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.     

Scientific LogAnalyzer: A Web-based tool for analyses of server log files in psychological research

Scientific LogAnalyzer is a platform-independent interactive Web service for the analysis of log files. Scientific LogAnalyzer offers several features not available in other log file analysis tools—for example, organizational criteria and computational algorithms suited to aid behavioral and social scientists. Scientific LogAnalyzer is highly flexible on the input side (unlimited types of log file formats), while strictly keeping a scientific output format. Features include (1) free definition of log file format, (2) searching and marking dependent on any combination of strings (necessary for identifying conditions in experiment data), (3) computation of response times, (4) detection of multiple sessions, (5) speedy analysis of large log files, (6) output in HTML and/or tab-delimited form, suitable for import into statistics software, and (7) a module for analyzing and visualizing drop-out. Several methodological features specifically needed in the analysis of data collected in Internet-based experiments have been implemented in the Web-based tool and are described in this article. A regression analysis with data from 44 log file analyses shows that the size of the log file and the domain name lookup are the two main factors determining the duration of an analysis. It is less than a minute for a standard experimental study with a 2 × 2 design, a dozen Web pages, and 48 participants (ca. 800 lines, including data from drop-outs). The current version of Scientific LogAnalyzer is freely available for small log files. Its Web address ishttp://genpsylab-logcrunsh.unizh.ch/.     

Visualizing individual differences in Web navigation: STRATDYN, a tool for analyzing navigation patterns

Navigational behavior on the Web can be analyzed with different methods. Log file data are an important source of behavioral traces of navigation. In this paper, we first discuss existing approaches to the classification and visualization of movement sequences that are important for understanding Web navigation. We then present STRATDYN, a tool that provides meaningful quantitative and qualitative measures from server-generatedlog files, as well as easy-to-follow visualizations of navigational paths of individual users. We demonstrate the usefulness of this new approach by reporting the results of two studies (with 44 students in education and vocational training), which show that navigational effectiveness is positively related to the ability to concentrate and selectivelyfocus attention, as measuredby the D2 Test of Attention and the FWIT, a German version of the Stroop test. Finally, we discuss implications for further research in this area and for the continuing development of the approach presented.     

Scientific LogAnalyzer: a web-based tool for analyses of server log files in psychological research.

Scientific LogAnalyzer is a platform-independent interactive Web service for the analysis of log files. Scientific LogAnalyzer offers several features not available in other log file analysis tools--for example, organizational criteria and computational algorithms suited to aid behavioral and social scientists. Scientific LogAnalyzer is highly flexible on the input side (unlimited types of log file formats), while strictly keeping a scientific output format. Features include (1) free definition of log file format, (2) searching and marking dependent on any combination of strings (necessary for identifying conditions in experiment data), (3) computation of response times, (4) detection of multiple sessions, (5) speedy analysis of large log files, (6) output in HTML and/or tab-delimited form, suitable for import into statistics software, and (7) a module for analyzing and visualizing drop-out. Several methodological features specifically needed in the analysis of data collected in Internet-based experiments have been implemented in the Web-based tool and are described in this article. A regression analysis with data from 44 log file analyses shows that the size of the log file and the domain name lookup are the two main factors determining the duration of an analysis. It is less than a minute for a standard experimental study with a 2 x 2 design, a dozen Web pages, and 48 participants (ca. 800 lines, including data from drop-outs). The current version of Scientific LogAnalyzer is freely available for small log files. Its Web address is http://genpsylab-logcrunsh.unizh.ch/.     

Listener: A tool for client-side investigation of hypermedia navigation behavior

Behavioral researchers have employed hypermedia-based software applications in their experiments for some time. More recently, interest in the World-Wide Web has developed among researchers in the social sciences, and popular use of this new medium continues to grow at an incredible rate. This paper describes Listener, a tool developed to log users’ hypermedia and World-Wide Web navigation behavior using Apple Macintosh computers in a laboratory setting. Listener is able to capture navigation actions through cached documents, overcoming some of the problems associated with analyzing standard web server logs.     

Pure orientation filtering: A scale-invariant image-processing tool for perception research and data compression

A method is described for scale-invariant segregation of image structure solely on the basis of orientation content. This kind of image decomposition is an unexplored image-processing method that is complementary to the well-explored method of filtering in spatial frequency bands; the latter technique is rotation-invariant, whereas the former technique is scale-invariant. The complementarity of these two approaches is explicit in the fact that orientation and spatial frequency are orthogonal variables in the two-dimensional Fourier plane, and the filters employed in the one method depend only on the radial variable, whereas those employed in the other method depend only on the angular variable. The biological significance of multiscale (spatial frequency selective) image analysis has been well-recognized and often cited, yet orientation selectivity is a far more striking property of neural architecture in cortical visual areas. In the present paper, we begin to explore some coding properties of the scale-invariant orientation variable, paying particular attention to its perceptual significance in texture segmentation and compact image coding. Examples of orientation-coded pictures are presented with data compression to 0.3 bits per pixel.     

ThurCatD: A tool for analyzing ratings on an ordinal category scale

An algorithm for analyzing ordinal scaling results is described. Frequency data on ordinal categories are modeled for unidimensional psychological attributes according to Thurstone’s judgment scaling model. The algorithm applies maximum likelihood estimation of model parameters. The Cramér-Rao bounds of the standard errors of the estimated parameters are calculated, and a stress measure and a goodness-of-fit measure are supplied.     

ALICE: A system for manipulating and analyzing multidimensional data

The computer program ALICE solves the two major problems of data manipulation and analysis. First, ALICE allows the user to treat data from an experiment in the form they are generated. Second, mathematical calculations and statistical analyses are included as an intrinsic part of the multidimensional approach to data handling. ALICE accepts raw data in the form and order they were collected; reorganizes, partitions, or selects any subset of them (including a single entry), and arithmetically combines, transforms, or evaluates any formula involving them. Furthermore, learning to use ALICE is simple, even for those who are naive to both computers and data analysis.     

ACT-PRO action protocol analyzer: A tool for analyzing discrete action protocols

This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, calleddiscrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, > 10⁵) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter’s attention on data that do not fit the prespecified patterns.     

DifScal: A tool for analyzing difference ratings on an ordinal category scale

An algorithm for analyzing difference scaling results is described. Frequency data on ordered categories that represent perceived differences for a unidimensional psychological attribute are modeled according to Thurstone’s judgment scaling model. The algorithm applies the gradient method for the maximum likelihood estimation of the model parameters. Two ways to calculate the start configuration for the model parameters are elaborated. The algorithm also provides asymptotic values for the standard errors of the estimates and three measures for the goodness of the model fit. An additional feature of DifScal is that it is suited to analyze incomplete data.     

VideoNoter: A productivity tool for video data analysis

The software described in this paper, VideoNoter, addresses the need for tools that support annotation and retrieval of video data and organize the presentation of multiple analyses of the same data. Video is widely perceived as an important medium for psychological research, because video recording makes the fleeting particulars of human interaction available as data for detailed analysis, while retaining much of the context of the event. Though the benefits of using video data are high, the process can be prohibitively time-consuming. We have developed a prototype computer-based video analysis tool that can enhance the productivity of the video analysis process. In this paper, we report on the design and implementation of VideoNoter, and we discuss how it facilitates video data analysis.     

RSCORE-J: Pooled rating-method data: A computer program for analyzing pooled ROC curves

RSCORE-J, a computer program for a signal-detection analysis of pooled rating-method data, is listed and described. RSCORE-J computes jackknife estimates of ROC parameters and their standard errors from rating-method data pooled over a group of observers.     

A practical method for analyzing factorial designs with heteroscedastic data

The Type I error rates and powers of three recent tests for analyzing nonorthogonal factorial designs under departures from the assumptions of homogeneity and normality were evaluated using Monte Carlo simulation. Specifically, this work compared the performance of the modified Brown-Forsythe procedure, the generalization of Box's method proposed by Brunner, Dette, and Munk, and the mixed-model procedure adjusted by the Kenward-Roger solution available in the SAS statistical package. With regard to robustness, the three approaches adequately controlled Type I error when the data were generated from symmetric distributions; however, this study's results indicate that, when the data were extracted from asymmetric distributions, the modified Brown-Forsythe approach controlled the Type I error slightly better than the other procedures. With regard to sensitivity, the higher power rates were obtained when the analyses were done with the MIXED procedure of the SAS program. Furthermore, results also identified that, when the data were generated from symmetric distributions, little power was sacrificed by using the generalization of Box's method in place of the modified Brown-Forsythe procedure.     

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.     

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.