BCIA Continuing Education Posts

Small-N designs are particularly powerful when the treatment effect is large and immediate, allowing researchers to infer causality with confidence.

A null result does not always mean that the independent variable (the factor manipulated by the researcher) does not affect on the dependent variable (the outcome being measured).

The goal of this post is to help you think like a scientist when it comes to evaluating questionable claims.

To support a causal claim, a study must meet three essential criteria: covariance, temporal precedence, and internal validity.

Interrogating association claims means asking three core questions: Were the variables measured well (construct validity)? Can the results be generalized to other populations (external validity)? And is the association statistically strong, significant, and precise (statistical validity)?

The four big validities offer a comprehensive toolkit for evaluating psychological research. Construct validity ensures that variables are accurately measured or manipulated. External validity tells us whether the results generalize. Statistical validity ensures the numbers are trustworthy. Internal validity confirms that cause-and-effect relationships are legitimate.

Association claims are foundational to psychological research because they help us identify variables that are connected and warrant further investigation. While they cannot prove causality, they provide essential groundwork for asking deeper questions.