Assessment & Research

Applying linear systems analysis to dynamic behavior.

McDowell et al. (1992) · Journal of the experimental analysis of behavior 1992
★ The Verdict

Linear systems math gives you a crystal ball for behavior that changes smoothly over time.

✓ Read this if BCBAs who track long-term skill acquisition or problem behavior that waxes and wanes.
✗ Skip if Clinicians who only run brief discrete-trial drills with no repeated time series.

01Research in Context

01

What this study did

Rasing et al. (1992) wrote a how-to guide for using linear systems math on behavior data.

They showed how to plug response streams into difference and differential equations.

The paper also moved the math up to second-order forms to catch smoother, slower trends.

02

What they found

The authors proved you can treat behavior like an electrical signal.

With the right equations you can forecast future response rates from past ones.

Second-order forms let the model bend, matching real-world data that drifts over days.

03

How this fits with other research

Vyse (2004) took the same math and asked why behavior stays stable across time and places.

He used the linear-systems lens to explain traits through molar accounts and behavioral momentum.

Krispin (2026) stretched the idea further, mapping systems rules onto how whole cultures evolve.

All three papers share one message: math that tracks change over time belongs in behavior analysis.

04

Why it matters

If your client’s data show waves, drifts, or slow rebounds, try plotting the session-by-session trend.

A simple moving average or difference equation can reveal the system’s natural rhythm.

You gain an early-warning chart that tells you when an intervention is truly working.

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→ Action — try this Monday

Graph the last 30 days of target responses, add a five-session moving average, and watch for slope changes before you tweak the intervention.

02At a glance

Intervention
not applicable
Design
theoretical
Finding
not reported

03Original abstract

In this paper we present an abbreviated discussion of the linear systems analysis in the time domain. We then consider the qualitative character of the behavioral dynamics predicted using the linear form of the analysis. The analysis is then extended to a second-order form. We illustrate some relevant new features introduced by the second-order form with a special case example.

Journal of the experimental analysis of behavior, 1992 · doi:10.1901/jeab.1992.57-377