Ordinal Versions of Coefficients Alpha and Theta for Likert Rating Scales

Two new reliability indices, ordinal coefficient alpha and ordinal coefficient theta, are introduced. A simulation study was conducted in order to compare the new ordinal reliability estimates to each other and to coefficient alpha with Likert data. Results indicate that ordinal coefficients alpha and theta are consistently suitable estimates of the theoretical reliability, regardless of the magnitude of the theoretical reliability, the number of scale points, and the skewness of the scale point distributions. In contrast, coefficient alpha is in general a negatively biased estimate of reliability. The use of ordinal coefficients alpha and theta as alternatives to coefficient alpha when estimating the reliability based on Likert response items are recommended. The choice between the two ordinal coefficients depends on whether one is assuming a factor analysis model (ordinal coefficient alpha) or a principal components analysis model (ordinal coefficient theta)

Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide

This paper provides a conceptual, empirical, and practical guide for estimating ordinal reliability coefficients for ordinal item response data (also referred to as Likert, Likert-type, ordered categorical, or rating scale item responses). Conventionally, reliability coefficients, such as Cronbach’s alpha, are calculated using a Pearson correlation matrix. Ordinal reliability coefficients, such as ordinal alpha, use the polychoric correlation matrix (Zumbo, Gadermann, & Zeisser, 2007). This paper presents (i) the theoretical-psychometric rationale for using an ordinal version of coefficient alpha for ordinal data; (ii) a summary of findings from a simulation study indicating that ordinal alpha more accurately estimates reliability than Cronbach\u27s alpha when data come from items with few response options and/or show skewness; (iii) an empirical example from real data; and (iv) the procedure for calculating polychoric correlation matrices and ordinal alpha in the freely available software program R. We use ordinal alpha as a case study, but also provide the syntax for alternative reliability coefficients (such as beta or omega). Accessed 35,197 times on https://pareonline.net from January 17, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Out-of- school time use in Pakistan: A qualitative study featuring youth\u27s voices

The current study addresses the lack of out-of-school time (OST) research in low- and middle-income countries by exploring OST use in the context of Pakistan and incorporating youth\u27s voices. Using a qualitative descriptive design with focus-group discussions, we conducted a study in three middle schools set in low- to middle-income neighborhoods in urban and rural areas of Karachi, Pakistan. We engaged 86 youth (50% girls; aged 10–15 years) that were purposefully selected from grade six (31.4%), seven (44.2%) and eight (24.4%) classrooms, balancing gender and locality. In each focus group, we asked participants to describe their afterschool activity routine on a typical weekday afternoon until bedtime. Digital recordings of discussions were transcribed verbatim and analyzed using content analysis. Based on sixteen focus groups with five to six participants in each group, we identified eight distinct categories: religious activities, schoolwork, screentime, helping adult family members, family time, outdoor play, indoor leisure activities, and hanging out with friends. We found that structured activities (e.g., religious activities and schoolwork supervised by an adult) were reported more frequently than unstructured activities (e.g., outdoor play and family time). Participation in activities varied by gender and location (i.e., urban vs. rural), highlighting disparities associated with the sociocultural context that marginalized youth face. Our findings provide a glimpse into the everyday lives of Pakistani youth outside of school. Additionally, they elucidate how economic resources, sociocultural norms regarding gender, and community safety shape youth\u27s time use and socialization patterns. Findings from this study can inform the development of OST activities and initiatives aimed at promoting the positive development of Pakistani youth

Measuring social-emotional development in middle childhood: the Middle Years Development Instrument

This paper discusses the conceptualization, development, validation, and application of the Middle Years Development Instrument (MDI) – a population-based child self-report tool that assesses children\u27s social-emotional development and well-being in the context of their home, school, and neighborhood. The MDI is administered at a population-level to 4th and 7th grade students within participating public school districts across British Columbia, Canada. Children respond to items in five domains: (1) social-emotional development, (2) connectedness to peers and adults, (3) school experiences, (4) physical health and well-being, and (5) constructive use of after-school time. Results are aggregated for schools and communities and reported back in comprehensive reports and community maps to inform planning and decision making at local and regional levels. Shared testimonials exemplify how MDI results have been used by educators, community organizers, and city planners as a catalyst for promoting children\u27s social and emotional competence and facilitating collaboration between schools and communities

Mental Health Inequities Amid the COVID-19 Pandemic: Findings From Three Rounds of a Cross-Sectional Monitoring Survey of Canadian Adults

Objectives: Adverse mental health impacts of the COVID-19 pandemic are well documented; however, there remains limited data detailing trends in mental health at different points in time and across population sub-groups most impacted. This paper draws on data from three rounds of a nationally representative cross-sectional monitoring survey to characterize the mental health impacts of COVID-19 on adults living in Canada (N = 9,061).Methods: Descriptive statistics were used to examine the mental health impacts of the pandemic using a range of self-reported measures. Multivariate logistic regression models were then used to quantify the independent risks of experiencing adverse mental health outcomes for priority population sub-groups, adjusting for age, gender, and survey round.Results: Data illustrate significant disparities in the mental health consequences of the pandemic, with inequitable impacts for sub-groups who experience structural vulnerability related to pre-existing mental health conditions, disability, LGBTQ2+ identity, and Indigenous identity.Conclusion: There is immediate need for population-based approaches to support mental health in Canada and globally. Approaches should attend to the root causes of mental health inequities through promotion and prevention, in addition to treatment

The Satisfaction with Life Scale adapted for Children : investigating the structural, external, and substantive aspects of construct validity

Measuring and monitoring children’s satisfaction with life is of great significance for improving children’s lives. In order to do this, validated measures to assess children’s satisfaction with life are necessary. This dissertation describes a program of research for the validation of the Satisfaction with Life Scale adapted for Children (SWLS-C). The introductory chapter provides a theoretical background for subjective well-being and validity/validation research and definitions of key terms. The first manuscript presents psychometric findings on the structural and external aspects of construct validity. A stratified random sample of 1233 students in grades 4 to 7 (48% girls, mean age of 11.7 years) provided data on the SWLS-C and measures of optimism, self-concept, self-efficacy, depression, empathic concern, and perspective taking. The SWLS-C demonstrated a unidimensional factor structure, high internal consistency, and evidence of convergent and discriminant validity. Furthermore, differential item functioning and differential scale functioning analyses indicated that the SWLS-C measures satisfaction with life in the same way for different groups of children. The second manuscript investigated the substantive aspect of construct validity for the SWLS-C by examining the cognitive processes of children when responding to the items. Think-aloud protocol interviews were conducted with 55 students in grades 4 to 7 (58 % girls, mean age of 11.0 years) and content analysis was used to analyze the data. In their responses, children mainly used an ‘absolute strategy’ (statements indicating the presence/absence of something they consider important for their satisfaction with life) or a ‘relative strategy’ (statements indicating comparative judgments). The absolute statements primarily referred to social relationships, personal characteristics, time use, and possessions. In the relative statements, children primarily compared what they have to what (a) they want, (b) they had in the past, (c) other people have, and (d) they feel they need. The results are in line with multiple discrepancies theory (Michalos, 1985) and previous empirical findings. These two studies provide evidence for the meaningfulness of the inferences of the SWLS-C scores. The concluding chapter highlights the contributions of the dissertation, discusses limitations of the presented research, and delineates a future validation program for the SWLS-C.Education, Faculty ofEducational and Counselling Psychology, and Special Education (ECPS), Department ofGraduat

The relationship between statistical power and predictor distribution in multilevel logistic regression: a simulation-based approach

Background: Despite its popularity, issues concerning the estimation of power in multilevel logistic regression models are prevalent because of the complexity involved in its calculation (i.e., computer-simulation-based approaches). These issues are further compounded by the fact that the distribution of the predictors can play a role in the power to estimate these effects. To address both matters, we present a sample of cases documenting the influence that predictor distribution have on statistical power as well as a user-friendly, web-based application to conduct power analysis for multilevel logistic regression. Method: Computer simulations are implemented to estimate statistical power in multilevel logistic regression with varying numbers of clusters, varying cluster sample sizes, and non-normal and non-symmetrical distributions of the Level 1/2 predictors. Power curves were simulated to see in what ways non-normal/unbalanced distributions of a binary predictor and a continuous predictor affect the detection of population effect sizes for main effects, a cross-level interaction and the variance of the random effects. Results: Skewed continuous predictors and unbalanced binary ones require larger sample sizes at both levels than balanced binary predictors and normally-distributed continuous ones. In the most extreme case of imbalance (10% incidence) and skewness of a chi-square distribution with 1 degree of freedom, even 110 Level 2 units and 100 Level 1 units were not sufficient for all predictors to reach power of 80%, mostly hovering at around 50% with the exception of the skewed, continuous Level 2 predictor. Conclusions: Given the complex interactive influence among sample sizes, effect sizes and predictor distribution characteristics, it seems unwarranted to make generic rule-of-thumb sample size recommendations for multilevel logistic regression, aside from the fact that larger sample sizes are required when the distributions of the predictors are not symmetric or balanced. The more skewed or imbalanced the predictor is, the larger the sample size requirements. To assist researchers in planning research studies, a user-friendly web application that conducts power analysis via computer simulations in the R programming language is provided. With this web application, users can conduct simulations, tailored to their study design, to estimate statistical power for multilevel logistic regression models.Other UBCReviewedFacult