiating groups of respondents. Each group is then further split until the analysis does not find any significantly discriminating predictor any more.
The predictors can be scaled (e.g. 1 to 10 scale rating) as well as categorical questions (e.g. products in pipeline).
The output is a tree of which the branches are the predictor variables that split the sample in discriminating groups.

Discriminant and logistic regression analysis are statistical techniques that point out the differences between two or more groups based on several characteristics (most often rating scales when discriminant analysis, while logistic regression can handle any type of variable).

	To determine which customers are likely to prescribe or use a company's product
	To decide whether a company should pursue a new indication or line extension for a product.
	To identify patients who can be classified as of high risk due to others
	conditions or medical problems

Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis and detect observed similarities or dissimilarities.
This outcome can be visualized in a 2 dimensional map, which gives the researcher an immediate feel of how differentiating the questions were. Questions which are clustered together did get very similar scores by all respondents.