# ::Free Statistics and Forecasting Software::

v1.1.23-r7
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### :: Survey Scores - Free Statistics Software (Calculator) ::

All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.

This free online software (calculator) computes the following numerical scores for a matrix containing results of a survey:
 Ps = the sum of all strictly positive scores Ns = the sum of all strictly negative scores the arithmetic average of all scores (assuming a quasi interval scale) the ratio (Ps-Ns)/(Ps+Ns) Pc = the count of all strictly positive scores Nc = the count of all strictly negative scores the ratio (Pc-Nc)/(Pc+Nc)

You must use the same Likert scale for every question. The software computes positive and negative scores by first subtracting the median of the Likert scale from the raw scores. This ensures that the computed ratios (Ps-Ns)/(Ps+Ns) and (Pc-Nc)/(Pc+Nc) are always contained in the interval [-1,1].
The data matrix must contain subjects in rows and variables (questions) in columns. Missing data should be coded as NA.

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

 Send output to: Browser Blue - Charts White Browser Black/White CSV MS Excel MS Word Data X (click to load default data) 4 4 4 4 5 4 4 4 3 5 2 4 4 4 2 2 4 4 4 1 4 4 4 4 4 3 4 5 2 4 1 5 4 3 3 4 5 4 2 3 2 4 4 4 3 3 4 3 3 2 3 4 4 4 3 3 4 4 2 2 4 4 4 3 4 2 2 3 2 4 3 4 5 4 4 2 3 2 4 2 4 4 2 3 4 4 4 4 2 3 1 4 4 3 2 3 3 4 4 1 3 3 3 3 2 3 3 2 1 3 3 4 3 2 1 4 3 3 3 2 2 4 4 5 5 5 5 5 4 5 2 4 5 4 5 1 4 4 4 2 5 4 5 4 5 3 5 3 4 4 5 4 5 5 4 2 3 3 4 3 4 4 4 2 4 4 4 5 3 4 3 5 5 4 4 2 3 4 4 3 3 4 4 4 4 4 2 3 3 3 2 4 5 2 3 2 3 3 4 2 4 3 3 4 5 3 4 4 3 4 1 5 3 3 3 2 3 3 4 3 3 3 4 4 3 2 4 4 2 5 4 4 5 4 4 4 4 4 4 3 3 5 4 4 5 5 3 5 2 4 3 4 4 4 3 2 2 3 2 2 3 4 4 4 4 4 3 4 3 4 4 4 2 2 3 3 4 3 4 3 4 3 3 3 2 4 4 2 4 3 3 3 3 3 4 3 2 3 3 4 4 4 2 2 2 5 4 5 4 5 3 4 4 3 4 1 3 4 3 2 3 5 4 4 3 4 2 3 4 3 3 3 4 4 4 3 4 3 3 4 2 4 2 4 3 2 2 4 2 4 1 3 5 2 2 1 1 1 3 1 3 5 4 4 4 3 5 4 3 4 5 4 4 5 2 2 4 5 2 2 3 4 2 3 4 5 4 4 3 3 4 5 2 4 4 3 5 4 4 2 3 4 2 4 4 3 3 4 3 4 2 3 4 4 2 2 4 2 3 3 3 4 4 2 4 3 3 5 3 3 2 4 1 5 3 2 4 3 2 4 2 2 2 4 2 2 3 2 3 2 4 2 4 2 4 4 2 3 2 2 4 3 4 4 3 4 4 4 4 4 3 4 4 3 4 3 3 3 3 3 5 5 4 4 4 4 4 5 3 4 2 4 2 3 2 1 2 2 2 1 4 4 4 3 4 4 4 4 3 4 2 4 5 3 2 2 4 2 4 3 4 4 2 2 2 2 2 2 4 2 4 2 2 3 2 4 2 2 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2 5 4 3 3 4 2 4 3 4 5 4 5 4 4 2 4 5 2 1 4 4 3 3 4 4 4 4 3 4 2 3 4 3 4 2 4 4 4 2 4 4 4 3 2 4 5 5 4 4 5 1 4 3 3 2 5 3 3 1 4 4 5 2 2 4 3 4 4 4 4 3 4 3 4 3 4 5 3 3 4 5 4 3 4 4 4 4 3 4 4 4 5 3 4 2 4 3 4 2 2 4 2 3 3 4 2 4 3 2 4 3 4 3 2 2 4 4 3 3 2 3 2 3 4 4 4 4 3 4 2 3 4 5 2 3 3 4 3 2 4 2 4 4 3 4 4 4 3 4 3 4 4 3 4 1 2 3 4 2 4 2 4 4 3 4 2 4 1 4 3 4 2 3 4 1 5 4 5 5 3 3 4 3 3 4 3 4 3 4 3 4 4 4 3 2 4 4 4 4 4 2 3 3 5 4 2 4 3 3 2 5 5 5 4 2 4 4 4 4 4 4 4 3 4 4 4 3 2 4 3 4 4 4 3 3 5 4 3 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 4 5 4 5 2 4 4 4 4 2 4 5 2 5 3 4 4 2 3 3 4 4 4 3 4 3 4 3 4 4 4 5 4 3 2 4 4 3 3 5 5 5 2 4 3 3 4 2 2 2 4 5 3 4 3 3 4 4 3 3 4 3 4 4 3 4 4 3 4 2 4 5 3 3 3 3 4 4 3 3 4 4 3 4 4 3 4 4 4 2 4 4 3 3 2 3 3 3 2 2 3 3 3 2 4 2 3 2 3 3 2 4 2 3 4 2 1 4 2 5 5 4 4 4 4 4 5 3 5 5 3 4 4 4 1 3 2 3 4 3 4 3 4 4 3 4 3 2 2 4 4 5 4 3 3 3 4 4 2 3 4 3 3 5 4 3 4 3 4 2 3 5 4 3 2 4 4 4 3 4 3 3 4 5 4 4 4 3 4 2 3 4 4 5 2 3 4 4 2 3 4 5 5 5 4 5 5 3 5 4 4 5 5 3 3 3 4 3 3 3 4 3 3 4 3 4 3 3 4 3 3 5 3 3 2 3 2 3 2 2 4 3 2 5 1 3 4 2 5 1 5 4 2 3 2 4 4 4 2 3 3 1 3 3 4 2 4 2 3 2 5 5 3 4 5 4 4 5 4 4 4 4 4 4 4 4 4 3 4 2 4 2 4 2 3 5 4 3 4 2 3 4 5 4 4 4 5 2 5 2 4 4 4 4 3 4 4 2 3 4 4 5 4 4 4 4 3 3 4 3 4 4 2 4 2 4 5 5 4 3 5 4 2 4 4 4 4 3 4 4 3 5 3 3 2 3 4 4 2 4 4 5 1 3 5 5 5 2 5 5 4 4 5 4 3 2 4 5 3 4 5 4 3 4 4 2 3 2 3 1 4 4 2 3 3 4 4 2 3 4 4 4 3 4 4 4 5 2 4 2 4 5 3 4 4 4 4 3 3 2 3 4 3 4 3 3 3 2 2 2 4 4 4 2 4 3 4 2 1 2 4 4 4 5 5 4 5 4 5 2 2 4 3 2 4 5 2 4 3 2 4 2 2 4 1 2 2 1 2 1 4 2 4 2 2 4 4 4 2 4 4 4 4 4 5 4 4 3 4 2 5 5 4 5 5 4 5 5 5 4 2 3 3 4 3 3 5 2 4 3 4 4 1 2 2 4 2 2 2 2 3 4 4 4 4 4 5 3 4 3 4 4 3 4 2 3 3 5 2 4 4 3 4 4 5 4 4 3 4 2 4 4 4 2 3 5 4 3 2 4 4 5 5 4 4 5 5 3 4 4 4 3 4 4 2 4 4 4 3 4 3 4 2 3 5 5 5 4 5 3 5 2 3 3 3 4 4 3 3 3 4 3 2 2 4 3 5 4 3 5 3 5 5 1 1 3 4 3 4 4 5 5 4 5 5 4 5 3 5 1 5 4 4 4 3 4 4 3 4 4 3 5 3 4 4 4 2 4 3 2 4 4 4 4 2 4 2 4 2 5 4 5 3 4 5 4 4 2 5 3 5 4 3 4 2 4 4 4 3 4 4 4 2 4 4 4 5 1 5 4 4 5 4 1 2 4 4 1 4 3 2 2 3 4 3 4 3 2 4 2 4 3 4 3 2 4 4 4 2 4 4 4 4 4 4 4 5 3 4 3 5 5 3 3 3 4 3 3 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2 5 4 1 5 5 5 5 4 4 2 4 2 4 2 2 2 4 4 1 2 4 2 2 2 4 4 5 2 5 2 5 4 5 2 4 4 4 3 2 4 3 4 4 4 4 3 4 2 4 3 4 5 3 3 1 3 4 2 2 4 4 4 2 4 4 4 5 1 4 2 3 5 4 3 1 3 4 4 3 4 4 4 4 4 4 4 4 1 4 4 5 5 4 4 2 4 4 4 4 2 4 3 5 5 2 3 3 1 3 3 4 4 2 4 1 3 4 4 3 5 5 5 5 5 5 5 5 3 5 1 4 4 4 4 2 3 3 4 1 4 4 4 4 4 3 4 4 3 5 1 4 5 3 3 4 3 3 3 3 2 3 2 2 4 2 4 2 1 2 3 4 4 2 3 4 3 4 4 4 5 5 3 4 4 4 4 5 3 5 2 5 4 3 4 3 3 3 4 3 3 3 3 3 4 4 4 4 3 5 3 4 4 5 3 3 3 3 3 3 4 5 4 4 4 4 3 4 3 4 3 3 5 2 3 1 3 3 4 4 5 4 5 4 4 3 4 5 4 5 2 5 5 5 3 2 3 4 4 5 3 4 4 4 4 4 2 4 2 3 1 3 4 4 2 2 3 4 4 2 3 3 4 4 4 3 5 4 2 4 3 4 4 3 3 2 3 3 3 4 4 5 5 4 4 4 4 4 3 5 1 4 4 2 4 2 3 5 4 2 4 5 4 5 5 4 5 5 4 5 2 5 5 4 4 3 4 2 5 4 4 4 4 4 3 5 3 4 3 4 2 4 5 4 3 4 4 4 4 2 3 4 5 3 4 5 3 4 2 3 4 2 5 3 4 2 3 2 5 1 3 2 2 4 3 3 4 4 2 2 3 4 4 3 3 2 3 3 3 3 4 4 3 4 2 5 2 3 2 3 5 3 5 4 1 1 3 3 3 1 5 5 3 4 3 5 4 5 4 5 5 4 4 5 5 3 3 4 5 4 3 4 4 4 5 4 3 5 3 3 4 4 4 4 4 3 3 3 3 3 5 5 5 4 5 5 5 5 3 5 3 5 5 4 4 4 3 5 4 3 4 4 2 3 4 4 3 4 2 4 2 4 4 3 3 2 4 3 3 2 4 5 4 3 3 4 3 3 4 4 3 4 4 3 4 2 3 4 4 2 4 4 4 4 4 4 3 3 2 4 2 4 4 3 3 3 3 2 4 1 4 4 3 3 3 4 4 4 4 4 4 4 4 3 4 4 2 4 3 4 1 5 5 5 4 5 2 4 1 4 3 5 5 3 4 4 1 3 2 1 3 3 2 4 4 4 4 4 4 4 3 3 4 3 3 2 3 4 3 2 4 4 4 4 4 3 4 4 3 4 2 4 2 4 4 2 4 4 4 3 4 4 2 3 5 5 4 5 3 4 3 5 5 5 3 2 2 2 3 4 4 4 4 4 4 3 4 4 2 4 4 2 4 3 3 2 2 4 3 3 4 4 2 3 3 5 2 4 3 4 4 4 4 2 2 1 4 5 3 2 3 4 3 3 4 4 3 4 3 4 3 5 5 4 5 4 4 4 4 4 4 4 3 4 3 4 4 4 4 4 3 4 4 3 3 3 4 4 4 3 2 2 4 2 3 4 5 5 1 5 2 4 4 5 3 1 4 1 4 1 2 2 4 3 4 5 4 5 4 5 5 4 2 4 3 3 3 4 3 3 3 4 5 4 4 4 4 4 4 5 3 3 5 3 3 3 4 3 3 1 5 4 4 5 3 4 4 4 3 5 1 4 5 5 2 2 4 4 4 4 3 3 4 4 4 5 4 4 4 4 1 4 5 3 3 1 4 4 4 1 4 4 4 2 5 4 4 4 2 5 4 4 2 5 2 2 4 4 5 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3 3 2 4 4 3 3 4 4 4 3 4 4 4 3 3 3 3 2 2 2 1 4 3 3 5 2 4 3 2 5 4 4 5 2 3 4 5 3 2 3 3 3 3 3 5 3 4 3 4 3 3 4 4 3 1 4 3 4 2 2 3 3 3 3 5 4 4 3 4 2 5 5 3 3 3 4 4 3 3 5 5 5 4 4 3 5 5 4 5 4 4 5 4 4 4 5 5 3 5 4 5 1 3 5 4 4 3 3 4 1 3 5 4 4 1 2 3 4 4 3 4 4 3 4 4 3 4 3 4 3 4 2 3 3 4 3 4 3 3 4 3 4 4 3 5 4 5 2 4 1 4 2 5 3 2 4 5 4 2 2 3 4 3 3 4 3 4 4 5 1 4 2 3 4 2 3 4 4 2 3 3 4 4 4 3 4 4 2 2 2 2 2 2 2 2 3 2 3 1 4 4 4 5 3 4 3 4 3 5 2 4 5 5 4 4 4 3 3 3 2 4 4 2 3 4 2 4 4 3 2 4 5 4 4 3 4 4 4 5 4 3 5 4 4 3 5 5 3 4 3 5 5 4 3 5 3 3 2 3 5 5 4 2 3 5 5 5 4 4 5 5 2 5 4 4 4 4 2 4 4 4 4 4 4 3 3 4 4 4 1 2 2 2 3 2 2 4 2 2 3 2 3 2 3 2 5 4 2 4 1 4 5 3 3 2 4 4 4 4 2 3 4 2 4 4 5 4 2 4 3 5 5 4 4 2 4 4 4 4 4 2 4 4 5 5 5 4 2 3 2 4 5 5 3 1 3 5 4 2 4 4 4 4 4 2 4 4 3 4 1 3 5 2 3 2 3 2 3 2 3 3 4 3 5 4 5 4 4 3 2 5 5 4 3 2 4 4 5 4 5 4 3 3 5 4 5 5 3 4 1 5 5 5 4 3 5 5 5 4 2 2 4 3 4 5 5 5 3 4 2 5 5 4 3 1 3 3 4 2 4 3 4 4 3 4 4 5 3 4 4 4 4 4 3 2 3 3 3 3 5 3 2 4 3 4 3 4 4 4 3 4 5 4 3 2 2 4 4 4 3 4 2 4 4 2 4 4 2 4 3 2 5 3 3 1 4 1 4 4 3 4 3 3 4 3 3 4 3 3 1 4 5 3 3 3 4 4 3 3 3 4 3 4 4 3 4 4 3 4 1 4 4 3 3 2 2 4 4 2 3 3 4 4 4 4 3 4 2 4 2 4 4 3 3 1 3 3 4 1 3 3 3 4 3 3 4 3 2 3 4 5 1 4 4 3 4 3 4 3 2 4 4 5 3 4 4 4 3 3 4 5 5 5 3 5 4 5 5 4 4 2 3 3 3 4 4 3 4 3 1 5 4 4 3 1 5 4 4 2 4 4 4 4 5 4 2 3 1 3 1 5 5 3 3 1 4 4 4 2 3 3 3 3 4 4 4 4 3 3 1 4 5 4 3 2 3 4 3 3 4 4 3 2 2 3 4 2 4 2 4 4 2 4 3 4 3 4 4 2 3 4 4 3 4 4 3 4 4 2 2 4 4 4 2 2 3 4 4 2 3 4 4 4 1 3 4 4 5 4 2 3 5 3 3 1 4 2 2 1 2 4 3 4 5 4 2 2 2 4 3 2 5 3 3 1 3 2 4 2 4 2 5 4 4 4 5 5 3 5 3 4 4 3 3 2 2 4 4 4 4 4 4 4 4 4 4 4 5 5 1 4 5 3 3 2 3 3 3 3 4 4 4 3 4 4 5 4 3 3 2 5 5 4 3 1 5 4 5 3 2 2 4 5 4 4 4 5 3 5 1 4 5 2 3 2 2 2 2 2 3 4 4 4 4 4 3 4 3 4 2 3 4 3 2 2 3 3 4 4 2 3 4 2 3 2 4 4 4 5 3 4 1 3 4 1 5 3 2 3 4 1 4 2 2 4 2 4 3 4 5 4 4 2 4 1 4 4 3 3 5 4 5 4 4 4 5 5 3 4 2 4 5 5 4 2 5 3 4 2 4 4 5 3 4 4 4 5 4 4 3 4 5 4 3 2 5 4 5 2 2 4 4 2 4 2 2 4 2 4 4 2 5 2 4 1 2 5 4 2 4 4 3 4 4 4 4 4 3 4 2 3 5 3 3 2 3 3 3 4 4 2 5 4 4 4 4 5 3 5 3 2 4 2 2 3 4 2 3 1 3 4 2 4 4 4 3 4 3 4 2 5 5 4 3 2 3 4 3 3 4 2 4 2 4 3 4 4 4 4 2 3 5 4 4 2 3 2 4 3 4 3 4 3 5 3 4 4 3 4 2 4 5 4 3 4 4 5 4 4 4 4 4 2 2 3 4 4 2 4 4 4 4 4 3 3 2 2 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 5 3 5 5 5 1 2 4 2 3 4 5 5 5 5 4 4 5 5 4 2 3 3 5 4 4 4 4 1 5 2 4 5 2 3 1 4 4 4 1 4 4 4 3 5 5 5 5 2 5 4 4 5 3 4 1 4 4 4 5 4 4 4 3 5 4 5 5 2 4 2 4 4 4 2 2 5 5 4 3 4 5 2 2 4 4 2 2 5 3 2 5 1 1 3 3 3 1 4 3 3 3 4 4 3 4 5 4 4 3 3 4 5 4 3 4 4 4 3 2 4 4 4 4 4 4 4 4 2 4 1 4 4 4 3 3 4 2 4 4 2 2 3 4 4 2 3 3 1 2 2 4 5 1 2 2 3 3 4 2 4 4 5 4 4 4 4 4 4 4 2 5 4 4 4 2 4 2 2 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 4 4 4 4 4 4 5 4 5 5 5 5 4 4 2 2 3 5 4 3 4 3 4 4 5 4 5 5 4 4 4 4 4 4 2 4 5 5 4 2 3 3 4 4 4 4 4 1 5 3 4 3 3 5 3 3 4 3 1 4 3 3 4 4 2 4 3 2 3 4 4 2 4 3 2 3 3 3 4 3 3 4 3 4 4 2 3 3 5 2 4 5 4 3 3 3 4 3 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 3 4 4 4 4 5 5 3 4 1 3 5 3 3 3 3 4 2 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3 3 3 4 3 5 4 4 3 4 2 4 4 5 2 2 2 4 4 4 4 4 2 3 4 4 3 4 4 3 3 5 5 4 3 2 3 4 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3 5 3 5 4 4 4 3 2 3 5 4 4 4 4 3 2 5 5 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 4 2 4 2 2 4 2 4 4 1 4 5 3 2 2 4 4 3 4 4 5 5 5 5 4 5 4 4 5 1 5 5 4 3 2 5 5 2 4 1 4 1 2 2 5 3 4 3 4 4 4 5 4 3 4 4 3 3 3 5 4 5 5 5 4 5 5 3 3 2 4 5 5 4 3 4 4 5 3 4 4 3 4 4 5 4 5 4 2 1 5 5 4 4 4 5 4 4 3 3 3 3 3 4 3 4 4 3 3 1 4 4 4 3 1 3 3 4 2 2 4 2 4 3 4 4 4 3 4 2 3 4 4 3 1 2 2 3 2 4 4 4 4 4 4 5 4 3 5 1 4 4 3 5 1 4 5 3 2 3 4 3 4 3 4 4 4 2 3 1 3 5 4 3 1 2 3 4 3 3 4 4 3 3 4 4 4 4 3 2 3 5 3 3 2 4 4 4 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3 4 3 5 5 4 4 4 3 3 3 4 4 2 3 3 3 3 3 4 3 3 3 4 4 3 3 4 2 4 3 4 5 3 3 2 4 3 4 3 3 4 3 4 5 5 4 4 3 3 1 4 5 4 4 3 4 4 3 4 2 3 2 4 4 3 4 4 2 4 3 4 5 4 2 3 2 2 2 2 4 4 4 4 4 4 5 5 3 4 3 5 5 5 4 3 4 3 4 4 4 4 2 4 3 5 4 4 4 4 2 4 5 5 3 3 4 3 3 3 4 5 4 4 4 3 4 5 4 4 1 5 5 3 3 3 3 4 5 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 3 4 2 4 2 4 3 2 4 3 4 5 4 4 1 2 3 4 4 3 4 3 3 4 2 3 3 2 2 4 3 2 2 3 3 2 3 3 4 4 2 4 4 2 4 3 3 2 3 4 3 2 2 3 3 2 4 3 3 3 3 2 2 1 1 2 1 4 2 3 2 1 2 3 4 2 3 2 4 3 4 3 4 4 4 3 4 2 3 1 4 2 3 3 2 3 3 4 3 3 3 2 4 4 3 4 1 3 3 1 3 5 2 3 1 4 4 4 4 4 4 5 3 3 3 4 4 3 4 3 5 4 4 4 2 4 4 3 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 5 2 4 2 5 1 4 3 2 2 4 5 4 3 3 2 2 4 4 4 2 2 1 1 2 2 3 1 2 2 4 1 2 2 2 3 2 1 2 2 4 2 3 4 3 2 3 4 2 4 4 3 5 2 3 2 4 4 3 4 5 5 5 5 3 5 5 5 5 5 1 5 5 2 5 4 4 5 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 3 4 2 4 3 2 4 4 3 2 5 5 2 4 1 3 5 4 2 4 4 4 3 4 4 4 2 3 4 3 3 4 4 3 3 2 3 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 4 3 4 4 4 4 3 3 4 3 4 4 5 2 1 2 3 4 1 4 4 4 4 3 4 4 4 1 5 2 4 4 4 4 2 3 4 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 5 5 5 4 5 4 5 5 5 5 1 5 5 5 4 4 5 5 4 4 Names of X columns: A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 Sample Range:(leave blank to include all observations) From: To: Scale (list separated by spaces)

 Source code of R module docor <- function(x,y,method) { r <- cor.test(x,y,method=method) paste(round(r\$estimate,3)," (",round(r\$p.value,3),")",sep="") } x <- t(x) nx <- length(x[,1]) cx <- length(x[1,]) mymedian <- median(as.numeric(strsplit(par1," ")[[1]])) myresult <- array(NA, dim = c(cx,7)) rownames(myresult) <- paste("Q",1:cx,sep="") colnames(myresult) <- c("mean","Sum of
positives (Ps)","Sum of
negatives (Ns)", "(Ps-Ns)/(Ps+Ns)", "Count of
positives (Pc)", "Count of
negatives (Nc)", "(Pc-Nc)/(Pc+Nc)") for (i in 1:cx) { spos <- 0 sneg <- 0 cpos <- 0 cneg <- 0 for (j in 1:nx) { if (!is.na(x[j,i])) { myx <- as.numeric(x[j,i]) - mymedian if (myx > 0) { spos = spos + myx cpos = cpos + 1 } if (myx < 0) { sneg = sneg + abs(myx) cneg = cneg + 1 } } } myresult[i,1] <- round(mean(as.numeric(x[,i]),na.rm=T)-mymedian,2) myresult[i,2] <- spos myresult[i,3] <- sneg myresult[i,4] <- round((spos - sneg) / (spos + sneg),2) myresult[i,5] <- cpos myresult[i,6] <- cneg myresult[i,7] <- round((cpos - cneg) / (cpos + cneg),2) } myresult load(file="createtable") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Summary of survey scores (median of Likert score was subtracted)",8,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Question",header=TRUE) for (i in 1:7) { a<-table.element(a,colnames(myresult)[i],header=TRUE) } a<-table.row.end(a) for (i in 1:cx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) for (j in 1:7) { a<-table.element(a,myresult[i,j],align="right") } a<-table.row.end(a) } a<-table.end(a) table.save(a,file="mytable.tab") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Pearson correlations of survey scores (and p-values)",4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"",header=TRUE) a<-table.element(a,"mean",header=TRUE) a<-table.element(a,"(Ps-Ns)/(Ps+Ns)",header=TRUE) a<-table.element(a,"(Pc-Nc)/(Pc+Nc)",header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"mean",header=TRUE) a<-table.element(a,docor(myresult[,1],myresult[,1],method="pearson"),align="right") a<-table.element(a,docor(myresult[,1],myresult[,4],method="pearson"),align="right") a<-table.element(a,docor(myresult[,1],myresult[,7],method="pearson"),align="right") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"(Ps-Ns)/(Ps+Ns)",header=TRUE) a<-table.element(a,docor(myresult[,4],myresult[,1],method="pearson"),align="right") a<-table.element(a,docor(myresult[,4],myresult[,4],method="pearson"),align="right") a<-table.element(a,docor(myresult[,4],myresult[,7],method="pearson"),align="right") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"(Pc-Nc)/(Pc+Nc)",header=TRUE) a<-table.element(a,docor(myresult[,7],myresult[,1],method="pearson"),align="right") a<-table.element(a,docor(myresult[,7],myresult[,4],method="pearson"),align="right") a<-table.element(a,docor(myresult[,7],myresult[,7],method="pearson"),align="right") a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable1.tab") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Kendall tau rank correlations of survey scores (and p-values)",4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"",header=TRUE) a<-table.element(a,"mean",header=TRUE) a<-table.element(a,"(Ps-Ns)/(Ps+Ns)",header=TRUE) a<-table.element(a,"(Pc-Nc)/(Pc+Nc)",header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"mean",header=TRUE) a<-table.element(a,docor(myresult[,1],myresult[,1],method="kendall"),align="right") a<-table.element(a,docor(myresult[,1],myresult[,4],method="kendall"),align="right") a<-table.element(a,docor(myresult[,1],myresult[,7],method="kendall"),align="right") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"(Ps-Ns)/(Ps+Ns)",header=TRUE) a<-table.element(a,docor(myresult[,4],myresult[,1],method="kendall"),align="right") a<-table.element(a,docor(myresult[,4],myresult[,4],method="kendall"),align="right") a<-table.element(a,docor(myresult[,4],myresult[,7],method="kendall"),align="right") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"(Pc-Nc)/(Pc+Nc)",header=TRUE) a<-table.element(a,docor(myresult[,7],myresult[,1],method="kendall"),align="right") a<-table.element(a,docor(myresult[,7],myresult[,4],method="kendall"),align="right") a<-table.element(a,docor(myresult[,7],myresult[,7],method="kendall"),align="right") a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable2.tab")
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 Cite this software as: Wessa P., (2012), Survey Scores (v1.0.3) in Free Statistics Software (v1.1.23-r7), Office for Research Development and Education, URL http://www.wessa.net/rwasp_surveyscores.wasp/ The R code is based on :
 Top | Output | Charts | References | History | Feedback
 Top | Output | Charts | References | History | Feedback
 To cite Wessa.net in publications use:Wessa, P. (2013), Free Statistics Software, Office for Research Development and Education, version 1.1.23-r7, URL http://www.wessa.net/ © All rights reserved. Academic license for non-commercial use only. The free use of the scientific content, services, and applications in this website is granted for non commercial use only. In any case, the source (url) should always be clearly displayed. Under no circumstances are you allowed to reproduce, copy or redistribute the design, layout, or any content of this website (for commercial use) including any materials contained herein without the express written permission. Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. We make no warranties or representations as to the accuracy or completeness of such information (or software), and it assumes no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site. Software Version : 1.1.23-r7Algorithms & Software : Patrick Wessa, PhDServer : www.wessa.net