Common core doesn't fix the real problem of education– poverty.

PUBLISHED in the Christian Science Monitor Weekly Magazine, September 15, 2014

Common core doesn't fix the real problem of education– poverty.

Arguments for opposing the common core presented by Gov. Jindal ("Common Core: Bobby Jindal says Obama forcing a national curriculum," August 27) do not include the

reasons many professional educators and researchers oppose it.

A central argument is that there is no need for a radical change in curriculum or testing. Substantial improvement will come only when we deal with the real problem: Poverty. When researchers control for the effect of poverty, American test scores are near the top of the world. Our unspectacular overall scores are because the US has the second highest level of child poverty among all 34 economically advanced countries (now over 23%, compared to high-scoring Finland’s 5.4%).

Poverty means poor nutrition, inadequate health care, and lack of access to books, among other things. All of these negatively impact school performance.  Instead of protecting children from the effect of poverty, the common core is investing billions in an untested curriculum and massive testing, despite research showing that increasing testing does not increase  achievement.

Stephen Krashen

Professor Emeritus

University of Southern California

This letter at: http://www.csmonitor.com/Commentary/Readers-Respond/2014/0913/Readers-Write-Education-s-real-problem-is-poverty-more-women-needed-in-parliament

Original article: http://www.csmonitor.com/USA/Education/2014/0827/Common-Core-Bobby-Jindal-says-Obama-forcing-a-national-curriculum-video

A more efficient, more pleasant way to teach English

Sent to the South Chine Morning Post, August 29, 2014

Philip Yeung's observations about Hong Kong English instruction ("Teach English that's actually of use to struggling students," August 28) applies to language instruction in other countries.  Yeung tells us that Sir Aker-Jones' daughter, after 12 years of English, could not "say or write a three word sentence." Survivors of foreign language classes in the US make similar statements. 

Yeung's analysis of the problem applies world-wide: Boring classes based on "fill-in-the-blanks" methods.  We know how to do it better.

Study after study confirms that we acquire language when we understand what we hear and what we read. Our vocabulary knowledge comes from "comprehensible input," not from memorizing vocabulary lists, and our ability to understand and use correct grammar comes largely from reading and listening, not from conjugating verbs.

For students to pay attention to the input, it has to be interesting: The best input is extremely interesting, or "compelling," so interesting that we are not even aware that it is in another language.

Methods developed over the last few decades, such as "TPRS", provide interesting activities for beginners (games, plays, stories, discussions) and encourage the development of a pleasure reading habit in English at the intermediate level.

Research shows that these methods produce far better results than traditional methods and are more pleasant for teachers and students.

Stephen Krashen

Original article (online): http://www.scmp.com/comment/article/1581131/teach-english-real-world-hong-kongs-struggling-students
Published version title: Teach English that's actually of use to struggling students

Groundhog Day: Americans Again Rate Local Schools Higher than Schools of the Nation

Groundhog Day: Americans Again Rate Local Schools Higher than Schools of the Nation

Stephen Krashen

As is the case every year, the PDK/Gallup poll (September 2014) found that people rate their local schools much more positively than they do schools in the US in general.  

The differences, as usual, were striking: Fifty percent of respondents said they would give the public schools in their neighborhood a grade or A or B, but only 17% would give public schools in the nation A or B.  When asked about the school their oldest child attends, 67% said they would give the school at A or B, suggesting that those who have more information about local schools rate them more highly.

Gerald Bracey (2009) gave a logical explanation for this phenomenon: "Americans never hear anything positive about the nation's schools," noting that "negative information flows almost daily from media, politicians, and ideologues."  The finding that American students score at the top of the world on international tests when poverty is statistically controlled (e.g. Carnoy and Rothstein, 2013) is never mentioned.

Education Secretary Duncan (also in 2009) gave his opinion of why people think local schools are better than schools in general: "Too many people don't understand how bad their own schools are." Duncan said that the public needs to be "woken up" to see that their own children are being short-changed. In other words, parents are not to be trusted on evaluating the quality of their own child's education, despite the fact that they are daily witnesses to the results of their child's schooling.

Bushaw, J. and Calderon, V. 2014. Try it again, Uncle Sam: The 46^th Annual PDK/Gallup Poll of the Public's Attitudes Toward the Public Schools. Phi Delta Kappan, 96(1): 8-20.

Bracey, G. 2009. Experience outweighs rhetoric. Phi Delta Kappen 91(1): 11

Carnoy, M and Rothstein, R. 2013, What Do International Tests Really Show Us about U.S. Student Performance. Washington DC: Economic Policy Institute. 2012. http://www.epi.org/).

Duncan, A. 2009. Quality education is our moon shot. Phi Delta Kappan 91(1), 24–9.

Krashen, S. and Ohler, J. 2009. The Bad Schools Syndrome. http://substancenews.net/articles.php?page=940&section=Article

Opposition to the common core

Sent to the Washington Post, August 20, 2014

The Post has reported that according to a recent PDK/Gallup poll, the "Common Core educational standards are losing support nationwide" (August 19). The poll asked those opposed to the Common Core why they were opposed, giving them several possible reasons.
The options did not include the reasons many educators oppose the Common Core: There is no need for a radical change in curriculum or testing. Substantial improvement will come only when we deal with the real problem: Poverty. When researchers control for the effect of poverty, American test scores are near the top of the world. Our unspectacular overall scores are because the US has the second highest level of child poverty among all 34 economically advanced countries (now over 23%, compared to high-scoring Finland’s 5.4%).
Poverty means poor nutrition, inadequate health care, and lack of access to books, among other things. All of these negatively impact school performance.  Instead of protecting children from the effect of poverty, the common core is investing billions in an untested curriculum and massive testing, despite research showing that increasing testing does not increase  achievement.
Stephen Krashen
Professor Emeritus
University of Southern California


original article: http://www.washingtonpost.com/local/education/common-core-educational-standards-are-losing-support-nationwide-poll-shows/2014/08/19/67b1f20c-27cb-11e4-8593-da634b334390_story.html

Pre-K = childhood's end?



Comment posted following “As the first day of school approaches, the city trains thousands of universal pre-K teachers,” on Chalkbeat
Posted on August 12.

I wonder how many of those interested in teaching Pre-K in New York have read the "New York State Prekindergarten Foundation for the Common Core." 



The document is 62 pages long, filled with an astonishing list of standards that cover Approaches to Learning (e.g. "Asks questions using who, what, how, why, when, where, what if," Physical Development (e.g."runs, jumps and walks in a straight line, and hops on one foot"), Social Development (e.g. "Identifies the range of feelings he/she experiences, and that his/her feelings may change over time, as the environment changes, and in response to the behavior of others"), Communication, Language and Literacy (e.g. " With prompting and support, demonstrate one-to-one letter-sound correspondence by producing the primary sound of some consonants,"), and Cognition and Knowledge of the World (e.g "Analyze, compare, and sort two- and three-dimensional shapes and objects, in different sizes, using informal language to describe their similarities, differences, and other attributes" (e.g., color, size, and shape)."

The document insists that the standards are not a curriculum, and not an assessment tool. But they "but can inform the development or selection of screening and progress monitoring tools" and they are intended to be a "bridge between the learning expectations of children birth through three and the standards for those attending K-12 in public schools." It is hard to imagine that the Pre-K standards will be used as anything a curriculum that teachers must teach, students must learn and be tested on, to make sure students are prepared for the rigors
of kindergarten. Standards and tests go together: Without tests, standards cannot be enforced. 



Nearly all children raised in a healthy environment develop these competencies in the course of hearing stories, interacting with others, and, of course, playing. Instead of allowing this to happen, Pre-K is in danger of turning play into work.

original article: http://ny.chalkbeat.org/2014/08/12/as-the-first-day-of-school-approaches-the-city-trains-thousands-of-universal-pre-k-teachers/#.U-u9lEh9mdT

High scores on reading tests, but little interest in reading

Sent to the Los Angeles Times, August 10, 2014

Walt Gardner  (letters, August 9) pointed out that high test scores may be accompanied by negative attitudes.  

Gardner may be right. Students in some countries with spectacular reading scores are not enthusiastic readers. Hong Kong ranked first in the 2011 PIRLS reading test, given to ten year olds in over 40 countries. But only 21% of Hong Kong children said they liked to read, less than the international average of 28%. 

The high test scores might be a result of massive amounts of required reading, combined with instructon on how to game the tests (test preparation).  In contrast, reading for interest and pleasure not only produces high test scores but also results in a love of reading and continuing growth in literacy. Required reading and test prep does not have this effect: Only 14% of Hong Kong parents said they liked to read; the international average is 30%.

Elizabeth Ka Yee Loh
University of Hong Kong

Stephen Krashen
University of Southern California

Gardner letter: http://www.latimes.com/opinion/readersreact/la-le-0809-saturday-california-api-20140809-story.html

Australia to require "the phonics method"

Sent to the Sydney Daily Telegraph (Australia)

Re the new "phonics requirement": ("Education minister orders universities to teach phonics or face losing accreditation," August 10)

"Phonics" could mean "systematic, intensive phonics,": teaching all phonics rules to all children in a strict sequence. Research done by Prof. Elaine Garan shows that systematic intensive phonics results only in better results on tests in which children are presented with words in a list and have to pronounce them outloud. It does not produce better results on tests in which children have to understand what they read. 

Systematic, intensive phonics has other problems. As literacy expert Frank Smith has reported, many rules of phonics are very complex and have numerous exceptions.  Many teachers say that they have to review the rules before trying to teach them: If experienced teachers can't remember the rules, how can six-year-old children remember them? Smith also notes that different phonics programs teach different rules, which makes it unlikely that learning all the rules is essential.

An approach that makes sense is "basic phonics": teaching those rules that children can learn, that teachers don't have to look up, and that actually help children understand what they read. 

Stephen Krashen

http://www.dailytelegraph.com.au/news/nsw/education-minister-orders-universities-to-teach-phonics-or-face-losing-accreditation/story-fni0cx12-1227019125456?nk=e8161d120ab17d620185f1eadb13ef3f

Rating Schools: Use Library Quality

Response to LA Times editorial: Grading California's Schools, August 7

More relevant than using outcomes (test scores, graduation rates) in rating schools is an "input" factor: the quality of the library.

Study after study shows that that students living in states with better school libraries do better on tests of reading achievement. 

California ranks near the bottom of the country in school library quality and is dead last in the ratio of school librarians per student.  Our students get little help from the public library: According to the latest "most literate cities" report, six California cities (including LA) are in the bottom ten out of 76 in library quality. Our low reading scores are no surprise.

Children of poverty have little access to books at home, in their neighborhoods, or at school, and few can afford e-readers and e-books. This lack of access helps explain their low reading achievement. For many of these children, the library is their only source of books.

Stephen Krashen

Professor Emeritus, University of Southern California

Most literate cities:

http://web.ccsu.edu/americasmostliteratecities/2012/rankbycategory/libraries.asp

Impact of libraries and librarians: Lance, K. C. The Impact of School Libraries on Student Achievement. http://www.lrs.org/impact.php; McQuillan, J. The Literacy Crisis: False Claims and Real Solutions. Heinemann; Krashen, Stephen, Syying Lee, and Jeff McQuillan. 1998. “Is the Library Important? Multivariate Studies at the National and International Level.” Journal of Language and Literacy Education 8.1 (2012): 26–36.

Poverty and access to books: Krashen, S (2004). The Power of Reading. Heinemann and Libraries Unliimted (Second Edition)

e-readers, e-books: Krashen, S. 2011. Kindelizaton: Are Books Obsolete? Journal of the California School Library Association, CSLA 3(2):10-11.

Listen to teachers, read the research (published in: The Jewish Journal)

Published in the Jewish Journal, August 13, 2014 as "Those who can't teach ..."

Ellie Herman points out that many of those driving education policy in the U.S. have never taught (“Why Aren’t We Listening to Our Teachers?” Aug. 8). The situation is even worse: Education policy makers are also ignorant of educational research. They are unaware that scientific studies published in professional journals provide no support for the massive amount of testing done in schools today, and that study after study shows that the most serious problem facing American education is our high rate of poverty, not the lack of tough standards.  

Educational practice should be influenced by the insights of experienced professional educators, as well as competent educational research. Policy makers today are ignoring both of these sources of wisdom. 

original article: http://www.jewishjournal.com/education/article/to_save_education_listen_to_teachers

Some sources:
No support for massive testing: Nichols, Sharon L., Gene V. Glass, and David C. Berliner. 2006. “High-Stakes Testing and Student Achievement: Does Accountability Pressure Increase Student Learning?” Education Policy Archives 14 (1). <http://epaa.asu.edu/ojs/article/view/72/198> (accessed October 14, 2013).
The US ranks second out of 34 economically advanced countries in level of child poverty: UNICEF Innocenti Research Centre (2012), ‘Measuring Child Poverty: New league tables of child poverty in the world’s rich countries’, Innocenti Report Card 10, UNICEF Innocenti Research Centre, Florence.
The impact of poverty: Carnoy, M and Rothstein, R. 2013, What Do International Tests Really Show Us about U.S. Student Performance. Washington DC: Economic Policy Institute. 2012. http://www.epi.org/). Payne, K. and Biddle, B. 1999. Poor school funding, child poverty, and mathematics achievement. Educational Researcher 28 (6): 4-13; Bracey, G. 2009. The Bracey Report on the Condition of Public Education. Boulder and Tempe: Education and the Public Interest Center & Education Policy Research Unit. http://epicpolicy.org/publication/Bracey-Report;

Access to books helps close the literacy gap in kindergarten

Access to books helps close the literacy gap in kindergarten

Stephen Krashen

Fryer and Levitt (2004) examined reading and math test scores in kindergarten and grade 1 from the Early Childhood Longitudinal Study (about 1000 schools). Their focus was the gap between black and white children.

For reading, the difference between black and white children at the start of kindergarten was .40 (where 0 = mean, sd = 1). Thus, black children scored 40% of a standard deviation below white children.

When Fryer and Levitt controlled for SES, (parents occupation, parent occupation, household income), the gap dropped to .134.

When they controlled for SES and number of children's books in the home the gap dropped to nearly zero, -.006.

This is a major result: the presence of children's books evens the playing field. And this is only for a test given at the start of kindergarten. Sadly, this report was buried deep in the paper: "Including number of books …. completely eliminates the gap in reading" (p. 452).

This result is consistent with studies that show that supplying even a modest number of books for read alouds to parents of young children of poverty helps close the vocabulary gap between children of poverty and national norms. (For a review of "Reach out and Read" studies, see Krashen, 2011). The finding is also consistent with studies done with older readers showing that access to a library can ameliorate the effect of poverty on reading achievement (Krashen, Lee and McQuillan, 2012).

When Fryer and Levitt controlled for more predictors, including age the child was when in kindergarten, birth weight, if the mother was a teenage mother at first birth, if the mother was 30 or older at first birth, the characteristics of neighborhood, whether the mother worked, preschool program participation, parental involvement in child's life, family size and structure, the difference was .093: Black children did slightly better.

References:

Fryer, R. and Levitt, S. 2004. Understanding the black-white test score gap in the first two years of school. The Review of Economics and Statistics 86 (2): 447-464.

Krashen, S. 2011. Reach out and read (aloud). Language Magazine 10 (12): 17-19. (languagemagazine.com/?page_id=2688)

Krashen, S., Lee, S.Y. and McQuillan, J. 2012. Is the library important? Multivariate studies at the national and international level. Journal of Language and Literacy Education, 8(1): 26-36. (http://sdkrashen.com/articles.php?cat=2)

Comprehensible Input-Based Methods vs. Traditional Methods

Below is a list of studies comparing comprehension-based methods with traditional methods that demand the conscious learning of grammar. The list includes studies contrasting comprehension-based methods with traditional methods for beginning foreign language teaching and intermediate foreign and second language teaching, as well as studies showing the superiority of self-selected reading over traditional instruction for intermediate second and foreign language students.

All studies included comparison groups and subjects were high school age or older.  In addition, there are a multitude of studies that confirm these results using multivariate techniques and case histories (Krashen, 2004).

References

Krashen, S. 2004. Explorations in Language Acquisition and Use. Portsmouth: Heinemann.

 

BEGINNING FOREIGN LANGUAGE: COMPREHENSION BASED METHODS

Asher, J. 1965. The strategy of the total physical response: an application to learning Russian. International Review of Applied Linguistics 3: 291-300.

Asher, J. 1969. The total physical response approach to second language learning. Modern Language Journal 53: 3-17.

Asher, J. 1972. Children's first language as a model for second language learning. Modern Language Journal 56: 133-139.

Asher, J., Kusudo, J. and De La Torre, R. 1974, Learning a second language through commands: the second field test. Modern Language Journal 58: 24-32.

Dziedzic, J. 2012. A comparison of TPRS and traditional instruction, both with SSR. International Journal of Foreign Language Teaching 7(2): 4-6.

Hammond, R. 1989. Accuracy versus communicative competency: The acquisition of grammar in the second language classroom. Hispania 71: 408-417

Isik, A. 2000. The role of input in second language acquisition: more comprehensible input supported by grammar instrution or more grammar instruction? ITL: Review of Applied Linguistics 129-130: 225-74.

Kunihara A, S. and Asher, J. 1965. The strategy of the total physical response: an application to learning Japanese. International Review of Applied Linguistics 4: 277-289.

Nicola, N. 1989. Experimenting with the new methods in Arabic. Dialog on Language Instruction. 6: 61-71.

Swaffer, J. and Woodruff, M. 1978. Language for comprehension: Focus on reading. Modern Language Journal 6:27-32.

Varguez, K. 2009. Traditional and TPR Storytelling instruction in the Beginning High School Spanish Classroom. International Journal of Foreign Language Teaching 5 (1): 2-11.

Watson, B. 2009. A comparison of TPRS and traditional foreign language instruction at the high school level. International Journal of Foreign Language Teaching 5 (1): 21-24.

Winitz, H. 1996. Grammaticality judgments as a function explitict and implicit instruction in Spanish. Modern Language Journal 80 (1): 32-46.

Wolfe, D. and Jones, G. 1982. Integrating total physical response strategy in a level 1 Spanish class. Foreign Language Annals 14: 273-80.

INTERMEDIATE  FOREIGN LANGUAGE: SHELTERED

Burger, S. 1989. Content-based ESL in a sheletered psychology course: Input, output, and outcomes. TESL Canada Journal 6:45-59.

Edwards, H., Wesche, M., Krashen, S., Clement, R., and Kruidenier, B. 1984. Second language acquisition through a subject-matter learning: A study of sheltered psychology classes at the University of Ottawa. Canadian Modern Language Review 41: 268-282.

Hauptman, P., Wesche, M., and Ready, D. 1988. Second language acquisition through subject-matter teaching: a follow-up study at the University of Ottawa. Language Learning 38: 433-71.

Lafayette, R. and Buscaglia, M. 1985. Students learn language via a civilization course – a comarison of second language acquisition environments. Studies in Second Language Acquisition 7: 323-42.

Sternfeld, S. 1993. Immersion in first-year language instruction for adults. In J. Oller (Ed.) Methods That Work. Boston: Heinle and Heinle.

INTERMEDIATE FOREIGN LANGUAGE: SUSTAINED SILENT READING

Bell, T. 2001. Extensive reading: Speed and comperhension. The Reading Matrix, 1 (1)

Hitosugi, C. I., and Day, R. 2004. Extensive reading in Japanese.  Reading in a Foreign Language 16 (1).  http://nflrc.hawaii.edu/rfl/April2004/abstracts.html#hitosugi

Hafiz, F., and I. Tudor. 1990. Graded readers as an input medium in L2 learning. System 18(1): 31-42.

Lao, C.Y. and Krashen, S. 2000. The impact of popular literature study on literacy development in EFL: More evidence for the power of reading. System 28: 261-270.

Lee, S.Y. 2007. Revelations from three consecutive studies on extensive reading. RELC Journal 38 (2), 150-170.

Lee, S. Y. and Hsu, Y. Y. 2009.  A three-year longitudinal study of in-class sustained silent reading with Taiwanese vocational college students. Indonesian Journal of English Language Teaching, 5(1): 15-29.

Lituanas, P. M., Jacobs, G. M., and Renandya, W. A. 1999. A study of extensive reading with remedial reading students. In Y. M. Cheah & S. M. Ng (Eds.) Language instructional issues in Asian classrooms (pp. 89-104). Newark, DE: International Development in Asia Committee, International Reading Association.

Liu, C.K. 2007. A reading program that keeps winning. Selected Papers from the Sixteenth International Symposium on English Teaching, English Teachers’ Association – Republic of China. Taipei: Crane Publishing Company.

Mason, B. 2006. Free voluntary reading and autonomy in second language acquisition: Improving TOEFL scores from reading alone. International Journal of Foreign Language Teaching 2(1), 2-5.

Mason, B. and Krashen, S. 1997. Extensive reading in English as a foreign language. System 25: 91-102.

Robb, T. N. & Susser, B. 1989. Extensive reading vs skills building in an EFL Context. Reading in a Foreign Language, 5, 2, 239-51.

Rodrigo, V., Krashen, S., and Gribbons, B. 2004. The effectiveness of two comprehensible-input approaches to foreign language instruction at the intermediate level. System 32(1): 53-60.

Sheu, S. P-H. 2004. Extensive reading with EFL learners at beginning level. TESL Reporter, 36(2), 8-26.

Sims, J. 1996. A new perspective: Extensive reading for pleasure. The Proceedings of the Fifth International Symposium on English Teaching, pp. 137-144. Taipei: Crane Publishing Company.

Smith, K. 2006. A comparison of “pure” extensive reading with intensive reading and Extensive Reading with Supplementary Activities. International Journal of Foreign Language Teaching (IJFLT), 2(2): 12-15.

Smith, K. 2007. The effect of adding SSR to regular instruction. Selected Papers from the Sixteenth International Symposium on English Teaching, English Teachers’ Association – Republic of China. Taipei: Crane Publishing Company.

Smith, K. 2011. Integrating one hour of in-school weekly SSR: Effects on proficiency and spelling. International Journal of Foreign Language Teaching, 7(1): 1-7.

Tudor, I., and Hafiz, F. 1989. Extensive reading as a means of input to L2 learning. Journal of Research in Reading 12(2): 164-178.

Tsang, W-K., 1996. Comparing the effects of reading and writing on writing performance. Applied Linguistics 17(2): 210-233.

Yuan, Y. P., and Nash, T. 1992. Reading subskills and quantity reading. Selected papers from The Eighth Conference on English Teaching and Learning in the Republic of China, pp. 291-304. Taipei: Crane.

Do American Rich Kids do Worse on International Tests than Rich Kids from Other Countries?

Do American Rich Kids do Worse on International Tests than Rich Kids from Other Countries?

Stephen Krashen

Hanushek, Peterson and Woessmann (2014) claim that when we examine students from "advantaged" families, American students do poorly in math: Our rich kids do worse than rich kids from other countries.  Hanushek et. al. conclude that this shows that poverty is not the only factor affecting school performance.  Their conclusions are based on their analysis of data from the 2012 PIRLS examination, tests given to 15-year-olds in a large number of countries.

Berliner (2014) argued that Hanushek et. al. used an invalid measure of "advantaged": at least one parent who graduated college. He also argued that a more valid measure is income. Many college graduates, Berliner pointed out, are not in high-income professions.

I present here a secondary analysis of the PISA data presented by Hanushek et. al. to determine the relative influence of parental education and poverty on math and reading achievement, as measured by PISA.  For the most part, the results support Berliner's claim.

Method and Results

Measures of poverty

Two measures of poverty were used in this analysis, one for poverty in the individual states of the US and another for poverty in other countries. Both measures are based on parental income.  Berliner (2014) points out that parental income and school achievement are strongly related: parental income often determines the school the student attends (Berliner, 2014), and the nutrition and health care the student receives (Berliner, 2009). All of these influence school achievement.

Levels of poverty in the states in the US were taken from Kids Count (2012). Poverty was defined as the percentage of children under 18 who live in families with incomes below the US poverty threshold, as defined by the US Census Bureau.

The measure used internationally comes from a report from UNICEF, from the Innocenti Research Centre (2012).  It presents the percentage of children, ages zero to 17, who live in "relative poverty,"  that is, in a household with less than 50% of the national median income, adjusted for family size.

Results: The states

For 49 states in the US,  I examined the relationship between levels of poverty with math scores for students with high parental education. The correlation was negative and substantial: r = -.70 (p < .0001), as was the correlation of income and reading scores (r = -.71; p < .0001). In other words, when we control for the effect of parent education, poverty has a considerable negative impact on test performance in both math and reading: parental education does not tell the whole story.

Results: International comparisons

For the analysis of the international results, I discarded three outliers. Japan and Poland had high levels of poverty (one standard deviation above the mean) and very high scores in reading and math,  two standard deviations above the mean. Iceland was discarded because of it's very low level of poverty and very low reading score, nearly two standard deviations below the mean.  (See appendix table 2).

In International Analysis I, as was done in the states analysis, the impact of parental education was controlled by limiting the analysis to test scores made by those with high parental education (at least one parent a college graduate). Data on child poverty was available for 24 countries.  Correlations of child poverty and achievement were not as high as the results presented above for 49 states of the US, but the correlation was clearly negative for math (r = -.43, p = .02, one-tail). It was also negative but low for reading (r = -.18, ns, p = .20, one-tail).  The correlations for international tests may be lower than those for the states of the US because the definitions of parental education are probably not as consistent from country to country as they are among the states.

For International Analysis II, data was obtained on the percent of college graduates in 18 countries in 2008  (National Center for Educational Statistucs, 2011).  The correlation of percent of colleage graduates in each country and child poverty was in the predicted direction – more poverty was related to less parental education, but the correlation was not high and fell short of statistical significance (r = -.22, ns, p = .20), confirming that the two variables are not strongly related. Similar results were reported by Berliner (2014).

International Analysis III used PISA math and reading scores for all students. Mean PISA scores for each country were used as the dependent variable, with child poverty and percent of college graduates for each country as predictors. The correlation between poverty and total math scores was considerably larger than the correlation between percentage of college graduates and math scores (for poverty, r = - .54, p < .01, one-tail); for percentage of college graduates, r = .19, ns, p = .23, one-tail), and the correlation between poverty and math was fairly close to the correlation that controlled for parental education, presented earlier (r = -.43).

Multiple regression yielded similar results. As presented in table 1, poverty was a much better predictor of math scores than was parental education (compare betas) and was statististically significant.

Table 1: Mutliple regression analysis  for PISA math

	B	beta	P
Poverty	-1.67	0.53	0.02
Grad	0.09	0.069	0.38

r2 = .20

Results for reading PISA scores were somewhat different.  Both poverty and percentage of college graduates correlated with reading scores but the correlation of percentage of college graduates and reading scores (r = .54, p = .01, one-tail) was higher than the correlation of poverty and reaiding scores (r = -.35, p = .08, one-tail).

Again, multiple regression yielded results similar to the correlational analysis: As presented in table 2, the impact (beta) of percentage of college graduates in a state was higher than the impact of poverty, and the college graduate predictor was easily statistically significant, while the poverty predictor fell somewhat short of statistical significance (p = .14). 

Table 2: Multiple regression analysis for PISA reading

	b	beta	P
Poverty	-0.60	0.24	0.14
Grad	0.51	0.51	0.02

r2 = .35

Summary and Conclusions

The states analysis for both reading and math showed that when we control for the effect of parental education, the effect of poverty is powerful.

The first international analysis, controlling for parental education, showed that poverty has a clear effect on math scores, but less on reading scores, and the impact of poverty on math and reading was not as strong as it was in the states analysis.

The second international analysis revealed that parental education and poverty are only modestly correlated.

The third international analysis, using total PISA scores, graduation rates, and poverty, showed that parental education is a much weaker predictor than poverty for math achievement, but the international analysis using total PISA reading scores, graduation rates and poverty showed a clear effect for parental education (college graduation rates), and a weaker effect for poverty.

At least some of the reading results could stem from the failure to include an obvious factor: Access to books, a significant predictor of reading achievement independent of the impact of socio-economic status using several different measures of SES (Krashen, 2011; Krashen, Lee and McQuillan, 2012). Also, before we conclude that poverty is not a good predictor of reading achievement, recall that poverty was a strong predictor of reading test scores among the states in the US when parental education was controlled.

In summary,  the results provide support for Berliner's claim that "One’s level of education and one’s level of income simply do not provide the same information" and that "Parental income and their child’s school achievement are strongly related, perhaps even more so than is parental education level and their children’s school achievement."  Poverty, as measured by income, was a strong predictor of achievement in the states of the US, independent of the effect of parental education,  and was clearly a stronger predictor of math scores, according two different analysis.  Parental education was a stronger predictor of international reading scores, but poverty also a made a clear contribution.

References

Berliner, D. 2014. Criticism via Sleight of Hand http://dianeravitch.net/2014/07/29/david-berliner-responds-to-economists-who-discount-role-of-child-poverty/

Berliner, D. 2009. Poverty and Potential:  Out-of-School Factors and School Success.  Boulder and Tempe: Education and the Public Interest Center & Education Policy Research Unit. http://epicpolicy.org/publication/poverty-and-potential

Hanushek, E. A., Peterson, P. E., and Woessmann, L. 2014. Not just the problems of other people’s children: U.S. Student Performance in Global Perspective. Harvard University, Program on Education Policy and Governance & Education Next, PEPG Report No. 14-01, May 2014.

Kids Count Data Center, 2012. http://datacenter.kidscount.org/data/tables/43-children-in-poverty#detailed/2/2-52/false/868/any/321,322.

Krashen, S. 1997. Bridging inequity with books. Educational Leadership  55(4): 18-22.

Krashen, S., Lee, SY., and McQuillan, J. 2012. Is the library important? Multivariate studies at the national and international level. Journal of Language and Literacy Education, 8(1)? 26-36. (available at www.sdkrashen.com, see "free voluntary reading" section and scroll down)

National Center on Educational Statistics, 2011. Youth Indicators 2011.  First time college gradution rates among 30 OECD countries. http://nces.ed.gov/pubs2012/2012026/tables/table_23.asp

UNICEF Innocenti Research Centre 2012, Measuring Child Poverty: New league tables of child poverty in the world’s rich countries,, Innocenti Report Card 10, UNICEF Innocenti Research Centre, Florence.

Appendix

Table A1: PISA scores from 49 states in the USA

STATES	Math	reading	Poverty
Mass	62.3	59	15
Vermont	59.3	55.4	15
Minnesota	59	48.1	15
Colorado	58.1	52.3	18
New Jersey	57.9	55.6	15
Montana	57.5	48.7	20
Washington	54.3	49.2	19
Texas	54.2	40.4	26
New Hamp	53	47.8	16
Virgina	52.6	46.2	15
Wisconsin	52.6	45.7	18
Kansas	52.2	47.1	19
Maryland	52	51.2	14
S. Dakota	51.6	44.7	17
Connecticut	51.3	56.8	15
Pennsylvania	50.7	48.9	20
N. Dakota	50.2	39	13
Ohio	49.8	48.1	24
Idaho	49.6	44.8	21
Maine	49.4	49	21
Arizona	49.1	41.6	27
Wyoming	48.2	47.2	17
N. Carolina	48.1	39.9	26
Rhode Island	48	46.4	19
Utah	47.8	46.4	15
Indiana	46.9	40.7	22
Oregon	46.3	45.6	23
Illinois	45.6	46.6	21
Iowa	44.9	42.3	16
Nebraska	44.8	45.1	18
Kentucky	44.1	46.2	27
S. Carolina	43.1	34.1	27
California	42.6	36.3	24
Missouri	42.3	47	23
Michigan	42	40.6	25
Oklahoma	41.8	33.6	24
Nevada	41.7	35.6	24
Delaware	40.9	41.1	17
Arkansas	40.2	36.9	29
New York	39.8	46.9	23
Georgia	38.2	36.2	27
Hawaii	37.5	34.4	17
Florida	37.5	37.3	25
New Mexico	37.1	34.5	29
Tennessee	34.1	37	26
West Virgina	31.9	33.9	25
Alabama	27.8	34%	27
Mississippi	25.6	25.9	35
Louisiana	28.1	29.4	28

Table 2: International Data, At least one parent completed college

	pov	math	reading
Finland	5.3	51.8	50.4
netherlands	6.1	54.7	49
Slovenia	6.3	42.8	40.1
Germany	8.5	50	53.1
New Zealand	11.7	43.4	53.3
Ireland	8.4	43.6	53.2
France	8.8	42.4	53
Belgium	10.2	50.3	52
Australia	10.9	45	51.1
Canada	13.3	51	50.6
Portugal	14.7	38.8	47.3
Switzerland	8.1	57.3	47
Czech Rep	7.4	43.7	45.9
Estonia	11.9	51.3	44.9
Luxembourg	12.3	40.3	44.3
Hungary	10.3	33.1	43.9
Norway	6.1	39.3	43.6
UK	12.1	41.3	43.4
USA	23.1	34.7	41.6
Austria	7.3	46.3	40.6
Denmark	6.5	42.8	39.1
Spain	17.1	36.9	38.3
Italy	15.9	37.4	37.7
Sweden	7.3	34.6	35.7
mean	10.4	43.9	45.8
sd	4.3	6.6	5.5

Outliers

	poverty	math	reading
Japan	14.9	59.2	60.4
Poland	14.5	59.3	62.6
Iceland	4.7	45.6	33.9

Table A3: Total scores, graduation rates , and poverty

	poverty	math score	reading score	grad rate
Finland	5.3	519	524	62.6
Netherlands	6.1	523	511	41.4
Germany	8.5	514	508	25.5
New Zealand	11.7	500	512	48.3
Ireland	8.4	501	523	46.1
Portugal	14.7	487	488	45.3
Switzerland	8.1	531	509	32.4
Czech Rep	7.4	499	493	35.8
Luxembourg	12.3	490	488	5.3
Hungary	10.3	477	488	30.1
Norway	6.1	489	504	41.5
UK	17.1	494	499	34.9
USA	23.1	481	498	37.3
Austria	7.3	506	490	25
Denmark	6.5	500	496	46.8
Spain	17.1	484	488	33.1
Italy	15.9	485	490	32.8
Sweden	7.3	478	483	39.9
mean	10.7	497.7	499.6	36.9
sd	5	15.8	12.5	12.05