Abstract
The study aimed to investigate the status of within-person state variability in neuroticism and conscientiousness as individual differences constructs by exploring their (a) temporal stability, (b) cross-context consistency, (c) empirical links to selected antecedents, and (d) empirical links to longer term trait variability. Employing a sample of professionals (
Keywords
Introduction
Over the past decade or so, there has been a surge of interest in the dynamic components of personality, reflecting a recognition that between-person rank-order stability co-exists with within-person change in personality responses. Conceptualising personality as ‘dynamic’ means bringing ‘change over time’ into the study focus, with both short- and long-term person change being of interest (e.g. Beckmann & Wood, 2020). There is now mounting evidence to show that personality varies short-term (e.g. Beckmann et al., 2010; Debusscher et al., 2014, 2016; Fleeson, 2001, 2007; Fleeson & Gallagher, 2009; Judge et al., 2014; Minbashian et al., 2010; Sosnowska et al., 2019b) and long-term (e.g. Liu & Huang, 2015; Roberts et al., 2006; Wille & De Fruyt, 2014; Woods et al., 2019), but also that individuals differ in the extent to which they experience such change (e.g. Smith et al., 2009; Wille et al., 2014). Indeed, variability as an
The current study makes the following three contributions: First, we aim to add to the small body of research literature on the linkages between short-term state and long-term trait variability using a non-student sample. Second, we seek to produce evidence on the antecedents and correlates of within-person variability by considering individual differences that are of relevance in learning and performance settings. These include implicit theories, goal orientations, and cognitive ability, in addition to Big Five traits. Third, we compare and contrast outcomes that are based on two commonly used forms of operationalising variability (an index of the total amount of variability, and a conditional response index) within the same sample and using lab and field data, to test whether different conceptual and operational approaches to short-term within-person variability result in differential correlation patterns with other individual differences variables.
Momentary and trait personality variability
Short-term personality state variability
Momentary (or state) personality has now been studied relatively widely in student and non-student samples (e.g. Beckmann et al., 2010; Debusscher et al., 2014, 2016; Fleeson, 2001, 2007, 2017; Huang & Ryan, 2011; Judge et al., 2014; Sherman et al., 2015; Sosnowska et al., 2019a; Zacher, 2016). As the focus is on short-term fluctuations in personality responses, the typical study employs an intensive repeated measurement design involving numerous measurement occasions within short time periods (typically one or multiple measurements per day for one or several weeks). For example, in a seminal series of studies, Fleeson (2001) collected Big Five personality state data in student samples five times per day over the duration of two to three weeks as students went about their daily activities. He showed that most students experienced considerable variability in their personality states during the study period and at the same time showed relative stability in their average responses. Variability in personality states has since been studied in employee samples and linked to work experiences and outcomes, such as job performance (e.g. Beckmann et al., 2010; Debusscher et al., 2014; 2016; Judge et al., 2014; Minbashian et al., 2010; Wood et al., 2019). Several insights can be derived from studies on momentary personality: First, within-person variability in state personality exists and is of a considerable amount. Second, this variability is at least to some extent systematic, i.e. non-random and substantive. Third,
Experiential versus construed components of personality
Variations in personality states have distributional properties, such as means and standard deviations, that are systematic and theoretically substantive (Fleeson, 2001). While most people experience a range of different state levels (e.g. high and low levels of state conscientiousness), they tend to experience some state levels more frequently than others, this is reflected in their mean state. Mean personality states tend to correlate moderately strongly with scores from conventional Big Five trait scales (e.g. Fleeson & Gallagher, 2009; Wood et al., 2019), suggesting momentary personality is related to, but also distinct from trait personality. From a measurement perspective, a reasonable explanation of such distinction is that the repeated, often in-the-moment, assessment of personality states reflects (in part at least) reactions to, and interpretations of, one’s experience of the proximal circumstances the situation presents – thus state measures yield an
An important question is whether variability is of consequence to an individual’s psychological functioning. If consequential, variability may present an asset or might turn out to be a liability. Arguably, if variability is of no consequence, antecedents and/or correlates of variability matter less. To date, findings from studies on variability as an individual difference present a mixed picture. A number of early studies suggested variability in affect or personality to indicate a vulnerability with undesirable consequences for well-being or performance (e.g. Donahue et al., 1993; Kuppens et al., 2007; Reddock et al., 2011; see also meta-analysis by Bleidorn & Ködding, 2013; Suh, 2002). More recently, variability has been shown to be facilitating, as reflected in higher well-being or performance indicators (e.g. Lievens et al., 2018; Magee et al., 2018). Others report no, or considerably reduced, relationships between measures of variability and outcome variables of interest, particularly when controlling for scale means (Baird et al., 2006; 2017; Magee et al., 2018). While the evidence available to date on the potential costs or benefits of variability is not yet conclusive, it is clear that findings vary by attribute studied and variability operationalisation used.
Total amount and conditional variability in states
Within-person variability has been quantified in various ways. A common approach is to calculate person-specific standard deviation scores. In repeated measurement designs, within-person standard deviation scores capture the total amount of variability in a variable observed for a given person across measurement occasions. Because cross-occasion variability is often related to the scale mean (e.g. very high and very low
Distinguishing between conditional and total response variability is important, because different operationalisations of variability are likely to emphasise different components of fluctuations in thoughts, feelings and behaviour, which likely contributes to the ‘mixed picture’ of findings mentioned earlier. A conditional response, as reflected in situation contingencies, implies conscious or subconscious adjustment of responses to situational demands or features. Such response adjustment is, however, not directly reflected in variability indices of the total amount of variability in states, as these indices capture both systematic and unsystematic components of variability (i.e. including non-situation-contingent but ‘true’ fluctuation). Hence, their relationships with antecedents and outcomes may differ. For example, total variability in state conscientiousness has been shown to present a liability in terms of employee performance (Debusscher et al., 2016), while a conditional response pattern, that is, variability in state conscientiousness as a function of task demand, can be performance-facilitative (Minbashian et al., 2010; 2018; Wood et al., 2019). Similarly, affect variability has been found to be associated with poorer health and well-being when operationalised as total variability and, in the same sample, with better psychological health and well-being when operationalised as a patterned, adaptive response variability (Hardy & Segerstrom, 2017).
To summarise, the operationalisation of personality state variability can be thought of comprising the following components: (a) systematic variance that is related to the mean and which partly reflects the boundedness of the scale; (b) systematic variance that is related to situational antecedents as captured by situation contingencies; (c) systematic but yet unexplained variance (e.g. contingent fluctuation in states based on unknown, or not measured antecedents which may or may not be related to the situation); and (d) unsystematic variance (e.g. measurement error). Total variability operationalisations – when controlled for the effect of the boundedness of the scale (Mestdagh et al., 2018) – therefore reflect several potential sources of systematic variance: the mean, situation-contingencies, and other non-situation related sources of systematic fluctuation (e.g. internal states). In other words, while in the strictest sense systematic variance is inherently ‘conditional’, the conditions or causes may lie outside the situation, or simply be unknown. In contrast, situation contingencies, as a patterned response variability measure, directly capture the component of systematic variance in states associated (typically linearly) with (known) situational antecedents. While there is overlap between different measures of variability, they also capture different components of variability to different degrees. At the same time, some personality variability will remain undetected and not all measured variability will be reflected in observable behaviour.
Long-term personality trait change
There has been a long-standing interest in the study of personality trait change over time, including work-related trait change. Compared with studies on variability in personality states, studies on trait change typically use longitudinal designs with fewer measurement occasions over longer timeframes, given their focus is on the more stable components of personality as indexed by conventional trait measures. A typical study involves two or three personality trait assessments over the course of several years, although some studies cover even larger timeframes (see Roberts et al., 2006). For example, employing a two-wave longitudinal design with a sample of over 1000 employees, Hudson and Roberts (2016) observed an increase in trait conscientiousness over the course of three years particularly for those employees who became increasingly invested in their work.
Several insights can be derived from the large body of work on long-term trait change. First, there is considerable evidence to support assumptions of trait malleability. For example, traits have been shown to change in response to vocational training and university education (e.g. Deventer et al., 2018; Lüdtke et al., 2011). Second, dependencies between traits and work demands are likely reciprocal in nature, such that traits function not only as antecedents, but also as outcomes of work experiences, such as career choices (e.g. Nieß & Zacher, 2015; Wille & De Fruyt, 2014; Woods et al., 2013; Woods et al., 2019). Third, it may be possible to actively evoke change in traits within relatively short timeframes. Recent research into the effectiveness of clinical intervention to trigger trait change suggests a time window of only about eight weeks to be sufficient (meta-analysis, Roberts et al., 2017). This is in contrast to commonly held assumptions of a more gradual, long-term developmental process of change in traits.
Finally, fewer studies have investigated
Theoretical frameworks on the developmental linkages between state variability and trait development have been proposed both in personality science and organisational psychology (TESSERA; Wrzus & Roberts, 2016; Woods et al., 2013; Woods et al., 2019). These essentially describe a process of accumulation of experienced states to prompt and shape trait development. To our knowledge, very few studies have however combined the study of short-term state variability with the longer term repeated assessment of traits that would permit insights into the empirical relationships between experiential and construed personality components and their variability (for an exception, see Borghuis et al.’s (2020) study on daily negative affect and trait neuroticism development in adolescence).
Individual differences as antecedents of personality variability in learning and performance contexts
Relatively few studies have explicitly investigated individual differences constructs as antecedents of short- and long-term personality variability. Such studies have almost exclusively focussed on Big Five traits and often used student samples (e.g. Geukes et al., 2017; Jones et al., 2017; cf. Noftle & Fleeson, 2015). Overall, and similar to findings related to outcomes of variability, the result pattern is somewhat inconclusive. Big Five traits that were found to be correlated with variability in one study often are not in another. One exception is neuroticism as we explain below. Other prominent individual differences include implicit theories and goal orientations. These traits are widely recognised to be of relevance in learning and performance contexts and, given their conceptual linkages with notions of variability and change, may function as potential antecedents of within-person variability in such contexts. In the following, we briefly review each of the traits.
Among the Big Five personality traits,
According to Dweck (2000), individuals differ in their
The current study, its objectives, aims, and expectations
The current study was undertaken in a learning and developmental context that was of professional relevance to participants, who were employees in mid-level managerial positions. There are three strata of effects that we explore. Our overarching framework begins with an antecedent network of change-facilitating and change-inhibiting factors (historical effects). These historical effects are expected to impact experiential state responses to situational demands manifested both in mean level and in degree of variability. State response and situation contingencies define potential sources of systematic within and between person variability. Together, these factors inform potentiality for higher order trait change, which we investigate over the course of two years. The exploration objectives and how they link to the analysis aims in the current study are presented in Table 1.
Overview of exploration objectives and analysis aims.
For (1) structured within-lab settings and (2) unstructured field settings, we investigated individual differences in short-term variability in conscientious and neurotic states with the aim to contribute to the relatively small literature on the antecedents and consequences of short-term variability in personality, and its links to trait variability. We focus on individual differences in (a) the total amount of within-person state variability (i.e. relative variability given the state mean, relative SD; Mestdagh et al., 2018), and (b) the strength of contingency between situational demands and variability in state responses (i.e. conditional, situation-state response patterns; Fleeson, 2007; Minbashian et al., 2010; Mischel & Shoda, 1995), as we expected these to be differentially related to antecedents and consequences, including trait change. Overall,
While links between individual difference variables (conventional/construed traits) and mean states (i.e. experiential traits) are well established, comparably less is known about associations between individual difference variables (particularly those outside the Big Five framework) and variability in states (e.g. Noftle & Fleeson, 2015). Findings are somewhat inconclusive; however, we expected trait neuroticism to be positively associated with state variability short- and long-term. Individual differences in state variability assessed at the start of the study were expected to remain relatively stable over the two-year study period. We also expected short-term variability in states to be associated with change in conventional traits, although the strength of the relationship was expected to vary depending on the variability operationalisation used. Specifically, we expected variability in state conscientiousness (or state neuroticism) to function as a temporal precursor of change in trait conscientiousness (or trait neuroticism) assessed over a two-year period. This was based on our reasoning that short-term variability may indicate a readiness for change in the respective trait. Given that variability in personality states is partly determined by variability in situations people encounter (e.g. stable situations may not provide much room for response variability), we studied state variability using data collected under lab-like conditions where all participants were exposed to the same tasks and therefore experienced similar situations. For a subset of participants, we then contrasted our findings for selected analyses with findings from data collected under more ecologically valid conditions, i.e. as participants went about their daily lives at work. The field data represent conditions in which participants were, at least to some extent, able to self-select and shape situations in line with their own preferences and traits.
The meaning of variability depends on the personality dimension for which variability is observed. We focussed on the two personality dimensions that have consistently been shown to be of relevance in organisational and other learning and performance contexts, conscientiousness and neuroticism. Variability in conscientious responses may indicate
Taken together, our approach allowed us to explore antecedents and consequences of variability in states along three dimensions: (1) attribute, i.e. personality dimension studied (conscientiousness vs. neuroticism), (2) type of operationalisation used to quantify variability (total amount of variability vs. conditional response variability), and (3) level of situational control (lab vs. field setting). The study was not preregistered and is explicitly exploratory in nature. 2
Methods
Participants
Our analyses are based on a dataset of 346 high-performing managers from large Australian organisations who participated in the study as part of a professional development programme offered by a major Australian university. For a subset of participants (
Materials
Experience sampling measure
The experience sampling measure included 21 items tapping into momentary thoughts, feelings, and behaviours. For the purpose of the current study, items were selected to provide broad coverage of various facets of neuroticism and conscientiousness (as per the NEO framework; Costa & McCrae, 1992) and to be representative of the types of states managers were likely to experience at work. Four items were used to assess state
Trait scales
State dynamic variables derived for lab and field data as situation contingencies (A) and total variability (B).
SHL reasoning tests
Cognitive ability was assessed using commercially sourced reasoning tests in three domains: verbal, numerical, and abstract reasoning (shl.com). For the purpose of the current study, scores for abstract reasoning were of relevance.
Design and procedure
The study comprised a longitudinal design. Data on personality states together with situation appraisals were collected using experience sampling methodology in field settings as participants went about their day-to-day activities, as well as under more controlled conditions when completing a predetermined set of tasks in lab-like settings. The lab-based experience sampling required participants to respond to a brief questionnaire immediately before, during or after completing a set task during up to five 3-day training sessions (referred to as ‘modules’) offered by a major Australian university. The five modules, during which data collection waves were conducted, were spread evenly over two years. The field-based experience sampling involved participants responding to a brief questionnaire up to five times per day for 15 working days over three weeks.
4
A signal-contingent approach was implemented. Signals occurred between 9am and 7pm and were at least 1 h and no more than 3 h apart. Participants were allowed a time-window of 30 min to respond to a signal. In total, we collected 6627 responses in lab settings (responses per participant:
Data analysis
Overview
To begin generally, the substantive focus of our analyses is the conceptualisation of personality as a dynamic attribute. The data clustering layout is summarised in Figure 1. Dynamic variables were derived at both ‘trait’ and ‘state’ levels. The dynamic ‘trait level’ variable was defined as the change in trait conscientiousness and trait neuroticism from the start (wave 1) to the end (wave 5) of the program (Figure 1(a)). The antecedents were also measured at this level. Dynamic state variables were derived and tested for lab and field data separately. For the lab data (Figure 1(b)), the repeated observations are clustered within data collection waves and within individuals. Accordingly, we conceived of a set of four dynamic state effects, three within-wave (mean state, situation-state contingency, and relative variability) and one set across waves (as linear change in each of the within-wave effects over wave 1 to 5). The field data (Figure 1(c)) was collected across a single three-week period, and therefore observations are clustered within individuals only. Accordingly, the same set of four dynamic state effects were considered, but without the cross-wave comparison. The generic dynamic state measures derived are summarised in Figure 1(d) and detailed in Table 2. The mean state measure (intercept, Figure 1(d)(i)) and task demand contingency (slope, Figure 1(d)(ii)) were derived and tested in the same models. However, because the relative variability index (i.e. relative SD; Figure 1(d)(iii)) aggregates over the clustering unit (by definition), it was necessary to derive these measures outside of the models and enter them as dependent variables in separate analyses (i.e. lab data using two-level clustering and the field data using single-level regression). The data analysis scripts together with supplementary material are openly accessible at https://osf.io/qp2nb/?view_only=3b85ace6b75e44b8b62a68f3fcf68d3a
Overview of data structure and derivation of common dynamic measures.
Aim A: Dynamic variables
Multilevel modelling was adopted (using MPlus software, Muthen & Muthen, 1998-2015) to investigate the correlates of short-term state variability with other individual differences variables that may function as antecedents. There were two preliminary analytic steps required to achieve this. First, a fully unconditional analysis was conducted in order to estimate the partitioning of variability in state consciousness and state neuroticism into within-wave (i.e. level 1) variability, between-wave (level 2) variability, and between-person (level 3) variability in the lab data; and within-person (level 1) and between-person (level 2) in the field data. Second, in the lab data, for both state conscientiousness and state neuroticism, we estimated multilevel models in which (group-mean centred) task demand (i.e. the situational characteristic) was entered as a predictor at level 1 and wave (with wave coded so that wave 1 = 0) was entered as a predictor at level 2. This analysis defines estimates of four fixed effects (and their associated variances as individual differences) that constitute dynamic personality variables, (i) typical state, (ii) short-term state dynamics, (iii) long-term state dynamics, and (iv) long-term change in short-term state dynamics, as summarised in Table 2. In the field data, we considered two-level models in which (group-mean centred) task demand was entered as a predictor at level 1. This analysis defines estimates of two fixed effects and their variances: (i) typical state and (ii) short-term state dynamics (Table 2).
We estimated indices of the total amount of variability (relative SD) for both state conscientiousness and state neuroticism for each person per data collection wave in the lab, and for each person in the field data using the approach proposed by Mestdagh et al. (2018). We then derived the dynamic variables from these dependent variables. For the lab data, we followed the same two steps as above. First, a fully unconditional analysis was conducted to partition the variability in
Aim B: Individual differences as antecedents of dynamic variables
To examine the relationships between personality traits assessed at the beginning of the learning and development program and the dynamic personality variables outlined above, we first extended the multilevel analyses from the Aim A investigations by including the (grand-mean centred) relevant personality trait as a between-person level predictor of the dynamic personality variables. Then, to more generally examine the independent effects of the individual difference
Aim C: Antecedents of long-term trait change
To address Aim C, we conducted a series of analyses to examine whether the individual difference antecedents directly relate to changes in personality traits that occur across waves, and whether our dynamic personality constructs (including conditional and relative state variability) contribute above and beyond. We did this by explicitly computing the change in the relevant personality trait (either conscientiousness or neuroticism) between the start and end of the learning and development program and regressing this change score on the individual differences antecedences, with the dynamic personality variables as both criterion and predictors in single-level regression analyses via Amos (Arbuckle, 2014), 5 i.e. using a path mediation analysis modelling approach.
Results
Aim A: Dynamic variables
Partitioning of variance
Table 3 shows descriptive statistics and correlation coefficients for the main study variables at the between-person level. This includes the individual differences variables assessed at baseline (i.e. during the first data collection wave). In general, the correlation pattern of the individual differences variables was in line with expectations. For example, trait neuroticism was negatively related to the traits conscientiousness (
Between-person descriptive statistics for main study variables, including individual differences variables at baseline.
O: openness; C: conscientiousness; E: extraversion; A: agreeableness; N: neuroticism; IT-P: implicit theories-personality; LGO: learning goal orientation; PGP: performance prove goal orientation; PGA: performance avoid goal orientation; Reasoning: abstract reasoning; TD: task demand; SC: state conscientiousness; SN: state neuroticism. Bold values indicate
Tables 4 and 5 display the decomposition of the cross-occasion variability in task demand, state conscientiousness, and state neuroticism for lab (Table 4) and field (Table 5) data. Across all three experience sampling measures in both contexts, the majority of observed cross-occasion variability in task demand, state conscientiousness, and state neuroticism lied at the within-person level (i.e. within- and between data collection wave for data collected under lab-like conditions).
Variability in experience sampling measures decomposed into three sources of variance for lab data.
Levels 1 and 2 represent within-person variability; lab experience sampling measures were scaled from 0 to 100. Mixed model: Yijk = β + uk + rjk + eijk.
Variability in experience sampling measures decomposed into two sources of variance for field data.
Field experience sampling measures were scaled from 0 to 6. Mixed model: Yjk = β + uk+ rjk.
Dynamic variables as individual differences
Results in Table 6 (section A) provide evidence in relation to the dynamic
Individual differences predictors of dynamic conscientiousness constructs (lab).
arSD analysis is based on two-level model, State and TCC analyses are based on three-level models;
TCC: task-contingent conscientiousness (= effect of task demand (TD)); rSD: relative SD; O: openness; C: conscientiousness; E: extraversion; A: agreeableness; N: neuroticism; IT-P: implicit theories-personality; IT-I: implicit theories-intelligence; LGO: learning goal orientation; PGP: performance prove goal orientation; PGA: performance avoid goal orientation; Est: beta (β) for mean of fixed effects in (A) and between-person regression coefficients in (B) and (C), and tau (τ) for variance of random effects in (A); for analyses Wave 1 was coded as 0. Italic values indicate
rSD models: Panel A: rSDjk = β0 + β1 × Wave + u0k + u1k × Wave + rjk; Panel B: rSDjk = β00 + β01 × Trait C + u0k + β10 × Wave + β11 × Wave × Trait C + u1k × Wave + rjk.
State/TCC models: Panel A: Yijk = β00 + β01 × Wave + β10 × TD + β11 × Wave × TD + u0k + u1k × Wave + u2k × TD + u3k × TD × Wave + r0jk + r1jk × TD + eijk; Panel B: Yijk = β000 + β001 × Trait C + β010 × Wave + β011 × Wave × Trait C + β100 × TD + β101 × TD × Trait C + β110 × Wave × TD + β111 × Wave × TD × Trait C + u0k + u1k × Wave + u2k × TD + u3k × TD × Wave + r0jk + r1jk × TD + eijk; Panel C models contain the same terms as Panel B as well as regression coefficients for each additional predictor and its interaction with Wave and (where applicable) TD.
The results for the lab data in Table 7 (section A) provide evidence in relation to dynamic
Individual differences predictors of dynamic neuroticism constructs (lab).
arSD analysis is based on two-level model, State and TCC analyses are based on three-level models;
TCN: task-contingent neuroticism (= effect of task demand (TD)), rSD: relative SD, O: openness, C: conscientiousness, E: extraversion, A: agreeableness, N: neuroticism, IT-P: implicit theories-personality, IT-I: implicit theories-intelligence, LGO: learning goal orientation, PGP: performance prove goal orientation, PGA: performance avoid goal orientation; Est: beta (β) for mean of fixed effects in (A) and between-person regression coefficients in (B) and (C), and tau (τ) for variance of random effects in (A)) ; for analyses Wave 1 was coded as 0; Italic values indicate
rSD models: Panel A: rSDjk = β0 + β1 × Wave + u0k + u1k × Wave + rjk; Panel B: rSDjk = β00 + β01 × Trait C + u0k + β10 × Wave + β11 × Wave × Trait C + u1k × Wave + rjk;
State/TCC models: Panel A: Yijk = β00 + β01 × Wave + β10 × TD + β11 × Wave × TD + u0k + u1k × Wave + u2k × TD + u3k × TD × Wave + r0jk + r1jk × TD + eijk; Panel B: Yijk = β000 + β001 × Trait C + β010 × Wave + β011 × Wave × Trait C + β100 × TD + β101 × TD × Trait C + β110 × Wave × TD + β111 × Wave × TD × Trait C + u0k + u1k × Wave + u2k × TD + u3k × TD × Wave + r0jk + r1jk × TD + eijk; Panel C models contain the same terms as Panel B as well as regression coefficients for each additional predictor and its interaction with Wave and (where applicable) TD.
In summary, our findings suggest substantial within-person variability in conscientiousness and neuroticism states, both in terms of short-term variability across tasks as reflected in the two situation contingencies (TCC, TCN) and, more generally, across occasions (relative variability in conscientious and neurotic states), and in terms of long-term variability over time. We also observed between-person differences in the majority of these effects.
For comparison purposes, we conducted analogous analyses using the field experience sampling data, for conscientiousness and neuroticism, respectively. Again, as reported in Table 8 (section A), we found a positive effect for task demand on state conscientiousness for the typical person (β = 0.300,
Individual differences predictors of dynamic conscientiousness constructs (field).
arSD analysis is based on a single-level model, State and TCC/TCN analyses are based on two-level models;
TCC: task-contingent conscientiousness; TCN: task-contingent neuroticism; rSD: relative SD; O: openness; C: conscientiousness; E: extraversion; A: agreeableness; N: neuroticism; IT-P: implicit theories-personality; IT-I: implicit theories-intelligence; LGO: learning goal orientation; PGP: performance prove goal orientation; PGA: performance avoid goal orientation; Est: beta (β) for mean of fixed effects in (A) and between-person regression coefficients in (B) and (C), and tau (τ) for variance of random effects in (A). Italic values indicate
State and TCC/TCN models: Panel A: Yjk = β0 + β1 × TD + u0k + u1k × TD + rjk; Panel B: Yjk = β00 + β01 × Trait C + u0k + β10 × TD + β11 × TD × Trait C + u1k × TD + rjk; Panel C: The same as Panel B with regression coefficients for each additional predictor and its interaction with TD.
Individual differences predictors of dynamic neuroticism constructs (field).
arSD analysis is based on a single-level model, State and TCC/TCN analyses are based on two-level models;
TCC: task-contingent conscientiousness; TCN: task-contingent neuroticism; rSD: relative SD; O: openness; C: conscientiousness; E: extraversion; A: agreeableness; N: neuroticism; IT-P: implicit theories-personality; IT-I: implicit theories-intelligence; LGO= learning goal orientation; PGP: performance prove goal orientation; PGA: performance avoid goal orientation; Est: beta (β) for mean of fixed effects in (A) and between-person regression coefficients in (B) and (C) ; and tau (τ) for variance of random effects in (A). Italic values indicate
State and TCC/TCN models: Panel A: Yjk = β0 + β1 × TD + u0k + u1k × TD + rjk; Panel B: Yjk = β00 + β01 × Trait C + u0k + β10 × TD + β11 × TD × Trait C + u1k × TD + rjk; Panel C: The same as Panel B with regression coefficients for each additional predictor and its interaction with TD.
Correlation pattern of variability indices
Table 10 shows the correlations across context (lab vs. field), operationalisation (contingent vs. relative variability) and dimension (conscientiousness vs. neuroticism). Three insights are offered: First, there was context alignment as evidenced by non-trivial correlations between lab and field indices (
Cross-dimension, -context, and -operationalisation correlations between variability indices.
Second, the operationalisation of variability (i.e. conditional vs. relative variability) mattered less for neuroticism than for conscientiousness. For neuroticism, the two types of variability indices were positively correlated, both under lab conditions where situations were more controlled (lab:
Third, there was no strong evidence to suggest the existence of an overarching ‘variability trait’ given the lack of pervasive positive correlations (i.e. positive manifold) across dimensions, operationalisations, and contexts. However, separate inspection of the set of correlations between contingent variability indices (Table 10, top-left shaded section) and the set of correlations between the relative variability indices (Table 10, bottom-right shaded section) suggests a more nuanced interpretation is needed. There was evidence of positive manifold in relative variability, whereas this is distinctly not the case for task contingencies. Relative variability in conscientiousness was substantially correlated with relative variability in neuroticism, both under lab (
Overall, if these variability indices were tapping an underlying variability trait, we would expect to find (a) substantive cross-dimension correlations in each context (lab, field) and (b) for those correlations to also hold across contexts given the alignment between field-and lab-based variability indices reported earlier. This was clearly not the case for contingent variability indices, but there was some evidence of positive manifold for relative variability indices. The stronger within-context compared to cross-context correlations between the relative variability indices for the different dimensions (neuroticism, conscientiousness) may still indicate that cross-dimension correlations reported in prior work (where there is typically only one context) may be somewhat inflated (for instance by a common method factor).
Two-year stabilities of variability indices
Having demonstrated that individuals differ in the amount of state variability they show and in their level of responsiveness to task demands, in a next step we investigated the stability of such individual differences in state variability over time. Stability may be interpreted as another characteristic of an individual differences variable. Table 11 shows the cross-wave correlations for situation contingences and relative variability indices for both conscientiousness and neuroticism. As can be seen, there was some evidence of stability (i.e. from wave 1 to 5) for both types of variability indices, and both personality dimensions. Effects were generally positive in sign and small in size. Overall, stability was weakest for TCC (mean
Stabilities of variability indices across a two-year period (lab).
a
b
Aim B: Antecedents of dynamic variables
In order to explore possible antecedents, we first examined the relationships between conscientiousness and neuroticism traits and the respective dynamic personality variables, ignoring other individual difference variables. Table 6 (section B) shows that trait
The pattern of findings for the effects of the construed trait on contingent variability (i.e. TCC and TCN) are somewhat similar (but not identical) to the effects of the trait on relative variability (i.e. rSDcon, rSDneu): (i) for neuroticism, the trait was positively related to contingent variability and relative variability in the state; (ii) for conscientiousness, the trait was positively related to relative variability, but not to contingent variability in the state; (iii) for both conscientiousness and neuroticism, the change in both relative and contingent variability across time was unrelated to the trait (as perhaps was to be expected given the lack of evidence for individual differences in contingent and relative variability change across time, as noted previously).
In a next step, we examined individual differences antecedents of the dynamic personality variables more generally by simultaneously including all 11 individual differences measures in the analyses. Table 6 (section C) summarises the effects of the individual difference variables on the dynamic
Table 7 (section C) summarises the effects of the individual difference variables on the dynamic
For comparison purposes, we conducted analogous analyses on the field data and note that the sample size is smaller for this set of analyses (
Aim C: Antecedents of long-term trait change
Finally, we tested whether variability in states and long-term change in conventional traits were related. Figure 2 depicts the change in trait scores from the first to last wave (A1 and B1), the density distribution of trait change scores (A2 and B2), and the relationship between baseline trait scores (trait scores at wave 1) and trait change scores (A3 and B3) for both conscientiousness (A) and neuroticism (B). While on average traits remained stable across waves, there were also individual differences, such that some individuals experienced a considerably increase or decrease in their trait standing over the course of the study.
Trait change: Mean trait scores at baseline (time 1) and time 2 (left), density of trait change scores (middle), and correlation of baseline with trait change scores (right) for conscientiousness (upper) and neuroticism (lower).
In a next step, we tested whether state variability operationalised in the form of conditional or relative variability indices predicted individual differences in trait change while controlling for the respective mean states and the selected set of individual differences antecedents. We again contrasted findings for data collected in lab vs. field settings. All models are depicted in Figure 3, and findings relating to the dynamic variables (i.e. mean states, conditional and relative variability indices) are presented in Table 12. The following insights can be drawn: First, more variance was explained in trait change for neuroticism compared to conscientiousness. Second, the strongest effects were generally found for mean states predicting trait change, rather than state variability. Third, effects were generally stronger for lab compared to field data; however, we note sample sizes differed considerably (
Individual differences and dynamic constructs as antecedents of trait change. All values reported are
Dynamic constructs as predictors of change in construed traits with (i) referring to regression coefficients, and (ii) referring to R2 of endogenous variables
Standardised coefficients;
C: conscientiousness; TCC: task-contingent conscientiousness; N: neuroticism; TCN: task-contingent neuroticism; rSD: relative SD.
Fifth, in relation to state variability as a predictor of trait change, the only statistically significant effect was found for relative variability in neurotic states. Figure 4(b) depicts this effect. For comparison purposes, in Figure 4(a) we also present the analogous but non-significant effect for conscientiousness. Negative change scores indicate a decrease in trait neuroticism and hence are generally desirable; while for conscientiousness positive change scores (i.e. an increase in trait conscientiousness) can generally be interpreted as beneficial. However, for some individuals adjusting their level of conscientiousness downwards may be strategically recommended (e.g. to prevent rigidity, and to use limited cognitive resources more strategically).
Relative variability as a predictor of trait change: (a) conscientiousness (non-significant effect), and (b) neuroticism (significant effect).
Figure 4 also shows that higher relative variability in neurotic states was associated with less positive and more negative trait change scores, suggesting that those whose response behaviour indicated a reduction in trait neuroticism at the end of the programme (wave 5, see Figure 1) tended to also display higher levels of relative state variability (controlled for the mean state and potential individual differences antecedents, including cognitive ability). In other words, individuals who had greater relative variability in state neuroticism at the beginning of the programme (wave 1) displayed a smaller increase/larger decrease in their level of trait neuroticism over time. Note however, the effect was small (β = –.23,
Discussion
Contributions and implications
The current study was undertaken to further investigate the individual differences status of within-person variability in personality states. To this end, we explored (1) the temporal stability and cross-context consistency of individual differences in state variability, (2) the potential antecedents of individual differences in state variability, and (3) the empirical links between short-term state variability and longer term trait variability. We also explored (4) the effects of different conceptualisations of variability and their respective operationalisations on above result patterns.
First, we consistently found evidence of individual differences in state variability, both from a conditional response and a total variability perspective. Importantly, such individual differences in state variability were relatively stable over time and consistent across contexts (lab vs. field). Second, overall, we observed relatively few associations of conditional and relative state variability indices with other individual differences variables that we had conceptually identified as potential antecedents. Predictive effects tended to be small, they also differed across dimensions (neuroticism vs. conscientiousness) and contexts (lab vs. field). However, results still indicated that state variability as operationalised in the current study reflected some systematic variability in some circumstances. Third, evidence in support of empirical links between short-term state variability and longer term trait change was limited. Fourth and finally, results often differed depending on the attribute studied (conscientiousness vs. neuroticism) and variability conceptualisation and operationalisation used (conditional vs. total amount of variability). In what follows we further elaborate on the implications of these four main findings.
Temporal stability and cross-context consistency
Our first aim was to explore the structure and relations between the dynamic variables at different levels. Evidence of individual differences in contingent responding and relative variability in states was observed under both lab and field conditions. This matters, as situations were held constant across individuals in lab conditions (compared to field conditions), and hence individual differences in response variability are more likely to be indicative of person-related differences in the way the situations are experienced (Fleeson & Law, 2015), rather than being an effect of the differences in situations that respondents may have encountered.
There was also evidence of temporal stability (i.e. from wave-to-wave) for both types of variability indices, again a finding that is supportive of an individual differences conceptualisation of state variability. Temporal stabilities of state variability operationalisations are often not reported, and where authors do report such stabilities these tend to encompass much shorter timeframes (e.g. a week or two, e.g. Fleeson, 2001; Jones et al., 2017; Minbashian et al., 2010; see also Podsakoff et al., 2019). In the current study, measurement intervals were of six months in duration spanning two years overall (see Table 11). Hence, these findings are promising in that they indicate that the short-term stabilities of state variability as defined and reported in prior research may hold more longitudinally. It is important to note, however, that the very concept of temporal stability in state variability is not yet fully determined in the study of personality dynamics (see also Beckmann & Wood, 2020). On the one hand, temporal stability is seen as a pre-condition of denoting variability indices as individual differences indicators. This was our starting position in the current study also. We expected mean-level changes, yet rank-order stability in state variability indices over time. On the other hand, the very premise for studying dynamic personality variables is to expect change, which may include or lead to change in interindividual rank ordering. Dynamic components of personality are by definition ‘dynamic’, which may manifest in absolute and relative terms (i.e. intraindividual changes in mean level and change in interindividual rank-order). We acknowledge more conceptual work needs to be done to deal with these psychometric challenges.
In addition to evidence of stability across time, there was evidence of consistency in variability across contexts – lab- and field-based variability indices were correlated. This is an important finding. It (a) indicates systematicity in the measurement of state variability (operationalised as conditional and relative state variability), (b) further supports an individual differences conceptualisation of state variability, and (c) shows that lab-based response variability can be indicative of response variability in every-day life settings. To our knowledge, this is the first study to provide lab vs. field comparisons of different state variability indices within the same sample.
Antecedents of individual differences in state variability
Our second aim was to explore possible antecedents of individual differences in state variability. We observed only a small number of predictive effects in relation to the dynamic variables under investigation, and these tended to differ between dimensions and contexts. It is important to note that our lab-based findings are arguably more robust, given the larger sample size and the between-person comparability of the situations participants were exposed to. Generally speaking, and as to be expected, the dimension-relevant trait tended to be a significant predictor of related dynamic variables, particularly with regard to the mean state, but importantly, in several instances, also with regard to conditional and relative variability indices. For example, trait neuroticism was a significant predictor of mean state neuroticism, and conditional and total variability in neurotic states. This is in line with prior work on the predictive effect of trait neuroticism on state variability (e.g. Dauvier et al., 2019; Geukes et al., 2017; Jones et al., 2017; Judge et al., 2014; Kuppens et al., 2007).
Across the range of individual differences variables, effects were typically stronger at bivariate level (as was to be expected since interdependencies among the potential predictors are not considered), and only few effects remained when controlling for all individual differences variables in the analyses (compare bivariate and combined effects in Tables 6 to 9). Under lab conditions, only abstract reasoning and trait openness explained unique portions of variance in selected dynamic variables across both dimensions (neuroticism, conscientiousness), while only implicit theories and performance prove goal orientation were unique predictors of selected dynamic variables across dimensions in field conditions. Nevertheless, the effect pattern at bivariate level – and in some instances at multivariate level – lend evidence to the suggestion that the variability indices capture, at least to some extent, systematic rather than mere error variance.
Important findings in this respect are those related to abstract reasoning as a significant predictor of conditional and total variability observed under lab conditions (see Tables 6 and 7). Given that the abstract reasoning test is an objective performance and non-self-report measure, concerns for example relating to common method bias when interpreting variable associations do not apply. In the current study, those with higher levels of abstract reasoning ability reported to think, feel, and behave more conscientiously when confronted with increasing task demand (higher levels of TCC). They were also less likely to respond with increases in neurotic states to increases in task demand (lower levels of TCN), and less likely to fluctuate in neurotic states overall (lower levels of rSDneu). A possible interpretation is that more cognitively resourceful participants were more strategic in their use of mental effort and less vulnerable affectively under demanding task conditions, at least in the more structured and controlled lab/training context. Given the scarcity of respective research reported in the literature and the explicit explorative focus of our study however, any such reflections are tentative and findings require replication.
Findings across the two contexts (lab, field) in relation to potential predictors of dynamic variables differ. In the field, unique predictors were tapping general dispositions for engaging in tasks (performance prove goal orientation) and views on malleability due to effort (implicit theories) – rather than general dispositions in cognitive ability (abstract reasoning) – that are more likely to be impactful in settings where there may be more freedom to select and shape situations, including tasks. However, note the direction of the effects were such that those who tended to hold incremental beliefs were more likely to vary in neurotic states, but less likely to vary in conscientious states. Implicit beliefs were also negatively related to TCC.
Short-term state and longer term trait variability
Our third aim was to explore the relationships between selected individual differences antecedents, short-term variability in states, and change in construed traits. There was limited evidence to suggest that conditional or total variability in states was related to change in traits; only one effect was found (when mean states and other individual differences antecedents were controlled). Specifically, those respondents with greater relative variability in state neuroticism showed less increase and more decrease in trait neuroticism over time. Taken at face value, this may suggest that variability in state neuroticism may indicate a potential for growth in emotional stability, but the effect was small. We highlight two considerations: First, given the training and developmental programme participants were enrolled in, the context in which the study was undertaken was conducive to personal development and change. Yet, on average, we did not observe major shifts in rank order (differential effectiveness) for trait conscientiousness or trait neuroticism for the majority of participants (individual differences in trait change notwithstanding). This may have limited our chances to detect associations of trait change with state variability. An experimental study that aims at trait change (see e.g. Hudson et al., 2019; Hudson & Fraley, 2015) may find stronger state-trait variability links. Second, even though a number of prominent individual differences variables were included in our models − notably indicators of personality and cognitive ability, as well as motivational mind sets − a considerable amount of existing variance in trait change remained unexplained (see Figure 3).
Nevertheless, our findings add to the small number of empirical studies on the possible associations between short-term personality state dynamics and trait development. In one of the few studies available to date (Borghuis et al., 2020), the authors reported evidence of a situation contingency being predictive of change in trait neuroticism, such that those who were more responsive (in terms of negative affect in response to conflict) showed an increase in trait neuroticism over time. None of the situation contingencies (TCC, TCN) investigated in the current study proved to be a significant predictor of trait change; however, we note that measures, sample, and study design differed between the two studies. In a more recent study using a daily diary approach to collect momentary states, Quintus et al. (2021) report that the repeated momentary experience of conscientious states was related to later trait change, which is in line with our findings (see Figure 3); although in the Quintus et al. (2021) study, this effect was not found for the neuroticism dimension. This, as the authors suggested, may be to do with the particular momentary state used to measure neuroticism (secure–insecure). Our finding of greater total variability in neurotic states being associated with change in trait neuroticism over a two-year timeframe may offer some optimism and encouragement for future studies to investigate links between short-term state and longer term trait change. One possible interpretation is that greater total state variability simply indicates a potential for trait change.
Different conceptualisations of variability
Finally, we were interested in establishing to what extent our findings relating to (a) temporal stability and cross-context consistency, (b) antecedents, and (c) trait change were a function of the specific variability conceptualisation and operationalisation used. While there was evidence of temporal stability and cross-context consistency for both types of variability indices, for all other analyses both the personality dimension for which variability was assessed (conscientiousness, neuroticism) and the variability operationalisation used (contingent variability, relative variability) made a difference. We discuss three observations in this respect. First, a number of authors have discussed the existence of an underlying variability trait based on substantive correlations between variability indices across personality dimensions assessed within the same context (e.g. Lang et al., 2019; Reddock et al., 2011; Storme et al., 2020). We found no strong evidence in support of an overarching variability trait across dimensions, operationalisations, and contexts. However, there was some evidence of positive manifold for total variability (including positive cross-dimension cross-context correlations), but not for contingent variability. This finding is relevant because it signals the necessity for a more nuanced interpretation of the ‘variability trait’. There are between-person rank-order consistencies in the unconditional variability one expresses, such that those who vary in one context seem to do so to similar extents relative to others, regardless of dimension. However, when this variability is conditionalised on the proximal demands of the situation, the rank-order stability breaks down, suggesting common situational triggers impact individuals idiosyncratically.
Second, taken together findings relating to neuroticism tended to be overall stronger, more consistent, and in line with prior research, including the discussion of neuroticism as an antecedent of state variability (Dauvier et al., 2019; Geukes et al., 2017; Jones et al., 2017; Kuppens et al., 2007). This may be indicative of greater within- and between-person systematicity in neurotic responses over time and across context. Our conclusions around neuroticism may therefore be more straightforward. Interestingly, we found substantial correlations between relative and conditional variability indices in both lab and field conditions for neuroticism, suggesting that the way state variability was operationalised made less of a difference for neuroticism. It is sensible to distinguish between a mere fluctuating in states (total variability) from contingent responding. The latter more strongly implies flexible adjustment (conscious or subconscious) to situational demands. However, with regard to neuroticism, independently of its operationalisation (total or conditional), variability constructs seem to tap similar underlying processes. As a consequence, any fluctuation in neurotic states (whether contingent on demand characteristics of the situation or not) may represent a potential liability. Such interpretation resonates with findings that different indices of variability in neuroticism were negatively correlated with performance indicators (Beckmann et al., 2020; Wood et al., 2019). Similarly, variability in negative affect has often been interpreted to indicate a vulnerability in terms of well-being and other outcomes of interest (e.g. Kuppens et al., 2007).
Third, others have distinguished within- from across-context variability in states and reported differential relationships of these state variability components with Big Five traits (Geukes et al., 2017). A change in context implies a change in situational demands (e.g. work vs. home, see also Beckmann et al., 2020). The current study was concerned with within-context state variability. We similarly found differential correlation patterns of variability components (i.e. total and conditional) with individual differences variables, going beyond the Big Five and including motivational mindsets and cognitive ability. Situation contingencies, as operationalised in the current study, are reflective of differences in (perceived) situation characteristics. When considering different variability operationalisations, it is important to recognise that a total variability index reflects both contingent and non-contingent variability components, while a situation contingency obviously reflects variability as a response to specific changes in a subset of situational characteristics. State fluctuation (as reflected in relative SD indices) may reflect an outcome of contingent responding (i.e. situation contingences, see Whole Trait Theory, Fleeson & Jayawickreme, 2015). If so, one would expect to find empirical associations between the two forms of operationalised variability. In our study this was the case for neuroticism in lab and field conditions with an even stronger effect under more controlled lab conditions (
Whether state variability is adaptive or maladaptive may depend on how and why one varies or merely fluctuates (e.g. Hardy & Segerstrom, 2017; Japyassú & Malange, 2014; Magee et al., 2018), and not all situation dependent changes are adaptive adjustments. This complexity may partly explain the mixed result pattern relating to the antecedents and consequences of state variability reported in the literature and in the current study.
Limitations and future directions
The current study is unique in a number of ways. We employed an authentic sample of non-student participants using repeated waves of state assessment. Observations in field and lab settings allowed for the analysis of context specificity of effects. We also considered a comprehensive set of potential cognitive and non-cognitive antecedents and correlates. Regardless, there are also some limitations to be considered. While we were able to build our models on several waves of data collection for states, we only had two data points of trait measures and this limited our options for modelling links between state and trait variability. Further research is needed to extend on this, although circumstances where this is possible are hard to come by. Different approaches to the conceptualisation and analysis of variability and contingencies are also possible. For instance, we, as have others (Fleeson, 2007; Minbashian et al., 2010; Sherman et al., 2015), conceptualised contingences in terms of linear effects. It is conceivable that non-linear contingencies may provide even further and more differentiated insights into the complex interplay between state affect and situational demands, and to the existence of an overarching variability trait.
In experience sampling studies the number of items that can be included is limited due to feasibility constraints. In the current study, we chose items to represent a range of neuroticism and conscientiousness facets – in most cases by using a single item – in order to cover the trait domain. However, our effects may be specific to the facets we included rather than be representative of the broad trait. Future research would benefit from further analyses of facet-level versus domain-level effects. Another potential issue is the scaling. We used a visual analogue scale which permitted keeping the answer format constant across state and trait self-report measures (except for the field experience sampling measure where the software used on the mobile devices did not accommodate the use of a visual analogue scale). For example, both a five-point and a seven-point scale can be translated into the same visual analogue scale. However, the ‘coarseness’ of the scale can impact the results of an analysis (e.g. Aguinis, 2004, p. 91); too few or too many scale points can affect the signal to noise ratio. If this were to have had an impact on our results, the reported effects would represent rather conservative estimations of ‘true’ effects. Finally, we note that across our analyses, effects were generally small in size. While not unusual for the personality field (see Gignac & Szodorai, 2016), this indicates the necessity for replication, and caution when drawing conclusions. While our focus was on exploration with the aim to provide conceptual stimulation, further investigations of the practical implications of short-term variability as an individual differences construct are called for (see e.g. Sosnowska et al., 2021).
Conclusion
In conclusion, there is relatively robust evidence to show that both situation contingent and non-contingent state variability indices have characteristics of individual differences variables in terms of observed between-person differences, temporal stability and cross-context consistency. However, relationships with antecedents, correlates and outcomes, including trait development, are complex and currently available evidence is certainly mixed with few replicated results. Findings that appear more consistent are those relating to the neuroticism dimension. One reason for such diversity in results is that the psychological meaning of state variability changes as a function of a number of factors, including dimension, operationalisation, and context, and, of course, various interactions of these factors. Future research into these complex processes – for which this paper may serve as an impulse – is expected to contribute to the conceptual and methodological maturation of the field by involving authentic samples and studying state variability within and across situations and contexts that have sufficient valence to participants.
Supplemental Material
sj-zip-1-erp-10.1177_08902070211017341 - Supplemental material for Personality dynamics at work: The effects of form, time, and context of variability
Supplemental material, sj-zip-1-erp-10.1177_08902070211017341 for Personality dynamics at work: The effects of form, time, and context of variability by Nadin Beckmann, Damian P Birney, Amirali Minbashian and Jens F Beckmann in European Journal of Personality
Footnotes
Data accessibility statement
The data analysis scripts used for this article together with supplementary materials can be accessed at 
In accordance with our ethics obligations (HREC HC06294) at the time of data collection, we are required to store electronic data password protected on the university’s internal server. Accordingly, we are unable to make the data freely available. However, researchers can request access to the data and such requests will be considered in light of the ethics regulations agreed upon at the time of data collection.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Australian Research Council (projects LP0669552 and DP0987584). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council.
Ethical approval
All procedures for the recruitment and treatment of participants in the current study were approved by the Ethics Committee of the UNSW Sydney.
Notes
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
