The Temporal Consistency of Personality Effects: Evidence fromthe British Household Panel Survey

Andrew J. BloeserAllegheny College

Damarys CanacheUniversity of Illinois

Dona-Gene MitchellUniversity of Nebraska

Jeffery J. MondakUniversity of Illinois

Emily Rowan PooreUniversity of Aberdeen

Personality traits have been posited to function as stable influences on political attitudes and behavior.Although personality traits themselves exhibit high levels of temporal stability, it is not yet known whether theeffects of these traits are marked by comparable temporal consistency. To address this question, this researchnote examines data from Wave 13 (2003–2004), Wave 15 (2005–2006) and Wave 17 (2007–2008) of the BritishHousehold Panel Survey (BHPS). Twenty-seven behavioral and 14 attitudinal dependent variables are studied.Consistency of effects is gauged via a series of multilevel models in which personality effects are permitted tovary by year. High levels of temporal consistency are observed for personality traits as represented by the BigFive framework.

KEY WORDS: Big Five, personality, temporal consistency, British Household Panel Survey

Introduction

This study examines the extent to which personality, as represented by the Big Five traitdimensions, exerts temporally consistent influences on social and political attitudes and behaviors.Scholars in numerous fields seek to identify factors that foster consistency in attitudes and behavior.As examples, two central questions among political scientists have been whether citizens maintainconsistency in policy attitudes and ideology and what factors contribute to such consistency(e.g., Converse, 1964; Ansolabehere, Rodden, & Snyder, 2008). Scholars also have focused oncontinuity when studying behavior, such as who votes in elections (e.g., Plutzer, 2002). We posit that

Political Psychology, Vol. xx, No. xx, 2013doi: 10.1111/pops.12067

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personality traits exert relatively consistent influences on attitudes and behavior over time, suggest-ing that personality operates as an inertial force in the political realm; if attitudes and behaviorschange, they are presumed to do so despite the stabilizing influence of personality. This thesis istested using data from three waves of the British Household Panel Survey (BHPS).1

To be clear, we do not examine whether attitudes, behaviors, or traits are stable. Attitudes andbehaviors are our dependent variables, and the Big Five are our key predictors. At question is whetherthe relationships between traits and our other variables are consistent. If we regress 2005 member-ship in environmental groups on measures of the Big Five, and a coefficient of 0.80 is found foropenness to experience, that effect would be deemed temporally consistent if a coefficient very closeto 0.80 also is obtained when the model is estimated using 2007 data. It is this matter—whether theeffects of personality are temporally consistent—that we consider. In the next section, we discusswhy consistency is to be expected, but we also note factors that may thwart such consistency. Thefollowing section describes the BHPS, provides information on our variables, and outlines theprocedures used to gauge temporal consistency. We then report results, with findings differentiatedfor behavioral and attitudinal dependent variables. The final section discusses the implications of thestudy’s findings.

Consistency or Inconsistency?

The question we pursue is whether personality exerts consistent effects over time on social andpolitical attitudes and behaviors. Past conceptualizations of personality suggest that such consistencyshould be expected. However, that outcome should not be presumed in the absence of relevantevidence, particularly given factors that potentially act to constrain longitudinal consistency.

The Case for Consistency

Personality is of interest to political psychologists because it can help us to understand whypeople take particular actions and maintain particular beliefs. Personality, especially as understoodin trait perspectives, is thought to be highly stable over time.2 Indeed, this stability is one ofpersonality’s defining features (e.g., McCrae & Costa, 2003, p. 25). For the Big Five, Costa andMcCrae (1988) report six-year error-corrected stability levels as high as 0.95, and Rantanen,Metsäpelto, Feldt, Pulkkinen, and Kokko (2007) report nine-year stability levels between 0.65 and0.97. If personality influences attitudes and behavior and if personality is characterized by temporalstability, then it follows that the effects of personality on attitudes and behavior also should exhibittemporal consistency. It would be illogical, for instance, for extraversion to increase a person’slikelihood of being an active group member one year and then have no effect, or even a negativeeffect, a few years later.

Possible Limits to Consistency

At least three factors may limit the consistency of personality effects over time. The first ischange in personality. In the present research, Big Five data are obtained only once, on Wave 15 ofthe BHPS (2005–2006). Trait scales from Wave 15 are then used to predict social and politicalattitudes at that time, again when the dependent variables are observed two years later (Wave 17,2007–2008), and also when the dependent variables were measured two years earlier (Wave 13,

1 Although our assumption is that any correspondence between personality and attitudes and behavior reflects the influenceof personality, our purpose in this study is to gauge the consistency of effects, not to test causal paths.

2 Although personality is conceived as being highly stable, it is not perfectly stable over time. One area of discussion pertainsto possible changes in personality over the life cycle (e.g., Costa & McCrae, 2006; Roberts, Walton, & Viechtbauer, 2006).

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2003–2004). One scenario that would attenuate consistency in effects is (1) personality influencesour dependent variables, but (2) values on the traits undergo at least some change in a four-year span.Thus, personality as measured on Wave 15 would be somewhat out of sync as a representation ofpersonality at Wave 13 and Wave 17.

A second reason effects may be inconsistent is that they may emerge via interactions withsituational variables. Life circumstances can change in a two- or four-year span. Trait-situationinteractions in effect today may differ from the interactions at work a few years later. This is a variantof Mischel’s (1968) critique of personality research, one premised on the claims that people exhibittrans-situational variation in behavior and that trait effects vary with situational factors.3 Modelsestimated below do not include trait × situation interactions. Thus, results for the Big Five measuresreflect averages for all respondents. The actual impact of personality should be heterogeneous acrossrespondents due to situational forces that magnify or mute a given trait’s impact. For example,extraversion may affect whether people become active members of neighborhood associations orparent-teacher organizations, but only if they are homeowners or parents. If people became home-owners or parents between the BHPS waves studied here, then the observed impact of personalitycould be inconsistent. In this case, inconsistency in personality effects may be seen even if person-ality itself is stable over time.

Measurement error is a third potential cause of inconsistency. Only one of three forms of errorcan affect this study’s tests. Sampling error is not a concern because we draw data from a panelsurvey, with the same individuals included when effects for one year are compared with those for twoother years.4 Measurement error in our independent variables also is not a concern. If respondentswere asked identical questions in two different years, it could be that how they interpreted the itemschanged over time. Our models side-step this by using only independent variables constructed withdata from the 2005–2006 wave. Where measurement error may be problematic is the left side of ourequations. Respondents were asked identical attitudinal and behavioral questions on three occasionsspanning just over four years, but we have no means to assure they interpreted those questionsidentically each time. Any changes in interpretation would mean that the personality variables wouldhave to predict values on a moving target.

Data and Method

The BHPS, started in 1991, is designed to be representative of the population of Great Britain.The project has surveyed as many Wave 1 respondents as possible in subsequent years, along withones added via supplemental samples in Northern Ireland, Scotland, and Wales or because theyreached adulthood as members of the original households.5 Independent variables in the presentstudy use data from Wave 15 of the BHPS (September 2005 to May 2006). Dependent variables drawon identical questions asked on Wave 15, Wave 13 (September 2003 to May 2004), and Wave 17(September 2007 to May 2008). The maximum number of respondents available to us (prior toaccounting for missing responses) is nearly 14,000.

3 Our dependent variables concern social and political attitudes and behavior. In recent years, political scientists haveemphasized that the Big Five are relevant for many of the phenomena they study but that personality effects most likelyemerge via interactions with situational and demographic variables (e.g., Mondak, Hibbing, Canache, Seligson, &Anderson, 2010).

4 There is some panel attrition on the BHPS. To test whether this attrition was systematic, we regressed attrition from Wave15 to Wave 17 (i.e., the respondent participated in Wave 15 and did (1) or did not (0) participate in Wave 17). Age, sex, andthree of the Big Five measures produced significant effects in this model. However, in a model with nearly 14,000 cases, evensubstantively minor effects can yield statistically significant coefficients. The Cox and Snell pseudo-R2 value for this modelis only 0.016, suggesting that the vast majority of sample attrition was unsystematic in its origins, at least with respect to thevariables used in the present study.

5 For additional background on the BHPS, see Lambert (2006) and Taylor (2010).

Temporal Consistency of Personality Effects 3

Models include seven main predictors: an indicator for gender (1 = female, 0 = male), therespondent’s age in years during the Wave 15 interview, and three-item scales for openness, con-scientiousness, extraversion, agreeableness, and emotional stability.6 The first dependent variablesare 27 self-reported behavioral measures.7 Three concern whether the respondent reported havingvoted in the last national election and how often the respondent reports talking, and socializing, withneighbors. The remaining 24 concern whether the respondent claims to belong to, or join in theactivities of, 12 types of organizations.8 The second dependent variables draw on data from 14attitudinal questions on matters such as whether the respondent agrees that employers should helpwith child care.9 All of the dependent variables are either dichotomous or use brief ordinal scales, andmodels are estimated using variants of binomial or ordinal logistic regression. We use as dependentvariables all relevant measures of social and political attitudes and behavior that were asked on allthree survey waves under consideration.

Given independent variables measured at T2, and dependent variables measured with the samerespondents in identical form at T1, T2, and T3, we require an estimation strategy that permits us togauge the temporal consistency of effects. Our approach is to estimate a series of multilevel models,three models for each of our 41 dependent variables. Each model includes two observations on thedependent variable per respondent, one from an earlier wave, and a second from a later wave. Threemodels are estimated for each dependent variable so that we can pair data as follows: Wave 13–Wave15; Wave 15–Wave 17; and Wave 13–Wave 17. There are 15 independent variables: the Big Five,gender, and age; an indicator variable for survey wave (1 = most recent wave, 0 = oldest wave), andinteractions between the survey-wave measure and each of the seven substantive predictors. This isa multilevel strategy because each model includes variables measured at the level of the respondentand the respondent-year. To account for this, estimates are calculated using robust standard errorswith clustering on the respondent case-identification number.

Our chief task is to gauge temporal consistency. Perfect temporal consistency would exist for thepersonality variables if all wave × trait interaction terms yield coefficients of zero. Put differently, weare hypothesizing null effects. With 41 dependent variables, five personality traits, and three pairs ofsurvey waves, a large quantity of output will be generated. Thus, questions arise regarding what sortsof evidence would demonstrate temporal consistency both with respect to individual effects andacross the full array of tests.

The test statistics we require are the opposite of those needed in most research. Typically,analysts propose the existence of substantive and statistical relationships and then seek evidence that

6 Thus far, on the BHPS, the Big Five battery has only been asked as part of Wave 15. It began “The following questions areabout how you see yourself as a person. Please tick the number which best describes how you see yourself where 1 means‘does not apply to me at all’ and 7 means ‘applies to me perfectly’.” Items for openness are “is original, comes up with newideas,” “has an active imagination,” and “values artistic, aesthetic experiences.” Data are averaged, with the scale coded torange from 1 (low openness) to 7 (high). The alpha value for the scale is 0.68 (mean = 4.45, s.d. = 1.23). For conscien-tiousness, items are “does a thorough job,” “tends to be lazy” (reverse coded), and “does things efficiently” (alpha = 0.54,mean = 5.26, s.d. = 1.10). For extraversion, items are “is talkative,” “is outgoing, sociable,” and “is reserved” (reverse coded;alpha = 0.54, mean = 4.49, s.d. = 1.18). Agreeableness items are: “is sometimes rude to others” (reverse coded), “has aforgiving nature,” and “is considerate and kind to almost everyone” (alpha = 0.53, mean = 5.45, s.d. = 1.01). For emotionalstability, the items are “worries a lot” (reverse coded), “gets nervous easily” (reverse coded), and “is relaxed, handles stresswell” (alpha = 0.67, mean = 4.33, s.d. = 1.33). The alphas are lower than ideal, a circumstance not unexpected given that theBig Five are broad trait dimensions; modest levels of internal consistency also are observed on other brief measures of theBig Five (e.g., Gosling, Rentfrow, & Swann, 2003; Rammstedt and John, 2007).

7 We refer to these dependent variables as “behavioral” so as to differentiate them from the attitudinal dependent variables.Still, it should be clear that these are self reports of behavior, not external measures such as would be derived from voterregistries or group-membership rosters.

8 The 12 types of organizations are: political parties, unions, environmental groups, parents’ groups, residential groups,religious groups, volunteer services, pensioner’s groups, Scout Masters, professional organizations, community groups, andsports groups.

9 The questions include measures of political interest, generalized trust, support for the Conservative, Labour, and LiberalDemocratic parties, and nine items regarding gender roles and the provision of child care.

Bloeser et al.4

such effects are present. In contrast, we posit the absence of effects. What results would show thepresence of null relationships? We will conduct two types of tests. The first is the inverse of aconventional hypothesis test. If statistically significant coefficients for the wave × personality inter-actions indicate the presence of temporal differences, then the absence of significant coefficients canbe viewed as an indicator of temporal stability. Thus, our first test simply tabulates insignificantinteractions.

Although the inverse of the conventional hypothesis test is often used to assess hypotheses thatthere are not meaningful effects, it should be acknowledged that this is not an ideal test (for adiscussion, see Rainey, 2013). First, the absence of a statistically significant coefficient is not thesame thing as evidence that no meaningful effect is present. In the current study, for instance, therewill be 615 parameters of interest (41 dependent variables × five traits × three pairs of survey waves).It is possible that every coefficient on every interaction will be statistically insignificant, but it ishighly unlikely that many of the coefficients will be exactly equal to zero. A coefficient of 0.83 maynot be significantly different from a coefficient of 0.90, but that does not mean they are the same.Second, as Rainey (2013) demonstrates, the inverse of the conventional hypothesis test is almostcertain to be biased; depending on sample size, there will be an inflated risk of either false positivesor false negatives.

As a complement to tabulating insignificant effects, we will use a variant of the approachsuggested by Rainey (2013). Rainey’s position is that analysts should, a priori, designate m, thesmallest substantive effect they would consider to be meaningful. This designation is inherentlysomewhat arbitrary. We might think, for instance, that an increase in annual salary must be at least$500 to be meaningful, whereas a professional athlete might set the minimum meaningful incrementat $100,000. Absent a fixed rule, the best the analyst can do is to articulate a rationale for the chosenstandard and be transparent about the standard in question so that other analysts have the opportunityto make the case for alternate standards.

Our analyses involve results from 123 binomial and ordered logistic regression models, withfocus on dependent variables with a wide array of distributions. Consequently, it would be useful ifwe could devise a simple, uniform standard for delineating the smallest meaningful substantiveeffect. With these pragmatic considerations in mind, we opt to focus on the odds ratio for movementacross the full range of each independent variable. We define the smallest meaningful substantivechange as a half-unit shift in the odds. For instance, starting with baseline odds of 1:1, we define achange as substantively meaningful if the odds increase to at least 3:2 (an odds ratio of 1.5) ordecrease to at least 2:3 (an odds ratio of 0.66). Thus, for any given wave × trait interaction, temporalstability is defined to exist if, for movement across the full range of the trait variable, 0.66 < oddsratio < 1.5. As an example, if the estimated likelihood of observing a 1 rather than a 0 on thedependent variable is 0.50 when a trait is at its minimum value, our definition considers thesubstantive change to be meaningful if this value rises to at least 0.60 or declines to at least 0.40 whenthe trait is at its maximum.10

10 We chose this standard because we think that, in most instances, changes of 10 percentage points are substantivelymeaningful. For binomial models, under our definition, any change in the predicted probability that reach at least 10percentage points in either direction is guaranteed to be defined as substantively meaningful. Changes smaller than 10percentage points will be defined as meaningful if the distribution on the dependent variable is relatively skewed. Forinstance, if the baseline distribution is 0.95–0.05, an odds ratio of 1.5 would yield a corresponding distribution of 97.4–2.6,meaning a shift of 2.4 percentage points would be defined as substantively meaningful. Again, we focus on shifts in the oddsratios across the full ranges of the predictors. For personality, this is always at least a 4.5-standard-deviation shift. For theodds ratio threshold used here, effects typically will be labeled as substantively significant if a two-standard-deviationchange in personality yields at least a three-percentage-point shift in the predicted probability. This is a minimal level ofchange, and thus we view our standard as conservative. A virtue of using odds ratios as the basis for our test statistic is thatthey are substantively comparable across disparate models. Again, though, the precise standard one selects is necessarilyarbitrary (Rainey, 2013).

Temporal Consistency of Personality Effects 5

Again, our analyses involve 615 tests. If some indicate temporal stability and some do not, oursecond requirement is a means to gauge how many successes would be needed for temporal stabilityto be claimed. One approach we will take on this point is purely relative: test results for personalityeffects will be contrasted with those for gender and age. This will enable us to ascertain whetherpersonality effects are stable relative to those for core demographic attributes. Our second approachwill be to construct test statistics based on the binomial distribution.11 In this test, we assume, first,that the hypothesis of no meaningful effect is correct, and second, that our 615 individual tests arehighly, but not perfectly, accurate. That is, even when temporal stability is the true effect, we maysometimes erroneously observe instability. As an arbitrary guide, we will assume 90% accuracy;hence, in any given test in which the true effect is temporal stability, we assume our test has a 90%chance of correctly designating the effect as stable. Our tests will be conducted in groups of 27(behavioral dependent variables) and 14 (attitudinal dependent variables). Following the binomialdistribution, the respective critical values for failure at the 95% confidence interval are x ≤ 21 andx ≤ 10. For example, if, in 27 tests of behavioral dependent variables, we observe stability 21 timesor fewer, we would reject the hypothesis of temporal stability.

A final matter is the relationship between the dependent variables on the survey’s three waves.If no response changed, there could be no inconsistency in the effects of the independent variables.However, it is also substantively important that the correlations be positive and significant, indicatingthat there is continuity in reported attitudes and behaviors. Personality is assumed to provide a stablefoundation to attitudes and behavior, and that foundation would be lacking if values on the dependentvariables bounce wildly over time. In actuality, significant positive correlations exist between wavesfor all dependent variables, with most being moderate in magnitude. The correlations range from0.18 to 0.94, and average 0.51.

Results and Discussion

Eighty-one models were estimated for the behavioral dependent variables and 42 for theattitudinal measures. Table 1 displays illustrative results. The first model reports estimates for aquestion concerning social interaction. Respondents were asked “How often do you meet friends orrelatives who are not living with you?” The five response options ranged from “never” to “on mostdays.” The second model depicts coefficients from an attitudinal model, with respondents askedabout the extent to which they agree that “a single parent can bring a child up as well as a couple.”The five response options ranged from “strongly disagree” “to strongly agree.”

In the first model, results reveal that respondents high in openness and conscientiousness haveinfrequent social interactions, whereas such interactions increase with extraversion and agreeable-ness. Among the personality × survey wave interactions, none reaches statistical significance oryields an odds ratio outside the bounds of our test statistic. Based on these results, we wouldconclude that the effects for the Big Five dimensions exhibit temporal consistency. Results for thesecond model also provide evidence of temporal stability in personality effects, albeit with oneexception in that the survey wave × extraversion effect is statistically significant. The correspondingwave × gender interaction is also significant.12

Results for all 123 models are summarized in Table 2. Findings are reported separately for thebehavioral and attitudinal dependent variables and for each pair of survey waves. The data reveal

11 We thank an anonymous reviewer both for emphasizing to us the importance of devising alternate tests when the hypothesisin question posits an absence of meaningful effects and for suggesting the specific summary test we use here, the binomialtest.

12 For both of these variables, the interactions reach statistical significance, whereas the odds ratios indicate temporal stability.These disparities are consistent with the critique advanced by Rainey (2013, 8), who notes that, when using the inverse ofthe conventional hypothesis test, “If the sample size is too large, the researcher will often conclude that there is a meaningfuleffect when there is strong evidence of no meaningful effect.”

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Table 1. Sample Models of the Effects of the Big Five on Social and Political Behavior

Sample Behavioral Dependent Variable(Frequency Respondent Meets with

Friends and Relativesfrom Outside of the Household)

Sample Attitudinal Dependent Variable(Extent to Which the Respondent

Agrees That a Single Parent Can Bringa Child Up as Well as a Couple)

Coefficient(s.e.)

Odds Ratio (for FullRange of Variable)

Coefficient(s.e.)

Odds Ratio (for FullRange of Variable)

Openness −0.05**(0.02)

0.73 −0.08***(0.02)

0.62

Conscientiousness −0.10***(0.02)

0.54 −0.03(0.02)

0.86

Extraversion 0.18***(0.02)

2.90 0.05**(0.02)

1.35

Agreeableness 0.12***(0.02)

2.01 0.08***(0.02)

1.65

Emotional stability 0.03(0.01)

1.17 0.01(0.01)

1.05

Gender −0.24***(0.04)

0.79 −0.83***(0.04)

0.44

Age −0.02***(0.00)

0.22 −0.03***(0.00)

0.06

Survey wave −0.01(0.16)

0.96 0.04(0.13)

1.03

Survey wave × openness 0.030.02)

0.83 −0.01(0.02)

0.92

Survey wave × conscientiousness 0.01(0.02)

1.07 −0.02(0.02)

0.89

Survey wave × extraversion −0.02(0.02)

0.86 0.04*(0.02)

1.27

Survey wave × agreeableness −0.00(0.02)

0.99 0.01(0.02)

1.06

Survey wave × emotional stability 0.01(0.02)

1.08 −0.03(0.01)

0.85

Survey wave × gender 0.01(0.04)

1.01 0.10**(0.03)

1.10

Survey wave × age 0.002(0.001)

1.23 −0.00(0.00)

0.98

Cut-point 1 −7.01***(0.26)

−4.86***(0.15)

Cut-point 2 −4.00***(0.15)

−2.54***(0.14)

Cut-point 3 −2.15***(0.15)

−1.43***(0.14)

Cut-point 4 0.05(0.14)

0.74***(0.14)

Model χ2 646.71 1765.85Number of cases 23,854 23,250

Source. BHPS Waves 15, 17.Note. cell entries are coefficients from multilevel ordered logistic regression models, with standard errors in parentheses.All independent variables are measured on Wave 15. Within each model, each respondent provides two observations onthe dependent variable, one recorded on Wave 15 and a second recorded on Wave 17. Models are estimated with robuststandard errors, with clustering on respondent case-identification number. Odds ratios indicate the change in oddsassociated with movement across the full range of each predictor. For odds ratios on the interaction terms, support for thehypothesis of a substantively stable effect is defined as 0.66 < odds ratio < 1.50.***p < .001 **p < .01 *p < .05.

Temporal Consistency of Personality Effects 7

high levels of consistency for the Big Five. Using statistically significant interaction terms as ourbarometer, the critical value for rejecting the hypothesis of temporal consistency is reached only onceacross 30 sets of tests, for openness in the Wave 13–Wave 15 models. Using the odds ratio metric,there are two failures in 30 sets of tests for openness and agreeableness in the Wave 13–Wave 17models.13 The only variable to consistently exhibit temporally unstable effects is age.

13 If instability in effects is seen because personality itself changes over time, we should observe the highest levels of temporalinstability in the Wave 13 – Wave 17 models, which span four years. The two failures for the odds ratio tests for Wave 13– Wave 17 arguably constitute weak evidence consistent with the claim that personality changes slowly and systematicallyover time, yielding a degree of temporal instability when trait variables are used as predictors. That said, the collectiveresults in Table 2 suggest that any such movement is rather slight, at least within a four-year span, in that the Wave 13 –Wave 17 effects all are highly similar, in terms of temporal consistency, to those in the other two contrasts. This mattercould, of course, be pursued in tests focused on longer panels.

Table 2. Consistency of Personality Effects on Social and Political Attitudes and Behavior

Number of Temporally Consistent Effects

Behavioral DependentVariables (27 tests)

Attitudinal DependentVariables (14 tests)

A. Wave 13–Wave 15 p > .05 0.66 < odds ratio < 1.50 p > .05 0.66 < odds ratio < 1.50Openness to experience 26 22 10* 13Conscientiousness 26 22 13 14Extraversion 27 24 14 14Agreeableness 26 24 12 14Emotional stability 24 24 13 14Gender 23 27 13 14Age 21* 21* 10* 13

B. Wave 15–Wave 17Openness to experience 25 23 14 14Conscientiousness 26 22 14 14Extraversion 26 25 12 14Agreeableness 26 23 14 14Emotional stability 27 25 13 14Gender 24 26 13 14Age 20* 19* 10* 13

C. Wave 13–Wave 17Openness to experience 27 21* 11 14Conscientiousness 25 22 11 14Extraversion 27 24 14 14Agreeableness 25 20* 12 14Emotional stability 26 24 13 14Gender 23 26 14 14Age 20* 16* 10* 12Critical value for rejecting the

hypothesis of temporal consistencyx ≤ 21 x ≤ 10

Note. Cell entries are based on analyses using data from the BHPS, Waves 13, 15, and 17. For the behavioral dependentvariables, 27 models were estimated for each pair of waves; 14 models were estimated for attitudinal dependent variables.In the first and third columns of results, categorization of effects as consistent is based on coefficients from multilevelbinomial and ordered logistic regression models (see Table 1 for examples). Significant coefficients on survey wave × traitinteraction terms signal temporal instability, and thus column entries are counts of statistically insignificant interactions.In the second and fourth columns of results, categorization of effects as consistent is based on odds ratios for the fullrange of the independent variables, with consistency defined to exist if 0.66 < odds ratio < 1.50.*Number of successes is at or below the critical value given the number of tests; thus, the null hypothesis of temporalconsistency is not supported. Critical values are obtained via the binomial distribution, assuming a 0.90 probability of asuccessful test (i.e., a true null will be retained). Cutoffs less than or equal to 0.05 are used. For instance, given 27 testsand a 0.90 probability of an individual test being successful, there is less than a 0.05 probability that outcomes between 0and 21 will be observed when the null is true.

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Conclusions

Personality traits are presumed to provide a firm grounding for attitudes and behavior, creatinga central tendency that persists across changes in time and circumstance. It is this quality ofpersonality that has drawn the interest of political psychologists conducting applied research (e.g.,Mondak, 2010, p. 6).14 To gauge the consistency of personality effects, scholars typically haveapproached the matter indirectly by testing whether personality itself remains stable over time orwhether personality effects are similar across related sets of dependent variables. A different strategyhas been introduced in the present study. Using panel data, we have examined whether the Big Fiveexert consistent effects on 41 attitudinal and behavioral dependent variables measured at two-yearand four-year intervals. Coefficient estimates for measures of the Big Five were found to be highlyconsistent. Values on the dependent variables were less stable, with correlations between the threereadings averaging 0.51. Hence, it appears that attitudes and self-reported behaviors change despitethe inertial pull of personality. Personality works to anchor attitudes and behavior, bringing anelement of constancy to human behavior.

A limitation of this study is that consistency is gauged over, at most, a four-year span. However,attitudes are notoriously unstable even within two-year time frames (e.g., Converse, 1964). Also, inthe current study, personality effects exhibited higher levels of consistency than did the effects of age.Thus, notwithstanding the limits of a two-year and four-year panel, it is reasonable to conclude thatpersonality effects are characterized by high levels of relative consistency.

ACKNOWLEDGMENTS

This study examines data from the British Household Panel Survey. These data are madeavailable by the UK Data Service. The authors thanks Jake Bowers, Justin Esarey, and Brian Gainesfor their helpful advice, and this journal’s editors and anonymous reviewers for their instructivecomments on earlier drafts of this article. Correspondence concerning this article should be sent toJeffery Mondak, 420 David Kinley Hall MC-713, 1407 W. Gregory Drive, Urbana, IL 61801. E-mail:jmondak@illinois.edu

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Temporal Consistency of Personality Effects 9

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