Is The Library Important? Multivariate Studies at the National and International Level Print E-mail
By Stephen Krashen, Syying Lee & Jeff McQuillan   
Abstract
 
Three multivariate analyses, all controlling for the effects of poverty, confirm the importance of the library. Replicating McQuillan’s analysis of 1992 NAEP scores, this study finds that access to books in school and public libraries was a significant predictor of 2007 fourth grade NAEP reading scores, as well as the difference between grade 4 and grade 8 2007 NAEP reading scores, suggesting that access is important for improvement after grade 4. Access (school/classroom libraries) was a significant predictor of scores on the PIRLS test, a reading test given to fourth graders in 40 countries.

 


 
It has been firmly established that more access to books results in more reading and that more reading leads to better literacy development (Krashen, 2004).
 
It is thus reasonable to hypothesize that more access means better reading. This prediction has been confirmed by a number of studies showing a positive relation between library quality and reading achievement (studies reviewed in Krashen, 2004; Lance, 2004; McQuillan, 1998).
 
In a multivariate study, McQuillan (1998) examined the relation between access to reading material and scores on the 1992 NAEP reading test given to samples of fourth graders in 42 states in the US. His measure of access was a combination of three measures of access to reading material at home, two of access to reading in school, and two of access to reading in the community. Table 1, a multiple regression analysis from McQuillan (1998), tells us that even after controlling for the effect of poverty, access to print was a significant and strong predictor of performance on the NAEP reading test: those with more access did better.
 
The combination of poverty and print access accounted for 72% (r2 = .72) of the variability on the NAEP, that is, if we know the level of poverty of families in a state, and how much reading material is available to children in that state, we have 72% of the information we need to predict how well fourth graders in that state scored on the NAEP.
 
Table 1: Predictors of NAEP reading test scores, grade 4, 1992, 42 states
 
 predictors  beta  t  p
 poverty  -0.45  5.07  0
 print access  1.12  4.3  0

 r2 = .7

The goal of this paper is to report some recent progress in this area, using multivariate analysis.

A replication

Table 2 presents a replication of McQuillan’s findings using the 2007 fourth grade NAEP and more recent measures of poverty and access to books (a combination of books per student in school libraries and per capita total circulation in public libraries in each state). (Means, standard deviations and inter-correlations among the variables are presented in the Appendix, Tables A1 and A2.) This analysis controls for the presence of English learners by only including scores for fluent English proficient children. Once again poverty is a strong predictor of scores, and once again access to books makes an independent contribution to reading achievement.
 
Table 2: Predictors of NAEP grade 4, 2007, 51 states
 
 predictors  beta  t  p
 poverty -0.72 7.42  0
 access 0.53 1.62 0.055

 r2 = .65; adjusted r2 = .63 (Fluent English proficient students only)

The Grade 4 to 8 difference

A separate analysis was performed to try to determine what factors are responsible for improvement after grade 4, or, more accurately in this case, the difference between grade 4 and grade 8 scores. This multiple regression analysis is presented in Table 3. This analysis indicates that, not surprisingly, grade 4 scores are a strong predictor of grade 8 scores. It is surprising, however, that poverty is a weak predictor of the difference between grade 4 and grade 8. Recall that the impact of poverty is strong, however, on the grade 4 test.
 
Table 3: Predictors of NAEP grade 8, 2007, 51 states 
 
predictors beta t p
NAEP grade 4 0.857 10.68 0
Poverty -0.076 0.96 0.17
Access 1.26 4.59 0
 r2 = .89 (Fluent English proficient only)
 
Of interest to us is that access to books, again a combination of school library holdings and public library circulation, is a significant predictor of the difference in NAEP reading scores between grade 4 and grade 8.
 
The r2 of .89 means that knowing the fourth grade NAEP scores for a state, the level of poverty, school library holdings, and public library circulation provides 89% of the information we need to predict a state’s grade 8 NAEP reading score.

Late intervention

The effect of poverty on fourth grade reading is enormous, but access to books can contribute to fourth grade reading, regardless of poverty. The analysis also indicates that those who read better in grade four also read better in grade eight, but access to books can help here as well. This finding agrees with data showing that “late intervention” in the form of recreational reading is not only possible but can be effective (Krashen & McQuillan, 2007).
 
To get a more precise idea of the impact of access to books, we can analyze the increase in r2 achieved by adding access to the effect of poverty. In grade 4, after controlling for poverty, access adds .02 to the r2, increasing our ability to predict reading scores by 2%. Access increases our ability to predict the grade 4 to 8 difference by nearly 5%. As indicated in Table 4, both public library circulation and school library holdings contributed to these increases.
 
Table 4: Gains in r2
 
predictors access public library  school library
grade 4 2%* 1.6% 1%
difference 4-8 4.8%* 2.7%* 3%*
 * = statistically significant, p < .10.
 
This investigation used states of the USA as units. Our second study expands the investigation of the relationship of access to reading to the international level, with countries as units.

The PIRLS study

PIRLS (Progress in International Reading Literacy Study) administered a reading test to fourth graders in over 40 countries. The PIRLS test attempts to measure both reading for literary experience and reading to acquire and use information (Mullis, Martin, Kennedy, & Foy, 2007). Students took the test in the national language of their country.
 
PIRLS provides not only test scores, but also the results of an extensive questionnaire given to teachers and students, including attitudes, reading behavior outside school, and classroom practices (Mullis et. al., 2007). PIRLS also supplies data on socio-economic class. The items on the questionnaire relevant to this study and SES statistics are presented in the Appendix (Table A3).
 
We present here two analyses of the PIRLS data, designed to further test the impact of access to books on scores on the PIRLS reading test. The first is a complex or full analysis that included as much of the information provided by PIRLS as possible, and the second is a simpler analysis, using only selected variables. We only included countries for which complete data were available for all factors (for a list of the countries included, see Appendix Table A4).

The full (complex) analysis

In order to deal with the vast amount of information supplied by the PIRLS questionnaire, the data were factor analyzed, a statistical technique that assigns predictors into groups that behave similarly, as one factor.
 
Factor analysis revealed four factors: SES/home (Socio-economic status and home resources, including books in the home), Literacy (free reading of fiction, sustained silent reading in school, parental reading, parental education), Libraries (school and classroom), and Instructional Factors. (Inter-correlations are in Table A5 of the Appendix, and details of the factor analysis are presented in Table A6 of the Appendix.)
 
The Library factor was by far the strongest predictor in the multiple regression analysis. The Literacy (free reading) factor was positively related to reading scores but did not reach statistical significance. Although the SES/home factor correlated highly with reading scores (r = .64; see Table A5 in the appendix), the SES/home factor was not a significant predictor of reading scores in the multiple regression analysis. The amount of formal reading instruction students received was negatively associated with reading proficiency. All factors combined accounted for 72% of the variation of PIRLS reading scores, which is very high (Table 5).
 
Table 5: Multiple Regression: Complex (Full) Analysis
 
predictors beta t P
SES home -0.02 0.122 0.9
Literacy 0.164 1.343 0.19
Library 0.493 4.801 0
Instruction -0.483 3.454 0.002
 r2 = .72

The simple analysis

In the simple analysis, one predictor was chosen to represent each factor, one that was felt to be most representative of the factor we were interested in investigating. For SES/Home, only one measure of socio-economic status was used, the Human Development Index (HDI) developed by the United Nations. The measure of literacy used was SSR (sustained silent reading), the percentage of students who read independently in school every day or almost every day in each country. The library factor was represented by the percentage of school libraries in each country with over 500 books.  Instruction was represented by the average hours per week devoted to reading instruction in each country. (Inter-correlations among these variables are in Table A7 of the Appendix.)
 
Table 6: Multiple Regression: Simple analysis
 
predictor beta t P
SES home -0.41 2.74 0.005
Literacy 0.161 1.343 0.143
Library 0.346 2.75 0.005
Instruction -0.186 1.4 0.085

 r2 = .63

The results are quite similar to the complex solution, except that SES, as measured by the HDI, is now a significant predictor (Table 6).

Conclusion

In all of the multivariate studies considered here, the library emerges as a consistent predictor of reading scores. This finding is remarkable, especially when we consider that the measures used are crude: library holdings, and even general circulation, in the case of public libraries.
 
Of course, providing access is only the first step: Even with access, some children (but surprisingly few) will not read. The research literature consistently indicates that rewards for reading are not effective (Krashen, 2003; 2004; McQuillan, 1997), but that read-alouds and conferencing do help. But in order for these approaches to work, the books need to be there.
 
But what is clear is that libraries definitely matter, and they matter a lot.
 
Inspection of the betas in the tables reveals that access to books in some cases had a larger impact on reading achievement test scores than poverty (Tables 1,3, 4), and in other cases had nearly as strong of an impact (Tables 2,5). This finding suggests that providing more access to books can mitigate the effect of poverty on reading achievement, a conclusion consistent with other recent results (Achterman, 2008; Evans, Kelley, Sikora, & Treiman, 2010; Schubert and Becker, 2010). This result is of enormous practical importance: Children of poverty typically have little access to books (Krashen, 2004). It seems that libraries can provide this access.

References

Achterman, D. (2008) Haves, halves, and have-nots: School libraries and student achievement in California. PhD dissertation, University of North Texas. Accessed at: http://digital.library.unt.edu/permalink/meta-dc-9800:1
 
Chutem, A. and Kroe, P. (2007) Public libraries in the United States: Fiscal year 2005 (NCES 2008-301). Washington, D.C.: National Center for Educational Statistics, Institute of Education Science, U.S. Department of Education.
 
Evans, M., Kelley, J., Sikora, J. & Treiman, D. (2010) Family scholarly culture and educational success: Books and schooling in 27 nations. Research in Social Stratification and Mobility, 28(2), pp. 171-197
 
Holton, B., Boe, Y., Baldridge, S., Brown, M., & Heffron, D. (2004) The status of public and private school library media centers in the United States.  Washington D.C.: National Center for Educational Statistics, U.S. Department of Education.
 
Krashen, S. (2003) ‘The (lack of) experimental evidence supporting the use of Accelerated Reader’ in Journal of Children’s Literature, 29(2): pp. 9, 16-30.
 
Krashen, S. (2004) The Power of Reading. Portsmouth, NH, & Westport, CT: Heinemann and Libraries Unlimited.
 
Krashen, S., & McQuillan, J. (2007) ‘Late Intervention’ in Educational Leadership, 65 (2), pp. 68-73. 
 
Lance, K. (2004) ‘The Impact of School Library Media Centers on Academic Achievement’ In C. Kuhlthau (Ed.), School Library Media Annual (pp. 188-197). Westport, CT: Libraries Unlimited.
 
Lee, J., Grigg, W. &  Donahue, P. (2007) The Nation’s Report Card: Reading 2007 (NCES 2007–496). Washington, D.C.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
 
McQuillan, J. (1997) ‘The Effects of Incentives on Reading’ in Reading Research and Instruction 36, pp. 111-125.
 
McQuillan, J. (1998) The Literacy Crisis: False Claims and Real Solutions, Portsmouth, NH: Heinemann.
 
Mullis, I, Martin, M., Kennedy, A. & Foy, P. (2007) PIRLS 2006 international report, Boston: International Study Center, Boston University.
 
Schubert, F. and Becker, R. (2010) ‘Social Inequality of Reading Literacy: A Longitudinal Analysis with Cross-Sectional Data of PIRLS 2001and PISA 2000 Utilizing the Pair Wise Matching Procedure’ in Research in Social Stratification and Mobility 29, pp. 109-133.
 
Editor’s Note: This article first appeared in the Journal of Language and Literacy Education. It is reprinted here with permission. Its full original citation is: Krashen, S., Lee, S., & McQuillan, J. (2012). 'Is the Library Important? Multivariate Studies at the National and International Level'. Journal of Language and Literacy Education [Online], 8(1), 26-38. Available at http://jolle.coe.uga.edu/wp-content/uploads/2012/06/Is-the-Library-Important.pdf

APPENDIX

Table A1: NAEP 2007 analysis: Means and standard deviations, 51 states
 
  mean standard deviation
NAEP 8 263.4 6.69
NAEP 4
222.4
6.74
Poverty
17.75
5.28
Public library circulation
7.52
2.82
School library holdings
19.57
6.21

 

The measure of poverty used was the percentage of families with children in each state at the poverty level or below for 2005, available at http://www.kidscount.org, from the U.S. Census Bureau, American Community Survey.

 
Access consisted of a combination of two variables:  (1) Per capita public library circulation for each state, from Chutem, A. and Kroe, P. (2007). Public Libraries in the United States: Fiscal Year 2005 (NCES 2008-301). Washington, D.C.: National Center for Educational Statistics, Institute of Education Science, U.S. Department of Education.  (2) School library holdings for each state (books per student), from Holton, B., Boe, Y., Baldridge, S., Brown, M., & Heffron, D. (2004). The Status of Public and Private School Library Media Centers in the United States. Washington D.C.: U.S. Department of Education, National Center for Educational Statistics.
 
Table A2: NAEP 4, 2007 analysis: Inter-correlations
 
  NAEP 4 Poverty Access
NAEP 8 0.92 0.72 0.64
    0.79 0.17
NAEP 4     0.47
 
 
Table A3: PIRLS Variables and Means
 
Predictor n mean standard deviation
Gross national income per capita 42 18458.7 14387
Gross National Income: Purchasing Power
40  20242.8  12081.8
Score on PIRLS reading test
45
505.9
67.91
Socio-economic status: Score on HDI index
45
0.8803
0.089
Percent children with high early home literacy activities
43
55.98
15.37
Percent of homes with high educational resources
43 11.86 6.72
Percent of homes with 100 books or more
43
15.14
11.55
Percent with university education or higher
42
27.48
12.88
Percent of parents reading more than five hours per week
43
37.67
9.78
Percent students reading fiction outside of school every day or nearly every day
45 34 10.55
Percent students reading nonfiction outside of school every day or nearly every day
45 15.33 7.45
Percent students reading for fun outside
45 40.69 8.57
Teacher reads aloud to entire class daily
45 59.5 22.24
Students read independently in school every day or almost every day
45 67.4 12.44
Students answer questions in workbooks about reading (almost) every day
45 36.33 14.15
Teacher reports giving written quiz or test after students read –at least weekly
45 24.53 17.4
Percent of schools with school libraries
44 89.84 16.35
Percent of schools with school libraries containing more than 500 books
44 73.64 27.4
School library has more than ten Magazines
44 25.67 22.07
Percent of students with access to classroom libraries
45 71.49 21.76
Average number of books in classroom Library
45 66.13 58.13
Average number of magazine titles in classroom library
45 3.36 1.84
Percent of students who can borrow books from classroom library to take home
45 57.78 20.15
Percent students using instructional software to develop reading skills
45 30.93 18.97
Percent students reading stories or other texts on computer
45 41.67 23.05
Hours per week on reading instruction 45 2.54  0.938
 
 
Table A4. PIRLS: Countries included in the analysis presented here: Austria, Belgium (French), Belgium (Flemish), Bulgaria, Canada-Alberta, Canada-British Columbia, Canada-Nova Scotia, Canada-Ontario, Canada-Quebec, Taiwan, Denmark, France, Georgia, Germany, Hong Kong SAR, Hungary, Iceland, Indonesia, Iran, Israel, Italy, Kuwait, Latvia, Lithuania, Republic of Macedonia, Republic of Moldova, Morocco, Netherlands, New Zealand, Norway, Poland, Romania, Russian Federation, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Trinidad and Tobago.
 
(PIRLS treated five provinces as separate countries, for some reason. Also, Hong Kong was included but China was not, and Flemish and French sections of Belgium were treated separately.)
 
Table A5: PIRLS: Complex (full) factor analysis: Inter-correlations

 

  Reading Proficiency SES home Literacy Library
SES home 0.64      
Literacy 0.47 0.51    
Library 0.57 0.35 0.51  
Instruction -0.64 -0.72 -0.18 -0.09

 

Table A6: PIRLS: Factor Analysis
 
  I: SES home II: Library III: Literary Activities IV: Instruction
Gross National Income per capita 0.85      
Gross Nat Income Purchasing Power 0.88      
Socio-economic status: HDI Index
0.87      
Percent of homes with high educational Resources
0.70      
Percent of homes with 100 books or more
0.81      
Percent of students using instructional software to develop reading skills
0.88      
Percent students reading stories or other texts on computer
0.84      
Percent of schools with school libraries   0.94    
Percent of school libraries with more than 10 magazines.
  0.62    
 
Percent of schools with classroom libraries
  0.89    
Average number of books in classroom Library
  0.74    
Average number of magazine titles in classroom library
  0.78    
Percent of students who can borrow books from classroom library to take home
  0.89    
Percent children with high early home literacy activities
    0.67  
Percent parents with university education or higher
    0.64  
Percent of parents reading more than five hours per week
    0.44  
Percent students reading fiction outside of school every day or nearly every day
    0.64  
Percent of students reading for fun outside of school every day or nearly every day
    0.38 0.71
Students read independently in school every day or almost every day
    0.65  
Teacher reads aloud to entire class daily     0.57 0.56
Teachers reports giving written quiz or test after students read—at least weekly
      0.60
Students answer questions in workbooks about reading (almost) every day
      0.32
Hours per week on reading instruction
      0.09
Percent students reading nonfiction outside of school every day or nearly every day
      0.36
Alpha       0.79
 
Some variables were not included in the multivariate analyses. For example, PIRLS reported data on hours spent on reading and writing instruction, but because of the vague description and the fact that it does not correlate with any of the other variables, it was not included. Also, among the library variables, PIRLS reported the percentage of students who reported borrowing books. This variable was omitted because it loaded on a single factor and reduced reliability.
 
A Principle Components Analysis extracted six factors and a Varimax Rotation produced three clear factors: SES/home, school library, and classroom library.
 
The literacy and instruction factors were determined based on the inter-correlations among the variables and the concept each variable represented.  We thus arrived at a four-factor solution, presented in Table A6. Table A6 also presents the results of the reliability test of the four factors, and the alpha for each factor was satisfactorily high.
 
Note that read-alouds were in Factor IV (Instruction) and correlated highly with other instructional variables, suggesting that read-alouds were used primarily as instruction, and not for enjoyment.
 
All raw scores of the variables selected were then converted to z scores and were added up and averaged to arrive at composite scores for the hierarchical regression analyses, presented in the text.
 
Table A7: PIRLS: simple analysis: Inter-correlations
 
  Reading Proficiency Poverty (HDI) SSR School Library
Poverty (HDI) 0.71      
SSR 0.5 0.43    
School Library 0.56 0.37 0.51  
Instruction -0.26 -0.4 0.04 0.17

 

The Human Development Index is an average of three factors: education (adult literacy rates, school enrollment), life expectancy, and wealth (logarithm of income); See http://hdr.undp.org/en/statistics/indices/hdi/.
 
The UN considers high HDI to be between .8 and .95, mid to be between .5 and .79 and low to be between .34 and .49.