Conceptualizing and assessing self-enhancement bias: A componential approach.

Kwan, Virginia S. Y.; John, Oliver P.; Robins, Richard W.; Kuang, Lu Lu

doi:10.1037/0022-3514.94.6.1062

Cited by 110 publications

(135 citation statements)

References 73 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…While the causes and working mechanisms of HHE remain to be identified, the following causes have been suggested to play a role: diverse child rearing and socialisation practices (Beloff, 1992), social and gender-role normative stereotyping and self-stereotyping (Guimond et al, 2006), self-enhancement and self-derogatory evaluation biases (Beyer, 1990(Beyer, , 1998(Beyer, , 1999Furnham, 2001;Kwan et al, 2008), lack of confidence and/or overconfidence (Sleeper & Nigro, 1982), gender differences in self-concept and inaccurate self-estimates (Pallier, 2003;Roberts, 1991), personality traits and male superiority in certain areas of cognition Hyde et al, 1990a,b;Lynn et al, 2002;Maccoby & Jacklin, 1974;Voyer et al, 1995).…”

Section: Resultsmentioning

confidence: 99%

The role of gender, task success probability estimation and scores as predictors of the domain-masculine intelligence type (DMIQ)

Storek

Furnham

2016

Learning and Individual Differences

View full text Add to dashboard Cite

This paper reports two experimental studies aimed at understanding correlates of self-estimated intelligence. In both studies participants twice estimated their mathematical and spacial intelligence (called Domain Masculine Intelligence Type: DMIQ) on a normal distribution. They also completed a number of short numerical and logical ability tests after which they estimated their performance at a similar, more difficult task. Males gave higher estimates than females and did better on the tests. Their estimates of their DMIQ reduced on the second occasion after testing. Gender, task score and estimated performance were all significant predictors of both DMIQ scores. Task confidence was the best predictor of DMIQ1 and DMIQ2, over and above gender and test score, explaining 17% and 23% of variance respectively. Results are discussed in terms of the expanding literature on self-estimated intelligence 2

show abstract

Section: Resultsmentioning

confidence: 99%

The role of gender, task success probability estimation and scores as predictors of the domain-masculine intelligence type (DMIQ)

Storek

Furnham

2016

Learning and Individual Differences

View full text Add to dashboard Cite

show abstract

“…This index compares each subject's self rating with his or her tendency to over-or underrate others, as well as with other's ratings of him or her. While previous indexes of self-enhancement were confounded with irrelevant components of interpersonal perception (Kwan, John, Robins, & Kuang, 2008), this index based on SRAs is an unbiased index of self-enhancement, as it takes each person's perceiver and target effect into account. To compute this index, a parameter has to be passed to the RR function: index = "enhance".…”

Section: Univariate Analysesmentioning

confidence: 99%

TripleR: An R package for social relations analyses based on round robin designs

Schönbrodt¹,

Back²,

Schmukle³

2016

Preprint

View full text Add to dashboard Cite

In this article, we present TripleR, an R package for the calculation of social Relations analyses (Kenny, 1994) based on round robin designs. The scope of existing Software solutions is ported to R and enhanced with previously unimplemented methods of significance testing in single groups (Lashley & Bond, 1997) and handling of missing values. The package requires only minimal knowledge of R, and results can be exported for subsequent analyses to other software packages. We demonstrate the use of TripleR with several didactic examples.

show abstract

“…This index compares each participant's self-rating with his or her tendency to over-or underrate others, as well as with others' ratings of him or her. While previous indexes of self-enhancement were confounded with irrelevant components of interpersonal perception (Kwan, John, Robins & Kuang, 2008), this index based on SRAs is an unbiased index of selfenhancement, since it takes each person's perceiver and target effect into account. To compute this index, a parameter has to be passed to the RR function: index=" enhance".…”

Section: Correlations Of Srm Effects With Other Variablesmentioning

confidence: 99%

TripleR: An R package for social relations analyses based on round-robin designs

2011

View full text Add to dashboard Cite

In this article, we present TripleR, an R package for the calculation of social relations analyses (Kenny, 1994) based on round-robin designs. The scope of existing software solutions is ported to R and enhanced with previously unimplemented methods of significance testing in single groups (Lashley & Bond, 1997) Interpersonal perceptions (e.g., liking) and behaviors (e.g., smiling) are complex and multiply determined social phenomena. The social relations model (SRM; Back & Kenny, 2010;Kenny, 1994;Kenny, Kashy, & Cook, 2006;Kenny & La Voie, 1984) is one way to understand these complexities. It is based on the analysis of interpersonal perceptions or social behaviors within dyads. The two people of a dyadic measurement are usually denoted as the actor and the partner. The actor provides the measurement, and the partner is the other person. The terms actor and partner are generic terms, and other terms can be used in different contexts. For instance, in interpersonal perception research, the terms perceiver and target are more commonly used. In line with this terminology, we use the more general terms actors and partners if we describe social relations analyses (SRAs) on the conceptual level or if the investigated phenomenon is a behavior. If the investigated phenomenon is an interpersonal perception, the dyadic members are called perceivers and targets.The SRM accounts for the interdependent nature of social relations and distinguishes conceptually distinct components necessarily entailed in interpersonal perceptions and social behaviors. On the basis of this componential approach, the perception that perceiver A likes target B can, for instance, be decomposed into A's general tendency to like others (a perceiver effect for liking attributed to A), B's general tendency to be liked by others (a target effect for liking attributed to B), and the unique liking of B by A (a relationship effect for liking from A to B). Similarly, actor A smiling at partner B can be decomposed into A's general tendency to smile (an actor effect for smiling attributed to A), B's general tendency to be smiled at (a partner effect attributed to B), and A's unique smiling toward B (a relationship effect for smiling from A to B). The SRM allows for a differentiated look at many important psychological topics, such as attraction, persuasion, helping, aggression, and cooperation, to name just a few (for overviews, see Back, Baumert, Denissen, Hartung, Penke, Schmukle & Wrzus, 2011a;Back & Kenny, 2010).Statistical analyses based on the SRM (called social relations analyses, SRAs) cannot be conducted, however, by using an individual-focused data collection and traditional methods of data analysis. The most comprehensive

show abstract

Conceptualizing and assessing self-enhancement bias: A componential approach.

Cited by 110 publications

References 73 publications

The role of gender, task success probability estimation and scores as predictors of the domain-masculine intelligence type (DMIQ)

The role of gender, task success probability estimation and scores as predictors of the domain-masculine intelligence type (DMIQ)

TripleR: An R package for social relations analyses based on round robin designs

TripleR: An R package for social relations analyses based on round-robin designs

Contact Info

Product

Resources

About