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Current loctn: about > methods > data analysis

Data Analysis

Initial stages of data analysis generally make heavy use of graphical data displays. Histograms are generally not used for display of distribution of values on an ordinal or nominal scale due to well-known problems with obscuring of data features when inappropriate category limits are chosen. Techniques employing empirical data distribution graphs are employed instead. 

Use of robust and non-parametric techniques is emphasized.  If a parametric analysis employing standard techniques such as least squares regression is employed, an alternative analysis employing a robust technique will generally be employed to verify the parametric results. 

Assumptions underlying applied statistical tests are always tested for violations. 

Statistical analyses is generally performed in either R or SPSS statistical software. Work can be completed using SAS if the client provides an appropriate SAS usage license. Microsoft Access is frequently employed for implementation of data transformations that are most efficiently performed using structured query language (SQL). Perl, Lisp, Fortran, Pascal, VisualBasic, MySQL, PostGreSQL may also be used if appropriate.

Documents providing short summaries of all major programs developed to implement an analysis are maintained. 

All programs are maintained in formal revision control systems. 

Documents summarizing all analysis interpretations are maintained throughout analysis.  More concise reports are generally provided at the end of the analysis or at important milestones.  These reports are targeted to assist the client in understanding the key results of the analysis.  

All documents and program code used during the analysis are provided to the client at completion of the project to facilitate reuse or reexamination of the work.