密集追踪数据分析：模型及其应用

在心理学、教育学和临床医学等领域, 越来越多的研究者开始关注个体内部的行为、心理、临床效果等随时间而产生的动态变化, 重视针对个体的差异化建模。密集追踪是一种在短时间内对个体进行多个时间节点密集追踪测量的方法, 更适合用于研究个体内部心理过程等的动态变化及其作用机制。近年来, 密集追踪成为心理学研究的一大热点, 但许多密集追踪的研究分析仍停留在较为传统的方法。方法学领域已涌现出较多用于密集追踪数据分析的模型方法, 较为主流的模型包括以动态结构方程模型(Dynamic Structural Equation Model, DSEM)为代表的自上而下的建模方法, 以及以组迭代多模型估计(Group Iterative Multiple Model Estimation, GIMME)为代表的自下而上的建模方法。二者均可以方便地对密集追踪数据中的自回归及交叉滞后效应进行建模。     

Assessing normal and abnormal personality functioning: strengths and weaknesses of self-report, observer, and performance-based methods

Assessing personality characteristics; distinguishing the boundaries between normal and abnormal functioning; identifying impairment in the domains of work, interpersonal relationships, and emotional state due to maladaptive personality traits; and translating these findings into effective, appropriate treatment interventions is a complicated endeavor. Valid, reliable conclusions about an individual's personality functioning and adjustment cannot be reached unless one has accurate information about that person's patterns of behavior, cognitions, emotions, and interpersonal relationships. I discuss strengths and weaknesses of assessment approaches utilizing explicit assessment methods, such as self-report measures and clinical interviews; information obtained from knowledgeable observers; and performance-based, implicit assessment methods such as the Rorschach Comprehensive System (Exner, 2003). In contrast to explicit methods of assessment, implicit methods can provide salient information about a personality construct whether or not individuals have accurately conceptualized that construct, have weighed how the construct describes them, are self-aware, and are willing to openly provide information relevant to that construct. I propose that the accuracy of conclusions about an individual's personality style, problems in adjustment, and treatment needs may be improved if conclusion are based on a multimethod assessment approach that incorporates information gathered using explicit assessment methods, information provided by significant others, and data from performance-based or implicit measures of personality.     

Temporal stability of WISC-III subtest composite: strengths and weaknesses

The Wechsler Intelligence Scale for Children--Third Edition (D. Wechsler, 1991; WISC-III) is often used to identify subtest-based cognitive strengths and weaknesses that are subsequently used to generate interventions. Given that intelligence is presumed to be an enduring trait, cognitive strengths and weaknesses identified via subtest analysis should also be stable over time. This was evaluated with 579 students who were twice tested with the WISC-III. Based on 66 subtest composites, 6 or 7 interpretable cognitive strengths and weaknesses were found on each WISC-III administration. However, subtest-based strengths and weaknesses replicated across test-retest occasions at chance levels. Because subtest-based cognitive strengths and weaknesses are unreliable, recommendations based on them will also be unreliable.     

The proof of the pudding: an illustration of the relative strengths of null hypothesis, meta-analysis, and Bayesian analysis

Some methodologists have recently suggested that scientific psychology's over-reliance on null hypothesis significance testing (NHST) impedes the progress of the discipline. In response, a number of defenders have maintained that NHST continues to play a vital role in psychological research. Both sides of the argument to date have been presented abstractly. The authors take a different approach to this issue by illustrating the use of NHST along with 2 possible alternatives (meta-analysis as a primary data analysis strategy and Bayesian approaches) in a series of 3 studies. Comparing and contrasting the approaches on actual data brings out the strengths and weaknesses of each approach. The exercise demonstrates that the approaches are not mutually exclusive but instead can be used to complement one another.     

Reviewing Automated Sensor-Based Visitor Tracking Studies: Beyond Traditional Observational Methods?

The method of timing and tracking has a long history within visitor studies and exhibition evaluation. With an increase in indoor tracking research, sensor-based positioning tool usage in museums has grown, as have expectations regarding the efficacy of technological sensing systems. This literature review identifies emerging trends in sensor-based tracking methods used for museum visitor studies. Ten studies are identified, in which five sensor-based solutions are used to access visitor movement in museum settings. These are compared with more established observational timing and tracking methods in terms of obtained level of detail, accuracy, level of obtrusiveness, automation of data entry, ability to time concurrent behaviors, and amount of observer training needed. Although individual sensor-based and traditional, observational methods had both strengths and weaknesses, all sensor-based timing and tracking methods provided automated data entry and the opportunity to track a number of visitors simultaneously regardless of the available personnel.     

An overview of Bayesian reasoning in the analysis of delay‐discounting data

Statistical inference (including interval estimation and model selection) is increasingly used in the analysis of behavioral data. As with many other fields, statistical approaches for these analyses traditionally use classical (i.e., frequentist) methods. Interpreting classical intervals and p‐values correctly can be burdensome and counterintuitive. By contrast, Bayesian methods treat data, parameters, and hypotheses as random quantities and use rules of conditional probability to produce direct probabilistic statements about models and parameters given observed study data. In this work, we reanalyze two data sets using Bayesian procedures. We precede the analyses with an overview of the Bayesian paradigm. The first study reanalyzes data from a recent study of controls, heavy smokers, and individuals with alcohol and/or cocaine substance use disorder, and focuses on Bayesian hypothesis testing for covariates and interval estimation for discounting rates among various substance use disorder profiles. The second example analyzes hypothetical environmental delay‐discounting data. This example focuses on using historical data to establish prior distributions for parameters while allowing subjective expert opinion to govern the prior distribution on model preference. We review the subjective nature of specifying Bayesian prior distributions but also review established methods to standardize the generation of priors and remove subjective influence while still taking advantage of the interpretive advantages of Bayesian analyses. We present the Bayesian approach as an alternative paradigm for statistical inference and discuss its strengths and weaknesses.     

Multiple outcome measures and mixed methods for evaluating the effectiveness of theory-based behaviour-change interventions: a case study targeting health professionals' adoption of a national suicide prevention guideline

Interest in behaviour-change interventions targeting health professionals' adoption of clinical guidelines is growing. Recommendations have been made for interventions to have a theoretical base, explore the local context and to use mixed and multiple methods of evaluation to establish intervention effectiveness. This article presents a case study of a behaviour-change intervention delivered to community mental health professionals in one Primary Care Trust, aimed at raising adherence to a national suicide prevention guideline. A discussion of how the theory-base was selected, the local context explored, and how the intervention was developed and delivered is provided. Time series analysis, mediational analysis and qualitative process evaluation were used to evaluate and explore intervention effectiveness. The time series analysis revealed that the intervention was not effective at increasing adherence to the guideline. The mediational analysis indicates that the intervention failed to successfully target the key barrier to adoption of the guidance, and the qualitative process evaluation identified certain intervention components that were well received by the health professionals, and also identified weaknesses in the delivery of the intervention. It is recommended that future research should seek to further develop the evidence-base for linking specific intervention strategies to specific behavioural barriers, explore the potential of theories that take into account broader social and organisational factors that influence health professionals' practice and focus on the process of data synthesis for identifying key factors to target with tailored interventions. Multiple and mixed evaluation techniques are recommended not only to explore whether an intervention is effective or not but also why it is effective or not.     

Myopic biases in competitions

In three studies using both laboratory and field data, we show that the focal competitor’s strengths and weaknesses feature more prominently in predictions of the outcomes of future competitions than do the strengths and weaknesses of the opponents. People are more confident when their own side is strong, regardless of how strong the competition is. We show that this effect is driven by the fact that people have better information about their own side than the other side, in part because they preferentially seek out information about their own side. Implications for theories of decision making in competitive settings are discussed.     

A Predictive Investigation of Reputation as Mediator of the Political‐Skill/Career‐Success Relationship1

Career success is determined by a number of factors, including some combination of specific competencies and a performance record, along with network development, organizational politics, and reputation building. Theory and research suggest that employees' political skill predicts their career success, and that this relationship is mediated by employees' reputation in the workplace. These hypotheses were tested in a predictive study, collecting 2 waves of data from 135 career employees, covering a 1‐year timeframe. Political skill at Time 1 predicted hierarchical position, income, and career satisfaction at Time 2. Furthermore, reputation mediated the relationships between political skill, hierarchical position, and career satisfaction. Contributions, implications, limitations, strengths, and future research directions are discussed.     

THE PRACTICE OF COMPETENCY MODELING

The purpose of this article is to define and explain a trend that has caused a great deal of confusion among HR researchers, practitioners, and consumers of HR-related services: competency modeling. The Job Analysis and Competency Modeling Task Force, a work group jointly sponsored by the Professional Practice Committee and the Scientific Affairs Committee of the Society For Industrial and Organizational Psychology, has recently concluded a 2-year investigation into the antecedents of competency modeling and an examination of the current range of practice. Competency modeling is compared and contrasted to job analysis using a conceptual framework (reflected in a 10-dimension Level of Rigor Scale) that practitioners and researchers may use to guide future work efforts, and which could be used as a basis for developing standards for practice. The strengths and weaknesses of both competency modeling and job analysis are identified and, where appropriate, recommendations are made for leveraging strengths in one camp to shore-up weaknesses in the other.     

The relationship between intra-cognitive scatter on the Woodcock-Johnson Psycho-Educational Battery-Revised and school achievement

The current study investigated the relationship between total number of significant Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R) intracognitive strengths and/or weaknesses (scatter) and low and underachievement for 2,974 subjects in grades 1 through 12 from the WJ-R standardization sample. Intra-cognitive strength and weakness base rate information was calculated using two common criteria used to define exceptionality. Independent sample t-tests compared subjects with high or low total numbers of intracognitive strengths and/or weaknesses on six achievement-related dependent variables. A total of up to two significant WJ-R intracognitive strengths and/or weaknesses was found to be common in the sample, and more than two intracognitive strengths and/or weaknesses was not related to low or underachievement. The results reinforce prior recommendations for abandoning the scatter concept in psychoeducational assessment.     

特殊儿童个案研究资料收集的方法

本文介绍了特殊儿童个案研究中资料收集的三种重要方法：观察法、访谈法和档案资料法。本文较详细地介绍了各种方法的定义、性质,包含的不同类型,不同角色的研究者与被试之间的关系,以及怎样合理利用不同方法获得的资料等。这三种方法各有自己的优点和缺点,为保证研究资料的多样性和有效性,在个案研究中常常是联合运用的。     

Stabilité à long terme des scores du WISC-IV : forces et faiblesses personnelles

Intelligence is considered as a stable trait over time; psychologists make predictions about the future performance of the child from the interpretation of the WISC-IV scores. However, stability at interindividual level does not necessarily imply stability at intra-individual level. By a test-retest procedure, the stability of categories that classify the performances, and the stability of the personal strengths and personal weaknesses are evaluated on a sample of 277 children aged from seven to 12 years. Categorical stability reveals that the classification into seven categories proposed by the designers of the WISC-IV does not allow reliable predictions. The stability of personal strengths and personal weaknesses is unsatisfactory for the different index scores.     

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights’ estimates unstable (i.e., the “bouncing beta” problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.     

Individual differences methods for randomized experiments

Experiments allow researchers to randomly vary the key manipulation, the instruments of measurement, and the sequences of the measurements and manipulations across participants. To date, however, the advantages of randomized experiments to manipulate both the aspects of interest and the aspects that threaten internal validity have been primarily used to make inferences about the average causal effect of the experimental manipulation. This article introduces a general framework for analyzing experimental data to make inferences about individual differences in causal effects. Approaches to analyzing the data produced by a number of classical designs and 2 more novel designs are discussed. Simulations highlight the strengths and weaknesses of the data produced by each design with respect to internal validity. Results indicate that, although the data produced by standard designs can be used to produce accurate estimates of average causal effects of experimental manipulations, more elaborate designs are often necessary for accurate inferences with respect to individual differences in causal effects. The methods described here can be diversely applied by researchers interested in determining the extent to which individuals respond differentially to an experimental manipulation or treatment and how differential responsiveness relates to individual participant characteristics.     

Detecting Outliers in Factor Analysis Using the Forward Search Algorithm

Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the interdependence of successive observations. Bootstrap methods can fill this need, however. The standard bootstrap of individual timepoints is not appropriate because it destroys their order in time and consequently gives incorrect standard error estimates. Two bootstrap procedures that are appropriate for dynamic factor analysis are described. The moving block bootstrap breaks down the original time series into blocks and draws samples of blocks instead of individual timepoints. A parametric bootstrap is essentially a Monte Carlo study in which the population parameters are taken to be estimates obtained from the available sample. These bootstrap procedures are demonstrated using 103 days of affective mood self-ratings from a pregnant woman, 90 days of personality self-ratings from a psychology freshman, and a simulation study.     

Charting the process of change: A primer on survival analysis

Survival analysis is a powerful and useful technique for understanding qualitative change. This article provides a practical, nontechnical introduction to the use of survival analysis for social scientists. Important issues in using survival analysis are discussed, including research design, data preparation and management, and data analysis. Attendance data from a self-helf organization are used to illustrate common survival analysis tasks such as describing the overall survival and hazard functions, examining covariate effects, and modeling the form of the hazard function over time. An appendix that discusses the strengths and weaknesses of current survival analysis computer programs is included. Editor's note: Edward Seidman served as action editor for this article while serving as Associate Editor for Methodology.I thank William Davidson, Susan Englund, Bruce Rapkin, Kurt Ribisl, and three anonymous reviewers for their helpful comments on earlier drafts. The example data presented here were collected with the support of an NIMH grant (MH37390) awarded to Julian Rappaport and Ed Seidman.     

The analysis of classroom talk: Methods and methodologies

This article describes methods for analysing classroom talk, comparing their strengths and weaknesses. Both quantitative and qualitative methods are described and assessed for their strengths and weaknesses, with a discussion of the mixed use of such methods. It is acknowledged that particular methods are often embedded in particular methodologies, which are based on specific theories of social action, research paradigms, and disciplines; and so a comparison is made of two contemporary methodologies, linguistic ethnography, and sociocultural research. The article concludes with some comments on the current state of development of this field of research and on ways that it might usefully progress.