Analysis and meta-analysis of single-case designs with a standardized mean difference statistic: A primer and applications

This article presents a d-statistic for single-case designs that is in the same metric as the d-statistic used in between-subjects designs such as randomized experiments and offers some reasons why such a statistic would be useful in SCD research. The d has a formal statistical development, is accompanied by appropriate power analyses, and can be estimated using user-friendly SPSS macros. We discuss both advantages and disadvantages of d compared to other approaches such as previous d-statistics, overlap statistics, and multilevel modeling. It requires at least three cases for computation and assumes normally distributed outcomes and stationarity, assumptions that are discussed in some detail. We also show how to test these assumptions. The core of the article then demonstrates in depth how to compute d for one study, including estimation of the autocorrelation and the ratio of between case variance to total variance (between case plus within case variance), how to compute power using a macro, and how to use the d to conduct a meta-analysis of studies using single-case designs in the free program R, including syntax in an appendix. This syntax includes how to read data, compute fixed and random effect average effect sizes, prepare a forest plot and a cumulative meta-analysis, estimate various influence statistics to identify studies contributing to heterogeneity and effect size, and do various kinds of publication bias analyses. This d may prove useful for both the analysis and meta-analysis of data from SCDs.     

No sex differences in spatial location memory for abstract designs

Previous research has demonstrated a female advantage, albeit imperfectly, on tests of object location memory where object identity information is readily available. However, spatial and visual elements are often confounded in the experimental tasks used. Here spatial and visual memory performance was compared in 30 men and 30 women by presenting 12 abstract designs in a spatial array for recall and recognition (visual memory) and spatial location ("object" location memory). Object location memory was measured via a sensitive absolute displacement score defined as the distance in mms between the position assigned to the object during recall and the actual position it originally occupied. There were no sex differences in either the visual or spatial location tests. Controlling for age and estimated IQ scores made no impact on the results. These data suggest an absence of a sex difference in purely visual and spatial aspects of object location memory.     

Overpredicting and Underprofiting in Pricing Decisions

This research examines sellers' price‐setting behavior and discovers a naturally occurring mismatch between sellers and buyers: Sellers who make a price decision often consider alternative prices and engage in the joint evaluation mode, whereas buyers who make a purchase decision see only the finally set price and are in the single evaluation mode. This mismatch in evaluation modes leads sellers to overpredict buyers' price sensitivity and underprice their products. However, these effects apply only to products unfamiliar to buyers and without salient reference prices and can be alleviated if sellers are encouraged to mimic single evaluation when making pricing decisions. These propositions are empirically tested and verified. Copyright © 2011 John Wiley & Sons, Ltd.     

Historically recontextualizing Sidman's Tactics: How behavior analysis avoided psychology's methodological Ouroboros

Psychology is undergoing major cultural changes methodologically, with efforts to redefine how psychologists analyze and report their data. Davidson (2018) argued that psychology's methodological crises stem from mechanical objectivity involving the adoption of an analytic tool as source of dependable knowledge. This has led to institutionalization, and eventually uncritical ritualistic use, such as happened with null hypothesis statistical testing. Davidson invoked the mythological symbol of the Ouroboros to represent the endless churning of statistical fads. Sidman (1960), in his Tactics of Scientific Research provided a shield from these problems in terms of the premium he placed on the experience, expertise, judgement, and decision-making of the scientist, that appear to be absent in psychology's ritualized processes.     

Response to the Comments by Larichev,Korhonen and Vargas on ‘Power Relations and Group Aggregation in the Multiplicative AHP and SMART’

We are pleased to continue the open discussion in this journal and we hope that it will contribute to the clarification of the main issues. Aggregation by geometric means has been accepted as a technically sound procedure. The critical comments now seem to concentrate on the establishment of the Multiplicative AHP, the publication of a method for weighted voting before empirical verification, and the significance of negotiations in decision making. Several numerical examples illustrate the arithmetic-mean and geometric-mean calculations, and the legitimacy of rank reversal is revisited. We discuss the issues in the same order here. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 171–174 (1997) No. of Figures: 0. No. of Tables: 5. No. of References: 9.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.