计划缺失设计——通过有意缺失让研究更高效

缺失值是社会科学研究中非常普遍的现象。全息极大似然估计和多重插补是目前处理缺失值最有效的方法。计划缺失设计利用特殊的实验设计有意产生缺失值, 再用现代的缺失值处理方法来完成统计分析, 获得无偏的统计结果。计划缺失设计可用于横断面调查减少(或增加)问卷长度和纵向调查减少测量次数, 也可用于提高测量有效性。常用的计划缺失设计有三式设计和两种方法测量。     

Proposing an ontological shift from intervention to intravention in sport and exercise psychology

Intervention research entails the measurement of change in a situation or individual after a modification has been imposed. In sport and exercise psychology, interventions have been implemented in a variety of situations (e.g., performance, doping, physical activity, mental health) for a variety of individuals (e.g., athletes, coaches, sedentary people). Despite their widespread use, accruing evidence indicates that interventions in sport and exercise psychology have had less than anticipated success in instigating change. While some scholars have pointed to the need to confront the methodological challenges of intervention implementation, others have called for the creation of viable alternative forms of inquiry that can complement intervention research. The purpose of the present article is to propose an ontological shift from intervention to intravention in sport and exercise psychology. This shift is undertaken through the deployment of Barad’s (2007) agential realist ontology. The paper is divided into six sections. First, the concept of interaction is situated as it forms the basis for how interventions are conducted. Second, the Baradian ontology of agential realism is explained, along with key concepts. Third, intravention is positioned in relation to five guiding principles that delineate the progression for conducting intravention inquiries. Fourth, alternate meanings are proposed for behaviour, change, and knowledge. Fifth, examples are provided as to how intravention inquiries can be deployed in sport and exercise psychology. Sixth, concluding thoughts are offered. As an ontological becoming of interventions, intraventions are situated as open-ended approaches to inquiry that can help researchers derive alternate understandings of existence and can take the psychology of sport and exercise in exciting and affirmative directions.     

Best practices for missing data management in counseling psychology

This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.     

Planned missing data designs in psychological research

The authors describe 2 efficiency (planned missing data) designs for measurement: the 3-form design and the 2-method measurement design. The 3-form design, a kind of matrix sampling, allows researchers to leverage limited resources to collect data for 33% more survey questions than can be answered by any 1 respondent. Power tables for estimating correlation effects illustrate the benefit of this design. The 2-method measurement design involves a relatively cheap, less valid measure of a construct and an expensive, more valid measure of the same construct. The cost effectiveness of this design stems from the fact that few cases have both measures, and many cases have just the cheap measure. With 3 brief simulations involving structural equation models, the authors show that compared with the same-cost complete cases design, a 2-method measurement design yields lower standard errors and a higher effective sample size for testing important study parameters. With a large cost differential between cheap and expensive measures and small effect sizes, the benefits of the design can be enormous. Strategies for using these 2 designs are suggested.     

Equipercentile equating via data-imputation techniques

In the design of common-item equating, two groups of examinees are administered separate test forms, and each test form contains a common subset of items. We consider test equating under this situation as an incomplete data problem—that is, examinees have observed scores on one test form and missing scores on the other. Through the use of statistical data-imputation techniques, the missing scores can be replaced by reasonable estimates, and consequently the forms may be directly equated as if both forms were administered to both groups. In this paper we discuss different data-imputation techniques that are useful for equipercentile equating; we also use empirical data to evaluate the accuracy of these techniques as compared with chained equipercentile equating.A paper presented at the European Meeting of the Psychometric Society, Barcelona, Spain, July, 1993.     

Evolutionary sport and exercise psychology: Integrating proximate and ultimate explanations

ObjectivesThis review aims to demonstrate the utility of integrating the insights of evolutionary psychology with sport and exercise psychology. Specifically, we offer a primer on evolutionary psychology that we then discuss in the context of several research avenues in sport and exercise. Next, we discuss how evolutionary psychology can inform our understanding of sporting culture.DesignReview paper.MethodsTheory and research are selectively reviewed in efforts to demonstrate the utility and limits of evolutionary psychology as an approach to sport and exercise psychology.Results and conclusionsEvolutionary psychology offers researchers in sport and exercise psychology an improved capacity to produce proximate explanations (i.e., how psychological mechanisms interact with the environment to produce behavior) by generating productive and novel hypotheses from ultimate explanations (i.e., why a psychological mechanism evolved a particular design; Tooby & Cosmides, 1992, 2005). The worth of integrating proximate and ultimate explanations is demonstrated by the ensuing novel insights of popular avenues of sport and exercise psychology including (a) the interrelation between motivation and reasoning and their relative influence on exercise behavior, (b) sex differences in sport participation, (c) performance in sport, and (d) group dynamics in sport. Unlike specific fields of psychology, evolutionary psychology is a metatheoretical approach that can foster mutually productive linkages between currently disparate areas within sport and exercise psychology, and with neighboring disciplines.     

Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

Identifying careless responses in survey data

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

2PLM下缺失数据处理方法及其比较

项目反应理论(IRT)是用于客观测量的现代教育与心理测量理论之一,广泛用于缺失数据十分常见的大尺度测验分析。IRT中两参数逻辑斯蒂克模型(2PLM)下仅有完全随机缺失机制下缺失反应和缺失能力处理的EM算法。本研究推导2PLM下缺失反应忽略的EM 算法,并提出随机缺失机制下缺失反应和缺失能力处理的EM算法和考虑能力估计和作答反应不确定性的多重借补法。研究显示：在各种缺失机制、缺失比例和测验设计下,缺失反应忽略的EM算法和多重借补法表现理想。     

Narrative inquiry in sport and exercise psychology: What can it mean,and why might we do it?

ObjectivesNarrative inquiry is one form of qualitative research that is burgeoning within the human sciences. However, in sport and exercise psychology little attention has been given to this approach. In this article, we seek to rectify this situation by offering an understanding of what narrative inquiry can be.ResultsIn order to begin to better understand what narrative inquiry as a methodology can be, and gain some theoretical purchase on a difficult field without aiming for a final answer, we first define narrative. Next, a distillation of guiding assumptions and characteristics are offered. Finally, some reasons as to why narratives may be of benefit for the field of sport and exercise psychology are highlighted.ConclusionNarrative inquiry is a useful and important way of theorising and doing research in the domain of sport and exercise psychology. It should not, however, be taken up or practised simply because it is new or fashionable. Informed, principled, and responsible choices must be made by researchers and applied professionals about why and when they might engage with narrative inquiry should they wish to do so.     

Participant dropout as a function of survey length in internet-mediated university studies: implications for study design and voluntary participation in psychological research

Internet-mediated research has offered substantial advantages over traditional laboratory-based research in terms of efficiently and affordably allowing for the recruitment of large samples of participants for psychology studies. Core technical, ethical, and methodological issues have been addressed in recent years, but the important issue of participant dropout has received surprisingly little attention. Specifically, web-based psychology studies often involve undergraduates completing lengthy and time-consuming batteries of online personality questionnaires, but no known published studies to date have closely examined the natural course of participant dropout during attempted completion of these studies. The present investigation examined participant dropout among 1,963 undergraduates completing one of six web-based survey studies relatively representative of those conducted in university settings. Results indicated that 10% of participants could be expected to drop out of these studies nearly instantaneously, with an additional 2% dropping out per 100 survey items included in the study. For individual project investigators, these findings hold ramifications for study design considerations, such as conducting a priori power analyses. The present results also have broader ethical implications for understanding and improving voluntary participation in research involving human subjects. Nonetheless, the generalizability of these conclusions may be limited to studies involving similar design or survey content.     

Developing mixed methods research in sport and exercise psychology: Critical reflections on five points of controversy

ObjectivesTo stimulate debate in sport and exercise psychology about the nature of mixed methods research as currently practiced and how this approach might develop in the future.DesignAn exploration of five points of controversy relating to mixed methods research.MethodA presentation of critical reflections on the following. (1) Mixing methods as a non-debate, (2) Purists, pragmatists and mixing paradigms, (3) Integrating findings and representational forms, (4) Judgment criteria and mixed methods research, and (5) Power, politics and what counts in mixed methods research.ResultsThe examples provided of mixed methods research in action indicate that a number of problematic issues regarding both process and product have been neglected.ConclusionsMixed methods research offers a number of conceptual, practical and pedagogical challenges that need to be addressed if this form of inquiry is to develop its full potential in sport and exercise psychology.     

Ethical beliefs and behaviors in applied sport psychology: The AAASP ethics survey

Abstract

AAASP members (N = 508) were surveyed to obtain preliminary data on ethical beliefs and behaviors specific to the practice of applied sport psychology. Completed surveys were received from 165 individuals. On the structured response portion of the survey, there were few differences in ethical beliefs and behaviors as a function of gender, professional/student status, and academic discipline. Nevertheless, a large number of controversial behaviors (N = 24) and difficult judgments (N = 8) were identified. Results of the open-ended portion of the survey indicated that most of the questionable ethical practices cited by respondents corresponded to violations of American Psychological Association (APA) Ethical Standards. The findings lend initial support for AAASP to adopt the APA Ethical Standards and suggest the need for ethics training specific to applied sport psychology.     

Reproducible research in sport and exercise psychology: The role of sample sizes

ObjectivesWe aim to introduce the discussion on the crisis of confidence to sport and exercise psychology. We focus on an important aspect of this debate, the impact of sample sizes, by assessing sample sizes within sport and exercise psychology. Researchers have argued that publications in psychological research contain numerous false-positive findings and inflated effect sizes due to small sample sizes.MethodWe analyse the four leading journals in sport and exercise psychology regarding sample sizes of all quantitative studies published in these journals between 2009 and 2013. Subsequently, we conduct power analyses.ResultsA substantial proportion of published studies does not have sufficient power to detect effect sizes typical for psychological research. Sample sizes and power vary between research designs. Although many correlational studies have adequate sample sizes, experimental studies are often underpowered to detect small-to-medium effects.ConclusionsAs sample sizes are small, research in sport and exercise psychology may suffer from false-positive results and inflated effect sizes, while at the same time failing to detect meaningful small effects. Larger sample sizes are warranted, particularly in experimental studies.     

概化理论缺失数据方差分量估计

各种心理调查、心理实验中, 数据的缺失随处可见。由于数据缺失, 给概化理论分析非平衡数据的方差分量带来一系列问题。基于概化理论框架下, 运用Matlab 7.0软件, 自编程序模拟产生随机双面交叉设计p×i×r缺失数据, 比较和探讨公式法、REML法、拆分法和MCMC法在估计各个方差分量上的性能优劣。结果表明：(1) MCMC方法估计随机双面交叉设计p×i×r缺失数据方差分量, 较其它3种方法表现出更强的优势; (2) 题目和评分者是缺失数据方差分量估计重要的影响因素。     

Assumptions in research in sport and exercise psychology

ObjectivesThe aim of this article is to outline how certain key assumptions affect the quality and interpretation of research in quantitative sport and exercise psychology.MethodsA review of three common assumptions made in the sport and exercise psychology literature was conducted. The review focused on three assumptions relating to research validity and the treatment and interpretation of observations. A central theme to this discussion is the assumption that research observations reflect true effects in a population.ResultsAssumptions often made in sport and exercise psychology research were identified in three key areas: (1) validity, (2) inferences of causality, and (3) effect size and the “practical significance” of research findings. Findings indicated that many studies made assumptions about the validity of the self-report psychological measures adopted and few provided a comprehensive evaluation of the validity of these measures. Researchers adopting correlational designs in sport and exercise psychology often infer causality despite such conclusions being based on theory or speculation rather than empirical evidence. Research reports still do not include effect size statistics as standard and confine the discussion of findings to statistical significance alone rather than commenting on “practical significance”.ConclusionResearch quality can only be evaluated with due consideration of the common assumptions that limits empirical investigation in sport and exercise psychology. We offer some practical advice for researchers, reviewers, and journal editors to minimise the impact of these assumptions and enhance the quality of research findings in sport and exercise psychology.     

Principles and practice in reporting structural equation analyses

Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.     

MISSING DATA: A CONCEPTUAL REVIEW FOR APPLIED PSYCHOLOGISTS

There has been conspicuously little research concerning missing data problems in the applied psychology literature. Fortunately, other fields have begun to investigate this issue. These include survey research, marketing, statistics, economics, and biometrics. A review of this literature suggests several trends for applied psychologists. For example, listwise deletion of data is often the least accurate technique to deal with missing data. Other methods for estimating missing data scores may be more accurate and preserve more data for investigators to analyze. Further, the literature reveals that the amount of missing data and the reasons for deletion of data impact how investigators should handle the problem. Finally, there is a great need for more investigation of strategies for dealing with missing data, especially when data are missing in nonrandom or systematic patterns.