Abstract
Null hypothesis significance testing is criticised for emphatically focusing on using the appropriate statistic for the data and an overwhelming concern with low p-values. Here, we present a new technique, Observation Oriented Modeling (OOM), as an alternative to traditional techniques in the social sciences. Ten experiments on judgements of associative memory (JAM) were analysed with OOM to show data analysis procedures and the consistency of JAM results across several types of experimental manipulations. In a typical JAM task, participants are asked to rate the frequency of word pairings, such as LOST-FOUND, and are then compared to actual normed associative frequencies to measure how accurately participants can judge word pairs. Three types of JAM tasks are outlined (traditional, paired, and instructional manipulations) to demonstrate how modelling complex hypotheses can be applied through OOM to this type of data that would be conventionally analysed with null hypothesis significance testing.
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