It is common for social scientists to use formal quantitative methods to compare ecological units such as towns, schools, or nations. In many cases, the size of these units in terms of the number of individuals subsumed in each differs substantially. When the variables in question are counts, there is generally some attempt to neutralize…

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…

In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…

Purpose: The lack of a definite standard for determining the sample size in qualitative research leaves the research process to the initiative of the researcher, and this situation overshadows the scientificity of the research. The primary purpose of this research is to propose a model by questioning the problem of determining the sample size,…

Mixed research is a methodology of growing importance both within and without education. This type of research forces researchers to reconcile conflicting ways of justifying and understanding research with results that have the potential to be forward pointing for all researchers. As mixed research has grown, mixed research has gained an…

Indirect questioning techniques such as the randomized response technique aim to control social desirability bias in surveys of sensitive topics. To improve upon previous indirect questioning techniques, we propose the new Cheating Detection Triangular Model. Similar to the Cheating Detection Model, it includes a mechanism for detecting…

Purpose: Critical realism is an increasingly popular "lens" through which complex events, entities and phenomena can be studied. Yet detailed operationalisations of critical realism are at present relatively scarce. This study's objective here is built on existing debates by developing an open systems model of reality, a basis for…

Educational research outcomes frequently rely on an assumption that measurement metrics have interval-level properties. While most investigators know enough to be suspicious of interval-level claims, and in some cases even question their findings given such doubts, there is a lack of understanding regarding the measurement conditions that create…

Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…

Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…

Randomized response techniques (RRTs) are useful survey tools for estimating the prevalence of sensitive issues, such as the prevalence of doping in elite sports. One type of RRT, the unrelated question model (UQM), has become widely used because of its psychological acceptability for study participants and its favorable statistical properties.…

Preventing panel members from attriting is a fundamental challenge for panel surveys. Research has shown that response behavior in earlier waves (response or nonresponse) is a good predictor of panelists' response behavior in upcoming waves. However, response behavior can be described in greater detail by considering the time until the response is…

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…

Bayesian approach is becoming increasingly important as it provides many advantages in dealing with complex data. However, there is no well-defined model selection criterion or index in a Bayesian context. To address the challenges, new indices are needed. The goal of this study is to propose new model selection indices and to investigate their…