An alternative to null hypothesis significance testing is presented and discussed. This approach, referred to as observation-oriented modeling, is centered on model building in an effort to explicate the structures and processes believed to generate a set of observations. In terms of analysis, this novel approach complements traditional methods based on means, variances, and covariances with methods of pattern detection and analysis. Using data from a previously published study by Shoda et al., the basic tenets and methods of observation-oriented modeling are demonstrated and compared with traditional methods, particularly with regard to null hypothesis significance testing.
Keywords: inference to best explanation; integrated model; null hypothesis significance testing; observation-oriented modeling.