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How to... choose the right statistical technique

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Advanced techniques

These tools and techniques have specialist applications, and will generally be designed into the research methodology at an early stage, before any data are collected. If you are considering using any of these, you may wish to consult a specialist text or an experienced statistician before you start.

In each case, we give some examples of Emerald articles which use the technique. 

Factor analysis

To reduce the number of variables for subsequent analysis by creating combinations of the original variables measured which account for as much of the original variance as possible, but allow for easier interpretation of the results. Commonly used to create a small set of dimension ratings from a large number of opinion statements individually rated on Likert scales. You must have more observations (subjects) than you have variables to be analysed.

For example

A Likert scale variable: "I like to eat chocolate ice cream for breakfast"

Strongly agree 

1

2

3

4

5

  Strongly disagree


Rob Dennis and Bruce E. Winston
Leadership & Organization Development Journal , vol. 24 no. 8


Yean Pin Lee, Suhaiza Zailani and Keng Lin Soh
Benchmarking: An International Journal , vol. 13 no. 5

Cluster analysis

To classify subjects into groups with similar characteristics, according to the values of the variables measured. You must have more observations than you have variables included in the analysis.

 
C. Fotopoulos and A. Krystallis
British Food Journal, vol. 104 no. 3/4/5


S. Gamesalingam and Kuldeep Kumar
Managerial Finance, vol. 27 no. 4

Discriminant analysis

To identify those variables which best discriminate between known groups of subjects. The results may be used to allocate new subjects to the known groups based on their values of the discriminating variables


S. Gamesalingam and Kuldeep Kumar
Managerial Finance, vol. 27 no. 4


Yean Pin Lee, Suhaiza Zailani and Keng Lin Soh
Benchmarking: An International Journal , vol. 13 no. 5

Methodology

Discriminant analysis was used to determine whether statistically significant differences exist between the average score profile on a set of variables for two a priori defined groups and so enabled them to be classified. Besides, it could help to determine which of the independent variables account the most for the differences in the average score profiles of the two groups. In this study, discriminant analysis was the main instrument to classify the benchmarking adopter and non-adopter. It was also utilized to determine which of the independent variables would contribute to benchmarking adoption.

Regression

To model how one, dependant, variable behaves depending on the values of a set of other, independent, variables. The dependant variable must be interval or ratio in type; the independent variables may be of any type, but special methods must be used when including categorical or ordinal independent variables in the analysis.


Jeremy Franks
British Food Journal, vol. 103 no. 9


Mohammed Al Madhoun
Journal of European Industrial Training, vol. 30 no. 2

Time series analysis

To investigate the patterns and trends in a variable measured regularly over a period of time. May also be used to identify and adjust for seasonal variation, for example in financial statistics.

 
Ming-Chi Chen, Yuichiro Kawaguchi and Kanak Patel
Journal of Property Investment & Finance, vol. 22 no. 1




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