RWA Web allows users to examine the relative importance of predictors in multiple regression using relative weight analysis (Johnson, 2000). Numerous capabilities are built in that allow users to:

• Produce raw relative weight values (epsilons) as well as rescaled weights (scaled as a percentage of predictable variance) for every predictor in the model.

• Produce the confidence interval around each relative weight with coverage probability specified by the user. The approach used here mirrors that described by Johnson (2004) with the notable exception of computing the confidence interval around the raw weights using the bias corrected accelerated method for generating the bootstrapped confidence intervals as recommended by Tonidandel, LeBreton, and Johnson (2009).

• Evaluate the statistical significance of the relative weights using the approach outlined by Tonidandel, LeBreton, and Johnson (2009). A weight is considered to be statistically significant if the confidence interval produced here does not contain zero (this confidence interval is different from the confidence interval described in the previous bullet point – see Tonidandel et. al. 2009 for clarification)

• Test whether the relative weights from two predictors are significantly different from one another using the approach suggested by Johnson (2004) with the updates recommended by Tonidandel, LeBreton, and Johnson (2009). Two relative weights are considered to be significantly different from one another if the confidence interval produced here does not contain zero.

• Test whether a predictor's relative weight differs significantly across two groups. As before, a description of the original approach can be found in Johnson (2004) with additional recommendations in Tonidandel, LeBreton, and Johnson (2009). The relative weight of a predictor is considered to be significantly different between two groups if the confidence interval produced here does not contain zero.

We have had to migrate our work off the old site and are in the process of building Shiny Apps to reproduce what was previously available. To use the Shiny app, user can simply go to the respective site. The calculations for confidence intervals and tests of significance require running multiple boostrapped replications. As a result, it can take some time (5 to 10 minutes or more) before a result is returned. Please be patient. These apps are in Beta. If you encounter issues, please email me.

User Input

1. Browse to their data file. The data format should be a .csv file with variable names in the first row. A valid variable name consists of letters, numbers and the dot or underline characters (other special characters including spaces are not permitted) and starts with a letter or the dot (not a number). If you start a variable name with a dot, it may not be followed by a number (e.g. names such as '".2way"' are not valid). There are also some reserved names that are not valid variable names. The list of reserved names is quite short and can be found here: http://stat.ethz.ch/R-manual/R-devel/library/base/html/Reserved.html

2. Specify the type of the input file. The input file can be either raw data or a correlation matrix. Please note that all of the confidence intervals/tests of significance described above require bootstrapping raw data so that output will not be available if your input data file is a correlation matrix. Example input data files of each type are available below.

   • Example data set from Tonidandel, S. & LeBreton, J. M. RWA-Web: A Free, Comprehensive, Web-based, & User-friendly Tool for Relative Weight Analyses

   • Multiple Regression Raw Data Example

   • Multiple Regression Correlation Matrix Example

   • Multivariate Multiple Regression Raw Data Example

   • Multivariate Multiple Regression Correlation Matrix Example

   • Logistic Regression Raw Data Example

3. Choose a missing data option. If the input file is raw data, one must indicate how missing data should be handled if it is encountered.

4. Choose your criterion variable from the dropdown.

5. Choose your predictor variables from the dropdown.

6. Select the number of iterations to use for the boostrapping procedures. We recommend at least 10,000.

7. Specify the alpha value used to compute confidence intervals/test for statistical significance.

8. Test to see if two predictors are significantly different from one another. If one wishes to test whether the relative weight of a predictor is significantly different from the relative weight of another predictor, one must select that option and then choose the relevant variable to be tested from the dropdown. The chosen listed here will be tested against all of the other predictors in your data set.

9. Compare groups If one wishes to test whether a predictor's relative weight differs significantly across two groups, one must first select this option, then choose the grouping variable from the dropdown list, and finally choose the values of the two groups to be compared.