A system for programming experiments and for recording and analyzing data automatically

A system designed for use in complex operant conditioning experiments is described. Some of its key features are: (a) plugboards that permit the experimenter to change either from one program to another or from one analysis to another in less than a minute, (b) time-sharing of permanently-wired, electronic logic components, (c) recordings suitable for automatic analyses. Included are flow diagrams of the system and sample logic diagrams for programming experiments and for analyzing data.     

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.     

A computer-automated laboratory for studying complex perception-action skills

In this paper, we describe a computer-based laboratory for collecting and analyzing real-time performance and eye-movement data by using video game-like tasks. These tasks assess skills that require the integration of varying numbers of perception and action components. Customized hardware and software are described for configuring tasks, collecting data, reconstructing performance, and organizing, editing, and reducing data. General design considerations are emphasized for solving several of the problems encountered in constructing a real-time data collection system. The paper concludes with examples of data that illustrate the system’s usefulness for studying complex perception-action skills.     

Quantifying social asymmetric structures

Many social phenomena involve a set of dyadic relations among agents whose actions may be dependent. Although individualistic approaches have frequently been applied to analyze social processes, these are not generally concerned with dyadic relations, nor do they deal with dependency. This article describes a mathematical procedure for analyzing dyadic interactions in a social system. The proposed method consists mainly of decomposing asymmetric data into their symmetric and skew-symmetric parts. A quantification of skew symmetry for a social system can be obtained by dividing the norm of the skew-symmetric matrix by the norm of the asymmetric matrix. This calculation makes available to researchers a quantity related to the amount of dyadic reciprocity. With regard to agents, the procedure enables researchers to identify those whose behavior is asymmetric with respect to all agents. It is also possible to derive symmetric measurements among agents and to use multivariate statistical techniques.     

USING MICROSOFT OFFICE EXCEL® 2007 TO CONDUCT GENERALIZED MATCHING ANALYSES

The generalized matching equation is a robust and empirically supported means of analyzing relations between reinforcement and behavior. Unfortunately, no simple task analysis is available to behavior analysts interested in using the matching equation to evaluate data in clinical or applied settings. This technical article presents a task analysis for the use of Microsoft Excel to analyze and plot the generalized matching equation. Using a data‐based case example and a step‐by‐step guide for completing the analysis, these instructions are intended to promote the use of quantitative analyses by researchers with little to no experience in quantitative analyses or the matching law.     

A program for parsing mouse movements into component submovements

Most current movement control theories include the idea that movement toward a target can be broken into several submovements. The complexity of analyzing a movement into its constituent submovement structure and the additional complexity imposed by the problem of noise in the data and hand tremor seem to be daunting to researchers. This paper discusses a program that can ameliorate both of these problems and parse movements into their constituent submovements. It also contains a graphing feature that is useful as a visual tool for analyzing submovement structure. The programs are easily modifiable, so that researchers can specify their own parsing rules on the basis of different assumptions about movement control and use the parser for data from different experimental tasks.     

Microcomputers in psychology laboratory courses

The implementation of a microcomputer-based laboratory curriculum is described and analyzed in terms of general guidelines for introducing computer-based instruction. Procedures are suggested for analyzing need, choosing a system, and implementing the project. Pedagogical considerations arising from the use of computer technology are also discussed.     

Multi-response permutation procedure as an alternative to the analysis of variance: An SPSS implementation

A permutation test typically requires fewer assumptions than does a comparable parametric counterpart. The multi-response permutation procedure (MRPP) is a class of multivariate permutation tests of group difference useful for the analysis of experimental data. However, psychologists seldom make use of the MRPP in data analysis, in part because the MRPP is not implemented in popular statistical packages that psychologists use. A set of SPSS macros implementing the MRPP test is provided in this article. The use of the macros is illustrated by analyzing example data sets.     

Some Arguments Against Analyzing Client Change Graphically

Although single-subject research techniques are relevant and valuable to counselors, analyzing change by graphs alone is open to major sources of error. The author discusses these sources and explores the issues of serial dependency, unreliability of graphs, interjudge disagreement on graphed data, and the use of a more reliable statistical procedure (the time-series statistic) with relevance to the counselor in the field.     

The study of emotional processes in communication: II. Peripheral blood flow as an indicator of emotionalization

Traditional methods of analyzing human peripheral blood flow (PBF) do not account for its continuous nature or small-scale variation. Sequel transformation is presented as a new methodology for measuring these variations. This analysis generates a richer record of aspects of activation of the autonomous nervous system than traditional interbeat-interval (IBI) measurement. Presumably, in addition to heartbeat (i.e., IBI), other information can be extracted that is consistent with the influence of several physiological and psychological factors. This kind of variation is reflected in the PBF signal. After determining and extracting relevant events from vascular volume data generated by sequel transformation, we demonstrate the use of template analysis to analyze patterns of events as time courses of activation. All procedures are included in the computer program VASC Analyzer.     

Modern robust statistical methods: an easy way to maximize the accuracy and power of your research

Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices.     

Nonlinear Bayesian analysis for single case designs

Several authors have suggested the use of multilevel models for the analysis of data from single case designs. Multilevel models are a logical approach to analyzing such data, and deal well with the possible different time points and treatment phases for different subjects. However, they are limited in several ways that are addressed by Bayesian methods. For small samples Bayesian methods fully take into account uncertainty in random effects when estimating fixed effects; the computational methods now in use can fit complex models that represent accurately the behavior being modeled; groups of parameters can be more accurately estimated with shrinkage methods; prior information can be included; and interpretation is more straightforward. The computer programs for Bayesian analysis allow many (nonstandard) nonlinear models to be fit; an example using floor and ceiling effects is discussed here.     

计算机动态测验中问题解决过程策略的分析：多水平混合IRT模型的拓展与应用

学生在完成计算机动态测验过程中, 会产生大量带有时间标记的过程性数据。本研究基于5个国家(地区) 3196名学生在PISA2012一道交通问题解决任务上的139990条数据, 将多水平混合IRT (MMixIRT)模型进行拓展, 用于探索问题解决过程策略的类别特点。结果表明, 该模型不仅可以基于行为序列对不同国家(地区)学生在解决问题时策略使用情况的典型特征进行分析, 还可以提供个体水平的能力估计值。拓展的MMixIRT模型可用于分析过程性数据的特征。     

Using Excel to estimate parameters from observed data: An example from source memory data

We present an application, using Excel, that can solve best-fitting parameters for multinomial models. Multinomial modeling has become increasingly popular and can be used in a variety of domains, such as memory, perception, and other domains in which processes are assumed to be dissociable. We offer an application that can be used for a variety of psychological models and can be used on both PC and Macintosh platforms. We illustrate the use of our program by analyzing data from a source memory experiment.     

Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.     

Modeling the effects of choice-set size on the processing of letters and words

Letters and words are better identified when there are fewer available choices. How do readers use choice-set restrictions? By analyzing new experimental data and previously reported data, the author shows that Bayes theorem-based models overestimate readers' use of choice-set restrictions. This result is discordant with choice-similarity models such as R. D. Luce's (1963a) similarity choice model, G. Keren and S. Baggen's (1981) letter recognition model, and D. W. Massaro and G. C. Oden's (1979) fuzzy logical model of perception. Other models posit that choice restrictions affect accuracy only by improving guessing (e.g., J. L. McClelland & D. E. Rumelhart's, 1981, interactive activation model). It is shown that these models underestimate readers' use of choice-set restrictions. Restriction of choice set does improve perception of letters and words, but not optimally. Decision models that may be able to explain this phenomenon are discussed.     

Analyzing individual differences in sentence processing performance using multilevel models

The use of multilevel models is increasingly common in the behavioral sciences for analyzing hierarchically structured data, including repeated measures data. These models are flexible and easily implemented via a variety of commercially available statistical software programs. We consider their application in the context of an eye-movement experiment testing readers' responses to a semantic plausibility manipulation in temporarily ambiguous sentences. Multilevel models were used to study the relationship between working memory capacity and the extent to which readers were disrupted by syntactic misanalysis. This represented a cross-level interaction between an individual difference measure and a sentence-level characteristic. We compare a multilevel modeling approach to a standard approach based on ANOVA.     

MCMC estimation and some model-fit analysis of multidimensional IRT models

A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization of the procedure to a model with multidimensional ability parameters are presented. The procedure is a generalization of a procedure by Albert (1992) for estimating the two-parameter normal ogive model. The procedure supports analyzing data from multiple populations and incomplete designs. It is shown that restrictions can be imposed on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data. The authors would like to thank Norman Verhelst for his valuable comments and ACT, CITO group and SweSAT for the use of their data.     

A method for the study of interstimulus similarity

A generalized method for collecting data on interstimulus similarity is presented and its special cases evaluated by use of information theory. A method of analyzing the data by the Unfolding Technique is presented which permits the study of the latent attribute structure underlying the similarity of stimuli for a single individual. This study was carried out under Office of Naval Research Contract Nonr 374 (00) NR 041-011.     

Polytomous Latent Scales for the Investigation of the Ordering of Items

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering property may be important in, for example, intelligence testing and person-fit analysis. We derive observable properties of the three latent scales that can each be used to investigate in real data whether the particular model adequately describes the data. We also propose a methodology for analyzing test data in an effort to find support for a latent scale, and we use two real-data examples to illustrate the practical use of this methodology.