Event-related potential as a measure of effects of report order and training on identification of multidimensional stimuli

This experiment investigated effects of report order and training on multidimensional stimulus identification. Subjects were required to identify each two-dimensional symbol. There were two orders of reporting the two dimensions of a stimulus. Subjects performed the task for three practice sessions. Both behavioral data and event-related potentials were recorded. Analysis showed order of report had no significant effect on behavior data. But when participants responded in Order Shape/ Part, they showed more N2 inhibition for Order Part/Shape. The P3 mean amplitude was marginally significantly greater for Order Part/Shape than Order Shape/Part. This indicated Order Part/Shape, according to natural language habits, was more appropriate and less cognitively demanding than Order Shape/Part. For the two report orders, along with reduction of reaction time, amplitude of N1 and N2 increased with practice. Amplitude of P2 decreased with practice, and no P3 habituation was found. ERP data indicated no habituation effect. Results suggested neural activity depended not only on perceptual mechanism but also on extent of learning.     

A Unified Approach to Power Calculation and Sample Size Determination for Random Regression Models

The underlying statistical models for multiple regression analysis are typically attributed to two types of modeling: fixed and random. The procedures for calculating power and sample size under the fixed regression models are well known. However, the literature on random regression models is limited and has been confined to the case of all variables having a joint multivariate normal distribution. This paper presents a unified approach to determining power and sample size for random regression models with arbitrary distribution configurations for explanatory variables. Numerical examples are provided to illustrate the usefulness of the proposed method and Monte Carlo simulation studies are also conducted to assess the accuracy. The results show that the proposed method performs well for various model specifications and explanatory variable distributions. The author would like to thank the editor, the associate editor, and the referees for drawing attention to pertinent references that led to improved presentation. This research was partially supported by National Science Council grant NSC-94-2118-M-009-004.     

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple correlation coefficients. The inversion principle and monotonicity property of the proposed formulations are used to describe alternative approaches to the exact interval estimation, power calculation, and sample size determination for correlation coefficients. The author thanks the referees for their constructive comments and helpful suggestions and especially the associate editor for drawing attention to several critical results which led to substantial improvements of the exposition. The work for this paper was initiated while the author was visiting the Department of Statistics, Stanford University. This research was partially supported by National Science Council Grant NSC-94-2118-M-009-004. Request for reprints should be sent to Gwowen Shieh, Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan 30050, ROC.     

Estimation of the simple correlation coefficient

This article investigates some unfamiliar properties of the Pearson product—moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson’s r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.     

Power and Sample Size Calculations for Contrast Analysis in ANCOVA

Analysis of covariance (ANCOVA) is commonly used in behavioral and educational research to reduce the error variance and improve the power of analysis of variance by adjusting the covariate effects. For planning and evaluating randomized ANCOVA designs, a simple sample-size formula has been proposed to account for the variance deflation factor in the comparison of two treatment groups. The objective of this article is to highlight an overlooked and potential problem of the exiting approximation and to provide an alternative and exact solution of power and sample size assessments for testing treatment contrasts. Numerical investigations are conducted to reveal the relative performance of the two procedures as a reliable technique to accommodate the covariate features that make ANCOVA design particularly distinctive. The described approach has important advantages over the current method in general applicability, methodological justification, and overall accuracy. To enhance the practical usefulness, computer algorithms are presented to implement the recommended power calculations and sample-size determinations.     

Optimal sample sizes for Welch’s test under various allocation and cost considerations

The issue of the sample size necessary to ensure adequate statistical power has been the focus of considerableattention in scientific research. Conventional presentations of sample size determination do not consider budgetary and participant allocation scheme constraints, although there is some discussion in the literature. The introduction of additional allocation and cost concerns complicates study design, although the resulting procedure permits a practical treatment of sample size planning. This article presents exact techniques for optimizing sample size determinations in the context of Welch (Biometrika, 29, 350–362, 1938) test of the difference between two means under various design and cost considerations. The allocation schemes include cases in which (1) the ratio of group sizes is given and (2) one sample size is specified. The cost implications suggest optimally assigning subjects (1) to attain maximum power performance for a fixed cost and (2) to meet adesignated power level for the least cost. The proposed methods provide useful alternatives to the conventional procedures and can be readily implemented with the developed R and SAS programs that are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.     

An empirical study of experiential value and lifestyles and their effects on satisfaction in adolescents: an example using online gaming

This study tests a consumer behavioral model on Taiwanese adolescents and young adults engaging in online gaming. The major focus is on how these consumers transfer the value of their experiences and lifestyles to satisfaction, which may assist in the further exploration of the specific consumption behavior characteristics of adolescents and young adults, particularly with regard to their social functioning and deep-seated psychological needs. Using a two-stage sampling design process, data were collected on a total of 211 consumers, with the statistical analysis methods adopted for this study including a reliability test, confirmatory factor analysis, and LISREL analysis. Our results indicate that causal relationships hold in certain experiential value and lifestyle constructs. In particular, two experiential value constructs (social function, empathy and escapism) and two lifestyle constructs (pursuit of recreation and taste for life, reference group) play major roles that affect satisfaction among adolescents and young adults in online gaming in Taiwan.     

Detection of interactions between a dichotomous moderator and a continuous predictor in moderated multiple regression with heterogeneous error variance

Moderated multiple regression (MMR) has been widely used to investigate the interaction or moderating effects of a categorical moderator across a variety of subdisciplines in the behavioral and social sciences. In view of the frequent violation of the homogeneity of error variance assumption in MMR applications, the weighted least squares (WLS) approach has been proposed as one of the alternatives to the ordinary least squares method for the detection of the interaction effect between a dichotomous moderator and a continuous predictor. Although the existing result is informative in assuring the statistical accuracy and computational ease of the WLS-based method, no explicit algebraic formulation and underlying distributional details are available. This article aims to delineate the fundamental properties of the WLS test in connection with the well-known Welch procedure for regression slope homogeneity under error variance heterogeneity. With elaborately systematic derivation and analytic assessment, it is shown that the notion of WLS is implicitly embedded in the Welch approach. More importantly, extensive simulation study is conducted to demonstrate the conditions in which the Welch test will substantially outperform the WLS method; they may yield different conclusions. Welch’s solution to the Behrens-Fisher problem is so entrenched that the use of its direct extension within the linear regression framework can arguably be recommended. In order to facilitate the application of Welch’s procedure, the SAS and R computing algorithms are presented. The study contributes to the understanding of methodological variants for detecting the effect of a dichotomous moderator in the context of moderated multiple regression. Supplemental materials for this article may be downloaded from brm.psychonomic-journals.org/content/supplemental.     

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconception of multicollinearity in MMR studies. The counterintuitive yet beneficial effects of multicollinearity on the ability to detect moderator relationships are explored. Comprehensive treatments and numerical investigations are presented for the simplest interaction model and more complex three-predictor setting. The results provide critical insight that both helps avoid misleading interpretations and yields better understanding for the impact of intercorrelation among predictor variables in MMR analyses.