Analysis

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Contents 1 Online Data Analysis Services 1.1 Interpreting Data and Statistics 1.2 Advanced Analysis: Multivariate Data Analysis 1.3 What is Multivariate Data Analysis?

[edit] Online Data Analysis Services

Need help with online data analysis? We provide you with easy to use online data analysis in real-time. You will have access to your data from anywhere in the world as long as you have access to the internet. And you can easily publish the results via email or to a website. We make online data analysis easy.

As the following table shows, our experts are able to perform the type of analysis that your project demands.

A GUIDE TO THE SELECTION OF STATISTICAL TOOLS
	Class of Statistics
Type of Data	Non-Parametric (Nominal - Ordinal)	Parametric (Interval Ratio)
One Variable In The Analysis
1. Discrete	a. Percentage	(Impossible)
2. Continuous	a. Median,Mode b. Quartile range	a. Mean b. Std. Deviation
Two Variables In The Analysis
1. Both Discrete	a. Chi Square b. Phi Coefficient (2 rows x 2 cols.) c. Contingency Coefficient d. Tetrachoric Corr. e. Yule's Q (2 x 2) f. Lambda	a. Conjoint Analysis (2 Attribute Tradeoff) b. Correspondence Analysis (2 rows x 2 cols.)
2. One Discrete and Other Continuous		a. Student t (if dependent variable is dichotomous) b. One-Way ANOVA
3. Both Continuous	a. Spearman Rank Correlation b. Kendall's R c. Gamma	a. Pearson Correlation b. Eta Curvilinear c. Simple Regression
Three Variables In The Analysis
1. Three Discrete	a. Cross Tabulation	a. Conjoint Analysis b.Multi-Dimensional scaling c. Correspondence Analysis (Multiple)
2. Two Discrete One continuous		a. ANOVA (2 Way)
3. One Discrete Two Continuous	a. Comparison of proportions	a. Analysis of Covariance b. Comparison of two Correlations (if dependent variable multichotomy) c. Multiple Discriminant Analysis
4. Three Continuous	a. Kendall's W	a. First-Order Partial Correlation b. Multiple Regression c. Multiple Correlation
Four or More Variables In The Analysis
1. K Discrete	a. Cross Tabulation b. Non-Metric MDS (Multi-Dimensional Scaling) c. Chaid	a. Conjoint Analysis b. Quasi Metric MDS
2. One Discrete, K Continuous		a. Probit Analysis (For normal probability distribution) b. Logit Analysis (For Logistic Probability Distribution) c. Multiple Discriminant Analysis
3. K Continuous Variables		a. Second-OrderPartial Correlation b. Multiple Regression c. Multiple Correlation d. Factor Analysis e. Cannonical Correlation f. Fully Metric Multi-Dimensional Scaling
4. One Continuous, K Discrete		a. CART b. Conjoint c. Multiple Regression (Dummy)

If you need more advanced data analysis, the data export feature creates an Excel spreadsheet or an SPSS data file. From there you can do what you need to with the data.

[edit] Interpreting Data and Statistics

We can help you interpret your data and make recommendations as to what decisions will bring you closest to your objectives. We can also put this in report or PowerPoint form.

[edit] Advanced Analysis: Multivariate Data Analysis

Do you need a Multivariate Analysis done? Let our professionals do it for you.

[edit] What is Multivariate Data Analysis?

Multivariate data analysis is the analysis of multiple variables at the same time. This type of analysis is used to find how a set of variables explain one or more other variables. For example, sets of variables may explain one overall variable (brand loyalty) or may differentiate between key market segments. Similarly, a set of brand attributes may be used to map relationships to the key brands competing in the marketplace, thereby showing the strengths and weaknesses of each brand.

Our research staff consists of Ph.D.s who are experts in conducting surveys and analyzing results using any number of techniques, including

• Conjoint Analysis (Full Profile Conjoint; Self Explicated Conjoint; Choice Based Conjoint Analysis)
• Monte Carlo Simulations
• Multidimensional Scaling Analysis
• Correspondence Analysis
• Cluster Analysis
• Factor Analysis
• Data Mining with CHAID, Classification and Regression Trees, and Neural Network Analysis
• Multiple Regression
• Discriminant Analysis
• Forecasting Analysis

Typical applications

• Quality optimization (food, beverages, consumer products, electronics, paints, cosmetics, pharmaceuticals, insurance, banking, and finance).
• Optimization of brand attributes in the design of new products.
• Multi-item Scale Development.
• Optimization of scale measures and methods.
• Classification of respondent and market segments.
• Development of new advertising and promotional materials.

Our professional researchers are also available to complete your data analysis for you. They will discuss your analysis options with you and help you to understand the types of decisions that can be made from the data you want to collect. The results will be quickly made available to you online and in a printable electronic format. Contact one of our research consultants for more information.