{'fraction_non_alphanumeric': 0.024036878498518273, 'fraction_numerical': 0.005926901547579849, 'mean_word_length': 5.422832980972516, '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}}
Quantitative Analysis and Effective Behavior

Unlike their counterparts in mainstream psychology, researchers in behavior analysis have long been known to have an affinity for inspecting raw behavioral data from individual organisms, and drawing conclusions from the visual patterns that emerge when these data are plotted in graphic form (e.g., Sidman, 1960). The cumulative record exemplifies this practice in its purest form. Casual inspection of recent volumes of the Journal of Applied Behavior Analysis (JABA) and the Journal of the Experimental Analysis of Behavior (JEAB) reveals that to this date, research methods and data-analytic techniques in both basic and applied behavior analysis differ substantially from the ANOVA-driven group comparisons that characterize much of experimental psychology. However, it also reveals that cumulative records of free-operant behavior are largely absent, and instead, modern-day behavior analysts typically engage in one form of data reduction or another. Researchers who publish in JABA tend to plot behavior from an entire observation period as one data point on a graph. This practice is also seen in JEAB, along with the practice of collapsing data across subjects. In addition, many articles published in JEAB, as well the occasional article in JABA, include the application of elaborate quantitative models to the data, which often involves a substantial amount of data transformation and reduction. Catania (1981) eloquently pointed out the perils of certain forms of data reduction (those involving quantitative modeling) relative to others (session-by-session data summaries in the context of an experimental analysis). I had not read Catania’s paper before being asked to comment on it, and I found it quite thought-provoking. Catania provides several examples in which the conclusions drawn from a quantitative analysis differ substantially from those drawn from a more intimate (cf. Fahmie & Hanley, 2008) analysis of the same data. The difference is sometimes alarming, suggesting a tendency for quantitative analysis to obscure important aspects of behavior as it appears in the raw, and thereby producing information that can be quite misleading. In spite of Catania’s admonishing, however, quantitative behavior analysis is alive and well more than 30 years later, as evidenced by continued publication in JEAB and elsewhere, and well-attended annual meetings of the Society for the Quantitative Analysis of Behavior. Do quantitative practices survive simply because the researchers who engage in them continue to reinforce the verbal behavior of one another, as Catania implies? Catania’s (1981) argument, of course, assumes that the more intimate data produced by an experimental analysis are necessarily more accurate than the less intimate data reported as part of a quantitative analysis. Thus, the conclusions drawn from an experimental analysis are “correct” and the conclusions drawn from a quantitative analysis are (in Quantitative Analysis and Effective Behavior