Raising Children in NM

Recently, an article was posted on a blog, Today's Mom,  that listed New Mexico as the absolute worst state in the nation to raise children.  First, offended at the status update that introduced this blog, "I will never raise my children in NM."(was the gist), I decided to defend my home state. I do not stand alone in reciting the countless benefits of raising children in the Land of Enchantment.  So, I obviously questioned the validity of this article.  The response, so vague, and often a cop out, "Like it or not, this article is backed by statistics, so it is accurate."  Really?

Let's Put This Article to the Test!

Using the Principles of Data Interpretation, as described by Gerald W. Bracey, I will debunk this bloggers claim that NM is the worst state for child well-being.  I will use their claims on the Basis of a Child's Well-Being and discuss how the claims of the research are based on faulty use of data.

"Basis" of a Child's Well Being

 According to the report the domains that define a child's well being are as follow:

1.  Family Economic Well-Being 
2. Health
3. Safe/Risky Behavior
4. Education Attainment
5. Community Engagement
6. Social Relationships
7. Emotional/Spiritual Well-Being 

Using the Principles of Data Interpretation

Defending NM on the Basis of Child Well Being #3:

  The first Principals of Data Interpretation that comes to mind seems to be the simplest, yet in this case, hold the most power. Principals 1 & 2.  We (the audience) are not given the opportunity to 1. Do the arithmetic because they neglected to follow the second principal which is, show me the data: No where in the report do they show the actual data rather they refer to data collected by 

O‘Hare, William P. 2006.―Developing State Indices of Child Well-Being. Chart is below:

Using the information from this chart alone, I can see that the authors are neglecting to follow Principal #3: Beware of selective data.  Refer to #5 on the chart above, "Teen Birth Rate (percent ages 16-19 not in school/not graduates), 2003.  In the study, this data was used in reference to Safe/Risky Behavior.  I feel safe to assume that the "risky behavior" is teen sex.  Why would they only use teen birth rates as a factor, and not teen pregnancies, or sexually transmitted diseases?  Selective data.

Another issue that falls under the Safe/Risky Behavior category is the drug abuse.  Whereas New Mexico, like many states, is plagued with the disease of drug abuse...it is not the worst state, as indicated in the demeaning information of the blog. View the map below:

According to the US Department of Health and Human Services, New Mexico has a high incident of drug abuse, but it is not the highest in the nation.  The data indicates that New Mexico has less of a problem than that of Colorado, Oregon, Washington, Hawaii, Wyoming, New Hampshire and Vermont.  So, again, New Mexico is not "the worst." 

 

Defending NM on the Basis of Child Well-Being #4: Educational Attainment

“Another a good example of state differences is support for public education, which is by far the largest program for children and is largely driven by state and local funds and decision making.” Foundation for Child Development (another source of data for the blog in question).

Per-pupil expenditures ranged from $6,951 in Idaho to $17,620 in New Jersey in 2007."

According to the Census Bureau’s report found on the Governing The States and Localities website shows that NM spends $9,070 per student.  This places New Mexico well in the middle with New York leading in revenue per student by spending $19,026 and Utah spending the least at $6,212.  According to the report, Utah was ranked as one of the top ten states; however, in referencing the characteristics of student wellness, Educational Attainment, Utah is the state the spends the least amount per student, not New Mexico. 

 

 

To conclude this blog post, by researching the data, I was able to find errors in a "statistically" based article.  The lesson learned from reading Gerald Bracey's book Reading Educational Research How to Avoid Getting Statistically Snookered is how often statistics are misused to mislead audiences in following an agenda or tooting ones own horn. I have yet to see where New Mexico comes in dead last in an area listed in the  "Basis of Child Well-Being." Is New Mexico the top in the nation?  No.  Is New Mexico the worst in the nation? No.  

Abuses of Data

Many books have been written on the misrepresentations of Data: How to Lie with Statistics (Huff 1954), Damned Lies and Statistics and More Damned Lies and Statistics (Best 2004). These books serve to bring to light the many ways in which data has been misused to serve an agenda or a motive.  The purpose of this post is the identify, according to Dr. Bracey, some of the most common abuses of data.

Naive Failure to Do the Arithmetic

Consider the following quote, "Every year since 1950, the number of American children gunned down has doubled." (Bracey, 19) This quote seems harmless.  Yet, when we do the arithmetic, the author of this statement is claiming that in 2004, approximately 18,014,398,510,000,000,000 children were killed by guns.  Aside from misrepresenting information, this comment also lacks specifics that could make a difference. Bracey states that the following questions should be answered:

• How does the author define children?
• Where did he/she get the statistics?
• What does he/she mean by "gunned down?

Deliberate Distortions:

So often statistics are deliberately misused to misinform people in relation to accept the agenda of a given policy, point of view, or program.  One example given in the text is:

"In a Washington Post oped, former secretary of education William Bennet write, 'Nationally, about half of all high school graduates have not mastered seventh grade arithmetic.' (2000, A25) This statement lacks specificity, too.  We don't test high school graduates, so how could he know?" (Bracey, 23)

Selective Use of Statistics: Principle of Data Interpretation: When comparing groups, make sure the groups are comparable.

Considered by Bracey as the most common abuse of statistics the selective use of statistics, "reveal only part of the picture, the part supporting the author's agenda.  Recently, on 60 Minutes, there was a claim that we are living three years longer now than we did 100 years ago.  This study was completed by averaging the median age of mortality.  When we take the infant mortality rate and death of mothers during birthing of 100 years ago out of the equation, the mean changes from three to six years longer.  Are we really comparing apples to apples here?  The study should be further broken in to categories to give a more accurate portrayal.  

 

 

Variables and No Child Left Behind

What Are Variables:

Variables are used to create categories. Basically they are things that vary:

• test scores
• attendance rates--teacher, student
• classroom disruptions
• ethnic mixture in a school
• socioeconomic mixture in a school
• height
• weight
• dropout rate
• teacher turnover rate
• distance between pupils of the eyes
• slope of the forehead
• etc.

No Child Left Behind 

No Child Left Behind, or previously known as the Elementary and Secondary Education Act(1965), was originally implemented to provide a more balanced budget between schools that service affluent communities and those which service communities of poverty. 
Currently schools must meet AYP and produce data through standardized testing that shows student growth.  
The purpose of ESEA was to improve the overall quality of education in the United States; however, even with multiple additions to the policy from 1965 ESEA to the current No Child Left Behind, I feel that many schools slip between the cracks and are allotted waivers.  Is it fair to put this policy in to place and then give waivers to the very schools that need the most improvements.  By giving waivers, are these schools getting the funding needed to improve? The following information was taken from the New American Foundation website, which offers a background and analysis of NCLB.  

"NCLB required states, school districts, and schools to ensure all students are proficient in grade-level math and reading by 2014. States define grade-level performance. Schools must make "adequate yearly progress" toward this goal, whereby proficiency rates increase in the years leading up to 2014. The rate of increase required is chosen by each state. In order for a school to make adequate yearly progress (AYP), it must meet its targets for student reading and math proficiency each year. A state’s total student proficiency rate and the rate achieved by student subgroups are all considered in the AYP determination."

 Variables of Concern Under the No Child Left Behind

•  grade
• ethnicity
• special education status
• free and reduced-price meal eligibility
• English language learner status
• percent of students taking the test
• math scores
• reading scores
• science scores
• percent of "proficient" students
• percent of high school graduates
• percent of "highly qualified" teachers
• adequate yearly progress  

 

Chapter 1 Cont...Tracking Growth

In the current system of education, data is used to monitor student growth which is then correlated to teachers' effectiveness.  This controversial use of data has caused tension with in the system it represents. 

Does this make any sense???

In some assessments, such as the Tennessee Value-Added Assessment System (TVAAS), the teacher is assessed by the students growth from one year to the next.  The frustration for many teachers was that is was not assessing the growth of the same student.  It would assess this year's third graders, with next year's third graders, to last year's third-graders.  Logically, one would conclude, that in order to assess growth accurately, one would have to assess the growth of a child from third grade to the fourth grade.


With the current teacher evaluation system, I have heard the following claim which presents no logic,  "...three years of effective teachers has an enormous impact on test scores."  This claim often leads teachers to believe they are in a lose/lose situation.  Secondary teachers often ask, "Why bother?"  Using the data collected from summative assessments as an indicator of a teacher's effectiveness can not be considered as a valid way to assess teachers.

Chapter 1: Data, Their Uses, and Their Abuses

Often, as we try to seemingly crack the code, data can be frustrating and overwhelming.  One thing to remember, is that it can be spun to represent a situation in either a positive or negative view.  It all depends on how the data is perceived.  As Dr. Bracey reminds us in chapter one, "Statistics, the language of data, are human constructions and must be interpreted by other humans for the numbers that have meaning." (Bracey, 2)

Data vs. Capta

Data: Latin for givens...there are no givens.
Capta: Dr. Bracey made up the term capta, which is derived from the proper Latin equivalent of data, captiva, which means taken.

To distinguish the difference between the two may seem like a small step, but it really speaks to the perception we have when data is referred to.  When we say, given we assume that the information was given for a single purpose.  In reality, information is taken from data and used/manipulated as proof of a theory. Before we are fooled by the use of statistics as pure fact, it is important to question the motive behind the theory being proven.  Then question the validity of the statistics by referring to the 32 principals of Dr. Bracey.

Rate vs. Number

While using statistics, a speaker/writer, can refer to a rate vs. a number to mislead their audiences.  One example is as follows:

"In his column of June 23, 2005, Washington Post pundit George Will wrote, 'Yet George W. Bush has increased the Department [of Education's] budget by 40 percent- more than the defense budget.'" (Bracey, 3)

This use of rate misleads audiences to believe that former President Bush's budget demonstrates higher increase to the Department of Education than to the Department of Defense.  Blind assumptions are dangerous.

Let's look at the numbers:

The Department of Defense made $402,635,000,000 in 2005. 

The Department of Education made $71, 477, 945,000 in 2005

If we have a small increase to an already large number, the large number is still made larger. 

This is just one, out of many examples, where the language used when statistics are thrown around can be misleading.

Simpson's Paradox

The 12th principal, according to Dr. Bracey, states: "Watch out for Simpson's Paradox." I further researched Simpson's Paradox:

The Simpson's Paradox was first discovered by Karl Pearson (1899) and Undy Yule (1903), but further elaborated by Edward H. Simpson (1951), and again by Colin R. Blyth (1972).

There are at least three variables related to Simpson's Paradox:
1. the explained
2. the observed explanitory
3. the lurking explanitory

For a more in depth explanations follow these links:
http://vudlab.com/simpsons/
http://en.wikipedia.org/wiki/Simpson%27s_paradox

32 Principals of Data Interpretation

One point that Dr. Bracey makes very certain, is the rule that you should never take statistics at face value.  He lists thirty-two principals that should be considered while interpreting data:

1. Do the arithmetic.
2. Show me the data!
3. Look for and beware of selectivity in the data.
4. When comparing gourps, make sure the groups are comparable. 
5. Be sure the rhetoric and the numbers match.
6. Beware of convenient claims that, whatever the calamity, public schools are to blame.
7. Beware of simple explanations for complex phenomena.
8. Make certain you know what statistic is being used when someone is talking about the "average."
9. Be aware of whether you are dealing with rates or numbers.  Similarly, be aweare of whether you are dealing with rates or scores.
10. When coparing either rates or scores over time, make sure the groups remain comparable as the years go by.  
11. Be aware of whether you are dealing with  ranks or scores.
12. Watch out for Simpson's paradox.
13. Do not confuse statistical significance and practical significance.
14. Make no causal inferences from correlation coefficients.
15. Any two variables can be correlated.  The resultant correlation coefficient might or might not be meaningful.
16. Learn to "see through" graphs to determine what information they actually contain.
17. Make certain that any test aligned with a standard comprehensively tests the material called for by the standard.  
18. On a norm-referenced test, nationally, 50 percent of students are below average, by definition. 
19. A norm-referenced standardized achievement test must test nly material that all children have had an opportunity to learn. 
20. Standardized norm-referenced testes will ignore and obscure anything that is unique about a school.  
21. Scores from standardized tests are meaningful only to the extent that we know that all children have had a chance to learn the material which the test tests. 
22. Any attempt to set a passing score or a cut score on a test will be arbitrary.  Ensure that it is arbitrary in the sense of arbitration, not in the sense of being capricious. 
23. If a situation really is as alleged, ask, "so what?"
24. Achievement and ability tests differ mostly in what we know about how students learned the tested sills. 
25. Rising test scores do not necessarily mean rising achievement.
26. The law of WYTIWYG applies: What you test is what you get.
27. Any tests offered by a publisher should present adequate evidence of both reliability and validity.
28. Make certain that descriptions of data do not include improper statements about the type of scale being used, for example, "the gain in math is twice as large as the the gain in reading."
29. Do not use a test for a purpose other than the the one it was designed for without taking care to ensure it is appropriate for the other purpose. 
30. Do not make important decisions about individuals or groups on basis of a single test.
31. In analyzing test results, make certain that no students were improperly excluded from the testing. 
32. In evaluating a testing program, look for negative or positive outcomes that are not part of the program.  For example, are subjects not tested being neglected? Are scores on other tests showing gains or losses?