Edward K. Schultz PhD
TEDA 2011
Padre Island
The participants of the session will be able to
understand data collection and analysis when
determining SLD via a pattern of strengths
and weaknesses.
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Recognize the similarities between SLD
evaluation and Research Processes (Discuss
rational-Educ. Diag. Program)
Increase professional judgment and
confidence in data analysis
Summarize third method PSW approaches
Describe organization and data collection
using a PSW approach
Data analysis principles.
SLD EVALUATION
GOOD RESEARCH
Case Study/Single Case
Reliable and Valid
methodology (design)
 Mixed-methods
 Referral Question
 Objective
 Data Collection
 Triangulation (PSW;
Variety of Assessment
tools)
 Analysis
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Case Study/Single Case
 Reliable and Valid
methodology (design)
 Mixed-methods
 Research Questions
 Objective
 Data Collection
 Triangulation
 Analysis
 Controls
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A pattern is defined as “a combination of
qualities, acts, tendencies, etc., forming a
consistent or characteristic arrangement
(Random House 2011).”
(a) multiple sources of data collected over a
period of time using a variety of assessment
tools and strategies
(b) data analysis which is grounded in the
techniques of pattern seeking (McMillan &
Schumacher, 2010)
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(c) possess predictive and treatment validity,
(d) uses logical and empirical evidence to
guide decisions-making.
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Integrating Response to Intervention and
Cognitive Processing Approaches
 Cognitive Processing Approaches
 Cognitive Hypothesis Testing (CHT)
 Discrepancy/Consistency Approach
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Idaho
Oregon
Texas
This type of data refers to data collected “prior
to and as part of the evaluation” (19 TAC
Chapter 89, Subchapter AA) as written in the
TX regulations concerning and the
Commissioners Rules Guidance document
(available
http://www.tea.state.tx.us/special.ed/guidan
ce/rules/89.1040.html)
Supporting documentation should be included for each area:
 Tutorial, compensatory, Response to Intervention, and other
academic or behavior supports available to all students. *
General education options should be thoroughly explored
prior to referral.
 Data that demonstrates the child was provided appropriate
instruction in reading and/or math within general education
settings delivered by qualified personnel.
 Review of curriculum and grade level performance by
class/subject area *this is typically accomplished through a
school improvement program that uses a grade-level or
campus level progress monitoring system.
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Data-based documentation of repeated assessments of
achievement at reasonable intervals “reflecting student progress
during classroom instruction” (formative assessment data)
RtI progress monitoring results (Curriculum–based measurement
(CBM)data)
In-class tests on grade level curriculum (state standards)
Benchmark assessment, criterion-referenced measures, or other
regularly administered assessments.
*Data from repeated assessments results used in the SLD
eligibility process should typically have been administered at
evenly-spaced intervals. Such as once per week, over a
reasonable period of time. (4-8 weeks, 6weeks on average)
Current work samples (Analysis of student work samples is a
special type of observation and is sometimes referred to as
“permanent product analysis” or “outcome recording.” This
method requires 1) collecting work samples, 2) scoring of
samples, 3) noting/analyzing errors, and 4) sorting errors into
meaningful categories.)
“prior to and as part of…”
Informal data collection should precede formal data collation and assist in
determining what other data is needed and assist assessment personnel in
determining a PSW.
 Informal Data includes:
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Interview data from multiple informants-student, parent, teachers.
Social/Educational History information provided by the parent
A thorough records review including: prior state assessment scores (TAKS),
report cards, attendance, discipline records, language survey, anecdotal
records/notes, and any other useful record.
Observational data-classroom, testing session, ect.
Work samples (Analysis of student work samples is a special type of observation
and is sometimes referred to as “permanent product analysis” or “outcome
recording.” This method requires 1) collecting work samples, 2) scoring of
samples, 3) noting/analyzing errors, and 4) sorting errors into meaningful
categories.)
Results from Screenings or any other district assessments (TPRI, Stanford, etc.)
Medical history
Vision/Hearing tests and results
Rating Scales
Formal data is generally regarded as data gathered from
standardized and/or norm-referenced assessments.
These measures have established reliability and
validity. Formal data should be collected and analyzed
in the context of other data collection techniques. This
includes such things as:
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Standardized individuals tests of cognitive processes
(WISC-4; KABC, WJ-III)
Standardized individual tests of achievement
Standardized tests of adaptive behavior, language
Standardized measures of grade level content
Curriculum Based Measurement (CBM)
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Integrative Data Analysis: “the analysis of
multiple data sets that have been pooled into
one.” (Curran & Hussong,p. 81 2010).
Inductive Analysis: process of synthesizing
and extracting meaning from data, starting
with specific data and ending with patterns
(McMillan & Schumaker, 2010)
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Begins with hypothesis
Thorough search of data-looking for negative
information and alternative explanations
(exclusionary factors)
Code and categorize data
Patterns of meaning will emeerge
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Trustworthiness: accuracy of sources, bias,
enough info.
Triangulation: cross-validating among
sources
Evaluating Discrepant and Negative
Evidence: exclusionary factors, explainable
Ordering and sorting categories
Constructing a Visual Representations
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Logical cross analysis
Plausability
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Interpretation is the essence of research
(exploit data fully, but do not go far beyond
the data-Professional judgment)
The data must speak for themselves
Defend your data: “to justify one’s
conclusions, to support’s ones statements
with the backing of solid data that have been
presented in the document” p. 296
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In practice:
The diagnostician must be confident that the data collectively identifies
a pattern consistent with the definition of SLD. The integrative approach
of data analysis is to ensure decisions concerning eligibility, instructional
implications, and learner profiles are based on data that has been
carefully examined in a way that logical and consistent. Each data
source has its unique value and should converge to strengthen decisions.
Conflicting data needs to be reconciled within an explanatory framework
(Gall, Gall, & Borg, 2005). Sound decisions cannot be made with
incomplete or conflicting data that cannot be explained. If the answer
does not lie in the data, additional questions must be asked. The use of
professional/clinical judgment to help guide an ARD committee to make
the most appropriate eligibility recommendation is embedded in the
integrative approach of data analysis.
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Guiding principle: "Good judgment requires good data" (Howell, Fox, &
Morehead, 2000, p. 95).
Edward.schultz@mwsu.edu