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Behavioral Landscapesand Change in BehavioralLandscapes: A Multiple Time-Scale Density DistributionApproachNilam Ram a , Michael Coccia b , David Conroy b

, Amy Lorek b , Brian Orland b , Aaron Pincus b ,Martin Sliwinski b & Denis Gerstorf ca Pennsylvania State University and Max PlanckInstitute for Human Developmentb Pennsylvania State Universityc Humboldt UniversityVersion of record first published: 20 Feb 2013.

To cite this article: Nilam Ram , Michael Coccia , David Conroy , Amy Lorek ,Brian Orland , Aaron Pincus , Martin Sliwinski & Denis Gerstorf (2013): BehavioralLandscapes and Change in Behavioral Landscapes: A Multiple Time-Scale DensityDistribution Approach, Research in Human Development, 10:1, 88-110

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Research in Human Development, 10(1), 88–110, 2013Copyright © Taylor & Francis Group, LLCISSN: 1542-7609 print/1542-7617 onlineDOI: 10.1080/15427609.2013.760262

Behavioral Landscapes and Change inBehavioral Landscapes: A Multiple

Time-Scale Density Distribution Approach

Nilam RamPennsylvania State University and Max Planck Institute

for Human Development

Michael Coccia, David Conroy, Amy Lorek, Brian Orland,Aaron Pincus, and Martin Sliwinski

Pennsylvania State University

Denis GerstorfHumboldt University

Multiple observations are needed to obtain a robust characterization of individ-uals’ behavioral tendencies across time and context. In this article, the authorsfuse core ideas from the study of lifespan development with intraindividual vari-ability based approaches to personality and with methods used to characterize thetopography of geographic landscapes. The authors generalize the notion of densitydistributions into bivariate and multivariate space and draw parallels between theresulting behavioral landscapes and geographic landscapes. The authors illustratethrough an empirical example how multiple time-scale study designs, measures ofintraindividual variability, and methods borrowed from geography can be used todescribe both an individual’s behavioral landscape and changes in the behaviorallandscape.

Notions of personality have progressed beyond the idea that people are well char-acterized by five or more scores obtained on a single occasion. In developmental

Address correspondence to Nilam Ram, Department of Human Development & Family Studies,Pennsylvania State University, S-110 Henderson Building, University Park, PA 16802. E-mail: nur5@psu.edu

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BEHAVIORAL LANDSCAPES 89

arenas it is well accepted that multiple observations are needed to obtain a robustcharacterization of individuals’ behavioral tendencies (including action, cogni-tion, emotion, physiology, etc.) across time and context. This article presents anovel fusion of methods from behavioral science and geography to extend thenotions of personality as density distributions (Fleeson, 2001) and behavioral sig-natures (Mischel & Shoda, 1995) towards personality as behavioral landscapes.Specifically, we draw parallels between behavioral landscapes represented by mul-tivariate density distributions and geographic landscapes that might be seen inthe real, physical world: mountains, hills, plains. Integrating core ideas from thestudy of intraindividual variability (Nesselroade, 1991) and life-span development(Baltes, Lindenberger, & Staudinger, 2006) we illustrate how three-dimensional(3D) visualizations of multivariate repeated measures data (e.g., from diary andecological momentary assessment studies) can be described analytically, and howthose descriptions may be used to characterize the topography of individuals’behavioral landscape and how those behavioral landscapes change over time.

Personality as Density Distributions

The person–situation debate in personality (Kenrick & Funder, 1988) could becharacterized as a clash between the person view, that a person will act simi-larly much of the time because behavior is determined in large part by a person’straits, and the situation view, that a person will behave differently on differentoccasions because behavior is determined in large part by the immediate situa-tion. These two points of view have been reconciled in a number of ways usingrepeated-measures data. For instance, when multiple observations of an indi-vidual’s behavior are available, it is possible to collect them into an ensembleof scores and use the intraindividual variability in observed behavior to modelperson-specific and situation-specific aspects of behavior (Nesselroade, 1988).Making use of the precision afforded by mathematics, a combined perspectivecan be written as

Xt = μx + xt,

where, Xt = the repeated measures of an individual’s behavior obtained at times t,are modeled as a combination of the individual’s behavioral tendency, μx = traitand situation-specific deviations from that tendency, xt = state.

Simplistically, the intraindividual mean, μx, is a measure of a person-level char-acteristic. Interindividual differences in this characteristic are noted by inclusionof an i subscript, noting that each individual’s ensemble of scores may have adifferent central tendency, μxi.

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90 RAM ET AL.

Abstracting from this simple trait + state model of behavior (the foundation ofclassical test theory; Lord & Novick, 1968), the density-distributions approach topersonality (Fleeson, 2001) suggests that the “leftover” state variance is itself anaspect of personality. That is, the range of states a person can exhibit or experi-ence is also a trait. Some individuals may behave in a relatively consistent mannerin many situations (xt scores are very similar across occasions), whereas others’behavior may vary more dramatically from situation to situation (xt scores are verydifferent across occasions). Formally, the range of an individual’s behavior can bequantified as the intraindividual variability of the xt scores, σ 2

x. The density dis-tributions approach acknowledges that individuals may differ in μxi and σ 2

xi and,as indicated by the inclusion of the i subscript, uses both parameters as measuresof between-person differences in personality. The density-distributions approachto personality uses measures of intraindividual mean (iMean) and intraindividualstandard deviation (iSD) as descriptors of person-level personality characteristics.For illustration, the univariate density distributions for repeated measures data fortwo hypothetical persons are shown in the upper panels of Figure 1 along with thecorresponding iMeans and iSDs.

Placed within a life-span developmental framework, the density distributionsapproach can be used to examine age differences or changes in density distribu-tions by examining between-person differences in iMeans and iSDs. For example,Noftle and Fleeson (2010) examined how individuals within different periods ofthe life span (young, middle age, older adults) differed in trait-relevant behavioraverages (iMean) and variabilities (iSD) in daily life. Similar to the hypotheticaldifferences depicted in the upper row of Figure 1, they found some initial evidencethat, compared to young adults (left panel, black distribution), middle-age andolder adults exhibited higher average levels and lower variabilities (right panel,red distribution) in agreeableness- and emotional stability-related behaviors.

Personality as Bivariate Associations

Extension from constructs based on univariate intraindividual variation to con-structs based on intraindividual covariation is straightforward. Two variables areused to define a bivariate space in which the observed data are located, as seenin the middle panels of Figure 1. The extent of association between the twovariables is described (1) visually by the oblong or circular nature of the ovalcovering the area in which the observations are located and (2) quantitativelyby the intraindividual correlation (iCorr) or an intraindividual regression (iReg).The measure of association is then tethered to a specific trait-like construct. Forexample, Carstensen and colleagues (2011) have used the intraindividual cor-relation between repeated measures of positive and negative affect to describepoignancy, an individual’s potential for experiencing mixed emotions. Others havereferred to the same operationalization as affective polarity (Ram, Conroy, Pincus,

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BEHAVIORAL LANDSCAPES 91

Intraindividual Means & Standard Deviations

Behavioral iLandscapes

Intraindividual Correlations

iM = 0.0iSD = 1.0

iM = 1.5iSD = 0.4

iCorr = .90 iCorr = 0.0

FIGURE 1 Simulated univariate and bivariate density distributions. The general featuresof the density distributions of intensive repeated measures obtained from an individual canbe described using intraindividual means (iMean), intraindividual standard deviations (iSD),intraindividual correlations (iCorr), and as behavioral landscapes (iLandscape). The upper pan-els for iMean and iSD illustrate that the person on the right (red distribution) exhibited higheraverage levels and lower variability than the person on the left (black distribution) on the behav-ior of interest. The middle and lower panels on the left depict strong associations between thetwo variables. The panels on the right depict weak associations between the two variables.(Color figure available online).

Hyde, & Molloy, 2012) or emotional complexity (e.g., Ong & Bergeman, 2004;Ready, Akerstedt & Mroczek, 2012). All of these constructs are considered asperson-level “traits” that are fixed over the short term (however, see, e.g., Zautra,

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92 RAM ET AL.

Affleck, Tennen, Reich, & Davis, 2005) but that may also develop over the longterm as individuals mature and/or transition through life events.

In the behavioral signatures approach to personality, individuals’ potential orlikelihood of behaving in particular ways in particular situation is described byif-then contingencies (Mischel & Shoda, 1995). For example, if person i is in sit-uation S = 1, then he or she exhibits behavioral response R = 10; if in situationS = 100 then behavior R = 50. In this approach, if-then traits are operational-ized using the pattern or strength of relation between situational features (S) andbehavior (R) within bivariate within-person time-series data. That is, an individ-ual’s if-then contingency is quantified by a person-specific regression parameter,β i from a model such as

Rt = βiSt,

where Rt are the repeated measures of person i’s behavior at times t and St arethe correspondent repeated measures of the situations the person was in. Withits i subscript, the regression parameter is explicitly defined as a characteristicof the person and could be relabeled Pi = β i to make literal the interaction-ist perspective. Importantly, Pi, is a “latent” trait (time-invariant) score that isinferred from the observed S-R relations. Differences in if-then contingenciesare quantified as between-person differences in the regression parameters andstraightforwardly tested by collecting the person-specific regressions within amultilevel modeling framework (see Bolger & Romero-Canyas, 2007). For exam-ple, Brose, Scheibe, and Schmiedek (2012) examined age-related differencesin the within-person association between daily stressors (S) and negative affect(R). Younger adults had stronger if-then contingencies than older adults (in partbecause of differences in stressor heterogeneity). From a birds-eye perspective, ayounger individual would tend to look more like the middle-left panel of Figure 1(strong correlation/regression), and an older individual more like the middle-rightpanel (no correlation).

Personality as Landscapes

The (univariate) density distributions and (bivariate) behavioral signaturesapproaches are all part of a “bottom-up approach to personality in which theo-rists start with the actual differences as they exist between individuals’ behaviors(including actions, cognitions, motivations, and emotions) and infer the per-sonalities that produced those differences” (Fleeson & Noftle, 2008, p. 1358).Methodologically, these approaches acknowledge and measure the within-personvariation or covariation in individuals’ behavior and use descriptive statistics(Mean, SD, correlation) to operationalize a wide variety of trait-like constructs

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BEHAVIORAL LANDSCAPES 93

that we have elsewhere labeled dynamic characteristics (see Ram & Gerstorf,2009). Although the accompanying visualizations are usually drawn in 2D, theyare occasionally rendered in 3D, as shown in the bottom panels of Figure 1, wherethe scatter-plots from the middle panels are now shown as bivariate density distri-butions. In these 3D distributions, individuals’ behavior “resides” within the hills.Visualized this way, we can immediately see similarities between the density dis-tributions and the natural world: mountains, hills, and plains, and may be remindedof the many theoretical perspectives that describe behavioral and developmentallandscapes (e.g., Hooker, 2002; Waddington, 1957).

It is important to note that the univariate and bivariate density distributionsdepicted in Figure 1 are “clean” univariate and bivariate normal (Gaussian) dis-tributions. They were simulated to conform to idealistic statistical assumptions.However, our experience with real data suggests that the observed distributionsrarely look so symmetric and smooth. Rather they are filled with extra hills, sharpspires, and flat zones (as will be seen later). Intrigued by the notion that the visu-alizations of real data are pictures of behavioral landscapes, we explored if andhow methods from geography might be used to generalize the interpretation ofpersonality of multivariate density distributions that change and/or remain stableover macro time (e.g., months, years).

Geographic landforms come in all shapes and have implications for the typesof ecosystems that can develop there (Bailey, 1996). In addition to land usecharacteristics (e.g., rural, urban) surface geometry is also sometimes used todifferentiate various mesoecosystems and landscape mosaics (Botequilha Leitao,Miller, Ahern, & McGarigal, 2006). In particular, Hammond (1954) developed asystem for differentiating landform classes into the categories listed in Table 1,which include general (e.g., plains, tablelands) and more specific categories (e.g.,irregular plains, open high hills). His classification was based on visual analy-sis of a local window (9.7 km x 9.7 km) that was moved without overlap acrossa 1:250 000-scale topographic contour map (Hammond, 1964). Specifically, therelative amount of gently sloping land (< 8%), local relief (meters), and gener-alized profile (where and how much of the gently sloping land is located in thevalley bottoms or uplands) is combined using the codes in Table 2 to classifyquadrangles delineated within the subject landscape into landform types. With themove to computer assisted, raster-based Geographic Information Systems (GIS)processing of satellite images such classification systems have been automated(Gallant, Brown, & Hoffer, 2005; Riley, DeGloria, & Elliot, 1999), greatly facili-tating generation of landform maps, investigation of regional land use patterns,and management of environmental resources (Botequilha Leitao et al., 2006).In an attempt to generalize the density distribution approach to personality, mapbehavior in multivariate space, and investigate between-person differences andwithin-persons changes in complex behavioral profiles, we consider how this

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94 RAM ET AL.

TABLE 1Landform Classes

PlainsA1 Flat plainsA2 Smooth plainsB1 Irregular plains, slight reliefB2 Irregular plains

TablelandsB3c,d Tablelands, moderate reliefB4c,d Tablelands, considerable reliefB5c,d Tablelands, high reliefB6c,d Tablelands, very high relief

Plains with hills or mountainsA3a,b Plains with hillsB3a,b Plains with hillsB4a,b Plains with high hillsB5a,b Plains with low mountainsB6a,b Plains with high mountains

Open hills and mountainsC2 Open low hillsC3 Open hillsC4 Open high hillsC5 Open low mountainsC6 Open high mountains

Hills and mountainsD3 HillsD4 High hillsD5 Low mountainsD6 High mountains

Source. Hammond (1954, 1964).

approach to describing geographic landscapes might be applied to investigationof individuals’ behavioral landscapes.

Multiple Time-Scale Study Design

The density distributions approach to personality, and more generally, the studyof intraindividual variability, is facilitated by intensive repeated measurement ofindividuals’ behavior at relatively fast, “micro,” time scales (e.g., hours, days).Advances in technology have facilitated researchers’ use of diary, ecologicalmomentary assessment (EMA), ambulatory, and other experience sampling studydesigns (Mehl & Connor, 2012). The intensive longitudinal data obtained in these

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BEHAVIORAL LANDSCAPES 95

TABLE 2Landform Classification Rules

Slope Character (% localarea gently sloping)

Local Relief(meters)

Profile Type (location ofgently sloping area)

A. >80% 1. 0–30 a. >75% in lowlandB. 50%–80% 2. 30–91 b. 50%–75% in lowlandC. 20%–50% 3. 91–152 c. 25%–50% in lowlandD. <20% 4. 152–305 d. <25% in lowland

5. 305–9146. >914

Source. Hammond (1954, 1964.)Note. Column categories are hierarchic, from left (most general) to

right (finest). Gently sloping defined as <8% grade. Lowland defined asbelow the median elevation of the area. Categories from each column arecombined to identify the landform classes listed in Table 1.

studies are being used in a wide variety of ways to measure the dynamic char-acteristics and processes that drive individuals’ moment-to-moment behavior (seeRam & Gerstorf, 2009).

In complement, the study of personality change, and more generally, thestudy of intraindvidual change, is facilitated by repeated measurement of indi-viduals’ behavior at relatively slow, “macro” time scales (e.g., years, decades).Longitudinal panel designs are often used to describe how individuals grow,maintain stability, and decline (see Hertzog & Nesselroade, 2003). The repeated-measures data obtained in these studies are being used in a wide variety of waysto describe the trajectories individuals follow as they develop, age, and transitionthrough life events (see Ram, Gerstorf, Fauth, Malmberg, & Zarit, 2010).

Combining “microtime” research designs with more “macrotime” designs forexamining intraindividual change, researchers can examine questions about thedevelopment of individuals’ personality. Nesselroade (1991) suggested makinguse of a measurement-burst design that involves measuring individuals on mul-tiple time scales. At the microtime scale, observations are obtained from one ormore individuals at closely spaced intervals (e.g., hours, days)—a “burst” of mea-surement. These intensive data are used to characterize individuals’ behavioraltendencies—density distributions and behavioral landscapes. At the macrotimescale, these same individuals are measured again at a wider interval (e.g.,months, years), each time providing another “burst” of information—anotherdensity distribution. Thus, the multitime scale repeated measures design com-bines the benefits of short-term longitudinal studies and the study of behavioraltendencies with the benefits of long-term longitudinal studies and the study ofdevelopment.

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96 RAM ET AL.

This Study

To illustrate how methods used in geography can be used to describe behaviorallandscapes and how they transform over time, we collected EMA data from asample of individuals from the local community across multiple bursts along withinformation about the life events they experienced during the 18-month studyperiod. Patterns of emotional behavior captured through the intensive repeatedmeasures within a burst were described using kernel density methods and visual-ized as behavioral landscapes. Typical descriptive statistics as well as algorithmsfrom geography were used to classify the landscapes into types and to describeif and how individuals’ landscapes changed over macro time as they did or didnot transition through major life events. Repeated measures obtained within-burstprovide for observation of individuals’ behavioral tendencies. Comparison acrossbursts provides for observation of individual-level change in those tendencies.

METHOD

Participants and Procedure

Participants in this study were four persons selected (see below) from a larger lon-gitudinal study on aging, health, and interpersonal behavior. After being recruitedinto the study and explained the intensive nature of the assessments, participantscompleted three 21-day measurement bursts (microtime) spaced at about 4-monthintervals (macrotime). During each 21-day burst, individuals reported about theirinterpersonal interactions as they went about their daily lives and provided end-of-day reports about their feelings, thoughts, and behaviors. As well, prior to andafter each 21-day burst, individuals completed a web-based battery of personal-ity, health, and life-event questionnaires. Participants were compensated $500 forcompleting the entire protocol.

Measures

Our main interest was in exploring if and how 3D landscape visualizationsof multivariate repeated measures data (P-data) could be described analyticallyusing methods adapted from geography. Concentrating here on illustration andextension of topography classification toward the density-distribution approach topersonality we selected a few measures and a few persons from the larger set ofmultiple-time-scale repeated measures.

Life Events

As a measure of the extent of (“developmental”) change that individuals expe-rienced between bursts (∼4-month intervals), we used a set of items assessing the

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BEHAVIORAL LANDSCAPES 97

occurrence of major life events (adapted from Holmes & Rahe, 1967; Sarason,Johnson, & Siegel, 1978). Prior to each burst, participants were asked, “Sinceyour last visit [or, at the first visit, “In the last 6 months”], did you experienceany of the following and, if so, how much did it affect you?” followed by a listof 12 events that included a change in relationship status, death of a loved one,serious illness or injury, change in living conditions, etc. Responses on a 0 (didnot experience), 1 (not affected), 2 (a little bit), 3 (somewhat), 4 (a lot) scale weresummed to a total score where higher scores indicated that an individual recentlyexperienced and/or was strongly affected by major life events. To explore the via-bility of a topographic approach to assessing changes in behavioral landscapes, weselected two persons who exhibited the least absolute change in life events acrossthe three repeated measures (� = 0 and 0 = stable persons; i.e., experienced nosuch events during the entire study period) and two persons who exhibited thegreatest absolute change (� = 23 and 20 = change persons).

Positive and Negative Affect

We describe person-specific density distributions in emotional aspects ofbehavior. Our choice of these two particular variables was driven by our famil-iarity and previous work operationalizing emotional variability and emotionalpolarity/poignancy constructs (e.g., Carstensen et al., 2011; Ram et al., 2012;Ram, Gerstorf, Lindenberger, & Smith, 2011). These constructs and variables areamong the “white rats” we have been using to test out various methods. However,as will be discussed later, any ensemble of two repeated measures variables canbe used.

In each end-of-day report, participants indicated the degree to which they felt avariety of affective states, “Today I felt _______” using a “touch-point continuum”(slider-type interface) that was digitally coded on a 0 (not at all) to 100 (strongly)scale. Items included adjectives for 10 pleasant feelings (enthusiastic, happy, alert,proud, excited, calm, peaceful, satisfied, content, relaxed), and nine unpleasantfeelings (nervous, embarrassed, upset, tense, sluggish, sad, bored, depressed, dis-appointed; Kuppens, van Mechelen, Nezlek, Dossche, & Timmermans, 2007). Foreach participant on each day, responses for the two sets of items were averagedseparately to obtain daily measures of positive affect (PA = x) and negative affect(NA = y).

Data Analysis

Within-person changes in affect across bursts were examined using descriptionsof the repeated measures of PA and NA from each burst as univariate densitydistributions, bivariate density distributions, and topographic landscapes.

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98 RAM ET AL.

Univariate: Intraindividual mean and variance. Each individuals’ dailymeasures of PA and NA during each burst were collected into ensembles anddescribed as separate univariate distributions using standard calculations for theintraindividual mean (iMean),

μxi = 1

T

∑T

t = 1xti

and intraindividual standard deviation (iSD),

σxi =√

σ 2xi =

√1

T − 1

∑T

t = 1(xti − μxi)2

where xti are the repeated measures of positive affect for person i on t =1 to T occasions. Correspondent equations with yti were used to describe theintraindividual distributions of NA.

Bivariate: Intraindividual Correlation

Considering the daily measures of PA and NA as a bivariate ensemble, weadditionally described the bivariate distribution using standard calculations forthe intraindividual correlation (iCorr),

rxyi = 1

T − 1

∑T

t=1

(xti − μxi

σxi

) (yti − μyi

σyi

)

where μxi, μyi, σ xi, and σ yi are obtained as noted above.

Landscapes: Landform Classification

Taking an explicitly geographic perspective, we also examined the topographyof the bivariate density distributions. We describe the approach in some detailbecause, to our knowledge, this is the first time the technique has been used inpsychological/developmental research.

Our first task was to obtain the bivariate emotional landscape for each individ-ual for each burst. A bivariate nonparametric kernel density surface—basically ahistogram in 3D was estimated with a binning width multiplier of 0.7 (which con-trols the width of the histogram bins and was selected after viewing the resultinglandscapes and choosing those that most closely matched the look of true land-scapes). Thus we obtain a grid-like surface where the height/elevation indicatesthe proportion of observations that have each specific x, y score combination (i.e.,0,1; 1,1; 2,1 . . . 98,100; 99,100; 100,100)—like the bottom panels of Figure 1.

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BEHAVIORAL LANDSCAPES 99

Our next task was to describe the topography of the resulting surface. To do so,we automated and adapted Hammond’s (1954, 1964) rule-based landform classi-fication method that is based on the local area’s slope character, local relief, andprofile type. Generally, we followed the automation procedures implemented byGallant et al. (2005). However, a few adaptations were necessary. Because ourdata were psychological dimensions rather than physical dimensions, we neededto consider various ways that our densities could be converted from proportions(frequencies) into an elevation metric (e.g., meters). After trying a few options,we settled on a method wherein we rescaled the maximum density in each indi-vidual’s bivariate density distribution so that it corresponded to the base-to-peakelevation of the world’s large rock spires, such as those of the Paine del Torres atthe southern end of Argentina (∼1,000 m). Automated classification according toHammond’s rules then proceeded with only slight adaptations.

Slope character. The slope for each cell (x, y combination) in the j = 101 xk = 101 (because the PA and NA response scales ranged from 0 – 100) grid of ele-vations was calculated as the slope of a plane fit to the elevations of each cell, Zj,k,in the 3 x 3 neighborhood surrounding the target cell. Specifically, calculations ofthe third-order finite difference weighted by the reciprocal of distance were,

slope% = 100 ·√

EW2 + NS2, where

EW = [(Zj+1, k+1 + 2Zj+1, k + Zj+1,k−1) − (Zj−1, k+1 + 2Zj−1, k + Zj−1,k−1)]/8, and

NS = [(Zj+1, k+1 + 2Zj, k+1 + Zj−1,k+1) − (Zj+1, k−1 + 2Zj, k−1 + Zj−1,k−1)]/8

(which also follows the implementation in ARC GIS, a commonly used geo-graphic information systems software, and in R by the slopeasp() function withinthe landsat library). Note that, because the 3 x 3 neighborhood is incomplete forcells along the edges of the grid, the resulting slope matrix contains only 99 x99 = 9,801 total cells. Then, the slope values for each cell were categorized as“gentle slope” if < 8%, and the number of gently sloping cells counted. Finally,the percentage of gently sloping area (100∗# gently sloping cells/total cells) wasclassified into four categories (A, B, C, D) based on the Hammond rules in thefirst column of Table 2.

Local relief. Local relief is usually quantified as the difference in elevationbetween the lowest and highest cells. However, because our landscapes always hadminimums at or very near zero and maximums of 1000 m, we instead simply usedthe median elevation as a general indicator of relief. The midlevel elevation valuewas classified into four categories (1, 2, 3, 4) based on the original Hammondrules shown in the second column Table 2.

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100 RAM ET AL.

Profile type. To obtain profile types, we first calculated the proportion ofgently sloping (<8% slope) lands that were below the midlevel elevation (whichin all of our landscapes was 500 m). These percentages were then classified intofour categories (a, b, c, d) based on the Hammond rules in the third column ofTable 2.

These three aspects of each individual’s emotional landscape at each burst des-ignated the Hammond landform class, as shown in Table 1 (e.g., irregular plains,open low hills).

Changes in iMean, iSD, iCorr, iReg, and iLandscape

Our final task was to examine if and how the quantifications and classificationsof individuals’ behavioral landscapes (kernel density distributions) changed acrossthe three bursts of measurement. Taking a person-specific (bottom-up) approachhere, between-person comparisons were made only after the person-specificchanges had been obtained. Given the small sample, we simply summarized thedifferences and similarities through qualitative description of the individual-levelquantifications and classifications, keeping in mind the differences/similarities weexpected might coincide with the differences in how these four individuals’ liveschanged across the 18 months of study.

RESULTS

Univariate, bivariate, and topographic descriptions of the PA and NA density dis-tributions obtained for each of the three bursts for each person are given in Table 3.Correspondent visual representations are shown in Figures 2 and 3.

Stable Persons

As can be seen in the top row of Table 3, Person #1 and #2’s life events scoresremained stable across the study at 3 and 0, respectively. This stability was alsoreflected in the univariate density distributions. For both individuals, the iMeansof PA and NA stayed within a 5-point range across the three bursts, and the iSDsstayed within a 2-point range across bursts. The stability of these distributions isevident in the top panels in Figure 2A and 2B where the locations and shapesof the PA and NA distributions are very consistent across bursts. The similarityof the density distributions across bursts could be interpreted as reflecting sta-bility in these two individuals’ positivity, negativity, and (positive and negative)emotional variability. The bivariate correlations were somewhat consistent acrossbursts. Person# 1’s iCorrs ranged between rPANA = –.61 to –.28 in raw correlationunits, and Person #2’s iCorrs ranged between rPANA = –.84 to –0.46, all in the

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3

TAB

LE3

Des

crip

tions

ofF

our

Indi

vidu

als’

Uni

varia

tean

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sity

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st1

Bur

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Bur

st2

Bur

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Bur

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st1

Bur

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Lif

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vent

sTo

tal

33

30

00

3222

922

25

iMea

n PA

54.8

155

.59

56.1

565

.44

61.6

661

.64

21.6

738

.28

54.5

945

.89

40.8

237

.72

iSD

PA7.

637.

68.

55.

585.

497.

6310

.69

14.0

911

.74

16.4

114

.28

6.89

iMea

n NA

34.4

924

.92

28.4

926

.33

29.6

129

.39

44.0

426

.76

9.98

28.3

629

.24

25.3

7iS

DN

A14

.18

11.6

10.7

16.

356.

995.

3218

.10

12.1

26.

9119

.22

14.0

915

.23

iCor

r PA

NA

−.49

−.28

−.61

−.46

−.62

−.84

−.58

−.88

−.36

−.68

−.40

−.38

iLan

dsca

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2B

2B

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ently

slop

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62.7

0%72

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68.7

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.61%

85.3

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40.4

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1%

Not

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(sta

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inL

ife

Eve

nts

scor

es.

101

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102 RAM ET AL.

Burst3Burst1 Burst2

iMNA = 34.49

iSDNA = 14.18

iMPA = 54.81

iSDPA

= 7.63

iCorrPANA = –.49

iMNA = 24.92

iSDNA = 11.6

iMPA = 55.59

iSDPA = 7.6

iMNA = 28.49

iSDNA = 10.71

iCorrPANA = –.28 iCorrPANA = –.61

iMPA = 56.15

iSDPA = 8.5

iLSPANA = B2 iLSPANA = B2 iLSPANA = B2

A. Person #1

Burst3Burst1 Burst2

iMNA = 26.33iSDNA= 6.35

iMPA = 65.44iSDPA= 5.58

iCorrPANA = –.46

iLSPANA = A1

iMNA = 29.61iSDNA = 6.99

iMPA = 61.66iSDPA = 5.49

iCorrPANA = –.62

iLSPANA = A1

iMNA = 29.39iSDNA = 5.32

iMPA = 61.64iSDPA = 7.63

iCorrPANA = –.84

iLSPANA = A1

B. Person #2

FIGURE 2 Stable persons. Univariate and bivariate density distributions of the emotionalbehavior during three 21-day bursts of measurement (positive affect = PA and negative affect =NA) of two individuals who exhibited no change in life events during 18 months of study time.Top panel: Univariate representations described by iMean and iSD. Middle panel: 2D bivariaterepresentations described by iCorr. Bottom panel: 3D bivariate representations described byclassification of iLandscapes into Hammond (1954, 1964) landform classes. For both stablepersons, the univariate density distributions (top panel: iMeans ∼ 5-point range; iSDs ∼ 2-pointrange) and the bivariate behavioral signatures and landscapes (middle and lower panel: com-parable iCorr; identical iLandscape classification) remained fairly consistent across the threebursts. (Color figure available online).

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BEHAVIORAL LANDSCAPES 103

Burst2 Burst3

iMNA = 44.04 iSDNA = 18.10

iMPA = 21.67 iSDPA = 10.69

iCorrPANA = -.58

iMNA = 26.76 iSDNA = 12.12

iMPA = 38.28 iSDPA = 14.09

iMNA = 9.98 iSDNA = 6.91

iMPA = 54.49 iSDPA = 11.74

iCorrPANA = -.88 iCorrPANA = -.36

iLSPANA = C3 iLSPANA = B3b iLSPANA = B2

Burst1 A. Person #3

Burst1 Burst2 Burst3

iMNA = 28.36 iSDNA = 19.22

iMPA = 45.89 iSDPA = 16.41

iMNA = 29.24 iSDNA = 14.09

iMPA = 40.82 iSDPA = 14.28

iMNA = 25.37 iSDNA = 15.23

iMPA = 37.72 iSDPA = 6.89

iCorrPANA = -.68 iCorrPANA = -.40 iCorrPANA = -.38

iLSPANA = C3 iLSPANA = C3 iLSPANA = B2

B. Person #4

FIGURE 3 Change persons. Univariate and bivariate density distributions of the emotionalbehavior during three 21-day bursts of measurement (positive affect = PA and negative affect =NA) of two individuals who exhibited substantial change in life events during 18 months ofstudy time. Top panel: Univariate representations described by iMean and iSD. Middle panel:2D bivariate representations described by iCorr. Bottom panel: 3D bivariate representationsdescribed by classification of iLandscapes into Hammond (1954, 1964) landform classes. Forboth persons with changing life circumstances, both the univariate density distributions (toppanel: iMeans ∼ 30-point range for Person #3 and iMeans ∼ 10-point range for Person #4;iSDs ∼ 10-point range) and the bivariate behavioral signatures and landscapes (middle andlower panel: changes in iCorr; transformation of iLandscape classification) changed across thethree bursts. (Color figure available online).

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104 RAM ET AL.

expected direction, but, as seen in the 95% confidence ovals in the middle pan-els of Figure 2A and 2B, changing some from burst to burst. Stability was alsoreflected in the continuity of behavioral landscapes across bursts. Person #1 and#2’s landscapes were consistently classified as type B2 (irregular plains) and A1(flat plains), respectively.

Change Persons

In stark contrast to Persons #1 and #2’s stability, changes in Persons #3 and #4’slife circumstances are clearly evident in the Life Events scores in the top row ofTable 3. For Person #3 changes were evident in the intraindividual means (30+range for iMeanPA and iMeanNA), in one aspect of the intraindividual variance(10+ range in iSDNA), and perhaps in the intaindividual correlation, iCorrs rangedbetween rPANA = –.88 to –.36. These changes can be seen readily in the top andmiddle panels of Figure 3A. Most striking, though, was that the behavioral land-scape transformed from C3 (open hills) to B3b (plains with hills) to B2 (irregularplains)—as seen in the lower panel of Figure 3A the landscape flattened out. ForPerson #4 changes were not so evident in the intraindividual means (less than 10-point range for iMeanPA and iMeanNA), although one aspect of the intraindividualvariance did show some change (almost 10-point range in iSDPA), as seen inthe top panel of Figure 3B. As with all the other participants the intaindividualcorrelation exhibited some change across bursts, iCorrs ranged between rPANA =–.68 to –.38. However, as seen in the lower panel of Figure 3B, the most strikingchange was in the transformation of the behavioral landscape, from C3 (open hillsin Bursts 1 and 2) to B2 (irregular plains)—again, a flattening of the landscapeaccompanied the drop in life events.

DISCUSSION

In this article, we attempted fusion of ideas from the study of life-span develop-ment with intraindividual variability based approaches to personality and withmethods used to characterize the topography of geographic landscapes. Thedensity distribution approach to personality has promoted the use of repeatedmeasures data, like those obtained in daily diary and EMA designs to describeand quantify individuals’ typical behaviors and the potential range of behaviorsexhibited across multiple situations and/or occasions (Fleeson, 2001). Acrossdomains (e.g., emotion, cognition, motivation), this intraindividual variabilitybased approach has promoted consideration of a wide set of dynamically-orientedconstructs (e.g., stability, inconsistency, flexibility, lability) as viable descriptorsof individuals’ personal characteristics (see Ram & Gerstorf, 2009). Generalizingfrom description of univariate density distributions to description of bivariate

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BEHAVIORAL LANDSCAPES 105

associations extends the possibilities and has promoted development and use ofadditional trait-like constructs (e.g., stress reactivity, Almeida, 2005; poignancy,Carstensen et al., 2011).

Behavioral Landscapes

Described in mathematical detail above, the operationalizations of such con-structs are based on calculation of person-specific iMean, iSD, or iCorr (or iReg),standard descriptive statistics of time-series data based on assumptions that therepeated measures obtained from each individual are independent and identicaldraws from a normal (Gaussian) distribution (for additional implications of theiid assumptions, see Ram & Gerstorf, 2009). Our experiences with empirical den-sity distributions suggested that the means, SDs, and correlations were not ableto capture some interesting features of the data. In particular, the bivariate den-sity distributions often look more like real geographic landscapes than like thesingle, symmetric hills in our (Gaussian-based) simulations. The real world (andreal-world data) is “rougher” than the utopian worlds of simulated data in whichmethods are often developed. Thus, we began searching for methods to describeand quantify individuals’ traits using empirical iLandscapes. As illustrated inour example, the Hammond (1954, 1964) landform classification rules used todescribe geographic landscapes can be adapted for description of behaviorallandscapes.

In this initial exploration of methodological viability, we used emotion datafrom a multitime scale study design to capture stability and change in emo-tional landscapes that proceeded in conjunction with changes (or stability) in lifeevents. The Hammond (1954, 1964) classifications provided access to between-person differences within a burst (e.g., Burst 1 landscapes included A1, B2, andC3 forms), and access to within-person stability (Persons #1 and #2) and change(Persons #3 and #4) across bursts. Taking a person-specific approach, we purpo-sively analyzed data from a very small set of “extreme” cases (with respect tolife event change) and found some basic evidence that points toward an emerginghypothesis that higher life events scores for a given period contribute to the forma-tion of hills and mountains in individuals’ emotional landscapes. These additionallumps in the distribution can be interpreted as emergence of additional “classes”of behavior. That is, the life events push individuals into previously unvisitedspaces (recall that individuals “reside” in the hills) and afford additional levelsand combinations of behavior. Although we remain cautious, the potential inter-pretability of the results suggests that the methods developed in geography canindeed be used to operationalize and investigate new behavioral science researchquestions.

Given intensive, multivariate repeated measures from a large sample of individ-uals we can investigate how interindividual differences are related to behavioral

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106 RAM ET AL.

landscapes. For example, we might hypothesize that the emotional landscapesof individuals higher in neuroticism are characterized by more hills and moun-tains than their lower neuroticism peers. Given multiple bursts of data fromthe same individual, we can examine how his or her behavioral landscapechanges with age—“the sharp bends in streams and the high, jagged moun-tain ranges, over time, becoming gently curving, wider rivers, and rounded,green hills” (Hooker, 2002, p. 329)—where some behavioral combinations geteliminated (or emerge) with development. With the automated version of theHammond classification algorithm we have developed here (adapting Gallantet al., 2005), it is possible to describe iLandscapes for large samples of per-sons or bursts quickly and efficiently and then to examine them as predictorsof consequential outcomes (e.g., health, longevity, interpersonal functioning,family outcomes, career development) genetic factors, early child variables,and/or environmental circumstances, and so on. The possibilities are endless forusing this new kind of personality variable on the right- and left-hand sides ofpredictive equations. Needed is simply some focused thinking about the spe-cific psychological constructs the iLandscapes represent, the specific meaningof between-person differences and within-person changes in those constructs,and how those differences and changes may relate to other aspects of humanbehavior.

Bivariate Density Landscapes

We described person-specific density distributions in emotional aspects of behav-ior using repeated measures of PA and NA. However, any two-variable setwill work, provided that the quantifications (iMean, iSD, iCorr, iLandscape)are tethered to theoretically meaningful constructs (Ram & Gerstorf, 2009).Many other bivariate spaces are well formulated and could be examined ina similar way (e.g., agency/communion, valence/arousal, approach/avoidance,goal engagement/disengagement, physical activity/inactivity). For example, pub-lic health researchers might apply these techniques to characterize behavioralprofiles in individuals’ physical activity volume (movement with energy expendi-ture) and sedentary behavior (sitting/reclining with minimal energy expenditure;Owen, Healy, Matthews, & Dunstan, 2010). The behavioral landscape for an“active couch potato” (e.g., a person who gets physical activity by bicycling toand from work but sits at a desk most of the day) looks substantially differentthan either ideal high-activity/low- sedentary or compromised low-activity/low-sedentary (e.g., lots of standing without much movement) landscapes. To theextent that individuals’ behavioral landscapes differ, tailored interventionsmay be needed to effectively erode unhealthy landscapes and form healthyones.

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BEHAVIORAL LANDSCAPES 107

Trivariate Association Landscapes

As a generalized measure of net-intraindividual variability, the Hammond (1954,1964) classification method demonstrated here can and should be generalized tothree-variable spaces. For example, rather than using density distributions wherefrequency/proportion of observations provides the vertical values, we could usethree observed variables for the x, y, and z axes (e.g., valence, activation, domi-nance; Russell & Mehrabian, 1977). Then the landscape method can be consideredas a map of the solution to a linear regression with two predictors (e.g., zti =β0 + β1xti + β2yti + β3 xtiyti) that defines a plane that bisects the 3D space.Of course, the surfaces (“planes”) defined by the raw trivariate data are likelyrougher and more irregular—and may in many cases be described as the surfacestructure of high or very high relief tablelands (e.g., B5c, B6d). Methodologically,we see good potential for using the landform classification methods to examinebetween-person differences and within-person changes in trivariate concepts—and like how these fit with other landform descriptors (e.g., terrain ruggednessindex; Riley et al., 1999). Although some additional theoretical work will beneeded, we are confident that it is possible to formulate trait-like constructs thatare truly operationalized as trivariate response surfaces—a worthy goal that canstretch personality theory in new directions.

Data Considerations

Our articulation of behavioral landscapes was both facilitated and constrained bythe relatively continuous 0 to 100 response scale (see discussion in Ram et al.,2012). The continuous scale provides the opportunity for a wide variety of irreg-ular shapes to emerge (whereas categorical variables only provide for a limitednumber of possible profiles), but also the chance that there are large, flat plains inthe landscapes (e.g., A1 type) driven by the fact that density is calculated relativeto the total number of responses. In future applications, researchers should explorehow to optimize the number of response scale options, the extent of smoothingneeded in the kernel density estimation, and if and how to engage person-leveland/or group-level standardization (e.g., conversion from raw metrics to z-scores).

The method introduced here fits in the space between the univariate densitydistributions (Fleeson, 2001), where repeated measures from many individu-als are used to examine between-person differences in iMeans and iSDs, and aP-technique approach (Cattell, Cattell, & Rhymer, 1947) where the common fac-tor model is applied to relatively long, many variable time series obtained from asingle individual using relatively few latent variables (see Brose & Ram, 2012 forstep-by-step implementation). Our initial experience here suggests that the iLand-scape approach does not require as much data as a P-technique analysis. It ismeant for low-dimensional time series (only two or three variables), and, as we

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108 RAM ET AL.

demonstrated, can be applied to relatively short time series (e.g., 21 daily obser-vations). Thus, it may be particularly useful in situations where a study design’soccasions/variables trade-off resulted in a small number of items that each oper-ationalize a distinct construct or piece of a 2- or 3D space—the exact approachused in many diary and EMA designs.

CONCLUSION

In developmental (and even personality) science, there is substantial move-ment towards use of intensive longitudinal, diary, and ecological momentaryassessment designs to collect time-series data (Laursen, Little, & Card, 2012;Ram, Lindenberger & Blanchard-Fields, 2009). Mobile technologies are becom-ing more and more ubiquitous, and facilitating the acquisition of high-densityrepeated measures within ongoing longitudinal studies (Mehl & Conner, 2012).The main goal of this article has been to extend our ability to deliberatelytether theoretico-methodological frameworks being used in geographic topogra-phy to the theoretical conceptions and time-series type data being collected inthe study of development. There may soon be tremendous opportunities and needfor methods that capture the richness of iLandscapes and afford the possibilityto examine between-person differences and within-person changes in those land-scapes. We have sought to refine and expand the methodological vernacular byexplicitly making use of taxonomies developed in the study of geographic topog-raphy and hopefully provoke a tsunami of new theoretical concepts in the study ofhuman development.

ACKNOWLEDGMENTS

This work was supported by the National Institute on Aging (RC1 AG035645) andthe Penn State Social Science Research Institute. The authors would like to offermany thanks to the study participants for providing a detailed glimpse of theirdaily lives for such extended periods of time, and to the many research assistantswho helped obtain such rich data.

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