Patterns of Strengths and Weaknesses(PSW): Conceptualization and Analysis

1. Patterns of Strengths and Weaknesses(PSW):  Conceptualization and Analysis Edward K. Schultz PhD TEDA 2011 Padre Island

2. Session Description The participants of the session will be able to understand data collection and analysis when determining SLD via a pattern of strengths and weaknesses.

3. Outcomes • 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.

4. Similarities between SLD evaluation and Research Processes SLD Evaluation Good Research Case Study/Single Case Reliable and Valid methodology (design) Mixed-methods Research Questions Objective Data Collection Triangulation Analysis Controls • Case Study/Single Case • Reliable and Valid methodology (design) • Mixed-methods • Referral Question • Objective • Data Collection • Triangulation (PSW; Variety of Assessment tools) • Analysis

5. Operational Definition of PSW • 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)

6. Operational Definition of PSW • (c) possess predictive and treatment validity, • (d) uses logical and empirical evidence to guide decisions-making.

7. Third Methods Approaches • Integrating Response to Intervention and Cognitive Processing Approaches • Cognitive Processing Approaches • Cognitive Hypothesis Testing (CHT) • Discrepancy/Consistency Approach

8. State Examples • Idaho • Oregon • Texas

9. Formative/Classroom Data 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/guidance/rules/89.1040.html)

10. Examples 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.

11. 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…”

12. Informal Data 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: • 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

13. Formal Data 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: • 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)

14. Data Analysis • 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)

15. Data Analysis: Pattern Seeking • 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

16. Techniques • 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

17. Techniques • Logical cross analysis • Plausability

18. Final Thoughts on Interpretation • 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

19. Decision • 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. • Guiding principle: "Good judgment requires good data" (Howell, Fox, & Morehead, 2000, p. 95).

20. Contact Edward.schultz@mwsu.edu