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Relations Among Apology, Forgiveness, and Communicative Responses to Hurtful Messages
Unformatted Document Text:  Forgiveness and Communication 16 general items found in the literature on responses to jealousy and relational dissatisfaction. Distributive communication was comprised of items tapping into face threatening behaviors that are aimed at making the partner feel guilty or bad for their actions. These items were derived from Guerrero et al.’s (1995) work on jealousy as well as Vangelisti and Crumley’s (1998) work on active verbal reactions to hurtful messages. Finally, negative avoidance focuses on behaviors designed to make the partner feel bad by strategically limiting contact, communication, and affection. Work on hurtful messages (Vangelisti & Crumley, 1998), uncertainty reduction (Emmers & Canary, 1996), responses to dissatisfaction (Rusbult, 1987), and responses to jealousy (Guerrero et al., 1995) has all included destructive avoidant behaviors such as these. Analysis Plan A bivariate correlation will be used to test H1, which predicted a positive association between apology and forgiveness. Bivariate correlations will also provide the primary statistical tests for H2 (predicting positive relationships between apology and conciliatory responses), H3 (predicting positive relationships between forgiveness and conciliatory responses), H5 (predicting negative relationships between apology and destructive responses), and H6 (predicting negative relationships between forgiveness and destructive responses). Because the various conciliatory and destructive responses are intercorrelated (average r for the conciliatory responses was .36; average r for the destructive responses was .39), it is appropriate to supplement the bivariate correlations with regression analyses utilizing the communicative responses to predict apology (for H2 and H5) and forgiveness (for H3 and H6). In addition to accounting for correlations between the responses, these regression analyses may also reveal suppression effects and provide information about the relevant strength of different associations. When reporting the regression analyses, we provide the information related to trimmed models

Authors: Bachman, Guy. and Guerrero, Laura.
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Forgiveness and Communication 16
general items found in the literature on responses to jealousy and relational dissatisfaction.
Distributive communication was comprised of items tapping into face threatening behaviors that
are aimed at making the partner feel guilty or bad for their actions. These items were derived
from Guerrero et al.’s (1995) work on jealousy as well as Vangelisti and Crumley’s (1998) work
on active verbal reactions to hurtful messages. Finally, negative avoidance focuses on behaviors
designed to make the partner feel bad by strategically limiting contact, communication, and
affection. Work on hurtful messages (Vangelisti & Crumley, 1998), uncertainty reduction
(Emmers & Canary, 1996), responses to dissatisfaction (Rusbult, 1987), and responses to
jealousy (Guerrero et al., 1995) has all included destructive avoidant behaviors such as these.
Analysis Plan
A bivariate correlation will be used to test H1, which predicted a positive association
between apology and forgiveness. Bivariate correlations will also provide the primary statistical
tests for H2 (predicting positive relationships between apology and conciliatory responses), H3
(predicting positive relationships between forgiveness and conciliatory responses), H5
(predicting negative relationships between apology and destructive responses), and H6
(predicting negative relationships between forgiveness and destructive responses). Because the
various conciliatory and destructive responses are intercorrelated (average r for the conciliatory
responses was .36; average r for the destructive responses was .39), it is appropriate to
supplement the bivariate correlations with regression analyses utilizing the communicative
responses to predict apology (for H2 and H5) and forgiveness (for H3 and H6). In addition to
accounting for correlations between the responses, these regression analyses may also reveal
suppression effects and provide information about the relevant strength of different associations.
When reporting the regression analyses, we provide the information related to trimmed models


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