Three experience-sampling studies explored the distributions of Big-Five-relevant states (behavior) across 2 to 3 weeks of everyday life. Within-person variability was high, such that the typical individual regularly and routinely manifested nearly all levels of all traits in his or her everyday behavior. Second, individual differences in central tendencies of behavioral distributions were almost perfectly stable. Third, amount of behavioral variability (and skew and kurtosis) were revealed as stable individual differences. Finally, amount of within-person variability in extraversion was shown to reflect individual differences in reactivity to extraversion-reievant situational cues. Thus, decontextualized and noncontingent Big-Five content is highly useful for descriptions of individuals' density distributions as wholes. Simultaneously, contextualized and contingent personality units (e.g., conditional traits, goals) are needed for describing the considerable within-person variation. The purpose of this article is to identify aspects of individuals' everyday trait-relevant behavior over time that are relevant to and explainable by personality psychology. The presumption is that, because the same individual behaves differently on different occasions, an individual's behavior over time forms a distribution; the central proposal is that the entire distribution and its several components are relevant to-and to be explained by-personality psychology. Specifically, on the basis of a view of individuals as actively reacting to context (Allport, 1937; Brown & Moskowitz, 1998; Cantor, 1990; Diener, Larsen, & Emmons, 1984; Mischel, 1968; Mischel & Peake, 1982; Mischel & Shoda, 1998; Moskowitz, 1982; Nesselroade, 1988 Nesselroade, , 1991 Revelle, 1995) , it is proposed, first, that the average individual routinely and regularly expresses all levels of all traits and that this within-person variability is predictable as individual differences in reactions to situational cues. Second, on the basis of work concerning the predictability of behavior (e.g., Epstein, 1979; Moskowitz, 1982) , it is proposed that although single behaviors are less predictable, the mean of the distribution is among the most predictable variables in psychology. Third, on the basis of theory that variability is itself a stable individual-differences characteristic (Fiske, 1961; Larsen, 1989; Murray, 1938) , it is proposed that parameters beyond the mean are also meaningful aspects of personality. That is, it is useful for personality to be conceived of as density distributions as wholes rather than as only one aspect of the distributions. To evaluate these claims empirically, three experience-sampling studies are presented in which many individ-I thank Anne Fleeson, Jason Fors, Alexandra Freund, Batja Mesquita, Benjamin Peterson, Daniel Green, Elizabeth Laney, and Adriane Malanos for their help with this line of work. I also thank the Max Planck Institute for Human Development, Center for Lifespan Development (Director, Paul B. Baltes) for support during the writing of this article. Correspondence concerning this article should be addressed to William Fleeson, Department of Psychology, Wake Forest University, WinstonSalem, North Carolina 27109. Electronic mail may be sent to fleesoww® wfu.edu. uals described their current behavior several times per day for 2-3 weeks. Directly assessing individuals' everyday trait-relevant behavior over time is intended as important for at least three reasons. Most important, it addresses a basic question about the nature of personality: How is trait content manifest in everyday behavior, and do individuals differ in such manifestation? If personality psychology is going to say something about behavior with its constructs, we must know how that behavior is patterned and where individual differences occur in those patterns (Barrett & Pietromonaco, 1997; Borkenau & Liebler, 1995; Borkenau & Ostendorf, 1998; Botwin, 1989; Buss & Craik, 1983; Larsen, 1989; Revelle, 1995; Schwartz, Neale, Marco, Shiffman, & Stone, 1999) . A second reason this is important is that considerable debate in personality psychology depends on the actual nature of behavioral distributions (e.g., Block, 1995; Cervone, 1991; Pervin, 1994) . Specifically, the usefulness of structural or noncontingent approaches to explaining behavior depends on the stability of aggregated means, whereas the usefulness of process or contingent approaches to explaining behavior depends on the amount of within-person variability to be explained. With both Epstein (1994) and Mischel and Shoda (1998) calling for compromise and integration between the structural and the process approaches to personality, it appears the field is prepared for the end of this debate. This article proposes one way to integrate the two factious approaches to personality by demonstrating that trait concepts are inclusive of both impressive levels of within-person stability and impressive levels of withinperson variability. The third reason for assessing everyday trait-relevant behavior is that it may reveal additional aspects of everyday behavior over time as relevant to personality. For example, it has long been theorized that the amount of variability itself is a stable, individualdifferences characteristic (Fiske, 1961; Larsen, 1989; Murray, 1938) , but it has not been investigated empirically for everyday behavior. The present article operationalizes such concepts as 1011 1012 FLEESON parameters of the density distributions and is thus able to empirically investigate their stability and validity as well. This article focuses in particular on the variability and stability of Big-Five-relevant behavior. Empirically demonstrating such phenomena for the Big Five is important for at least two reasons. On the one hand, it would be trivially easy to choose behaviors that show very little or very much stability and variability ; what is important is to investigate the amount of variability and stability of specifically that content of behavior that the leading structural theory implies should be stable. On the other hand, this investigation is useful for further testing and elaboration of the viability (usefulness) of the Big Five. Specifically, this article tests whether there is sufficient predictability (stability) in everyday manifestations of the Big Five traits to justify their usefulness as descriptions of individuals' behavior. Determining what in behavior is to be explained requires measuring an extensive sample of several individuals' everyday behavior, using items that allow (a) quantification of variability on a meaningful scale and (b) direct translation of trait concepts into behavior. The present studies accomplish this by assuming that, just as an individual can be characterized by a level on a trait, representing the degree to which that individual expresses the trait (e.g., a 5 on a 7-point Extraversion dimension represents a moderately extraverted individual), a behavior can be characterized by a level on the same dimension, representing the degree to which that behavior expresses the trait (e.g., a 5 on 7-point Extraversion dimension represents a moderately extraverted behavior). That is, states-short-term, continuous, concrete ways of acting, feeling, and/or thinking (Cattell, Cattell, & Rhymer, 1947; Fridhandler, 1986; Nesselroade, 1988; Patrick & Zuckerman, 1977; Spielberger, Lushene, & McAdoo, 1977) -can be described with the same content and scales as are traits. Once it is accepted that behavior can be characterized as traitrelevant states, it is easy to see that over time, one individual's behavior forms a distribution. Figure 1 shows two extreme but possible natures of state distributions. The left possibility is one of low within-person variability. The three depicted individuals overlap very little; although each individual occasionally enacts states along the entire dimension, individuals typically act in a narrow and characteristic way. Thus, the central tendency is an adequate description of such distributions, and the remaining variance can be dismissed as error. The right extreme is one of sizeable withinperson variation, depicting the same three individuals. In this case, each distribution is wide, such that each individual routinely and regularly manifests all levels of the trait, and, as a result, the primary feature of the figure is overlap (i.e., individuals are highly similar in how they behave). Nonetheless, there are differences between individuals in the locations (central tendency or level) and also in the sizes and shapes of the distributions. Structural approaches are intended to describe what an individual is like in general and, thus, tend to emphasize the mean alone and dismiss the remainder of the distribution as small or meaningless; process approaches are intended to describe how individuals differentially react to situations and, thus, tend to emphasize the variability within the distribution, dismissing individuals' unique distributional parameters or contours as unpredictable Pervin, 1994) . The proposal of this article is that the most adequate characterizations of behavior focus on the entire distribution. That is, personality psychology should conceive of behavior as consisting of density distributions of states. Five empirical outcomes would support the shift toward a focus on entire density distributions for adequate description of individual differences in behavior. First, the wider the typical individual's distribution, the more of any given individual's behavior left undescribed by his or her mean alone. Second, single states would not be stable (predictable), as predicting an individual's precise momentary location within the distribution would be inefficient. Third, means of the distributions nonetheless would be highly stable, because the distribution is the reliable characteristic of individuals' behavior and the location (e.g., mean) is an important feature of a distribution. Fourth, at least one other parameter of distributions would also be stable. That is, if the distribution is the stable characteristic of individuals, some mean-independent way of describing that distribution must show stability. Fifth, withinperson behavioral variability must be meaningful (predictable) and, preferably, differentially so for different individuals. If such variability were only error (capricious or meaningless), it would not be worth describing. In sum, if individual differences in behavior are best described as density distributions, a large amount of behavioral variability will be present within the typical individual, individual differences in distribution parameters will be highly stable, and within-person variability will be meaningful. The remainder of the introduction elaborates each of these three ideas in turn. Adams Personality-Relevant Behavior? Within-person variability describes the same individual acting differently on different occasions, and the first hypothesis of the proposed model is that the amount of within-person variability should be sizeable, near the high end of expectations. In the DENSITY DISTRIBUTIONS OF STATES 1013 present studies, participants described their states several times per day for 2-3 weeks using adjectives rated on 7-point scales, and within-person variability is defined as the standard deviation across the resulting large number of states (i.e., how much the states belonging to one person differ from each other). The amount of variation within the average individual is first located between the minimum extreme of little variation and the maximum extreme of total variation across the entire sample of states (across and within individuals). Although it is trivially obvious that within-person variation is greater than zero, the closer within-person variation is to total variation, the closer individuals are to perfect and complete overlap in actual manifestation of traits in everyday behavior. Thus, the distance from total variation describes the amount of information gained by knowing which individual is acting. In addition to being compared against total variation, withinperson variation is compared against two other observed variations: between-person variation in traits, and within-person variation in affect. Because the amount of variability between people is sufficient to conclude that different individuals have different personalities, comparing the amount of variability within the average individual against the amount of variability between individuals describes how close within-person variability is to the magnitude sufficient for concluding that one individual's behavior represents different personalities. Within-person variation in affect is a useful comparison standard, because affect is so variable that it is conceived of primarily as a state and only secondarily and recently as a trait (e.g., see the Ekman & Davidson, 1994, volume) and because several studies have quantified the amount of withinperson variability in affect (e.g., Cattell, 1973; Eid & Diener, 1999; Eckenrode, 1984; Larsen, 1987; Larson, Csikzentmihalyi, & Graef, 1980; Zelenski & Larsen, 2000) . Thus, comparing withinperson variability in behavior against within-person variability in affect indicates whether behavior is variable enough to justify it also being studied as a variable state concept. Another advantage to affect is that it is collected with the same scale and the same method as is behavior variability, so that any biases introduced by the method should affect each estimate of variability equivalently and, thus, not be relevant to comparisons between them. Personality psychologists have long assumed there is withinperson behavioral variability (e.g., Allport, 1937; Fiske, 1961; Murray, 1938) ; the purpose of this article is to find out how much there is. Existing theory and research cannot tell us whether the amount is closer to the left or to the right extreme in Figure 1 . However, the larger the degree of within-person variability, the more need there is for research that examines within-person processes of personality; emphasizes flexibility, adaptiveness, and responsivity; and explains behavior as (differential) responses to situations, even in Big Five content. That is, the greater the within-person variability, the greater is the opportunity for integration of two approaches to personality, making quantification of the amount of within-person variability an important goal. of Traits Stable (Predictable)? Individual differences in states are important to the usefulness of the trait concept. That is, one way trait descriptions of individuals may acquire usefulness is by referring to the way the trait is manifest in those individuals' daily behavior. For example, describing an individual as moderately agreeable acquires usefulness when that individual in fact behaves in a moderately agreeable way. The second, third, and fourth hypotheses of the proposed model concern the existence of such individual differences in stable manifestation of traits. Because behavioral regularities provide usefulness for the trait concept, it has always been an important task in personality psychology to examine the degree to which behavior shows regularities. The current view is based on empirical work demonstrating distressingly low levels of predictability of one behavior from another. At best, behavior on one occasion predicts behavior on another occasion at around the .30 level (see Mischel, 1968 , for a review). However, the current view also proposes highly stable and predictive individual differences in the distributions of states, as long as these differences are conceived of at the distributional level. Epstein (1979 ) and, later, Moskowitz (1982 and were able to show impressive levels of predictability by examining averaged behavioral aggregates. Rather than predict one behavior from one other behavior, Epstein (1979) successfully predicted the aggregate of several behaviors from the aggregate of several other behaviors. Thus, individual differences in everyday behavior are present and evident. The present studies build on this work by testing whether aggregated means are stable when specifically Big-Five-relevant behavior is assessed and when it is assessed with adjectives. It is predicted that such means will be highly stable and, thus, that noncontingent Big Five descriptions of individuals can be highly useful. The current view, however, differs from the traditional conception of behavioral distributions as a mean or tendency plus some error. In contrast, I propose that personality psychologists focus on the entire distribution, that it is meaningful as a whole, and that the mean is only one parameter of such distributions. One test of this view is whether at least one distributional parameter other than the mean also shows regularity (Berdie, 1969; Larsen, 1989) . The average tendency describes the location of the bulk of the individual's states. For example, a person with a mean of 6 on a 7-point Extraversion scale is not seen as someone who acts highly extraverted but rather as one who acts in the upper half of the Extraversion dimension slightly more frequently than in the lower half. Another distributional property is its size (standard deviation), which describes the diversity of an individual's behavior-how differently he or she acts from moment to moment. Finally, shape (skew and kurtosis) describes the frequency and direction of an individual's extreme behaviors. Although it is a decades-old idea that people have characteristic ranges of variability in behavior (Fiske, 1961; Murray, 1938) , it has been studied mainly with mood, affect, or emotion (e.g., Eid & Diener, 1999; Larsen & Diener, 1987; Larson et al., 1980) . It is not known whether the amount of everyday behavioral variability within persons is a stable individual-differences characteristic. Stability of a distributional parameter in addition to the mean, combined with the large size of the average distributions, would suggest that the entire distribution become the focus of individual differences. The Role of Situational Cues Paying attention to within-person variability in behavior is worthwhile only if that variability is meaningful (predictable) rather than measurement error (unpredictable). Much withinperson variance in behavior is likely to be a response to variability in relevant situational cues (e.g., people are more extraverted in large groups than in small groups): Because situational cues vary in everyday behavior, behavior varies as well. Explaining situational cues' joint influence with personality on behavior is an important mission for personality psychologists. However, it is likely to be particularly interesting to personality psychologists, because individuals are likely to differ in their sensitivity or reactivity to such cues (e.g., some individuals may not increase or may even decrease their level of Extraversion with the size of the group). These differences have been theorized as Person X Situation interactions (Magnusson & Endler, 1977) and as conditional, if-then traits (Shoda, Mischel, & Wright, 1994; Thome, 1989 ). An additional consequence of individual differences in sensitivity to cues may be individual differences in the amount of within-person variability (Brown & Moskowitz, 1998) . Generalized responsivity to cues (e.g., self-monitoring; Snyder, 1974; or affect intensity; would lead to heightened variability in all traits. However, if different cues are relevant to different traits (the size of the group is less likely to be a cue for Conscientiousness than for Extraversion), being sensitive to the cues for a particular trait would result in the individual acting more variably on that trait, in particular. That is, a person whose actions differ from each other on a trait may be a person who responds strongly to the momentary cues for that trait. Testing these ideas requires identifying the relevant situational cues for a given trait and then assessing them along with behavior. Doing so for all traits is clearly beyond the scope of this article, but it is possible to do so for Extraversion as an illustration. I use time of day and the number of present others as situational cues, because they both are intuitively powerful for Extraversion and have been shown to be relevant to Extraversion (Blake, 1971; Brown & Moskowitz, 1998; Eysenck, 1981; Larsen, 1985; Larsen & Kasimatis, 1990 ; less is known about their relevance for other traits, although see Rusting & Larsen, 1998) . Four predictions are made in support of the hypothesis that sensitivity to cues and individual differences in sensitivity provide within-person variance with psychological meaning. First, if within-person variability represents meaningful variance in behavior rather than capricious or random responding, then it will vary significantly with time of day and with number of present others, but different traits will respond differently to these situational cues. Second, if personality is at least somewhat expressed in differential sensitivity to cues, individuals will differ in how their trait levels react to the cues. Third, if sensitivity to cues is trait specific, variability will not be strongly correlated across traits. Finally, if individual differences in the amount of variability in a trait represent sensitivity to that trait's cues, then variability of Extraversion will be predictable from reactivity of Extraversion to time and to number of others (the same will not hold true for other traits, unless time and number of others turn out to be relevant for another trait as well). The following three experience-sampling studies assess the average individual's distribution across time as well as individual differences in such distributions. The proposed model predicts that (a) density distributions evince sizeable and meaningfully predictive variability, near the high end of possibilities; (b) means of density distributions are highly stable; and (c) additional parameters of density distributions (size and shape) are also stable characteristics of individuals. Beyond adding a useful fifth empirical demonstration that aggregated means are highly stable, the present studies are the first to directly and quantifiably assess the manifestation of trait content in an extended period of the typical individual's everyday behavior, test the stability of size and shape properties of behavioral state distributions, and examine stability and predictability of the specifically Big-Five-relevant properties of behavior. These tasks are important for discovering how, in fact, people differ from each other in manifestations of traits in daily life, for contributing to the integration of process and structure within the very trait concept itself, and for empirically identifying additional individual differences in everyday behavior. Study 1 Participants. Forty-six students at a small southeastern university participated in the experiment in partial fulfillment of the requirements for an introductory psychology course. Procedure. Five times per day for 13 days, participants described how they had been acting and feeling during the previous hour. These reports were completed on a regular schedule, every 3 hrs (noon, 3 p.m., 6 p.m., 9 p.m., and midnight), and took about 1-2 min to complete. Reports were completed on Palm Pilots (U.S. Robotics, Los Angeles, CA), hand-held computers about the size of a calculator. Each question was printed on a small screen, and participants responded by pressing a number with a plastic stylus. Every 2 days, participants downloaded their data, and those who missed a download were contacted. The last day of the study, participants also completed a standard Big Five and affect inventory (the Positive and Negative Affect Scale [PANAS]; Watson, Clark, & Tellegen, 1988) . The first report occurred during a 45-min introductory session. The procedure was explained, questions were answered, and participants chose code names to make their anonymity salient. The unique nature of this study, that it investigated a complete picture of 2 weeks of each individual's life, was stressed as well as that it was important that they complete as many reports as honestly as possible. At the end of the introductory session, participants were invited to withdraw for partial credit if they felt the study would be too intrusive. The response rate was within normal range for such experiencesampling studies. Of the possible 65 reports per participant, the mean was 49.6 reports (76%), and the median was 53.5 reports (82.3%), with a range of 13-63 reports. Participants had been instructed to miss a report if it would be a major inconvenience to complete (e.g., while driving, during an exam, during practice, while sleeping). Participants were also told they could complete a report up to 3 hrs later than the scheduled time but to nonetheless describe the scheduled hour. Reports were also missed because participants forgot or had computer problems. In the interest of maintaining data quality, I excluded completed reports if they did not meet strict criteria. First, reports that contained four or more missing values or at least 85% identical responses were excluded (e.g., reports in which a participant responded "1" to every item-leaving these reports in would artificially inflate within-person variability). Second, an advantage to Palm Pilots is that they surreptitiously record the date and time of completion. Thus, all events completed at least 1 hr earlier or 3 hrs later than the scheduled time were excluded, guaranteeing that all reports were completed close in time to the described behavior. In total, 342 of the 2,281 reports were excluded for one of these reasons (15%). Materials. The daily reports were the same format as traditional, adjective-based Big Five and affect scales, with the exception that rather than describing themselves in general, participants described their behavior and emotion during the previous 1 hr (e.g., "During the previous hour, how well does 'talkative' describe you?"). Each Big Five and affect factor was represented by four items (Extraversion: talkative, energetic, assertive, adventurous; Agreeableness: cooperative, trustful, rude, warm; Conscientiousness: organized, undependable, hardworking, responsible; Emotional Stability: perturbable, insecure, optimistic, vulnerable; Intellect: intelligent, philosophical, inquisitive, creative; Positive Affect: excited, enthusiastic, proud, alert; Negative Affect: distressed, irritable, nervous, guilty). The Big Five is appropriately assessable with a large variety of adjectives (Goldberg, 1992) ; for this study, I chose adjectives that (a) loaded on the correct factor in Goldberg (1992), either alone or as part of a bipolar item, or in De Raad, Hendriks, and Hofstee (1994); (b) together represented the breadth of a factor; (c) were easily used to describe behavior; and (d) contained no emotion words, especially for Extraversion and Emotional Stability (to avoid redundancy with the affect scales). In addition, perturbable and inquisitive were used rather than their opposites for ease of participant understanding. For affect, eight representative items were chosen from the PANAS. Items were presented in Goldberg's (1992) opaque order: The five traits were cycled through one adjective per cycle in the above-listed order, followed by alternating negative and positive affect adjectives. All adjectives were responded to on scales ranging from 1 to 7, with higher numbers meaning that the adjective was more descriptive. Scale scores for the five traits and two affects were computed for each report by taking the mean of the corresponding four items (after reversing). Thus, each participant produced about 45 descriptions of how well his or her behavior in 45 different 1-hr periods was describable by each of the Big Five factors and how he or she was feeling during those same 1-hr periods. Differences across these descriptions represent variability in states, and similarities across the descriptions represent stability in states. Participants also indicated whether 0, 1-3, 4-10, or 10 or more others were present. After the daily study, participants completed standard Big Five and affect scales, describing what they are like in general. The Big Five scale consisted of the same 20 adjectives (except that imperturbable and uninquisitive were used in this assessment). Affect was measured with the same eight items. Reliability was calculated across all included reports and was found to be similar to reliability of Big Five traits and affect scales in previous work: for Extraversion, Cronbach's a = .72; for Agreeableness, a = .66; for Conscientiousness, a = .68; for Emotional Stability, a = .62; for Intellect, a = .69; for Positive Affect, a = .80; and for Negative Affect, a = .74. Results are divided into three parts. First, the distribution of states across time within the average individual is described. Specifically, the amount of variability is quantified and then compared with several other variability quantities. Second, individual differences in density distributions are assessed by testing splithalf stabilities of several distributional parameters. Third, the role of sensitivity to situational cues in producing within-person variability is tested. Amount of variability. The first section addresses how much the behavior of one individual changes from hour to hour. I attempt to locate the amount of variability between the one extreme view that there is little within-person variability and the other extreme view that individuals overlap completely in the expression of traits in their daily behavior. I assessed the latter extreme by taking the standard deviation, separately for each trait and affect, across the entire data set of states (thus, it is the total and individual-ignorant variation in states); the left bars in Figure 2 depict these standard deviations. Within-person variation was assessed by calculating seven standard deviations per participant, one for each trait and affect factor, with each such standard deviation representing the amount the individual varies from hour to hour in how he or she manifests a given trait. The second set of bars in Figure 2 shows the averages across individuals of these standard deviations; thus, they represent the magnitude of the average participant's across-time state variability. It is not surprising that the five trait within-person variabilities are larger in magnitude than zero; however, they are Total variation is the standard deviation calculated across all experience-sampling reports, thus representing how much states differ from each other (ignoring the actor). Between-person variation is the standard deviation across trait levels as assessed in a standard questionnaire or across behavioral mean levels from the experience sampling. As predicted, within-person variation is close to the total, about the same as between-person variation in traits, and nearly as much as within-person variation in affect. FLEESON relatively close to the total variability in the sample and, thus, near the high end of possibilities and to complete overlap across individuals. Two additional ways of evaluating the magnitude of withinperson variability are provided in Figure 2 . First, trait standard deviations are almost equal to affect standard deviations, and some are larger. A within-subject one-way analysis of variance (ANOVA) revealed that the seven standard deviations differ from each other, F(6, 270) = 15.16, p < .001 (any pairwise differences greater than .09 are significantly different from each other at p < .05). Most important, Extraversion was more variable than Negative Affect, all other traits were less variable than Positive Affect, and Agreeableness was the only trait less variable than was Negative Affect. Second, Figure 2 also depicts between-person variation in traits, represented by how much individuals differ from each other in their average levels. This study provides two ways to calculate average levels: scores on standard questionnaires, and averages of states across the 2 weeks. The rightmost bars in Figure 2 depict standard deviations across these two measures of average levels. F tests on differences between two variances revealed that within-person variance in Extraversion was greater than between-person variance in Extraversion questionnaire scores, F(41,43) = 1.90, p< .05, and greater than between-person variance in Extraversion behavioral averages, F(41, 45) = 2.06, p < .05; that within-person variation in Conscientiousness was greater than between-person variance in Conscientiousness behavioral averages, F(41, 45) = 2.15, p < .01; and that within-person variation did not differ from between-person variation for any other trait. In sum, individuals differ from themselves over time at least as much as they differ from each other at the average level, and more so for the traits of Extraversion and Conscientiousness. The first purpose of this article is to describe the average individual's distributions of states over a short time period. Figure  3 shows normal distributions calculated with the means and standard deviations observed for the average individual for each trait. The width of these distributions makes clear that the average individual routinely and regularly manifests all levels of Extraversion, Conscientiousness, and Intellect and most levels of Agreeableness and Emotional Stability in his or her daily behavior. In sum, one individual's behavior varies from hour to hour over a 2-week period close to the maximum extreme possible, almost as much as affect varies from hour to hour and at least as much as individuals differ from each other. Such variability is near the high end of expected possibilities. Individual differences in state distributions. The second purpose of this article is to identify the individual differences in behavior that are available to be described in a nomothetic manner. The basic assumption is that the individual differences in behavior that are available to be described are those that show some regularity over time-thus, those that are correlated across two independent assessments (i.e., split-half reliability). The central claim is that the individual differences that show regularity are the distributions of states, as opposed to single states or to only one parameter of the distributions. If this claim is correct, then (a) single states are not highly correlated across independent assessments, (b) locations of the distributions (means) are highly correlated across assessments, and (c) at least one other distributional parameter is also highly correlated across assessments. Level of State  First, two reports were randomly selected for each participant (with the constraints that none were within the first five reports and that the two reports were at least six reports apart from each other) and correlations were computed for each of five traits across those two reports (i.e., from one randomly selected hour to another). This was repeated for 20 trials, and the average of the 20 resulting correlations for each trait are presented in the top line of Table 1 (because participants differed in the number of completed reports, different numbers of participants contributed to each trial; note also that the standard errors are all less than 0.05, suggesting that additional trials would be unlikely to substantially change the results). These correlations are slightly higher than the traditional .30 limit but are low enough to indicate that levels of single states do not exhibit sufficient regularity to be the focus of personality descriptions. Second, the across-subjects mean and standard deviation for each of the within-subject parameters are shown in Table 1 (skews greater than ± 2.6 and kurtoses greater than ± 2.1 were windsorized). The between-subjects distributions were relatively normal, indicating that such parameters can be treated as individualdifferences variables (kurtoses were less normal because of outliers). Finally, regularity at the distribution level was tested, starting with the mean. Each participant's reports were randomly split into two approximately equal halves; means for each half were calculated for each trait, and correlations were calculated between the two halves. The Location Stability line of Table 1 shows the average correlations obtained from 10 such trials (the standard errors are all under 0.02, suggesting that additional trials would be unlikely to substantially change the results). These correlations approach 1.00, showing that the location of an individual's distribution is highly predictable. For example, the .90 correlation for Extraversion means that an individual's average level of Extra version in half the data was nearly identical to his or her average level of Extraversion in the other half of the data. (Stepping up the single-state correlations with the Spearman-Brown prophecy formula predicted values similar to these empirically observed values.) Figure 4 shows two example scatterplots from single trials, one predicting Extraversion on a single occasion from Extraversion on another single occasion, and the second predicting averaged Extraversion in one half of the data from averaged Extraversion in the other half of the data. The important point in these figures is that the correlations are based on the entire range of possible means. That is, individuals differed from each other in the location of their distributions, and such differences were matched precisely in two independent halves of the data. However, to show that it is the distribution that is stable and not only the average tendency, it is important to show that at least one other parameter of the distributions also shows regularity. Each participant's data were randomly divided into two approximately equal-size halves; standard deviations, skews, and kurtoses were computed for each half; and the parameters were correlated across halves (skews greater than ± 2.6 were recoded as ± 2.6 to avoid outliers dominating the results; extreme kurtoses were recoded to ± 2.1). Ten such trials were repeated, and the averages of the correlations across the 10 trials are shown in Table 1 . The size of an individual's distribution was clearly a reliable characteristic of the individual, although not as reliable as was the location. Skew demonstrated some regularity, although not much more than did single states. Kurtosis is of questionable status: Independent assessments were positively correlated, but only slightly. It may be that kurtosis requires more reports for a stable assessment, or it may be that kurtosis is not an important individual difference (the low correlations for kurtosis also point out that aggregation does not artificially create stability-only when the characteristic is a stable aspect of the individual does aggregation produce large correlations). In sum, average tendency is a highly regular char- Note. Mean and standard deviation are across-subjects statistics calculated on the within-subjects parameters. Mean describes the typical participant score on the parameter, and standard deviation describes how much participants differ from each other in the parameter. Stability describes correlations computed from one randomly selected hour to another from the same individual or from one randomly sampled half to the other half belonging to the same individual; stability is thus similar to split-half reliability. acteristic of individuals' behavior, and more than average tendency is required to adequately describe all that is regular about individuals' behavior. Discriminant validity of parameters. Individuals who were more variable, skewed, or kurtotic on one trait tended to be slightly more variable, skewed, or kurtotic, respectively, on other traits. Across-trait correlations of standard deviations ranged from -.08 to .70, with an average r = .38; across-trait correlations of skews ranged from -.12 to .50, average r = .28; and across-trait correlations of kurtoses ranged from .04 to .42, average r = .20. This positive manifold in variability of responding, as is true of individual differences in affect variabilities (Eid & Diener, 1999; Larsen & Diener, 1987) , likely represents both meaningful personJevel tendencies to behave more or less variably for all behavioral contents (e.g., generalized situational cue sensitivity) as well as less meaningful method variance or individual differences in response styles (e.g., the tendency to use extreme numbers). Although these across-trait correlations are substantially smaller than even the split-half, same-trait correlations reported above, it is important to establish that behavioral regularities represent more than response styles and that they are trait specific. Thus, the above stability analyses were repeated for 10 more trials, except that the standard deviation of a given trait in one half of the data was predicted simultaneously from the standard deviations of all five traits in the other half of the data. The resulting unique standardized betas, shown in Table 2 , describe the stability of standard deviations that is independent of generalized response styles. The pattern of stronger correlations along the diagonals shows that removing generalized response styles had little effect on same-trait stabilities yet eliminated the across-trait correlations. Thus, individual differences in parameters represent more than response styles, and participants responded discriminatively to the different traits. Skew and kurtosis stabilities dropped somewhat more when generalized response styles were removed: Skew same-trait unique betas ranged from .28 to .47, average r = .37, and kurtosis same-trait unique betas ranged from .05 to .33, average r = .22. However, these were higher than across-trait unique betas: skew = -.18 to .29, average r = .06, kurtosis = -.11 to .14, average r = .05.
Moreover, the expression of personality traits fluctuates within persons across time REF .
Toward a structure- and process-integrated view of personality: Traits as density distributions of states.
Journal of personality and social psychology
Journal of personality and social psychology
Psychology Medicine