BANOVA: Bayesian Analysis of Experiments in Consumer Psychology

This article introduces a Bayesian extension of ANOVA for the analysis of experimental data in consumer psychology. The approach, called BANOVA (Bayesian ANOVA), addresses some common challenges that consumer psychologists encounter in their experimental work, and is specifically suited for the analysis of repeated measures designs. There appears to be a recent surge in interest in those designs based on the recognition that they are sensitive to individual differences in response to experimental treatments and that they offer advantages for assessing causal mediating mechanisms, even at the individual level. BANOVA enables the analysis of repeated measures data derived from mixed within–between‐subjects experiments with Normal and nonNormal‐dependent variables and accommodates unobserved individual differences. It allows for the calculation of effect sizes, planned comparisons, simple effects, spotlight and floodlight analyses, and includes a wide range of mediation, moderation, and moderated mediation analyses. An R software package implements these analyses, and aims to provide a one‐stop shop for the analysis of experiments in consumer psychology. The package is illustrated through applications to a number of data sets from previously published studies.     

A Clinician’s Primer for Idiographic Research: Considerations and Recommendations

Theorists and clinicians have long noted the need for idiographic (i.e., individual-level) designs within clinical psychology. Results from idiographic work may provide a possible resolution of the therapist’s dilemma—the problem of treating an individual using information gathered via group-level research. Due to advances in data collection and time series methodology, there has been increasing interest in using idiographic designs to answer clinical questions. Although time series methods have been well-studied outside the field of clinical psychology, there is limited direction on how clinicians can use such models to inform their clinical practice. In this primer, we collate decades of published and word-of-mouth information on idiographic designs, measurement, and modeling. We aim to provide an initial guide on the theoretical and practical considerations that we urge interested clinicians to consider before conducting idiographic work of their own.     

Multilevel modelling: Beyond the basic applications

Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.     

应对研究的成果、问题与解决办法

应激和应对问题 ,由于同人的心理适应和健康密切相关 ,目前在国外已经成为心理学所有领域中研究最多的一个重要课题。然而 ,该课题的研究 ,特别是关于应对方式及其效果评价的研究 ,存在着较突出的问题。通过对国内 4种最主要的有关学术期刊的检索发现 ,同国外相比 ,国内的应激和应对的研究在数量和质量上均有较明显的差距。文章结合国内外有关研究的现状 ,分析、说明了在应对研究中存在的 5个主要问题 :(1)忽略防御机制的有效评估和在心理适应中的作用 ;(2 )应对方式的评定方法不当 ;(3)在对于应激的影响和应对后果的估价中存在着片面性和策略上的错误 ;(4)应对研究的设计同关于应激和应对的“过程”理论不一致 ;(5 )应对研究同临床实践之间存在着较明显的脱节。作者探讨并提出了对于这些问题的可能的解决办法。     

贝叶斯统计方法的应用与现状

贝叶斯统计方法是心理学数据分析的热门方法。研究全面论述贝叶斯方法在心理学领域的应用与方向。现阶段贝叶斯方法以模拟研究为主,应用方向为心理学研究常用的项目反应理论、认知诊断、计算机自适应、结构方程模型。同时,评述发现贝叶斯方法正逐步被国内心理学研究者所接受。最后,文章讨论了当下贝叶斯统计在心理学研究中应用的局限性及可能的原因,建议统计学者开发界面友好的贝叶斯软件,并在心理学课程中加入贝叶斯知识。     

Minimally Sufficient Research

ABSTRACT— Psychology would be improved if researchers stopped using complicated designs, procedures, and statistical analyses for the sole reason that they are able to do so. The present article reviews some of the classic studies in psychology, all of which are breathtakingly simple. The notion of minimally sufficient research is suggested as an ideal worth following. More generally, questions should dictate research methods and statistical analyses, not vice versa.     

从元分析看传统心理统计的局限性

当前,心理学研究文献中占主导地位的数据分析和解释方法是传统的统计方法。元分析方法已经表明传统的统计方法延缓了心理学理论的创新和知识累积的增长。该文从元分析方法的角度阐述了传统的心理统计在处理研究结果方面的局限性。     

Reinvigorating the Study of Careers

Before discussing how to innovate the study of career development using insights from life-span psychology and life course sociology, researchers might benefit from reconsidering whether they are studying careers or studying vocational behavior. While the word “career” seems ubiquitous in vocational psychology research, there are important differences between the study of vocational behavior, occupational roles, and career development. In this article, I urge researchers to (a) reconsider the meaning of career and research on career development, (b) adopt prospective, longitudinal research designs, and (c) concentrate first on processes of development and then on the content of careers. Considering these suggestions may ready vocational psychology for true innovations such as studying human development through work and relationships.     

Regression analyses of repeated measures data in cognitive research

Repeated measures designs involving nonorthogonal variables are being used with increasing frequency in cognitive psychology. Researchers usually analyze the data from such designs inappropriately, probably because the designs are not discussed in standard textbooks on regression. Two commonly used approaches to analyzing repeated measures designs are considered in this article. It is argued that both approaches use inappropriate error terms for testing the effects of independent variables. A more appropriate analysis is presented, and two alternative computational procedures for the analysis are illustrated.     

Multiway frequency analysis for experimental psychologists.

Many research designs in experimental psychology generate data that are fundamentally discrete or categorical in nature, and produce multiway tables of frequencies. Despite an extensive and, more recently, accessible literature on the topic, multiway frequency analysis is rarely used in experimental psychology. A reason may be the form of exposition in the literature, with emphases and concerns far removed from those of the typical experimental psychologist. An approach to multiway frequency analysis for experimental psychologists is described that has the features we want: asymmetrical designs, factors assessed for their respective main and interactive effects in a manner analogous to ANOVA, and the ability to handle within-subject designs.     

Performing high‐powered studies efficiently with sequential analyses

Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, or the study can be terminated whenever it is extremely unlikely that the predicted effect will be observed if data collection would be continued. Such interim analyses can be performed while controlling the Type 1 error rate. Sequential analyses can greatly improve the efficiency with which data are collected. Additional flexibility is provided by adaptive designs where sample sizes are increased on the basis of the observed effect size. The need for pre‐registration, ways to prevent experimenter bias, and a comparison between Bayesian approaches and null‐hypothesis significance testing (NHST) are discussed. Sequential analyses, which are widely used in large‐scale medical trials, provide an efficient way to perform high‐powered informative experiments. I hope this introduction will provide a practical primer that allows researchers to incorporate sequential analyses in their research. Copyright © 2014 John Wiley & Sons, Ltd.     

Using the analysis of covariance to increase the power of priming experiments.

Although priming paradigms are widely used in cognitive psychology, the statistical analyses typically applied to priming data may not be optimal. Conceiving of priming paradigms as change-from-baseline designs suggests that the analysis of covariance (ANCOVA), using baseline performance as the covariate, is a more efficient (i.e., powerful) analysis. Specifically, ANCOVA provides more powerful tests of 1) the presence of priming and 2) between-group differences in priming. In addition, for within-subject designs with multiple baseline conditions, ANCOVA may increase the power of within-subjects effects. Efficiency gains are demonstrated with a re-analysis of priming datasets from implicit memory research. It is suggested that similar gains may be realized in other areas of priming research. Important assumptions of this procedure, which must be evaluated for the appropriate application of ANCOVA, are discussed.     

Making friends with your data: Improving how statistics are conducted and reported1

Aim: This paper highlights some of the areas where there are problems with the way that statistics are conducted and reported in psychology journals. Recommendations are given for improving these problems. Sample: The choice of topics is based largely on the questions that authors, reviewers, and editors have asked in recent years. The focus is on null hypothesis significance testing (NHST), choosing a statistical test, and what should be included in results sections. Results: There are several ways to improve how statistics are reported. These should improve both the authors' and the readers' understanding of the data. Conclusions: Psychology as a discipline will improve if the way in which statistics are conducted and reported is improved. This will require effort from authors, scrutiny from reviewers, and stubbornness from editors.     

Ethical Issues and Guidelines for Conducting Data Analysis in Psychological Research

Psychologists are directed by ethical guidelines in most areas of their practice. However, there are very few guidelines for conducting data analysis in research. The aim of this article is to address the need for more extensive ethical guidelines for researchers who are post–data collection and beginning their data analyses. Improper data analysis is an ethical issue because it can result in publishing false or misleading conclusions. This article includes a review of ethical implications of improper data analysis and potential causes of unethical practices. In addition, current guidelines in psychology and other areas (e.g., American Psychological Association and American Statistical Association Ethics Codes) were used to inspire a list of recommendations for ethical conduct in data analysis that is appropriate for researchers in psychology.     

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.     

2007-2010年心理学专业基础综合考试的多元概化理论研究

摘 要 本研究使用多元概化理论分析2007-2010年心理学专业基础综合考试。结果表明：1.从考查的学科内容看,心理统计与测量、普通心理学的测量精度较高,而发展与教育心理学、实验心理学的测量精度偏低;2.从设置的题型看,多选题的测量精度偏低,其他题型的测量精度较高;减少单选题数量、增加多选题数量可在保障全卷测量精度的基础上大幅提高多选题的测量精度;3.全卷测量精度很高,不同年度的试卷在学科内容和题型结构上可看成是“平行”试卷。     

Multilevel modeling: overview and applications to research in counseling psychology

Multilevel modeling (MLM) is rapidly becoming the standard method of analyzing nested data, for example, data from students within multiple schools, data on multiple clients seen by a smaller number of therapists, and even longitudinal data. Although MLM analyses are likely to increase in frequency in counseling psychology research, many readers of counseling psychology journals have had only limited exposure to MLM concepts. This paper provides an overview of MLM that blends mathematical concepts with examples drawn from counseling psychology. This tutorial is intended to be a first step in learning about MLM; readers are referred to other sources for more advanced explorations of MLM. In addition to being a tutorial for understanding and perhaps even conducting MLM analyses, this paper reviews recent research in counseling psychology that has adopted a multilevel framework, and it provides ideas for MLM approaches to future research in counseling psychology.     

Univariate repeated measures techniques applied to multivariate data

Repeated measures designs in psychology have traditionally been analyzed by the univariate mixed model approach, in which the repeated measures effect is tested against an error term based on the subject by treatment interaction. This paper considers the extension of this analysis to designs in which the individual repeated measures are multivariate. Sufficient conditions for a valid multivariate mixed model analysis are given, and a test is described to determine whether or not given data satisfy these conditions.     

Likelihood ratios: A simple and flexible statistic for empirical psychologists

Empirical studies in psychology typically employ null hypothesis significance testing to draw statistical inferences. We propose that likelihood ratios are a more straightforward alternative to this approach. Likelihood ratios provide a measure of the fit of two competing models; the statistic represents a direct comparison of the relative likelihood of the data, given the best fit of the two models. Likelihood ratios offer an intuitive, easily interpretable statistic that allows the researcher great flexibility in framing empirical arguments. In support of this position, we report the results of a survey of empirical articles in psychology, in which the common uses of statistics by empirical psychologists is examined. From the results of this survey, we show that likelihood ratios are able to serve all the important statistical needs of researchers in empirical psychology in a format that is more straightforward and easier to interpret than traditional inferential statistics.     

An analysis-of-variance model for the intrasubject replication design

One- and two-way analysis-of-variance procedures are shown logically to be appropriate for testing hypotheses in successive treatment reversal designs for one-subject and N-subject experiments, respectively. The applicability of these designs is demonstrated through analyses of typical data.