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OCR for page 206
Appendix E
Coordination of Strategies for
Collecting Data
Data collection strategies must be planned in such a way that
there is effective coordination in conducting the activities to develop
indicator variables, and in cooperating with other organizations, such
as the Center for Education Statistics and the National Assessment of
Educational Progress (NAEP), that are collecting similar data. The
committee therefore recommends that the collection of data for in-
dicator objectives and for other ongoing and planned data collection
activities be coordinated to the maximum extent feasible and effec-
tive. Indefensible multiplication of surveys or excessive burden on
individual respondents would increase the costs of the program and
decrease response rates. Effective coordination can lead to reduced
costs through possible reduction in sample sizes and to improve-
ments in data quality through sharing of frame development and
maintenance, sampling operations, and improved training and qual-
ity control in survey administration. The purpose of this appendix is
to discuss some of the considerations in achieving such coordination.
The specific approaches and designs should be developed by those
assigned responsibility for the new activities.
Elements of data collection activities that affect the feasibility of
coordination are:
Target population
Data elements
206
OCR for page 207
APPENDIX E
207
Methods of data collection
Frequency of data collection
~ Sample design (related to data collection methodology and
frequency)
~ Time of year (related to data elements and time cycles within
the school year)
. Respondent burden and its impact on the cooperation of
sample respondents
Much of the thrust of the committee' recommendations is in the
direction of developing new assessment tools and descriptive data. It
is far from certain that they will require separate vehicles. However,
coordination with other assessment and data collection activities
may depend upon the feasibility of compromise between the parallel
surveys and gradual shifting to the new tools. Along with the devel-
opment and testing of data items, some attention must be given to
logistical concerns.
The committee considers the assessment of student learning to
be of primary importance. For this purpose, the committee suggests
testing of students in three grade levels, for example, grades 4, 8, and
12. The committee also recommends that data for additional key
indicators about the students and about their teachers be obtained,
as well as data to provide several supplementary indicators. These
key indicator variables, for example, might include:
~ For students: Semesters of science and mathematics taken
by students in the 12th grade; time per week spent on science and
mathematics study by students in 4th and 8th grades.
to teach.
~ For teachers: Knowledge in subject matter they are expected
Collection of these additional data, linked to student learning, can
serve two purposes: (1) to provide descriptive statistics about stu-
dents, teachers, and schools with regard to the distribution of factors
linked to student learning and (2) to help understand and explain
differences in student achievement.
For the first purpose, linkages between individual students and
their teachers are not necessary. Samples of schools, teachers, and
students (and possibly their parents) not necessarily linked to the
sample of students selected for testing at specified grades can be used
to provide general descriptive statistics. Consideration should be
given to coordinating these samples with the Elementary/Secondary
Integrated Data Systems (ESIDS) program being developed by the
OCR for page 208
208
APPENDIX E
Center for Education Statistics. Preluninary specification of sample
sizes for schools, students, and teachers could be based on design pa-
rameters used by the Center for Education Statistics. In any event,
however, steps should be taken to ensure that the data would be com-
parable to corresponding data from schools and teachers associated
with the sample of students selected for testing.
For the second purpose, the committee recommends that the
sample be tied to students classified by race or ethnicity, gender,
grade level or age, socioeconomic status, type of community, and
region or state. It must be recognized that, realistically, it wiD not be
possible with the levels of effort now represented by ESIDS or NAEP,
for example, to provide enough data for all cross-cIassifications of
these variables. It will not even be possible to provide for, say, cross-
cIassification of race or ethnicity and gender with equal precision
in every cell. One design approach would be to set limited goals
but establish designs that could readily be extended; for example, a
design for national data that could be expanded to provide state-level
data.
Testing of students might be coordinated with NAEP, depending
on the time needed. NAEP now requires about an hour of student
tune, and this could not be reduced substantially without jeopardiz-
ing it. Coordination would then depend on the feasibility of adding
to the time per student or of sampling additional students.
Certain new activities are likely to be special efforts, although in
each case it would be desirable to associate them with an organization
having related data collection or analysis responsibilities. Among
them are the following:
Recommendation bv Committee
Salary survey
Federal support of science and
mathematics education
Support of scientific bodies
Observation of classroom processes
Constructions of curriculum
frameworks
Existing Orzanization/Acti~rit~r
U.S. Census Bureau
National Science Foundation
International Association for the
Evaluation of Education Achievement
National Assessment of Educational Progress
Elementary and Secondary Integrated Data Systems
Linkage to developing teacher evaluation programs,
in the R&D phase
American Association for the Advancement of Science
Mathematical Sciences Education Board
National Council of Teachers of Mathematics
Representative terms from entire chapter:
collection activities
