Interval estimation for linear functions of medians in within‐subjects and mixed designs

2019 ◽  
Vol 73 (2) ◽  
pp. 333-346
Author(s):  
Douglas G. Bonett ◽  
Robert M. Price
Keyword(s):  
Interval Estimation ◽  
Linear Functions ◽  
Within Subjects ◽  
Mixed Designs

Design and Analysis of Experimental Research II

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Multivariate Analysis ◽  
Repeated Measures ◽  
Experimental Designs ◽  
Factorial Designs ◽  
Repeated Measures Anova ◽  
Data Analyses ◽  
Research Designs ◽  
Within Subjects ◽  
Design Type ◽  
Mixed Designs

This chapter introduces the reader to more possibilities for thinking about causal questions and for laying the foundational concepts necessary for conducting data analyses that correspond to more complex experimental designs. The discussion of experimental design types presented in chapter 8 is expanded to include within-subjects designs, factorial designs, mixed designs, and designs for multivariate outcomes. Prototypical examples of each design type are presented along with the typical analysis tools used for testing the associated experimental hypotheses. Hypothetical examples of research designs that are suitable for illustrating analyses with repeated-measures ANOVA, factorial or multiway ANOVA, and MANOVA (multivariate analysis of variance).

scholarly journals Dial M̂ for Monotonic: A Kernel-Based Bayesian Approach to State-Trace Analysis

2018 ◽  
Author(s):  
Gregory Edward Cox ◽  
Michael Kalish
Keyword(s):  
Outcome Measures ◽  
Bayesian Approach ◽  
Trace Analysis ◽  
The Other ◽  
Outcome Variable ◽  
Binomial Data ◽  
Posterior Odds ◽  
Within Subjects ◽  
Mixed Designs ◽  
Derivatives Of

A monotonic state-trace implies that a single latent factor is sufficient to explain the joint variation between two outcome variables across a set of conditions. There are, however, few methods available for assessing how much evidence a sample of data provides about whether the variables are truly monotonically related or not. We present a model that allows researchers to estimate the statistic M̂ which reflects the amount of evidence a dataset provides about whether two outcome variables are jointly monotonically related. This model is based on modeling the covariation between outcome measures in terms of a kernel function, which allows for computation of the latent derivatives of each outcome variable with respect to the other. M̂ is the posterior odds that these derivatives are all of the same sign and are thus monotonic. Simulations show that M̂ discriminates between monotonic and non-monotonic state traces and an example illustrates how the model can be applied to both continuous and binomial data from between-subjects, within-subjects, or mixed designs.

Design-Effects in Prospective and Retrospective Memory for Actions

2003 ◽  
Vol 50 (1) ◽  
pp. 4-15 ◽  
Author(s):  
Petra Jahn ◽  
Johannes Engelkamp
Keyword(s):  
Prospective Memory ◽  
Memory Task ◽  
Prospective Memory Task ◽  
Retrospective Memory ◽  
Ample Evidence ◽  
Relational Information ◽  
Design Effects ◽  
Verbal Tasks ◽  
Within Subjects

There is ample evidence that memory for action phrases such as “open the bottle” is better in subject-performed tasks (SPTs), i.e., if the participants perform the actions, than in verbal tasks (VTs), if they only read the phrases or listen to them. It is less clear whether also the sole intention to perform the actions later, i.e., a prospective memory task (PT), improves memory compared with VTs. Inconsistent findings have been reported for within-subjects and between-subjects designs. The present study attempts to clarify the situation. In three experiments, better recall for SPTs than for PTs and for PTs than for VTs were observed if mixed lists were used. If pure lists were used, there was a PT effect but no SPT over PT advantage. The findings were discussed from the perspective of item-specific and relational information.

The Limits of Ego Depletion

2019 ◽  
Vol 50 (5-6) ◽  
pp. 292-304 ◽  
Author(s):  
Mario Wenzel ◽  
Marina Lind ◽  
Zarah Rowland ◽  
Daniela Zahn ◽  
Thomas Kubiak
Keyword(s):  
Research Agenda ◽  
Statistical Power ◽  
Interindividual Variability ◽  
Ego Depletion ◽  
Crossover Design ◽  
Effect Sizes ◽  
Future Research ◽  
Future Research Agenda ◽  
Depletion Effect ◽  
Within Subjects

Abstract. Evidence on the existence of the ego depletion phenomena as well as the size of the effects and potential moderators and mediators are ambiguous. Building on a crossover design that enables superior statistical power within a single study, we investigated the robustness of the ego depletion effect between and within subjects and moderating and mediating influences of the ego depletion manipulation checks. Our results, based on a sample of 187 participants, demonstrated that (a) the between- and within-subject ego depletion effects only had negligible effect sizes and that there was (b) large interindividual variability that (c) could not be explained by differences in ego depletion manipulation checks. We discuss the implications of these results and outline a future research agenda.

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