5 Study 2: Concurrent Assessment of the Implicit Self-Concept of Anxiousness and Angriness

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5.1  Introduction

The results of Study 1 showed that indirect measures (shyness IAT and IAP) do not yet meet psychometric criteria that are necessary for individual diagnosis and that are typically shown for direct measures, that is, satisfactory test-retest stability and high convergent validity. The main purpose of Study 2 was to examine another important aspect concerning the practical implications of indirect assessment. Direct self-reports, for example, the NEO-PI-R (Costa & McCrae, 1992), allow for the concurrent assessment of different traits within one questionnaire. Therefore, Study 2 explored whether IATs also allow for the assessment of two different traits within one sample when the IATs are applied as two consecutive tests. Although several studies employed more than one IAT within one sample (e.g., Gawronski, 2002; Nosek, Banaji, & Greenwald, 2002), there appears to be no research that would systematically carry out position effects on the IAT.

Therefore, the sequence of an anxiousness and an angriness IAT was counterbalanced across participants in Study 2 and three main research questions were explored. First, it was expected that the validity of the IAT is affected if the IAT is preceded by another IAT. Second, it was expected that the IATs add incremental validity to the prediction of anxious and angry behavior. Third, it was explored whether social desirability does moderate the relationship between direct and indirect measures. These research questions are discussed in more detail in the following sections.

5.1.1  Research Question 1: Position Effects on IATs

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The sequence of IATs is often counterbalanced in studies that explore several IATs. Usually, results are not discussed separately for the groups of different IAT order. Concerning test-retest comparisons, the study by Asendorpf et al. (2002) provided evidence that the second, parallel shyness IAT tended to show lower correlations with direct shyness measures and with shy behavior than the first IAT. Similarly, other studies found that the retest reliabilities of IATs are lower than their internal consistencies (cf. for an overview, Egloff, Schwerdtfeger, & Schmukle, 2003). Thus, IAT measures showed both a validity decrease for the second test and relatively low test-retest reliabilities. Both aspects might be caused by the two factors, that is, state influences and changes in response strategies.

State influences were ruled out in Study 1 and in the study by Schmukle and Egloff (2003) as a systematic bias in IAT results. Therefore, it is most likely that differences between the first and the second IAT measures are due to changes in response strategies. Working on the IAT, participants might develop cognitive strategies to respond faster, and try out different response styles, for example, avoiding errors because errors increase test duration. Recently, De Houwer (2003a) stated that changes in response strategies may emerge because participants try to make the IAT tasks as simple as possible. Therefore, participants recode the double discrimination task in terms of a simple discrimination (e.g., positive versus negative; see Mierke & Klauer, 2001). De Houwer pointed out that recoding in terms of a simple discrimination may be based on the associations one aims to measure (e.g., the associations of flowers and insects with positive and negative attributes). Alternatively, recoding may be based on any type of similarity between target and attribute concept (e.g., word length, color, etc.). Importantly, IAT effects are likely to be distorted if the similarity-based task-recoding is unrelated to the associations one tries to assess.

Study 2 examined whether the completion of an IAT distorts the validity of the succeeding IAT due to any change in response strategies. Therefore, the order of the anxiousness and the angriness IAT was counterbalanced in Study 2, and results were inspected separately for both groups of different IAT order.

5.1.2 Research Question 2: Prediction of Anxious and Angry Behavior

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Recently, a study by Egloff and Schmukle (2002) showed that self-reported state anxiety during a stressful speech was predicted by direct anxiousness measures but not by the anxiousness IAT. More importantly, the observer judgments of anxious behavior and several behavioral indicators of anxiety were predicted by the anxiousness IAT but not by direct anxiousness measures. Using the same rationale, Study 2 examined whether indirect measures significantly increase the prediction of behavior even if two traits are assessed within one study. It was expected that both, the anxiousness and the angriness IAT show predictive validity for anxious and angry behavior, respectively.

Anxiousness and angriness were employed as traits under investigation because they were expected to be uncorrelated at least at the level of direct self-reports. Uncorrelated traits facilitate the study of convergent and discriminant validity between direct, indirect, and behavioral measures (see Chapter 2.5 and the following). Additionally, anxiousness and angriness allow for the study of the predictive validity of direct and indirect measures because anxious and angry behavior may be observed after emotion inductions.

5.1.3 Research Question 3: Social Desirability as a Moderator Variable

One of the main reasons for research interest in indirect measures is that they are expected to circumvent the validity problems that are associated with direct measures (Greenwald et al., 2002). An example of a validity problem in direct measures is their susceptibility to social desirability concerns . For example, it was shown in Study 1 and in other studies (e.g., Asendorpf et al., 2002; Egloff & Schmukle, 2002) that direct self-report measures were, in contrast to IAT measures, significantly correlated with social desirability. Social desirability is a tendency to portray oneself in a favorable light (Crowne & Marlowe, 1960). Therefore, the more negative the correlations between direct measures and social desirability are, the more biased by social desirability the direct measures are assumed to be.

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More importantly, social desirability may also act as a moderator variable in the relationship between direct self-report and IAT measures. Individuals with a weak tendency to present themselves in a socially desirable way should show higher correlations between direct measures and IATs than the individuals with a strong tendency to socially desirable responding. Previous studies indicated that the correlations between direct measures and IATs were not moderated by social desirability (Egloff & Schmukle, 2003; Hofmann, Gschwendner, & Schmitt, 2003). In contrast, moderator variables were found to be significant if they asked for self-presentational motivation more directly with regard to the attribute that was measured (e.g., Banse & Gawronski, 2003; Hofmann, Gschwendner, et al., 2003; Nosek & Banaji, 2002). Nevertheless, social desirability was explored as a moderator variable in Study 2 in order to replicate the results from other studies for the anxiousness and the angriness IAT.

5.2  Hypotheses

Study 2 tested the following hypotheses.

Hypothesis 1 (Increase of state anxiety and state anger). Participants report more state anxiety and state anger after the emotion inductions as compared to the baseline.

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Hypothesis 2 (Validity of the bipolar anxiousness and angriness self-ratings). The bipolar anxiousness self-rating correlates with direct anxiousness but not with direct angriness measures whereas the opposite is true for the bipolar angriness self-rating. This validates the word material that was used in the IATs.

Hypothesis 3 (Zero correlation between social desirability and the IATs). In contrast to direct self-ratings neither the anxiousness nor the angriness IAT are correlated with social desirability scores.

Hypothesis 4 (Social desirability is not a moderator variable). Social desirability does not moderate the correlations between indirect and direct measures.

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Hypothesis 5 (Zero correlation between anxiousness and angriness). Anxiousness and angriness are neither correlated for the direct, nor the indirect or the behavioral measures, confirming their conceptualization as orthogonal dimensions.

Hypothesis 6 (Validity decrease for the second IAT). The IAT tends to show smaller convergent validity with direct and behavioral measures when it is preceded by another IAT.

Hypothesis 7 (Independent contribution of IATs to behavior prediction). The anxiousness and the angriness IAT predict behavioral anxiety and anger even when direct self-ratings are controlled for. In contrast, self-reported state anxiety and state anger are predicted by direct self-ratings but not by the anxiousness and angriness IAT.

5.3 Methods

5.3.1  Participants

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A total of 103 university students were recruited as participants on the campus of Humboldt University, Berlin, none of whom were psychology students or had participated in the lab’s previous studies. Most participants were directly approached by an experimenter (not identical with the experimenter at the lab). Some participants were recruited using postings at the university buildings. Participants were asked to take part in a study on concentration and personality. As a compensation, participants were offered € 10 (approximately US $ 10 at the time) for completing a questionnaire of about 15 minutes duration at home and for participating in a lab experiment of about one hour duration. In addition, they could receive individual feedback on their results after the study is complete. All participants claimed to be native German speakers. Three female participants refused to complete the speaking task during the lab session, and were therefore excluded from analysis. This led to a final sample of 100 participants (50 male, 50 female; age M = 24.0 years, range 19-32 years).

5.3.2 Assessments and Measures

Overall procedure. The overall procedure of Study 2 is depicted in Table 11. All participants (a) judged themselves on several trait measures at home within one week before the lab session. After arrival at the lab they (b) completed a short form of the d2 Attention-Stress Test, (c) judged themselves on a short optimistic risk perception measure (not relevant to this research), (d) completed the anxiousness IAT and the angriness IAT, (e) indicated their state anxiety and state anger on bipolar items, (f) received instructions for an anxiety-inducing speech, (g) completed a retest of (e), (h) prepared their speech, (i) were video-taped during their speech, (j) were videotaped during an anger-inducing computer crash, (k) completed a retest of (e), (l) were interviewed about the experiment, and (m) were completely debriefed.

The anxiousness and angriness items of the two IATs were included as direct self-ratings in step (a), (e), (g), and (j). The order of the anxiousness IAT and the angriness IAT in step (d) was varied between participants such that half of the participants completed the anxiousness IAT first and the other half completed the angriness IAT first. The assignment to the two orders was balanced for gender and alternated between successive participants. In contrast, the order of the anxiety and the anger induction was fixed, such that the anxiety induction always came first, because of the faked computer crash during the anger induction.

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Finally, the participants were thanked and asked to give their consent for the evaluation of the videotapes (all agreed). They were also paid and promised individual feedback about their results. Four months after finishing data collection, participants received a letter containing the principal findings of the study along with an invitation for an individual feedback session, where they were informed about their personal results.

Table 11
Overall Procedure of Study 2

Cover story: Concentration and personality

Duration
(Min.)

At home:

(a) Direct trait measures

- Trait form of the STAI, STAXI, and two subscales of the TAI-G
- Speaking Anxiety Scale
- Bipolar self-ratings of anxiousness, angriness, conscientiousness, and intellect
- Social desirability scales and MAS
- Biographical data

15

At the lab:

(b) d2 Attention-Stress Test

5

(c) Optimistic risk perception measure

2

(d) Anxiousness and angriness IAT (counterbalanced for order across participants)

20

(e) Direct state measures (baseline)

Bipolar self-ratings of anxiety, anger, and conscientiousness

1

(f) Anxiety induction: Instructions for the speech

2

(g) Direct state measures

Bipolar self-ratings of anxiety, anger, and conscientiousness

1

(h) Preparation of the speech

3

(i) Behavior observation: Speech before video camera

5

(j) Anger induction and behavior observation: Computer crash, which was

(1) pretended to be caused by the participant
(2) destroyed all his / her data
(3) made payment of the reward impossible

5

(k) Direct state measures

Bipolar self-ratings of anxiety, anger, and conscientiousness

1

(l) Interview: Identification of participants who doubted the computer crash

5

(m) Debriefing about the true purpose of the study

5

70

Note. STAI = State Trait Anxiety Inventory, STAXI = State Trait Anger Expression Inventory, TAI-G = German version of the Test Anxiety Inventory, MAS = Manifest Anxiety Scale, IAT = Implicit Association Test.

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Trait measures. In order to minimize transfer effects between direct and indirect measures, direct trait measures were mailed to the participants within one week before the lab session. The instructions explained to participants that the study was about concentration and personality and consisted of two parts: a set of questionnaires concerning several personality traits, that was attached and had to be completed at home, and a subsequent lab session assessing attention and concentration. I avoided to tell participants that the study was about anxiousness and angriness because I (a) did not want anxious persons to avoid participation in the study, and (b) had to keep participants naive about the anger induction, as most people would not get angry knowing that it was intended to provoke their anger (Stemmler, Heldmann, Pauls, & Scherer, 2001).

The mailed questionnaire contained the following measures (test references list the used German version first, and the English equivalent second, if such equivalent existed). The questionnaire started with the trait forms of the State Trait Anxiety Inventory STAI (Laux, Glanzmann, Schaffner, & Spielberger, 1981; Spielberger, Grousch, & Lushene 1970) and the State Trait Anger Expression Inventory STAXI (Schwenkmezger, Hodapp, & Spielberger, 1991; Spielberger, 1988) together with the subscales Interference and Lack of Confidence (without the item “Ich bin überzeugt, dass ich gut abschneiden werde.” [“I am sure, that I will receive good marks.”]) of the Test Anxiety Inventory TAI-G (Hodapp, 1991; expanded German version of the TAI, Spielberger, 1980). These questionnaires assess enduring symptoms of anxiousness, angriness, and test anxiousness on a 4-point scale (1 = Almost never, 4 = Almost always) with 20, 10, and 11 items, respectively. The TAI-G subscales were added, and all scales were mixed in a fixed random order, because participants of a pilot study doubted the cover story when the STAI and the STAXI were presented separately. When both scales were mixed with the TAI-G, the STAI and the STAXI were less salient cues for the true content of the experiment.

The trait measures proceeded with the second series of the Speaking Anxiety Scale (Spitznagel, Schlutt, and Schmidt-Atzert, 2000). This questionnaire assesses habitual emotionality (e.g., “I am quite nervous”) and worries (e.g., “I fear negative consequences”) immediately before giving a speech with 8 items each. Items were presented on a 4-point scale (1 = I do not agree at all, 4 = I agree completely).

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Subsequently, participants had to rate their conscientiousness, intellect, attentiveness, anxiousness, and angriness on 33 bipolar adjective pairs (e.g., “ self-confident 1-2-3-4-5-6-7 anxious”). The pairs were mixed in a fixed random order and presented with a trait instruction. The 10 intellect and 10 conscientiousness pairs were the same as in Asendorpf et al.’s Study 1 (2002). I further added 3 attentiveness pairs to make the cover story more plausible. The first pair was “aufmerksam” [“attentive”] versus “durcheinander” [“jittery”] that was adapted from the Positive and Negative Affect Schedule PANAS (Krohne, Egloff, Kohlmann, & Tausch, 1996; Watson, Clark, & Tellegen, 1988). Two additional pairs were synonymous.

The 5 anxiousness pairs (anxious versus self-confident) and the 5 angriness pairs (angry versus self-controlled) were constructed on the basis of 430 unipolar and 179 bipolar adjective items provided by Ostendorf (1990). He had factor analyzed them and reported their loadings on the first five factors that could be interpreted as the factors of the Five Factor Model of personality. Within the Five Factor Model (see Chapter 2.6.2), anxious versus self-confident was conceptualized as being strongly related to neuroticism, moderately related to introversion, and as being unrelated to agreeableness. In contrast, angry versus self-controlled was conceptualized as being weakly related to neuroticism and extraversion, but as being strongly negatively related to agreeableness.

Consequently, concerning the anxious pole, I selected unipolar items with factor loadings above .25 on both introversion and neuroticism, and below .10 on agreeableness. For the opposite pole, self-confident, unipolar items representing the inverse factor loadings were selected. Concerning the angry pole, I selected unipolar items with factor loadings above .20 on extraversion and neuroticism, and below -.25 on agreeableness. For the opposite pole, self-controlled, unipolar items representing the inverse factor loadings were selected. 9 items met these requirements. Then, I searched for bipolar adjective pairs that showed the same pattern of factor loadings, and received another 13 adjectives. Finally, I added 6 self-generated, semantically similar adjectives. This procedure resulted in a list of 7 bipolar items describing anxious versus self-confident, and 7 bipolar items describing angry versus self-controlled. These items were pre-tested in a student sample (N = 42; age M = 22.6 years, range 19-39 years) together with three scales of seven bipolar adjectives from the 179 items list, which had the highest factor loadings on either the neuroticism, the extraversion, or the agreeableness factor and cross-loadings below .30. Within the 7 anxiousness pairs, 5 showed significant negative correlations with extraversion (r < -.32 p < .05); the two noncorrelating items were excluded. The resulting 5 item bipolar anxiousness scale showed acceptable internal consistency, α = .84, and correlated strongly with neuroticism (r = .82; p < .001), intermediately with extraversion (r = -.45; p = .003), and nonsignificantly with agreeableness (r = -.19). From the seven angriness pairs, two pairs that showed significant positive correlations with the anxiousness scale were excluded. The resulting 5 item bipolar angriness scale showed acceptable internal consistency, α = .77 and correlated marginally with neuroticism (r = .21; p = .18) and extraversion (r = .22; p = .17), highly with agreeableness (r = -.78; p < .001), and was not correlated with the 5 item anxiousness scale (r = .01). All items of the bipolar anxiousness and angriness scale were used as word material within the IATs and are listed in Table 12.

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Finally, the questionnaire concluded with the social desirability scales by Lück and Timaeus (1969; Crowne & Marlowe, 1960) and Stöber (1999; without the Item “Have you ever consumed drugs”). These scales contain 16 and 23 items, respectively, and measure socially desirable responding by asking for socially desirable but infrequent or socially undesirable but frequent behaviors on a true-false format. Items of both scales were presented in a fixed random order together with the Manifest Anxiety Scale MAS (Lück & Timaeus, 1969; Taylor, 1953). The 23 items of this scale assess various symptoms of anxiousness (e.g., “I work under a great deal of tension”). The reliability of all trait measures was satisfactory and is reported in Table 13 of the Results section.

After answering these personality items, participants had to report their age, sex, height, dominant hand, academic subject, length of time spent at university, whether they were still students (all were), and whether they had a permanent partner.

Lab session. Upon arrival at the lab participants were reminded that the experiment was about attention and concentration. The experimenter briefly explained that the lab session contained different concentration tests, two of which were on the computer, and one being a paper-and-pencil test, as well as a situation demanding attention and concentration that would be videotaped. Subsequently, participants received instructions for the first concentration test. Because men might repress their anger facing a woman, and women might avoid getting angry with a physically superior man, the experimenter was always of the same gender as the participant.

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d2 Test. Instructions and procedures of the d2 Test (Attention-Stress Test, Brickenkamp, 1994) corresponded to the test manual but I only presented the first 5 rows instead of the complete 14 rows version. During the d2 Test participants are given 20 seconds per row with 47 stimuli each to cross out relevant stimuli (letter “d” with exactly two lines) and ignore irrelevant stimuli (letter “d” with more or less than two lines and any letter “p”). The test score is calculated as the difference between processed stimuli and errors (false alarms and misses). The d2 Test was primarily used to give a better justification for the cover story. Therefore, results for the d2 Test will not be reported here.

Optimistic risk perception measure. After the d2 Test and before the IATs I presented a German translation of the optimistic risk perception measure from Lerner and Keltner (2001, Study 4) as a short break from concentration tasks. The questionnaire was presented on the computer and was added for the purpose of another study. The internal consistency of this 15-item questionnaire was low, α = .58.

Anxiousness and angriness IAT. The procedures for the anxiousness and the angriness Implicit Association Test (IAT) were identical to the shyness IAT in Study 1. Consequently, both IATs were the same except for the attribute dimension, being anxious versus self-confident within the anxiousness IAT, and angry versus self-controlled within the angriness IAT. Task sequence and stimuli are depicted in Table 12. IAT scores were computed as D measures with an error penalty of 600 ms, and without the exclusion of trials below 400 ms (for details on the complete algorithm, see Greenwald et al., 2003). Like conventional scores, D measures were based on the difference between mean response latencies in sequence 5 and sequence 3 (see Table 12), but were scaled in units of the individuals’ standard deviations and included an error penalty for incorrect responses. In contrast to Greenwald et al. (2003), all trials were considered equally and the first 20 trials were not weighed as more important as the succeeding trails, because I (a) did not declare the first 20 trials as training trials and (b) had 60 instead of 40 succeeding trials.1 The measures were coded so that high scores represented quicker associations of Me-anxious and Others-self-confident relatively to Me-self-confident and Others-anxious, or of Me-angry and Others-self-controlled relatively to Me-self-controlled and Others-angry, respectively. Internal consistencies are reported in the Results section.

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Table 12
Implicit Association Tests for Anxiousness and Angriness: Task Sequence and Stimuli

Response key assignment

Sequence

N of trials

Task

Left key

Right key

1

40

Target discrimination

Me

Others

2

40

Attribute discrimination

Anxious
(angry)

Self-confident
(self-controlled)

3

80

Initial combined task

Me, anxious
(angry)

Others, self-confident
(self-controlled)

4

40

Reversed target discrimination

Others

Me

5

80

Reversed combined task

Others, anxious
(angry)

Me, self-confident
(self-controlled)

Stimuli

Anxiousness IAT

Angriness IAT

Me

Others

Anxious

Self-confident

Angry

Self-controlled

I

they

anxious

self-confident

angry

self-controlled

self

them

timid

daring

hot-tempered

thoughtful

My

your

insecure

secure

undercontrolled

self-disciplined

Me

you

worried

unconcerned

hot-headed

adaptable

Own

other

overly cautious

carefree

irritable

calm

Note. The procedures of the anxiousness and the angriness IAT were identical. Words in parentheses refer to the task sequence within the angriness IAT. The original German single word stimuli are listed in the appendix.

State measures. As a manipulation check for the emotion inductions I used bipolar items for anxiousness and angriness together with a state instruction. These items were mixed in a fixed random order with 3 attentiveness and 7 out of 10 conscientiousness items. The items were presented in a paper-pencil version, and were identical to those completed as a trait measure at home. 3 conscientiousness items were dropped, because I expected them not to match the state instruction (e.g., “fleißig” [“industrious”] versus “faul” [“lazy”]). State measures were presented after the IATs (baseline), the instructions for the speech (anxiety induction), and after the computer crash (anger induction). Reliabilities for the state measures were satisfactory, internal consistencies were for the anxiety scale α = .89, for the change in anxiety (speech minus baseline) α = .78, for the anger scale α = .80, and for the change in anger (computer crash minus baseline) α = .74.

Anxiety induction. Participants received instructions for the speech on a piece of paper. The paper informed participants that they should give a speech that would be videotaped and later on analyzed by experts. The duration of the speech was asked to be five minutes. Directly after this announcement participants completed the state measures. Subsequently, they were told about the subject of the speech (terminal illness and euthanasia: immoral or humane; adapted from Schmukle & Egloff, 2003) and were given three minutes for preparation. Participants were allowed to make notes during preparation, but the speech was supposed to be given without notes. Then, participants gave their speech directly in front of the video camera that was operated by the experimenter from a nearby room. Exactly after five minutes the experimenter thanked the participants and informed them that this was enough. When participants stopped talking before the five minutes were over, the experimenter prompted them to continue talking until full five minutes were up. The time period before participants continued their speech was defined as missing. For the judgments and codings of anxious behavior secondary tapes were prepared that contained the first three minutes of noninterrupted speech. The speech task was followed by the anger induction.

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Anger induction. The general procedure was adapted from Wiedig (2003) and is similar to a procedure used by Bargh et al. (1996, Experiment 3). Participants completed a STROOP-Test on the computer. Again, participants were videotaped and were told that this was to evaluate their eye-blink-rate as an indicator of concentration. In fact, this was to give good reason for videotaping the interaction with the experimenter. Three minutes after starting the STROOP, the screen froze and the words “FATAL ERROR” appeared in the center of the screen. In addition, a short but intensive error sound was given, whenever a key was pressed. The experimenter, then, approached the participant and pretended to be astonished by the accident. The subsequent interaction between experimenter and participant comprised 3 different provocations. First, the experimenter accused the participant of causing the crash by incorrectly using the enter-key. Second, she or he said that all computer-based data of the participant were now destroyed. Third, due to loss of data participants could not receive any money for the experiment. After this, participants were asked to complete the state measures, waiting for a computer expert who may help to save the data. For the judgments and codings of angry behavior secondary tapes were prepared. The recording started when the computer crashed and ended when participants began completing the state measures. For the anger judgments, a three second blue screen interval was inserted after the end of each of the 3 provocations to enable separate ratings for each provocation.

Interview. The aim of the interview was to identify participants who doubted the computer crash. Participants were first asked whether they had difficulties with any part of the experiment. Afterwards they had to say whether they noticed anything remarkable during the experiment and whether anything in the experiment seemed strange to them. All participants (11 females and 12 males) who were suspicious about the computer crash being part of the experiment were excluded from the analysis of the anger induction. These participants did not differ significantly from the remaining participants on any of the anxiousness and angriness measures.

Debriefing. Finally, participants were completely and thoroughly debriefed about the true purpose of the study. It was made sure that participants had an opportunity to relax after the disturbing computer crash, and would not leave the lab angry or upset. In the beginning of debriefing, the participants were offered some sweets by the experimenter as a compensation for a rather harsh preceding interaction. Then, participants were informed that the study was not on concentration and attention but on anxiousness and angriness, and aimed to validate new computer based measures for these traits. Thereby, the experimenter went through the crucial parts of the study (direct and indirect measures, emotion inductions) and explained why these procedures were designed to assess anxiousness and angriness. In order to keep the true purpose of the study undisclosed for the subsequent participants, the experimenter asked the participants to keep the information about the study confidential until they would receive a letter from the experimenter. This letter was sent out four months after finishing data collection and comprised the main findings of the study together with an invitation for an individual feedback session.

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Judgments of anxious and angry behavior. Four student judges that were unfamiliar with the participants and blind to their data independently rated their overall impression of the participants' anxiety and anger on 7-point scales. On these scales, 7 was labeled "very anxious" or "very angry" and 1 was labeled "not at all anxious" or "not at all angry". For the anxiety judgment each minute of the 3-minute speech was judged. For the anger judgment each of the 3 provocations after the computer crash (alleged misuse of the enter key, loss of data, no money) was judged separately. This resulted in 12 anxiety and 12 anger judgments for all participants that were averaged in each case. Similarly, two judges independently rated the anxiety and the anger within the participants' voices with three ratings per scene but without watching participants. This resulted in 6 anxious and 6 angry voice judgments that were averaged in each case. The anxiety judgments (both overall and voice judgments) were anchored by a female and a male example of extremely anxious and extremely nonanxious participants from the study by Egloff and Schmukle (2002). In the same way, the anger judgments were anchored by extremely angry and nonangry examples from a study by Wiedig (2003). Interrater reliability was satisfactory for all judgments (see Results section).

Codings of anxious behavior. All Codings were done on a PC using the Computer Aided Observation System (CAOS) software. This program synchronizes video player and PC, and registers onset and offset of behavioral codings when the appropriate key is pressed. Anxiety codings were carried out for body movements and nervous mouth movements. Following Ekman and Friesen's (1972) classification, body movements were coded as illustrators (movements illustrating speech), facial adaptors (self-stimulations of the face), and body adaptors (self-stimulations of the body). For data analysis body movements were considered in terms of their relative duration of the 3 minute speech. Nervous mouth movements were coded according to Egloff and Schmukle (2002) defined as lip biting, lip licking, twitches of the mouth, and pressing of the lips. As the nervous mouth movements were short and discrete events, Egloff and Schmukle (2002) examined their frequency rather than their duration. I also considered their frequency, because their duration might in this case be overly confounded by the noise in the coders’ reaction time during the on-off coding. In order to control cross-lab reliability with the Egloff and Schmukle (2002) coding system, one coder first coded 10 female and 10 male participants of Egloff and Schmukle's Study 4. This coder correlated highly with the mean of two coders of Egloff and Schmukle's (2002) study and showed therefore substantial agreement between the coding in both labs, r = .80. In addition, within-lab reliability of all anxiety codings was assessed by a second coder with independent codings of 20 randomly selected participants and was satisfactory in all cases (see Results section).

Codings of angry behavior. Anger codings were completed for emotional facial expressions of the Ekman and Friesen's (1978) coding system that were shown to co-occur with anger (Friesen & Ekman, 1984). These were the Action Units brow lower (AU 4), upper lip raise (AU 10), lip funnel (AU 22), lip tight (AU 23), lip press (AU 24), and chin raise (AU 17), that were coded in independent runs. As the coded facial expressions were short and discrete events, I further considered their frequency per minute rather than their relative duration of observation time. Reliability estimates were provided by independent codings of 20 participants by another coder. Reliability was not satisfactory for the AU 10, 22, and 17, because they occurred so rarely (mean frequency less than 0.25 times per minute) that intercoder reliability was hard to obtain. Therefore, these Action Units were not considered for data analyses. Reliability for the other three Action Units was acceptable (see Results section).

5.4 Results

5.4.1  Efficacy of Emotion Inductions

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To investigate whether the speech and the computer crash were apt to observe anxious and angry behavior, I first needed to examine the efficacy of these emotion inductions. As expected by Hypothesis 1, participants reported more state anxiety after the announcement of the speech (M = 3.39) than at the beginning of the experiment (M = 3.02), t (99) = 4.11, p < .001, d = .58. Similarly, participants reported more state anger after the computer crash (M = 2.53) than at the beginning of the experiment (M = 2.35), t (76) = 1.98, p < .05, d = .31. It should be noted that the degrees of freedom were smaller for the anger induction because I had to exclude participants who were suspicious about the computer crash. Considering the increase in self-reported state measures, both of the emotion inductions worked.

5.4.2 Validity of the Bipolar Anxiousness and Angriness Self-Ratings and the IAT Stimuli

This section inspects the convergent and discriminant validity of the bipolar anxiousness and angriness self-ratings that were also used as word material IATs. The reliabilities and correlations of all direct trait measures are depicted in Table 13. Reliability (Cronbach’s α) was satisfactory for all measures, in particular it was .84 for the bipolar anxiousness and .80 for the bipolar angriness self-rating. As one can see in the first two rows of Table 13, the bipolar anxiousness self-rating correlated highly with the Manifest Anxiety Scale and the trait form of the State Trait Anxiety Inventory, and intermediately with the subscales of the Speaking Anxiety Scale. These subscales assess habitual emotionality and worries immediately before giving a speech and, in contrast to general anxiousness questionnaires, are more situation-specific. The bipolar anxiousness self-rating also showed a small correlation with the trait form of the State Trait Anger Expression Inventory.

Table 13
Correlations between the Trait Measures in Study 2

1

2

3

4

5

6

7

8

 

1. Bipolar anxiousness self-rating

.84

-.08

.30**

.35***

.72***

.73***

.23*

-.08

 

2. Bipolar angriness self-rating

.80

-.05

-.05

.12

.07

.45***

-.30**

 

3. Speaking Anxiety Emotionality

.88

.72***

.36***

.28**

.13

-.16

 

4. Speaking Anxiety Worries

.84

.44***

.40***

.23*

-.23*

 

5. Manifest Anxiety Scale

.82

.78***

.39***

-.30**

 

6. State Trait Anxiety Inventorya

.90

.37***

-.25*

 

7. State Trait Anger Expression Inventorya

.78

-.34***

8. Social Desirability

.81

 

Note. N = 100. Internal consistencies (Cronbach’s α) are printed in italics along the diagonal.
a Trait form. + p < .05 *p < .05 **p < .01 ***p < .001.

↓85

In contrast, the bipolar angriness self-rating did not even marginally correlate with any direct anxiousness measure and correlated intermediately with the trait form of the State Trait Anger Expression Inventory. Thus, the correlation for the angriness self-rating with the corresponding trait measure was somewhat lower than for the anxiousness self-rating. Nevertheless, a Steiger’s (1980) test of correlation differences revealed that the bipolar angriness self-rating correlated marginally higher with the trait form of the State Trait Anger Expression Inventory, r = .45, than the bipolar anxiousness self-rating, r = .23, t (97) = 1.65, p = .05 (one-tailed). Moreover, the trait form of the State Trait Anger Expression Inventory did not only correlate with the bipolar anxiousness self-rating but also with other direct anxiousness measures. This indicated a lack of discriminant validity for the trait form of the State Trait Anger Expression Inventory rather than for the bipolar anxiousness self-rating. This may further account for the only intermediate correlation between the bipolar angriness self-rating and the trait form of the State Trait Anger Expression Inventory. As a result, as expected by Hypothesis 2, convergent and discriminant validity with established measures were shown for both bipolar self-ratings. This validated the word material I used as attributes within the IATs, at least at the level of direct measures.

5.4.3 Descriptive Statistics for the Anxiousness, the Angriness IAT, and the Behavioral Measures

Before I explore the correlations of the IATs and the behavioral measures, I will discuss briefly their descriptive statistics. The mean raw score (in milliseconds) of the anxiousness IAT was M = -171.1, SD = 156.9, and ranged from -640.6 to 179.2. Only 9 (6 female, 3 male) out of 100 participants had positive IAT scores. Thus, most of the participants were quicker to combine Me+self-confident and Others+anxious than for the reverse mapping. The mean raw score of the angriness IAT was M = -186.6, SD = 133.2, and ranged from –533.3 to 161.0. Only 4 (1 female, 3 male) out of 100 participants had positive scores. Thus, most of the participants were quicker to combine Me+self-controlled and Others+angry than for the reverse mapping. Mean error rates were for the anxiousness IAT M = 4.2%, SD = 2.6%, and for the angriness IAT M = 3.6%, SD = 2.3%. In any IAT, no participant had error rates higher than 15% or more than 10% of the latencies faster than 300 ms. The distributions of the improved and individually standardized D measures were not even marginally different from a normal distribution in both IATs, Z < 1. Internal consistency was computed across the two test halves and was acceptable for the anxiousness IAT, α = .72, but somewhat unsatisfactory for the angriness IAT, α = .66.

The descriptive statistics of the behavioral measures are depicted in Table 14. It should be noted that the reliability of several behavioral anger indicators was below .70. Nevertheless, the reliabilities of the global observer judgments for anxiety and anger were completely satisfactory.

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Table 14
Descriptive Statistics of the Behavioral Measures in Study 2

Behavioral anxiousness measure
(range of scores)

N a

M

SD

Range

Reliabilityb

Observer anxiety judgment (1-7)

100

3.27

1.06

1.33-6.42

.89

Anxious voice rating (1-7)

100

3.68

1.01

1.83-6.33

.72

Nervous mouth movements (per minute)

100

5.49

2.72

0.33-15.66

.87

Facial adaptor duration (%)

100

1.69

4.97

0 – 27

.99

Body adaptor duration (%)

100

13.52

23.10

0 – 96

.99

Illustrator duration (%)

100

8.55

14.66

0 – 72

.96

Behavioral angriness measure
(range of scores)

Observer anger judgment (1-7)

77

3.80

.83

1.75-6.08

.87

Angry voice rating (1-7)

77

2.96

.92

1.50-5.67

.69

Lips tight (per minute)

76

2.36

1.82

0-7.94

.65

Lips pressed (per minute)

76

0.44

.64

0-3.60

.82

Brows lower (per minute)

76

0.39

.69

0-2.77

.64

Note. M, SD and range refer to raw scores, reliabilities to log-transformed scores in the case of the behavior codings.
a sample size is smaller for anger indicators because participants, who realized that the anger induction was part of the experiment, had to be excluded from the analyses of the anger induction. One participant stood up during the anger induction, and his facial expression could, therefore, not be coded.
b agreement α of 4 observers for observer judgments, and of 2 observers for voice ratings, correlation r between 2 independent codings for behavior codings (n = 20).

5.4.4  Correlations of Direct, Indirect and Behavioral Measures with Social Desirability

The correlations between the direct anxiousness and angriness measures and social desirability are depicted in the last column of Table 13. As expected by Hypothesis 3, almost all direct measures showed small to intermediate correlations with social desirability. On the contrary, the anxiousness and the angriness IAT were not significantly correlated with social desirability, r = .02, r = .-08. Likewise, the observer anxiety and anger judgments showed no substantial correlations with social desirability, r = .06, r = .-05.

↓87

Unexpectedly, the bipolar anxiousness self-rating did not, in contrast to the Manifest Anxiety Scale and the trait form of the State Trait Anxiety Inventory, significantly correlate with social desirability, r = -.08. A possible explanation might be that, although the anxious pole of the bipolar self-rating represents socially undesirable traits, the opposed self-confident pole does not clearly stand for socially desirable traits. Conceptually, social desirability scales aim to assess the degree to which persons describe themselves in socially desirable terms (e.g., “I am always polite.”). Therefore, social desirability is strongly related to agreeableness. Thus, although traits like self-confident, secure and unconcerned have a clear positive valence (cf. Chapter 0), these traits do not refer to socially adaptive and considerate behaviors. In contrast, the angry pole of the bipolar angriness self-rating clearly represents socially undesirable traits, and the opposed self-controlled pole clearly stands for socially desirable traits. This was consequently reflected in the negative correlation between the bipolar angriness self-rating and social desirability, r = -.30, p < .01.

5.4.5 Moderation of the Relationship between Direct and Indirect Measures by Social Desirability

To examine whether social desirability moderated the relationship between direct and indirect measures according to Hypothesis 4 I conducted stepwise multiple regression analyses. In these regressions, the direct anxiousness or angriness measures were the criteria. In the first step, social desirability and the anxiousness or angriness IAT were entered as predictors. In the second step, the interaction term (cross product) of both variables (each scored as deviation of the original scale from its own mean) was added as a predictor. The results for every direct anxiousness and angriness measure are depicted in Table 15. As indicated by the zero-order correlations (Table 13), direct anxiousness measures were predicted by the anxiousness IAT, and direct anxiousness and angriness measures were predicted by social desirability in almost every case. However, when the interaction term of social desirability and the IAT was entered in step 2, there was never a significant increment in the explained variance. Thus, as expected from Hypothesis 4 social desirability did not moderate the relationship between either direct and indirect anxiousness or direct and indirect angriness measures. The results were the same when regression analyses were conducted separately for both groups of different IAT order.

Table 15
Moderation of the Relationship between Direct and Indirect Measures by Social Desirability

Step 1

Step2

Direct measure

R 2

F (2, 97)

IATb
β

SD
β

ΔR 2

F (1, 96)

IATbx SD
β

Bipolar anxiousness self-rating

.071*

3.72*

.26*

-.08

.000

.04

-.02

Speaking Anxiety Emotionality

.025

1.23

-.01

-.16

.000

.00

.00

Speaking Anxiety Worries

.086*

4.59*

.18+

-.24*

.019

2.00

.14

State Trait Anxiety Inventorya

.094**

5.03**

.18+

-.25*

.005

.51

.07

Manifest Anxiety Scale

.135***

7.60***

.22*

-.30**

.000

.00

.00

Bipolar angriness self-rating

.097**

5.22**

.09

-.29**

.000

.05

-.02

State Trait Anger Expression Inventorya

.118**

6.51**

.00

-.34***

.000

.01

.01

Note. N = 100. IAT = Implicit Association Test, SD = social desirability. a trait form.
b Anxiousness IAT for prediction of direct anxiousness measures and angriness IAT for prediction of direct angriness measures. + p < .10 *p < .05 **p < .01 ***p < .001.

↓88

5.4.6 Zero Correlation between Anxiousness and Angriness

Conceptualizing anxiousness and angriness as orthogonal dimensions, it was expected by Hypothesis 5 that both these traits were not correlated. Hypothesis 5 was confirmed for the correlation between the bipolar anxiousness and angriness self-rating, r = -.08, n.s., and the observer anxiety and anger judgment, r = .00, n.s.. Nevertheless, the trait form of the State Trait Anger Expression Inventory showed intermediate correlations with the trait form of the State Trait Anxiety Inventory and the Manifest Anxiety Scale, and small correlations with the bipolar anxiousness self-rating (see Table 13). This replicated the results of some previous studies (Schwenkmezger et al., 1992), that showed that the State Trait Anger Expression Inventory was correlated with anxiousness because individuals high in neuroticism are more concerned with their anger expression than those individuals who are emotionally stable. When anxiousness and angriness were conceptualized as orthogonal dimensions, the bipolar self-ratings did not correlate with each other, and the angriness self-rating was not correlated with any direct anxiousness measure.

In contrast, Hypothesis 5 was not confirmed for the correlation between the anxiousness and the angriness IAT that was significantly positive, r = .32 p < .01. Moreover, order effects affected this correlation. The sequence of the anxiousness and the angriness IAT was counterbalanced across participants such that two groups with different IAT orders could be compared with each other. In the group that completed the anxiousness IAT as first test, both IATs were substantially correlated, r = .49, p < .001, whereas they were not even marginally correlated in the group that completed the angriness IAT first, r = .17, n.s.. This correlation difference was marginally significant, z = 1.77, p < .10 (two-tailed). The discrepancy might not be attributed to sample effects, as anxiousness and angriness were neither correlated for the bipolar self-ratings nor for the observer judgments in both groups, all r < .17, n.s..

↓89

A possible explanation might be that anxiousness normally shows higher correlations with neuroticism than angriness. This was also the case in the pilot study that was performed to select the bipolar items. In that pilot study (N = 42), anxiousness and neuroticism were strongly correlated, r = .82, p < .001, whereas angriness and neuroticism showed only a weak correlation, r = .21, p = .18. Working on the anxiousness IAT, participants could have possibly developed a classification heuristic, discriminating anxious versus self-confident as neurotic versus non-neurotic or even as positive versus negative attributes. In other words, participants recoded the IAT task because a discrimination of positive versus negative is easier than a discrimination of anxious versus self-confident (cf. De Houwer 2003a). This task-recoding was salient during the anxiousness IAT. Upon completion of the anxiousness IAT the task-recoding could have been transferred onto the angriness IAT, which would lead to a positive correlation between both IATs. In contrast, the angriness IAT is less likely to elicit to a positive-negative task-recoding, because angry versus self-controlled is less associated with neuroticism. Consequently, when the angriness IAT was the first test, the participants did not use a positive-negative classification, and the IATs did not correlate with each other.

To examine whether a positive-negative dimension is more salient in anxious versus self-confident than in angry versus self-controlled judgments, participants of two different groups rated the valence of the IAT stimuli. Instructions for the self-relevant group (41 undergraduate psychology students) asked to estimate how positive or negative one would rate a trait if it was one’s own. This was done because the self-concept IATs ask participants to combine ‘Me’ with personality traits, for example, anxious. “Anxious” may be judged more negatively when it refers to oneself rather than to anxiousness in general. Instructions for the control group (10 PhD psychology students) simply asked respondents to rate the positiveness or negativeness of traits in general. In both groups, the anxiousness and angriness traits were presented in a paper-pencil questionnaire, and respondents judged the valence of those traits on a 7-point scale (negative [---] [--] [-] [0] [+] [++] [+++] positive). Answers were coded such that higher values indicated more positive valence. The results are shown in Table 16.

↓90

Table 16
Valence Ratings of the IAT Stimuli from Two Different Samples

Undergraduates
(n = 41)

PhD students
(n = 10)

Group difference
(df = 49)

Attributes

M

SD

Range

M

SD

Range

t

p

d

Anxious (ängstlich)

2.68

1.15

1-6

2.20

.79

1-3

1.25

.22

.36

Timid (furchtsam)

2.49

1.08

1-5

2.20

.92

1-4

.78

.44

.22

Insecure (unsicher)

2.20

.84

1-4

2.50

1.18

1-5

-.95

.35

-.27

Worried (besorgt)

3.78

1.44

1-6

3.80

1.55

3-8

-.04

.97

-.01

Overly cautious (übervorsichtig)

2.17

1.00

1-5

2.20

.63

1-3

-.09

.93

-.03

Mean anxious attributes

2.66

.83

1.2-5.0

2.58

.60

1.6-3.6

.30

.77

.09

Self-confident (sicher)

6.02

.82

4-7

6.10

.74

5-7

-.27

.79

-.08

Daring (wagemutig)

4.85

1.20

3-7

4.10

1.10

3-6

1.81

.08

.52

Secure (selbstvertrauend)

6.37

.66

5-7

6.60

.52

6-7

-1.04

.30

-.30

Unconcerned (sorglos)

4.24

1.56

1-7

4.00

1.25

2-6

.46

.65

.13

Carefree (unbeschwert)

5.44

1.23

2-7

5.40

1.07

4-7

.09

.93

.03

Mean self-confident attributes

5.39

.69

3.6-6.6

5.24

.52

4.6-6.0

.62

.54

.18

Angry (ärgerlich)

3.07

1.27

1-6

3.00

1.33

1-6

.16

.87

.05

Hot-tempered (aufbrausend)

2.34

1.28

1-6

2.60

1.43

1-5

-.56

.58

-.16

Undercontrolled (unbeherrscht)

1.88

.87

1-4

1.90

.88

1-3

-.07

.94

-.02

Hot-headed (hitzköpfig)

2.83

1.30

1-7

2.20

1.03

1-4

1.42

.16

.41

Irritable (motzig)

1.95

1.09

1-6

1.80

.79

1-3

.41

.68

.12

Mean angry attributes

2.41

.70

1.4-4.6

2.30

.60

1.6-3.4

.47

.64

.14

Self-controlled (kontrolliert)

4.76

1.37

2-7

4.30

1.34

2-6

.95

.35

.27

Thoughtful (bedächtig)

4.73

.92

3-7

4.90

.88

3-6

-.52

.60

-.15

Self-disciplined (selbstbeherrscht)

4.98

1.19

3-7

5.00

1.15

2-6

-.06

.95

-.02

Adaptable (fügsam)

2.46

1.16

1-6

2.40

.52

2-3

.17

.87

.05

Calm (friedlich)

5.54

1.05

3-7

5.70

1.16

3-7

-.43

.67

-.12

Mean self-controlled attributes

4.49

.61

3.4-6.0

4.46

.65

3.4-5.4

.15

.88

.04

Note. The scale format was a 7-point scale with 1 indicating negative, 4 indicating neutral, and 7 indicating positive valence.

As it can be seen from Table 16, the valence of the traits was not judged differently by the undergraduates and the PhD students, although the undergraduates rated the valence as if the traits were their own. “Daring” was judged marginally more positive by the undergraduates. However, given the amount of tests performed, this might may very well be a chance finding. It should be noted that the design of this valence check confounded group membership (undergraduates versus PhD students) and instruction (self-relevant versus control). However, it is unlikely to expect that the PhD students and the undergraduates had a different self-concept in anxiousness or angriness. Thus, the direct valence estimates seemed to be unaffected by the instruction to judge the traits as if one’s own. More importantly, the group differences did not consistently point in the same direction, neither for the positively (M > 4) nor for the negatively (M < 4) evaluated traits. Since the sample size of the undergraduate group was larger, and the undergraduate group is more similar to the sample of Study 2, the results of this group will be discussed in regards to the valence estimates.

As one may recall, the category label of the attribute concept was anxious versus self-confident for the anxiousness IAT, and angry versus self-controlled for the angriness IAT, respectively. Since the category label has a chief influence on the IAT effect (cf. Chapter 2.4.2), the valence estimates for the category labels as well as for the category means were compared. Concerning the labels, anxious was rated more negatively than self-confident, d = 3.17. (The effect size d for repeated measures was computed as √2(M 1  - M 2 )/SD where SD is the standard deviation of the difference scores; see Cohen, 1988). Similarly, angry was rated more negatively than self-controlled, d = 1.33, but the effect size was less than half than for anxious versus self-confident. Thus, a positive-negative dimension was stronger for anxious versus self-confident than for angry versus self-controlled. More importantly, self-confident was also rated more positively than self-controlled, d = 1.17. “Anxious” was not judged more negatively than angry, t (40) 1.41, p = .17, d = .31, although the effect pointed in the expected direction (see the first column of Table 16).

↓91

Concerning the category means, the five anxious attributes were rated more negatively than the five self-confident attributes, d = 3.03. However, the five angry attributes were also rated more negatively than the five self-controlled attributes, d = 3.31. Thus, at the level of category means, a positive-negative dimension was as strong for anxious versus self-confident as for angry versus self-controlled. The five self-confident attributes were judged more positively than the five self-control attributes, d = 1.50. The five anxious attributes were not judged more negatively than the five angry attributes, t (40) = -1.35, p = .18, d = -.31, and the effect did not even point in the expected direction (see the first column of Table 16).

In summary, the positive-negative difference was stronger for anxious versus self-confident than for angry versus self-controlled. This was true only at the level of the category labels but not at the level of the category means. Nevertheless, self-confident was judged more positively than self-controlled concerning the category labels as well as the category means. More importantly, within the self-control attributes, one attribute (adaptable) was judged negatively when it was tested against the neutral scale midpoint, t (40) = -8.45, p < .001. In contrast, none of the anxious attributes was judged positively, and none of the self-confident attributes was judged negatively.

Altogether, a positive-negative dimension was less clear in the angriness IAT than in the anxiousness IAT concerning both the level of category labels and the level of category exemplars. Consequently, a positive-negative dimension was less salient within the angriness IAT. As a result, there might have been a transfer effect from the anxiousness IAT on the angriness IAT, but not vice versa. In the next section, I present the effects of different IAT order on the correlations of the anxiousness and the angriness IAT concerning the correlations with direct self-ratings and the observer judgments.

5.4.7 Order Effects on IAT Correlations

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It was expected by Hypothesis 6 that the second IAT tended to be less valid than the first IAT. Given the transfer effect from the anxiousness on the angriness IAT this should be especially true for the angriness IAT. Table 17 depicts the overall correlations and the correlations by IAT order for both IATs. Concerning the anxiousness IAT, all correlations with direct anxiousness measures declined in the second test, except for the trait form of the State Trait Anxiety Inventory. When the anxiousness IAT was the first test, it showed significant or marginally significant correlations with several direct anxiousness measures, whereas these correlations were not even marginally significant when it was the second test. This correlation decrease was marginally significant for the subscale Worries of the Speaking Anxiety Questionnaire, z = 1.39, p < .10 (one-tailed), and not even marginally significant for the correlations of the anxiousness IAT with other direct anxiousness measures, all z < 1.12, n.s. (one-tailed). Contrary to Hypothesis 6, the correlation with the observer anxiety judgment tended to be higher, and was significant only when the anxiousness IAT was the second test. However, this correlation difference was small and nonsignificant, z = -.58, n.s. (one-tailed). In regards to the anxiousness IAT, a pattern of reduced validity for the second test was confirmed for the correlations with direct anxiousness measures but not for the correlation with the observer judgment.

Table 17
Overall and Correlations by IAT Order for the Anxiousness and the Angriness IAT (Study 2)

Anxiousness IAT

Angriness IAT

Overall

1st test

2nd test

Overall

1st test

2nd test

Angriness IAT

.32**

.49***

.17

-

-

-

Bipolar anxiousness self-rating

.25*

.28*

.23

-.04

-.18

.15

Bipolar angriness self-rating

-.03

-.06

.03

.11

.16

.06

Speaking Anxiety Emotionality

-.01

.02

-.08

-.03

-.24+

.20

Speaking Anxiety Worries

.17+

.27+

-.01

.05

-.16

.27+

Manifest Anxiety Scale

.21*

.31*

.09

.00

-.15

.22

State Trait Anxiety Inventorya

.17+

.17

.20

.02

-.06

.12

State Trait Anger Expression Inventorya

-.01

-.01

.01

.03

-.16

.25+

Social Desirability

.02

-.07

.16

-.08

.02

-.20

Observer anxiety judgment

.26**

.22

.33*

-.07

-.05

-.09

Observer anger judgment

-.09b

-.04c

-.17d

-.11b

-.23d

.00c

Note. N = 100 for overall correlations, n = 50 for correlations by different IAT order. IAT = Implicit Association Test. a trait form. b n = 77. c n = 41. d n = 36.
+ p < .10 *p < .05 ***p < .001.

In regards to the correlations of the angriness IAT, the pattern was less clear. This might be due to the fact that the angriness IAT showed only small convergent validity with direct angriness measures already in the first test. As expected by Hypothesis 6, the correlation with the bipolar angriness self-rating decreased when the angriness IAT was the second test. However, this correlation difference was small and nonsignificant, z = .49, n.s. (one-tailed). Moreover, the opposite was true for the trait form of State Trait Anger Expression Inventory. When the angriness IAT was the second test, its correlation with the trait form of the State Trait Anger Expression Inventory was marginally significant and higher than in the first test, z = -2.02, p < .05 (two-tailed). The correlation with the observer anger judgment was even nonsignificantly negative when the angriness IAT was the first test and zero when it was the second test, but did not differ significantly between both groups, z = -1.14, n.s. (one-tailed). Thus, a pattern of reduced convergent validity for the second test was not found for the angriness IAT, and validity was small in both cases.

↓93

With regard to discriminant validity, the anxiousness IAT did not correlate with direct angriness measures in any case. In contrast, the angriness IAT correlated with the anxiousness IAT and tended to correlate with direct anxiousness measures, when it was the second test. Thus, there was a marginally positive correlation with the subscale Worries of the Speaking Anxiety Scale (see Table 17). On the other hand, these correlations tended to be negative, when the angriness was the first test. Specifically, there was a marginally negative correlation with the subscale Emotionality of the Speaking Anxiety Scale. Possibly, for some participants, the category self-controlled, that was the opposite of angry within the angriness IAT, was more related with high rather than low anxiousness. Therefore, the small negative correlation between the angriness IAT and direct anxiousness measures might have appeared. In contrast, when the angriness was the second test, it tended to positively correlate with direct anxiousness measures due to the assumed transfer effect from the anxiousness IAT onto the angriness IAT. The correlation differences (two-tailed tests) between both groups were significant for the two subscales of the Speaking Anxiety Scale (in both cases z > 2.12, p < .05), marginally significant for the Manifest Anxiety Scale (z = 1.82, p < .10), and nonsignificant for the bipolar anxiousness self-rating and the trait form of the State Trait Anxiety Inventory (in both cases z < 1.61, n.s). Consequently, the increase in correlation with direct anxiousness measures provides further evidence for a transfer effect from the anxiousness IAT on the angriness IAT.

The positive correlations of direct anxiousness measures with the angriness IAT in the second test might also lead to the positive correlation between the angriness IAT and the trait form of the State Trait Anger Expression Inventory. Table 13 shows that the trait form of the State Trait Anger Expression Inventory was positively correlated with direct anxiousness measures. Thus, the positive correlation between the angriness IAT and the trait form of the State Trait Anger Expression Inventory might be mediated by the correlation of both measures with direct anxiousness measures. However, when the correlation between both measures was controlled for their correlation with direct anxiousness measures the partial correlation was only a little smaller than the zero-order correlation, r = .20, n.s. versus r = .25, p < .10. Thus, the angriness IAT seemed to show at least some convergent validity with the trait form of the State Trait Anger Expression Inventory.

Altogether, the anxiousness IAT showed a pattern of reduced validity for the second test with respect to direct measures but not for the observer anxiety judgment. The angriness IAT showed small convergent validity in general, and was affected by a transfer effect from the anxiousness IAT. This led to positive correlations between both IATs, and a trend to positive correlations between the angriness IAT and direct anxiousness measures.

5.4.8 Prediction of the State and the Behavioral Measures by Direct and Indirect Measures

↓94

In this section, I report the results of hierarchical regression analyses that explored whether state and behavioral measures of anxiety and anger were predicted by direct and indirect measures. According to Hypothesis 7, it was expected that self-reported state measures were predicted by self-reported trait measures, and that the IATs added incremental validity to self-reported measures to the prediction of behavior.

To examine the prediction of anxiety I performed separate hierarchical regressions with self-reported state anxiety and behavioral anxiety as criteria. Predictors were direct and indirect anxiousness measures, as well as direct state anxiety and its change when behavioral anxiety was the criterion. Direct measures (the bipolar anxiousness self-rating, the subscales Emotionality and Worries of the Speaking Anxiety Questionnaire, the trait form of the State Trait Anxiety Inventory, the Manifest Anxiety Scale, plus, for the prediction of anxious behavior, the bipolar state anxiety self-rating and its change) were entered in one step, and the anxiousness IAT was entered in the other step.

↓95

Table 18
Predictions of the State Anxiety Measures and the Behavioral Anxiety Indicators by Direct Measures and the Anxiousness IAT

Hierarchical regression

Step 1: Direct measuresa

Step 2: Anxiousness IAT

Measure

R 2

ΔR 2

Bipolar state anxiety self-rating

Speech

.448***

.000

Change (speech minus baseline)

.131*

.001

Behavioral anxiety indicators

Observer anxiety judgment

.171*

.072**

Anxious voice rating

.131+

.043*

Facial adaptor duration

.083

.003

Body adaptor duration

.080

.006

Illustrator duration

.055

.004

Nervous mouth movements (frequency)

.054

.000

Note. N = 100. IAT = Implicit Association Test.
a For the regression analysis on direct state anxiety all direct anxiousness measures (the bipolar anxiousness self-rating, the subscales Emotionality and Worries of the Speaking Anxiety Questionnaire, the trait form of the State Trait Anxiety Inventory, and the Manifest Anxiety Scale) were entered. For regression analysis on behavioral anxiousness indicators all direct anxiousness plus the state anxiety measures (bipolar self-rating and its change) were entered. + p < .10 *p < .05 **p < .01 ***p < .001.

In order to evaluate the contribution of every single predictor and to control for suppressor effects, I carried out different regressions considering the following points: (a) The contribution of each direct measure was individually analyzed in a separate regression entering the direct measure in one step, and the IAT in the other step. (b) Both orders of these hierarchical regressions were organized such that the direct measure was entered in Step 1 and the IAT in Step 2, as well as the opposite order of both steps . (c) Predictive validity of the IAT was inspected separately for both groups of different IAT order (anxiousness IAT as first versus as second test). To avoid accumulation of α-error I first performed overall hierarchical regressions entering all direct measures in Step 1 and the IAT in Step 2. Then, I performed further analyses following points (a) to (c). To report the results for each criterion I begin with the overall analysis, as depicted in Table 18. Results are then outlined more clearly with the findings of points (a) to (c). I conclude with examining the standardized βs of all predictors in Step 2 of the overall analysis. To keep these analyses manageable I did not consider marginally significant results.

As it can be seen in the first row of Table 18, direct anxiousness measures significantly accounted for self-reported state anxiety immediately before the speech, whereas the anxiousness IAT did not. This was (a) true for every single direct anxiousness measure, (b) independent of the regression order, and (c) not affected by different IAT orders. However, although all direct anxiousness measures share significant portions of variance with self-reported state anxiety (see Table 19), they did not independently contribute to the criterion. When all predictors were entered into the overall regression (Step 2 in Table 18), only the bipolar anxiousness self-rating and the Emotionality subscale of the Speaking Anxiety Scale were significant predictors, β = .41, t = 3.42, p < .001, β = .43, t = 3.89, p < .001, all others |β| < .09, |t| < .70, n.s..

↓96

Direct anxiousness measures also predicted state anxiety change after the anxiety induction, whereas the anxiousness IAT did not. This was (a) only true for the Emotionality subscale of the Speaking Anxiety Questionnaire, and independent of (b) regression order and (c) IAT order. Accordingly, only the Emotionality subscale accounted for the increase in state anxiety in the overall analysis (Step 2 in Table 18), β = .46, t = 3.22, p < .01, all others |β| < .27, |t| < 1.81, n.s..

Table 19
Correlations of State Anxiety (Study 2)

Bipolar state anxiety self-rating

State anxiety

Speech

Change
(speech minus baseline)

Bipolar state anxiety self-rating (speech)

-

.66***

Anxiousness measures

Bipolar anxiousness self-rating

.56***

.09

Speaking Anxiety Emotionality

.52***

.29**

Speaking Anxiety Worries

.40***

.09

Manifest Anxiety Scale

.47***

.13

Trait form of the State Trait Anxiety Inventory

.45***

.03

Anxiousness IAT

.11

.00

Behavioral anxiety

Observer anxiety judgment

.38***

.23*

Anxious voice rating

.30***

.14

Facial adaptor duration

.07

-.02

Body adaptor duration

.16

.07

Illustrator duration

.09

.19+

Nervous mouth movements (frequency)

.15

.06

Note. N = 100. IAT = Implicit Association Test.
+ p < .10 * p < .05 ** p < .01 *** p < .001.

↓97

As it can be seen in Table 18 the observer anxiety judgment was predicted by direct and indirect measures. (a) This was true for the bipolar anxiousness self-rating, the Emotionality subscale, the bipolar state anxiety self-rating, and the change in state anxiety. The trait form of the State Trait Anxiety Inventory and the Manifest Anxiety Scale contributed marginally to the observer anxiety judgment. The Worries subscale was not even a marginal predictor. The anxiousness IAT accounted for the observer anxiety judgment independently from all direct measures. (b) When the anxiousness IAT was entered first, only the Emotionality subscale, the self-reported state anxiety and its change additionally contributed to the observer judgment. (c) As indicated by the zero-order correlations (Table 20), the anxiousness IAT showed significant correlations with, and was, therefore, a significant predictor for the observer anxiety judgment when it was preceded by the angriness IAT. When the anxiousness IAT was the first test it marginally predicted the observer anxiety judgment. To conclude, only the Emotionality subscale, the bipolar state anxiety self-rating, and the anxiousness IAT were significant predictors in the overall analysis, β = .33, t = 2.26, p < .05, β = .36, t = 2.05, p < .05, β = .29, t = 2.94, p < .01, all others |β| < .27, |t| < 1.81, n.s..

Table 20
Correlations of Behavioral Anxiety Measures in Study 2

Anxiousness IAT

Explicit anxiousness

Speaking Anxiety

Behavioral measure

Observer judgment

1st test

2nd test

Both

Bipolar self-rating

MAS

STAI

Emotio-nality

Worries

Observer judgment

-

.22

.33*

.26**

.22*

.19+

.19+

.29**

.15

Anxious voice rating

.61***

.23

.19

.22*

.14

.06

.13

.23*

.22*

Facial adaptor duration

-.14

-.11

-.01

-.07

-.01

-.16

-.09

-.02

-.07

Body adaptor duration

.26**

.02

.13

.06

.07

-.06

-.03

.07

-.05

Illustrator duration

.02

.23

-.15

.05

.00

-.01

-.05

-.05

-.11

Nervous mouth movements (frequency)

.22*

.00

.11

.04

.13

. 07

.15

-.03

.00

*Note. N = 100 (n = 50 for different IAT orders). IAT = Implicit Association Test, MAS = Manifest Anxiety Scale, STAI =  trait form of the State Trait Anxiety Inventory.
* p < .05 ** p < .01 *** p < .001.

The anxious voice rating was marginally predicted by direct measures and significantly predicted by the anxiousness IAT. (a) With regards to single direct measures, the Worries and the Emotionality subscale, as well as the bipolar state anxiety self-rating were significant predictors. The anxiousness IAT marginally contributed to the anxious voice rating, when entered after the bipolar anxiousness self-ratings, the Worries subscale, or the state anxiety self-rating. The anxiousness IAT was always a significant predictor, when entered after any other direct measure. (b) When the direct measure was entered after the anxiousness IAT, the Emotionality subscale as well as the self-reported state anxiety were significant predictors and the Worries subscale was a marginal predictor. (c) As it may be seen in Table 20, the anxiousness IAT significantly correlated with the anxious voice rating only when both groups with different IAT order were pooled. When the groups were inspected separately, sample sizes were smaller, and the small positive correlations failed to reach the significance criterion although the effect sizes were almost the same. This was also true for the regression analysis. Thus, the IAT was a significant predictor only when the groups with different IAT order were analyzed simultaneously. Finally, in the overall regression only the anxiousness IAT was a significant predictor β = .22, t = 2.17, p < .05. The bipolar state anxiety self-rating marginally accounted for the anxious voice rating, β = .37, t = 1.98, p < .10, all others |β| < .27, |t| < 1.54, n.s..

↓98

The duration of facial adaptors, body adaptors, and illustrators as well as the frequency of nervous mouth movements was neither predicted by direct measures nor by the anxiousness IAT. (a) When entered as single predictors, only the increase in state anxiety was a marginal but surprisingly positive predictor for illustrator duration. However, the effect was only small and might be due to chance. All other direct measures were not even marginal predictors for any of the anxiety codings. This pattern was not affected by (b) regression or (c) IAT order. In the overall analyses, neither any direct measure nor the anxiousness IAT was a significant predictor, all |β| < .19, |t| < 1.19, n.s.. This was the case even though nervous mouth movements and the duration of body adaptors showed small correlations with the observer anxiety judgment (see Table 20). Thus, although the observers interpreted nervous mouth movements and body adaptors as anxious behavior, these codings were not related to self-reported anxiousness and anxiety measures or the anxiousness IAT (see Table 20). It should be noted that the observer anxiety judgments showed a large correlation with the anxiety rating of the participants’ voices (see Table 20). Therefore, important anxiety indicators might not be found in the gestures or the facial expressions, but in the verbal expression and the sound of the participants’ voices.

To summarize these findings, self-reported state anxiety was predicted by direct anxiousness measures but not by the anxiousness IAT. This confirmed Hypothesis 7. Important predictors for state anxiety were the bipolar anxiousness self-rating and the Emotionality subscale of the Speaking Anxiety Scale. In contrast, the anxiousness IAT added incremental validity over direct measures to the prediction of the observer anxiety judgment and the anxious voice rating. This confirmed, again, Hypothesis 7. It should be noted that the observer anxiety judgment and the anxious voice rating was also predicted by direct measures. Important predictors were, again, the Emotionality subscale and the self-reported state anxiety. Codings of anxious behavior were neither predicted by direct measures nor by the anxiousness IAT.

To examine the prediction of anger, I carried out the same hierarchical regressions as for the prediction of anxiety but with self-reported state anger and behavioral anger as criteria. Predictors were direct and indirect angriness measures as well as direct state anger and its change when behavioral anger was used as the criterion. Again, direct measures (the bipolar angriness self-rating, the trait form of the State Trait Anger Expression Inventory, plus, for the prediction of angry behavior, the bipolar state anger self-rating and its change) were entered in one step, while the angriness IAT was entered in the other step. Results are reported considering the same aspects as for the prediction of anxiety. I start with the results of the overall analysis as depicted in Table 21. Then, I explore (a) the contribution of single direct measures, (b) different regression orders, and (c) different IAT orders. Finally, I examine the standardized βs of all predictors in Step 2 of the overall analysis.

↓99

As it is shown in Table 21, the bipolar state anger self-rating after the computer crash and the state anger change as compared to the baseline were neither predicted by direct angriness measures nor by the angriness IAT. This was (a) the same for the trait form of the State Trait Anger Expression Inventory and the bipolar angriness self-rating, and not affected by (b) regression or (c) IAT order. Thus, in both overall analyses neither direct angriness measures nor the angriness IAT were significant predictors, all |β| < .18, |t| < 1.64, n.s..

Table 21
Predictions of the State Anger Measures and the Behavioral Anger Indicators by Direct Measures and the Angriness IAT

Hierarchical regression

Step 1: Direct measures

Step 2: Angriness IAT

Measure

R 2

ΔR 2

Bipolar state anger self rating

Computer crash

.044

.000

Change (computer crash minus baseline)

.003

.006

Behavioral angriness indicators

Observer anger judgment

.224***

.015

Angry voice rating

.154*

.014

Lips tight (frequency)

.029

.144***

Lips pressed (frequency)

.020

.023

Brows lower (frequency)

.110+

.003

Note. N = 77. IAT = Implicit Association Test.
a
For the regression analysis on direct state anger all direct angriness measures (the bipolar angriness self-rating and the trait form of the State Trait Anger Expression Inventory) were entered. For regression analysis on behavioral angriness indicators all direct angriness plus the state anger measures (bipolar self-rating and its change) were entered. + p < .10 *p < .05 ***p < .001.

The observer anger judgment was predicted by direct measures but not by the angriness IAT. (a) This was true for the trait form of the State Trait Anger Expression Inventory and for the bipolar angriness self-rating but not for self-reported state anger or its change. This pattern was not affected by (b) regression or (c) IAT order. The only significant predictor in the overall analysis was the bipolar angriness self-rating, β = .34, t = 2.94, p < .01. The trait form of the State Trait Anger Expression Inventory and the self-reported change in state anger were only marginally significant predictors, β = .21, t = 1.87, p < .10, β = .25, t = 1.90, p < .10. The self reported state anger did not significantly account for the observer anger judgment, β = -.12, t = -.87, n.s..

↓100

Table 22
Correlations of State Anger (Study 2)

Bipolar state anger self-rating

State anger

Computer crash

Change (computer
crash minus baseline)

Bipolar state anger self-rating (computer crash)

-

.60***

Angriness Measures

Bipolar angriness self-rating

.18

-.04

State Trait Anger Expression Inventorya

.17

.02

Angriness IAT

-.06

-.08

Behavioral Anger

Observer anger judgment

.14

.18

Angry voice rating

.28*

.28*

Lips tight (frequency)

.03

-.03

Lips pressed (frequency)

-.07

-.08

Brows lower (frequency)

-.04

.20+

*Note. N = 100, n = 77 for behavioral anger measures. IAT = Implicit Association Test.
a Trait form. + p < .10 * p < .05 *** p < .001.

Similarly the angry voice rating was predicted by direct measures but not by the angriness IAT. (a) When direct measures were analyzed individually, the bipolar angriness self-rating, the bipolar state anger self-rating, and its change significantly accounted for the angry voice rating. In contrast, the trait form of the State Trait Anger Expression Inventory failed to be a significant predictor. This pattern was not affected by (b) regression or (c) IAT order. In the overall analysis the bipolar angriness self-rating was the only even so marginally significant predictor, β = .24, t = 1.96, p < .10, whereas all others were |β| < .23, |t| < 1.66, n.s..

The frequency of putting the lips tight was predicted by the angriness IAT but not by direct measures. However, contrary to expectations, the angriness IAT and the frequency of tight lips were negatively correlated (see Table 23). When (a) direct measures were inspected individually or (b) the regression order was varied, results were the same. Nevertheless, this pattern was true only when (c) the angriness IAT was the first test. As already indicated by the zero-order correlations (Table 23), the frequency of tight lips and the angriness IAT share common portions of variance only when the angriness IAT was completed before the anxiousness IAT. Concerning the overall analysis, only the angriness IAT was a significant, although negative predictor, β = -.38, t = -3.48, p < .001, all others were |β| < .14, |t| < 1.19, n.s..

↓101

Table 23
Correlations of Behavioral Anger Measures in Study 2

Angriness IAT

Explicit angriness

Behavioral measure

Observer judgment

1st test

2nd test

Both

Bipolar self-rating

STAXI

Observer anger judgment

-.23

.00

-.05

.38**

.33**

Angry voice rating

.51***

-.17

-.10

-.10

.25*

.16

Lips tight (frequency)

.08

-.55**

-.21

-.34**

.05

.16

Lips pressed (frequency)

-.05

-.28+

.00

-.13

.00

-.11

Brows lower (frequency)

.08

-.12

.00

-.12

-.07

-.21+

Note. n = 77 (n = 36 for angriness IAT as first test, and n = 41 for angriness IAT as second test). IAT = Implicit Association Test. + p < .10 * p < .05 ** p < .01 *** p < .001.

The frequency of pressing the lips together (lips pressed) was neither predicted by direct measures nor the angriness IAT. This pattern (a) was true for every single direct measure. When the angriness IAT (b) was entered before the direct measures and (c) was the first test, it marginally accounted for the frequency of pressed lips. However, as it is indicated by the correlations in Table 23, the angriness IAT was then, once more, a negative predictor. In the overall analysis, none of the predictors was even marginally significant, all |β| < .15, |t| < -1.31, n.s..

The frequency of frowns (brows lower) was marginally predicted by direct measures but not by the angriness IAT. (a) Concerning single direct measures, this was true for the change in bipolar state anger and the trait form of the State Trait Anger Expression Inventory. However, the prediction was positive only for the former and again surprisingly negative for the latter (see the zero-order in Table 22 and in Table 23). This pattern was neither affected by (b) regression nor (c) IAT order. In the overall regression, only the change in state anger was a significant predictor, β = .32, t = 2.27, p < .05, all others |β| < .20, |t| < 1.64, n.s.. Since the frequency of frowns did not correlate with the observer anger judgment, and the effects of the direct measures were small and contradictory, these results might be due to chance.

↓102

To summarize these findings, Hypothesis 7 was not confirmed with regard to the prediction of state anger through direct angriness measures. Hypothesis 7 was also not confirmed with regard to the incremental validity of the angriness IAT for the prediction of angry behavior. The negative correlation of the angriness IAT with the frequencies of tight and pressed lips was contrary to expectations, and true only when the angriness IAT was the first test. Moreover, as it can be seen from Table 23, none of the anger codings were correlated with the observer anger judgment. Therefore, the anger codings might not be valid indicators for angry behavior. However, the observer anger judgment correlated substantially with the anger rating of the participants’ voices (see Table 23). Thus, important anger indicators might not be found in the facial expressions, but in the verbal expression and the sound of the participants’ voices. Finally, the observer anger judgment and the angry voice rating were only predicted by direct measures, whereby the bipolar angriness self-rating was the most important predictor.

5.5 Discussion

In the Discussion section, I first summarize the main findings of Study 2, and then briefly refer to gender differences. Subsequently, I discuss the differences between direct and indirect measures concerning the prediction of anxious and angry behavior. Finally, I refer to the conceptualization of angriness within the present study.

5.5.1  Summary of the main findings

Study 2 explored the psychometric properties of an anxiousness and an angriness IAT. Thereby, the sequence of the IATs was counterbalanced. The IATs’ convergent and discriminant validity was examined both for self-reported anxiousness and angriness, as well as for anxious versus angry behavior after emotion inductions. Study 2 tested seven hypotheses.

↓103

First, the efficacy of the emotion inductions for anxiety and anger was reflected in an increase of self-reported state anxiety and state anger, respectively. Second, the anxiousness and angriness attributes of the IATs were validated by their correlations with established questionnaire measures. Third, in contrast to direct self-ratings, the anxiousness and the angriness IAT were not correlated with social desirability. Fourth, social desirability did not moderate the correlation between direct and indirect measures. Fifth, the bipolar anxiousness and angriness self-ratings, as well as the observer judgments for anxiety and anger did not correlate with each other. In contrast, the anxiousness and the angriness IAT were correlated when the anxiousness IAT was the first test, r = .49, but not when the angriness IAT was the first test, r = .17. This correlation difference was marginally significant, and was attributed to a task-recoding in terms of a positive-negative self-dimension that was transferred from the anxiousness IAT onto the angriness IAT. Sixth, the validity of the anxiousness and the angriness IAT was marginally affected, if the test was the second rather than the first indirect test. Seventh, the anxiousness, but not the angriness IAT, added incremental validity over direct measures to the prediction of behavior.

5.5.2 Gender Differences

The sample in Study 2 was counterbalanced for gender. However, sex was not introduced as an independent variable in the results section because female and male participants did not differ significantly from each other with respect to the correlational analyses (Hypotheses 2-7). The only significant influence of gender was found in the anxiety induction effect (bipolar state anxiety items after the announcement of the speech minus baseline, F (1, 98) = 17.71, p < .001) that was qualified by an interaction effect with gender, F (1, 98) = 5.58, p < .05. Post hoc comparisons with Bonferoni correction (p < .025) indicated that the increase in state anxiety was true only for women, t (49) = 4.35, p < .001, but not for men, t (49) = 1.41, n.s.. This finding is different from Spitznagel et al. (2000) who found that women generally report more speech anxiety than man, but do not differ with respect to the increase in state anxiety. However, the studies from Spitznagel et al. used a different scale that asked for self-reports of habitual speech anxiety before, during, and after an imagined speech without a real anxiety induction. In the present study, there was no main effect of gender on any direct, indirect, or behavioral measure.

5.5.3 Behavior Prediction Through Direct and Indirect Measures

In Study 2, the observer judgments of anxious and angry behavior were predicted by the direct self-ratings. Additionally, the observer judgment of anxious behavior was predicted by the anxiousness IAT, and correlated with the duration of body adaptors and the frequency of nervous mouth movements. However, none of the behavioral anxiety and anger codings correlated significantly with either the direct measures or the IATs. The same pattern of results was found for the shyness measures in Study 1. Thus, it is a difficult task to identify valid behavioral indicators that correlate with the observer judgments and direct or indirect personality self-concept measures. There might be several reasons why Study 2 failed to succeed in the search for valid behavioral cues.

↓104

Concerning the behavioral anger measures, the interaction between the experimenter and the participant after the computer crash was probably too short for aggregating sufficient anger indicators. The mean duration was 117 (SD = 21) seconds and ranged from 72 to 168 seconds. In contrast, the duration of the speech was three minutes for all participants. Importantly enough, the duration of the anger sequence did not correlate with either the observer anger judgment, direct or indirect angriness measures, or any of the anger codings (that were coded in frequencies per minute). Thus, behavioral anger measures were not confounded with the duration of the anger sequence. Nevertheless, the anger sequence was relatively short, and most behavioral anger indicators were so infrequent that even intercoder reliability was unsatisfactory. This does not, however, imply that the anger induction was inapt for the observation of angry behavior since the direct angriness measures showed predictive validity for the observer anger judgment. As many earlier attempts to study anger in the lab (e.g., Pauls & Stemmler, 2003; Wiedig, 2003), the present study only partially solved the problem that the anger sequence has to be both (a) long enough and (b) unrecognized by the participants.

Concerning the behavioral anxiety measures, the results from Egloff and Schmukle’s (2002) Study 4 were only partially replicated in the present study. In both studies the anxiousness IAT added incremental validity over direct measures to the prediction of anxious behavior. In contrast, the observer anxiety judgment was significantly predicted by direct anxiousness measures in the present study but not in Egloff and Schmukle’s Study 4. This might be due to the fact, that more direct anxiousness measures were included in the present study. The situation-specific direct measures, that is, the emotionality subscale of the Speaking Anxiety Questionnaire and the bipolar state anxiety items, were particularly strong predictors for the observer anxiety judgment in the present study. Yet, the trait form of the State Trait Anxiety Inventory also correlated marginally with the observer anxiety judgment, r = .19, p < .10, whereas this was not true for Egloff and Schmukle’s study, r = .12, n.s.. However, this correlation difference was only small, and the lack of predictive validity of the direct anxiousness measure in Egloff and Schmukle’s Study 4 might also be attributed to the relatively small sample size (N = 33). Thus, the present study is in line with the expectation that direct measures show small to moderate validity for the prediction of behavior (Funder, 1999).

Differently from Egloff and Schmukle’s Study 4, the frequency of nervous mouth movements was not predicted by the anxiousness IAT in the present study. This could not have been attributed to a lack of cross-lab reliability of the behavioral coding since consistency between both labs was completely satisfactory. However, the anxiousness IATs of both studies differed with respect to the attribute categories and the attribute exemplars. Attribute categories were anxiety versus calmness in Egloff and Schmukle’s studies, and anxious versus self-confident in the present study. A possible post hoc explanation is that behavioral nervousness is more directly linked to a lack of calmness than to a lack of self-confidence. Therefore, the anxiety versus calmness IAT from Egloff and Schmukle might have shown better predictive validity.

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Additionally, the participants in Egloff and Schmukle’s Study 4 showed more nervous mouth movements than the participants in the present study, t (131) = 2.74, p < .01. Possibly, behavioral anxiety was higher in Egloff and Schmukle’s Study 4 due to the more evaluative nature of the speech task that asked the participants to summarize a scientific text instead of talking about euthanasia. Importantly enough, participants of both studies differed only marginally on the trait form of the State Trait Anxiety Inventory. Thus, differences between the studies should not be attributed to a general sample effect. In summary, it seems that interindividual differences in the personality self-concept of anxiousness are observed best, if one maximizes the evaluative character of the anxiety induction.

Altogether, the search for valid behavioral codings was not successful in Study 2. However, I refrained from further behavioral analysis due to the position effects and the lack of convergent and discriminant validity that were found in the angriness IAT. Nevertheless, the high correlations between the voice ratings and the observer judgments indicate that valid cues for interindividual differences in anxiousness and angriness may be found within the vocal expression of participants. Future studies of more objective vocal cues should explore this possibility.

5.5.4 Angriness, Agreeableness, Anger Expression, and Approach Behavior

Study 2 explored the implicit and explicit representations of the personality self-concept of anxiousness and angriness. Explicit representations were assessed with bipolar anxiousness and angriness self-ratings. Implicit representations were assessed by using the same words as stimuli within the IATs. The convergent validity of the bipolar anxiousness self-ratings with widespread anxiousness scales was high, r > .70. In contrast, the correlation between the bipolar angriness self-ratings and the trait form of the State Trait Anger Expression Inventory was only moderate, r = .45. This might be due to the conceptualization of anxiousness and angriness in the present study as orthogonal factors within the Big Five model of personality.

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Conceptually and empirically, anxiousness versus self-confidence was strongly related to neuroticism, and unrelated to agreeableness. Angriness versus self-control was weakly related to neuroticism, and strongly related to agreeableness. In contrast, the trait form of the State Trait Anger Expression Inventory is intermediately related with emotional instability or neuroticism (Spielberger, 1988), and was also significantly correlated with all direct anxiousness measures in the present study. Differently from the trait form of the State Trait Anger Expression Inventory, the present conceptualization of angriness refers more to agreeableness and less to emotional instability or neuroticism. This may account for the moderate correlation between the bipolar angriness self-ratings and the trait form of the State Trait Anger Expression Inventory. Nevertheless, the scale was labeled as angriness because it is less broad than the Big Five dimension of agreeableness.

Alternatively, angriness versus self-control may be considered as a combination of high anger-out and low anger-control, which are strongly negatively correlated. Moreover, anger-out and anger-control show the same intermediate correlations with the trait form of the State Trait Anger Expression Inventory as the bipolar angriness self-ratings (Schwenkmezger et al., 1992). Thus, the bipolar angriness self-ratings may more directly refer to styles of anger expression than the trait form of the State Trait Anger Expression Inventory. A more direct relation to angry behavior within the bipolar angriness self-ratings is also suggested by the somewhat higher correlations with the observer anger judgment and the angry voice rating than it was obtained for the trait form of the State Trait Anger Expression Inventory (see Table 23).

Anger is a negative emotion that is related to approach behavior (see Chapter 2.6.1). In contrast, anxiety is related to avoidance behavior that is true for most of the negative emotions (e.g., sadness, disgust). Due to the relation of state anger to approach motivation, anger is associated with different EEG activation than anxiety (Harmon-Jones & Sigelman, 2001). Possibly, the automatic categorization of stimuli within the angriness IAT was somehow obstructed because angry versus self-control combines approach-related words (e.g., angry) with negative valence, and avoidance-related words (e.g., self-control) with positive valence. In contrast, avoidance-related words (e.g., anxious) are combined with negative valence, and approach-related words (e.g., self-confident) with positive valence in the anxiousness IAT. Generally, positive valence is more strongly associated with approach motivation whereas negative valence is more strongly associated with avoidance motivation (e.g., Neumann, Förster, & Strack, 2003). However, within the angriness IAT motivational direction and valence of the stimuli are inversely related. This might distort the automatic categorization of angry versus self-controlled, and further accounts for (a) the lower internal consistency within the angriness IAT (.66) than within the anxiousness IAT (.72), (b) the lack of convergent validity of the angriness IAT, and (c) the susceptibility of the angriness IAT to the transfer effect from the anxiousness IAT.


Fußnoten und Endnoten

1  Different procedures of weighing the first 20 trials more than the succeeding trials did only minimally change the results.



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