Statistics come in many forms. We want to ensure you get the very best out of your data, and understand what it is saying.
This is not a full list, but gives you some idea of the range of statistics we deal with, from the relatively simple to the complex.
Type |
Method / Technique |
Usage |
---|---|---|
S |
Analysis of variance (ANOVA, MANOVA and MANCOVA) |
General techniques that test whether means between groups are equal or not |
MR |
BPTO (brand price trade off) |
Respondents select preference for different brands at different prices |
S |
Canonical correlation |
An extension of multiple regression, where there are multiple dependent variables as well as multiple independent variables |
S |
CHAID |
A decision tree model with multiple splits |
S |
Classification trees (decision trees) |
An analysis that predicts group membership |
S |
Cluster analysis |
Allocates records (people, businesses, etc) into mutually exclusive groups, where the members of a cluster are more like other members of the same cluster than members of any other cluster |
MR |
Conjoint analysis |
Measures the relative importance of attributes so that the most attractive offers are identified for target groups |
S |
Correlation |
Level of association between two measures |
S |
Correspondence mapping |
A method of dimension reduction and perceptual mapping |
S |
Discriminant analysis |
Predicting the likelihood that an individual belongs to a series of groups or segments |
S |
Factor analysis |
Reduces many variables to fewer factors which represent the underlying dimensions of the data |
MR |
Gabor Grainger |
Respondents say how likely they are to buy a product at a series of prices |
MR |
Key driver analysis (KDA) |
Identifies what measures have the most impact on a dependent variable, such as customer satisfaction, employee satisfaction, likelihood of choice |
S |
Locational analysis |
Systematic method using evaluation of distances or cost-distance-time calculations for site planning and market analysis |
S |
Logistic regression analysis |
Predicts the probability of group membership |
S |
Logit analysis |
Produces linear probability models, usually used if predicting one of two alternatives |
S |
Multidimensional scaling (MDS) |
Restructures respondent comparisons of similarity or preference into distances plotted in a multidimensional space |
S |
Multiple regression analysis |
Understanding the relationship between multiple variables and how these influence a dependent variable, such as likelihood to buy, or customer satisfaction, or employee satisfaction |
S |
Perceptual maps |
The position of brands relative to their competitors, and the inter-relationship of brand attributes |
MR |
Price sensitivity meter (Van Westendorp) |
Uses set of four questions to establish cheap and too cheap price, expensive and too expensive price for each respondent |
S |
Principal components analysis |
A form of factor analysis that uses correlation to identify super-variables |
MR |
Quadrant charts |
Divides results into four, identifying what is important and needs maintaining, what is important and needs improving, what is less important and needs reduced investment, and what is not important and requires no change |
MR |
Social network analysis |
Understanding relationships in terms of volumes, direction, and spread. For example, transactions between companies, buying and selling behaviour, communications between group members. |
S |
Structural equation modelling / path analysis |
Maps the relationships between variables using factor analysis, canonical correlation and multiple regression to evaluate predictive power |
S |
Time series analysis |
Used both for forecasting and influences over time to analysis a series of events / measures |
MR |
TURF (Total Unduplicated Reach and Frequency Analysis) |
Used to provide estimates of market potential and identifying the optimal mix of products / flavours / options |
Looking for something that isn't listed? Get in touch and see if we can help - the list is not exhaustive!
ANOVA | ||||||
Model | Sum of Squares | df | Mean Square | F | Sig. | |
1 | Regression | 36.197 | 9 | 4.022 | 8.844 | .000 |
Residual | 55.024 | 121 | .455 | |||
Total | 91.221 | 130 |