Sabotaging relationships why

But relationship skills can be learned. Healthy relationships can help foster relationship skills and in turn lessen the effects of defensiveness and trust difficulty.

Relationship sabotage does not necessarily end relationships. This depends on whether these patterns are long term. For singles, relationship sabotage might prevent you from starting a relationship in the first place. For people in relationships, a long-term effect of repeatedly using self-defensive strategies might be to see your fears turn into reality, like a self-fulfilling prophecy.

Difficulties in intimate relationships are among the top main reasons for seeking counselling. Such difficulties are also significant contributors to anxiety, depression and suicidal thoughts.

I have seen countless testimonials from people who sabotaged their relationships and felt helpless and hopeless. But here are three ways to do something about it :. Be honest with yourself and your partner about your fears and what you might be struggling with.

Understand what you can realistically expect of yourself and your partners. This means learning how to communicate better across all topics, while being honest and showing flexibility and understanding, especially when dealing with conflict. Portsmouth Climate Festival — Portsmouth, Portsmouth. Edition: Available editions United Kingdom. Become an author Sign up as a reader Sign in. Raquel Peel , University of Southern Queensland. Popular culture has plenty of examples of people sabotaging their romantic relationships.

Altogether, there are three main examples of how sabotage is presented in relationships. Nevertheless, one common theme to explain motivation amongst all these cases is fear. Fear was also mentioned as a motive for why individuals avoid committing to relationships.

Additionally, participants explained they avoid working on their relationships due to poor self-esteem or self-concept and loss of hope. Overall, it is fear which motivates individuals to engage in defensive strategies. Yet, to be discussed are possible self-defeating attitudes and behaviors which could be classified as symptomatic of relationship sabotage.

To this end, the next two section will review themes discussed in the and studies conducted by Peel et al. This was an inductive qualitative study, conducted prior to defining the phenomenon of relationship sabotage, towards understanding possible accounts for individual motivation and representative self-defeating attitudes and behaviors. Overall, this study has provided preliminary evidence for how to define relationship sabotage and how to identify attitude and behaviors that are symptomatic of relationship sabotage.

Psychologists described attitudes and behaviors that are well understood to be maladaptive in romantic relationships in accordance with experts in the field, such as John Gottman and Susan Johnson and colleagues [ 20 , 21 , 22 , 23 , 24 , 25 ].

It seems that people sabotage romantic relationships primarily to protect themselves, as a result of insecure attachment styles and past relationship experiences, and the many ways they do this was represented over 12 main themes: 1 partner attack e. Interestingly, practitioners interviewed in this study highlighted that the same attitudes and behaviors that are initially employed to make the relationship function well are the contributors to relationship dissolution in the short or long term.

Please see Peel et al. More specifically, participants explained how relationship sabotage happened for them over several relationships. Defensiveness, trust difficulty, and lack of relationship skills were the most salient themes contributing to relationship sabotage. Defensiveness is a self-protection strategy used as a counterattack when feeling victimised against a perceived attack. In support, Gottman [ 26 ] explained that defensiveness is often a result of perceived criticism and contempt, and in turn, can trigger a cascade of behaviours leading to relationship dissolution e.

Trust difficulty is often a result of past experiences of betrayal. This theme included being unable to trust romantic partners and feeling overly jealous. For instance, lack of experience, inflexibility, immaturity and learned helplessness were categorized under this theme as contributors.

Although the literature discussed thus far is abundant, a major gap in understanding relationship sabotage still exists. Presently, there is no instrument to conceptualise and empirically measure how people continue to employ self-defeating attitudes and behaviors in and out of relationships. To this end, the twelve main themes identified by psychologists in the study [ 14 ] have helped inform the generation of the initial item pool.

However, not all twelve themes are expected to be confirmed as separate constructs, as some are attitudes and behaviors to explain why individuals sabotage their relationships, while others represent attitudes and behaviors to explain how sabotage happens.

Therefore, the results from the study [ 15 ], which highlighted defensiveness, trust difficulty, and lack of relationship skills, have also served to identify the most prominent themes and those most likely to be represented as separate constructs.

A series of three studies were conceptualized for the current project to fill the need for scale development and to build empirical evidence on the topic of sabotage in romantic relationships. The first study was designed to pilot test the list of items using an exploratory factor analysis EFA.

This was an important step as not all 12 themes were expected to be represented as unique and separate constructs in the final scale. The second study aimed to refine the scale and factor structure using a two-part EFA and one-congeneric model analyses.

Lastly, a third study examined the final structure for the Relationship Sabotage Scale RSS with a confirmatory factor analysis CFA and reliability and construct validity analyses.

The following section will collectively present the methods employed and the results found to best illustrate the sequential steps of scale development, refinement, and validation. The current studies were conducted online as an anonymous survey. Snowball recruitment i.

It is estimated that participants took around 15—30 min to complete the survey. Data for the current studies were collected between June and December in three separate campaigns.

The initial items pool were generated based on the 12 main themes extracted from the thematic analysis of interviews conducted with psychologists specializing in relationship therapy, reported in the study [ 14 ]. Although items were created based on these broad themes, it was not expected all themes would be represented as separated constructs. Instead, it was expected that constructs would be an agglomeration of the specified themes as per the study [ 15 ].

Both reviewers are practicing psychologists with experience in relationship counselling. Feedback from the reviewers resulted in additional items being added three items were added to the initial pool of 57 items, resulting in a total of 60 items, with an approximately equal number of items per theme and changing the wording of some items for better comprehension. The items were randomly presented as a survey to prevent question order from affecting scores. We are interested in how you generally experience relationships, not just in what is happening in a current relationship.

If you are not in a relationship, think back to your last relationship. See Table 1 for a complete list of the items included in the survey. Participants for the three studies were English speaking individuals of diverse gender orientation, sexual orientation, and cultural background, with lived experience of relationship sabotage.

A sample of participants was recruited for this study. A sample size above is considered acceptable for EFA [ 27 , 28 , 29 , 30 ], especially given that the sample item communality values were within the recommended range 0. The distribution included 98 male participants This complies with the parameters recommended by Fabrigar et al. Lastly, the sample did not include missing data. The aim of this initial analysis was to assess the original item pool, the underlying factor structure for the proposed inventory, reduce the number of items, and determine the highest loading items.

This extraction method is arguably the most robust choice for normally distributed data, as it provides more generalizable results and allows for the computation of goodness-of-fit measures and the testing of the significance of loadings and correlations between factors [ 28 , 29 , 30 , 31 ]. These are important considerations [ 32 ] for future analysis of the scale using structural equation modelling SEM.

The KMO statistic measures whether the correlations between pairs of variables can be explained by other variables [ 33 ]. These are necessary conditions to support the existence of underlying factor structures. Factorability was established with a KMO above the recommended i. Factor 1, the strongest factor, accounted for Overall, the factor correlation matrix showed that factors were not highly correlated i. An inspection of the screeplot revealed a break after the sixth component.

To ensure a conservative approach at this stage, eight components were retained for further investigation. The eight-component solution explained a total of To aid in the interpretation of results, a direct oblimin rotation with Kaiser normalization was performed, which allowed for factors to correlate. It was assumed that factors within the construct of relationship sabotage should all correlate [ 30 ], as this is often the case when measuring psychological constructs [ 28 , 29 ].

The pattern and structure matrices were reviewed, and the rotated solution showed all components included moderate to strong loadings i. Further investigation to ensure the quality of items was also applied.

Items loading with coefficient values below 0. This resulted in 19 items dropped, with a total of 41 items remaining. This sample size was deemed appropriate based on specific recommendations.

Bentler and Chou [ 37 ], Worthington and Whittaker [ 27 ], and Kline [ 32 ] recommended a sample of a minimum of participants and a minimum of participants per parameter.

In the current study, the most complex model estimated 16 parameters a ratio of Therefore, the current sample was adequately powered to detect significant misspecifications in the models examined. Further, Browne [ 38 ] developed the Asymptotic Distribution Free ADF estimator for sample sizes based on a weight matrix in the function for fitting covariance structures.

This method is considered too stringent [ 39 ] and other methods, such as the aforementioned, are most often used. Nevertheless, it is noted that the current study met the sample size suggested by the ADF estimator, with participants for 8 observable variables and 1 latent variable in the most complex model. In addition, a total of The sample did not include missing data.

A two-part EFA was conducted. The first part was the scale refinement process including factor and scale-length optimization. The second part, recommended by Henson and Roberts [ 40 ] and Worthington and Whittaker [ 27 ], was to ensure that factor and item elimination does not result in significant changes to the instrument. The 41 items derived from the previous study were tested for the first part of Study 2. Factorability was established with a KMO at 0.

Eigenvalues indicated eleven factors over 1. These factors explained Using the results from the parallel analysis, seven components were retained for further investigation. Overall, this approach is to ensure constructs can be represented, ensure good model identification [ 43 ], and avoid an inadmissible solution [ 32 ] prior to conducting one-congeneric model analyses the next step.

This resulted in six items dropped due to low coefficient values, three items dropped due to low inter-item correlation values, and four factors dropped due to insufficient number of items and low factor reliability, with a total of three factors and 20 items remaining. This process was used to identify and scale the model [ 44 ].

Also, alternative marker variables were examined as a means of checking for the robustness of the final models. No items were allowed to covary within constructs. The error terms associated with observable and latent variables were also set to the value of 1 and measurement error was assumed to be uncorrelated between items [ 44 ].

The t -rule method [ 43 ] was used to assess model identification. Model identification is assumed if the number of parameters to be estimated in a model does not exceed the number of unique variances and covariances in the sample variance—covariance matrix calculated using k. The most complex model analyzed in this study Factor 1 had 16 free parameters and 8 observable variables; therefore, it met the t -rule requirement i. Free parameters in the model were also estimated using the ML procedure.

In SEM, this practice is recommended by several researchers—e. The ML approach is robust for normal, or near normal data, as it provides close estimates of measurement error and a chi-square distribution closely related to the population of estimation. In this step, factor score regression weights, variance explained, and measurement error were used to assess the quality of items.

Modifications were only applied to improve the model when existing literature, previous research findings, and the results from the current set of studies supported the proposed alterations. Overall, the one-congeneric model approach allows for factors of different weights within the same construct to contribute uniquely and does not assume that items are parallel i.

Model specifications analysis showed high covariance associated with four items 16, 22, 24, Therefore, these items were removed. Altogether, this factor contains three items from the original defensiveness theme items 18, 19, and 23 and one item from the original contempt theme item Factor 2.

Model specifications analysis showed high covariance associated with three items 6, 9, Altogether, this factor contains two items from the original trust difficulty theme items 44 and 45 , one item from the original partner pursue theme item 8 , and one item from the original controlling tendency theme item Factor 3. However, item 60 showed a weak regression weight i. Altogether, this factor contains three items from the original lack of relationship skills theme items 40, 41, and 42 and one item from the original contempt theme item These analyses resulted in eight items dropped.

The final EFA was performed on 12 items. The three-component solution explained a total of No other factor showed eigenvalues above 1. The rotated solution showed all components included moderate to strong loadings i. Factor 1 Overall, this result demonstrated the three-factor model is superior to the eight and seven factor solution previously identified.

The final inventory of 12 items and their respective loadings can be viewed in Table 2. A sample of participants were recruited for this study. The same specifications to access the appropriateness of sample size as Study 2 were used. The distribution included male participants Also, the sample did not include missing data. A full multi-factor CFA was conducted with the final set of items and the same sample and specifications as the one-congeneric model analyses.

The aim of conducting this CFA was to evaluate the EFA-informed factor structure and psychometric properties and to test the fit of the global model. The three factors were represented in the full model by latent variables fitted as a second-order g model , with each item loading on its respective latent factor, as predicted by the EFA.

Factor loadings from one of the observable variables from each set of constructs was randomly set to the value of 1. Also, alternative marker variables were examined as a means of checking for the robustness of the final model.

Items were not allowed to load on multiple factors. The three factors were allowed to covary and measurement error was assumed to be uncorrelated between items. All factors and items significantly loaded in their respective latent factor. Items loaded with t values between 6 and Also, items squared multiple correlations ranged between 0.

Overall, this indicates items were strong and reliable indicators of the latent variables [ 44 ]. The RMSEA takes into account the error of approximation in the population and reduces the stringent requirement on the chi-square that the model should hold exactly in the population [ 44 , 46 ].

An issue with the chi-square statistic is that the more complex the model, the bigger the value and the more likely it is that the model will be rejected. The normed chi-square takes model complexity into account and can also be referred to as an index of model parsimony [ 47 ]. This indicates the hypothesised model accounts for variance in the data well in comparison with the null model. The TLI was 0. This indicates the model is parsimonious.

Finally, the SRMR, which is a residual statistic that assesses the residual variance unexplained by the model, showed a level of 0. Overall, the final item inventory was supported by the CFA. According to Hancock and Mueller [ 51 ], coefficient H provides a more robust way to assess latent measures created from observable construct indicators, such as regression coefficients, especially if items are not parallel.

Alternatively, coefficient H is not limited by the strength and sign of items and draws information from all indicators even from weaker variables to reflect the construct. The standard cut-off indicators recommended by the most stringent researchers [ 50 , 53 , 54 ] were followed for both analyses i.

As all sub-scales contain less than ten items, which can affect the reliability value, the mean inter-item correlation value was also inspected. The mean inter-item correlation value for all sub-factors showed a strong relationship between items i. Traditional approaches to assess construct validity i. Therefore, assessing validity with a correlation matrix alone is limited and does not account for the effect of variables with different regression weights and measurement errors.

To remedy this limitation, SEM-based approaches to construct validity were also performed. SEM-based approaches highlight how constructs are affected differently and allows them to correlate freely among themselves. Further, these approaches assess how well each construct fits within the model with regards to variance explained and measurement error [ 55 ]. Convergent and discriminant validity were assessed using the MTMM matrix, which assesses construct validity by comparing the correlation matrix between the proposed constructs and constructs measured by different scales, which are either conceptually similar or dissimilar [ 56 ].

This approach is unlike adding raw scores to represent subscales, which assumes that the items are parallel. Further, weighted composite variables are continuous, as opposed to Likert scale scores, which are ordinal.

Therefore, for the purpose of creating weighted composite variables, factor score regression weights were rescaled to add up to a total of 1. Regarding divergent validity, all three factors showed a near zero positive relationship with self-handicapping ranging between 0.

See Table 3 below. According to Bagozzi et al. Further, Holmes-Smith and Rowe [ 42 ] recommended a threshold value of 0. Standardized item loadings were in between 0. Additionally, Hair [ 61 ] proposed an all-encompassing and more stringent set of criteria for convergent validity, which requires that in addition to standardized factor loading of all items greater than 0.

All AVE between factor were above 0. Further, all factor CR were above 0. These results fully supported convergent validity for Factors 1 and Factor 3 and partially support convergent validity for Factor 2.

The criterion adopted by Kline [ 32 ] was considered for discriminant validity analyses, which stipulates that validity can be assumed if the correlation between two factors is less than 0. This was further supported by Cheung and Wang [ 64 ], who recommended the correlation not be significantly greater than 0. However, this approach is often criticized for its reliance on the correlation matrix approach, which does not consider variance explained and error measurement [ 55 ].

Therefore, two additional approaches were considered. This method showed that all pairs of constructs were distinct, thereby supporting discriminant validity i. Further, discriminant validity was assessed using the Bagozzi et al.

This procedure involves measuring the difference between the constrained and unconstrained models with correlations between constructs set to 1 between each two pairs of variables. The difference between models should show that constraining the correlation between the two constructs worsens the model fit i. The nested model approach was performed between factors showing divergent constructs.

This confirms there are three distinct factors. Additionally, this approach has gained favor as a technique to compare alternative models [ 27 ]. The results from this test fully supported discriminant validity—see Table 5. As predicted, not all themes derived from the study [ 14 ], as shown on Table 1 , were represented as unique factors in the final scale. Instead, the three themes from the study [ 15 ] study—i. Nevertheless, some concepts were represented as minor sub-themes within the identified constructs in the final measure.

For instances, two items from the contempt theme item 26 and 28 were represented in the defensiveness and lack of relationships skills factors. Another example is the one item from the partner pursue theme item 8 , which was represented in the trust difficulty factor. These findings are all a part of the process of scale development, which although based on a strong literary background, needs to undergo exploratory tests to strengthen the original predictions [ 30 ].

Sometimes the only thing standing between us and a happier relationship is ourselves. Many psychologists call this self-sabotaging behavior, which is broadly defined as behavior that creates problems in your own daily life and interferes with your long-standing goals.

In relationships, self-sabotage is when you're actively trying to ruin your own relationship or make it fall apart, whether consciously or subconsciously. For some people, this is such an ingrained behavior that it can be hard to even recognize, let alone stop it. Although often subconscious, there are several reasons someone might want to sabotage a perfectly healthy relationship.

One big reason is low self-esteem and self-worth, according to clinical psychologist Maggie Dancel, Psy. If you're worried your partner may like you enough, you might subconsciously act out or push them away so you don't have to feel the sting of rejection.

Stirring up relationship drama can also be a way to keep your partner interested, Dancel tells mbg: "Individuals may not feel that they can get better, so they settle for any attention, affection, and connection, negative or positive.

On the other side of the spectrum, some individuals might fear commitment due to what the relationship will mean for their independence, leading them to self-sabotage the relationship in order to keep their distance and maintain a sense of freedom.

Your attachment style is the way you deal with relationships, which is learned from our earliest childhood relationships with caregivers. Individuals with anxious attachment styles often desire intimacy and fear rejection because of experiences of abandonment in childhood, which can lead them to project these negative outcomes of the relationship onto their partner.

Individuals with avoidant attachment styles often avoid closeness and intimacy because their childhood taught them to be self-sufficient, which may lead them to delay commitment or demonstrate a dismissive nature.

Because the desire to self-sabotage is so linked to our attachment style, people can often self-sabotage relationships subconsciously by repeating the relational patterns that we learned as children. A big red flag for self-sabotage is having negative emotions about your partner or relationship but refusing to address them. Feeling anxiety, anger, frustration, or doubt in any relationship, romantic or not, is totally normal—but refusing to speak to your partner about these fears signals that you're not interested in fixing the problems you're seeing or keeping your relationship alive.

It's hard not to get paranoid sometimes in relationships, but if you are constantly worried that your partner is cheating or wants to leave you, this could be a projection of your own fears and anxieties about the relationship.

The best partnerships involve at least some constructive criticism, but if you are always criticizing your partner for small behaviors, this could also be a sign of self-sabotage. Critiquing your partner when they do not deserve it could mean that you are subconsciously trying to create a wedge between you two or drive them away. While it might not seem like it, eating poorly, drinking or smoking excessively, and overall not taking care of yourself can be a sign of self-sabotage in a relationship.

These negative behaviors can function as a coping mechanism for individuals who are unhappy in a relationship but do not know how to fix it. These unhealthy patterns can also be a scapegoat for the issues in a relationship—if someone is focused on their excessive smoking, for example, they can blame their relationship troubles on that rather than looking for deeper problems.

Everyone holds a grudge once in a while, but if you are constantly annoyed by small things your partner does and can't seem to let go of that anger, this may be a sign of self-sabotage. Often, holding grudges in a relationship can lead to poor communication and delayed anger and fighting, which can greatly hurt any partnership. You may be subconsciously holding a grudge to avoid talking to your partner about the issues in your relationship.

A big sign of self-sabotage is if you are concerned about the state of your relationship but also not putting time into mending it.

If you have suddenly become hyper-focused on work, your hobbies, or the other people in your life and are ignoring your partner completely, you might be trying to convince yourself you don't have time to fix the issues in your relationship, when really you are just prioritizing other things.

Intimate relationships can be difficult to manage, and it's hard to always have a perfect set of expectations for what you and your partner owe each other. That being said, if you are regularly upset that your partner is not meeting your expectations and are not communicating your disappointment to them, this could also be a sign that you have already deemed your partner unfit for you in your head and don't think the relationship is worth fighting for.

Small things add up. If you regularly break promises regarding what time you will be home or when you and your partner will be spending quality time, this could mean that you are training your partner to resent you. Another red flag is if you are unable to see the good in your partner or relationship and can instead only focus on small imperfections on both sides.

This negative pattern is often a sign that you are trying to drive a wedge between yourself and your partner. It's normal for couples to go through phrases of lackluster sex or no sex at all , but Cooper says it's telling when one person has given up and accepted the unfulfilling sexual relationship.

This can lead to frustration, resentment, or even 'the grass is greener' syndrome, where someone wonders if something else is better," she says.