Parkway’s Math Common Assessments

1 / 13

# Parkway’s Math Common Assessments - PowerPoint PPT Presentation

Parkway’s Math Common Assessments. A brief study of their validity and ability to predict MAP scores. Reliability versus Validity. Reliability measures consistency. Does a test consistently give us the same result time after time? Validity measures accuracy.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## Parkway’s Math Common Assessments

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Parkway’s Math Common Assessments

A brief study of their validity and ability to predict MAP scores

Reliability versus Validity
• Reliability measures consistency.
• Does a test consistently give us the same result time after time?
• Validity measures accuracy.
• Does a test measure what it is supposed to be measuring?
Target Practice

http://www.socialresearchmethods.net/kb/relandval.php

How do we determine validity?
• Validity can be determined by running correlations.
• The most common is Pearson’s Product-Moment Correlation Coefficient (r).
• In this case, we compare student common assessment scores to MAP test scores.
Pearson’s Coefficient (r)
• A value between -1 and 1.
• -1 is a perfect negative correlation.
• 1 is a perfect positive correlation.
• 0 is no correlation.
• -0.8 is a strong negative correlation.
• 0.8 is a strong positive correlation.

Correlations measure the relationship between two variables. They do not measure causation.

Quick way to determine what a specific coefficient means

Size of coefficientGeneral Interpretation

• .8 to 1.0 Very strong relationship
• .6 to .8 Strong relationship
• .4 to .6 Moderate relationship
• .2 to .4 Weak relationship
• 0 to .2 Weak or no relationship

Salkind (2005) – cited in report

Correlation Coefficients

http://mste.illinois.edu/courses/ci330ms/youtsey/scatterinfo.html

Pearson Coefficients of Correlations (r) forCorresponding 2009 Common Assessments and MAP Math Tests
So what could we do about it?
• Perform an item by item validity analysis by calculating correlations between common assessment items and MAP test items .
• Obviously, the common assessment items and the MAP items we compared would have to be testing the same concept.
• Then delete the poorly correlated items and replace them with new items.
• Check how the new items correlate the following year.
Predicting MAP scores using multiple regression
• It is very common to use accepted data to offer a prediction of the future. The opportunity of using existing data to predict future outcomes is viewed as model-building. That is to say, existing data are used to build a model of the future, with a predetermined degree of error built into the model. Multiple regression is a common and useful tool for model building. (Thomas W. MacFarland, 1998)

http://www.nyx.net/~tmacfarl/STAT_TUT/reg_sion.ssi

Common Assessment Percentages and Corresponding Predicted Achievement LevelOn the MAP Math Test