Good judge characteristics of distinctive accuracy and normativity for judgments of personality traits and affective states
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The registered analyses will examine how characteristics of judge-participants, who observed recorded interactions and rated the personality traits and affective states of the people in the videos, are related to their levels of distinctive accuracy and normativity. Judges completed three self-report measures and provided demographic information. The self-reports were 1) the Big Five Inventory (BFI; John, Naumann, & Soto, 2008) that assess the personality traits of extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, 2) the trait version of the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), and 3) the Interpersonal Reactivity Index (IRI; Davis, 1983) that assesses perspective-taking, empathy, fantasy, and personal distress. Demographic information included gender, age, ethnicity, and level of education. Ethnicity is a nominal variable, and groups will be determined based on sample sizes within each group. At minimum, two groups will be used that consist of Caucasian/European American and all other ethnicities. If at least 20% of the sample falls within an ethnicity other than Caucasian/European American, then that group will be retained as its own group. Groups will be compared with the use of dummy coding, with the Caucasian/European American group serving as the comparison group because it is expected to be the largest group. Education is an ordinal variable with 5 response options: some high school, high school graduate or GED, some college or technical school, college graduate, and graduate school/advanced degree. Categories that will be used in the analyses will depend on the number of participants in each category. Groups will be formed so that they contain at least 20% of the sample, and compared with the use of dummy coding with the lowest level of education serving as the comparison group.
The following hypotheses for trait ratings will be tested:
1. Distinctive accuracy will be positively related to judges’ levels of agreeableness, conscientiousness, perspective-taking, empathy, and fantasy (Colman, 2019; Colman, Letzring, & Biesanz, 2017; Hall, Goh, Schmid Mast, & Hagedorn, 2016; Letzring, 2015).
2. Distinctive accuracy will be negatively related to judges’ levels of neuroticism and personal distress (Colman, 2019; Colman et al., 2017).
3. Distinctive accuracy will be unrelated to judges’ levels of positive and negative affect (Human & Biesanz, 2011; Letzring, 2015).
4. Normativity will be positively related to judges’ levels of perspective-taking, empathy, fantasy, positive affect (due to the link with psychological adjustment), and being female (Chan, Rogers, Parisotto, & Biesanz, 2011; Colman, 2015; Colman et al., 2017; Human & Biesanz, 2011; Krzyzaniak, 2018; Letzring, 2015; Winquist, Mohr, & Kenny, 1988).
5. Normativity will be unrelated to judges’ levels of distress and negative affect (Colman et al., 2017; Letzring, 2015).
Additionally, exploratory analyses will examine relationships among the following variables:
1. Distinctive accuracy for judgments of traits and judges’ levels of extraversion, openness, gender, ethnicity, age, and education level (Christiansen et al., 2005; Colman, 2019; De Kock et al., 2015; Letzring, 2015).
2. Normativity for judgments of traits and judges’ levels of extraversion, agreeableness, conscientiousness, openness, ethnicity, age, and education level.
3. Distinctive accuracy and normativity for judgments of individual traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness) with all of the judge characteristics.
4. Distinctive accuracy and normativity for judgments of affective states, both combined and for positive and negative affect separately, with all of the judge characteristics.
The Social Accuracy Model (SAM; Biesanz, 2010, 2020) will be used to analyze the data. Distinctive accuracy and normativity will be estimated for ratings of all five traits combined, and for positive and negative affect combined. First, analyses will include all participants in each study, and studies will be analyzed separately. Then, experimental groups within each study will be analyzed separately.
For Study 1, judges observed videos and then rated both personality traits and affective states, and the order of ratings was counterbalanced across judges. A moderator variable will be included in the models to represent whether judges rated affect or traits first. Differences are not expected between these groups.
For Study 2, judges either received valid, invalid, or no information about one domain before observing the videos and then rating the other domain. Separate analyses will be conducted for judges who rated traits vs. affect, as judges in this study only rated one domain. Type of information will be dummy coded and used as a moderator variable, with no information serving as the comparison group, and valid and invalid information will be the other groups. Hypotheses are expected to be more consistently supported in the valid information and no information conditions, as these conditions are more consistent with the methodology used in previous studies.
For Study 3, videos were determined to have either high or low congruence between the self-reported affect of the targets and affect as coded by trained research assistants. Judges observed and rated three targets with high affect congruence and three targets with low affect congruence, and therefore this was a within-subjects design. A moderator variable will be included in the models to represent whether judges observed high vs. low affect congruence targets. Hypotheses are expected to be more consistently supported with high affect congruence targets because individual differences among judges are easier to detect when good targets are being judged (Rogers & Biesanz, 2019), and targets who express affect in a way that is consistent with their self-reported experiences of affect are likely to be better targets.
For Study 4, targets were instructed to focus on one of three domains while observing videos of targets: personality traits, affective states, and physical appearance. Domain of focus will be dummy coded and used as a moderator variable, with physical appearance serving as the comparison group, and traits and affect as the other groups. Differences in relations between judge characteristics and distinctive accuracy and normativity are not expected among these groups. This is based on levels of distinctive accuracy and normativity not differing across the groups, and therefore it is expected that judge characteristics will be related to distinctive accuracy and normativity in similar ways across the groups.
Biesanz, J. C. (2020). The Social Accuracy Model. In T. D. Letzring, & J. S. Spain (Eds.). The Oxford Handbook of Accurate Personality Judgment. Oxford University Press. doi:10.1093/oxfordhb/9780190912529.013.5
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