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Identifying relevant papers for your literature review can be time consuming.

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Full Text

Why should we consider random effects models in some studies?

Random effects models are a powerful statistical tool that can provide a more robust and generalizable analysis compared to fixed effects models.

Here are some key reasons why we might opt for a random effects model:

1. Accounting for Unmeasured Heterogeneity:

? Unobserved Variables: Random effects can capture the influence of unobserved variables that might affect the outcome.

?Increased Precision: By accounting for this variability, we can obtain more precise estimates of the model parameters.

1. Generalizability:
   ? Beyond the Observed Groups: Random effects models allow us to make inferences about a larger population, even if we only have data from a limited number of groups.

?Reduced Bias: By treating the group effects as random samples from a larger population, we can reduce bias in our estimates.

1. Hierarchical Data Structures:
   ? Nested Data: Random effects are particularly useful for hierarchical data structures, such as data collected from multiple schools or hospitals.

?Accounting for Correlation: They can account for the correlation between observations within the same group.

?When to choose Random Effects:

1-When the groups are a random sample from a larger population.

2-When the goal is to make inferences about the population of groups, not just the specific groups in the study.

3-When there is significant heterogeneity between groups.

In contrast, fixed effects models are more appropriate when:

1-The groups of interest are the only groups of interest.

2-The goal is to make precise comparisons between the specific groups in the study.

Ultimately, the choice between fixed and random effects models depends on the specific research question and the underlying assumptions about the data.

By carefully considering the nature of the data and the research objectives, we can select the most appropriate modeling approach.

The latest and most recent recommendations and suggestions for conducting narrative inquiry in the field

Considering points in this methodological synthesis will boost the quality of the works and in trun increase the chance of publishing

Ghanbar, H., Cinaglia, C., Randez, R. A., & De Costa, P. I. (2024). A methodological synthesis of narrative inquiry research in applied linguistics: What's the story?. International Journal of Applied Linguistics.‏

https://onlinelibrary.wiley.com/doi/10.1111/ijal.12591?af=R

Just out: A simple statistical framework for small sample studies (Schwarzkopf & Huang, in press/2024, in Psychological Methods).

Fascinating idea being put forth here with potential relevance for AL where lots of studies rely on smaller samples.
#openaccess

Abstract
Most studies in psychology, neuroscience, and life science research make inferences about how strong an effect is on average in the population. Yet, many research questions could instead be answered by testing for the universality of the phenomenon under investigation. By using reliable experimental designs that maximize both sensitivity and specificity of individual experiments, each participant or subject can be treated as an independent replication. This approach is common in certain subfields. To date, there is however no formal approach for calculating the evidential value of such small sample studies and to define a priori evidence thresholds that must be met to draw meaningful conclusions. Here we present such a framework, based on the ratio of binomial probabilities between a model assuming the universality of the phenomenon versus the null hypothesis that any incidence of the effect is sporadic. We demonstrate the benefits of this approach, which permits strong conclusions from samples as small as two to five participants and the flexibility of sequential testing. This approach will enable researchers to preregister experimental designs based on small samples and thus enhance the utility and credibility of such studies.

https://doi.org/10.1037/met0000710