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The Discrete Emotions Questionnaire: A New Tool for Measuring State Self-Reported Emotions


Several discrete emotions have broad theoretical and empirical importance, as shown by converging evidence from diverse areas of psychology, including facial displays, developmental behaviors, and neuroscience. However, the measurement of these states has not progressed along with theory, such that when researchers measure subjectively experienced emotions, they commonly rely on scales assessing broad dimensions of affect (positivity and negativity), rather than discrete emotions. The current manuscript presents four studies that validate a new instrument, the Discrete Emotions Questionnaire (DEQ), that is sensitive to eight distinct state emotions: anger, disgust, fear, anxiety, sadness, happiness, relaxation, and desire. Emotion theory supporting the importance of distinguishing these specific emotions is reviewed.


Beginning with the theories of Darwin [1] and James [2], several theories of discrete emotions have been proposed [3, 4, 5, 6]. Evidence for sets of discrete emotions has been garnered from research on facial expressions and other behavioral expressions, as well as from direct brain stimulations of non-human animals. Surprisingly, these empirical advances have not been met with advances in the measurement of discrete emotions via self-report questionnaires. Instead, the dominant method of assessing self-reported affect is through questionnaires aimed at assessing broad categories of positive and negative affect. The research reported in this article aims to provide a new measure of discrete emotions that can be used to assess multiple state emotions. Doing so will aid the field by improving our ability to measure the subjective experience of emotions, which will aid in understanding not only emotional reactions but the emotional processes that underlie much of our cognitive and social processes and behavioral responses.

On Accurately Assessing Self-Reported Emotions

Psychologists prefer to use established, published measures of constructs. As a result, many psychologists have chosen to use the Positive and Negative Affect Schedule (PANAS) [7] to measure self-reported positive and negative affect when testing whether their manipulation influenced emotions. Indeed, the PANAS, which was originally published in the Journal of Personality and Social Psychology (JPSP), has been cited over 18,000 times (from Google Scholar, 22 July 2015), and is the most cited paper in JPSP behind Baron and Kenny’s [8] mediation-moderation paper (according to Google Scholar, 22 July 2015).

Often, research uses the PANAS to measure responses to situational manipulations of emotion, and often null effects are reported. Because emotions are only probabilistically linked to measures of emotion, it is inappropriate to assume that a null effect on a self-report measure means that emotional processes were not involved in the psychological phenomena of interest. That is, the (often poor) measurement of emotion should not be conflated with emotion itself. Emotion can be involved in driving a phenomenon despite null effects on measures of self-report.

Many psychological scientists use poor measures of emotion to discount emotion prematurely. In fact, we are scarcely immune from the allure of premature discounting, having done so in the not-so-distant past [9]. In this study, inspired by Terror Management Theory, participants were assigned to groups based on their ostensible painting preferences (minimal groups) and either prompted to write about their own death or watching television. They then completed the PANAS. We found no increase in negative affect or decrease in positive affect in the mortality salience condition, as measured by the PANAS, and argued that this suggested that affect was not involved in the effects of mortality salience. Similar flawed logic has been used in numerous published papers.

The way in which the field dismisses emotion based on self-report alone demonstrates the necessity for more comprehensive scrutiny of emotional processes in mainstream psychology. Emotion is a primary influence on psychological processes and behavior. It need not be controlled for; rather, it should always be considered and measured appropriately. Researchers do not administer cognitive measures in an attempt to “rule out” cognition as a factor in a psychological process (because cognition is assumed to play a role) and emotional/motivational processes are at least as fundamental as cognition. If an organism is behaving, it can safely be assumed it is motivated and emoting. Measuring emotion should be directed at identifying the contribution of emotion to the behavior.

As we have already alluded to, emotions involve more than self-reported feelings. Although assessing self-reported emotional responses may be appropriate (and accurate) in some cases, one of the most widely used measures of self-reported emotional experience, the PANAS [7], is fraught with problems that may lead to it failing to yield support for the role of affective processes as resulting from and being involved in psychological phenomena.

First, consider a bit of history about the development of the PANAS. When Watson and colleagues [7] developed it, they were intent on arriving at a measure that would provide statistically independent measures of positive versus negative affect, “that is, terms that had a substantial loading on one factor but a near-zero loading on the other” (p. 1064) [7]. So they began with a long list of emotion labels, and then discarded all words that did not fit the statistical independence of positive versus negative affect. Perhaps consequently, they arrived at a measure of positive affect that does not include many common positive emotion words such as happy or joyful, but instead includes items such as alert, active, and attentive that may not always be positive in valence (e.g., a person could be alert, active, and attentive during threat). Similarly, the measure of negative affect does not include many common negative emotion words such as sad, down, grieved, and angry. Watson and colleagues [10] later suggested that their measures should be referred to as positive activation and negative activation because the emotion words on the scale were activated (i.e., high arousal) positive and negative emotions. This is partly accurate and explains why sadness-related words are missing but it fails to explain why some positive affects are missing (e.g., joyful) and why less common activated negative affect words are used for some concepts (i.e., irritated is included, rather than angry).

It would seem that the PANAS may be limited only to those contexts where activated emotion states are involved, indicating the need for other validated measures of emotion. Yet, even the use of the PANAS in these contexts could be problematic. Firstly, recent factor analytic studies have revealed that the factor structure of the PANAS is not maintained under emotion-evoking manipulations. Instead, items load on additional factors reflecting discrete emotions. For example, from before, to during, to after taking an exam, the PA items clustered into three discrete positive emotions, “joy”, “interest”, and “activation” [11]. Whereas when participants were angered, PANAS items loaded on three factors: the PA items loaded on the first factor, the anger items on the second factor, and the fear/anxiety items on the third factor [12].

Another issue is that PA scores have been found to increase during anger (an approach-motivated state that is negative in valence), suggesting that the positive activation scale does not always measure positivity, but may be sensitive to approach motivation [12]. In support of the idea that PA reflects approach rather than pure positivity, recent research has revealed that the PANAS is contaminated by a large first factor that could be referred to as evaluation or social desirability [13]. This research has revealed that responses to PANAS positive activation words (e.g., proud) are highly positively correlated with responses to positive antonyms of those words (e.g., humble). When this first factor is statistically removed, anger correlates with positive activation, as revealed in other studies [12,14].

Other Measures of Self-Reported Emotions

The PANAS is not the only way to measure self-reported emotions, yet other questionnaires are also commonly based on dimensional models, similar to the PANAS. Some of these questionnaires use multiple items to assess the broad dimensions of positive and negative affect, such as the Brief Mood Introspection Scale [15], or use single items to assess broad dimensions of valence, arousal, and dominance, such as the self-assessment manikin [16].

Some affect measures that do focus on discrete emotions have been developed for clinical purposes. The Profile of Mood States (POMS) [17] is a measure of emotion blends (i.e., anger-hostility, tension-anxiety, depression-dejection) and other affective states (e.g., fatigue-inertia; vigor-activity; confusion-bewilderment). It is a scale that must be purchased from the publisher in order to be used for research purposes. Since it was produced in 1971, the POMS has been used primarily in research and applied settings with psychiatric outpatients, medical patients, and in sports psychology. The Multiple Affect Adjective Checklist (MAACL) [18] and its revised version (MAACL-R) are comprised of subscales that are designed to assess self-reported Anxiety, Depression, Hostility, Positive Affect, and Sensation Seeking. Like the POMS, the MAACL has been used primarily in clinical psychology and psychiatry, and it has been used to assessed trait affect or day-to-day changes in affective states. Both the POMS and MAACL lack subscales for certain basic emotions (e. g., disgust). Furthermore, their usefulness in assessing the full range of emotional experience is limited by their focus on maladaptive affect.

Other studies examining situationally induced emotions have used single items to measure the discrete emotions evoked [19]. The use of single items to measure emotions has proven effective in these published studies, but generally speaking, methodologists express concerns over the use of single items to measure psychological constructs. Short measures are more likely to contain error variance [20] because they cannot benefit as much from aggregation across multiple items [21]. The use of a single item to measure a discrete emotion may not accurately capture an individual’s subjective emotional state because the individual may interpret the item differently at that moment.

In support, a review of personality questionnaires ranging from two to eight items in length found a positive correlation of r = .77 between questionnaire length and reliability [22]. If self-report questionnaires of emotions have low reliability, the chances of observing correlations between specific emotions and other measured constructs is reduced, because the correlation between two measures is limited by the reliability of each measure. In addition, because unreliable measures contain more measurement error, the determination of effect size between the unreliable measure and other measures will be more difficult than if reliable measures were used. Short measures of emotion, however, may prove to be more valid, as they assist in reducing participant boredom and fatigue [23]. Some research has suggested that the optimal trade-off between reliability and validity occurs with two to four items [24].

Other measures of discrete emotions have been produced, but these discrete emotions measures do not measure a larger set of emotions. For example, measures have been developed to assess the trait tendency to experience various discrete emotions, such as proneness to anger, awe, compassion, disgust, embarrassment, envy, gratitude, guilt and shame, happiness, and pride [25, 26, 27, 28, 29, 30, 31, 32, 33, 34]. However, these questionnaires measure traits and are not designed to measure state emotions.

Self-report questionnaires designed to assess state emotions exist for a few emotions, such as anxiety [35], pride [34], and shame and guilt [36]. But these state emotion questionnaires, like the trait emotion questionnaires, stand in isolation of other emotions and are not part of a questionnaire assessing a broad array of emotions. The current research was designed to provide a state measure of a wide array of emotions.

A single instrument that assesses discrete emotions relatively comprehensively is important for several reasons. One is that the same manipulation may evoke primarily one emotion in a particular individual, but a different emotion in another. For example, negative feedback on an essay might be intended to increase anger, and may in fact elevate anger in a majority of participants. However, if this manipulation evokes primarily sadness in a subset of participants, this would be missed if the researcher used only a measure of the single emotion of anger. Additionally, individuals may experience mixed emotions, which may have very different effects than the same emotion when not blended with other emotions. For example, anger is usually an approach-related emotion. However, when anger is blended with anxiety, it may be associated with withdrawal motivation [37]. A single instrument that assesses the broad range of emotions would allow researchers to identify when emotions are relatively pure versus when they are blended.

Theories of Basic Emotions

In designing our Discrete Emotions Questionnaire (DEQ), we aimed to include emotions that are considered "basic" by prominent emotion theories. These theories of emotion were developed by Paul Ekman [3, 38], Carroll Izard [4, 5], Jaak Panksepp [6], and Phillip Shaver and colleagues [39]. Although none of these theorists consider their lists to encompass the entire range of possible emotions, they (and we) consider these viewpoints on the structure of emotions a useful starting point. These various theories have considerable overlap in the basic emotions proposed, even though they were arrived at using diverse methods, including facial expressions of adult humans (Ekman), behaviors of human infants and children (Izard), direct brain stimulation and behavioral responses of non-human animals (Panksepp), and sorting of emotion words by adult humans [39]. Below we consider each emotion for which we included a subscale in the DEQ.

The DEQ is predicated on a discrete, versus a dimensional, approach to emotions. Regarding the ‘debate’ between discrete approaches and dimensional approaches, we believe that both have value. Discrete emotions can be categorized along dimensions, including valence (positivity/negativity), arousal (high/low), and motivational direction (approach/avoid). However, discrete emotions also possess other characteristics that do not fall neatly into dimensions, such as specific action tendencies, subjective feelings, evoking situations, and cognitive appraisals. Whether emotions are viewed as discrete or dimensional, people can still name specific emotion types and having a measure that allows them to distinguish between these different emotion types is useful and important. The emotions measured by the DEQ are listed below.

Anger is often regarded as a negative, high arousal emotion that is associated with approach motivational tendencies [40, 41, 42]. Neurally, anger involves testosterone, substance P (a neuropeptide that acts as a neurotransmitter and neuromodulator), the medial hypothalamus, amygdala, and the periaqueductal gray [6, 43, 44]. The full facial expression of anger involves the muscles of the brow moving inward and downward; the eyes fixed in a hard stare; and the nostrils and wings of the nose expanded [4, 5, 45]. When laypersons sort emotion words into groupings, the Anger cluster includes words like anger, rage, irritation, and exasperation [39].

Disgust is also regarded as a negative, high arousal emotion, but it is associated with withdrawal motivational tendencies. Disgust is considered a basic emotion by Ekman and Izard but not by Panksepp, who views disgust as a sensory affect. Shaver et al. [39] found that the two disgust-related words (disgust, revulsion) included in their emotions list clustered with the anger words, forming a branch along with contempt. Thus, if disgust is a basic emotion, researchers may need to devote additional effort to finding words that capture the construct. Neurally, disgust uniquely involves the inferior frontal gyrus/anterior insula relative to other emotions [46]. The full-face expression of disgust involves the brows being drawn down and together and a wrinkled nose. The upper lip is also pulled up and the lower lip is pulled downward. The tongue may be pushed forward out of the mouth [4, 5, 45].

Fear is often regarded as a negative, high arousal emotion that is associated with withdrawal motivational tendencies. Neurally, fear involves corticotrophin releasing factor, adreno-cortico-trophic-hormone, cholecystokinin, central and lateral amygdala, anterior and medial hypothalamus, PAG, and the lower brain stem [6]. The full-face expression of fear involves the eyebrows lifted and slightly pulled together; the eyes opened wide; and the corners of the mouth retracted straight back with the mouth slightly open [4, 5, 45]. When laypersons sort emotion words into groupings, the Fear cluster includes words like horror, alarm, terror, and fear [39].

Anxiety is often regarded as a negative, high arousal emotion that is most likely associated with behavioral conflict [47]. Anxiety is not characterized as a "basic" emotion by emotion theorists such as Ekman, Izard, or Panksepp. Similarly, Shaver et al. [39] found that anxiety words (e. g., anxiety, nervousness, tenseness) form a branch within the Fear cluster. However, many areas of research point to the theoretical and empirical importance of distinguishing anxiety from fear. Fear is often evoked by discrete, acute threats, whereas anxiety is evoked by vague, potential threats [48, 49]. Neurally, anxiety is not as well understood as fear, but the brain systems that contribute to anxiety overlap with those that contribute to fear. One key neural difference between fear and anxiety is that the peptide corticotropin-releasing factor (CRF) has a special role in anxiety [49]. The facial expression of anxiety is distinguished from other facial expressions of emotion by eye darts and head swivels that presumably increase the spatial area seen by the eyes [50]. When laypersons sort emotion words, the anxiety branch includes words like anxiety, nervous, and tense [39]. For the purposes of the DEQ, we saw value in attempting to distinguish anxiety from fear, although we expected that these constructs would show some overlap.

Sadness is often conceptualized as a negative, low arousal emotion; it appears to be mostly associated with the approach motivational system. In Panksepp's system, the affect that maps onto sadness is called PANIC/GRIEF. Although this terminology may be surprising to human researchers, PANIC/GRIEF, like human sadness, is a response to loss (particularly the infant animal's separation from the mother), and characterized by distress vocalizations (crying). Neurally, PANIC/GRIEF (i.e., sadness) involves endorphins, corticotrophin releasing factor, cortisol, glutamate, midbrain PAG, medial diencephalon, ventral septal area, preoptic area, bed nucleus of stria terminalis, cingulate gyrus, amygdala, and hypoththalamus [6]. In human neuroimaging studies, sadness uniquely activates middle frontal gyrus and head of the caudate/subgenual anterior cingulate cortex [46]. The full facial expression of sadness involves the inner corners of the brows being drawn obliquely upward and together, with the eyes begin slightly narrowed; the corners of the mouth are pulled downward and the chin may be pushed up and quiver [4, 5]. When laypersons sort emotion words, the Sadness cluster includes words like suffering, sadness, depression, and disappointment [39].

Joy or happiness is conceptualized as a positive emotion that could be associated with a variety of intensities of approach motivation; most theoretical perspectives are silent or mixed regarding where joy sits on the arousal dimension. Joy is described as a basic emotion by Ekman and Izard, and joy words form a major cluster in the analysis by Shaver et al. [39]. The conceptually closest emotion in Panksepp's [6] system may be PLAY, which seems similar to the joy posited by other theories. When laypersons sort emotion words, the Joy cluster includes words like cheerfulness, joy, enthusiasm, and contentment [39].

Most theories concerned with basic emotions have only carefully considered one or two positive emotions, and we believe that this is unfortunate for emotion science. Of course, theorists, including Ekman [51], have discussed many other positive emotions but research has not delved too far into these discrete positive emotions. One reason for the difficulty in distinguishing discrete positive emotions may be the overly dominant influence of valence on self-reports. In a stark demonstration of this, Pettersson and Turkheimer [13] found that a large first factor, which they refer to as "evaluation" confounds self-reported moods. They created a measure, based on the PANAS, by adding words of opposite meaning but identical valence (for example, proud was matched with humble, and determined was matched with easy-going). To assess evaluation, the PANAS items and matched antonyms were rated on how desirable it would be for a person to endorse each item. Separate participants rated the items on how much they felt these words applied to themselves, in general. When the first principal component was extracted from participants' self-endorsement of the items, it correlated r = .96 with the evaluation of the items, strongly suggesting that individuals endorsed the items based on valence, rather than semantic content. Similar results have been found in early research [52]. The evaluation component may be equivalent to social desirability [53, 54, 55]. In creating the DEQ, we considered that it may be important to control for this large evaluation component, in order to accurately measure other discrete emotions, particularly positive emotions.

In addition to including a Joy/Happiness subscale (which we suspect would be equivalent to evaluation, or valence) on the DEQ, we aimed to include subscales for two other positive emotional states that have received much theoretical and empirical attention. Broadly speaking, these two states can be thought of as low- and high-approach positive affect. We refer to these subscales as Satisfaction and Desire, respectively. These concepts align with perspectives that examine appetitive or pre-goal positive states as being distinct from consummatory or post-goal positive states [56], or "wanting" as distinct from "liking" [57]. The positive affective states of high-approach (desire) and low-approach (satisfaction) are associated with different neural structures and neurochemicals [6, 56, 57, 58]. Low-approach positive affect involves a small area within the nucleus accumbens and the posterior half of the ventral pallidum, brain regions which are sensitive to opioids and endocannabinoids [57]. High approach positive affect (SEEKING, in Panksepp's system) involves the amygdala, nucleus accumbens, and frontal cortex [59]. The neurotransmitter dopamine, produced in the mesolimbic and mesocortical dopamine circuits, activates these regions. Pregoal, high-approach-motivated positive affect (desire) presumably assists in promoting reward acquisition, whereas postgoal, low-approach-motivated positive affect (satisfaction) presumably assists in promoting reward enjoyment.

The research we review concerning the brain regions, hormones, and neurotransmitters involved in discrete emotions is primarily based on non-human animal evidence, which is typically more invasive and therefore more precise than the research that can be conducted with humans. There is evidence regarding brain regions involved in discrete emotions that is obtained from functional magnetic resonance imaging (fMRI) of humans lying supine while experiencing emotions. A supine posture has been found to reduce certain physiological responses associated with certain emotions [60, 61, 62]. This fMRI evidence presents a more mixed picture as to whether there is any discrete emotions specificity with brain regions. That is, some meta-analyses have suggested there is no support [63], whereas other meta-analyses have suggested there is support for discrete emotions being linked to specific neural circuits [46]. Hamann’s [64] interpretation of this literature is prescient, “… although neuroimaging studies have identified consistent neural correlates associated with basic emotions and other emotion models, they have ruled out simple one-to-one mappings between emotions and brain regions, pointing to the need for more complex, network-based representations of emotion” (p. 458).

Because the DEQ is intended primarily for use in measuring self-reported emotions among lay participants, we were careful to ensure that it was made up of words that are 1) clearly understood by lay English speakers (as opposed to psychological or emotion scientists), and 2) actually used by ordinary individuals in describing their emotional experiences. Instead of developing the DEQ from an existing list of emotion words, we preferred to build the instrument on a foundation of words that are spontaneously used and understood by lay persons. Thus, Study 1 used a bottom-up, open-ended method to collect words used by individuals to name the emotions they experienced in situations that would, according to emotion theory, evoke the emotions of interest: Anger, disgust, fear, anxiety, sadness, joy, desire, and satisfaction.

Study 1

In Study 1, we aimed to generate a list of words that individuals use to name emotions. Our goal in doing so was to base our preliminary instrument around words that individuals actually use in describing emotional states. To this end, we asked participants to recall emotional events from their own lives, and to name the words that best described the emotions they experienced during these events.



We created story prompts based on the themes that have been identified in emotional accounts in past research. Smith and Lazarus [65] identified "core relational themes" for anger, guilt, sadness, and fear/anxiety. Shaver et al. [39] analyzed emotional stories and identified the prototypical themes for fear, sadness, anger, joy, and love. The story prompts for Study 1 were based on these themes from past research (Table 1). It is important to note that the prompts avoided the use of emotion words. Instead, they focused on the prototypical characteristics of events that tend to produce the target discrete emotions.

Table 1. Study 1: Prompts Used to Elicit Emotional Recall.


Participants were 337 individuals (186 women, 148 men, 1 other, 2 declined to answer) residing in the United States who completed the study online using Amazon's Mechanical Turk. The ethnicities reported by participants were White (261), African American (16), Asian (31), Hispanic/Latino (19), Native American (3), and Other (6). Age ranged from 18 to 73 years (M = 34.28, SD = 12.78). They were recruited for a study on memories and emotions. This and all other studies in this article were reviewed and approved by The University of New South Wales Human Research Ethics Advisory Panel C: Psychology.


Because this and the other studies were conducted online, participants did not provide signed consent. They were presented with the consent form and, if they chose to consent, they entered their initials into a box and clicked the option "I consent to participate." The other option was "I do NOT consent to participate." If potential participants selected this option, they were exited from the survey. This was approved by the ethics committee. After giving informed consent, participants saw the following: “On the next screen, you will see instructions regarding the situation we would like you to remember. It is very important to the study that you understand and follow these instructions. Please read carefully!”

Next, participants were randomly assigned to one of the prompts in Table 1. After reading their prompt, they viewed these instructions: “Take a few moments to remember the situation that you thought of. As you remember the incident, re-experience the emotions you felt at that time as strongly as possible. When you have the experience in mind, and the emotions are strong, please go on to the next screen.”

When participants proceeded to the next screen, they read, “At this time, please write down the events of the emotional experience that you remembered. In as much detail as possible, describe what happened, paying particular attention to your thoughts and feelings.” They were given 180 seconds to write about the experience they had remembered.

On the following screen, participants read, “In the space below, write ONE WORD that BEST names the emotion you experienced most strongly during this event.” After giving their open-ended response, participants proceeded to the next screen and read, “In the spaces below, write 4 more words that name the emotion you experienced most strongly during the event you wrote about. If you can't think of 4 words, write as many words as you can, and write 'none' in the other spaces.”

On the following screen, the instructions stated, “Now please imagine that you are in the middle of the situation that you wrote about. You want to tell a close friend how you are feeling. You say, "I am so ___________!" In the space below, please fill in the blank with the word, or words, that you would say to tell a close friend how you feel.” The purpose of this question was to encourage participants to generate vernacular words that they would actually use, in the moment, to name their emotional experiences.

Thus, participants had the opportunity to generate up to 6 words/phrases naming the emotions they experienced in the situation they had remembered.

Manipulation checks.

To determine whether the emotion manipulation was successful, a judge who was blind to condition coded each story for whether it included each of the eight emotion themes (non-exclusive, S1 Data). Results appear in Table 2. To assess reliability, a second judge coded a random sample of 15% of the stories. Inter-rater reliability was good, Cohen’s Kappa = 0.73.


We performed a count of the words generated for each condition. Different grammatical forms of a word (e. g., anger, angry) were counted as a single word. Table 3 shows the most common words generated in response to each prompt. Words were included in the table if the word was generated at least twice for “Best Word” or “Vernacular,” or at least three times for “Other Words.”

Table 3. Study 1: Most Frequently Generated Words for Each Condition.

From this list, we created a preliminary word list for the emotion instrument, with six items per emotion category. This preliminary list included the most frequently generated words for each emotion prompt which overlapped minimally with words generated in response to the other emotion prompts. For the categories for which participants did not generate at least six common words, we supplemented the list with items from Shaver et al. (1987) that fell near the participant-generated words in a cluster analysis, and that were rated as being good exemplars of emotion (rated at least 3.00 on a 4-point scale where 1 = I definitely would not call this an emotion and 4 = I definitely would call this an emotion) [38].

Study 2

For Study 2, participants recalled an emotional experience (similar to Study 1), but instead of generating emotion words, they rated the extent to which they had experienced emotions during the experience, using the preliminary emotion scale developed in Study 1.



Participants were 244 individuals (139 female, 104 male, 1 other) who completed the study on Amazon’s Mechanical Turk. Their reported ethnicities were White (197), African American (15), Asian (14), Hispanic/Latino (11), Native American (1), and Other (5). Age ranged from 18 to 68 years (M = 32.91; SD = 11.02). Of these participants, 25 were in the love condition, which was dropped from the ANOVA (in all studies) because the love words never loaded on their own factor. Despite including additional positive affect words, a unique factor for Love words did not emerge in the factor analysis. The word “love” and its synonyms had high loadings on the first factor (Happiness). Therefore, we dropped the Love subscale from the DEQ. However, the love words were used in all factor analyses because all participants in all conditions responded to them as well as the other words.


The method was identical to Study 1, except that after participants finished writing about the emotional experience they had recalled, they rated, on a 7-point scale (1 = Not at all and 7 = An extreme amount), the extent to which they had experienced the emotions, using the items on the preliminary scale. The emotion items were presented in a random order. We assumed all participants followed the instructions because each one wrote about an emotional experience.


Using Statistica Version 12 (StatSoft, Inc.), the data (S2 Data) were subjected to a factor analysis using maximum likelihood with varimax (raw) rotation; cases with missing values were rejected on pairwise basis (Table 4). Initially, the number of factors allowed was 8, because we predicted that 8 factors would result. This analysis yielded five factors with Eigenvalues greater than 1.00. The first factor had an Eigenvalue of 24.92, and all of the positively valenced words loaded negatively on this factor. The second factor had an Eigenvalue of 5.58, and all of the fear and anxiety words loaded positively on this factor (except “shock,” which had a loading of 0.44). The third factor had an Eigenvalue of 2.14, and all of the anger words loaded positively on this. The fourth factor had an Eigenvalue of 2.43, and all of the sad words loaded on this factor. The fifth factor had an Eigenvalue of 1.58, and all of the disgust words loaded on this factor.

These results suggested that the negative emotion words differentiated into specific emotions, with the exception of fear and anxiety. However, the positive emotion words on our preliminary scale loaded on the large first factor, which, based on past research [13], is equivalent to evaluation/valence.

Composite variables (subscales) were created by taking the mean of the four items with the highest loadings on each factor and small cross-loadings on other factors (shown in bold in Table 4). Subscales were not created for the positive affect items, because these did not load on separate factors. To examine whether the subscales were sensitive to our emotion manipulation, one-way analyses of variance (ANOVA) were conducted with the emotion manipulations as independent variables and the mean ratings for each emotion category as the dependent measures (Table 5). These results showed that these “best four” items were elevated by the expected manipulations, with the exception of the anger subscale and the anxiety subscale. For the anger subscale, the anger prompt elevated ratings significantly more than any except the fear prompt. The increase in the mean for the anger prompt compared to the fear prompt was not quite statistically significant (p = .06). For the anxiety subscale, the anxiety prompt elevated ratings more than any prompts except the anger and fear prompts. Means for the anger prompt did not differ from those for the anxiety prompt (p = .73). Furthermore, means for the fear prompt elevated ratings on the anxiety subscale more than any other prompt, including anxiety (p = .03). These results suggested that our preliminary scale discriminated between all negative emotions except anxiety and, to some extent, anger. Based on these results, we conducted Study 3 with a more controlled emotion manipulation and additional positive emotion words.

Table 5. Study 2: Mean Responses on Preliminary Subscales in Response to Emotional Recall Prompts.

Study 3

For Study 3, we wanted to examine how well the scale discriminated among discrete emotions using a different manipulation of emotion. As the coding results from Study 1 showed, participants’ responses to the prompts were very rich: many of the stories that participants wrote included multiple emotion themes (Table 2). For this current study, guided imagery was used to elicit a more specific emotional response.

In addition, because the positive items on the preliminary scale all loaded on the same factor, we added additional positive items in an attempt to identify items that would discriminate between discrete positive affects. We based these additional items on theoretical and empirical distinctions between high- and low-approach positive affect [6, 57]. For high-approach positive affect, the additional items were desire, craving, wanting, longing and need. For low-approach positive affect, the additional items were peace, easygoing, chilled out, savoring, calm, and relaxation.



Participants were 439 individuals (253 women, 184 men, 1 other, 1 declined to answer) recruited from MTurk. Their reported ethnicities were White (352), African American (26), Asian (24), Hispanic/Latino (29), Native American (3), and Other (3). Age ranged from 18 to 68 years (M = 32.21; SD = 10.57). Of these participants, 55 were in the love condition, which was dropped from the ANOVA because of the reasons stated previously. At the end of the questionnaires, seven participants failed to correctly answer a multiple-choice question indicating the gist of their assigned story. For two pairs of stories, the themes were similar and thus the response options were similar.


The stimuli were composite stories created from typical memories submitted by participants in Study 1 and Study 2. Two scenarios were created for each emotion category (Table 6).


The method was identical to Study 2, except that, instead of being asked to recall an emotional memory, participants read, “On the next screen, you will read about the situation we would like you to imagine. Please read carefully! It is very important to this study. The story you will read is a composite of several similar stories written by different people. These are the kinds of stories people often write when they're asked to remember an emotional experience. As you read the story, please put yourself in the place of the person who is describing the experience. Try to imagine, as vividly as possible, how you would feel if this situation were happening to you.” Participants were then randomly assigned to read one of the scenarios from Table 6. Participants then rated the emotions they experienced while imagining the story, on our emotion scale. Finally, they answered a multiple choice question that assessed which story they read.


The data (S3 Data) were subjected to the same factor analysis parameters as used in Study 2. Our original conceptual prediction was eight factors or emotion subscales. However, Study 2 revealed fear and anxiety to be highly related and to load on the same factor. Thus, in Study 3, we set the factor analysis to have seven factors (Table 7). The first factor had an Eigenvalue of 25.02 and all of the positive items loaded negatively on this factor. The second factor had an Eigenvalue of 8.65 and all of the fear and anxiety items loaded on this factor. The third factor had an Eigenvalue of 3.71 and all of the anger items loaded on this factor. The fourth factor had an Eigenvalue of 2.73 and all of the sadness items loaded on this factor. The fifth factor had an Eigenvalue of 1.99 and all of the desire items loaded on this factor. The sixth factor had an Eigenvalue of 1.47 and all of the disgust items loaded on this factor. The seventh factor had an Eigenvalue of 0.94 and all of the relaxation items loaded on this factor; however these items also cross-loaded on the first factor. Although we began this research thinking that low approach positive affect might be best captured by a word like “satisfaction,” the factor analysis did not reveal that “satisfaction” loaded separately from other positive words. Because the words that did load (somewhat) separately from happiness were associated with and included the word “relaxation,” we labelled this last factor “relaxation.”

Composite variables (subscales) were created by taking the mean of the four items with the highest loadings on each factor (shown in bold in Table 7). For all of the negative affect subscales, these words were identical to those used in Study 2. To examine whether these subscales were sensitive to our emotion manipulations, one-way ANOVAs were conducted with the emotion scenarios as independent variables and the average ratings for each subscale as the dependent measures (Table 8). These results showed that average ratings on the subscales were elevated by the intended manipulations, with a few exceptions.

Table 8. Study 3: Mean Responses on Subscales in Response to Emotional Scenarios.

For the fear subscale, only fear scenario 2 elevated ratings more than any other manipulation. For the anxiety subscale, ratings following anxiety scenario 1 did not significantly differ from fear scenario 2, and ratings following anxiety scenario 2 did not differ significantly from fear scenario 1 or 2. For the desire subscale, only desire scenario 1 elevated ratings more than any other scenario. For the relax subscale, ratings following relax scenario 2 differed only marginally (p = .07) from ratings following desire scenario 1.

These results showed that each of the subscales was sensitive to manipulations of the intended discrete emotion, although some of the emotional scenarios were more effective than others. The only exception was the anxiety subscale, which was sensitive to both anxiety and fear scenarios.

Study 4

In Study 4, we wanted to assess whether our emotion instrument was sensitive to a different manipulation of emotion. Study 2 used autobiographical recall, whereas Study 3 used guided imagery. For Study 4, we chose a pictorial manipulation, because photographs are often used to manipulate affect in psychological research.



Participants were 511 individuals who completed the study through MTurk. Three participants exited the study after completing the baseline questionnaire. Seventeen participants were excluded for failing the manipulation check question (they did not correctly identify the content of the two picture sets, baseline and emotional, they had viewed), leaving 491 individuals (290 women, 193 men, 3 other) for the analyses. Their reported ethnicities were White (366), African American (32), Asian (55), Hispanic/Latino (21), Native American (2), and Other (10). Age ranged from 18 to 79 years (M = 32.19; SD = 11.02). Five participants did not respond to these demographics questions.


We created seven sets of photographs, intended to evoke the seven emotions measured by the instrument. Each set included five photographs. The emotional sets were anger (militants burning the American flag) [43, 66], disgust (dirty toilets and rotten food), fear (dangerous animals and weapons), anxiety (people taking tests or being questioned by police), sadness (people crying) [67], desire (delicious-looking desserts) [68], and relaxation (beaches). We also created a neutral set, which was made up of pictures of rocks [69]. Although some of the photographs were photos from the International Affective Picture System [70] (disgust IAPS numbers 7359, 7380, 9300, 9320, 9570; fear IAPS numbers 1050, 1220, 1300, 6230, 6350; sad IAPS numbers 2205, 2800, 9050, 9210, 9220), many were selected from the Internet and had been used in our previous research [42, 65, 66, 67, 68].


Before beginning the task, participants read these instructions, “On the next few screens, you will see several pictures. Please view each picture for the entire time it is presented. Each picture will be shown for 4 seconds, and then the display will automatically advance to the next picture. As you view the set of pictures, imagine yourself experiencing the events that the pictures show. As vividly as you can, try to place yourself in the scene and imagine that what is shown in the photos is actually occurring to you right now. After you view several pictures, you will rate how the set of pictures made you feel while viewing them.” We included these instructions to manipulate emotion as well as possible, to better understand the self-reported subjective experience of emotions. Other researchers have used a different (and more minimal) set of instructions for picture viewing manipulations, but we are unsure why they have chosen to use the instructions they use. We suspect that their choice reflects their primary research agenda of focusing more on psychophysiological responses to individual pictures (averaged over many of the same valence and arousal level) than on an extensive measurement of self-reported subjective experience of emotions.

To familiarize them with the task, participants then viewed the neutral set of rock pictures. They rated their emotions while viewing the set on the items active, alert, attentive, jittery, hostile, guilty, inspired, ashamed, upset and strong. Next participants read the picture viewing instructions again.

Participants were then randomly assigned to one set of pictures. They viewed each photograph for 4 seconds. After viewing all the pictures of their assigned set, they rated how they felt during viewing on the DEQ (that is, an instrument made up of the best 4 items from each of the 8 factors).

Participants then completed the Attitudes Toward Emotions scale (results of which are not of interest for this study and are not reported), demographic questions, and attention check questions that asked them to describe the two picture sets they had viewed.

Composite variables for the data (S4 Data) were created by taking the mean of each subscale, using the same items as in Study 3.


To examine whether these subscales were sensitive to our emotional picture manipulation, one-way analyses of variance (ANOVA) were conducted with the picture sets as independent variables and the average ratings on each emotion subscale as the dependent measures (Table 9). These results showed that ratings on the discrete emotion subscales were elevated by the expected picture sets, with minor exceptions. Although ratings on the fear subscale were elevated more by the fear pictures than by the anxiety pictures, ratings on the anxiety subscale were elevated equally by fear and anxiety pictures. Also, ratings on the desire subscale were only marginally higher for desire pictures compared to relaxation pictures (p = .06). Reliability for all subscales was high (Cronbach’s αs > 0.80, Table 10).

Table 9. Study 4: Mean Responses on Subscales in Response to Emotional Photographs.

Given the strong predictions derived from past theory and research, we thought the use of controls for family-wise error rate was unnecessary. However, when Bonferroni corrections were applied to the ANOVAs (.05/7 = .0071), the p value needs to be less than .007 to be acceptable. In all cases, the p values were acceptable. For Studies 2, 3, and 4, all p values for the ANOVAs were less than .001. Study 1 did not use ANOVA.

General Discussion

The results of four studies support the validity of the DEQ, a discrete emotions questionnaire (S1 Appendix). Study 1 was intended to generate a starting base of emotion words that are used by laypeople to name the emotions they experience in various situations. In Study 2, these words, supplemented as needed with emotion words from Shaver et al. [39], were used to create a preliminary emotion measure. Participants recalled emotional experiences, as in Study 1, and indicated their endorsement of these words. Exploratory factor analysis revealed 5 factors which we called positive emotion, fear/anxiety, anger, sadness, and disgust. In Study 3, additional positive words were included to better capture discrete positive emotions, and participants rated their endorsement of these words after guided imagery of emotional scenarios. Exploratory factor analysis revealed 7 factors which we called happiness, fear/anxiety, anger, sadness, desire, disgust, and relaxation. Results of Study 3 were conceptually replicated in Study 4 which revealed that the ratings on the DEQ subscales were influenced in predictable ways by a different manipulation of emotion states.

The DEQ consists of the 4 items with the highest loadings on each of these factors (8 from the fear/anxiety factor) and small cross-loadings on the other factors. Moreover, the internal consistency of each subscale is more than adequate and experimental manipulations of emotion influence the emotion subscales in the predicted manner in all studies. The results from the ANOVAs clearly indicate that the Discrete Emotions Questionnaire is sensitive at assessing discrete emotional responses to discrete emotional events. The exploratory factor analyses also supported the existence of discrete emotions, even though they were unable to take into account the experimental manipulation of emotions. It is important to note that, in the validation studies for other affect scales such as the PANAS, emotions were not manipulated; rather participants were asked to endorse the items while in a neutral setting, presumably not experiencing strong emotion [7]. In contrast, participants in the current studies were assigned to experience one of 8 emotions, adding an important source of variance to their ratings. Past research has revealed that inducing emotions changes the factor structure of affect scales[11, 12].

Although we could have conducted a study similar to the validation studies for the PANAS, by asking participants to respond to the DEQ by indicating how they feel in a non-emotional context, we believe that such a study would be uninformative. The DEQ is intended to measure state emotions, so in a non-emotional context, responses to the negative emotion subscales of the DEQ (anger, disgust, fear, anxiety, and sadness) should be near the floor for most individuals. Past research and the current studies show that persons report fairly high positive emotions (happiness, relaxation, and desire) at rest, either because people feel fairly positive at baseline or because of ‘evaluation’ or social desirability [13]. The goal in creating the DEQ was to create an instrument that would be sensitive to state emotions. In our view, whether the instrument has good factor structure in the absence of emotion-inducing situations is not informative. The ANOVA results of the current research show that the subscales of the DEQ appropriately detect manipulated state emotions.

These studies showed that the DEQ is sensitive to several different manipulations of emotion: autobiographical recall, guided imagery, and pictorial stimuli. Scores on the target subscales were elevated significantly more than scores on the other subscales by these stimuli. The only exception was the Fear and Anxiety subscales: these items loaded on a single fear/anxiety factor, however, participants endorsed them differently in response to emotional manipulations. Fear manipulations elevated both Fear and Anxiety ratings, whereas anxiety manipulations elevated only Anxiety ratings, but not Fear ratings. We surmise that the subjective experiences of both fear and anxiety include the uncertainty and anticipation of an unpleasant future expressed in words such as “worry” and “dread,” however, only the experience of fear also includes the expectation of physical harm expressed in words like “terror” and “fear” [65]. Given that the Fear and Anxiety items behave differently in this way, we chose to retain both subscales in the DEQ. We based our design of the studies and inclusion of both anxiety and fear on existing theories and research that have revealed differences between anxiety and fear, as reviewed earlier. Although anxiety and fear “typically loaded together” in the factor analyses, when we consider responses to the emotional inductions, we find that situations that evoke fear cause increases in DEQ fear and anxiety, but situations that cause anxiety only cause increase in DEQ anxiety but not fear. If both fear and anxiety were not included in the DEQ, then future research might miss these important differences in outcomes between fear and anxiety situations. Thus, we included the two subscales in the DEQ.

The DEQ can be used as a relatively short measure of the “basic” emotions of anger, disgust, fear, sadness, and happiness, plus the theoretically interesting emotions of anxiety, desire (high approach), and relaxation (low approach positive). Although these are certainly not the only discrete emotions, they cover the broad categories considered basic and important by a number of emotion theorists [3, 4, 5, 39].

We have developed a measure of emotion that is sensitive to discrete emotional states, and maintains a decent overall factor structure when capturing dynamic emotional states. Yet, it may not be necessary to use the entire DEQ when measuring emotion. In our view, researchers should use emotion theory, past research, and the purposes of his/her research question to determine whether to use the entire DEQ or only a selection of its subscales. In some cases, the use of a few subscales or a single subscale of the DEQ might be sufficient. Using the full scale may be beneficial when a researcher wants to examine whether participants are experiencing mixed emotions, or when a manipulation may cause one emotion for some participants and a different emotion for other participants. In other cases, the participant fatigue brought on by completing many items might outweigh the benefits of measuring more emotions than those directly of interest.

We note that although we have validated a coherent measure comprising eight subscales that are sensitive to situational changes in emotional states, we have not exhausted full possibilities of the DEQ. Rather we view this as a work in progress that may be contributed to over time. For instance, researchers might find that they are interested in specific emotions not currently included in the DEQ. We would suggest that in these cases the DEQ, as specified here, could be useful for assisting those researchers to construct measures of additional emotions (by modelling the approach used in the DEQ), and to demonstrate the discriminant validity of those measures (by showing they do not overlap with other measures in the DEQ). Overtime, additional emotions may be added to, and validated as part of, the DEQ.

In the end, we hope that the DEQ will be of use to researchers interested in measuring self-reported discrete emotions associated with emotional states. As reviewed earlier, much past research has used a flawed measure presumably because it appeared to have good measurement properties, was published in the field’s top empirical journal, and was so widely used. We believe our understanding of psychological and behavioral processes will be better understood by using a measure such as the DEQ that more accurately captures emotional responses to events. Indeed, we have already reported the results of three experiments that revealed the DEQ to be more sensitive than the PANAS at detecting self-reported emotions to discrete events [71]. Emotions are vital psychological constructs that need to be measured as well as possible so that research can fully understand their impact on psychological processes.

Supporting Information

S1 Appendix. The Discrete Emotions Questionnaire. DEQ with instructions.


S1 Data. Data file for Study 1. Generate words following emotion-evoking prompts.


S2 Data. Data file for Study 2. Rate words following emotion-evoking prompts.


S3 Data. Data file for Study 3. Rate words following emotion-evoking scenarios.


S4 Data. Data file for Study 4. Rate words following emotion-evoking photographs.


Author Contributions

  1. Conceived and designed the experiments: CHJ BB EHJ.
  2. Performed the experiments: CHJ.
  3. Analyzed the data: CHJ EHJ.
  4. Contributed reagents/materials/analysis tools: CHJ EHJ.
  5. Wrote the paper: CHJ BB EHJ.


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