Aggressive driving in Romania: Psychometric properties of the Driving Anger Expression Inventory

Transportation Research Part F 15 (2012) 556–564

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Aggressive driving in Romania: Psychometric properties of the Driving Anger Expression Inventory Paul Sârbescu ⇑ West University of Timisßoara, Bld. V. Pârvan, 4, 300233 Timisßoara, Romania University of Bucharest, Bld. M. Koga˘lniceanu, 36-46, 050107 Bucharest, Romania

a r t i c l e

i n f o

Article history: Received 13 February 2012 Received in revised form 14 April 2012 Accepted 31 May 2012

Keywords: Aggressive driving Reliability Validity DAX

a b s t r a c t The present research verified the psychometric properties of the Driving Anger Expression Inventory (DAX; Deffenbacher, Lynch, Oetting, & Swaim, 2002) on a Romanian sample (n = 262). Exploratory factor analysis revealed a three-factor structure: Verbal and Physical Aggressive Expression (a = .86), Adaptive/Constructive Expression (a = .88) and Using the Vehicle for Aggressive Expression (a = .83). The aggressive forms of expressing anger were summed up in the Total Driving Aggressive Expression Index (a = .90). Confirmatory factor analysis supported the three factors solution, by showing a good fit into a 30 items version of the DAX. All the aggressive forms of anger expression correlated positively with each other and with trait Aggression-Hostility, while the adaptive way of expressing anger correlated negatively with them, thus supporting the convergent validity of the DAX. Age differences were identified, in that younger drivers scored higher in Using the Vehicle for Aggressive Expression scale and in Total Driving Aggressive Expression Index, while older drivers scored higher in Adaptive/Constructive Expression scale. Several directions for future research concerning the DAX are suggested. Overall, our results showed that the Romanian version of the DAX is a valid tool for assessing aggressive driving. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Nearly two decades ago, a new concept called ‘‘driving anger’’ appeared in the research field of Traffic and Transportation Psychology. It was defined as a situation-specific form of trait anger (Deffenbacher, Oetting, & Lynch, 1994). Until now, many studies have linked driving anger to risky driving behaviour (Dahlen, Martin, Ragan, & Kuhlman, 2005; Dahlen & White, 2006; Iversen & Rundmo, 2002; Lajunen & Parker, 2001; Sullman, 2006) and to implication in road accidents (Constantinou, Panayiotou, Konstantinou, Loutsiou-Ladd, & Kapardis, 2011; Dahlen, Edwards, Tubré, Zyphur, & Warren, 2012; Dahlen & Ragan, 2004; Deffenbacher, Filetti, Richards, Lynch, & Oetting, 2003). In Romania, driving anger and its forms of expression appear to be serious problems. In a recent study performed on a national sample of 1.119 active drivers, the following driving anger outcomes emerged: 78.70% reported horns and flashes, 48.80% reported threatening or obscene gestures, 43.50% reported verbal aggressions, 39.10% reported having their car blocked in a parking lot, 6% reported traffic chases and 3.30% reported physical aggression (Traffic Direction, 2011). Therefore, research is needed in order to verify the way Romanian drivers express their anger while driving. Because of the need to measure driving anger, several instruments have been developed and investigated among different samples and in different cultures. One of the most frequently used instrument for assessing driving anger as a personality trait related to a general anger trait is the Driving Anger Scale (DAS; Deffenbacher et al., 1994). The DAS has been adapted ⇑ Address: Psychology Department, West University of Timisßoara, Bld. V. Pârvan, 4, 300233 Timisßoara, Romania. Tel.: +40 726 863 806. E-mail address: [email protected] 1369-8478/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trf.2012.05.009

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for research use in UK (Lajunen, Parker, & Stradling, 1998), Turkey (Yasak & Esiyok, 2009), Spain (Sullman, Gras, Cunill, Planes, & Font-Mayolas, 2007), France (Delhomme & Villieux, 2005) and New Zealand (Sullman, 2006). The convergent validity of the DAS was supported by its correlations with the Propensity for Angry Driving Scale (DePasquale, Geller, Clarke, & Littleton, 2001) (Dahlen & Ragan, 2004), and with trait anger (van Rooy, Rotton, & Burns, 2006; Villieux & Delhomme, 2010). One important issue that emerged in the study of trait driving anger is the need to examine the ways of expressing this emotion. This is particularly important because two drivers reporting the same amount of anger can respond to the same anger-provoking situation differently (Herrero-Fernández, 2011). Thus, in order to assess the usual ways that drivers express their anger in driving context, the Driving Anger Expression Inventory (DAX) was developed (Deffenbacher, Lynch, Oetting, & Swaim, 2002). The DAX measures four forms of expressing anger (the first three are aggressive forms of anger expression): Verbal Aggressive Expression (12 items, a = .88), which assess the verbal ways of expressing anger (item example: ‘‘I make negative comments about the other driver.’’); Physical Aggressive Expression (11 items, a = .84), which assess the physical forms of expressing anger (item example: ‘‘I give the other driver the finger.’’); Using the Vehicle for Aggressive Expression (11 items, a = .86), which assess the ways drivers use their vehicle for expressing anger (item example: ‘‘I try to cut in front of the other driver.’’); and Adaptive/Constructive Expression (15 items, a = .90), which assess the adaptive forms of expressing anger (item example: ‘‘I try to think of positive solutions to deal with the situation.’’). The three aggressive forms of expressing anger can be added together into the Total Driving Aggressive Expression Index (a = .90). The validity of the DAX has been verified in several studies, which basically reported similar results: the convergent validity of the DAX was supported by the high correlations between all aggressive forms of expressing anger and also with the DAS, but negatively with the adaptive form of expressing anger (Dahlen & Ragan, 2004; Deffenbacher, Lynch, Deffenbacher, & Oetting, 2001). The incremental validity of the DAX was supported by the variance added by all four forms of expressing anger in predicting aggressive and risky behaviour, after controlling for demographic variables and trait anger (Deffenbacher, Kemper, & Richards, 2007; Deffenbacher, White, & Lynch, 2004). So far, the DAX has been validated in a few countries, and different factor structures were revealed. In Spain, a 53 items version was tested, and a five factor structure was identified, where the fifth factor was labelled Displaced Aggression (Herrero-Fernández, 2011). This factor was also found in the original study, but was discarded because of its low reliability (Deffenbacher et al., 2002). The authors concluded that this fifth factor seems to be characteristic for Spanish drivers. In France, the original 49 items DAX was applied, but the authors removed the Physical Aggressive Expression items because French drivers were unlikely to report expressing their anger in the manner described by this factor. They also found the Verbal Aggressive Expression factor to be problematic, and kept only the three nonverbal aggressive expression items. Finally, they found that an 11 items version seemed to be the most reliable, consisting of three factors: Nonverbal Aggressive Expression, Adaptive/Constructive Expression and Using the Vehicle for Aggressive Expression (Villieux & Delhomme, 2008). Thus, it appears that the structure of the DAX is susceptible to specific cultural modifications. It is worth mentioning that, to our knowledge, no study in Eastern Europe verified the factorial structure of the DAX. The characteristics of the Romanian transportation system (crowded roads, increasing number of cars, etc.) are quite similar to those of other Eastern European countries. Therefore, any possible differences that might emerge between the factorial structure of the DAX in Romania and other US or Western Europe countries could be of great importance, because of their possible replicability in other Eastern European countries. In Romania, researches in the field of Traffic and Transportation Psychology have low international visibility, mainly because of the lack of properly adapted instruments. So far, only the Dula Dangerous Driving Index (Dula & Ballard, 2003) has been validated (Iliescu & Ionescu, 2009), and different versions of the Manchester Driving Behaviour Questionnaire (Reason, Manstead, Stradling, Baxter, & Campbell, 1990) were adapted for research use (Havârneanu, Gheorghiu, & Hohn, 2010; Hohn, 2000; Sârbescu, accepted for publication). To our knowledge, neither the DAS nor the DAX have been properly validated in Romania. However, one study discussed the theoretical framework of aggressive driving (Anitßei, 2003), and another recent study used the DAX, but without verifying its factorial structure (probably because the aim of the study was to account for variance in the Total Driving Aggressive Expression Index, rather than to explore its factorial structure) (Sârbescu, Costea, & Rusu, 2012). The main objective of this study was to assess the psychometric properties and the factorial structure of the DAX on a Romanian sample. Also, this study verified the convergent validity of the DAX, by testing its relations with trait Aggression-Hostility from the Alternative Five-Factor Model (Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993). Last but not least, this study checked possible links between the DAX and self-reported road accidents, as well as the differences between younger and older drivers in ways of expressing their anger on the road. 2. Method 2.1. Participants The questionnaires were sent by email to 400 people, randomly selected from the active members of several auto forums in Romania. Of these, 303 (75.75%) were returned, and 41 were removed because of their high scores in our test validity measures (infrequency and social desirability). This removal was necessary in order to make sure that the results used for our analysis are neither extremely desirable, nor random. Setting the cut-off points for excluding was not very difficult because in the Infrequency scale (scores possible range form 0 to 10, M = 2.78, SD = 1.91) 86.47% of the original sample reported

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scores ranging from 0 to 6. The other 13.53% (41) of the respondents scored between 8 and 10. Out of them, 36 respondents also reported high scores (between 29 and 32) in the Social Desirability scale (scores possible range form 0 to 33, M = 15.65, SD = 5.14). Therefore, we decided to exclude those 41 respondents because their responses seemed to be extremely desirable and/or random. The final sample consisted of 262 participants, out of which 90.8% were male. The age of the respondents ranged from 18 to 45 years (M = 28.17, SD = 6.49). Although the research sample is not very large, it is adequate for identifying the factorial structure of the DAX, because it provides a participants-item ratio larger than 5 to 1 (Gorsuch, 1997; MacCallum, Widaman, Zhang, & Hong, 1999). The overwhelming majority of male sample is, to some extent, desirable, because of the following reasons: many authors suggested that the sample used for validating a psychometric instrument should consist of people similar to those with whom the scale will be ultimately used (Fabrigar, Wegener, MacCallum, & Strahan, 1999; Ford, MacCallum, & Tait, 1986; Gorsuch, 1997). In Romania, women represent about 30% of people who hold a driving license, and report lower driving frequency than men (National Statistical Institute, 2011). Thus, because in Romania there are more male than female drivers on the road, it appears that a predominant male sample (although not as unbalanced as ours) is desirable for this research. 2.2. Instruments 2.2.1. Driving Anger Expression Inventory (DAX) The DAX (Deffenbacher et al., 2002) contains 49 Likert scale items (1 = Almost Never, 4 = Almost Always), conceptualized to express the way people express their anger while driving. In its original version, four different types of expression could be distinguished: Verbal Aggressive Expression (Ver), Physical Aggressive Expression (Phy), Using the Vehicle for Aggressive Expression (Veh) and Adaptive/Constructive Expression (Adp). The adaptation of the DAX was done using the back-translation method. The items were translated into Romanian by a Romanian native, proficient in both languages. Afterwards, they were translated back into English by a professional translator, and compared with the original version of DAX (Deffenbacher et al., 2002). No major differences were identified. 2.2.2. Zuckerman–Kuhlman Personality Questionnaire (ZKPQ) The ZKPQ (Zuckerman et al., 1993) contains 99 true–false items and measures the following dimensions: NeuroticismAnxiety (N-Anx), Impulsive Sensation Seeking (ImpSS), Aggression-Hostility (Agg-Host), Activity (Act), Sociability (Sy), and Infrequency, which is a control scale used as a validity measure. The internal alpha consistencies ranged from .69 (for the Act scale) to .88 (for the N-Anx scale). 2.2.3. Marlowe–Crowne Social Desirability Scale (MCSDS) The MCSDS (Crowne & Marlowe, 1960) contains 33 true–false items measuring social desirability. Internal consistency was satisfactory (a = .78). 2.2.4. Demographic questions Beside the usual demographic data required in this type of research (age, gender, mileage, driving frequency), we requested information about the number of accidents in which the participants were involved. More specifically, we asked the participants to estimate the number of minor (accidents resulting in reduced damage to the car, but without any person being injured), and major accidents (accidents resulting in significant damage to the car, and/or with injured or deceased people) they have been involved in for the past 5 years. 2.3. Procedure This research was conducted via email, using randomly chosen members of Romanian auto forums as participants (approval from the forums’ main admin was received before the emails were sent). All of the questionnaires and the demographic questions were included in a Microsoft Office Word file, which was sent to the selected participants. 3. Results 3.1. Exploratory factor analysis The factorial structure of the DAX was checked through exploratory factor analysis (EFA). Before we present the EFA results, a few details need to be mentioned. According to Deffenbacher et al. (2002), three items from the Verb factor (items 11, 37 and 40) were conceptualised as non-verbal behaviours accompanying verbal aggression. Some authors (Villieux & Delhomme, 2008) found that the Verb factor was problematic, because the three items loaded on a different factor than the other Verb items, thus suggesting the distinction between verbal and non-verbal aggressive expression. In our initial EFA, most of the Verb and Phys items (including the three non-verbal items) loaded on a single factor, while all Adap and Veh items loaded on their expected factor. The forth factor has drawn our attention, because it was formed out

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of three items from the verb scale (items 14, 31 and 39). When inspecting the items’ content, we found that they all included the phrase ‘‘. . .under my breath.’’ in their construction (item example: ‘‘I swear at the other driver under my breath.’’). We believe the reason for the different factor loading of these three items is that, due to their formulation, they surprise a type of aggressive expression which is not reflected on other drivers. In the case of all other Verb, Phys and Veh items, the aggressive expression is definitely felt by other drivers. Thus, it appears that these items capture an indirect form of aggressive expression, rather than a direct one. Therefore, because the DAX is supposed to measure mainly direct aggressive expression (Deffenbacher et al., 2002), we decided to remove the three items from our future analysis. The analysis of all the 46 items (after excluding the three items from the Verb scale) was accomplished with Principal Axis Factoring followed by Direct Oblimin rotation. We used parallel analysis (Horn, 1965) for selecting the number of factors because it is one of the most accurate methods for determining the optimal factor solution, and it provides more reliable results than Kaiser’s criterion (eigenvalues greater than 1) (Fabrigar et al., 1999). Parallel analysis suggested the extraction of three factors, which explained 32.45% of the total variance. The first factor consisted of the Verb and Phys items, and was labelled Verbal and Physical Aggressive Expression; it accounted for 20.09% of the explained variance. The next two factors

Table 1 Results of the EFA. Item nr.

A priori scale

it_5 it_6 it_9 it_11 it_28 it_37 it_38 it_40 it_43 it_1 it_8 it_10 it_12 it_13 it_17 it_18 it_20 it_21 it_34 it_41 it_2 it_3 it_4 it_7 it_15 it_16 it_19 it_22 it_27 it_33 it_46 it_23 it_24 it_25 it_26 it_29 it_30 it_32 it_35 it_36 it_42 it_44 it_45 it_47 it_48 it_49

Verb Verb Verb Verb Verb Verb Verb Verb Verb Phys Phys Phys Phys Phys Phys Phys Phys Phys Phys Phys Veh Veh Veh Veh Veh Veh Veh Veh Veh Veh Veh Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap Adap

% Variance

F I: Verb&Phys

F II: Adap

F III: Veh

.79 .59 .55 .38 .57 .16 .70 .36 .29 .58 .64 .75 .52 .13 .20 .51 .31 .28 .61 .49 .29 .24 .26 .37 .33 .41 .39 .54 .18 .44 .43 .09 .00 .19 .22 .31 .23 .03 .08 .21 .02 .00 .33 .08 .31 .09

.16 .04 .00 .17 .14 .24 .08 .03 .25 .13 .14 .10 .14 .01 .07 .13 .08 .19 .14 .01 .14 .11 .15 .04 .19 .21 .10 .15 .04 .16 .15 .51 .30 .51 .65 .60 .66 .37 .71 .73 .49 .40 .53 .44 .69 .56

.33 .45 .37 .34 .35 .28 .40 .40 .24 .26 .34 .31 .31 .09 .02 .43 .41 .50 .38 .16 .65 .58 .59 .47 .47 .58 .52 .52 .61 .55 .63 .30 .00 .26 .25 .31 .28 .09 .14 .25 .06 .03 .24 .00 .18 .16

20.09

8.63

3.73

h2 .62 .39 .33 .19 .34 .16 .50 .21 .19 .34 .41 .57 .28 .02 .05 .31 .19 .27 .38 .25 .43 .34 .35 .25 .25 .38 .30 .39 .38 .34 .43 .33 .09 .30 .45 .43 .48 .16 .51 .56 .25 .17 .35 .20 .53 .32

Notes: Verb&Phys = Verbal and Physical Aggressive Expression, Veh = Using the Vehicle for Aggressive Expression, Adap = Adaptive/ Constructive Expression, h2 = Communality.

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Table 2 Goodness of fit statistics for the six models of DAX. Model 49 49 49 46 30 30 30 ** a b

items, items, items, items, items, items, items,

one factor four factorsa four factorsb four factorsb one factor three factorsa three factorsb

v2

df

GFI

AGFI

RMSEA

IFI

CFI

3991.25** 331.39** 2967.16** 2254.89** 1619.31** 905.01** 590.60**

1127 1127 1121 983 405 405 393

.53 .66 .68 .73 .60 .82 .87

.49 .63 .65 .70 .54 .79 .85

.099 .086 .079 .070 .107 .069 .044

.42 .56 .63 .71 .59 .83 .93

.42 .56 .62 .71 .58 .83 .93

[.095–.102] [.083–.090] [.076–.083] [.067–.074] [.102–.113] [.063–.075] [.036–.051]

p < .01. The DAX factors are uncorrelated with each other. The DAX factors are correlated with each other.

Table 3 Means, standard deviations skewness, kurtosis and a coefficients of the Romanian version of the DAX. Scale

M

SD

Verb&Phys Veh Adap DAE

1.43 1.40 2.27 1.41

.43 .41 .72 .37

Skewness 1.58 1.36 0.17 1.35

Kurtosis 3.43 1.88 0.83 2.11

Cronbach’s a .86 .83 .88 .90

Notes: Verb&Phys = Verbal and Physical Aggressive Expression, Veh = Using the Vehicle for Aggressive Expression, Adap = Adaptive/Constructive Expression, DAE = Total Driving Aggressive Expression Index.

were Adaptive/Constructive Expression and Using the Vehicle for Aggressive Expression, which accounted for 8.63% and 3.73% of the explained variance, respectively. The results of the EFA are presented in Table 1. All Adap items had their primary loadings on the expected factor. One Veh item (22) loaded strongly on the Verb&Phys factor. Also, three Verb&Phys items (20, 21 and 37) had their primary loading on the Veh factor. 3.2. Confirmatory factor analysis In order to verify the adequacy of the identified factor structure, confirmatory factor analysis (CFA) was used. Seven different models were compared. Firstly, we tested the original 49 items form, with three possible solutions: one factor, four uncorrelated factors and four correlated factors. Then, we excluded the three problematic Verb items, and tested a 46 items form, with four correlated factors. Afterwards, we excluded items with low factor loadings (<.30), and verified the content of item pairs with high correlations among them (r > .30). This content analysis showed that they were somewhat redundant and, as suggested by several authors, one item from each pair was removed (Aluja, García, & García, 2003). We obtained a 30 items model (10 items for each factor1), with three possible solutions: one factor, three uncorrelated factors and three correlated factors. Table 2 presents the results of the CFA. All 49 items models showed very poor fit, the weakest being achieved by the one factor solution. The 46 items model showed improved fit, but none of the indices (except RMSEA) reached at least an acceptable level. The 30 items models showed improved fit, with the exception of the one factor solution. The best fit was achieved by the three correlated factor solution, of the 30 item model. All indices reached at least acceptable level, and some of them (CFI, IFI and RMSEA) showed optimal values. Thus, it appears that a 30 items model with three correlated factors seems to be the most representative solution. 3.3. Scale statistics Table 3 summarizes means, standard deviations, skewness, kurtosis, and Cronbach’s a coefficients. The internal consistencies ranged from .83 (for the Veh scale) to .88 (for the Adap scale), while the Total Driving Aggressive Expression Index (DAE) showed very good reliability (a = .90). The obtained alphas are similar and even higher than those reported by other authors (Herrero-Fernández, 2011; Villieux & Delhomme, 2010). The skewness and kurtosis values indicate that the distributions do not deviate substantially from normality.

1

Verb&Phys items: 1, 5, 6, 8, 9, 10, 12, 18, 34, 38. Veh items: 2, 4, 7, 15, 16, 19, 21, 22, 33, 46. Adap items: 23, 25, 26, 29, 30, 35, 36, 45, 48, 49.

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P. Sârbescu / Transportation Research Part F 15 (2012) 556–564 Table 4 Pearson correlations between the DAX and the ZKPQ scales. Scale

Verb&Phys

Verb&Phys Veh Adap DAE Act Agg-Host ImpSS N-Anx Sy

–

Veh

.61** .31** .90** .01 .50** .19** .08 .12*

Adap

DAE

Act

Agg-Host

ImpSS

N-Anx

– .30** .30** .02

– .02 .27**

–

– .38** .89** .09 .33** .11 .06 .06

– .39** .20** .37** .07 .16** .05

– .06 .46** .17** .08 .11

– .08 .22** .23** .23**

.22**

Notes: Verb&Phys = Verbal and Physical Aggressive Expression, Veh = Using the Vehicle for Aggressive Expression, Adap = Adaptive/Constructive Expression, DAE = Total Driving Aggressive Expression Index, Act = Activity, Agg-Host = Aggression-Hostility, ImpSS = Impulsive Sensation Seeking, N-Anx = Neuroticism-Anxiety, Sy = Sociability. * p < .05. ** p < .01.

Table 5 Spearman correlations between DAX scales and accident types. Scale

Verb&Phys

Verb&Phys Veh Adap DAE Minor accidents Major accidents

–

Veh

.62** .31** .90** .16** .13*

Adap

DAE

Minor accidents

– .16** .11

– .17**

– .37** .89** .14* .07

– .38** .04 .05

Notes: Verb&Phys = Verbal and Physical Aggressive Expression, Veh = Using the Vehicle for Aggressive Expression, Adap = Adaptive/Constructive Expression, DAE = Total Driving Aggressive Expression Index. * p < .05. ** p < .01.

Table 6 ANCOVA results for age differences. Scale

Age 18–23 (n = 71)

Verb&Phys Drv.Freq Veh Drv.Freq Adap Drv.Freq DAE Drv.Freq

24–30 (n = 113)

F

Partial g2

2.28 10.91** 5.47** 11.45** 8.54** 3.06 4.61* 14.09**

.02 .04 .04 .04 .06 .01 .03 .05

31–45 (n = 78)

EM

M

SD

EM

M

SD

EM

M

SD

14.71

14.52

4.74

14.55

14.52

4.21

13.39

13.60

3.95

14.64c

14.45

3.96

14.45c

14.42

4.56

12.74ab

12.95

3.13

23.87c

24.04

7.54

26.11c

26.13

6.86

28.68ab

28.49

6.77

29.35c

28.97

7.80

29.00c

28.95

7.91

26.13ab

26.55

6.30

Notes: EM = Estimated Mean, Verb&Phys = Verbal and Physical Aggressive Expression, Veh = Using the Vehicle for Aggressive Expression, Adap = Adaptive/ Constructive Expression, DAE = Total Driving Aggressive Expression Index, Drv.Freq = Driving Frequency. Differences by age: 18–23 (a), 24–30 (b) and 31– 45 (c), according to the Bonfferoni Post Hoc test. * p < .05. ** p < .01.

3.4. Correlation matrices The correlation matrix between the DAX and the ZKPQ scales is presented in Table 4. Correlations ranged from .39 to .90. The highest inter-scale correlations were reported between Agg-Host and Verb&Pshys (.50), between Agg-Host and DAE (.46), and between Agg-Host and Adap ( .37). Table 5 presents the correlation matrix between the DAX scales and the types of accidents reported by the participants. Because the distribution of both minor and major types of accidents deviate substantially from normality (skewness > 2, kurtosis > 7), Spearman’s q was used to calculate the correlation matrix. Small intensity correlations were reported between Verb&Phys and both types of accidents (.16 for minor, .13 for major), and between Veh and minor accidents (.14). Also, the DAE positively correlated with minor accidents (.16).

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3.5. Differences by age In order to verify age differences regarding aggressive expression, an analysis of covariance (ANCOVA) was conducted. The age groups included 18–23, 24–30 and 31–45 aged participants. The covariate included in the analysis was driving frequency. Although usually covariates are interval variables, some authors argue that ordinal variables can be used successfully as covariates, as long as they use a Likert scale that has at least five values (Pedhazur & Schmelkin, 1991). In this case, driving frequency was measured on a six-point Likert scale, therefore being suitable for this analysis. The results of the ANCOVA are displayed in Table 6. The results show that no significant differences were identified on the Verb&Phys factor. Instead, significant differences between participants were revealed for the Veh and Adap factors, as well as for the DAE. The Bonfferoni Post Hoc test showed that differences were identified between the 18–23 and the 31–45 age groups, and between the 24–30 and the 31–45 age groups, with small to moderate effect sizes (.02–.06). No significant differences were noticed between the 18–23 and the 24– 30 age groups. Driving frequency had a significant effect on all forms of aggressive driving expression, but not on the adaptive form of expression. 4. Discussion The main goal of this research was to assess the psychometric properties and the factorial structure of the DAX on a Romanian sample. Exploratory factor analysis identified a three factor structure, where the first factor consisted of the original Verbal and Physical Aggressive Expression factors, while the next two factors were Adaptive/Constructive Expression and Using the Vehicle for Aggressive Expression. Confirmatory factor analysis revealed a good fit of these three factors in a 30 items version of the DAX. All factors had optimal reliability (a > .80), while the Total Driving Aggressive Expression Index showed even better reliability (a = .90). To some extent, the unification of the two factors is not very surprising: several studies reported the correlation between the Verbal and Physical Aggressive Expression factors as one of the highest correlation among all forms of aggressive expression (Deffenbacher et al., 2002; Esiyok, Yasak, & Korkusuz, 2007; Herrero-Fernández, 2011). Also, there are several explanations for the unification of the Verbal and Physical Aggressive Expression factors in our sample. Firstly, it is very likely that the two types of aggressive expression tend to manifest consecutively, or even simultaneously. For example, in is not a very unusual thing (at least in Romania) to see a driver rolling down his window and starting yelling and cursing at another driver (verbal aggressive expression), and then making obscene gestures at the same driver (physical aggressive expression). Secondly, the effects of the aggressive expression on the target are very similar in both cases. More specifically, no physical damage is done to the respective driver, or to its vehicle. Thus, the Verbal and Physical Aggressive Expressions differ from Using the Vehicle for Aggressive Expression, in which case serious damage can occur on the target’s vehicle. At this point we can say that, in comparison with previous researches concerning the DAX, the unification of the Verbal and Physical Aggressive Expression factors represents the main different finding. Also, because (as stated in Section 1) traffic conditions in Romania aren’t very different from the ones in other Eastern Europe countries, this shortened and very robust version of the DAX could prove itself to be more reliable than the original one in other studies regarding the factorial structure of the DAX, in Eastern Europe countries. Also, unlike the French DAX which is shorter because the Verbal and Physical Aggressive Expression factors were totally removed (as a specific cultural-related modification), this version of the DAX measures all the four forms of aggressive expression initially identified (Deffenbacher et al., 2002), but simply unites the two most similar forms of aggressive expression into one unitary factor, thus resulting in a three factor structure. The results also support the convergent validity of the DAX. The two aggressive forms of expressing anger correlated positively between them at high intensity, and negatively correlated with the adaptive/constructive form of expressing anger, at moderate intensity. Also, both aggressive forms of expressing anger and the Total Driving Aggressive Expression Index correlated positively with trait Aggression-Hostility, at moderate intensity, while a similar magnitude but negative correlation was identified between trait Anger-Hostility and the adaptive/constructive form of expressing anger. Also, small intensity correlations were found between the aggressive forms of expressing anger and self-reported accidents. Because a few studies have identified somewhat stronger connections between aggressive driving and a mixture of driving outcomes, including crashes and tickets (Dahlen et al., 2012), but other studies failed to identify such relations (van Rooy et al., 2006), future research is necessary in order to verify the links between aggressive driving and specific driving outcomes in Romania. Identified age differences are, to some extent, similar to those reported by other authors (Esiyok et al., 2007; HerreroFernández, 2011). Younger drivers (<30 years) scored higher than older drivers on Using the Vehicle for Aggressive Expression scale, and on Total Driving Aggressive Expression Index. Unlike in the Spanish or Turkish samples, no significant differences were identified in Verbal and Physical Aggressive Expression, and older drivers scored higher than younger drivers on Adaptive/Constructive Expression scale. Small to moderate effect sizes were noticed. But the overall picture of age differences is incomplete, because no driver older than 45 years participated in our study. Thus, future research is necessary in order to outline the entire portrait of age differences in Romania, regarding the ways of expressing anger in traffic. Regarding gender differences, the results obtained so far are not definite. While some authors identified gender differences, but with small effect sizes (Deffenbacher et al., 2002; Esiyok et al., 2007), others failed to identify such differences

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(Herrero-Fernández, 2011). Because of the low number of female participants in our research, gender differences could not be calculated. Although, as explained before, a predominantly male sample was desirable for our research, future researches should use more balanced samples, in order to capture the possible gender differences regarding aggressive driving, in Romania. In conclusion, the Romanian version of the DAX is a valid and reliable tool for assessing driving anger expression forms. Exploratory and confirmatory factor analysis supported a three factor structure, with the first factor labelled Verbal and Physical Aggressive Expression, and the other ones labelled Adaptive/Constructive Expression and Using the Vehicle for Aggressive Expression. The internal consistencies were at optimal level, and all DAX scales also showed good convergent validity. Thus, the Romanian version of the DAX can be successfully used in future researches in the field of Traffic and Transportation Psychology research. Also, being the first reported study in an Eastern Europe country concerning the ways people express their anger while driving, this research raises questions regarding whether the three factor structure identified (with the Verbal and Physical Aggressive Expression factors united) could be more representative than the original four factor structure, for Eastern Europe.

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