Multilevel multivariate meta-analysis made easy: An introduction to MLMVmeta.

The basic random effects meta-analytic model is overwhelmingly dominant in psychological research. Indeed, it is typically employed even when more complex multilevel multivariate meta-analytic models are warranted. In this paper, we aim to help overcome challenges so that multilevel multivariate meta-analytic models will be more often employed in practice.

Conceptualizing and evaluating replication across domains of behavioral research.

We discuss the authors' conceptualization of replication, in particular the false dichotomy of direct versus conceptual replication intrinsic to it, and suggest a broader one that better generalizes to other domains of psychological research. We also discuss their approach to the evaluation of replication results and suggest moving beyond their dichotomous statistical paradigms and employing hierarchical/meta-analytic statistical models.

Multilevel Multivariate Meta-analysis with Application to Choice Overload.

We introduce multilevel multivariate meta-analysis methodology designed to account for the complexity of contemporary psychological research data. Our methodology directly models the observations from a set of studies in a manner that accounts for the variation and covariation induced by the facts that observations differ in their dependent measures and moderators and are nested within, for example, papers, studies, groups of subjects, and study conditions. Our methodology is motivated by data from papers and studies of the choice overload hypothesis.

Adjusting for Publication Bias in Meta-Analysis: An Evaluation of Selection Methods and Some Cautionary Notes.

We review and evaluate selection methods, a prominent class of techniques first proposed by Hedges (1984) that assess and adjust for publication bias in meta-analysis, via an extensive simulation study. Our simulation covers both restrictive settings as well as more realistic settings and proceeds across multiple metrics that assess different aspects of model performance. This evaluation is timely in light of two recently proposed approaches, the so-called p-curve and p-uniform approaches, that can be viewed as alternative implementations of the original Hedges selection method approach.

Planning sample sizes when effect sizes are uncertain: The power-calibrated effect size approach.

Statistical power and thus the sample size required to achieve some desired level of power depend on the size of the effect of interest. However, effect sizes are seldom known exactly in psychological research. Instead, researchers often possess an estimate of an effect size as well as a measure of its uncertainty (e.

You Cannot Step Into the Same River Twice: When Power Analyses Are Optimistic.

Statistical power depends on the size of the effect of interest. However, effect sizes are rarely fixed in psychological research: Study design choices, such as the operationalization of the dependent variable or the treatment manipulation, the social context, the subject pool, or the time of day, typically cause systematic variation in the effect size. Ignoring this between-study variation, as standard power formulae do, results in assessments of power that are too optimistic.

Statistical Learning with Time Series Dependence: An Application to Scoring Sleep in Mice.

We develop methodology which combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and computationally tractable fashion. We apply our methodology to the scoring of sleep behavior in mice.

Assessing REM sleep in mice using video data.

Study Objectives: Assessment of sleep and its substages in mice currently requires implantation of chronic electrodes for measurement of electroencephalogram (EEG) and electromyogram (EMG). This is not ideal for high-throughput screening. To address this deficiency, we present a novel method based on digital video analysis.

Characterization of the bout durations of sleep and wakefulness.

Study Objectives: (a) Develop a new statistical approach to describe the microarchitecture of wakefulness and sleep in mice; (b) evaluate differences among inbred strains in this microarchitecture; (c) compare results when data are scored in 4-s versus 10-s epochs.

Design: Studies in male mice of four inbred strains: AJ, C57BL/6, DBA and PWD. EEG/EMG were recorded for 24h and scored independently in 4-s and 10-s epochs.