Exploring the use of network meta-analysis in education: examining the correlation between ORF and text complexity measures

Network meta-analysis in psychology and educational sciences: A systematic review of their characteristics

Network meta-analysis (NMA) allows the combination of evidence on the effectiveness of several interventions. NMA has mainly been applied in the medical science field, whereas in the domain of psychology and educational sciences its use is less frequent. Consequently, systematic reviews that describe the characteristics of published NMAs are limited to the field of medicine, and nothing is known about the characteristics of NMAs published in the psychology and educational sciences field. However, this information is still relevant for the design of future simulation studies and for detecting good and bad research practices. Thus, this study describes the features of the meta-analytic datasets of NMAs published in the field of psychology and educational sciences, as well as their methodological characteristics, and compares them to those observed in the medical domain. Results show that the number of studies included is larger in NMAs from psychology and educational sciences, the most commonly used effect size is the standardized mean difference (unlike the odds ratio in medicine), the sample size is smaller, more intervention groups are included, and inconsistent effects are observed more often. These results can be used in future simulation studies to generate realistic datasets. Finally, we warn about the poor quality of reporting of some technical aspects of the NMA, such as the statistical model used.

The Role of Emotion Regulation in Eating Disorders: A Network Meta-Analysis Approach

Background

Previous theoretical models and reviews have documented a strong connection between emotion dysregulation eating disorder (ED) psychopathology among the general and clinical populations. The aim of this review was to build on this previous work by conducting a network meta-analysis to explore associations between adaptive and maladaptive emotion regulation strategies and ED psychopathology trans-diagnostically across the ED spectrum to identify areas of emotion dysregulation that have the strongest association with symptomatology.

Methodology

A total of 104 studies were included in the meta-analysis and correlation coefficient representing the associations between specific emotion regulation strategies and ED symptomatology were extracted. We ran a Bayesian random effects network meta-analysis and the initial network was well-connected with each emotion regulation strategy being linked to at least one other strategy. We also conducted a network meta-regression to explore whether between-study differences in body mass index (BMI), age, and whether the sample consisted of solely female participants explained any possible network inconsistency.

Results

The network meta-analysis revealed that ruminations and non-acceptance of emotions were most closely associated with ED psychopathology. There was no significant network inconsistency but two comparisons approached significance and thus meta-regressions were conducted. The meta-regressions revealed a significant effect of BMI such that the associations between different emotion regulation strategies and ED symptomatology were weaker among those with low BMI.

Discussion

The present findings build on previous work and highlight the role of rumination and difficulties with accepting emotions as key emotion regulation difficulties in EDs. Additionally, the finding that the associations were weaker among ED patients with low BMI may point toward a complex relationship between ED behaviors and emotion regulation. Taken together, our findings call for interventions that target emotion regulation, specifically rumination and difficulties accepting emotions, in the treatment of EDs.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249996, PROSPERO, identifier: CRD42021249996.

Initial validation of a measure of decoding difficulty as a unique predictor of miscues and passage reading fluency

Quantifying the decoding difficulty (i.e., 'decodability') of text is important for accurately matching young readers to appropriate text and scaffolding reading development. Since no easily accessible, quantitative, word-level metric of decodability exists, we developed a decoding measure (DM) that can be calculated via a web-based scoring application that takes into account sub-lexical components (e.g. orthographic complexity), thus measuring decodability at the grapheme-phoneme level, which can be used to judge decodability of individual words or passages. Here we report three experiments using the DM: two predicting children's word-level errors and one predicting passage reading fluency. Generalized linear mixed effect models showed that metrics from the DM explained unique variance in children's oral reading miscues after controlling for word frequency in two samples of children (experiments 1 and 2), and that more errors were made on words with higher DM scores for poor readers. Furthermore, the DM metrics predicted children's number of words read correctly per minute after accounting for estimated Lexile passage scores in a third sample (experiment 3). These results show that after controlling for word frequency (experiments 1 and 2) and estimated Lexile scores (experiment 3) the model including the DM metrics was significantly better in predicting children's word reading fluency both for individual words and passages. While further refinement of this DM measure is ongoing, it appears to be a promising new measure of decodability at both the word and passage level. The measure also provides the opportunity to enable precision teaching techniques, as grapheme-phoneme correspondence profiles unique to each child could facilitate individualized instruction, and text.