Tools for Social Work

Using Standardization and the Normal Curve to Enhance your Social Work Practice, Education & Research

Resources Via Social Work Search

Posted by nepeht on September 12, 2009

Don’t let Social Work Statistics twist up your mind.

Social Work Statistics Tailspin (c.2009, WTB)

Social Work Statistics Tailspin (c.2009, WTB)

Hey, you can go to Social Work Search for other websites that might help with Statistics and Research!

Posted in Potentially Helpful Sites | Tagged: , , | 2 Comments »

Use of Standardized Measures in Agency Based Research and Practice

Posted by nepeht on May 5, 2009

Article:

  Berkman B., & Maramaldi P. (2001). Use of standardized measures in agency based research and practice.  Social Work in Health Care, 34(1-2), 115-29. Retrieved on May 5, 2009 from http://www.ncbi.nlm.nih.gov/pubmed/12219762 .

Quoting the PubMed.com source:

“This article reviews criteria for social workers’ selection and use of standardized outcome measures for practice and research. Issues related to reliability and validity are discussed. The utility of standardized Health Related Quality of Life (HRQL) measures, either generic or disease specific, is presented utilizing one measure, the SF-36+ Social Work, as an exemplar. The article concludes that such measures are viable and necessary for social work to demonstrate its value-added qualities in the emerging healthcare environment.”

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Article: “The Bell Curve: Race, Socioeconomic Status, and Social Work”

Posted by nepeht on December 28, 2008

http://findarticles.com/p/articles/mi_hb6467/is_/ai_n28743565

This is and it IS NOT exactly what this blog is about. 

However, if one can understand the basic issues in this article (i.e., how the Normal Curve works, Social Work’s reaction to the words “Normal Curve”) cited here, one can come closer to getting why this blog may be necessary.

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Solving Problems with the Standard Normal Curve

Posted by nepeht on December 24, 2008

http://videos.howstuffworks.com/hsw/11359-solving-problems-with-the-standard-normal-curve-video.htm

A video from “How Stuff Works”.

Posted in Notes | Tagged: , | 1 Comment »

Normal Curve Tests of Means and Proportions

Posted by nepeht on December 24, 2008

http://faculty.chass.ncsu.edu/garson/PA765/normal.htm

By Professor G. David Garson

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Slideshow About the Basics of Standard Deviation and Variance

Posted by nepeht on December 22, 2008

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Slideshow Demonstrating Use of The Normal Distribution to Analyze Medical Data

Posted by nepeht on December 21, 2008

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Probability Slideshow posted by Mike Shelly on Slideshare.com

Posted by nepeht on December 19, 2008

Posted in Notes | Tagged: | 1 Comment »

Graph and Importance of the Normal Distribution

Posted by nepeht on December 19, 2008

Click here to see graph at Stattucino.com.

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Percentile and Percentile Rank

Posted by nepeht on November 24, 2008

http://en.wikipedia.org/wiki/Percentile.

A percentile is the value of a variable below which a certain percent of observations fall. So the 20th percentile is the value (or score) below which 20 percent of the observations may be found. The term percentile and the related term percentile rank are often used in descriptive statistics as well as in the reporting of scores from norm-referenced tests.

The 25th percentile is also known as the first quartile(Q1); the 50th percentile as the median or second quartile(Q2); the 75th percentile as the third quartile (Q3).

 http://en.wikipedia.org/wiki/Percentile_rank.

The percentile rank of a score is the percentage of scores in its frequency distribution which are lower. For example, a test score which is greater than 85% of the scores of people taking the test is said to be at the 85th percentile. Percentile ranks are commonly used to clarify the interpretation of scores on standardized tests. For the test theory, the percentile rank of a raw score is interpreted as the percentages of examinees in the norm group who scored below the score of interest.[1]

Percentile ranks (PRs or “percentiles”) are normally distributed and bell-shaped while normal curve equivalents (NCEs) are uniform and rectangular in shape. Percentile ranks are not on an equal-interval scale; that is, the difference between any two scores is not the same between any other two scores. For example, 50 – 25 = 25 is not the same distance as 60 – 35 = 25 because of the bell-curve shape of the distribution. Some percentile ranks are closer to some than others. Percentile rank 30 is closer on the bell curve to 40 than it is to 20.

From Wikipedia

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