Conflict + Anxiety = Turmoil! Introducing a Measure of Conflict Response Derailers

Conflict is notorious for making most people anxious. When asked to free associate to the term, most of us respond with words such as problem, aggression, tension, violence, confrontation, or war. When we find ourselves face‐to‐face with a tricky conflict, often our stomach sinks, our blood pressure rises, and we start to feel a creeping sense of dread that the worst will happen (Heffner et al. 2004). And when our conflicts mingle with our anxiety… watch out!

Thus, it seems odd that most of the more popular conflict style assessments in use today seem to neglect the role of anxiety. For that matter, most of these assessments avoid altogether the effects of emotions in conflict (Shapiro 2004). For example, the Thomas–Kilmann Conflict Mode Instrument (Thomas 1976), based on the dual concern framework originally developed by Blake and Mouton (1964) and arguably the most utilized conflict style assessment of our time, focuses on different behavioral responses to disputes but neglects the foundational role of emotions completely. The same can be said for other commonly employed tools, such as the Intercultural Conflict Style Inventory (Hammer 2005) and the Rahim Organizational Conflict Inventory‐II (ROCI‐II) (Rahim 1983; Weider‐Hatfield 1988).

This article seeks to address this missing link in our understanding of our emotive selves in conflict. It builds on the clinical observations and insights of one of the founders of the field of conflict resolution, and one of the most eminent psychologists of all time, Morton Deutsch (APA Monitor 2002; Coleman 2018). It offers a summary of Deutsch's model of conflict anxiety and then presents an overview of a series of studies conducted to develop and test a new assessment tool, the Conflict Anxiety Response Scale (CARS). The article summarizes the history of this research and discusses in detail two recent studies that offer reliability estimates of the scales and initial validation of the instrument. It also presents preliminary findings on the relationship of scores on the scale to differences in gender, positive and negative affect, generalized anxiety, and well‐being.

Social conflict is defined as an encounter between people over differences that matter (Follett 1924; Coleman and Ferguson 2014). It can be more destructive, involving harms and losses for some or all disputants, or more constructive, where the disputants walk away feeling mostly satisfied or enhanced by the dispute, depending on a variety of factors (Deutsch 1973; Coleman, Deutsch, and Marcus 2014). However, most conflictual encounters trigger tension and anxiety initially (Larkin, Frazer, and Semenchuk 1996), which, in turn, makes us more inclined to perceive or instigate conflict (Etkin et al. 2010). When lingering, this state can stress our relationships, lower our self‐esteem (Ozdemir 2014), and increase our blood pressure (Honeycutt, Hample, and Hatcher 2016), thereby making us feel even more anxious and disagreeable.

Anxiety is a psychological, physiological, and behavioral state induced in animals and humans by a real or perceived threat to well‐being or survival (Steimer 2002). It is associated with increased arousal, autonomic and neuroendocrine activation, and chronic behavioral‐response patterns, which are believed to facilitate coping with adverse circumstances. Anxiety can be a normal and healthy reaction to worrisome events, and moderate amounts have been shown to improve performance on some tasks by helping to focus attention (Teigen 1994). But prolonged states of anxiety, particularly when coupled with an underlying anxiety disorder, can have serious negative consequences for our physical and psychological health and social relationships.

In the 1970s, Morton Deutsch, an experimental social psychologist and psychoanalytically trained psychotherapist, observed an intriguing pattern of responses to conflict in his clinical practice with romantic couples (Deutsch 1993). Initially, Deutsch noted that many of the individuals in his practice typically evidenced a palpable level of anxiety when disputes would emerge between them and their partners. Trained in psychoanalysis, Deutsch understood this angst as the result of the partners' early experiences with their respective families of origin, and their related fears of causing or suffering from severe harm in response to conflict. He wrote, “In clinical work, I have found that the anxiety is often based on unconscious fantasies of being overwhelmed and helpless in the face of the other's aggression or of being so angry and aggressive that you will destroy the other” (Deutsch 1993: 514).

Over time, Deutsch also began to observe that the individuals comprising these couples tended to respond to the anxiety instigated by their conflicts in one or more of the following ways:

• chronically avoiding conflict or obsessively seeking out conflict;

• becoming hard and unyielding or becoming overly soft and unassertive;

• overintellectualizing and rationalizing or becoming overwhelmingly saturated in emotion;

• standing rigid and controlling or falling into a state of loose disorganization;

• skyrocketing into escalation or shrinking back into minimization of problems; and

• compulsively revealing everything or stonewalling and concealing everything.

Deutsch observed that moderate tendencies along these dimensions were quite normal—and often adaptive—responses to the feelings and circumstances his clients faced. However, the more anxious the disputants became in response to conflict and the longer their distress persisted, the more chronic and extreme their responses, and the more problematic it was for their relationships and psychosocial well‐being.

Deutsch, the social‐psychological theorist, found this pattern of dynamics fascinating and instructive, and described it later in an article in American Psychologist (Deutsch 1993). Deutsch, the psychotherapist, found that helping individuals and couples begin to recognize their own response tendencies along these dimensions often helped them to keep such tendencies in check when they found themselves becoming derailed by their own “go to” responses (Table One).

Although Deutsch's clinical observations of his clients' responses to conflict‐induced anxiety proved useful in his clinical practice, he never empirically tested the basic assumptions of the model that he articulated (Deutsch 1993). They included:

1. Individuals experiencing higher levels of conflict anxiety will evidence more of at least some of the six‐dimensional reactions to conflict.

2. The twelve types of reactions to conflict anxiety fall into six bipolar dimensions (e.g., individuals will tend to either avoid conflict or become overly involved).

3. The more extreme the responses to conflict anxiety along each dimension, the more problematic it will be for the affect, well‐being, and relationships of the individual.

4. The more extreme the responses to conflict anxiety along each dimension, the more likely individuals will evidence less constructive and more destructive conflict dynamics.

5. Increasing awareness of one's more chronic conflict–anxiety response tendencies may allow one to modify them when they are inappropriate in a given conflict.

6. More optimal responses to conflict anxiety will likely fall in between the more extreme poles of each dimension, thus incorporating the beneficial aspects of some of the responses.

The studies discussed below seek to develop the empirical means to begin to test these and other related hypotheses.

Ultimately, we seek to develop an assessment tool for individuals, couples, and families that could help to increase awareness of each person's chronic conflict “derailer” tendencies—thus providing them with useful information feedback for their personal and professional development. This research program seeks to parallel similar initiatives, such as the decades of research conducted on the Hogan Development Survey (Hogan et al. 2021), a well‐validated leadership assessment tool that offers managers feedback on their “Dark Side”—the more problematics aspects of their leadership tendencies that have been shown to have negative consequences for job performance.

Over the past few years, we at the Morton Deutsch International Center for Cooperation and Conflict Resolution at Columbia University have been studying these hypothesized conflict tendencies to better understand their psychometric properties and test the basic assumptions of Deutsch's model and its consequences for mental health, conflict management, and well‐being.

Here, we briefly summarize a series of studies leading up to the development of the current scale. We then report in depth on two studies that examine the factor structure, reliabilities, and predictive validity of the current version of the Conflict Anxiety Response Scale. Study 1 was conducted to examine overlaps in the 12 CARS dimensions and enhance the distinctiveness of the subscales. Study 2 tested the factor structure, interitem reliability, and predictive validity of the scale in relation to standard measures of affect, anxiety, and well‐being, thereby allowing for the examination of Hypotheses 1–3 from above (Figure One).

We previously conducted seven iterations of studies on the CARS survey development designed to assess respondents' conflict–anxiety tendencies along the proposed six bipolar dimensions identified by Deutsch (1993). For each study, the participants were asked questions about how they typically respond to important conflicts in their personal lives. Based on Deutsch's framework (1993), we drafted item sets for each of the twelve hypothesized tendencies included in the six bipolar dimensions. Over each iteration, we honed the scales and their psychometric properties to increase construct clarity, reliability, and validity. Each iteration resulted in a separate empirical study, leading to further refinement of the scale's items and format. Across these seven studies, we revised our scale from 53 items in Study 1 (N = 100), to 83 items in Study 2 (N = 180), 75 items in Study 3 (N = 215), 53 items in Study 4 (N = 204), 61 items in Study 5 (N = 212), 60 items in Study 6 (N = 298), and 62 items in Study 7 (N = 620). The format of each study was an online questionnaire, and data were collected from participants through Amazon's Mechanical Turk (mTurk) online platform, a Qualtrics Panel, or the Prolific recruitment system. For each study, confirmatory factor analyses were run to determine whether each item set fit with the twelve dimensions as described by Deutsch's framework.

Over the course of these seven studies, we improved model fit with each iteration. We want RMSEA to be close to zero, preferably at 0.05. MacCallum, Browne, and Sugawara (1996) have used 0.01, 0.05, and 0.08 to indicate excellent, good, and medium fit, respectively. Thus, the latest versions of our models can be considered between good and medium fit. Fit statistics, descriptive statistics, and Cronbach's alphas for each iteration are presented in Tables Two and Three.

The goal of this new study (Study 8 in our sequence but Study 1 for this article) was to build on prior iterations of studies of the questionnaire to assess the twelve response tendencies to interpersonal conflict. In this study, we describe the items and the process by which we identify items negatively impacting interitem reliability of the subscales and the factor structure.

Subjects were 760 participants recruited via Prolific, an online survey recruitment platform. Subjects ranged in age from 18 to 80 years (M = 33.12, SD = 11.89). 50.3% of the participants were women (N = 383), while 48.1% of the participants were men (N = 366), and 1.2% identified as a gender not listed (N = 9). Our sample consisted of people of various ethnic identities: American Indian/Alaskan Native (N = 10, 1.3%); Asian/Pacific Islander (N = 90, 11.8%); Black/African American (N = 59, 7.8%); Hispanic/Latin American (N = 76, 10%), White (N = 567, 74.5%); and other (N = 12, 1.6%). Two hundred and fourteen participants have a high school diploma or GED (28.1%), 115 have an associate degree (15.1%), 295 have a bachelor's degree (38.8%), 89 have a master's degree (11.7%), 13 have a doctorate (1.7%), 20 have a professional degree (2.6%), and 15 have some form of education not listed (2.0%). Subjects were paid $2.50 for their time in completing a 15‐minute survey.

Seventy‐six items, identified and revised through the prior seven studies, were tested in this iteration of the scale. The introduction to the scale read, “People face many different types of conflict in their lives. Some conflicts are minor, others are more serious; some are with strangers, others with colleagues or within intimate relationships; some are in public places, others at home or at work. Below is a list of statements that reflect different ways people respond to conflict. Please indicate how often you tend to respond to conflicts in your personal life in the following ways.” The response options were 5‐point Likert scales with choice options from “Never to Always.”

The dataset of 760 respondents was randomly split in half, leaving two datasets with 380 participants each. On the first dataset, we conducted exploratory factor analyses with principal axis factoring and promax rotation on every possible combination of the 12 factors, resulting in 66 EFAs. For example, we ran EFAs on all the Avoiding items in combination with the set of items for each of the Soft, Hard, Involved, Escalating, etc. subscales, one at a time. These paired analyses were conducted because the 76 items of the 12 hypothesized dimensions failed to resolve after countless iterations. This alternative paired EFA is recommended by Watkins (2018). Factors were identified for each EFA based on having eigenvalues >1. In each of these EFAs, we examined the loadings of each item to factors in the pattern matrix, using a minimum loading of 0.40. These loadings represent the relationship between a factor and the item, when controlling for the other factors. We conducted these 66 EFAs to examine whether any of our items cross‐loaded with other factors, causing fuzzy demarcations between subscales. If an item loaded on a factor other than the one it was hypothesized to (e.g., if an Avoiding item loaded on a factor containing mostly Soft items), we dropped it from the scale. Finally, we estimated Cronbach's alpha for each subscale and looked for items that were negatively influencing interitem reliability. Based on these analyses, we arrived at a smaller set of items. These analyses were conducted in SPSS Version 26.

On the testing dataset (N = 380), confirmatory factor analysis (CFA) was performed using R's lavaan package. Six CFAs were conducted between each hypothesized opposing pair (e.g., Avoid versus Involve), using the trimmed‐down set of items resulting from our analyses of the first dataset of 380 respondents (Exploratory Factor Analysis). We attempted to run CFAs on all twelve dimensions together, but the model did not converge. Thus, we ran the CFAs in pairs and a CFA for all twelve dimensions using the full dataset. Confirmatory factor analysis models were estimated using the lavaan package in R studio version 3.6.3.

Fit was determined based on several indices: the comparative fit index (CFI), Tucker–Lewis Index (TLI), root mean square error of approximation (RMSEA), and AIC (Akaike's Information Criteria) and BIC (Bayesian Information Criteria) to compare models. Hu and Bentler (1999) recommend a cutoff of 0.95 or higher for CFI and TLI and close to or below 0.05 on the RMSEA to indicate excellent fit between the model and the data. We also examined significance tests for factor loadings and R² values. When the models were finalized, composite scores were calculated for each of the twelve dimensions. Pearson's correlations were calculated between each of the hypothesized opposing dimensions and between each dimension and Generalized Anxiety (Table Four).

Here, we report findings for each of the six bipolar dimensions proposed by Deutsch from the EFA, CFA, and various tests of fit. See Table Five for items that were kept after analyses and Table Six for items that were dropped.

Six items were tested for the Avoiding subscale and six for the Involved subscale. Items that cross‐loaded with another CARS dimension are marked with a †, while items that decreased interitem reliability as measured by Cronbach's alpha are marked with a ®. Three models were tested: one with all the items, one without items that reduced Cronbach's alpha, and the final one without cross‐loading items. Our final model had an RMSEA of 0.086, which would be considered below medium fit by Browne and Cudeck (1993) but was an improvement on the first model, which had an RMSEA of 0.109. This model was close to meeting standards for CFI (0.926) and TLI (0.897) of >0.95, as set by Hu and Bentler (1999). This indicated that the Avoiding and Involved dimensions needed additional honing. Avoiding had good interitem reliability after removal of items (α before removal = 0.835, final α = 0.808). Involved had improved interitem reliability after removal of items (α before removal = 0.755, final α = 0.789). The final subscales for Avoiding and Involved had an insignificant negative correlation (r = −0.063, p = 0.226), suggesting they are orthogonal dimensions.

Four items were tested in Study 1 for Soft and six items were tested for Hard. The final model had an RMSEA of 0.067, which would be considered medium fit and was an improvement on the first model, which had an RMSEA of 0.091. This model met standards for CFI (0.96) and TLI (0.945). Soft had sufficient interitem reliability after removal of items (α before removal = 0.707, final α = 0.752). No items needed to be removed for Hard (α = 0.861). The final subscales for Soft and Hard had a significant but moderate negative correlation (r = −0.302, p < 0.001).

Six items were tested for Intellectual and five items were tested for Emotional. With an RMSEA of 0.088, CFI of 0.95, and TLI of 0.927, the results of the fit of this final model were mixed but showed improvement on the original model (RMSEA = 0.08, CFI = 0.893, TLI = 0.869). After removal of items, interitem reliability for Intellectual decreased greatly (α before removal = 0.66, final α = 0.374). No items needed to be removed for Emotional (α = 0.882). The final subscales for Intellectual and Emotional showed a significant negative correlation (r = −0.412, p < 0.001).

Six items were tested for Escalating and eight items were tested for Minimizing. With an RMSEA of 0.086, CFI of 0.905, and TLI of 0.874, the results of the fit of this final model are not good but were still an improvement on the original model (RMSEA = 0.097, CFI = 0.806, TLI = 0.767). The interitem reliability for Minimizing decreased after removal of items (α before removal = 0.621, final α = 0.561). No items needed to be removed for Escalating (α = 0.849). The final subscales for Escalating and Minimizing had only a marginally significant negative correlation (r = −0.092, p = 0.077).

Seven items were tested for Revealing and seven items were tested for Concealing. The final model had an RMSEA of 0.117, CFI of 0.848, and TLI of 0.81, indicating inadequate fit. The interitem reliability for Revealing decreased after removal of items but was still adequate (α before removal = 0.842, final α = 0.827). The interitem reliability for Concealing decreased minimally after removal of items but was still adequate (α before removal = 0.837, final α = 0.836). The final subscales for Revealing and Concealing had a significant negative correlation (r = −0.580, p < 0.001).

Six items were tested for Rigid and seven items were tested for Loose. The final model had an RMSEA of 0.063, which would be considered moderate fit, and was an improvement on the first model, which had an RMSEA of 0.117. This model was close to meeting standards for CFI (0.941) and TLI (0.89). Rigid had a slight improvement in interitem reliability after removal of items (α before removal = 0.531, final α = 0.564), while interitem reliability for Loose worsened (α before removal = 0.687, final α = 0.519). The final subscales for Rigid and Loose did not show a significant negative correlation (r = −0.01, p = 0.843).

All Study 1 goodness of fit statistics, reliabilities, and correlations between opposing factors are presented in Table Seven.

The results from Study 1 showed positive but marginal improvements from changes in the last iteration of the CARS (from Study 7) in improving model fit and reliability of the subscales. However, because of our aggressive approach to trimming the scales, some subscales became too small (i.e., fewer than three items). So, we conducted an additional study and revaluated items from previous studies to determine our next item set, which we tested in Study 2.

In addition, while our previous studies showed significant positive correlations between higher scores on each of the 12 response types and Generalized Anxiety, the current study evidenced mixed results with Involved, Soft, Emotional, Escalating, and Revealing showing significant positive correlations with anxiety, and Minimizing showing a significant negative correlation. These effects were further examined in Study 2.

Finally, while three of the subscales from the proposed bipolar dimensions did evidence negative correlations between the opposing pairs of responses (e.g., Avoiding and Involved), they were moderate in effect, and three dimensions did not show significant negative relations between the pairs. This suggests that the six proposed dimensions may not in fact always function as bipolar dimensions, a question we further explored in Study 2.

The goal of Study 2 was to test the factor structure of the Conflict Anxiety Response Scale Version 9.0, and to assess the relations of each of its 12 dimensions to measures of positive and negative affect, well‐being, and generalized anxiety. Based on the results of Study 1, a series of smaller studies were conducted to hone the scales further, which will not be reported here. Study 2 describes the final scale and psychometric evidence of its reliability and validity.

We gathered data through Prolific in 2021. The sample size was 324 (125 men, 191 women, 8 other). Participants reported being in romantic relationships for greater than three months. Subjects ranged in age from 18 to 74 years (M = 30.52, SD = 9.80). In our sample, 58.41% of the participants were women (N = 191), while 38.23% of the participants were men (N = 125), and 2.45% identified as a gender not listed (N = 8). See Table Eight for full demographics.

In this version of the survey, participants were primed to think of and write about their last personal conflict before answering the questions. The introduction to the survey stated, “Please recall and reflect on the last difficult conflict you were involved in with a romantic partner, friend or family member (someone from your personal life). Please use the space below to briefly write down your recollections of the dispute.” We chose to focus participants on recent specific conflicts of a more difficult nature in order to better allow for the exploration of the relations between conflict anxiety and conflict response tendencies. We used results from the previous study, as well as additional studies of subsets of the 12 dimensions, to determine the items. Subjects were remunerated $2.38 for their time in completing this 15‐minute survey.

Fifty‐eight items were tested in this study. The introduction to the scale read, “People face many different types of conflict in their lives. Some conflicts are minor, others are more serious; some are with strangers, others with colleagues or within intimate relationships; some are in public places, others at home or at work. Below is a list of statements that reflect different ways people respond to conflict. Please indicate how often you tend to respond to conflicts in your personal life in the following ways.” The response options were 5‐point Likert scales with choice options from “Never to Always.”

The PANAS is a tool designed to assess general affective states. It includes ten negative and ten positive affective words. Participants assess the extent to which they have experienced each affective state within the past two weeks on a 5‐point Likert scale. Participants' responses to this questionnaire allowed us to assess potential relationships between affective states and styles of responding to conflict anxiety as specified in Hypothesis 3. Cronbach's alpha for the positive subscale of the PANAS was 0.918, while alpha for the negative subscale is 0.929.

The PWB scale is a tool designed to assess multiple facets of well‐being beyond just positive and negative emotional experience, specifically including: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self‐acceptance. This 18‐item version of the scale (the shortest of three validated versions) is assessed on a 7‐point Likert scale, with three questions per dimension of psychological well‐being. In this study, we utilized this scale to test the relationship between conflict anxiety response styles and measures of PWB, as specified in Hypothesis 3. The PWB had a Cronbach's alpha of 0.857.

The GAD‐7 is a validated, brief clinical assessment of generalized anxiety disorder. It is a 7‐item scale that asks participants to reflect on how often in the last two weeks they experienced 7 presented “problems” by responding on a 4‐item Likert scale ranging from “not at all” to “nearly every day.” This measure is included to help us assess the relationship between base levels of generalized anxiety and different conflict anxiety coping responses as specified in Hypothesis 3. The GAD had a Cronbach's alpha of 0.93.

For Study 2, we ran a confirmatory factor analysis using all 12 of the CARS dimensions, with statements specifying each of the “opposites” to be negatively correlated to each other. Three models were tested. A preliminary model tested six latent factors where opposing constructs (e.g., Avoiding and Involved) were entered into the same factor. This also allowed us to identify whether certain items loaded with the hypothesized opposing factor. For example, two Involved items loaded positively with the Avoiding subscale items (“I ruminate over what I might say or do in a conflict,” and “I can't stop thinking about conflicts long after they've occurred”). The fit statistics for this model were quite poor (RMSEA = 0.089, CFI = 0.60, TLI = 0.58). In subsequent models, we tested different factor structures and the removal of these two items.

In the next model (Model 1), we separated the six factors into twelve factors, such that each of the twelve CARS subscales were tested on their own, but additional statements were entered that specified that they would be correlated. This model greatly improved the fit statistics (RMSEA = 0.070, CFI = 0.75, TLI = 0.74). In Model 2, we tested the removal of the two Involved items that had loaded positively with the Avoiding subscale. This further improved model fit (RMSEA = 0.069, CFI = 0.77, TLI = 0.75). As will be discussed, the removal of these items also created a stronger negative correlation between Avoiding and Involved. In the final model, Model 3, the specified correlations were removed. This allowed us to test whether the twelve factors were negatively correlated with their bipolar opposite (e.g., Avoiding and Involved) or were negatively impacting model fit. Removing these correlations from the model improved model fit (RMSEA = 0.060, CFI = 0.83, TLI = 0.82).

The fit statistics, although much improved, are still not optimal. As mentioned above, an RMSEA less than 0.05 is an indicator of good fit, but we also want to maintain CFI/TLI above 0.90 to 0.95. However, AIC and BIC, which can be used to compare models, were lowest in the final model, indicating that the model without correlated latent factors and with the two items trimmed was better. However, AIC tends to prefer parsimony, which is when there are fewer parameters to be measured, so that might be one source of the improvement in fit. Factor loadings and items are presented in Table Nine. Goodness‐of‐fit statistics for all models are reported in Table Ten.

We were also interested in whether there were in fact negative correlations between each of the hypothetically opposing dimensions. Four of the six dimensions evidenced significant negative correlations in Study 2: between Soft and Hard (r = −0.244, p ≤ 0.001), Escalating and Minimizing (r = −0.531, p ≤ 0.001), Intellectual and Emotional (r = −0.518, p ≤ 0.001), and Concealing and Revealing (r = −0.463, p ≤ 0.001). Prior to removing the two items from the Involved subscale, Avoiding and Involved did not have a significant correlation, but after removal, there was a significant negative correlation (r = −0.14, p = 0.015). However, Rigid and Loose did not evidence a significant negative correlation, but rather a nonsignificant correlation (r = 0.105, p = 0.07). Correlations can be found in Table Eleven.

To estimate the internal consistency among the final set of Conflict Anxiety Response items, Cronbach's alpha was calculated for each subscale. We had sound Cronbach's alphas on each dimension (ranging from 0.67 to 0.895). Avoiding had α = 0.81, Involved had α = 0.67, Hard had α = 0.85, Soft had α = 0.77, Intellectual had α = 0.90, Emotional had α = 0.88, Escalating had α = 0.84, Minimizing had α = 0.86, Revealing had α = 0.81, Concealing had α = 0.87, Rigid had α = 0.80, and Loose had α = 0.75. Alphas are recorded in Table Twelve.

To test the predictive validity of the CARS (Hypothesis 3), we measured its relationships with positive and negative affect (PANAS‐N), generalized anxiety (GAD), and psychological well‐being (PWB). We predicted that more extreme presentations of conflict anxiety response styles on each of the twelve dimensions would be negatively correlated with psychological well‐being and positive emotions, and positively correlated with generalized anxiety and negative emotions. Final composite scores were calculated for each of the dependent variables of interest. Descriptive statistics, alphas, and correlations are presented in Tables Eleven and Twelve.

In testing Deutsch's model, we hypothesized that more extreme scores on the twelve CARS dimensions would be negatively associated with lower levels of psychological well‐being. Of the twelve subscales, seven—Involved (r = −0.276**, p < 0.001), Soft (r = −0.368**, p < 0.001), Escalating (r = −0.186**, p = 0.001), Emotional (r = −0.259**, p < 0.001), Concealing (r = −0.278**, p < 0.001), Rigid (r = −0.279**, p < 0.001), and Loose (r = −0.125*, p = 0.031)—had significantly negative correlations with psychological well‐being. However, Avoiding, Rigid, and Revealing had no significant correlations and Minimizing (r = 0.249**, p < 0.001) and Intellectual (r = 0.385**, p < 0.001) had significantly positive correlations with psychological well‐being.

We also hypothesized that extreme scores on the twelve CARS dimensions would be positively associated with higher levels of generalized anxiety. Again, of the twelve subscales, seven—Involved (r = 0.41, p < 0.001), Soft (r = 0.29, p < 0.001), Escalating (r = 0.24, p < 0.001), Emotional (r = 0.31, p < 0.001), Concealing (r = 0.17, p = 0.003), Revealing (r = 0.15, p = 0.009), and Rigid (r = 0.15, p = 0.009)—had significant positive correlations with GAD. However, Avoiding, Hard, Loose, and Minimizing had no significant correlations while Intellectual (r = −0.22, p < 0.001) had a significantly negative correlation with GAD.

Finally, we hypothesized that those who evidenced more extreme conflict anxiety responses would experience more negative emotional states. Of the twelve subscales, six—Involved (r = 0.35, p < 0.001), Soft (r = 0.23, p < 0.001), Escalating (r = 0.20, p < 0.001), Emotional (r = 0.27, p < 0.001), Revealing (r = 0.14, p < 0.019), and Loose (r = 0.16, p < 0.007)—had significant positive correlations with the PANAS‐Negative, while Avoiding, Rigid, Hard, and Concealing had no significant correlations and Minimizing (r = − 0.12, p < 0.042) and Intellectual (r = −0.18, p < 0.002) evidenced negative correlations with it.

In addition to predictive validity, we explored post hoc differences in the CARS' conflict anxiety response styles associated with gender differences. For this analysis, we conducted two sample t‐tests for the difference between male‐ and female‐identifying individuals on each CARS dimension. We found significant differences on six of the subscales: Involved, Emotional, Escalating, Revealing, Intellectual, and Concealing. Women evidenced higher scores than men on Involved (M = 2.72, SD = 0.81) (M = 2.53, SD = 0.75), (t(240) = −2.04, p = 0.0426), Emotional (M = 3.21, SD = 0.79) (M = 2.55, SD = 0.6), (t(272) = −8.03, p < 0.0001), Escalating (M = 2.43, SD = 0.72) (M = 2.27, SD = 0.64), (t(250) = −2.01, p = 0.0455), and Revealing (M = 2.84, SD = 0.67) (M = 2.67, SD = 0.59), (t(250) = −2.23, p = 0.0269). Men reported higher scores than women on Intellectual (M = 3.42, SD = 0.68) (M = 3.1, SD = 0.73), (t(240) = 3.80, p < 0.0001) and Concealing (M = 2.71, SD = 0.76) (M = 2.42, SD = 0.7), (t(214) = 3.21, p = 0.0015).

Proportions of men and women who scored highly on each dimension (defined in this study as having a mean of the items higher than 3) were also calculated. Although women scored higher than men in being highly involved in conflict, less than a third followed this pattern (28.49%). Almost two‐thirds of men scored high on the intellectualize dimension, though less than half of women did (66.07% vs. 43.55%). Over half of women prefer highly emotional responses to conflict (51.08%), though only about one‐sixth of men do (16.96%). Although women were more likely to escalate a conflict, the proportions of both men's and women's tendencies to do so were low (7.14% and 18.82%, respectively). More women than men conceal information in a conflict, despite the previous t‐test showing the opposite trend. Lastly, the difference in proportions was insignificant for Revealing. These gender difference findings are mainly consistent with other gender‐related findings on anxiety, such as the finding that anxiety disorders are about twice as prevalent in women than in men (Donner and Lowry 2013) and findings related to more traditional manifestations of gender role identities (Schrock and Knop 2014). Gender differences and proportions are reported in Table Twelve.

This article describes a multiyear process of developing a new scale for the measurement of different types of individual responses to anxiety in situations of interpersonal conflict. Based on a model derived from the clinical observations of Morton Deutsch, we developed and tested a measure that could allow us to examine the hypotheses embedded or assumed in the model. The resulting measure, the Conflict Anxiety Response Scale or CARS, although not yet perfect in terms of its psychometric properties, offers users a sufficiently reliable and valid measure of responses to conflict anxiety—the first measure of its kind. CARS also provides an individualized feedback report for increasing self‐awareness of one's conflict‐response tendencies.

Empirically testing, validating, and refining a model about conflict anxiety and conflict behaviors that has between six and twelve subdimensions of response types is a highly complicated task. However, over time, we have been able to observe patterns in the data across our multiple studies that increase our sense of how study participants experience these dimensions, and how we might go about meaningfully measuring them.

First, although five of the six hypothesized opposing‐paired responses showed significant negative correlations with each other, the model with twelve independent dimensions seemed to offer the best fit of the data. In other words, although responses such as Involved and Avoiding or Escalating and Minimizing were found to be significantly negatively correlated, the data suggested that the twelve response options in the model are best understood not as opposing, but as somewhat independent of one another. This may simply be because people can differ in how they respond to conflict anxiety in different types of personal situations—for example, whether the conflict is with family members, romantic partners, or friends. However, it also raises the possibility that individuals may in fact score high on some of the hypothesized “opposing” responses, such as when people respond in a highly Rigid and Loose manner. These are questions to be explored further in future research.

Second, the final reliabilities of each of the subscales that emerged from Study 2 were strong, ranging from 0.67 to 0.895. Third, the predictive validity of the measures found in Study 2 provided some general but qualified support for Deutsch's original hypothesis that the more extreme the response to conflict anxiety along each dimension, the more problematic it is for the respondents' emotions, well‐being, and relationships. However, higher tendencies on some response types, particularly Intellectual and Minimizing, were found to be nonproblematic or even beneficial. This may be due to some type of curvilinear or threshold effect of these response types, where, for example, intellectualizing may be beneficial in conflicts some of the time or to a degree, unless it becomes chronic or extreme, which may show deleterious effects. These curvilinear effects should also be explored in future research.

Finally, an exploratory post hoc analysis identified some self‐reported gender differences in anxiety response tendencies. Women had higher scores than men on the subscales of Involved, Emotional, Escalating, and Revealing, while men reported higher scores than women on Intellectual and Concealing. These findings are not surprising and are even stereotypical, and are supported by other research on gender and conflict resolution (see Brahnam et al. 2005; Rahim and Katz 2020).

Study 2 was the ninth study we ran on the Conflict Anxiety Response Scale, and yet there are still challenges and limitations to the scale to be addressed. This was an iterative process whereby as we improved one of our requirements for the psychometric properties of our scales, others worsened. In this case, model fit and correlations between a hypothesized opposing pair of subscales at times resulted in a worse Cronbach's alpha.

Given that this research is based solely on correlational data, it is difficult to determine whether greater levels of anxiety and negative affect predict more extreme responses on the CARS, vice versa, or both. Either way, Deutsch's observation that conflict + anxiety = turmoil was generally supported for most subdimensions of the CARS.

However, these findings suggest that Intellectualizing and Minimizing responses to conflict may in fact shield respondents from mental health consequences of conflict anxiety, at least up to some threshold. Although we hypothesized that more extreme levels of all our subscales would be found to be unproductive derailers of a conflict, in previous studies, these two response types often predicted better outcomes. Although we have formulated our scales to emphasize more extreme and negative responses to conflict, it could be that these two subscales need more refinement in that regard.

The Rigid and Loose subscales were the most challenging to operationalize of the twelve dimensions. In previous studies, we looked specifically at these two scales and investigated a larger pool of items for each. We ran EFAs within these two subscales and found the three items used here to be the best, as they were significantly correlated with GAD‐7. That finding was replicated here, but we must consider whether Rigid inflexibility and Loose informality responses are relatively less related to conflict anxiety than the other dimensions.

Fit statistics of the full model were also not ideal. It could be that trying to fit all twelve subscales into one model quite negatively impacts fit, and that it would be more proper to run each pair separately.

Despite these limitations, many of our subscales show different ways that anxiety about conflict manifests, and the importance of these conflict anxiety responses in predicting mental and relational health. Counselors who work with individuals, couples, and families in conflict can use these scales and the feedback derived from them to suggest different ways for clients to work together in communicating disagreements. To that end, a free version of the CARS and individualized feedback reports can be found here.

The current studies advance our ability to assess the critical role of conflict anxiety on people's responses to conflicts in their personal lives. Nevertheless, there are many questions left to answer through research. One next step would be to assess whether and how differing levels of anxiety—sparked specifically by conflict (as opposed to generalized anxiety)—are related to the extremity of responses on the CARS dimensions. Another would be to assess the relationship of the CARS to general conflict outcome satisfaction with the use of scales such as the Subjective Values Inventory (Curhan, Elfenbein, and Xu 2006), to verify that the conflict anxiety scale correlates strongly with a standard conflict outcome scale. Similarly, it would also be informative to assess the relationship of the CARS to other standard measures of conflict behavior, such as the Thomas–Kilmann Conflict Mode Instrument (Thomas 1976), the Intercultural Conflict Style Inventory (Hammer 2005), and the Rahim Organizational Conflict Inventory‐II (ROCI‐II) (Rahim 1983; Weider‐Hatfield 1988).

Another future area of research is the value of “optimality” in conflict anxiety responses. Deutsch (1993) proposed several types of optimal responses that are situated between the more extreme types of responses, including the following:

• “A healthy predisposition to conflict involves the readiness to confront conflict when it arises without needing to seek it out or to be preoccupied with it.”

• “A more appropriate stance in conflict is a firm support of one's own interests combined with a ready responsiveness to the interests of the other.”

• “An approach to conflict that allows for both orderliness and flexibility in dealing with the conflict seems more constructive than one that is compulsive either in its organizing or in its rejection of orderliness.”

• “The ideal mode of conflict communication combines thought and affect: The thought is supported by the affect, and the affect is explained by the thought.”

• “One, in effect, should be open and honest in communication but realistically take into account the consequences of what one says or does not say and the current state of the relationship.”

The value of such forms of optimal conflict resolution needs to be interrogated empirically.

Further research into the CARS could also investigate the validity of a short version of the scale using 1–2 items per tendency, resulting in a brief 12–24‐item short scale. Prior iterations of this short scale were found to be valid, as it had positive correlations with their longer counterparts and was predictive of higher generalized anxiety and lower psychological well‐being. The authors recommend the 22‐item brief scale in Table Thirteen, which was created by selecting one to two items with the highest coefficients from the final model in Study 2. Additional research may be conducted on the long‐term benefits to conflict resolution styles by providing feedback based on measuring these tendencies.

We seem to be living in increasingly anxious times due to spikes in everyday incivility, political polarization, racial injustice, COVID concerns, and runaway climate change‐related catastrophes, just to name a few sources of our anxiety. In these times, how we treat each other becomes more and more paramount, as often we all are sitting on explosive levels of emotional distress. The development and use of CARS is one small way to help us be more mindful—and hopefully less harmful—in how we respond to our many differences with others over the things that matter.

The authors wish to thank Rebecca Bass and Rebecca Mohr for their contributions to this research.

The data that support the findings of this study are openly available in the Negotiation Data Repository (NDR) at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4UYOEP.