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negative social exchan!That is, frequency of Idicted negative affectlwhen positive exchang(frequency of positive {tive affect and not negalexchanges were held Ihand, longitudinal an{dent crossover effects: ]were held constant, frlchanges at baseline prtnegative affect at folllexchanges, independen]did not predict either Iat follow-up.

The foregoing resusistencies in the valenc{of social interacrions mldiffering temporal dyn]negative exchanges. Vjexamined these tempdseries analyses of conrassociations between p(and time spent in vari(actions. Data consistedthree times daily for 4gative affect were concqualitatively distinct tybut there was no evideciations within days, paffective states and infrthe ability to detect timtthe other hand, the resryses reported by Newsthat negative dspects rimpact on subsequent rthese effects may be mreffects of positive aspecsitive affect.

Our interest is in exiof loneliness moderatrlinking interaction qualsearch indicates that inabilities to benefit fromregulation (reviewed b5Pereg, 2003). Extraverample, are not only mo:interaction partner but iare shy or introvertedtheir interactions as poPietromonaco, 1997; Vcially anxious individualonly perceive interactivironment as poorer

some individuals report dissatisfaction with theirsocial relationships and a feeling of isolation orlack of relational or collective connectednesswith friends or groups (Hawkley, Browne, &Cacioppo, 2005). We have begun to learn thatpotentially adverse health consequences mayensue from these feelings of loneliness and iso-lation (e.g., Cacioppo et al., 2002; Hawkley,Burleson, Berntson, & Cacioppo, 2003). Inmarked contrast, very little research has ex-amined how those who are low in feelings ofloneliness are protected from these con-sequences. Studying these socially connectedindividuals-their cognitions, motives, and in-teractions-could help us understand what itmeans to be a successful social being.

In prior research (Hawkley et al., 2003), wefound that socially connected individuals re-ported less negativity and more positivity infeelings about their interaction partners than didtheir lonely counterparts. Socially connected in-dividuals also reported lower negative affect andhigher positive affect over the course of theweek than did their less connected counterparts.Several explanations could contribute to thesedifferences in affect and interaction quality. First,the social interactions of socially connected in-dividuals could be objectively different than thoseof lonely individuals. For example, socially con-nected individuals may attract or choose thekind of interaction partners that facilitate positivesocial exchanges, and this could subsequentlycontribute to higher positive affect and lowernegative affect among socially connected thanlonely individuals. Second comparable socialinteractions may be perceived more positively bysocially connected than by lonely individuals.The same perceptual bias may contribute to re-ported mood differences between socially con-nected and lonely individuals. Third, sociallyconnected individuals may experience a larger orlonger-lasting boost in mood following positivesocial interactions, or a smaller or shorter-lastingdecrement in mood following negative social in-teractions. This could help to explain why, acrossa typical week, average positive affect was higherand average negative affect was lower amongsocially connected than lonely individuals. Foreach of these explanations, the underlying as-sumption is that affective aspects of everyday lifemay contribute to downstream health ef{ects, andmore interaction positivity or less negativitycould put socially connected individuals at a dis-tinct advantage.

Theoretical Background

Numerous studies have examined the relationshipbetween social interactions and mood, and resultssuggest a reciprocal causal relationship. For ex-ample, in a series of experimental studies, Cun-ningham (1988a, 1988b) induced mood states andfound that a positive mood, relative to a negativemood, stimulated greater interest in social inter-actions and increased conversation quantity andquality (i.e., self-disclosure). The reverse causaldirection has also garnered support, however. In aseries of experimental srudies, Mclntyre and col-leagues found that either spontaneous or arrangedsocial interactions increased positive affect relativeto affect during a neutral control setting (Mcln-tyre, Watson, Clark, & Cross, 1991; Mclntyre,Watson, & Cunningham ,1990). Similarly, when adiary methodology was employed" state positiveaffect was higher when individuals were socializ-ing (Watson, Clar[ Mclntyre, & Hamaker, 1992),engaging in physically active social events (Clark& Watson, 1988), interacting with familiar asopposed to relatively unfamiliar partners (Vittengl& Holt, 1998a), and experiencing fun or necessarysocial interactions (Vittengl & Holt, 1998b).Conversely, negative affect was elevated duringarguments and confrontations (Clark & Watson,1988; Vittengl & Holt, 1998b), interactions markedby the receipt of social support (Vittengl & Holt,1998b), and interactions with poor communicationquality (Vittengl & Holt, 1998a).

Positive and negative affect are largely in-dependent dimensions of mood (Watson, Clark,& Tellegen, 1988) and may be differentially af-fected by positive and negative qualities of socialinteractions. In cross-sectional analyses, Rook(2001) found that number of daily positive socialexchanges was related to greater daily positivemood but was unrelated to negative mood,whereas number of negative social exchangeswas related to dampened positive mood and in-creased negative mood. Similarly, Finch, Okun,Barrera, Zautra, and Reich (1989) showed thatnumber of positive social ties was associatedwith well-being, whereas number of negativeties was associated with well-being and psycho-logical distress. More recently, Newsom, Nish-ishiba, Morgan, and Rook (2003) reportedsignificant correlations between positive andnegative social exchanges and both positive andnegative affect in both cross-sectional and lon-gitudinal analyses. However, independent ofeach other, concurrent effects of positive and

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f f i z P A R T V . I N T E R I N D I V I D U A L D I F F E R E N C E S I N I N T R A I N D I V I O U A L C H A N G E

One of the advantages of MLM is that missingdata typically do not pose a serious problem;individuals can be measured for different num-bers of days, and different days can involve asfew or as many measurement occasions as ne-cessary.

In this chapter, we illustrate how MLM hasmade it possible to examine the following kinds oftheoretical questions in incomplete time-seriesdata such as that obtained using an experience-sampling methodology (ESM). For didactic clar-iry, subsequent MLM analyses will address thesequestions in the sequence enumerated here.

First, MLM can be used to evaluate the pro-portion of total variation in a given outcomevariable (e.g., affect) that is attributable to var-iation at a given level of the data hierarchy (e.g.,diary, day, or person). Predictors (e.g., lone-liness) can then be introduced to explain varia-tion at some of these levels. We expectconsiderable variability in affect and interactionquality at each level (diary, day, person). Basedon our prior work (Hawkley et al., 2003), wehypothesize that loneliness will predict lowerlevels of positivity and higher levels of nega-tivity in affect and interaction quality.

Second, MLM permits examination of theconcurrent relationship between affect and in-teraction quality in a way that avoids the biasthat would result if ordinary linear regressionwere employed without regard to the hier-archical structure of the data. Based on oriorresearch (Cunningham, 7988a, 1988b; Mclntyreet al., \990,1991), we expect to find a reciprocaland valence-specific relationship between socialinteraction quality and affect (i.e., positive in-teraction quality and positive affect will bemutually predictive, as will negative interactionquality and negative affect). Concurrent inter-action quality and affect may also exhibitcrossover effects (e.g., Newsom et al., 2003), butprior research has been mixed in this regard.

Third" MLM makes it possible to srudy intra-and interindividual variability in the concurrentrelationship between lowerJevel variables. Spe-cifically, multilevel modeling permits regressionparameters (e.g., slopes) at lower levels to bemodeled as dependent variables in regressionequations at higher levels. Such effects aretermed cross-leztel interactions because they in-volve variables at higher levels predicting slopesat lower levels. In our case, we study the effects ofindividual differences in loneliness on the asso-ciation between interaction quality and affect. Weexpect that individuals low in loneliness (i.e.,

socially connected individuals) may exhibit morerobust positive affect such that negative interac-tion quality may not have as great an impact onconcurrent positive affect as it does among thosehigh in loneliness.

Fourth, MLM can be employed to examine thetemporal relationship between variables. Typi-cally, time-series analyses are used to examinetime-lagged effects. We introduce a novel al-ternative means of assessing temporal relation-ships by testing lagged effects within the contextof MLM. Specifically, we use multilevel model-ing to examine the lagged effects linking inter-action quality and affect. Moreover, by varyingthe lag between predictor and criterion, we canuse the MLM approach to test the duration of theeffects of social interaction quality on affect-that is, we can examine the duration as well as thecausal direction of effects.

Prior experimental research has shown re-ciprocal causal effects relating interaction qual-ity and affect (Cunningham, 1988a, 1988b;Mclntyre et al., 1990, 1991), and we expect thatlagged effects will support this causal strucrure.Namely, we hypothesize that mood will influ-ence subsequent interaction quality and viceversa. In general, negative exchanges have agreater impact on psychological outcomes thando positive exchanges (a negativity effect; Rook,1990). The potency of negative exchanges maytherefore lengthen the duration of their influ-ence on a{fect. If this is the case, we expect thatnegative interaction quality will continue to in-fluence affect when the temporal lag is extended.

Fifth, adding yet another level of complexity,MLM makes it possible to study intra- and in-terindividual variability in the lagged relation-ships between lower-level variables. A temporallag between lower-level variables allows us toask whether the causal structure linking inter-personal interactions and affect is the same forindividuals regardless of degree of loneliness. Inaddition, we can examine whether lonelinessmoderates the duration of the effects of inter-action quality and affect on each other. If socialconnectedness facilitates effective emotionalregulation, then a given degree of positivity in asocial interaction may elicit greater or longer-lasting increases in subsequent positive affectwith or without greater or shorter-lasting de-creases in subsequent negative affect amongindividuals low versus high in loneliness. Inaddition, reciprocal reinforcement between po-sitive interactions and positive affect may leadto more effective buffering of the effects of

negative interactrons atopposed to high in lcconnected individuals I

tive interactions morelonely counterparts.

Sample and Methods

Over 2,000 students weto represent the lower (

(total score >33 and <3>46) quintile of scoreliness Scale (Russell, PThe R-UCLA Lonelinitems that were originexperiences that best inonlonely individuals.are "I feel isolated frcpanionship"; and "Thto." Notably, none oterms lonely or loneliton the R-UCLA Lonrpositively correlated r,rliness and inversely cmeasures of social exptime spent alone eaclfriends; Russell et al.,scores on the R-UCIpresent a high degreebeddedness and objecti

Participants were 13(83% Caucasian;7o/"Asian, Asian Americalother or undeclared), ethe loneliness groups,were equally represerExclusionary criteria lwhere (Hawkley etof recruitment, stude(SD: 1.0) and they hand, on average, 3.2 (

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-lIIII

the equation for Pql1rq

Pmtr!

The full, conditionaf r{now be: q

Yai.: Yooof Yoro(@d

Models such as th'ationly the tip of the iceber{of MLM to test comp!magnitude and directilarchically stmcrured datllevels. Multilevel mo{include nonlinear effect]and multivariate outc!entered into the model ]error distributions and ibe specified. In ad&tio]times be centered (i.e.,

their means) in order tiestimation and enharjmore involved discussiilimitations of MLM, seor any introductory tex2002; Kreft & de LeerBryk,2002; Snijders &

Software

Facilities for specifyirlevel models are inchsoftware packages, in<LISREL (8.54) and SMLM packages suchbush, Cheong, & Cor(Rasbash et al., 2000'(fcireskog & Srirbom,ease of use. Comple:be easily specifiedLISREL using PRELIS

TABLE 39.1 Pr

1 A '

43.t7-!

32.!

Nofer Numbers*Values for posi

the grand mean fo

present data set, MLM was chosen as the mostappropriate analytical tool.

Three-Level Models

In MLM, the lowest level of the hierarchy (herethe diary level) is commonly denoted Level 1,the next highest as Level 2, and so on. Mostapplications of MLM involve only two levels, forexample, students nested within schools or re-peated measures nesred within individuals.There is theoretically no limit to the number oflevels a multilevel model can contain, but thereare often practical and software-related limita-tions, and few sources discuss MLM with morethan two levels. Our data could be organized intothe three-level hierarchy depicted in figure 39.1.

The three-level, fully unconditional multi-level model can be represented in terms of Level1, Level 2, and Level 3 submodels. The Level 1(diary level) submodel is:

Y;i1, : n01t<l elik e4t-N(0, o2) (1)

where Y4t represents the response at diary i onday I for individual k, nsip represents the meanresponse on day i for individual k, and. eqp re-presents the deviation of Y;;7. from no,k.r TheLevel 2 (day-level) model is:

n6;1 : pe61 *r6ip rsil-N(0, t,) (2)

where Beep represents the mean response forindividual k and 1671 represents the deviation ofns;p from 0oor, or the random effect for day l,The Level 3 (person-level) model is:

Foor: Yooo*root z6e1-N(0, rB) (3)

where 16ss represents the grand mean and a961represents the deviation of goor from yooo.Combining Equations 1,2, and 3 yields the fol-lowing composite model:

Yi ik: losolusu,Irsipl eirk (4)

which explicitly models Xir as the sum of thegrand mean and deviations at Level 3, Level 2,and Level 1, respectively.

Predictor variables may be entered at any le-vel in order to explain variability in randomeffects at lower levels. For example, if a day-levelvariable is entered to explain variation in theLevel 1 intercept, Equation 2 becomes:

no;r : Foor * F uk(wft) -t rolk (5)

In this equation, the predictor ru is hypothesizedto predict intraindividual (intraday or inter-diary) variability in the outcome. The slopeparameter describing the expected increase innsik given a unit change in ra is here specified as a

fixed et't'ect, meaning that it is not permitted tovary across individuals or days. Consequently,

l-r"ror"r.l Tr',tor"tl l-r"ror"t.l l-r"iror"r.lI Diary l 1"""'l oiarv g

| | Diary l 1"""'l oiary e Ift"i-t or"t I fi"- o""L II oiarv t l"-"" 1 oiarv s I

?ooo(''in

ipFigure 39.1 Three-level data structure. Data used in this study could be organized into a three-level hierarchy. Level 1 (diary level) consists of repeated measures of affect (positive andnegative) and interaction quality (positive and negative) nested within days (Level 2), which inturn are nested within individuals (Level 3).

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-0.130 (0.050)43.828 (0.813)7.s81 (0.70s)

30-868 (4.062)

0.120 (0.029)14.84s (0.276)3.015 (0.252)

10.272 (7.362)

-0.140 (0.049)s4.527 (7.s28)11.s1s (1.438)27.zsr (3.9r3)

0.1s1(0.029)19.219 (0.s39)3.349 (0.467)9.978 (7.404)

_-'1IIII

experienced more Poslnected individuals.

II

Concurrent Effects I

As has been not.d in plHolt, 1998a, fg98b; Wlond key question invqinteraction quality arelother words, does pol

predict the positivity ojrnteractron (or vlce r',1

possibility, we specifie{with posint and neginldictors of concurrent a{serving as Level 1. iquality. The Seneral ff

posat't',,u: nsftItl

no*:0oot+1

Foot : Yooo"]

Tlril : Frot

Frot : Troo

The combined equatio

posat't' ;i1:"t ooo * ^l t

+fojk-l (

Code for this model crdix. Results are reportlestimates, standard eciated with'i166.

In every case, thelrelationship betvveenquality. The directiorexpected: positive inthigher positive and Ictive interaction qualiland higher negativeconcurrent relationshcific is consistent witl

TABLE

Outcomc

posaffposaffnegaffnegaffposintposlntnegintnegint

interaction quality variables, but does not alterthe statistical significance of any reported effects.

In the null model, the point estimate of theintercept is an unconditional estimate of thesample mean, which in all four cases is sig-nificantly different from zero (p <.00001). Thefact that the Level 1, Level 2, and Level 3 var-iances also are all significantly different fromzero implies that there exists variation to bepotentially explained in all four outcome vari-ables by adding predictor variables to the model.Variation is additive across levels, such that thevariation which exists at a given level in a givenoutcome variable can be expressed as a propor-tion of the total variation by the intraclasscorrelation. For example, the proportions of var-iability in negaff that exist at the diary, day, andperson levels are, respectively:

Y d i a r v -

o2 f t ^ * rg74.844

t4.844+ 3.014+17.729: 0.502 (10)

'cT

Y d a y - R 2 f r r , ^

3.014 : 0.102 (11)14.844+3.014+\1..729

gauged by noting the magnitude of the regres-sion coefficient associated with loneliness, itssignificance, and any reduction in Level 1, Level2, or Level 3 variation when compared to thenull model. A typical equation for this type ofmodel is:

Posat't' ,,1,: Yooo * Yoot (l onelinessp)

I usu< * rsik I eiik (13)

Results of fitting this model to data for all fouroutcome measures are reported in table 39.2.The y61 coefficient in eacir case represents theslope of the dependent variable regressed onloneliness.

Individual differences in loneliness explainvariabi l i ty in al l four ourcome variables, butcomparison of the variance estimates in tables39.1 and 39.2 reveals that the explanation oc-curred mostly at Level 3 (the person levell. Thismeans that loneliness does not tend to accountfor fluctuations in affect or interaction qualityobserved across diaries or across days withinpersons. In line with expectations, however,loneliness predicted decreased positivity andincreased negativity in affect and interactionquality. Referring to the coefficients in table39.2, a one-unit increase in loneliness producedan increase in negative affect of 0.120 and adecrease in positive affect of 0.130. Values forposint and negint were multiplied by 10 beforeanalysis, so we can say that a 10-unit increase inloneliness produced a 0.151 increase in interac-tion negativity and a 0.140 decrease in inter-action positivity.

Conversely, in mood and social interact-ions, experiential domains that arguably are theprimary determinants of overall well-being, so-cial connectedness provides a distinct advan-tage. Specifically, social connectedness predictshigher positive and lower negative affect, andmore positive and less negative qualities insocial interactions. Not surprisingly, life is

o2

n - T FY P e r s o n - o 2 + r n + r p

1L.729 :0.396 (12)14.844+3.014 +11.729

Most of the explainable variation in all fouroutcome variables occurs at Level 1, or acrossdiaries (within days and persons).

After specifying null models, the next step ina multilevel analysis is usually to introducepredictor variables. Of primary interest to uswas the ability of loneliness to predict variabilityin affect and interaction quality. Loneliness wasmeasured only once per individual, making it aLevel 3 predictor. The success of loneliness inpredicting affect and interaction quality can be

TABLE 39.2 Parameter Estimates for Models With Loneliness as a Predictor

Dependent Variable

posaff negaff poslnt neglnt

ioor62in

i p

Note: Numbers in parentheses are standard errors.

roooo > d (sro o) szg'oIoooo'> d (zro o) zgo o-roooo'>d (zeodwz'o-roooo'> d (ero'o) ree'oroooo >d (sro o) sre oroooo'> d (ooo'o) zzo'o-toooo'> d (szo o) rur'o-roooo'> d (rro'o) rez'o

_ 00lJ

= oori_ 00It

: oori- 00rJ

: oori- 00I.1

: oori

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568 P A R T V . I N T E R I N D I V I D U A L D I F F E R E N C E S I N I N T R A I N D I V I D U A L C H A N G E

-'1IIII

rABLE 3el

Outcome I

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negaltneg3ftr

ry:1'PUrr r r rneglntr

n"gin,,

modets srmilar to thoq

Iag of two diaries. Res39.6, with point estimavalues associated with

These results indicistill in evidence even IFor example, negativdtinued to precipitate nelater. Positive interaclhibit effects of comparwith prior research do<tency of negative intering affect (Rook, L9902003). Notably, rnoodduring valence-specifiquality even two timesitive affect enhancednegative affect exaceriity during interaction:

Predict ing Intra- andDifferences in Lagger

Of primary interest tointra- or interindividurelating lagged predicmight be functionallywe investigated whetl

T A B L E :

Outcome

posaff,posaff,negaff,negafftposlntfposint,neSrntrneSlnt,

posaff

posaff

negaff

negaff

poslnt

posrnt

neglnr

negrnr

posrnr

negint

poslnt

negmr

posaff

negaff

posaff

negaff

TABLE 39.4 Parameter Estimates for ConcurrentRandom Effects at the Day and Person Levels

Outcome Predictor Effect

causality. Here we depart from assessing purelyconcurrent effects, and instead focus on ex-amining the relationship between affect and in-teraction quality when these measures areseparated in time. Lagging predictor variablespermits us to investigate whether, for example,neSative interaction quality at diary f - 1 influ-ences affect at time f. Using lagged predictors alsoallows us to make stronger claims about causalitythan have heretofore been possible. Granted, thelag intervals are not constant, but diaries tendedto be around 92.5 (SD:9.5) minutes apart. Thegeneral form of the combined equation is, forexample:

p o s af t',,1,,, : f s6s t u00k I I sit<

* y rcs(p o sint iit,t-) -l e ijr(1e)

The LISREL syntax for this model is the same asfor the concurrent fixed-effects model; the dataare lagged within-day before submitting them toanalysis in LISREL. Results are reported intable 39.5, with point estimates, standard errors,and p values associated with i166.

In virtually every case, there is a strong laggedrelationship between affect and interactionquality. These effects were in the expected di-rection and were not valence-specific. Positiveinteraction quality at time r - 1 positively pre-dicted positive affect and negatively predictednegative affect at the subsequent time point (i.e.,about 90 minutes later), and negative interactionquality at time f - 1 positively predicted nega-tive affect and negatively predicted positive a{-fect at the subsequent time point. These resultssuggest that the crossover effects of interactionquality on positive and negative aspects of moodare not limited to concurrent effects, but extendto influence mood as much as 90 minutes later.

The reverse causal direction linking affect andinteraction quality was also supported, with onlyone exception. Negative affect at time f - 1predicted less positivity and more negativity ininteractions at the subsequent time point, andpositive affect at one time point predicted morepositivity (but not less negativity) in interac-tions at the subsequent time point. Thus, ingeneral, mood had relatively persistent effectson interaction quality.

The strong lagged effects relating interactionquality and affect suggest that their reciprocalinfluence may last even longer than 90 minutes.We investigated the duration of these effects bylengthening the temporal separation betweenpredictor and outcome. We specified eight

tz - 0.o22 (0.008) p:.004i: :0.022 (0.007) p:.001

iz : 0.014 (o.0r4) p -.324i: - 0.041 (0.016) p:.010

iz - 0.01'4 (0.004) p < .0001is : 0.011 (0.003) p:.001

iz :0.062 (0.013) p < .00001i: : 0.015 (0.009) p:.084

iz :0.058 (0.014) p< .0001t: : 0.0L1 (0.009) p: .22L

iz :0.103 (0.036) p= - joais :0 .175 (0 .048) p< .001

iz : 0.010 (0.00a) p:.915i: - 0.007 (0.003) p:.040

iz - O.746 (0.028J p< .00001i: : 0.049 (0.02r) p: .o20

For every pairing of predictor and outcomereported in table 39.4, slopes vary randomly ateither the day level or the person level, some-times both. But do interindividual differences inloneliness explain some of these intra- and in-terindividual differences in slopes? To addressthis question, we introduced loneliness as a Level3 predictor of slopes. Loneliness was not cen-tered because grand-mean centering would notalter the effect of interest, and it is unclear howgroup-mean centering should be approached orhow the results should be interpreted. The fullthree-level combined equation incorporating across-level interaction is (e.g.):

posat't' ,,1, : yooo * (yroo * r1k I urck)(posintilk)

+ yssl(lonelinessik)

* y rc1(loneline s s it< x p o sint iit<)

1_ruootrlroik* eiik

Code for this model can be found in the appen-dix. However, no cross-level interaction effectswere found to be significant. That is, thestrength of the relationship between affect andconcurrent interaction quality did not differ as afunction of degree of social connectedness.

Lagged Effects

The results reported thus far do not address thetemporal separation (lag) required for establishing

(18)

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570 P A R T V . I N T E R I N D I V I O U A L D I F F E R E N C E S I N I N T R A I N D I V I D U A L C H A N G E

TABLE 39.7 Parameter Estimates for Lag 1 Random Effects at the Dayand Person Levels

Outcome Predictor tttect

posaff iz : 0.008 (0.009) p:.378t: - 0.004 (0.005) p :.483

?z : -0.011 (0.017) p:.509i: : 0.029 (0.017) p:.083

iz - 0.005 (0.003) p:.135t: : -0.003 (0.002) p:.154

iz : 0.03s (0.015) p: .024i: - 0.001 (0.015) p:.934

iz : 0.007 (0.010) p:.456i: : 0.004 (0.006) p :.s58

tz : 0.010 (0.02! p:.659iz -- 0.072 (0.028) p:.411

iz : -0.001 (0.004) p:./25i: : 0.002 (0.003) p: .334

iz : 0.007 (0.032) p: .8r7ig : 0.014 (0.018) p:.43s

What distinguishesresearch is that weof the co-occurringtures of socialdeparture from typspectrve/ assessmentsdividuals with whompositive or negative1989; Rook, 2001), orsocial exchanges aretive (Newsom et al.,interaction qualityvariance, and this maas9ocratlons we

negative aspects ofHowever, this did noting possible explanatness/loneliness divariables and their

What mightpositivity and lowerteraction qualitydividuals? Our datasocially connected indiindividuals in theirsocial experiences,characterized bytions of positivityne8atlvrty ln

results of our laggedcially connected indilonger-lasting effectsmood, or of positivethan do lonely individoes not account forliness differences inand negativity inknow from past reseathat these same indiconnectedness/lonelispent with others, ruliopportunities as anferences. On thethe possibility thatdividuals haveuon partners/ painclined towardinteraction qualitymaintain positivity iindividual.

In sum, our dataof greater perceivedconnected individualssitivity to be self-reinof affect and i

posaff

negaf{

negaff

poslnt

posint

negint

negint

posrntf, 1

negintr - l

poslntl - 1

ne$rntl 1

posaff, - 1

negaff, 1

posaff, 1

negaff, 1

In additioru the effect of negative affect at timef - 1 on positive interaction quality at time tvaried significantly across people. We investigatedthe ability of loneliness to predict this randomeffect. The combined threelevel eouation is:

posint,,p,,

: Yooo _F (Yroo * r1i1,* ulsk)(negot't' iir,,-)

a y6rr(lonelinessip)

I y .61(I o n e line s s p x n e gat' t',,p,, -r)

I ussk -l rsir< * eiik (27)

Intraindividual variability in slopes was not sig-nificantly predicted by loneliness, ?ror :0.0004

(0.004), p: .e27.Given that there was some evidence for Lag 2

effects (see table 39.6), we were curious to discoverwhether there existed significant intraindividualvariability in dayJevel or person-level slopes forLag 2 effects and, i{ so, whether this variabilitycould be predicted by interindividual differencesin loneliness. We examined day-level and person-level random effects of Lag 2 predictors. None ofthese slopes exhibited significant variance at eitherthe day or person level.

Goncluding Remarks

In this study, MLM afforded theoretical tests andinsights that would not have been visible fromother perspectives. Had we taken an ordinary

least squares regression approach, for example,we would not have seen that variance in affectand interaction quality was evident not onlyacross diaries, but also across days and acrosspersons. Specifically, variability in mood andinteraction quality associated with momentarycircumstances exceeded day-to-day and person-to-person variability in mood and interactionquality. Moreover, MLM enabled us to see thatloneliness/social connectedness was more pow-erful in explaining interindividual variance thanmomentary or daily variance. This finding pro-vides a first clue that loneliness/social con-nectedness is characterized to a greater extent bya pervasive enduring influence over the affectiveexperience of everyday circumstances (includingsocial interactions) than by a transitory influenceon the experience of momentary circumstances.

Second, we saw that concurrent affect and in-teraction quality were reciprocally related acrossvalence domains, as has been suggested by priorresearch (Cunningham, 1988a, 1988b; Mclntyreet al., 1990, 1991; Newsom et al., 2003). Im-portantly, lag analyses revealed potentially causalrelationships linking interaction quality and af-fect: These relationships appeared reciprocal, werein the expected directions, and acted across va-lence domains. In addition, negative aspects ofsocial interactions had a particularly long-lastinginfluence on negative affect, consistent with the"negativity effect" reported by other researchers(Rook, 1990; see also Newsom et al., 2003).

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measures and addressed important theoreticalquestions that could not otherwise be addressed.For instance, the relationship between variablescan be studied within nesting units (e.g., days,people), which allows us to ask questionsabout both interindividual variability and in-traindividual variability.

We further demonstrated that longitudinaldesigns need not focus on time as a variable oreven on trends. The capability of testing laggedeffects is very useful, and is an interesting al-ternative to incorporating time as a predictor(which is how most researchers study trends).We were not interested in trends over time(i.e., growth curves), but rather concurrent andlagged effects of other relevant variables. Ourstudy was characterized by repeated measure-ments of the same individuals, yet our predic-tions and hypotheses had little to do withexamining growth or trajectory over time.

Finally, in psychological research, there is anunfortunate tendency to address longitudinal

hypotheses using cross-sectional data. Temporalseparation is a necessary, but not sufficient,condition for causality (Gollob & Reichardt,7987). To address causality, we used laggedprediction in MLM of longitudinal data. Theexamination of lagged effects as illustrated inthis chapter should be undertaken more oftenbecause this modeling strategy recognizes ex-plicitly that independent variables require timeto exert effects on dependent variables. Modelinglagged responses in MLM is a novel but powerfulstatistical approach that overcomes these lim-itations in traditional longitudinal analyses.

Acknowledgments This work was supportedby Program Project Grant PO1 AG18911(social isolation, loneliness, health, and theaging process) from the National Institute onAging and by the Mind-Body Network ofthe John D. and Catherine T. MacArthurFoundation.

Appendix Selected LISREI Gode

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_lIIII

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lrnteracnon on poslJBulletin ot' the Psych

Mclntyre, C. W., Watsoj(1990). The effects olland test stress on p!Bulletin ot' the Psychdi

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Vy"tr,-n. C., A Oi"n.fPsychological Sciencl

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Rasbash, J., Browne, WPlewis, L, Healy, M., eMLzoiN. London: Cen

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2. Centering sometimes improves the stability ofestimation by reducing collinearity with other pre-dictors, and often renders uninterpretable parameterestimates interpretabie. For example, the intercept intraditional regression is interpretable as the va-lue ofthe dependent variable when all predictors equalzero.If a predictor variable has no meaningful zeropoint, then mean centering allows the intercept to beinterpretable as the predicted value of the dependentvariable at the mean of the predictor. In two- andthree-level models, centering is more complicated(see Kreft & de Leeuw, 1998; Kreft, de Leeuw, &Aiken, 1995; Raudenbush & Bryk, 2002, for generalguidance on centering). In this chapter, we limitanalyses to uncentered data because the effects ofgreatest interest are not altered by the most widelyemployed kind of centering.

3. For the sake of brevity, only those resultsdirectly relevant to the question at hand are re-ported. More details are available from the authorsupon request.

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