Focusing Personality Assessment on the Person: Modeling General, Shared, and Person Specific Processes in Personality and Psychopa

Group Model (General Paths)

Group level paths are depicted in black in Figures 1 and and2. 2 . All six autoregressive paths were freely estimated for each individual. For all six variables, estimates ranged from small, non-significant, negative values to large, significant, positive values when looking across individuals (see Table 1 ). Large significant values for the autoregressive effects indicate relative consistency across days for a given variable (holding constant the influence of any other variables that predict the target variable). Conceptually, the AR weight has been described as a measure of inertia (Kuppens, Allen, & Sheeber, 2010) or stability in the variable of interest. This does not imply absolute stability, but rather that a given day’s measure can be predicted well by the prior day’s measure on that construct. Dominance and Functioning were the only variables to have significant negative autoregressive estimates for any participant, and these were for two and one individuals, respectively. Negative autoregressive paths have been thought to reflect a feedback system whereby the system cycles between high and low values. Only one additional path surpassed the group-level cutoff: contemporaneous NA regressed on Stress . The average effect was strong, all individuals exhibited positive estimates for this relation, and all but seven were significant (92.5%). This suggests that in general, experiencing stress predicts experiencing negative affect during the same day after taking into account the lagged influence of negative affect on the prior day as well as other covariates included as needed for each individual.

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Figure 2.

Diagrams of group iterative multiple model estimation results by subgroup (excluding two ungrouped individuals). Group level paths are in black, subgroup paths are in green, and individual paths are in gray. Line thickness indicates the number of individuals with the path proportionate to the total group size. Contemporaneous effects are represented with solid lines, and lagged effects are represented with dashed lines.

Sub-Group Models (Shared Paths)

Three subgroups were found, and participants were unevenly divided across subgroups. The largest, Subgroup 1, included 60 individuals, next largest, Subgroup 3, included 23 individuals, followed by Subgroup 2 with 11 individuals. Importantly, the subgroup order and numbering is arbitrary. One must examine and interpret the results for each subgroup to arrive at any inferences. Subgroup plots can be found in Figure 2 . Additionally, summary tables similar to Table 1 , but specific to each subgroup, can be found in the online supplemental materials.

Starting with Subgroup 1, we found one shared path, Affiliation regressed on NA. Estimates for this path in the sample ranged from small, non-significant, positive effects, to large, significant, negative effects. Thus, for a subset of the sample, affect and interpersonal behavior were associated contemporaneously, such that experiencing negative affect predicted withdrawing from others. Descriptively this is important information – that a relatively large subset of individuals may have this relation. Hence when working with individuals who have personality disorder diagnoses this may be an aspect of their emotional and behavioral processes to attend to and assess.

Subgroups 2 and 3 provide interesting examples of groupings of individuals that share no paths (with the exception of the group-level paths all in the sample share). Recall that the subgrouping algorithm within GIMME uses the total similarity of individuals’ model patterns, not solely based on the presence of shared paths (Gates, Lane, et al., 2017 ). As a result, it is possible for the algorithm to form groups based on overall similarity of their group-level estimates, but lacking in any shared paths that cross the 50% threshold. Subgroups 2 and 3 are examples of such groups.

We recognize that a feature that is intended to provide interpretive facility of the resulting models does not guarantee easily observable similarities. When this occurs, researchers may be interested to examine differences in paths across groups statistically in order to clarify shared features within a subgroup. Here we explored some differences on baseline personality pathology features and differences in group-level paths to illustrate how one might go about this. We found that the largest subgroup (number 1) had lower autoregressive estimates than Subgroup 2 on stress and PA, as well as a lower average AR for affection than seen in Subgroup 3 (See Table 2 ). Perhaps most interesting were the differences on the Stress → NA path (F _2,91 = 17.89, p < .001). Post hoc tests revealed that Subgroup 2 had a lower average estimate for the path from Stress to NA when compared to Subgroup 1 (average difference = −0.43, p < .001) and Subgroup 3 (average difference = −0.40, p < .001). This suggests that Subgroup 2 has a particularly lower propensity to have negative affect on days in which they experience stress (controlling for other variables in their model). Hence, we can see that the estimates contributed to the separation of individuals into subgroups.

Table 2.

Means and standard deviations of group-level beta coefficients by subgroup.

	Subgroup 1		Subgroup 2		Subgroup 3
	Mean	SD	Mean	SD	Mean	SD

Dominance (AR)	.14 ^a	.15	.10 ^a	.21	.06 ^a	.18
Stress (AR)	.10 ^a .31 ^b	.23	.19 ^ab	.28
PA (AR)	.20 ^a .37 ^b	.26	.29 ^ab	.17
NA (AR)	.19 ^a	.15	.30 ^a	.20	.18 ^a	.17
Functioning (AR)	.16 ^a	.17	.28 ^a	.27	.23 ^a	.20
Affiliation (AR)	.13 ^a	.13	.19 ^ab	.16		.16
STR→NA	.55 ^a	.22	.12 ^b	.16	.52 ^a	.23

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Note. Subscripts that differ indicate significant differences at p < .05; significant differences bolded.

We also wanted to see if the subgroups related to constructs of interest. Since borderline or narcissistic personality disorder diagnoses were common in the sample, we focused on these variables. Interestingly, Subgroup 1 contained almost all of the individuals who had a narcissistic personality disorder diagnosis ( n = 15 out of 19; χ ² = 5.0, df = 2, p = .082) and a significantly larger number of individuals with borderline personality disorder than would be expected by chance ( n = 28 out of 37, χ ² = 6.4, df = 2, p = .041). Taken together, inferences from these results suggest most individuals with a personality disorder may have a relation between experiencing stress and negative affect on the same day. However, this relation varies across individuals. As seen from the subgroup results, those with borderline and/or narcissistic personality disorder diagnoses may be more likely to withdraw from others when experiencing negative affect. In this way, the subgroups help to identify patterns that exist for a subset of individuals that may be meaningful or offer new insights.

Individual Models

Person-specific (i.e., individual-level) models exist for each participant in the sample. A diagram and path coefficients for each participant can be found in the online materials, and here we present a selection of diagrams in Figure 3 for demonstration purposes. Each participant’s map is detailed and invites in-depth interpretation, as might be expected when working with an individual in an applied setting (e.g., assessment before, during, or after psychotherapy, consultation in industry). This sort of detailed interpretation for each individual goes beyond the scope of this presentation. Rather, we wish to draw the reader’s attention to several aspects of the obtained person-specific models and use the examples presented in Figure 3 to guide this discussion. In following with color schemes often used in heat map depictions of networks, red here indicates positive (“hot”) values with blue reflecting negative (“cool”) values. Path width corresponds to the absolute value.

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Figure 3.

Example diagrams of person-specific (i.e., individual-level) models from group iterative multiple model estimation. Contemporaneous effects are represented with solid lines, and lagged effects are represented with dashed lines. Positive effects are in red, negative effects are in blue. Line thickness denotes strength of effect.

In terms of overarching observations, note that the various presented models differ in their density. That is, some individuals have more significant paths than others, suggesting that daily interpersonal behavior, affect, stress, and functioning are more intertwined, whereas for others these variables function separately or are governed by separate processes. Also, note that lagged effects, which were absent in the general and shared paths, now emerge with greater frequency. This suggests that these variables do, in fact, carry over from day to day in their effects, but that there is high heterogeneity in these effects, with one individual sharing little in common with the next. In line with that, notice the high degree of heterogeneity across individuals in their global structures. A single model would fail to do this diversity justice. It is also useful to compare any given model to the summary matrices in Table 1 . Those matrices also highlight the high degree of heterogeneity, and also illustrate that certain paths are much more common than others, even if they do not rise to the level of subgroup or group paths.

Inspecting each person’s model reveals interesting hypotheses about their daily processes. For instance, some individuals have lagged links between the same two variables, but the effects go in opposite directions, suggesting different dynamics. To the extent that Participant 55 experiences NA, the next day s/he engages in dominant behavior, whereas for Participant 51 the opposite is true. Or, take Participant 69, who appears to have an amplification dynamic between stress and NA, such that the more stress s/he experiences, the more s/he experiences NA, which leads to more stress the next day. This sort of vicious cycle is commonly the target of therapeutic interventions. In contrast, Participant 69 exhibits what appears to be a regulatory process between dominance and daily stress, such that increases in dominance lead to more stress, which in turn leads to greater submissiveness, presumably as s/he seeks to interrupt or arrest the process. Participant 86 also has a pattern of paths suggestive of a regulatory process between affiliation and NA. We also see variability in the estimates for NA → Stress. While all of the individuals shown here had positive estimates for this group-level path, we see that individual 80 has a notably smaller value (as seen in the thinner lines). Other models share several features (e.g., 80 and 94), but differ in others. Some models demonstrate what appear to be the central importance of a specific variable (e.g., NA in participant 55), and other models differ in the overall density and complexity (e.g., compare 86 to 51). Many more specific interpretations could be made about just these six individuals. Our goal is not to interpret every parameter, but instead demonstrate the richness of the emergent person-specific models, and show their relevance to the dynamics of interest in understanding how individuals function in daily life.

Discussion

There is substantial field-wide interest in measuring people intensively and repeatedly to capture dynamic personality processes. The resulting data, which often involve many observations per person, have motivated calls for personalized or person-specific modeling. This poses a challenge, how to develop a nomothetic science based on idiographic models? GIMME provides a potential solution to this thorny issue, and we have demonstrated it in a sample of patients who completed a long (100-day) daily diary protocol. Our main goal was not to draw firm conclusions about general personality processes. Instead, we sought to provide a proof-of-concept of GIMME with subgrouping capabilities in a large and heterogeneous sample using major domains of personality (affiliation, dominance, PA, NA) and commonly associated variables (stress and dysfunction).

Although our empirical example was not motivated by specific study questions, we nonetheless found interesting results that point to what can be expected in future work in this area. In the results section, we summarized our findings going from the general to the specific, following GIMME’s order of operations. Here we reverse the order, beginning with the person-specific level and expanding to the general-group level. Perhaps the most notable finding is the high-degree of heterogeneity across person-specific models. This highlights the motivation for idiographic or personalized models of dynamic personality assessment data: people differ from each other in the structure of their specific processes. Figure 3 illustrates this to some degree, and we encourage the interested reader to peruse the full set of figures (hosted online at https://osf.io/95hyr/ ) to gain a full appreciation of the individual differences in these models. Although the current sample was highly heterogeneous by design, we anticipate based on past experience using GIMME in several data sets of different sorts ( Beltz et al., 2016 ; Price et al., 2017 ) that massive heterogeneity is likely to be the rule rather than the exception.

The subgrouping feature provides one way to parse through this heterogeneity, identifying individuals that appear to be governed by similar processes. In line with this, we identified a number of subgroups that differed from each other in notable ways. One subgroup was demarcated by shared paths, whereas the others stood out for their lack of shared paths. Subgroups differed in their size considerably ( n s = 60, 23, 11). The largest subgroup had only a single shared path, which was a contemporaneous association. As mentioned, two groups had no shared paths, which highlights that the subgrouping is not based solely on the presence of shared paths, but also on the individual estimates of the group-level paths (i.e., direction and strength) and the overall pattern of paths. Shared paths offer a natural first step towards interpreting the groupings, as the overall networks are likely to be too complex to interpret by eye. However, when subgroups lack shared paths to facilitate interpretation, comparisons on external validators may be needed. Here we explored several baseline personality disorder features and path features, and identified differences in narcissistic and borderline features that distinguished group 1 from the others, and several group differences in the strength of lagged effects. We emphasize that these were exploratory and are but a few of the possibly approaches one might take understanding group differences when they are not transparent. We further reiterate that these subgroups are not intended to identify firm subtypes, but rather to provide another tool for parsing through the heterogeneity and facilitating interpretation and comparison of individuals.

A single contemporaneous path emerged as the only group-level effect, daily perceived stress predicted NA. In other words, knowing someone’s daily level of experienced stress is informative of their level of NA above other predictors in the model, but the opposite is not true. That there was only a single group-level effect speaks to the heterogeneity in patterns in the person-specific models. Group-level paths are important since, by definition, they replicate across individuals in the sample. Hence these are paths that might be expected to be found across other samples from the same population. In fact, NA predicted by perceived stress (after accounting for its autoregressive effect) is a well-established finding in the daily diary of personality and stress literature (e.g., Bolger et al., 1995; Mroczek et al., 2004). Thus, this finding is consistent with prior literature in other distinct samples using different analytic methods, thereby lending confidence that our group-level results would replicate in other samples.

An important note is that GIMME likely provides more reliable group-level relations than other approaches for data-driven models. For instance, one could feasibly do model selection search with MLM. However, this is less common and not fully evaluated. The researcher would have to make critical choices, such as does one conduct forward-selection or backward-deletion of potential relations among variables? Because there were so few group-level paths in the present example it is highly possible that spurious relations would result ( Molenaar & Campbell, 2009 ). Since MLM estimates paths for all individuals, none of the individual-level paths would be considered – unless spuriously estimated for the sample, which would likely attenuate estimates since they would be constrained to follow a normal distribution with a mean near zero (since for most individuals, zero would be the estimate). Another popular option is to concatenate individuals’ data such that one person’s time series is pasted above another’s and this continues until the whole sample is in one data set. The problem with this approach is that a few outliers could drive all of the results. GIMME circumvents these issues by requiring that all group-level paths be significant for the majority of individuals.

Potential Applications for GIMME of Dynamic Personality Data

GIMME offers a number of exciting avenues in the domain of dynamic personality assessment. Perhaps the most obvious application is to data collected from putatively homogenous groups, such as diagnostic categories. It is well established that even the best treatments for behavioral disorders only successfully treat a portion of afflicted individuals, and data-driven approaches such as GIMME may help to identify match effective interventions to specific behavioral processes. However, it is relatively unknown how heterogeneous the behavioral processes are of individuals who share a diagnostic category. Our results here would suggest that they are likely to be highly heterogeneous, although we did not limit our search to a group with a circumscribed diagnostic profile. It is possible, if not likely, that more characteristic group- and subgroup-level paths might emerge in more homogenous samples.

Moreover, subtyping clinical diagnoses based on traditional symptom measures has been largely unsuccessful. Subtyping based on shared processes might be more successful at identifying individuals who share meaningful processes. For instance, when it comes to alcohol use, it is difficult to identify who will go on to develop problematic use over time. In early adulthood, heavy use is normative among many, but only some develop lasting patterns of misuse. Hypotheses related to who is at elevated risk, even among heavy drinkers, are related to dynamic processes of use (e.g., positive reinforcement vs. negative reinforcement pathways). Early identification of cycles of positive and negative reinforcement would allow early identification of at risk individuals, which may not be evident from profiles of use, consequences, or other cross-sectional assessments. Similar examples could be developed for other issues. We wish to be clear that we view GIMME’s subgrouping capabilities as a useful tool for identifying individuals with similar networks of processes, but we do not necessarily assume these groupings will represent formal subtypes. Nevertheless, developing and refining clinical phenotypes based on shared patterns of processes is a promising avenue for future work.

On a more basic level, GIMME could be used to investigate heterogeneity in processes of individuals with the same trait or symptom profiles. Currently, it is assumed that individuals who share similar profiles of traits or clinical features are likely to behave similarly. But, as discussed in the introduction, cross-sectional assessments of personality do not directly capture the dynamics that are presumed to give rise to what are recognized as traits. As such, it remains an open question whether and to what degree individuals with similar profiles share similar patterns of dynamic processes. The processes could be examined at a fine-grained level, incorporating specific indicators from within a single trait domain (e.g., facets). Alternatively, this question could be expanded to incorporate broader range of traits to understand how they interact with each other. This type of approach could unpack gross statements like “narcissists are disagreeable extraverts,” because not all disagreeable extraverts (or extraverted antagonists for that matter) are likely to be narcissistically organized ( Wright, 2011 ).

More broadly, GIMME might be used any time one is interested in studying patterns of dynamic processes in highly multivariate systems. For example, contemporary taxonomies of situations use up to eight situational assessments ( Rauthmann et al., 2014 ), which when included with the five canonical traits, means studying 13 variables over time. Running individual models to examine each of the possible paths would generate a dizzying number of results to be interpreted (e.g., Rauthmann, Jones, & Sherman., 2016 ). Furthermore, it takes the focus away from the person and places it on the variables. GIMME allows for these to be analyzed simultaneously, retaining the focus on the person.

Implications for Applied Assessment

As we highlighted in the first paragraph, there is now a great deal of interest in using ambulatory assessment to develop personalized models of an individual’s psychological and behavioral processes. This is particularly the case in clinical settings (i.e., mental health), although this could be extended to occupational or other healthcare settings. The main engine of the GIMME approach, uSEM, can be estimated for an individual (For exemplars see Beltz et al., 2016 ; Fisher et al., in press; or Foster & Beltz, in press ), and the GIMME software package includes this capability. This makes it feasible to apply this model using the same tools in practical settings using easy to use and freely available software.

However, a challenge with this approach is that fully idiographic modeling of an individual in a high-stakes setting risks leaving the practitioner adrift when it comes time to interpret the results. Enthusiasm for understanding the individual should not unmoor fundamental nomothetic principles that play such an important part of practical assessment. Running a full GIMME in a large sample provides the necessary tethers to anchor the interpretation of an individual to a firm foundation. Specifically, if the variables used in the current study were given to someone in an applied setting, we would expect to find that there was a link between daily stress and NA, and we could compare the individual’s coefficient to the distribution found here. We could similarly compare any additional emerging paths to those reported in Table 1 to (a) determine how frequently the path is observed in a large sample of patients, and (b) the relative strength of this path. Without these sorts of comparisons, it is difficult to know whether the individual’s model is aberrant or merely normative. Many additional ways to compare models might be useful (e.g., overall density of associations).

Practical Guide to using GIMME

GIMME has been tested under a number of conditions to identify when the results are most reliable. Ideally, the researcher will use between 5 and 25 variables (with all individuals required to have the same variables). Simulation studies suggest that reliable results can be obtained with as few as 10 individuals when using regular GIMME ( Gates & Molenaar, 2012 ) or 25 individuals when the subgrouping option is invoked ( Gates et al., 2017 ). GIMME requires the same assumptions be met that exist for typical time series analysis. By that we mean that the data: (1) are collected at approximately equal time intervals; (2) are continuous in nature (i.e., a scale as opposed to categorical); and (3) have constant mean and variance across time (i.e., are stationary). More details on preparing data can be found in Beltz & Gates (2017) . At the time of writing this manuscript it is unknown how well GIMME performs when data are not collected at equal intervals or with categorical data. Interestingly, while the GIMME approach assumes that data are stationary across time, it still is able to recover the underlying relations when this assumption is violated ( Gates & Molenaar, 2012 ). However, it is recommended that if such shifts are expected across time that analysis aimed towards accommodating these changes are conducted. For instance, when the mean level changes across time, this can often be rectified by detrending prior to conducting GIMME analysis.

As with any analysis, the number of observations (in this case, time points) is also an important consideration. Much literature within SEM has focused on rules of thumb regarding the minimum number of observations, particularly as it relates to the ratio of the number of variables and the number of cases (e.g., Bentler & Chou, 1987 ; MacCallum et al. 1999 ; Wolf et al., 2013). Specific to GIMME, a Monte Carlo simulation study revealed that GIMME is successful for use on data with qualities similar to those seen in daily diary data when time series are at least 60 observations long and 5 or 10 variables ( Lane et al., in press ). A related aspect concerns that of missing data. GIMME can still be conducted if there are missing time points for a given individual as long as the researcher indicates the missing data with “NA”. In fact, GIMME can be run if individuals have different numbers of observations for any reason. For more details on conducting analysis with the gimme R package and interpreting results please refer to Lane and Gates (2017) .

The subgrouping capabilities of GIMME are an option, and not required for estimation of a model. Subgrouping does seem to help recovery of the true paths in a model, but as we have shown here it may or may not lead to easily interpretable groupings. As with all modeling choices, researchers may wish to give careful consideration to whether invoking the subgrouping feature would be advantageous or desirable given their investigative aims. In some cases, this may be a motivating feature for selecting GIMME, and in others it may be superfluous to the research questions. In either case, subgrouping can be used to further identify participants who share similar overall patterns of effects for further interrogation, as we did in our empirical example.

Challenges and Future Directions

Intensive longitudinal data present unique challenges for the study of psychological and behavioral processes. Although GIMME circumvents a number of these to provide highly reliable data-driven results, four main issues with data have yet to be resolved: how to handle variables with low variability, unequal spacing of assessments across time, and errors in measurement. Low variance occurs when a given participant repeatedly select the same response option, resulting in a constant or low-variance variable. Researchers can attempt to prevent low variance at the time of collection with the development of measures sensitive enough to capture day-to-day variability. However, some measures simply will not have variance for some participants as a result of the individuals’ behaviors (e.g., someone who does not drink alcohol will report zero drinks every day, someone who has a highly regimented sleep schedule will report the same number of hours slept each night). As seen in the present study, two individuals had to be removed from analysis because their responses on one measure were constant across time. Other studies using time series or network approaches have also reported removing individuals for this reason (e.g., Lebo & Nesselroade, 1978 ; Whitley, Ford, & Livingstone, 2000 ). Current developments in GIMME aim at allowing all individuals to be included even if some of their variables are constant or have low variance.

The second challenge, unequal assessment, is at times inherent to the process of completing daily surveys. It can be requested that participants complete the survey in a given window of time (much like the present study requested completion between 8pm and 12am), but realistically, participants cannot be expected to complete a survey at exactly the same time every day. Another source of unequal spacing occurs when data are collected multiple times per day (e.g., ambulatory assessment designs with random assessments throughout the day). Data might be collected every few hours when the participant is awake, but of course while the participant is asleep there will be no self-report measurements. For these reasons, solutions for dealing with unequal assessment should focus on corrections at the time of analysis. Many approaches for dealing with unequal spacing have been suggested, including marking the data as missing ( van der Heijden, 2006 ), interpolation ( Ram & Nesselroade, 2007 ), induction of equal spaces ( Koopman & Ooms, 2003 ), and an exponential moving average approach ( Ellis & Parbery, 2005 ). These have yet to be fully tested in the GIMME context.

Third, another future direction is to include measurement models within the GIMME search procedure. Oftentimes self-report data contains measurement error and multiple questions are used to quantify one latent construct. While the use of observed variable models such as the ones provided by GIMME is gaining popularity ( Fried & Cramer, 2017 ), given the nature of self-report data, there will likely still be a need for including a measurement component in some cases. Current efforts aim to improve upon much of the published network literature by using scales composed of multiple items instead of single items to represent complex constructs (cf. Fried & Cramer, 2017 ).

Fourth, although group- and sub-group level effects are readily interpretable when they emerge, fully exploiting the information contained in the person-specific models is likely to pose challenges for researchers. For instance, one can immediately examine the plots provided by the gimme R package to identify nomothetic relations among behaviors of interest, relations that exist among subsets, and the degree to which heterogeneity exists in the individual-level paths. Work is needed on developing useful summary statistics that might allow for feature extraction and association with external variables. One possibility is using graph theory metrics (e.g., network density) when they can be linked with psychologically meaningful constructs. Others have applied these to dynamic data analyzed in different frameworks (e.g., Pe et al., 2015; Bringmann et al., 2016 ), but much work remains to be done.

A final consideration is whether an individual’s model is representative of his or her behavior beyond the assessment period (i.e., is the model stable across time?). Certainly, questions of measurement invariance are as applicable to intensive samples from an individual as they are from cross-sectional samples of many individuals. Indeed, a main message of the current work is that assessment considerations that are important at the between-person level of analysis remain important when modeling an individual. Whether an individual’s model is likely to be more or less invariant over time is likely to track with how more or less representative their assessments were of their general pattern of behavior. For instance, assessing an individual twice while in highly distinct psychological circumstances (e.g., during a manic phase and during a depressed phase) may lead to expectations of non-invariance. The goal might even be to compare the individual’s model across assessments to establish the differences. Alternatively, across repeated assessments of the same psychological state (e.g., random bursts of assessing the same individual), the expectation would be that the models would be identical or close to it. We draw the reader’s attention to the fact that each individual’s coefficient has a standard error, reflective of sampling variability in the estimate. This is unlike MLM, where each individual has an estimate, but no distribution of that estimate. Thus, like between-person statistics, when modeling the individual one must keep in mind whether and how representative the sample is of the population. Future work could implement time-varying effects by design if relevant.

Conclusion

The study of personality and psychopathology, and therefore their assessment, must move beyond a nomothetic approach, whereby individuals scores are only understood as relative to others. With the rapid development of ubiquitous computing and ambulatory assessment techniques, intensive sampling of behavior can now be used to develop personalized (i.e., idiographic) models of personality. This is particularly exciting, because these are models of an individual’s dynamic processes ( Wright & Hopwood, 2016 ). Dynamic processes are often inferred from static (i.e., cross-sectional assessments), but this is not appropriate and require dynamically sampled behavior ( Bos et al., 2017 ). However, the challenge posed by these models is that they are person-specific, and therefore, can quickly become unwieldy. GIMME provides a way forward, by searching for general, shared, and person-specific contemporaneous and lagged associations in intensive data. Thus, it can be used to build generalizable models from the bottom up. Here we presented a proof-of-concept of GIMME in dynamic personality data in a clinical sample. Although a number of issues remain to address in continued model development, GIMME offers a promising new avenue as personality shifts from the static to dynamic and places the person at the center of personality assessment.

Public Significance Statement:

Ambulatory assessment techniques (e.g., ecological momentary assessment) generate information that can be used to develop personalized models of an individual’s behavior. Techniques are presented that allow for the simultaneous development of many personalized models and searches for shared features across models.

Acknowledgments

We would like to thank Barbara De Clerq, Filip De Fruyt, Lize Verbeke, Bart Wille, Joeri Hofmans, Karla Van Leeuwen, & Leonard Simms for organizing and inviting the first author to the Expert Meeting on Personality Assessment in Oostduinkirke, Belgium, and the European Association for Personality Psychology for providing funding for the meeting.

This research was supported by grants from the National Institute of Mental Health (F32 MH097325, L30 MH101760, Wright; R01 MH080086, Simms). The opinions expressed are solely those of the authors and not those of the funding source.

Footnotes

¹ Here we use the term “behavior” very generally, including thoughts, feelings, and overt behavior.

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