Survey Analysis

Raw data from a completed survey can be useful, but further analysis is often needed to get the most out of survey results. The specific components in an analysis toolkit will depend on the project, but there are a few commonly used tools and methods to keep in mind.

About the links in “Survey Analysis”

  • Importance of Crosstabs

    A crosstab is a frequency table that shows data for more than one variable.

  • Cluster Analysis

    Cluster analysis is a data reduction method that focuses on respondents as opposed to variables. It reduces a large pool of respondents into groups (or clusters) based on common themes. The members of each cluster group are more similar within a group than they are between groups.

  • Conjoint Analysis

    Conjoint Analysis is a type of analysis based on a Conjoint module in a quantitative survey. The Conjoint module asks respondents assess their interest in a wide range of variations on a particular product or service offering.

  • Factor Analysis

    In situations where many variables are at play, factor analysis identifies the key underlying factors or constructs within a set of data. A factor analysis may start with twenty known (or manifest) variables from a questionnaire and reduce them to, for instance, five constructs, or latent factors.

  • Kano Analysis

    Kano analysis measures customer perceptions of product or service features, and classifies them based on satisfaction with the product according to the presence or absence of a feature.

  • MaxDiff

    MaxDiff is a form of analysis where survey respondents are shown a subset of possible items, often three to five at a time, and asked to indicate the two elements of the subset shown that are the most and least important to them. This forces respondents to make choices between options, in contrast to standard rating scales.

  • Multiple Analysis

    Multiple regression is an extension of simple regression that is used to predict the value of an unknown variable based on the value of two or more known variables.

  • Regression Analysis

    Regression analysis is a technique used to understand the relationship between two variables and can reveal how closely two factors are related, as well as how certain one can be about the resulting predictions.

  • Weighting

    Weighting is a data processing technique that allows researchers to change the value of a particular respondent’s answers relative to the rest of the responses in a dataset. This allows researchers to correct for sampling imbalances by adjusting the data to be more representative of their desired target population.