ED372759 May 94 The Impact of School Library Media Centers on Academic Achievement. ERIC Digest.
Author: Lance, Keith Curry
ERIC Clearinghouse on Information and Technology, Syracuse, NY.

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INTRODUCTION

Advocates of school library media programs have long been convinced of
the relationship between quality library media programs and academic
achievement. Most studies of this relationship were conducted between
1959 and 1979, were limited in scope, and usually used a small number
of subjects in a limited geographical area. This study was designed
both to update the existing research and to examine the relationship
between library media programs and student achievement.

METHODOLOGY

Ideally, schools included in the sample for a study such as this would
be selected on a random, stratified, or quota basis. None of these
sampling designs was possible, because schools included in the sample
had to have library media centers that responded to the 1989 survey of
school library media centers in Colorado and had to use the Iowa Tests
of Basic Skills (ITBS) or Tests of Achievement and Proficiency (TAP).
These data were available for only 221 of 1,331 public elementary and
secondary schools in Colorado during the 1988-89 school year. The
study relied entirely upon available data about school library media
centers and their school and community contexts to predict
norm-referenced test scores.

FINDINGS

Findings of this study provided bases for measuring the relative
impact of potential predictors on academic achievement. Correlation
analysis of community variables identified the following
relationships:

* Rural and urbanized populations within school districts are almost
mutually exclusive. In addition, there is little variation between
districts that are 100 percent rural and 100 percent urbanized.

* Where more adults have graduated from high school, family incomes
are higher.

* Where more adults have graduated from high school, more adults have
graduated from college.

* Where more adults have graduated from college, family incomes are
higher.

* Where family incomes are lower, more families live in poverty.

* Where fewer adults are high school graduates, more families live in
poverty.

On the basis of these findings, the following community variables were
discarded as predictors of student achievement:

* urbanized and rural percentages of population,

* college graduation and median family income, and

* percentage of families living below poverty level.

Correlation analysis of school variables identified these
relationships:

* Schools with more teachers with master's degrees tend to pay higher
salaries.

* Schools which spend more on instruction in general almost always
spend more on supplies and materials, support services, and community
services.

On the basis of these findings, the following actions were taken:

* Teacher-related variables were referred to factor analysis for
potential combination into a single variable.

* Proportions of total expenditures per pupil spent on instruction,
supplies and materials, support services, and community services were
discarded as redundant.

Correlation analysis of library media center (LMC) variables
identified the following noteworthy relationships:

* LMCs with larger book collections tend also to have more periodical
subscriptions.

* LMCs which have more to spend on materials tend to have more to
spend on equipment.

* LMCs which have more endorsed staff tend to have staff who spend
more time identifying materials for instructional units developed by
teachers and more time collaborating with teachers in developing such
units.

* Numbers of books, periodical subscriptions, software packages, and
videos in LMC collections tend to rise and fall together.

* Use of LMC materials, particularly audio-visual materials, appears
likely to increase as teachers begin to involve LMC staff in their
instructional planning.

* The well-known impact of periodical subscription prices on LMC
materials expenditures is evident.

On the basis of these findings, the following actions were taken:

* A collection size factor based on numbers of books and periodical
subscriptions was attempted.

* Separate dollar figures on LMC materials and equipment spending were
added together to form one variable.

* Additional combinations of LMC variables were sought solely to
reduce their numbers.

In terms of student achievement, in every grade, students who scored
better on reading tests were likely to test better on their use of
language and use of the library media center. For this reason, reading
scores alone were used to represent academic achievement in this
study.

After eliminating redundant variables, the next step in refining the
database of potential predictors was to submit related sets of
variables to factor analysis, generating several scores that were used
to represent groups of related variables. Community variables
submitted to factor analysis were: percentage of minority students,
percentage of free lunch students, percentage of adults who graduated
from high school, and average family size. The first three variables
were combined into an "At-Risk" factor. Average family size was
dropped from further consideration when it was realized that it was a
poor way to operationalize a student's access to parental support,
such as homework assistance. (If average family size is three, the
typical family might be composed of two parents and one child, in
which case the student is likely to be in a relatively advantageous
position. Alternatively, the three might be a single parent with two
children, in which case the students are likely in a relatively
disadvantaged position.)

School variables submitted to factor analysis were: total expenditures
per pupil, teacher-pupil ratio, percentage of teachers with master's
degrees, average years of experience for teachers, and average teacher
salary. The three latter variables were combined into a "Career
Teacher" factor. Both total expenditures per pupil and teacher-pupil
ratio were retained as separate variables because of their presumed
relationships to academic achievement.

Library media variables submitted to factor analysis were:

* numbers of materials by format (books, periodical subscriptions,
videos, software packages, audio-visual materials);

* numbers of microcomputers;

* numbers of media-endorsed and total staff hours per typical week;

* numbers of hours typically spent each week assisting teachers or
collaborating with them in designing instructional units;

* numbers of service transactions (print and non-print circulation,
information skills instruction contacts, microcomputer uses); and

* expenditures on materials and equipment.

These variables were reduced to five, four of which were factor scores
representing two or more of the original variables. Anticipated
factors representing staffing levels and collection size did not
emerge. Instead, total staff hours per typical week and per pupil
holdings of books, periodicals, and videos comprise a factor
representing the staff and collection size of the library media
center. This score was named the "LMC Size" factor.

Media-endorsed staff hours per week and hours library media staff
spend assisting and collaborating with teachers comprised a second
factor. This score, which taps the instructional role of the library
media specialist, was named the "LMS Role" factor.

Weekly statistics on print and non-print circulation and information
skills instruction contacts comprised a factor representing use of
library media centers. This score was named the "LMC Use" factor.

Surprisingly, numbers of microcomputers in or under the jurisdiction
of the LMC were unrelated to holdings figures, and weekly
instructional use of microcomputers was unrelated to other kinds of
LMC use. Instead, these two figures were combined in a single score
called the "LMC Computing" factor.

Predictably, expenditures on library media materials and equipment
were strongly related to each other. Because they are both dollar
figures, these data were summed into a single amount for the remainder
of this study. This total is called "LMC expenditures per pupil."

Entering the model-testing phase of this study, the original data were
reduced and refined to the following variables:

* the At-Risk factor;

* Teacher-Pupil Ratio, the Career Teacher factor, Total Expenditures
Per Pupil;

* the LMC Size factor, the LMS Role factor, the LMC Use factor, the
LMC Computing factor, LMC Expenditures Per Pupil; and

* ITBS/TAP Reading Scores.

In the preliminary regression analyses, reading scores for almost
every grade were predicted by two variables: the At-Risk factor and
the LMC Size factor. Other variables predicted reading scores for only
one or two grades. A second and final analysis was conducted to
measure the effects of the two implicated predictors without
"statistical static." At-risk conditions appear to exert great
influence as younger students come into the public schools from the
community, less influence during the middle years, and even greater
influence as older students prepare to leave public schools. In a
complementary fashion, library media programs appear to exert more
influence during the middle years of elementary and secondary
schooling. These apparent relationships certainly bear further study.

Multiple regression techniques also calculated the percentage of
variation in test scores explained by the two direct predictors.
Consistently, the At-Risk and LMC Size factors explained half or more
of the variation in reading scores. After identifying and measuring
the impact of the two direct predictors, the indirect effects of other
potential predictors were considered with the following findings:

* The size of a library media program, as indicated by the size of its
staff and collection, is the best school predictor of academic
achievement.

* LMC expenditures predict the size of the LMC's staff and collection
and, in turn, academic achievement.

* The instructional role of the library media specialist shapes the
collection and, in turn, academic achievement.

* LMC expenditures and staffing vary with total school expenditures
and staffing.

* The degree of collaboration between library media specialist and
classroom teacher is affected by the ratio of teachers to pupils.

CONCLUSIONS

The findings of this study provide evidence needed to answer three
major questions about the impact of school library media centers and
academic achievement.

1. Is there a relationship between expenditures on LMCs and test
performance, particularly when social and economic differences between
communities and schools are controlled?

Yes. Students at schools with better funded LMCs tend to achieve
higher average reading scores, whether their schools and communities
are rich or poor and whether adults in the community are well or
poorly educated.

2. Given a relationship between LMC expenditures and test performance,
what intervening characteristics of library media programs help to
explain this relationship?

The size of the LMC's total staff and the size and variety of its
collection are important characteristics of library media programs
which intervene between LMC expenditures and test performance. Funding
is important; but, two of its specific purposes are to ensure adequate
levels of staffing in relation to the school's enrollment and a local
collection which offers students a large number of materials in a
variety of formats.

3. Does the performance of an instructional role by library media
specialists help to predict test performance?

Yes. Students whose library media specialists played such a role
tended to achieve higher average test scores.

LIMITATIONS OF THE STUDY

1. The Sample. Although the self-selected sample employed in this
study fit the profile of public schools in Colorado and the U.S. by
school level, enrollment range, and district setting, it is
conceivable that some other important characteristic might distinguish
this sample from the universe of public schools it was intended to
represent. Numbers of schools involved in this analysis at upper grade
levels were sometimes quite small. A larger overall sample would
probably eliminate this problem.

2. The Data. By far the greatest data limitation is the use of the
ITBS and TAP to operationalize academic achievement. During this
study, a revolution in testing has begun. Future research may enjoy
the benefit of more authentic assessment data. Subsequent studies will
also have the advantage of access to 1990 U.S. Census data on a wide
variety of demographic, social, and economic conditions that probably
affect academic achievement. Other potential school predictors of
academic achievement should be considered in future research.
Alternative teaching styles, disciplinary issues, and student turnover
are just a few such variables for which data were unavailable to this
study. Subsequent studies might also consider other library media
variables, such as: how access to the LMC is scheduled, how
information skills are taught, and how technology is used in the LMC.

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This ERIC Digest is based on Lance, K.C., Welborn, L., &
Hamilton-Pennell, C. (1993). The impact of school library media
centers on academic achievement. Castle Rock, CO: Hi Willow Research
and Publishing. (ED 353 989) For a complete description of this study
and a comprehensive annotated bibliography, the reader is directed to
this work.



Keith Curry Lance is Director of the Library Research Service, a unit
of the State Library and Adult Education Office of the Colorado
Department of Education. The research on which this Digest is based
was supported by Library Research and Demonstration Grant No.
R039A00008-90 from Library Programs, U.S. Department of Education.



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