{'fraction_non_alphanumeric': 0.020012911555842477, 'fraction_numerical': 0.0025823111684958036, 'mean_word_length': 6.045454545454546, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Ordinal pattern analysis in comparative psychology - A flexible alternative to null hypothesis significance testing using an observation oriented modeling paradigm

Author(s): Craig, David Philip Arthur; Abramson, Charles I | Abstract: The data of comparative psychology generally differ from the majority of data collected within mainstream psychology in several key respects – most notably in the diversity of forms of measurement and fewer number of subjects. We believe null hypothesis significance testing may not be the most appropriate method of analysis for comparative psychology for these reasons. Comparative psychology has a rich history of performing several analyses on a few subjects due to a philosophical interest in individual subject behavior, along with group assessments. Since first being published in 2011, Observation Oriented Modeling has successfully been used to analyze individual subjects’ responses from honey bees, horses, humans, and rattlesnakes. Observation Oriented Modeling is highly flexible and has allowed comparative researchers to perform a variety of assessments comparable to null hypothesis significance testing’s T-Tests, One-way ANOVA, and Repeated-Measures ANOVA while producing easily-interpretable and, most importantly, relevant results. This paper describes the diverse manners in which comparative psychologists can assess individual and group performances without concerns of statistical assumptions and limitations that complicate assessments when employing Null Hypothesis Significance Testing.