[Rasch] Rasch-inspired open-source software now available for highly multidimensional data

Iasonas Lamprianou liasonas at cytanet.com.cy
Mon Apr 2 17:03:10 EST 2012

(a) can the Damon software be used to solve regression-like problems? For example, can I run the ussual Rasch model, also using independent variables for latent regressions? 
(b) Does it support many-facets designs?



----- Original Message Follows -----
From: Mark Moulton <markhmoulton at gmail.com>
To: <rasch at acer.edu.au>
Subject: [Rasch] Rasch-inspired open-source software now available for highly multidimensional data
Date: Fri, 23 Mar 2012 20:06:14 -0700
> *Dear Rasch Colleagues,*
> I want to let you know about *Damon*, a Python-based software package that
> I'm releasing free to the Rasch psychometric community and general public.
>  The result of some 20 years hard labor (initiated by Ben Wright to his
> surprise), it has been in private commercial use for the last five years.
>  It was designed specifically to apply Rasch's objectivity criterion to *highly
> multidimensional* datasets, such as the infamous Netflix movie ratings
> dataset.
> It is documented and available for download through
> www.pythiasconsulting.com .  If there is any interest, I will hold a *Damon
> workshop in Vancouver* at my hotel on the morning of *Friday, April 13,*after
> *IOMW*.
> Contact me if you have any questions.
> Thanks!
> Mark H. Moulton, Ph.D.
> Pythias Consulting
> markhmoulton at gmail.com
> 408-307-2794
> *Features/Bugs (depending on point of view):*
>    - *Multidimensionality.*  Easily handles data with 1, 5, 10, 50, 100+
>    dimensions, including items that are negatively correlated.
>    - *Data types.*  Handles interval, ordinal, ratio, dichotomous,
>    polytomous, and nominal data, including within the same dataset.
>    - *Missing Data. * Handles pretty much any proportion of missing data,
>    random or non-random.
>    - *Labels.*  Datasets support row labels and column labels of any depth.
>     Queries are label-based.
>    - *Objectivity.*  Damon offers clear criteria for determining and
>    optimizing the objectivity of all reported statistics, including "best
>    dimensionality".  These include Rasch's parameter invariance requirement.
>    - *Measures.*  Person measures consist of cell estimates, in either the
>    original or a linear (logit) metric, averaged across one or more items
>    within a dataset.  They answer the question:  *How able is Person A on
>    the construct embodied by a defined subset of items?*
>    - *Predictions.*  Predictions are of the form:  *How would Person A have
>    performed on Item 1 if he had taken the item?*
>    - *Equating.*  Parameters from one dataset are transferrable as anchors
>    to a comparable dataset.
>    - *Analysis of Fit.*  Damon reports fit statistics, standard errors,
>    etc., for determining the degree to which the observed data fits into the
>    objective space.
>    - *Rasch.*  Damon includes a Rasch module based on the Winsteps JMLE
>    implementation.
> *Algorithm*
>    - *ALS/Rasch.*  The algorithm is classified as a multidimensional
>    alternating least squares matrix decomposition subjected to a strict
>    Rasch-based objectivity optimization criterion.
> *Usability*
>    - *Python.*  Damon is built on top of the Python scripting language and
>    the Numpy numerical package (both free).  It is accessed through a
>    command-line shell or run from scripts.  If you have experience with the
>    statistical language *R*, or *MatLab*, you will feel at home.  Although
>    Damon is written in Python, it requires very little expertise in Python
>    programming.   The website includes a tutorial.  Damon's inline help
>    resources are extensive.
> *A Sample Damon script*
> >>>  import damon1.core as dmn
> >>>  Data =
> dmn.DamonObj('California_May2012.csv','TextFile',nHeaders4Rows=5,nHeaders4Cols=1,ValidChars=['All',[0,1]])
> >>>  Data.standardize()
> >>>  Data.coord([range(1,11)],RunSpecs=[0.0001,20])
> >>>  Data.baseEst()
> >>>  Data.finEst()
> >>>  Data.summStat()
> >>>  Data.export(['summStat_out','baseEst_out','finEst_out'],'TextFile')
> This short program imports and formats a text file, standardizes the data,
> looks for the optimal (most objective) dimensionality in a range of 10
> dimensions, computes coordinates (multidimensional abilities and
> difficulties) for each person and item, computes an array of cell estimates
> (for both missing and non-missing cells), computes another array of cell
> (0,1) predictions, computes person measures, and exports three of the
> outputs as text files.
> Various other statistics, functions, and methods are available in Damon,
> plus the massive libraries in Numpy and related Python packages.
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