Teacher and Parent Ratings of Seven–Year–Old Children'S Personality and Psychometrically Assessed Cognitive Ability

2012 ◽  
Vol 26 (5) ◽  
pp. 504-514 ◽  
Author(s):  
Jan–Erik Lönnqvist ◽  
Mari–Pauliina Vainikainen ◽  
Markku Verkasalo
Keyword(s):  
Regression Model ◽  
Cognitive Ability ◽  
Multiple Informants ◽  
General Factor ◽  
Parent Ratings ◽  
Rater Agreement ◽  
Descriptive Data ◽  
Personality Ratings ◽  
And Performance ◽  
Teachers And Parents

We linked seven–year–old children's personality ( n = 406), as rated by both teachers and parents, to a wide array of cognitive ability measures. Besides (i) providing descriptive data on the associations between childhood personality and cognitive ability, we (ii) investigated the benefits of having multiple informants provide personality ratings and (iii) examined the recent proposition that the general factor of intelligence be partialled out of associations between personality and narrower domains of intelligence. In a regression model, the shared variance of teacher and parent ratings of personality explained 14% of the variance of cognitive ability. Both teacher and parent ratings of Openness to Experience (O) were positively associated with cognitive ability, and both explained unique variance in cognitive ability. Moreover, the associations were stronger the stronger the inter–rater agreement on O. When the unique variances of each perspective and the moderating effect of inter–rater agreement were added to the aforementioned regression model, personality explained 18% of the variance of cognitive ability. Controlling for the general factor of intelligence caused the correlations between personality and performance on the specific cognitive ability measures to diminish to near zero. Copyright © 2011 John Wiley & Sons, Ltd.

What teachers and parents can add to personality ratings of children: Unique associations with academic performance in elementary school

2021 ◽  
pp. 089020702098843
Author(s):  
Naemi D Brandt ◽  
Michael Becker ◽  
Julia Tetzner ◽  
Martin Brunner ◽  
Poldi Kuhl
Keyword(s):  
Elementary School ◽  
Academic Performance ◽  
Test Scores ◽  
Elementary School Children ◽  
Teacher Ratings ◽  
Multiple Informants ◽  
Parent Ratings ◽  
Self Reports ◽  
Personality Ratings ◽  
Teachers And Parents

Adults’ ratings of children’s personality have been found to be more closely associated with academic performance than children’s self-reports. However, less is known about the relevance of the unique perspectives held by specific adult observers such as teachers and parents for explaining variance in academic performance. In this study, we applied bifactor (S–1) models for 1411 elementary school children to investigate the relative merits of teacher and parent ratings of children’s personalities for academic performance above and beyond the children’s self-reports. We examined these associations using standardized achievement test scores in addition to grades. We found that teachers’ unique views on children’s openness and conscientiousness had the strongest associations with academic performance. Parents’ unique views on children’s neuroticism showed incremental associations above teacher ratings or self-reports. For extraversion and agreeableness, however, children’s self-reports were more strongly associated with academic performance than teacher or parent ratings. These results highlight the differential value of using multiple informants when explaining academic performance with personality traits.

Parent–Teacher Agreement on 7–Year–Old Children's Personality

2011 ◽  
Vol 25 (5) ◽  
pp. 306-316 ◽  
Author(s):  
Jan–Erik Lönnqvist ◽  
Markku Verkasalo ◽  
Mari–Pauliina Vainikainen
Keyword(s):  
Big Five ◽  
Teacher Ratings ◽  
Multiple Informants ◽  
Limited Attention ◽  
Big Five Personality ◽  
Parent Ratings ◽  
Socially Desirable Responding ◽  
Personality Item ◽  
Personality Ratings ◽  
The Mean

Agreement between multiple informants on child personality has received limited attention. Focusing on factor structure, gender differences and the influence of socially desirable responding (SDR), we compared parent and teacher Big Five personality ratings of around 600 7–year olds. Although parent ratings were more desirable than teacher ratings, differential agreement was generally similar to that found for adults, and especially high for ratings of boys. The more evaluative the personality item, the larger the mean–level difference between parents and teachers on that item. However, undesirable items showed the highest levels of differential agreement. In parent ratings, the two poles of Agreeableness formed separate factors. To view Pro–sociality as independent of Antagonism could enable parents to view their child more positively. Copyright © 2010 John Wiley & Sons, Ltd.

Does Age Moderate the Influence of Early Life Language Experiences? A Naturalistic Home Observation Study

2019 ◽  
Author(s):  
Katrina d'Apice ◽  
Sophie von Stumm
Keyword(s):  
Cognitive Ability ◽  
Early Life ◽  
Cognitive Skills ◽  
Cognitive Testing ◽  
Parent Ratings ◽  
Language Experience ◽  
Observation Study ◽  
Home Environments ◽  
Lexical Diversity ◽  
Audio Recordings

We explored if children’s age moderated associations between their early life language experiences and their linguistic and cognitive skills. For 107 British children, aged 24 to 48 months, and their families, we collected 3 day-long audio-recordings of their naturalistic home environments (M = 15.06 hours per day, SD = 1.87). Children’s cognitive ability was assessed by parent-ratings and with a cognitive testing booklet that children completed at home. We found that the quantity, lexical diversity and vocabulary sophistication of adult speech were associated with children’s linguistic and cognitive skills. However, these associations were not moderated by children’s age. Our findings suggest that the influence of early life language experience is not differentiated at age 24 to 48 months.

scholarly journals Application of Multinomial Regression Model to Identify Parameters Impacting Traffic Barrier Crash Severity

2019 ◽  
Vol 13 (1) ◽  
pp. 57-64
Author(s):  
Mahdi Rezapour ◽  
Amirarsalan Mehrara Molan ◽  
Khaled Ksaibati
Keyword(s):  
Regression Model ◽  
Multinomial Logistic Regression ◽  
Crash Severity ◽  
Multinomial Regression ◽  
Interstate Highways ◽  
The Road ◽  
Run Off ◽  
Multinomial Regression Model ◽  
Proportional Odds Assumption ◽  
And Performance

Background: Run Off The Road (ROTR) crashes are some of the most severe crashes that could occur on roadways. The main countermeasure that can be taken to address this type of crashe is traffic barrier installation. Although ROTR crashes can be mitigated significantly by traffic barriers, still traffic barrier crashes resulted in considerable amount of severe crashes. Besides, the types of traffic barriers, driver actions and performance play an important role in the severity of these crashes. Methods: This study was conducted by incorporating only traffic barrier crashes in Wyoming. Based on the literature review there are unique contributory factors in different crash types. Therefore, in addition to focusing on traffic barrier crashes, crashes were divided into two different highway classes: interstate and non-interstate highways. Results: The result of proportional odds assumption was an indication that multinomial logistic regression model is appropriate for both non-interstate and interstates crashes involved with traffic barriers. The results indicated that road surface conditions, age, driver restraint and negotiating a curve were some of the factors that impact the severity of traffic barrier crashes on non-interstate highways. On the other hand, the results of interstate barrier crashes indicated that besides types of barriers, driver condition, citation record, speed limit compliance were some of the factors that impacted the interstate traffic barrier crash severity. Conclusion: The results of this study would provide the policymakers with the directions to take appropriate countermeasures to alleviate the severity of traffic barrier crashes.

Importance-Performance Analysis Based on Choquet Integral with Respect to L-Measure

2010 ◽  
Vol 44-47 ◽  
pp. 3844-3848
Author(s):  
Shu Juan Lee ◽  
Hsiang Chuan Liu ◽  
Shih Ming Chen ◽  
Yu Du Jheng
Keyword(s):  
Performance Analysis ◽  
Regression Model ◽  
Choquet Integral ◽  
Ministry Of Education ◽  
Software Application ◽  
Simple Logistic Regression Model ◽  
Survey System ◽  
And Performance ◽  
Set Up ◽  
Simple Logistic

In this study, the Web-based Multi-Survey System was adopted, one or two choices available among five options, to set up questionnaire to get a nine-point scale with network programs. As an empirical research, which was conformed to the policy of Ministry of Education to promote technology abilities for teachers in Taichung County in 2009, the important and satisfied survey from Teachers’ Free Software Application workshops was analyzed by four approaches which were Importance-Performance Analysis, Simple Logistic regression model, Choquet integral regression model with respect to λ-measure, and Choquet integral regression model with respect to L-measure. By comparing MSE, Choquet integral regression model with L-measure obtained the best performance. Two crosshairs which were Hollenhorst’s overall mean and Choquet integral regression model with L-measure were positioned for I-P matrix. From results, the L-measure model had shown better sensitivity about quadrant distribution, and reflected participants’ real responses. At the same time, it was definitely known what to keep up the good work, concentrate here, or possible overkill through assessing Importance and Performance of teacher’s workshops.

Using Learning Analytics to Enhance Student Learning in Online Courses Based on Quality Matters Standards

2016 ◽  
Vol 45 (2) ◽  
pp. 165-187 ◽  
Author(s):  
Florence Martin ◽  
Abdou Ndoye ◽  
Patricia Wilkins
Keyword(s):  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Online Course ◽  
Design Quality ◽  
Descriptive Data ◽  
Quality Matters ◽  
Frequency Of Use ◽  
And Performance ◽  
Enhance Student Learning

Quality Matters is recognized as a rigorous set of standards that guide the designer or instructor to design quality online courses. We explore how Quality Matters standards guide the identification and analysis of learning analytics data to monitor and improve online learning. Descriptive data were collected for frequency of use, time spent, and performance and analyzed to identify patterns and trends on how students interact with online course components based on the Quality Matters standards. Major findings of this article provide a framework and guidance for instructors on how data might be collected and analyzed to improve online learning effectiveness.

Consideration of Demographics and Variance in Regression Approaches to Estimating Body Dimensions for Spatial Analysis of Design

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Gopal Nadadur ◽  
Matthew B. Parkinson
Keyword(s):  
Regression Model ◽  
Target Population ◽  
Residual Variance ◽  
Use Of Data ◽  
Target User ◽  
Digital Human Models ◽  
And Performance ◽  
Marginal Improvement ◽  
The Military ◽  
Digital Human

A common objective in designing for human variability is to consider the variability in body size and shape of the target user population. Since anthropometric data specific to the user population of interest are seldom available, the variability is approximated. This is done in a number of ways, including the use of data from populations that are well-documented (e.g., the military), proportionality constants, and digital human models. These approaches have specific limitations, including a failure to consider the effects of lifestyle and demography, resulting in products, tasks, and environments that are inappropriately sized for the actual user population, causing problems with safety, fit, and performance. This paper explores a regression-based approach in a context where the demographic distributions of descriptors (e.g., race/ethnicity, age, and fitness) are dissimilar for the database and target population. Also examined is a stratified regression model involving the development of independent anthropometry-estimation models for each racial group. When using regression with residual variance, stratification on the predictor demographics to obtain estimates of gender, stature, and BMI distributions is shown to be sufficiently robust for usual database-target population combinations. Consideration of demographic variables in development of the regression model provides marginal improvement, but could be appropriate in specific situations.

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