Data Mining and Rasch Measurement

Data mining is finding useful relationships in large datasets. "When you mine data (by "drilling down"), you use data to improve your business by predicting and understanding behavior." (Peter Frometa, SPSS Inc., 2001)

According to a press release, "in May 1998, more than 20 key players in the data mining market met to discuss the first draft of a new process model, CRISP-DM ("CRoss-Industry Standard Process for Data Mining"). This is designed to help businesses plan and work through the complete data mining process - from problem specification to deployment of results. The core consortium consists of NCR, ISL, Daimler-Benz and OHRA. At the centre of the CRISP-DM project is a Special Interest Group (SIG) of data mining service suppliers and large-scale commercial users."

Data mining employs a 6-stage approach to extracting meaning from business data. This parallels Rasch-based approaches to measurement construction in the social sciences. The Table below focusses on the Data Cleaning component of data mining. It is in marked contrast to the conventional "data is inviolable" approach of social science research.

The Figure shows the six phases of a data mining process. The sequence of the phases is not rigid. Moving back and forth between different phases is always required. It depends on the outcome of each phase which phase or which particular task of a phase, has to be performed next. The arrows indicate the most important and frequent dependencies between phases.

The outer circle symbolizes the cyclical nature of data mining itself. Data mining is not over once a solution is deployed. The lessons learned during the process, and from the deployed solution, can trigger new, often more focused business questions. Subsequent data mining processes will benefit from the experiences of previous ones. In the following, we outline each phase briefly:

1. Business understanding
This initial phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan designed to achieve the objectives.

2. Data understanding
starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data or to detect interesting subsets with hidden information.

3. Data preparation
constructs the final dataset from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order. Tasks include table, record and attribute selection as well as transformation and cleaning of data for modeling tools.

4. Modeling
selects and applies modeling techniques and calibrates their parameters to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often necessary.

5. Evaluation
thoroughly reviews the model and the steps executed to construct the model to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. A decision on the use of the data mining results should be reached.

6. Deployment
organizes and presents the knowledge gained in a way that the customer can use it. It often involves applying "live" models within an organization's decision making processes, for example in real-time personalization of Web pages or repeated scoring of marketing databases. However, depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases it is the customer, not the data analyst, who carries out the deployment steps. However, even if the analyst will not carry out the deployment effort it is important for the customer to understand up-front what actions need to be carried out in order to actually make use of the created models.

Excerpted from CRISP-DM 1.0 Step-by-step data mining guide (2000)

Data Mining Stages	Rasch Measurement
1. Business Understanding Determine business objectives Determine data mining goals "You must have a clear idea of what success would be."	1. Conceptualize the latent variable What to measure? How to do it? What marks success?
2. Data Understanding "Do the data match your objectives?"	2. Collect relevant data
3. Data Preparation Select data Clean data Reconstruct data	3. Organize data Select data Orient data Rescore data
4. Modeling Build model Assess model	4. Construct measures Select measurement model Explicable data fit? Refine model
5. Evaluation Evaluate results Review process "Can results be repeated and verified by someone else?"	5. Evaluate results Meaningful construct? Useful measures? Reproducible results?
6. Deployment Report "Communicate! Impress! Compel!" Activate	6. Utilize measures Reporting Decision making Knowledge building

Data Mining and Rasch Measurement CRISP-DM, Linacre J.M. … Rasch Measurement Transactions, 2001, 15:2 p. 826-7

Rasch Publications
Rasch Measurement Transactions (free, online)	Rasch Measurement research papers (free, online)	Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch	Applying the Rasch Model 3rd. Ed., Bond & Fox	Best Test Design, Wright & Stone
Rating Scale Analysis, Wright & Masters	Introduction to Rasch Measurement, E. Smith & R. Smith	Introduction to Many-Facet Rasch Measurement, Thomas Eckes	Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.	Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar	Journal of Applied Measurement	Rasch models for measurement, David Andrich	Constructing Measures, Mark Wilson	Rasch Analysis in the Human Sciences, Boone, Stave, Yale
in Spanish:	Análisis de Rasch para todos, Agustín Tristán		Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez

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