Discrete-time survival analysis (DTSA) models are a popular way of modeling events in the social sciences. However, the analysis of discrete-time survival data is challenged by missing data in one or more covariates. Negative consequences of missing covariate data include efficiency losses and possible bias. A popular approach to circumventing…

Previous research extensively studied reasons for and ways to avoid low response rates, but it largely ignored the primary research issue of the degree to which response rates matter, which we address. Methodological survey research on response rates has been concerned with how to increase responsiveness and with the effects of response rates on…

Imputation or likelihood-based approaches to handle missing data assume the data are missing completely at random (MCAR) or missing at random (MAR). However, little research has examined the missingness pattern before using these imputation/likelihood methods. Three missingness mechanisms -- MCAR, MAR, and not missing at random (NMAR) -- can be…

Recent, high-profile, large-scale, preregistered failures to replicate uncover that many highly-regarded experiments are "false positives"; that is, statistically significant results of underlying null effects. Large surveys of research reveal that statistical power is often low and inadequate. When the research record includes selective…

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…

Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead…

Technology education research is a growing field, with the rate of growth increasing over the last 2 decades. As the field grows, it is paramount that credibility is maintained in published findings. To date there is no evidence to suggest a lack trust is warranted, however in the midst of the replication crisis there is need to ensure continued…

Randomized controlled trials have proliferated in education, in part because they provide an unbiased estimator for the causal impact of interventions. It is increasingly recognized that many such trials in education have low power to detect an effect if indeed there is one. However, it is less well known that low powered trials tend to…

Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…

The deficit narrative is a critical component of the white racial frame and attributes disparate outcomes to the racialized groups themselves rather than the policies and actions that create conditions that produce these disparities. Educational research that employs racialized groups as a variable in quantitative research holds potential to…

Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions…

This study systematically reviews randomly selected 155 experimental studies in education field originated in the Republic of Turkey between 2010 and 2020. Indiscriminate choice of sample size in recent publications prompted us to evaluate their statistical power and precision. First, above and beyond our review, we could not identify any…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

Meta-studies are often conducted on empirical findings obtained from overlapping samples. Sample overlap is common in research fields that strongly rely on aggregated observational data (eg, economics and finance), where the same set of data may be used in several studies. More generally, sample overlap tends to occur whenever multiple estimates…

Standard generic inverse variance methods for the combination of single proportions are based on transformed proportions using the logit, arcsine, and Freeman-Tukey double arcsine transformations. Generalized linear mixed models are another more elaborate approach. Irrespective of the approach, meta-analysis results are typically back-transformed…