Random Permutation Testing Applied to Measurement Invariance Testing with Ordered-Categorical Indicators

We describe and evaluate a random permutation test of measurement invariance with ordered-categorical data. To calculate a p-value for the observed (?)χ², an empirical reference distribution is built by repeatedly shuffling the grouping variable, then saving the χ² from a configural model, or the ?χ² between configural and scalar-invariance models, fitted to each permuted dataset. The current gold standard in this context is a robust mean- and variance-adjusted ?χ² test proposed by Satorra (2000), which yields inflated Type I errors, particularly when thresholds are asymmetric, unless samples sizes are quite large (Bandalos, 2014; Sass et al., 2014). In a Monte Carlo simulation, we compare permutation to three implementations of Satorra’s robust χ² across a variety of conditions evaluating configural and scalar invariance. Results suggest permutation can better control Type I error rates while providing comparable power under conditions that the standard robust test yields inflated errors.     

A theory of instruction for introductory programming skills

ABSTRACT

Background and Context: Current introductory instruction fails to identify, structure, and sequence the many skills involved in programming.

Objective: We proposed a theory which identifies four distinct skills that novices learn incrementally. These skills are tracing, writing syntax, comprehending templates (reusable abstractions of programming knowledge), and writing code with templates. We theorized that explicit instruction of these skills decreases cognitive demand.

Method: We conducted an exploratory mixed-methods study and compared students’ exercise completion rates, error rates, ability to explain code, and engagement when learning to program. We compared material that reflects this theory to more traditional material that does not distinguish between skills.

Findings: Teaching skills incrementally resulted in improved completion rate on practice exercises, and decreased error rate and improved understanding of the post-test.

Implications: By structuring programming skills such that they can be taught explicitly and incrementally, we can inform instructional design and improve future research on understanding how novice programmers develop understanding.     

Some observations on dropping out in distance education

This paper is an appraisal of drop‐out from a German perspective. This perspective is refreshingly and challengingly different — even to the rather casual German expression, ‘Karteileiche’, which means literally ‘file corpse’. The paper argues that drop‐out cannot and even should not readily be compared across systems, since significant parameters differ. The paper offers different professional viewpoints then moves to consider solutions to the problem. For die FemUniversitat the solution is not pragmatically driven but rather requires a theoretical research base.     

Students’ Emotive Reasoning Through Place-Based Environmental Socioscientific Issues

Research in Science Education - Engaging environmental socioscientific issues (SSI) requires navigating diverse positions regarding people and nature. This qualitative investigation determined how...