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Personality consists of stable patterns of cognitions, emotions, and behaviors, yet personality psychologists rarely study behaviors. Even when examined, behaviors typically are considered to be validation criteria for traditional personality items. In the current study (N = 332,489), we conceptualize (self-reported, yearlong) behavioral frequencies as measures of personality. We investigate whether behavioral frequencies have incremental validity over traditional personality items in correlating personality with six outcome criteria. We use BISCUIT, a statistical learning technique, to find the optimal number of items for each criterion’s model, across three pools of items: traditional personality items (k = 696), behavioral frequencies (k = 425), and a combined pool. Compared to models using only traditional personality items, models using the combined pool are more strongly correlated to four criteria. We find mixed evidence of congruence between the type of criterion and the type of personality items that are most strongly correlated with it (e.g., behavioral criteria are most strongly correlated to behavioral frequencies). Findings suggest that behavioral frequencies are measures of personality that offer a unique effect in describing personality-criterion relationships beyond traditional personality items. We provide an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.

This dissertation investigates two ways in which personality psychology should move beyond the traditional approach of measuring personality with broad domains composed of trait descriptors, as exemplified by the Big Five taxonomy. The first study (Chapter 2) suggests an alternative to the traditional approach of aggregating personality items into domains. Mounting evidence indicates that, compared to domains, narrower measures of personality account for more variance in criteria and describe personality-criterion relationships more accurately. Analysis of individual personality items is the most granular approach to studying personality and is typically performed with statistical learning techniques (SLTs). The first study: (a) champions a new statistical learning technique, BISCUIT; (b) finds that BISCUIT provides a balance between prediction and parsimony; and (c) replicates previous findings that the broadness of the Big Five traits hinder their predictive power.The second study (Chapter 3) suggests an alternative to the traditional approach of measuring personality with trait descriptors, or "traditional personality items." Of the three patterns commonly associated with personality (cognitions, emotions, and behaviors), behaviors are the least studied; traditional personality items tend to measure cognitions and emotions. Historically, yearlong patterns of specific behaviors have been thought of as criteria of personality measures, but the second study posits they should be classified as personality items because they measure patterns of behavior, a component of personality. The second study reviews and extends two pilot studies that indicated behavioral frequencies predict life outcomes, sometimes better than traditional personality items. The second study: (a) estimates the extent to which behavioral frequencies strengthen personality-criterion relationships above traditional personality items; (b) determines that some criteria are differentially predicted by personality item type; and (c) publishes an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.