Jon Tapp

A multiple option observation system for experimental studies: MOOSES

The purpose of this article is to outline the development of the Multiple Option Observation System for Experimental Studies (MOOSES), a flexible data collection package for applied behavioral research. Several data collection options are available to users of MOOSES. Event-based recording, interaction-based recording, duration recording, and interval recording are available to the users and can be used individually or together, depending upon the research question. The collection program can incorporate any of the keys on the keyboard. Function keys on the top or side are used for toggle (duration states) type data collection. Types of analysis include frequency and duration of discrete events, frequency of general behavior states, frequency and duration of events within behavioral states, percent interval analysis, sequential analysis, and interobserver agreement. Data obtained from MOOSES is easily incorporated with other data for further statistical analysis with standard statistical packages or popular spreadsheet programs. Applications of MOOSES and its uses in social interaction research are presented. Comparisons with other similar systems are provided.

PROCODER: a professional tape control, coding, and analysis system for behavioral research using videotape

PROCODER is a software system for observing and coding events that have been recorded on videotape. The system uses a personal-computer-based tape controller to control a VHS tape while observations are recorded. Frequencies of events, durations of events, and calculations of inter-observer agreement of events or intervals are included. Data can be output in ASCII format for use with other statistical programs. A sample study in which the system is used is described as well.

Comparing observational software with paper and pencil for time-sampled data: A field test of Interval Manager (INTMAN)

In this article, we describe the Interval Manager (INTMAN) software system for collecting timesampled observational data and present a preliminary application comparing the program with a traditional paper-and-pencil method. INTMAN is a computer-assisted alternative to traditional paper-and-pencil methods for collecting fixed interval time-sampled observational data. The INTMAN data collection software runs on Pocket PC handheld computers and includes a desktop application for Microsoft Windows that is used for data analysis. Standard analysis options include modified frequencies, percent of intervals, conditional probabilities, and kappa agreement matrices and values. INTMAN and a standardized paper-and-pencil method were compared under identical conditions on five dimensions: setup time, duration of data entry, duration of interobserver agreement calculations, accuracy, and cost. Overall, the computer-assisted program was a more efficient and accurate data collection system for time-sampled data than the traditional method.

Comparing sequential associations within a single dyad

We present a new application of sampled permutation testing to examine whether two sequential associations are different within a single dyad (e.g., a teacher and a student). A Monte Carlo simulation with the same (i.e., 100 vs. 100) or a different (100 vs. 400) number of event pairs was used to simulate designs that use time-based (typicallyproducing equal-length comparisons) and event-based (typically producing different-length comparisons) data, respectively. For these pairs of simulated data streams, we compared the Type I error rates and the kappa for agreement on significance decisions, using the sampled permutation tests and the more traditional asymptotic log linear analysis. The results provide the first evidence relevant to evaluating the accuracy of log linear analysis and sampled permutation testing for the purpose of comparing sequential associations within a single dyad.

Partial-Interval Estimation of Count: Uncorrected and Poisson-Corrected Error Levels.

A simulation study that used 3,000 computer-generated event streams with known behavior rates, interval durations, and session durations was conducted to test whether the main and interaction effects of true rate and interval duration affect the error level of uncorrected and Poisson-transformed (i.e., corrected) count as estimated by partial-interval recording. For both count estimates, shorter intervals and lower true rates resulted in less error than longer intervals and higher rates. For all conditions tested, Poisson-corrected estimates were more accurate than uncorrected estimates. Therefore, using Poisson-corrected estimates and short intervals are recommended when partial-interval recording is used to estimate counts. Generality of results might be restricted to events that are about 1-s long. A URL was provided to aid in the computation of corrected counts.