ActSds and OdfSds: Programs for converting INTERACT and The Observer data files into SDIS timed-event sequential data files

In this article, we describe programs for converting Mangold International's INTERACT and Noldus Information Technology's The Observer data files to sequential data interchange standard (SDIS) timed-event sequential data files. Users who convert their INTERACT or The Observer data files can then take advantage of various flexible and powerful data modification and computational procedures available in the Generalized Sequential Querier, a program that assumes SDIS-formatted files.     

OTS: A program for converting Noldus Observer data files to SDIS files

A program for converting Noldus Observer data files (ODF) to sequential data interchange standard (SDIS) files is described. Observer users who convert their data files can then take advantage of various flexible and powerful data modification and computational procedures available in the Generalized Sequential Querier, a program that assumes SDIS-formatted files.     

C-quence: A tool for analyzing qualitative sequential data

C-quence is a software application that matches sequential patterns of qualitative data specified by the user and calculates the rate of occurrence of these patterns in a data set. Although it was designed to facilitate analyses of face-to-face interaction, it is applicable to any data set involving categorical data and sequential information. C-quence queries are constructed using a graphical user interface. The program does not limit the complexity of the sequential patterns specified by the user.     

Analyzing sequential relations in observational data: A practical guide

Many observational systems in basic and applied research produce a record of sequences of events over time. Within such observational systems, important information may be found in the frequency of the transitions between events that does not emerge in the typical researcher's focus on absolute event frequency. Indeed, if many behaviors are controlled by closely adjacent preceding events, then substantial prediction and control can be obtained through knowledge and manipulation of causal event transitions. A method for analyzing and testing sequential dependencies between events is proposed as part of an integrated package of computer-based data entry, storage, and analysis procedures. The mathematical portion of these techniques is based on the statistic, kappa,which is applicable to determining whether particular transitions among events differ from chance and whether particular transitions differ significantly across groups of subjects. Low-cost hardware and software to implement the proposed procedures are described.This work has been supported by Grant 1 RO1 HD19245-01A1 from the National Institute of Child Health and Human Development to the two senior authors and by grants of equipment from Commodore Computers, Inc., Koala Corporation, and NEC Telephones, Inc.     

A note on interobserver reliability for sequential data

It is well accepted that reliability measures based on simple frequency counts of designated codes are inappropriate for sequential analysis. The stricter point-by-point method, however, is problematic, particularly because it is too strict. It is suggested that a less strict reliability measure based on the number of transitions be used. This method also avoids problems encountered with the point-by-point method and another less stringent method.     

ANSEC: A BASIC package for lag sequential analysis of observational data

Three BASIC programs for processing observational, nonconcurrent sequential data are presented. The programs follow Sackett’s lag sequential analysis method and have the innovations of running on interactional microcomputers and of providing plots of results. The outcome of the analysis is stored on a magnetic disk, facilitating a further application of probabilistic models to the transformed data. The Allison-Liker correction for the comparison test between expected and observed lag probabilities is included in the programs.     

Streamlining experiment data manipulation with psychology experiment data interchange format (PXDIF)

The use of small computers to automate psychology experiments has raised a number of development and logistical problems that interrupt the smooth flow of work from hypotheses to conclusions and publication. Currently, the outstanding bottleneck is the tedious and error-prone manipulation of raw experiment data in preparation for use in commercial data visualization and statistics packages. This problem is addressed by a self-describing data format PXDIF. Companion software can perform most needed manipulations automatically or with minimal experimenter involvement.     

Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms

Observer agreement is often regarded as the sine qua non of observational research. Cohen’s κ ?is a widely used index and is appropriate when discrete entities—such as a turn-of-talk or a demarcated time interval—are presented to pairs of observers to code. κ-like statistics and agreement matrices are also used for the timed-event sequential data produced when observers first segment and then code events detected in the stream of behavior, noting onset and offset times. Such κs are of two kinds: time-based and event-based. Available for download is a computer program (OASTES; Observer Agreement for Simulated Timed Event Sequences) that simulates the coding of observers of a stated accuracy and then computes agreement statistics for two time-based κs (with and without tolerance) and three event-based κs (one implemented in The Observer, one in INTERACT, and one in GSEQ). On the basis of simulation results presented here, and due to the somewhat different information provided by each, the reporting of both a time-based and an event-based κ?is recommended.     

A graphical method to aid the sequential analysis of observational data

In this paper, two methods of sequential analysis are applied to hypothetical observational data. The first method employs the conventional “conditional probability” approach, illustrated using the GSEQ program (Bakeman & Quera, 1995). In order to overcome some of the difficulties associated with the conditional probability approach, the second method employs a new “normalized and pooled” approach. Essentially, by normalizing periods of time preceding, during, and following each occurrence of a nominated “given” behavior, the proportion of time units devoted to a “target” behavior can be estimated and then pooled across all occurrences of the given behavior. A summary diagram representing the likelihood that the target behavior precedes, occurs concurrently with, and follows the given behavior can then be constructed. Elements of this summary diagram can also be quantified. Given the graphical nature of the output, and its ease of use, the normalized and pooled approach may help to promote the use of sequential analysis in applied settings.     

Analyzing sequential categorical data: Individual variation in markov chains

Markov chains are probabilistic models for sequences of categorical events, with applications throughout scientific psychology. This paper provides a method for anlayzing data consisting of event sequences and covariate observations. It is assumed that each sequence is a Markov process characterized by a distinct transition probability matrix. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. The elements of the transition probability matrices are written as functions of a vector of latent variables, with variation in the latent variables explained through a multivariate regression on the covariates. The regression is estimated using the EM algorithm, and requires the numerical calculation of a multivariate integral. An example using simulated cognitive developmental data is presented, which shows that the estimation of individual variation in the parameters of a probability model may have substantial theoretical importance, even when individual differences are not the focus of the investigator's concerns.Research contributing to this article was supported by B.R.S. Subgrant 5-35345 from the University of Virginia. I thank the DADA Group, Bill Fabricius, Don Hartmann, William Griffin, Jack McArdle, Ivo Molenaar, Ronald Schoenberg, Simon Tavaré, and several anonymous reviewers for their discussion of these points.     

Dismal: a spreadsheet for sequential data analysis and HCI experimentation

Dismal is a spreadsheet that works within GNU Emacs, a widely available programmable editor. Dismal has three features of particular interest to those who study behavior: (1) the ability to manipulate and align sequential data, (2) an open architecture that allows users to expand it to meet their particular needs, and (3) an instrumented and accessible interface for studies of human-computer interaction (HCI). Example uses of each of these capabilities are provided, including cognitive models that have had their sequential behavior aligned with subject’s protocols, extensions useful for teaching and doing HCI design, and studies in which keystroke logs from the timing package in Dismal have been used.     

Hierarchical modeling of sequential behavioral data: an empirical Bayesian approach

The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches. They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.     

Mother-child verbal interchange: A descriptive study of young children's verbal behavior

The theoretical importance of the linguistic data available to the child in his natural environment has been increasingly emphasized in recent work on child language acquisition. This study sought to describe a portion of such data as contained in maternal verbal behavior and to analyze its influence on the child's usage of speech. The subjects were five 21-month-old children and their mothers. The mother-child verbal interchange was recorded during free-play situations carried out both at the subject's home and in a playroom. The interactive patterns of mother-child utterances were analyzed with regard to the kind and frequency of verbalizations, and the temporal intervals between them. Results showed that the temporal pattern of mother-child interchanges was characterized by significantly shorter pauses (<2 s) for mothers than for children. A variation in this pattern, consisting of pauses longer than 4 s, was associated with certain kinds of ongoing mother-child verbal behaviors. Thus mothers showed selectiveness, in varying degrees, in responding to the child's utterances, and their verbal responses had differential effects on the child's subsequent verbal performance. These effects were described as “initiating”, “maintaining”, or “ending” verbal chains, or else as “non-reacted” utternances. Moreover, the amount of verbalization by the children was positively correlated with the proportion of the mother's output that consisted of immediate responses to the child, and not with the total verbal output.     

A FORTRAN program for lag sequential analysis of contingency and cyclicity in behavioral interaction data

One method for identifying contingency relationships in a sequence of observed behaviors studies occurrences of matching behaviors at successive event or time steps from a criterion event. This paper briefly summarizes the method and presents a computer program for generating lag conditional probabilities and associated measures for assessing statistical significance.     

Hierarchical modeling of sequential behavioral data: examining complex association patterns in mediation models

This article presents new methods for modeling the strength of association between multiple behaviors in a behavioral sequence, particularly those involving substantively important interaction patterns. Modeling and identifying such interaction patterns becomes more complex when behaviors are assigned to more than two categories, as is the case for most observational research. The authors propose multilevel empirical Bayes methods to overcome the challenges inherent in such data. Furthermore, these methods allow the study of how variation in interaction patterns can mediate the effects of antecedents or intervention on distal outcomes. New procedures are developed to compare alternative mediation models and pinpoint which random effects operate as mediators. These models are then applied to observational data taken from a study of the behavioral interactions of 254 couples.     

A note on sequential blanking

Five-letter pseudowords were presented in all possible spatiotemporal orders at each of nine combinations of letter duration and interletter interval. Subjects were required to report as many letters of the pseudoword as possible. The results confirmed the sequential blanking effect; under certain timing conditions, some spatiotemporal orders result in perceptual blanking. The results show, however, that blanking does not follow the two rules previously thought to describe the phenomenon.     

Searching for the sequential line-up advantage: A distinctiveness explanation

Signal Detection models as well as the Two-High-Threshold model (2HTM) have been used successfully as measurement models in recognition tasks to disentangle memory performance and response biases. A popular method in recognition memory is to elicit confidence judgements about the presumed old/new status of an item, allowing for the easy construction of ROCs. Since the 2HTM assumes fewer latent memory states than response options are available in confidence ratings, the 2HTM has to be extended by a mapping function which models individual rating scale usage. Unpublished data from 2 experiments in Bröder and Schütz (2009) validate the core memory parameters of the model, and 3 new experiments show that the response mapping parameters are selectively affected by manipulations intended to affect rating scale use, and this is independent of overall old/new bias. Comparisons with SDT show that both models behave similarly, a case that highlights the notion that both modelling approaches can be valuable (and complementary) elements in a researcher's toolbox.     

Identification of sequential dependencies in conversations: A Pascal program

A Pascal program that provides an analysis of the sequential dependencies between conversational events (e.g., speech acts) generated by two sources is described It produces z scores, chirsquare values, and confidence limits for the difference between estimated unconditional probabilities of the occurrence of selected events (valid events) and estimated conditional probabilities of these events following specified target events immediately (at Lag 1) and, if required, more distantly (at Lags 2, 3, etc.). Valid events and target events may be independently selected from the set of events generated by the first source, the second source, or both. Events not selected are ignored. The user may also elect to exclude the target event from the set of valid events (an adjustment for autocorrelation). The program is especially appropriate for conversational events because it does not assume that two events from the same category can never follow one another.