angry thoughts and daily emotion logs: validity of the angry cognitions scale

ORI GIN AL ARTICLE

Angry Thoughts and Daily Emotion Logs: Validityof the Angry Cognitions Scale

Ryan C. Martin • Lauren E. Vieaux

Published online: 28 September 2013

� Springer Science+Business Media New York 2013

Abstract The Angry Cognitions Scale (ACS; Martin and Dahlen in J Ration Emot

Cogn Behav Therapy 25:155–173, 2007) was designed to measure six cognitive

processes related to anger: misattributing causation, overgeneralizing, catastro-

phizing, demandingness, inflammatory labeling, and adaptive processes. Pre-

liminary evidence on the reliability of the ACS has been positive, demonstrating

that the ACS is correlated with the experience and expression of anger, anger

consequences, hostile thoughts, and responses to provocation (Martin and Dahlen in

J Ration Emot Cogn Behav Therapy 25:155–173, 2007; J Ration Emot Cogn Behav

Therapy, 29:65–76, 2011). This previous research, however, has suffered from

limitations associated with demand characteristics and the reliance on retrospective

data. The current study sought to address these limitations by using daily emotion

logs. Results showed that the ACS predicted the daily experience of anger and anger

related consequences, contributing to the literature on the ACS as a valuable

measure of anger-related cognitions.

Keywords Anger � Cognition � Assessment � Emotion noted

Introduction

Though anger is a commonly experienced emotion, it is well known that it can have

highly negative repercussions when experienced too often or when expressed in

maladaptive ways. In fact, anger is known to be associated with a number of

R. C. Martin (&)

Departments of Human Development and Psychology, University of Wisconsin-Green Bay, Mary

Ann Cofrin Hall C318, 2420 Nicolet Dr., Green Bay, WI 54311-7001, USA

e-mail: [email protected]

L. E. Vieaux

Psychology Department, University of Wisconsin-Green Bay, Green Bay, WI, USA

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J Rat-Emo Cognitive-Behav Ther (2013) 31:219–230

DOI 10.1007/s10942-013-0171-2

negative correlates, such as health problems, other emotional problems like

depression and anxiety, occupational dysfunction, a decrease in social support, and

aggression (Bruehl et al. 2012; Dahlen and Martin 2005; Deffenbacher 1992; Eiden

et al. 2012; Helmers et al. 1994; Sirois and Burg 2003). Consequently, dysfunctional

anger is a problem frequently dealt with in therapy.

Over the last 10 years, the field has seen a growing body of literature on the value

of cognitive-behavioral therapy for maladaptive anger (Deffenbacher 2006;

DiGiuseppe and Tafrate 2007; Loehman et al. 2012; Sukhodolsky and Scahill

2012). Interestingly, however, until recently, there have been few measures of

anger-related cognitions to assist researchers and clinicians in better understanding

the thoughts commonly experienced by people when angry. Those measures of

anger-related cognitions that do exist tend to focus on some of the thoughts that

people have once they are angry. For instance, the Anger Rumination Scale

(Sukhodolsky et al. 2001) measures the tendency to ruminate about an event after it

has occurred. Likewise, the newly developed Anger Regulation and Expression

Scales (DiGiuseppe and Tafrate 2011) measure cognitive styles like bitterness and

suspiciousness. Finally, the Hostile Automatic Thoughts Scale (Snyder et al. 1997)

measures the types of antagonistic thoughts people have when angry. None of these

instruments measure the primary types of cognitive errors outlined in the cognitive

literature on the treatment of problematic anger (e.g., Beck 1999; Deffenbacher

1996; Dryden 1990; Ellis 1977). Such a measure would assist in understanding the

relationship between the thoughts, behaviors, and emotions of angry people.

The Angry Cognitions Scale (ACS; Martin and Dahlen 2007) was developed to

measure five types of cognitive processes described in the cognitive literature on the

treatment of anger problems (e.g., Beck 1999; Deffenbacher 1996; Dryden 1990;

Ellis 1977): (1) overgeneralizing, (2) inflammatory labeling, (3) demandingness, (4)

catastrophic evaluation, and (5) misattributing causation. These five cognitive

processes have been demonstrated in multiple studies to be important in predicting

the experience and expression of anger in adults (e.g., Eckhardt and Jamison 2002;

Eckhardt and Kassivove 1998; Martin and Dahlen 2011; Martin and Dahlen 2007;

Martin and Dahlen 2004; Mizes et al. 1990; Tafrate et al. 2002). However, until the

development of the ACS, limited means of assessing anger-related cognitive

processes has been a significant hurdle to research and clinical work.

Two previous studies by Martin and Dahlen (2007, 2011) provided preliminary

evidence of support for the construct validity of the ACS. In the first (2007), they found

that all five cognitive processes measured by the ACS were related to the experience

and expression of anger, the experience of negative anger-related consequences, and

the experience of hostile thoughts. Likewise, the adaptive processes subscale of the

ACS was related to anger control, positive thoughts, and negatively correlated with the

experience and maladaptive expression of anger. These findings were replicated in the

second study (2011) which also tested the temporal stability of the ACS and found that

the ACS predicted anger and hostile thoughts in response to a provocation.

These initial findings have been promising and support the ACS as a valid

measure of anger-related cognitive processes. However, these initial studies have

not been without limitations. First, the research on the ACS has relied largely on

retrospective data with regard to negative anger-related consequences. Specifically,

220 R. C. Martin, L. E. Vieaux

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the Anger Consequences Questionnaire (Deffenbacher et al. 1996), which asks

participants how many times they have experienced certain negative consequences

in the last month as a result of their anger (e.g., negative emotions, physical fights),

was used in both studies. Though there is a significant literature base supporting the

Anger Consequences Questionnaire as a valid measure of anger consequences

(Dahlen and Martin 2006; Deffenbacher et al. 1996), there is also reason to believe

that retrospective data, particularly for such a long period of time as 1 month, is less

accurate than other means when it comes to behavioral checklists. For instance,

participants are less likely to be able to give an accurate count of how many times

they may have engaged in a particular behavior (e.g., yelled at someone) in the last

month than if they were asked about the last day.

Likewise, early research on the ACS may have suffered from problems related to

demand characteristics. In both of the previous ACS projects, participants were

likely able to surmise early on in the data collection that the focus of the study was

on anger as the instructions and the items for several of the questionnaires, including

the ACS, contain references to anger. Previous research on demand characteristics

has shown that when participants are aware of the purpose of a study, they respond

in ways that are consistent with that purpose (Nichols and Maner 2008).

The current study seeks to address these two limitations by exploring the value of the

ACS in predicting the experience and expression of anger through the use of daily

emotion logs. By using daily emotion logs, it was possible to better disguise the purpose

of the study as being about emotion in general, rather than about anger specifically, and

to limit the potential problems of retrospective data by asking participants about anger

consequences at the end of each day rather than the end of each month.

Method

Participants

One hundred and eighty-one (158 female, 23 male) participants were recruited from

undergraduate psychology and human development courses (Mdn age = 19) at a

public school in the Midwest. Students received course credit for their participation.

Instruments

Angry Thoughts

Anger-related cognitive processes were measured using the 54-item Angry

Cognitions Scale (ACS; Martin and Dahlen 2007). The ACS has six, nine-item,

subscales: misattributing causation (MC; attributing the cause of an event to the

wrong source), overgeneralizing (OV; using broad language like ‘‘always’’ or

‘‘never’’ in your interpretation of events), inflammatory labeling (IL; labeling others

in highly derogatory or offensive ways), demandingness (DE; asserting your own

needs as more important than the needs of others), catastrophic evaluating (CE;

elevating the degree to which an event is negative), and adaptive processes (AP;

Angry Cognitions Scale 221

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those thoughts that are associated with anger reduction). Scores for each subscale

range from 9 to 45. Higher scores indicate greater endorsement of the thought type.

A total maladaptive processes score (TM) can be created by combining the five

maladaptive thought types. The total maladaptive processes score ranges from 45 to

225. The ACS has demonstrated acceptable internal consistency (a[ .70) and

6-week, test–retest, reliability (r [ .60; Martin and Dahlen 2011). Subscales are

correlated with trait anger, outward anger expression, many adverse consequences

of anger, and angry response to provocation (Martin and Dahlen 2007, 2011).

Anger Experience and Expression

The State-Trait Anger Expression Inventory-2 (STAXI-2; Spielberger 1999) was

used to measure the experience and expression of anger. The STAXI-2 has six

subscales. First, the 10-item trait anger scale (T-Ang) was used to measure

participants’ tendency to experience anger. Next, the anger expression and control

scales were used. Anger expression-out (AX-O) measures the outward expression of

anger. Anger expression-in (AX-I) reflects the tendency to turn anger inwards by

suppressing anger. The two anger control scales, anger control-out (AC-O) and

anger control-in (AC-I), reflect different strategies for controlling anger. AC-O

reflects controlling anger by preventing the expression and AC-I reflects the use of

strategies like deep breathing to calm down. The sixth subscale, the state anger

scale, was not used in this study. The STAXI-2 uses Likert-type items with higher

scores indicating greater experience. The validity of the STAXI-2 has been

established through correlations with various measures of anger and hostility

(Deffenbacher 1992; Spielberger 1999).

Daily Emotion Log

The daily emotion log consisted of 24 items, to be completed at the end of each day.

It had three parts. First, four mood states (i.e., happy, sad, angry, fearful) were

measured using a brief version of Izard’s (1972) Differential Emotions Scale.

Participants were presented with one 5-inch line for each mood state. The lines were

labeled ‘‘not at all’’ on one end, ‘‘moderately’’ in the middle, and ‘‘very much’’ at

the other end and were marked at 1-inch intervals with 0, 20, 40, 60, 80, and 100.

Participants were instructed to mark the line at the point indicating the amount of

the emotion they felt that day.

Second, participants were asked to describe the most emotional situation they had

experienced that day, the most prominent emotion they experienced during that

situation, and to rate how emotional they had been during that experience on a scale

from 0 (‘‘very little’’) to 100 (‘‘as much as could be’’).

Finally, participants were provided with a 17-item checklist of experiences they

might have had that day (e.g., used a substance, drove dangerously). Several of

these items were taken from the Anger Consequences Questionnaire (ACQ;

Deffenbacher et al. 1996) and reflect the six consequence subscales found in Dahlen

and Martin’s (2006) revision of the ACS: Negative emotions, aggression, drug use,

self-harm, risky driving, and damaged friendships. It included other items, however,


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to distract participants from the focus on anger (e.g., cried out of happiness, laughed

out of joy). Although the ACQ has not been used in this format (i.e., as a daily

emotion log), it has demonstrated impressive validity as a measure of anger

consequences through its relationships with measures of trait anger and anger

expression (Dahlen and Martin 2006).

Procedures

Participants signed up online through a research management system to participate

in this study and received credit in their introductory psychology or human

development course for their participation. Participants were emailed by a

researcher 6 days before the time slot they had signed up for with five copies of

the emotion log attached to the email. They were instructed to complete one

emotion log at the end of each day for five consecutive days and were sent email

reminders each day. On the sixth day, participants brought their emotion logs to an

in-person data collection with approximately 20–30 other participants. At this data

collection, they completed the ACS, the STAXI-2, and a demographic question-

naire. The participants were asked to link their emotion logs to the questionnaire

packet before they turned them in. Though it is possible participants could have

simply completed all 5 days’ worth of emotion logs on the last day, they were

reminded of the emotion log each day to try and minimize that possibility.

Results

Preliminary Analyses

Alpha coefficients for the ACS subscales demonstrated acceptable consistency with

alpha coefficients ranging from .68 (MC) to .82 (AP). As previous research has

identified gender differences on several of the variables, a one-way (gender)

MANOVA was computed on all variables. There were no multivariate gender

effects, F(22, 147) = 1.13, p = .33. Means and standard deviations are reported for

all variables in Table 1.

Primary Analyses

Correlations

Bivariate correlations were calculated to assess the relationships among the

subscales of the ACS, STAXI-2, and variables measured by the emotion logs (see

Table 2). The five maladaptive subscales and the total maladaptive processes score

were shown to be positively correlated with T-Ang, AX-Out, and AX-In and

negatively correlated with AC-Out and AC-In. In contrast, the adaptive processes

subscale was negatively correlated with T-Ang, AX-Out, and AX-In and positively

correlated with AC-Out and AC-In.


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With regard to the emotion logs, the total maladaptive processes score and the

five maladaptive subscales were positively correlated with anger, sadness, and fear.

The only exception to this pattern was misattributing causation and fear, which were

uncorrelated. The AP subscale of the ACS was uncorrelated with the four average

daily emotions. All subscales but catastrophic evaluating and overgeneralizing were

correlated with the number of days in which anger was the most prominent emotion.

Finally, with regard to the six anger consequences, all subscales were correlated

in the expected directions with aggression. All subscales but misattributing

causation and adaptive processes were correlated with negative emotions. Likewise,

all but demandingness and adaptive processes were correlated with risky driving.

Table 1 Means, standard deviations, skewness, and kurtosis for all variables (N = 181)

Variable M SD S K

1. TM 116.44 24.49 -.23 -.31

2. MC 18.24 4.75 .13 -.76

3. CE 25.48 5.78 -.12 .17

4. OV 23.94 5.81 -.11 -.14

5. DE 27.72 5.39 -.41 -.02

6. IL 21.36 7.31 .48 .47

7. AP 29.84 6.72 -.16 .12

8. T-Ang 18.60 4.52 .90 .81

9. AX-Out 14.90 3.57 1.04 1.64

10. AX-In 17.79 4.40 .44 .22

11. AC-Out 24.33 4.66 -.26 -.67

12. AC-In 23.00 4.92 .03 -.59

13. Log: Anger 15.62 12.89 1.16 1.30

14. Log: Happy 70.24 14.72 -.23 -.50

15. Log: Sad 20.19 15.08 1.32 2.67

16. Log: Fear 12.60 13.54 2.36 9.56

17. Log: NE 3.47 2.64 1.47 2.04

18. Log: Agg .29 .46 2.25 5.72

19. Log: DU 1.42 3.44 3.77 15.43

20. Log: SH .34 .67 2.79 8.32

21. Log: RD .52 1.22 3.47 15.84

22. Log: DF .35 .78 3.42 16.74

23. Log: Days angry .81 .98 1.21 .98

TM total maladaptive, MC misattributing causation, CE catastrophic evaluating, OV overgeneralizing, DE

demandingness, IL inflammatory labeling, AP adaptive processes, T-Ang trait anger, AX-Out anger

expression-out, AX-In anger expression-in, AC-Out anger control-out, AC-In anger control-in, Log: Anger

average anger rating, Log: Happy average happiness rating, Log: Sad average sadness rating, Log: Fear

average fear rating, Log: NE negative emotion, Log: Agg aggression, Log: DU drug use, Log: SH self-

harm, Log: RD risky driving, Log: DF damaged friendships, Log: Days angry number of days anger was

the primary emotion

* p \ .05; ** p \ .01


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Only inflammatory labeling was correlated with damaged friendships, and nothing

was correlated with drug use or self-harm.

Regression Analyses

To determine which ACS subscales were most valuable in the prediction of anger-

related variables, stepwise multiple regressions were conducted. Trait anger, the

four anger expression scales, average daily anger rating, four of the anger

consequences, and the number of days anger was the primary emotion served as

dependent variables. Drug use and self-harm were not included in the analyses

because none of the ACS subscales were correlated with these two variables.

Table 2 Intercorrelations between the subscales of the ACS and all other variables (N = 181)

TM MC CE OV DE IL AP

1. TM –

2. MC .80** –

3. CE .90** .67** –

4. OV .89** .66** .81** –

5. DE .86** .62** .75** .76** –

6. IL .79** .53** .54** .58** .56** –

7. AP -.35** -.24** -.25** -.28** -.30** -.35** –

8. T-Ang .57** .42** .49** .50** .57** .42** -.33**

9. AX-Out .36** .28** .24** .31** .35** .32** -.29**

10. AX-In .42** .34** .45** .42** .34** .27** -.08

11. AC-Out -.44** -.35** -.31** -.39** -.37** -.40** .46**

12. AC-In -.40** -.32** -.32** -.36** -.30** -.35** .55**

13. Log: Anger .24** .19* .20** .22** .17* .23** -.14

14. Log: Happy -.33** -.25** -.31** -.31** -.23** -.27** .11

15. Log: Sad .25** .19* .24** .24** .19* .20** -.07

16. Log: Fear .19** .12 .20** .15* .17* .17* -.01

17. Log: NE .21** .12 .22** .19* .21** .15* -.04

18. Log: Agg .27** .18* .22** .21** .25** .26** -.18*

19. Log: DU .02 -.04 .03 .02 -.01 .05 .08

20. Log: SH .07 .04 .11 .27 .07 .08 -.00

21. Log: RD .21** .21** .19* .18* .12 .20** -.01

22. Log: DF .13 .12 .10 .06 .12 .21** -.10

23. Log: Days angry .16* .15* .11 .04 .16* .20** -.33**

TM total maladaptive, MC misattributing causation, CE catastrophic evaluating, OV overgeneralizing, DE

demandingness, IL inflammatory labeling, AP adaptive processes, T-Ang trait anger, AX-Out anger

expression-out, AX-In anger expression-in, AC-Out anger control-out, AC-In anger control-in, Log: Anger

average anger rating, Log: Happy average happiness rating, Log: Sad average sadness rating, Log: Fear

average fear rating, Log: NE negative emotion, Log: Agg aggression, Log: DU drug use, Log: SH self-

harm, Log: RD risky driving, Log: DF damaged friendships, Log: Days angry number of days anger was

the primary emotion

* p \ .05; ** p \ .01


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All regressions were significant at the p \ .01 level (results for all regressions are

presented in Table 3). The ACS predicted between 4 % (risky driving) and 34 %

(trait anger and anger control-in) of the variance in the dependent variables.

Between one and three ACS subscales emerged as useful predictors for each

dependent variable. With regard to which predictors were selected most often,

adaptive processes emerged as useful for five of the dependent variables,

inflammatory labeling emerged for four variables; overgeneralizing, demanding-

ness, and catastrophic evaluating emerged for two variables; and misattributing

causation emerged for just one variable.

Table 3 Stepwise multiple regressions of the subscales of the ACS in predicting anger-related variables

(N = 181)

Variables B SEB b R2 DR2

Trait anger scale

1. DE .43 .05 .52** .32**

2. AP -.11 .04 -.17** .35** .03**

Anger expression-out

1. DE .19 .05 .29** .12**

2. AP -.11 .04 -.21** .16** .04**

Anger expression-in

1. CE .34 .05 .45** .20**

Anger control-out

1. AP .24 .05 .35** .21**

2. OV -.16 .06 -.20** .29** .08**

3. IL -.10 .05 -.16* .31** .02*

Anger control-in

1. AP .36 .05 .49** .30**

2. OV -.19 .05 -.22** .35** .05**

Log: Average anger rating

1. IL .40 .13 .23** .05**

Log: Negative emotions

1. CE .10 .03 .22** .05**

Log: Aggression

1. IL .02 .01 .26** .07**

Log: Risky driving

1. MC .05 .02 .21** .04**

Log: Damaged friendships

1. IL .02 .01 .21** .05**

Log: Number of days anger was the primary emotion

1. AP -.05 .01 -.33** .11**

MC misattributing causation, CE catastrophic evaluating, OV overgeneralizing, DE demandingness, IL

inflammatory labeling, AP adaptive processes, T-Ang trait anger, AX-Out anger expression-out, AX-In

anger expression-in

* p \ .05; ** p \ .01


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Discussion

The current study extended the findings from Martin and Dahlen’s (2007, 2011)

studies on the reliability and validity of the ACS. In addition to demonstrating

strong internal consistencies for the ACS and replicating patterns of correlations

between the ACS and the STAXI-2, it also addressed limitations of the previous

research by utilizing emotion logs. In doing so, it adds to the validity of the ACS by

demonstrating relationships between the ACS and average daily anger ratings,

number of days where anger was experienced more than any other emotion, and

anger-related consequences. Finally, results from the regression analyses revealed

differing patterns of predictive validity for the ACS subscales with regard to anger-

related variables.

The pattern of relationships between the ACS subscales and the STAXI-2 were

exactly the same as those reported in Martin and Dahlen’s (2007) original paper on

the ACS and similar to those found in their (2011) follow-up study. The current

study addressed an important inconsistency found between the previous two articles

with regard to anger control-out. Like the original study (2007), the current project

found that all subscales of the ACS were correlated with anger control-out. This was

not found by Martin and Dahlen (2011), however, where it was found that the ACS

subscales were mostly uncorrelated with anger control-out. The inconsistency

between the two studies was addressed as an area of future research by Martin and

Dahlen (2011) and the findings from the current study suggest that the results from

the 2011 paper were an anomaly.

By using an emotion log to measure anger consequences, the current study

addressed two significant limitations of the previous evaluation of the ACS and

anger consequences (Martin and Dahlen 2007), demand characteristics and reliance

on retrospective report. In comparing the findings from these analyses, there are

both consistencies and inconsistencies. Both studies identified positive relationships

between the ACS subscales and three types of consequences: negative emotions,

aggression, and risky driving. However, the current study did not find any

relationships between the ACS subscales and drug use or self-harm, like was found

in the previous study. Similarly, the current study only identified a correlation

between one ACS subscale, inflammatory labeling, and damaged friendships, while

the previous study identified relationships between all subscales of the ACS and

damaged friendships.

It is likely that the differences found here reflect, in part, the use of emotion logs

as it eliminates the impact of demand characteristics. In that sense, the current study

provides a more accurate assessment of the ACS’s predictive validity than the

previous research. While that does mean that the ACS has less predictive utility than

was once thought, it should also be noted that the findings here suggest that how a

person tends to think when provoked affects not just how angry they tend to get (i.e.,

trait anger) and how they tend to express it (i.e., anger expression) but also that they

tend to get into physical or verbal fights, drive dangerously, or experience other

negative emotions associated with their anger. The fact that the thoughts we have, as

measured by the ACS, predict the types of consequences we experience as a result

of our anger suggests an impressive level of predictive utility.


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Another new and important finding from the current study is that all of the ACS

subscales but adaptive processes were correlated with average daily anger ratings,

and four of the six subscales were correlated with the number of days that anger was

the most prominently experienced emotion. This has not been explored before with

the ACS and offers another example of its predictive utility. It was found that the

subscales of the ACS also correlated, by and large, with sadness, which could

suggest a lack of specificity of the ACS in measuring angry thoughts. However,

many of the thought types measured by the ACS (e.g., catastrophic evaluations,

overgeneralizing) are noted in the emotion literature as being relevant to multiple

emotions so this pattern of correlations would be expected. For instance, a tendency

to blow things out of proportion is associated with sadness and fear, as well as

anger, so one would expect it to be correlated with all three variables. Meanwhile, a

tendency to label people in offensive ways is predominantly associated with anger

so the stronger correlation between inflammatory labeling and anger than with

sadness or fear is also expected. Likewise, the finding that misattributing causation

(clearly more associated with anger than fear) is uncorrelated with fear is to be

expected.

Results from the regression analyses show how different patterns of angry

thoughts predict different experiences, expressions, and consequences of anger. For

instance, the findings that adaptive processes emerged as a useful predictor in five of

the eleven regressions and that inflammatory labeling emerged in four of these

regressions suggests that these types of thoughts, in particular, should be the focus

of cognitive therapies.

The current study was designed to explore the relationships between the ACS and

various anger-related variables while addressing limitations in the previous

literature. Though it could be argued that the current study still used retrospective

data because participants completed the questionnaires at the end of the day, it is

fair to assume that participants are much more able to reliably indicate how many

verbal fights, physical fights, etc. they had that day than they would be in the last

month.

To date, there have been three papers identifying the reliability and validity of the

ACS. There are, of course, still limitations of this study. For instance, we relied on

self-report data and used a college sample of participants. Likewise, because

participation was voluntary and students had many options for participation, it is

possible that our participants chose to get involved in this study because of its focus

on anger, suggesting that our sample was biased in some way. Future research

should take these concerns into account.

Despite these limitations, it would appear that the ACS is likely a valuable

measure of anger-related thoughts. Given that cognitive treatments of anger

emphasize the identification and restructuring of maladaptive thoughts, the logical

next steps are to explore the reliability and validity of the ACS with a sample of

clinically angry participants and to explore the clinical utility of the ACS in the

treatment of anger. It seems likely, however, that the ACS will be a valuable tool in

that regard.


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