ERIC Identifier: ED372759 Publication Date: 1994-05-00
Author: Lance, Keith Curry Source: ERIC Clearinghouse on
Information and Technology Syracuse NY.
The Impact of School Library Media Centers on Academic
Achievement. ERIC Digest.
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
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 of this study provided bases for
measuring the relative impact of potential predictors on academic achievement.
Correlation analysis of community variables identified the following
* 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
* 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
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
* 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
Correlation analysis of library media center (LMC) variables identified the
following noteworthy relationships:
* LMCs with larger book collections tend also to have more periodical
* LMCs which have more to spend on materials tend to have more to spend on
* 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
* 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
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
* 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
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
* 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
* The degree of collaboration between library media specialist and classroom
teacher is affected by the ratio of teachers to pupils.
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
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
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.
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