"A correlation analysis of individual difference variables on ill-structured problem-solving"

2008 ◽  
Vol 24 (3) ◽  
pp. 125-157
Author(s):  
Sun Yun Kim ◽  
Kyoo-Lak Cho
Keyword(s):  
Problem Solving ◽  
Correlation Analysis ◽  
Individual Difference ◽  
Individual Difference Variables ◽  
Structured Problem Solving

Coaches’ and Players’ Perceptions of Goal Setting in Junior Tennis: An Exploratory Investigation

1997 ◽  
Vol 11 (4) ◽  
pp. 426-439 ◽  
Author(s):  
Robert S. Weinberg ◽  
Kevin L. Burke ◽  
Allen Jackson
Keyword(s):  
Problem Solving ◽  
Individual Difference ◽  
Goal Setting ◽  
Developmental Differences ◽  
Physical Conditioning ◽  
Tennis Players ◽  
Individual Difference Variables ◽  
Exploratory Investigation ◽  
Junior Tennis ◽  
Overall Performance

This study examined the various aspects of goal setting of youth tennis players and their coaches. To examine this multifaceted technique, an extensive goal-setting questionnaire was administered to 224 youth tennis players and 35 youth tennis coaches. Results indicated that improving overall performance, fun/enjoyment, and winning were the three most important goals for youth tennis players and that they most preferred setting moderately difficult goals. The most effective type of goals for players were physical conditioning, practice, and skill/technique, whereas the top reasons for setting goals were focusing attention, problem-solving, and increasing effort. Results also revealed numerous significant differences between coaches’ and players’ goal setting, with the coaches generally having a higher frequency of using different goal-setting strategies and finding them more effective. Results were discussed in terms of developmental differences between youth and college athletes, as well as individual difference variables such as gender and ability.

Individual Difference Variables as Predictors of Error during Multitasking

2007 ◽  
Author(s):  
Elizabeth M. Oberlander ◽  
Frederick L. Oswald ◽  
David Z. Hambrick ◽  
L. Andrew Jones
Keyword(s):  
Individual Difference ◽  
Individual Difference Variables

Individual difference variables associated with the willingness to engage in various minor moral and legal violations

2005 ◽  
Author(s):  
Natalie Brown ◽  
Mark Barnett ◽  
Fred Sanborn ◽  
Jennifer Sperfslage ◽  
Andrea Shane
Keyword(s):  
Individual Difference ◽  
Individual Difference Variables ◽  
Willingness To Engage

Experiencing Instigations and Trait Aggression Contribute to Harsh Parenting Behaviors

2016 ◽  
Author(s):  
Randy J. McCarthy
Keyword(s):  
Individual Difference ◽  
Parenting Behaviors ◽  
Situational Factors ◽  
Harsh Parenting ◽  
Total N ◽  
Individual Difference Variables ◽  
Trait Aggression ◽  
Reported Study

Three studies (total N = 1,777 parents) examined whether harsh parenting behaviors would increase when parents experienced an instigation and whether this increase would be especially pronounced for parents who were high in trait aggression. These predictions were tested both when parents’ experience of an instigation was manipulated (Studies 1 and 2) and when parents’ perceptions of their child’s instigating behavior was reported (Study 3). Further, these predictions were tested across a variety of measures of parents’ harsh behaviors: (1) Asking parents to report their likelihood of behaving harshly (Study 1); (2) using proxy tasks for parents’ inclinations to behave harshly (Study 2); and (3) having parents report their past child-directed behaviors, some of which were harsh (Study 3). Both child instigations and parents’ trait aggression were consistently associated with parents’ child-directed harsh behaviors. However, parents’ trait aggression only moderated the extent to which the instigation was associated with their harsh parenting for self-reported physical harsh behaviors (Study 1). The results of the current studies demonstrate that both situational factors, such as experiencing an instigation, and individual difference variables, such as trait aggression, affect parents’ likelihood to exhibit harsh behaviors, but found little evidence these factors interact.

Individual Difference Variables, Ethical Judgments, and Ethical Behavioral Intentions

1999 ◽  
Vol 9 (2) ◽  
pp. 183-205 ◽  
Author(s):  
Kenneth Bass ◽  
Tim Barnett ◽  
Gene Brown
Keyword(s):  
Individual Difference ◽  
Behavioral Intentions ◽  
Structural Equation ◽  
Equation Modeling ◽  
Just World ◽  
Ethical Judgments ◽  
Individual Difference Variables ◽  
The Individual ◽  
Ethical Behavioral Intentions ◽  
The Relationship

Abstract:This study examined the relationship between the individual difference variables of personal moral philosophy, locus of control, Machiavellianism, and just world beliefs and ethical judgments and behavioral intentions. A sample of 602 marketing practitioners participated in the study. Structural equation modeling was used to test hypothesized relationships. The results either fully or partially supported hypothesized direct effects for idealism, relativism, and Machiavellianism. Findings also suggested that Machiavellianism mediated the relationship between individual difference variables and ethical judgments/behavioral intentions.

scholarly journals Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Relationship Quality ◽  
Individual Difference ◽  
Large Scale ◽  
Well Being ◽  
Attachment Anxiety ◽  
Self Report ◽  
Individual Difference Variables ◽  
Partner Satisfaction

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

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