Washington School Library Survey, 2004

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Washington School Library Survey, 2004
WLMA Conference Bellevue, Washington October 8,
2004

• Gayle Pauley, OSPI
• Kathleen Plato, OSPI
• Dave Pavelchek, WSU-SESRC

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Outline

• Background, Sample, Data, Methods
• Findings
• Does library quality have an effect on student
  learning?
• Other factors related to student learning
• Characteristics of better performing libraries
• How to use the data from the survey at your
  building or district
• Questions/Answers

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Background Sample

• Background
• Survey and study are based on research started in
  Colorado
• Survey topics include hours worked, staff
  activities, students use of the library, library
  resources including computers, library budget,
  interactions with the local public library, and
  district-level support
• Sample
• Surveys sent to all K-12 public school libraries
  last spring
• 656 School librarians completed the survey (36
  response rate)
• Sample of respondents is very similar to the
  overall population of school libraries in terms
  of size and location

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Response Rates and Sample Sizes by Level
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Data Sources

• Data on student and school characteristics
  maintained by OSPI
• Census data on community level characteristics
  (like adult education levels)
• The number and percentage of students passing the
  Reading portion of the WASL in 2001, 2002, and
  2003 (data for the 3 years was combined for the
  study)
• The online survey completed by 656 school
  librarians

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Study Objectives

• Objective 1 Determine whether library quality
  has an effect on students WASL performance
• Objective 2 Identify factors that contribute or
  correlate with student learning
• Objective 3 Examine characteristics of better
  performing libraries

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Objective 1Does library quality have an effect
on student learning?
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Creating the initial model

• We identified the community and school
  characteristics that best predict the percentage
  of students passing the reading WASL.
• Separate models were developed for Elementary,
  Middle, and High Schools.
• Data elements included
• From OSPI Students receiving free reduced
  price lunch, student ethnicity, teacher education
  and experience, school enrollment,
  student-teacher ratios, urban/rural location
• From Census Number of families children in
  poverty, household income, and adult educational
  attainment

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Elementary School Base Model

• A model was developed that explained 69 of the
  variance in the percentage of students passing
  the Reading WASL
• Variables in the model are
• Percent of students receiving free or reduced
  price lunch
• Student racial composition
• Average years of teacher experience
• Percent of adult females in the community who
  earned at least an Associate of Arts degree
• Total school enrollment
• Whether the school is in an urban or rural part
  of the state

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Middle School Base Model

• A model was developed that explained 82 of the
  variance in the percentage of students passing
  the Reading WASL
• Variables in the model are
• Percent of students receiving free or reduced
  price lunch
• Student racial composition
• Percent of adults in the community who earned at
  least an Bachelors degree
• Percent of children in poverty
• Percent of individuals in poverty

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High School Base Model

• A model was developed that explained 67 of the
  variance in the percentage of students passing
  the Reading WASL
• Variables in the model are
• Percent of students receiving free or reduced
  price lunch
• Student racial composition
• Percent of adults in the community who earned at
  least an Bachelors degree
• Per student expenditures at the district level

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Creating the enhanced model with library survey
data

• To each of these models, survey data about school
  libraries was added to see if any aspects of the
  quality of libraries have a statistically
  significant impact on the percentage of students
  passing the WASL.

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Elementary School Significant Library Factors

• Adding variables from the library survey
  increased the precision of the model for
  elementary schools by 3 percentage points
• Variables with a statistically positive effect on
  WASL scores include
• Number of classroom visits to the library
• Non-classified library staff hours per week
• Number of volunteers assisting in the library
• Hours doing collection development
• Number of types of interactions between school
  and local public library

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Middle School Significant Library Factors

• Adding variables from the library survey
  increased the precision of the model for middle
  schools by 2 percentage points
• Variables with a statistically positive effect on
  WASL scores include
• Number of volunteers assisting in the library
• Hours library staff spent on all teaching-related
  activities

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High School Significant Library Factors

• Adding variables from the library survey
  increased the precision of the model for high
  schools by 1 percentage point
• The only variable with a statistically positive
  effect on WASL scores was
• Number of books per student

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Objective 2Other Factors that Correlate with
WASL Performance
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Identifying Other Factors

• Regression model allows for the exploration of
  factors that contribute to student performance,
  while taking into account other local
  characteristics
• Quartile analysis identifies factors that are
  related to student performance but does not test
  whether it is independently important or simply
  related

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Quartile Analysis Methodology

• The regression models that did not use survey
  data (explained in the previous section) were
  used to predict the percentage of students who
  would pass the reading WASL given the number of
  people with free or reduced price lunch, adult
  education levels, etc.
• The predicted value from the model for each
  school was compared to the actual percentage who
  passed the reading portion of the WASL in 2001,
  2002, and 2003.
• Schools were ranked by how much they exceeded or
  fell short of their predicted scores.

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Quartile Analysis Methodology (continued)

• The survey responses from the top 25 of schools
  (generally those who did 3 to 6 better than
  predicted) were compared to the bottom 25 (3 to
  6 below predicted)
• Results are reported if there is a statistically
  significant difference between the two mean
  values
• The analysis focused on student performance on
  the reading portion of the WASL only

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Elementary Factors Affecting Reading

• The following have statistically significant
  differences (top 25 and bottom 25)
• Positive Difference
• Percent of books that are science related (12
  vs. 9)
• Number of science books published in last 5 yrs
  (270 vs. 182)
• Percent of science books published in last 5 yrs
  (23 vs. 19)
• Total circulation (823 vs. 788)
• Negative Difference
• Dollars from Title V (442 vs. 954)
• Note Values marked with are computed on a
  per-student basis and displayed for a typical
  elementary school of 400 students.

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Middle School Factors Affecting Reading

• The following have statistically significant
  differences (top 25 and bottom 25)
• Positive Difference
• Number of CD-Rom and online resources (4.2 vs.
  2.6)
• Dollars from Other (1,652 vs. 665)
• Volunteer hours per week (2.6 vs. 0.9)
• Number of volunteers (1.9 vs. 0.8)
• Number of hours spent on teaching activities per
  week (22.0 vs. 18.9)
• Negative Difference
• Total library budget (14,104 vs. 15,920)
• Note Values marked with are computed on a
  per-student basis and displayed for a typical
  middle school of 700 students.

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High School Factors Affecting Reading

• The following have statistically significant
  differences (top 25 and bottom 25)
• Positive Difference
• Number certified librarian hours per week (50
  vs. 35)
• Negative Difference
• Overtime hours on all other non-library
  activities (0.6 vs. 6.7)
• Note Values marked with are computed on a
  per-student basis and displayed for a typical
  high school of 1,100 students.

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Objective 3Examine the Characteristics of
Better Performing Libraries
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Section 3 Methodology

• As with the section above, the predicted value
  from the model for each school was compared to
  the actual percentage who passed the reading
  portion of the WASL in 2001, 2002, and 2003.
• Schools that did better than their predicted
  score were identified as better performing
  schools. This is approximately half of the
  sample.
• The average responses for the group of better
  performing schools and for all schools were
  tabulated.
• No statistical test was used. The differences
  between the groups were considered and the most
  noticeable are mentioned here.

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Better Performing Elementary Schools

• What are the differences between the better
  performing schools and elementary schools in
  general? The better performing schools have
  the following characteristics
• More likely to be flexibly scheduled
• Fewer paid staff working fewer hours
• More volunteers
• Less time teaching non-library subjects, more
  time focused on library work
• More circulation of materials in a typical week
• Fewer books but more other catalogued holdings
• Slightly more computers in building that are
  directly linked to library
• Smaller overall library budget
• More book talks presented by the public library
• More homework alerts provided to the public
  library

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Better Performing Middle Schools

• What are the differences between the better
  performing schools and middle schools in
  general? The better performing schools have
  the following characteristics
• More volunteers
• More individual students using the library and
  fewer groups
• More circulation of materials and in-library use
• Fewer books and other catalogued holdings
• Fewer technology demonstrations by public library
  staff
• Fewer shared networks links with public library
• More cataloging assistance from district

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Better Performing High Schools

• What are the differences between the better
  performing schools and high schools in general?
  The better performing schools have the
  following characteristics
• More credentialed librarian staff and hours and
  fewer classified and other paid staff
• Fewer individuals using the library
• Less circulation of materials but more in-library
  use (in-library use is greater than circulation)
• More other catalogued holdings (besides books)
• Older science collection
• More computers with access to library databases
• Fewer book talks presented by the public library
• Fewer technology demonstrations by public library
  staff
• More reference questions directed to the public
  library
• More shared access to the public library catalog
• More shared access between school and ESD
  resources

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Using Library Survey Data

• Use State Level Results for
• Examining building WASL Reading scores
• Allocating resources to types of school libraries
• Understanding the characteristics of better
  performing libraries
• Use Survey Average and Better Performing School
  Data for
• Comparing your library/media center to the
  average and better performing models by-
• Service, staffing, use collections, budget
• Staff roles, training, work load,
  teaching/learning time, technology use to the two
  averages
• Comparing library/media centers within a district

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Other Uses for the Data

• District/Building Budget Development
• Teaching and Learning Involvement/Support
• Organizational and Inter-organizational policy
  development
• Library/media center services
• Relationship to local public libraries
• Technology resource acquisition/use
• Grant applications
• Your own research

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Questions? Contact us
Gayle Pauley (Project History)360-725-6100gpaul
ey_at_ospi.wednet.edu Kathleen Plato (Using the
data and OSPI data support)360-725-6097kplato_at_os
pi.wednet.edu Dave Pavelchek Paul Stern
(Understanding the data and methods
used)360-586-9292dpavel_at_wsu.edu
sternpo_at_wsu.edu