Data processing

Nova Insights provides data processing services to support other market research firms.  With our strong background in research, we are able to be consultative in the services we offer.

Our data processing services include:

Sample Excel Output

Sample Excel Output

  • Cross tabulations (bivariate) in an easy-to-read MS Word® or Excel® format showing statistically significant differences at two confidence levels of your choice (e.g., 90%, 95%).  Versions can also be created in Excel that make creation of charts and graphs very easy.
  • Weighting of the data to better reflect known distribution proportions within the population being sampled.
  • Creation of relevant indices helpful for a greater depth of understanding in trending groups of variables.
  • Correlation analysis: A statistical technique that measures the degree of relationship between variables.  Correlation coefficients can range from -1.00 to +1.00.  The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation.  If a correlation is 0, this means there is no relationship.
  • Factor analysis: A technique used to determine the underlying themes of a set of variables. It reduces a larger number of variables to a smaller number of factors that can be used to explain the principle themes of the data.
iStock_000012914706_ExtraSmall.jpg
  • Cluster analysis: As factor analysis creates themes among a set of variables, cluster analysis creates a set of thematic segments among respondents representing the population under analysis.

CHAID.gif
  • CHAID: Chi-squared Automatic Interaction Detector is a type of decision tree based on adjusted significance testing that identifies interaction between variables in a data set.  This technique allows us to identify relationships between a ‘dependent variable’ and other explanatory (attitudinal) variables.  CHAID does this by identifying discrete groups of respondents and, by taking their responses to explanatory variables, seeking to predict what the impact will be on the dependent variable.