Longitudinal
data
analysis
in
education
is
the
study
growth
over
time.
A longitudinal
study
is
one
in
which
repeated
observations
of
the
same
variables
are recorded
for
the
same
individuals
over
a
period
of
time.
This
type
of
research
is known
by
many
names
(e.g.,
time
series
analysis
or
repeated
measures
design),
each of
which
can
imply
subtle
differences
in
the
data
or
analysis,
but
generally
follows the
same
definition.
The
purpose
of
this
paper
is
to
provide
an
overview
of longitudinal
data
analysis
in
education
for
practitioners,
administrators,
and
other consumers
of
educational
research,
focusing
on:
the
purposes
of
longitudinal
data analysis
in
education,
some
of
its
benefits
and
limitations,
and
the
various
analyses used
to
model
student
growth
trajectories. A Primer on Longitudinal Data Analysis in Education Longitudinal data analysis in education is the study of student growth over time. A longitudinal study is one in which repeated observations of the same variable(s) are recorded for the same individuals over a period of time. This type of research is known by many names (e.g., time series analysis or repeated measures design), each of which can imply subtle differences in the data or analysis, but generally follows the same definition. The purpose of this paper is to provide an overview of longitudinal data analysis in education for practitioners, administrators, and other consumers of educational research, focusing on: the purposes of longitudinal data analysis in education, some of its benefits and limitations, and the various analyses used to model student growth trajectories. 1. Purposes Longitudinal data analysis, also known as growth modeling and growth curve analysis, has as its primary purpose the measurement of change, or trajectories. Growth trajectories refer to both the intercept (initial or starting point) and the slope (growth, or change over time). There are two general objectives that are addressed by longitudinal data analysis: (a) how the outcome variable changes over time, and (b) predicting or explaining differences in these changes (Singer & Willett, 2003). The first purpose is more narrow, and looks at the description of the functional form of growth; that is, is growth linear, or non-linear. It is important to note here that growth can increase and/or decrease, accelerate and/or decelerate, and that an important part of longitudinal data analysis is modeling the correct functional form of growth. The second purpose is much broader than the first, and addresses the relation between the trajectory and independent variables of interest (e.g., instructional program, public vs. private schooling, absences, socioeconomic status). In the coming sections, examples of these two purposes are provided, and different analyses that help answer questions related to these purposes are illustrated. Specific longitudinal educational data, described next, is used to help elucidate these purposes. 1.1 Description of Data The following longitudinal data are used to help illustrate examples about growth and analyses throughout this paper. These data come from a larger study conducted in 2009-2010 to develop a comprehensive reading and mathematics assessment system. The sample includes 186 students in grade 4 who were administered eight oral reading fluency (ORF) measures over one academic year. Measures were administered in October, November, December, January, February, March, April, and May. Students with ORF results from at least four testing occasions were included in the sample. For the ORF administration, students were shown a narrative passage (approximately 250 words) and were given 60 seconds to “do their best oral reading.” The assessor followed along as the student read, indicating on the test protocol each word the student read incorrectly (producing the wrong word or omitting a word). If a student hesitated for more than three seconds, the assessor provided the correct word, prompted the student to continue, and marked the word as read incorrectly. Student self-corrections were marked as correct responses. After one minute, 1
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