Published by WMF Press, the Compositator
creates custom composite scores from subtests of the *WJ
III NU*. It uses multiple regression, path analysis, and
a host of other features to enhance the clinical
interpretation of the WJ III NU.

Compositator video tutorial here.

In its original incarnation, the Compositator was an Excel
spreadsheet with far fewer capabilities. However, because it
is more generic (it can be used with any tests for which you
know the test intercorrelations and test reliability
coefficients), I have made it available here. It is not
published by WMF Press.

The TableMaker is
designed to help providers of psychological assessments
organize and present test data in a simple, efficient, and
theoretically informed manner. You enter an evaluee's test
scores in an order that is convenient and theoretically
organized tables are generated in MS Word.

TableMaker video
tutorial here.

The software is free. For now, it runs on Windows only.

For non-Windows users, I made an Excel spreadsheet that
accomplishes much of what TableMaker does. Download it here. Also see the video tutorial for the
spreadsheet version.

I wrote a commentary in a special issue
of the Journal Psychoeducational Assessment. My article
proposes a new way to interpret cognitive profiles. The basic
idea is to use the best available latent variable model of the
tests and then estimate an individual's latent scores (with
confidence intervals around those estimates). I have made two
spreadsheets available, one for the WISC-IV and one for the
WAIS-IV.

Five-Factor Model of the WISC-IV

Four-Factor Model of the WAIS-IV

I decided not to provide a spreadsheet for the five-factor
model of the WAIS-IV because Gf and *g* were so highly
correlated in that model that it would be nearly impossible to
distinguish between Gf and *g* in individuals. You can
think of Gf and *g* as nearly synonymous (at the latent
level).

Schneider, W. J. (2013). What if we
took our models seriously? Estimating latent scores in
individuals. *Journal of Psychoeducational
Assessment, 31*, 186–201.

I have created a suite of over 25 repeatable tests. For now, none of them have been normed but they can be used for cognitive rehabilitation and qualitative assessment. The suite includes tests of attention, long-term and working memory, reasoning, reaction time, executive functions and visual-spatial processing.

With an explanation of your credentials, this is available upon request.

Making predictions about
individuals using multiple regression and custom composite
scores (Allows you to combine psychological test scores
and create multiple regression formulas that can be applied to
individuals)

Making
predictions about individuals using multiple regression
(Allows you to create multiple regression formulas that can be
applied to individuals)

Generalized Relative Proficiency Index (Extends the
usefulness of the RPI from the Woodcock family of test
batteries)

Difference Score
Replication Predictor (Calculates the probability of
replicating difference scores larger than a specific
criterion)

Base Rates (Calculates
positive and negative predictive power and other useful
statistics given a test's specificity and sensitivity)

Confidence Intervals
(Calculates the confidence interval of a test, given the
reliability coefficient)

Standard Score
Converter (Convert any standard score to any other
standard score (e.g., scaled scores, T-scores, and index
scores)

**Statistical Tools**

Area Under
the Normal Curve (Graphic tool that calculates the
proportion under a normal curve)

Understanding
Statistical Power (Basic tool for calculating
statistical power for known distributions. Has an interactive
graph of the null and alternative distributions.)

Z-test (Conducts
a z-test. Has an interactive graph of the normal distribution)

1-Sample
t-test (Conducts a 1-sample t-test. Has an
interactive graph of the t distribution.)

Basic
Statistical Tables and Tools** **(F, t, and Z-Score
tables, Area under normal curve, Z-test, 1-sample t-test)

**ExcelToR Matrix Maker**

ExcelToR Matrix Maker (A
simple tool for making R matrices in Excel)

- Make a matrix anywhere in this spreadsheet.
- Select the matrix.
- Click one of the buttons.
- Make Matrix: A simple matrix with no names

*Becomes*

rbind(

c(1,2),

c(3,4)) - Make Named Matrix: Place names like so:

*Becomes*

MatrixName <- rbind(

c(1,2),

c(3,4))

MatrixNameRows <- c("RowName1", "RowName2")

MatrixNameCols <- c("ColName1", "ColName2")

colnames(MatrixName) <- MatrixNameCols

rownames(MatrixName) <- MatrixNameRows - Make String Vector: A row or column of cells with text

*Becomes*

c("Text1", "Text2", "Text3", "Text4")

- Make Matrix: A simple matrix with no names
- It will look like nothing happened. However, a VBA macro
constructed the code and copied to the clipboard. Simply go
to R and click paste.

**Latent Structure Simulations**

A spreadsheet that offers a simple way to simulate data according to a structural model that you specify.

A video tutorial that shows how to use the spreadsheet: