Statistical and Mathematical Modeling versus NHST? There’s No Competition!

Some of Robinson & Levin’s critique of Rodgers (2010) is cogent, helpful, and insightful – although limiting. Recent methodology has advanced through the development of structural equation modeling, multi-level modeling, missing data methods, hierarchical linear modeling, categorical data analysis, as well as the development of many dedicated and specific behavioral models. These methodological approaches are based on a revised epistemological system, and have emerged naturally, without the need for task forces, or even much self-conscious discussion. The original goal was neither to develop nor promote a modeling revolution. That has occurred; I documented its development and its status. Two organizing principles are presented that show how both perspectives can be reconciled and accommodated. A program of research that could not have occurred within the standard NHST epistemology, without a modeling perspective, is discussed. An historical and cross-disciplinary analogy suggests their view is similar to Galileo’s world view, whereas some branches of social and behavioral science may be ready for something closer to a Newtonian perspective

An Empirical Evaluation Of The Retrospective Pretest: Are There Advantages To Looking Back?

This article builds on research regarding response shift effects and retrospective self-report ratings. Results suggest moderate evidence of a response shift bias in the conventional pretest-posttest treatment design in the treatment group. The use of explicitly worded anchors on response scales, as well as the measurement of knowledge ratings (a cognitive construct) in an evaluation methodology setting, helped to mitigate the magnitude of a response shift bias. The retrospective pretest-posttest design provides a measure of change that is more in accord with the objective measure of change than is the conventional pretest-posttest treatment design with the objective measure of change, for the setting and experimental conditions used in the present study

Dimensions Underlying Student Ratings of Instruction: A Multidimensional Scaling Analysis

↵MARTHA L. BANZ, Graduate Student, Department of Psychology, University of Oklahoma, Norman, OK 73019.Specializations:Quantitative methods, educational psychology.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Matrix and Stimulus Sample Sizes in the Weighted MDS Model: Empirical Metric Recovery Functions

The only guidelines for sample size that exist in the multidimensional scaling (MDS) literature are a set of heuristic "rules-of-thumb" that have failed to live up to Young's (1970) goal of finding func tional relationships between sample size and metric recovery. This paper develops answers to two im portant sample-size questions in nonmetric weight ed MDS settings, both of which are extensions of work reported in MacCallum and Cornelius (1977): (1) are the sample size requirements for number of stimuli and number of matrices compensatory? and (2) what type of functional relationships exist between the number of matrices and metric recov ery ? The graphs developed to answer the second question illustrate how such functional relation ships can be defined empirically in a wide range of MDS and other complicated nonlinear models.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Seriation and Multidimensional Scaling: A Data Analysis Approach to Scaling Asymmetric Proximity Matrices

A number of model-based scaling methods have been developed that apply to asymmetric proximity matrices. A flexible data analysis approach is pro posed that combines two psychometric procedures— seriation and multidimensional scaling (MDS). The method uses seriation to define an empirical order ing of the stimuli, and then uses MDS to scale the two separate triangles of the proximity matrix defined by this ordering. The MDS solution con tains directed distances, which define an "extra" dimension that would not otherwise be portrayed, because the dimension comes from relations between the two triangles rather than within triangles. The method is particularly appropriate for the analysis of proximities containing temporal information. A major difficulty is the computa tional intensity of existing seriation algorithms, which is handled by defining a nonmetric seriation algorithm that requires only one complete itera tion. The procedure is illustrated using a matrix of co-citations between recent presidents of the Psychometric Society.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Statistical Tests of Group Differences in ALSCAL-Derived Subject Weights: Some Monte Carlo Results

Several techniques to test for group differences in weighted multidimensional scaling (MDS) subject weights have recently been proposed. The present study presents monte carlo results to evaluate the op erating characteristics of two of these with ALSCAL- derived subject weights. The first uses the analysis of angular variation (ANAVA) on raw subject weights. The second applies the analysis of variance (ANOVA) to the flattened subject weights generated by ALSCAL. The ANOVA on flattened weights was less affected by the presence of error and by distortions caused by ALSCAL'S normalization routine than was the ANAVA.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us

Modelling the coupling between intracellular calcium release and the cell cycle during cortical brain development

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasibl