The Inductive-Deductive Research Continuum and Single-Case Experimental Design

Tincani (2026) wrote a how-to paper, not an experiment. He asks researchers to label where their single-case question sits on a line. One end is 'I am just looking.' The other end is 'I already know what will happen.'

He says most ABA studies land closer to the 'I already know' end. Yet many still write neutral questions. That mismatch clouds peer review and slows field progress.

The paper finds no new data. Instead it finds a habit: we often hide our hunches. If you expect the intervention will raise behavior, say so up front. Write a one-sentence directional prediction and pick a design that tests it.

Doing this helps reviewers judge your success criterion. It also stops you from fishing for any change that looks good.

Hawkes et al. (1974) show the old way still works. They used a multiple-baseline across laps, starts, and turns. They simply graphed the data and let readers eyeball the change. Tincani does not reject that style; he just wants the question typed out first.

Zhao et al. (2024) validate a new FBA survey. They state clear hypotheses about factor loadings. Their paper is an example of the transparent framing Tincani wants.

Sawyer et al. (2021) test Fit Lite in schools with a simple pre-post. They gain 16 percentile points but call it 'preliminary.' Tincani would nudge them to declare the expected size and direction before week one.

Next time you write a single-case study, start with one line: 'I predict the intervention will raise the target behavior by at least one level on my rubric.' Pick an analysis that matches that claim. Reviewers will spot your plan faster. Your graph will tell a clearer story. The field gets a stack of studies that are easier to compare and meta-analyze.