International Journal of Quantitative Research in Education (7 papers in press)

Regular Issues

• A multiple indicators multiple causes (MIMIC) model of engagement in English language learning and self-directed behaviours  
  by Ernest Afari, Myint Swe Khine 
  Abstract: The present study sought to examine the effect of gender on student engagement in English language learning and self-regulation in a tertiary institution. Three hundred and eighty-nine first-year students from a Gulf state responded to the engagement in English language learning and self-regulation (EELLS) survey developed by Zubaidi et al. (2016). Multiple indicators multiple causes (MIMIC) approach was used to investigate the effect of gender on the structure of a four-factor model of EELLS survey consisting of learning goal orientation (LGO), task value (TV), self-efficacy (SE), and self-regulation (SR). The results indicated statistically significant differences in gender in LGO, TV, SE, and SR.
  Keywords: multiple indicators multiple causes; MIMIC; confirmatory factor analysis; CFA; covariates; self-regulation; task-value; self-efficacy; learning goal.
  DOI: 10.1504/IJQRE.2023.10052278
   
  
• Personality, Self-regulation and Academic Success: Differences and Interactions across Academic Major  
  by Tianna Loose, Alejandro Vasquez-Echeverría 
  Abstract: Personality traits would be meaningfully associated with academic performance and distinguish students across academic majors. One recent study suggested that personality traits had different associations with achievement as a function of students major. As this was the case for personality traits, self-regulated learning and feelings of belonging could also differ. Self-report questionnaires were administered to 265 students from the psychology and engineering department. Agreeableness, openness, conscientiousness, self-regulation, intrinsic value and use of cognitive strategies differed across groups. Academic major moderated relationships between neuroticism or intrinsic value and grade point average, as well as the relationship between openness and feelings of belonging. Dispositional and motivational characteristics distinguish students across academic majors. The prevalence and associations of these characteristics differed across field, unveiling a crucial need to account for academic major and cautioning generalisations.
  Keywords: personality; self-regulation; intrinsic value; self efficacy; cognitive strategies; moderation; higher education; academic major; feelings of belonging; academic performance.
  DOI: 10.1504/IJQRE.2024.10052984
   
  
• Social marginalisation and academic performance: a multigroup SEM analysis of key factors underlying inequality in Danish public schools  
  by Martin Brygger Andersen 
  Abstract: This study's purpose was to analyse key factors underlying social marginalisation and academic performance. The 2017 data from the Danish PLM survey (N = 42,703) were analysed which contained responses by students (grades 4-10), parents, and class teachers. Multigroup structural equation modelling was applied to explore anticipated gender differences. Two critical factors were identified that were associated with reduced levels of social marginalisation: 1) the degree of teacher support; 2) the strength of the parental community. Finally, the study indicated that girls, and students at lower grade levels, tend to experience greater social marginalisation.
  Keywords: marginalisation; academic performance; academic achievement; teacher support; parental involvement; parental community; SEM; students; school.
  
  
• The mediating role of self-efficacy between emotional intelligence and academic achievement: a study among postgraduate students  
  by Akhund Ahammad Shamsul Alam 
  Abstract: This study examined the mediating effect of self-efficacy on the relationship between emotional intelligence and academic achievement. A total of 257 postgraduate diploma students from the Bangladesh Institute of Management were conveniently sampled. The Schutte Self-Report Emotional Intelligence Test (SSEIT) advanced by Schutte et al. (1998) was applied for measuring 'emotional intelligence'. In assessing self-efficacy, the study adopted the 'Generalised Self-Efficacy Scale (GSES)' formulated by Schwarzer and Jerusalem (1995). The study revealed that all the variables included in the study, i.e., emotional intelligence, self-efficacy, and academic achievement were significantly correlated to each other. Moreover, the results of the study showed that the link between emotional intelligence and academic achievement was fully mediated through self-efficacy. Based on the findings of the study, academic institutions are recommended to include emotional intelligence and self-efficacy in their curriculum.
  Keywords: emotional intelligence; self-efficacy; academic achievement.
  DOI: 10.1504/IJQRE.2022.10047085
   
  
• On the use of inclusive strategy when some participants fail to provide data on all studied variables  
  by Yan Xia, Yachen Luo, Mingya Huang, Yanyun Yang 
  Abstract: Behavioural research scientists have become increasingly aware of the importance of missing data methods. Including auxiliary variables in data analysis can increase the plausibility of meeting the missing at random assumption, leading to increased parameter estimation accuracy and a more trustworthy goodness-of-fit evaluation. This study addresses a missing data pattern typically mishandled by using listwise deletion. The missing data pattern echoes a common research scenario in which some participants fail to respond to all the studied variables but provide information on auxiliary variables. Researchers commonly delete these participants from further data analyses in practice. Using confirmatory factor analysis models, this study shows that including effective auxiliary variables to analyse data with this missing data pattern can substantially improve the estimation accuracy, particularly when auxiliary variables correlate with latent factors.
  Keywords: structural equation modelling; SEM; missing at random; full information maximum likelihood; FIML; auxiliary variables.
  
  
• Likelihood of observing transformative learning amongst profession changers: a predictive analysis  
  by Tanuj Negi, Shashi Jain 
  Abstract: In this paper, we explore the possibility of whether the likelihood of observing transformative learning may be predicted using information related to personal history and current and previous professions. We examine empirical data collected from a group of Indian profession changers using a machine learning method: random forest algorithm. Results indicate that the following variables play an important role in prediction: 'overall formality (previous profession)', 'community sanction (previous profession)', 'professional authority (current profession)', 'bridge course', and 'gender'. Additionally, this provides empirical support to the position that profession change may have a transformative effect. A discussion and a list of areas for further research are provided.
  Keywords: transformative learning; profession change; career change; machine learning; prediction.
  DOI: 10.1504/IJQRE.2023.10051240
   
  
• Analysing observed categorical data in SPSS AMOS: a Bayesian approach  
  by Hongwei Yang, Lihua Xu, Mark Malisa, Menglin Xu, Qintong Hu, Xing Liu, Hyungsoo Kim, Jing Yuan 
  Abstract: This study has a didactic purpose to help applied investigators and practitioners to understand the roles of observed categorical data (OCD) in structural equation modelling (SEM) and the appropriate ways of analysing such data under SPSS AMOS. To that end, the study reviews types of OCD (nominal, ordinal, dichotomous and polytomous) and their incorporation into SEM under AMOS to play different roles. The study presents two applications from the health and retirement study where Bayesian statistical inference is used to analyse one set of OCD variables serving as endogenous variables with/without groups created by another OCD variable. Besides, the study demonstrates the typical ways of summarising, reporting and interpreting the results from Bayesian statistics, and compares AMOS with several other SEM programmes (Mplus, R lavaan, Stata and SAS PROC CALIS) on handling OCD. The study concludes with summaries of the findings for its intended audience.
  Keywords: structural equation modelling; SEM; multi-group analysis; Bayesian statistics; SPSS AMOS; categorical data analysis; Mplus; R lavaan; Stata; SAS PROC CALIS; health and retirement study; HRS.
  DOI: 10.1504/IJQRE.2023.10051888