Technical Brief
Factors Associated with Student Achievement
Factors
Associated with Student Achievement
Prior achievement is the most powerful predictor of school success, accounting for about
50% of the variation in student achievement scores. It is important to note that about
half of the variation in student achievement is still 'up for grabs" when we are
talking about student populations as a whole. This 50% of unaccounted for variation in
student achievement may be markedly affected by things like good teaching practices,
strong parent support for learning, community or cultural expectations, and student
motivation. Barring unforeseen circumstances, a child who is achieving academically will
probably continue to achieve - the exact amount of achievement being influenced strongly
by prior achievement and a host of other factors. No matter what the family's
circumstances, any child who consistently arrives at school adequately nourished, rested,
healthy, and feeling safe and stable will be able to acquire and accumulate learning. This
is what we mean by "ready to learn." A child's readiness to learn, especially in
the early years, positions that child for lifelong success with learning.
A wealth of studies shows that family background characteristics are also closely related
to student achievement. Schools with less economically privileged students, for example,
almost always have lower achievement scores. (When RI rank orders their state test
results, the results closely mirror the socioeconomic status (SES) of the district; thus,
high income districts have high scores and scores drop with a strong correspondence to the
relative drop in income.) Changes in many other characteristics (variables) have also been
shown by many research studies to correlate closely to student achievement. These include:
- Prior achievement on aptitude
- Participation in free and reduced lunch program
- Minority status
- Educational level of the mother
- Father's occupation
- Family income
- Number of siblings
- Students receiving special services (e.g., special
education, bilingual or LEP education)1
Additionally,
at higher levels of the system beyond groups of individual students, we know that a number
of other factors are associated with student achievement such as school settings (urban,
rural, suburban), per pupil expenditure, policies and practices within schools or school
districts, and community characteristics (e.g., job market, tax support).2
Performance indicators of school effects have been systematically collected in a variety
of places in the U.S. and elsewhere.3
Attempts to identify effective schools have created many controversies over the kinds of
data to be collected, the appropriate methodologies to be employed and the interpretation
of specific results. The first few years of Information Works! will probably witness
similar controversies. The researchers who constructed the 2000 RI model are not wedded to
it. This year's model was based on various data sources that were already available and
took into account the strengths and weaknesses associated with each available data set. As
both the quality of the data improves and new research is accomplished, the model will
evolve and become increasingly sophisticated.
The following few sections of this brief describe some general statistical principles that
are important for understanding the statistical model used in this year's Information
Works! These principles are then applied specifically to the model that was created to
generate the second field of the Rhode Island school and district reports. Readers already
familiar with hierarchical regression analysis may wish to skip directly to the sections
on Multiple Regression and the RI Model.
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