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Problem Analysis

By: Jessie Kember, Ph.D., NCSP

This blog is a continuation of the previous topic explored: problem identification. While the first blog of this series examined the first step in the problem-solving process (problem identification), the purpose of this blog is to dive deeper into the second step of the problem-solving process: problem analysis. As a brief review, the problem-solving process is outlined below:

  1. Problem identification
  2. Problem analysis
  3. Plan development
  4. Plan implementation
  5. Plan evaluation

Problem analysis occurs after problem identification and before plan development. As mentioned in a previous blog, Christ and Arañas (2014) defined a problem as an unacceptable discrepancy between expected and observed performance (i.e., when a student’s performance deviates from a set expectation or standard of success). For example, if the expectation is that Jerome turns in 80% of his homework in order to pass his History course, and Jerome is submitting only 40%, there is a 40 percentage-point discrepancy between the expectation and his performance.

Helping to define a problem is a critical skill for any educator within a Multi-Tiered System of Support (MTSS) framework. Problem analysis can occur at the system, group, and individual level. Problem analysis is similar to Deno’s (1995), problem definition stage in which the size of the problem is described in a way that is measurable. In addition, a hypothesis about the cause is developed. Problem analysis sets the stage for the remaining steps in the problem-solving process.

Regardless of the severity of a problem, problem analysis requires operationalization. In other words, two pieces of information must be operationalized: (a) What is the individual (or group or system) expected to do? and (b) What is the individual actually doing? (Tilly, 2008). Tilly (2008) identifies several implications when a problem is defined in regard to discrepancies:

  1. A discrepancy allows the problem solver to remain objective about the nature of the problem. This objectivity allows those involved to identify variables related to the problem, as well as identify when there are improvements and when an implemented plan or intervention is in fact successful (i.e., when the discrepancy is lessening).
  2. A discrepancy allows educators to identify the magnitude (i.e., urgency) of the problem. For example, the larger the discrepancy, the larger the magnitude of the identified problem. In other words, students whose current performance is close to, but below expectations, have less urgent problems than those whose performance is substantially below expectations.
  3. A discrepancy allows educators to identify a clear means of analysis and intervention. For example, if the behavior of concern is non-compliance, direct measurement of the problem can include documenting the frequency of work refusal incidents within a specified time frame during an observation. The number of work refusal incidents can be compared to that of a same-gender peer during the observation period.

Within a multi-tier framework, educators aim to answer four questions essential to the problem-solving process (Batsche et al., 2005):

  1. Is there a problem and what is it? (Problem Identification and Problem Analysis)
  2. Why is the problem happening? (Problem Analysis)
  3. What can be done about the problem? (Plan Development and Plan Implementation)
  4. Did the intervention work? (Plan Evaluation)

As shown above, two of these questions are answered during the identification and analysis stages of problem-solving. Therefore, following the identification of the existing discrepancy, educators begin the analysis of problem etiology: what is the cause of the problem (i.e., why is the problem happening?). Tilly (2008) described clear hypotheses as the key to effective problem analysis, as they link observed performance to hypothesized causes.

Referring back to our example of Jerome’s homework completion rate, we could propose the following hypothesis: Jerome submits 40% of the total homework in his History course because he does not have a reading fluency level that matches the reading fluency level of the textbook from which assignments are created. This hypothesis serves as a direct link to probable intervention approaches. In this specific example, the logical intervention approach is to increase Jerome’s reading fluency through a reading fluency intervention. The intervention is logically linked to the analysis of the problem.

How Can FastBridge Learning® Help Me with Problem Analysis?

FastBridge Learning® provides users with the tools to define problems within an MTSS framework. The suite of assessments offered for the purpose of screening can serve as the starting point for problem identification, and subsequently problem analysis. By providing evidence-based standards and expectations in the form of both normative scores and benchmark scores, FastBridge Learning® allows for problems to be defined in terms of a discrepancy between expectation and actual performance, informing both instruction and intervention.


Batsche, G., Elliott, J., Graden, J., Grimes, J., Kovaleski, J., Prasse, D., et al. (2005). Response to intervention: Policy considerations and implementation. Alexandria, VA: National Association of State Directors of Special Education.

Christ, T.J., & Arañas, Y.A. (2014). Best practices in problem analysis. In A. Thomas & J. Grimes (Eds.), Best Practices in School Psychology VI. Bethesda, MD: National Association of School Psychologists.

Deno, S. (1995). The school psychologist as problem solver. In J. Grimes & A. Thoms (Eds.), Best practices in school psychology III (pp. 471-484). Silver Spring, MD: National Association of School Psychologists.

Tilly, D.W. III (2008). The Evolution of School Psychology to Science-Based Practice. In A. Thomas & J. Grimes (Eds). Best Practices in School Psychology V (pp. 17-36). Bethesda, MD: National Association of School Psychologists.

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