Gender differences on general knowledge tests: Are they due to Differential Item Functioning?

Validation of the Chinese version of the autism spectrum knowledge scale general population version (ASKSG) with a parent sample

BACKGROUND

The early detection and diagnosis of autism spectrum disorder (ASD) in children are a first step in determining follow-up assessments and personalized treatment plans. Parents play an extremely crucial role in children's development; therefore, it is essential to identify a valid and reliable instrument to evaluate the knowledge of parents about ASD. The aim of this study was to translate and determine the psychometric properties of the Autism Spectrum Knowledge Scale General Population Version (ASKSG) with a parent sample.

DESIGN AND METHODS

A cross-sectional study was conducted with 858 parents of children aged 0-14 years in China. The demographic characteristics form and ASKSG were used for data collection. The psychometric properties of the ASKSG were evaluated by examining reliability, exploratory factor analysis (EFA), categorical confirmatory factor analysis (CCFA), unidimensionality, person and item reliability, item fit and item difficulty, and item-person mapping.

RESULTS

The Cronbach's alpha coefficient of the ASKSG was 0.865. In terms of EFA, principal component analysis revealed a fixed number of five factors, explaining 41.642% of the total variance. For the CCFA, the structure of the thematically derived five-factor model was observed to be a marginal fit to the data. The eigenvalue was 31.00, with a variance of 62.7%. The person reliability was 0.85, and the person separation was 2.38. The item reliability was 1.00, and the item separation was 15.02. The infit and outfit statistical analysis fell within acceptable ranges from 0.75 to 1.35, and the item difficulty ranged from -2.43 to 2.44. An item-person map with excellent targeting would be symmetric along the vertical axis from more difficult endorsement at the top (question 1 and question 20) to easier endorsement at the bottom (question 1). Some items do appear to function differently across sexes, and they assess the latent trait unequally across both sexes.

CONCLUSIONS

The Chinese version of the ASKSG is a reliable and valid tool for assessing knowledge of parents about ASD.

PRACTICE IMPLICATIONS

Chinese ASKSG now offers medical professionals and teachers a robust means of assessing parental knowledge about ASD. Therefore, professionals may use the scale to help them screen or identify parents who are in need of additional support or advice.

Does intelligence predict development of investment traits from mid to late adolescence? Evidence from a 3-year longitudinal study

INTRODUCTION

Investment theories have claimed reciprocal relations between intelligence and investment traits (i.e., personality traits related to seeking out, and dealing with, cognitive challenges). However, previous research has primarily addressed the effects of investment traits on intellectual development (environmental enrichment hypothesis) and often focused on either childhood or later adulthood. The present study investigated the effects of intelligence on investment traits (environmental success hypothesis) from mid to late adolescence.

METHOD

In a 3-year longitudinal survey (2008-2011) covering four measurement occasions, the predictive effects of both fluid and crystallized intelligence on intraindividual change in both the achievement motive (i.e., hope for success and fear of failure) and need for cognition were examined. Overall, 476 adolescents (t₁ : M_age  = 16.43, SD = 0.55; 51.3% girls) from Germany participated.

RESULTS

Second-order latent growth models indicated that fluid intelligence predicted a steeper growth in hope for success (β = .40), but was unrelated to change in the other investment traits. Crystallized intelligence had no effects on the investment traits under study.

CONCLUSIONS

The results contribute to the research on the bidirectionality of intelligence and investment traits and add to our understanding of personality development from mid to late adolescence. Specifically, they underline the importance of nurturing hope for success especially in individuals with lower intelligence, but also show that support for the environmental success hypothesis seems to be limited to certain investment traits.

The predictors of general knowledge: Data from a Spanish megastudy

Studies on sociodemographic data and crystallized intelligence have often struggled to recruit enough participants to achieve sufficient validity. However, the advent of the internet now allows this problem to be solved through the creation of megastudies. Yet, this methodology so far has only been used in studies on vocabulary size, while general knowledge, another key component of crystallized intelligence, remains unexamined. In the present study, regression models were used to examine the impact of sociodemographic variables—gender, age, years of study and socioeconomic status—on general knowledge scores. The sample comprised 48,234 participants, each of whom answered 60 general knowledge questions, their data being fully available online. Men were found to score higher than women in general knowledge. Years of study and socioeconomic status acted as strong and weak positive predictors, respectively. Age acted as a strong positive predictor until the age of 50, where it became progressively detrimental. These results are discussed relative to other studies on crystallized intelligence, highlighting the need to study each of its components individually.

Personality and Intelligence Interact in the Prediction of Academic Achievement

Personality predicts academic achievement above and beyond intelligence. However, studies investigating the possible interaction effects between personality and intelligence when predicting academic achievement are scarce, as is the separate investigation of broad personality factors versus narrow personality facets in this context. Two studies with 11th grade students (Study 1: N = 421; Study 2: N = 243) were conducted to close this research gap. The students completed the Intelligence-Structure-Test 2000 R measuring general reasoning ability, and a well-established personality inventory based on the Five Factor Model. Academic achievement was operationalized via Grade Point Average. Using hierarchical regression and moderation analyses, Study 1 revealed that Conscientiousness interacted with intelligence when predicting academic achievement: there was a stronger association between intelligence and academic achievement when students scored higher on the Conscientiousness scale. Study 2 confirmed the findings from Study 1 and also found a moderation effect of Neuroticism (stronger association between intelligence and academic achievement with lower values on the Neuroticism scale). Analyses at the facet level revealed much more differentiated results than did analyses at the domain level, suggesting that investigating personality facets should be preferred over investigating personality domains when predicting academic achievement.

Hidden Markov Item Response Theory Models for Responses and Response Times

Current approaches to model responses and response times to psychometric tests solely focus on between-subject differences in speed and ability. Within subjects, speed and ability are assumed to be constants. Violations of this assumption are generally absorbed in the residual of the model. As a result, within-subject departures from the between-subject speed and ability level remain undetected. These departures may be of interest to the researcher as they reflect differences in the response processes adopted on the items of a test. In this article, we propose a dynamic approach for responses and response times based on hidden Markov modeling to account for within-subject differences in responses and response times. A simulation study is conducted to demonstrate acceptable parameter recovery and acceptable performance of various fit indices in distinguishing between different models. In addition, both a confirmatory and an exploratory application are presented to demonstrate the practical value of the modeling approach.