Collecting and Analyzing Continuous Behavioral Data with the TRS-80 Model 100/102 Portable Laptop Computer (1989)

©1989 by Dallas Denny & James Fox

Source: Denny, Dallas, & Fox, James. (1989, Summer). Collecting and analyzing continuous behavioral data with the TRS-80 Model 100/102 portable laptop computer. Journal of Special Education Technology, 9(4), 183-189.

This article and the others in this issue of JSET was based upon a presentation Dr. Fox and I did as part of a symposium on electronic processing of behavioral data in Gatlinburg, Tennessee. Dr. Fox chaired the symposium and edited the resulting special issue.

Source: Denny, Dallas, & Fox, James. (1987). Collecting and analyzing sequential data with the TRS‑80 Model 100 and the Behavioral Observation Code Computerized (BOSCO). Twentieth Annual Gatlinburg Conference on Research and Theory in Mental Retardation and Developmental Disabilities, Gatlinburg, TN, 25‑28 March.

The behavioral data gathering and analysis program Behavioral Observation System, COmputerized is a program written in the BASIC programming language for the TRS-80 Models 100 and 102 portable computers. I am the sole author. It was used extensively by researchers at the Special Education Department at George Peabody College, which is a part of Vanderbilt University.

 

BOSCO Program Operations Manual

Collecting and Analyzing Continuous Behavioral Data with the TRS-80 Model 100/102 Portable Laptop Computer

By Dallas Denny

And James Fox

Department of Special Education

George Peabody College of Vanderbilt University

During observation sessions, discontinuous re­cordings of behavioral events (time sampling and interval recording) introduces sampling error in­to the data (Johnston & Pennypacker, 1980; Sackett, 1978). “Such sampling error is a major contributor to measurement-induced variability, which then becomes thoroughly confounded with treatment-imposed variability, severely compro­mising the accuracy and generality of experimen­tal interpretations” (Johnston & Pennypacker, 1980, p. 149). Continuous recording of data is methodologically superior to discontinuous col­lection, but makes greater demands on observer energy and concentration and often requires ex­pensive and cumbersome equipment (Sackett, 1978).

Currently there are portable electronic devices available that allow the collection of continuous observational data (Sackett, 1978). The OS-3 by Observational Systems, Inc., is an example of one such device (cf. Bakeman & Gottman, 1986). Data from these “electronic event recorders” typically is transferred to a desktop computer for editing and analysis. Also available are programs for desktop computers that permit data collection and detailed analyses of the data (e.g., Semmel & Frick, 1980), and others that calculate agreement between observers (Mac­Lean, Tapp, & Johnson, 1985).

However, there is a need for software and hard­ware that can integrate these and other functions yet remain truly portable, reasonably priced, and flexible in their application. For example, obser­vational data collection typically occurs in situa­tions that require that observers and their equip­ment remain mobile. At the same time it is often desirable (sometimes necessary) to edit data or obtain results of analyses and checks of inter-observer agreement on-site without having to resort to a desktop computer, which may be many miles away from the actual research site. This may be particularly true in single-subject design studies because decisions about changes in experimental conditions are made on the basis of data trends rather than a priori criteria. When they do not allow relatively immediate access to the data, even electronic event recorders can serve to “distance” the experimenter from his or her data (Martin & Bateson, 1986). Inductive reasoning, a valuable tool of the behavioral researcher, is impeded when data are not readily available for inspection.

“Stand-alone” programs for portable laptop computers are beginning to appear, as witnessed by this special issue. Our purpose is to briefly describe and illustrate the use of one such in­tegrated system, the Behavioral Observation System Computerized (BOSCO) and the Radio Shack TRS-80 Model 102, which we have been developing and using over the last several years. Its advantages and disadvantages over other, more traditional, paper-and-pencil and electronic devices will also be addressed.

The Computer

 The TRS-80 Model 102 portable laptop com­puter (and its nearly identical predecessor, the Model 100) is an inexpensive battery-operated microcomputer that can be purchased and ser­viced at Radio Shack stores throughout the world. The Model 102 is available with up to 32K (32,000 bytes) of random access memory (RAM). Second-party suppliers offer plug-in devices that can increase RAM to one megabyte and more. The built-in software includes the BASIC pro­gramming language and text editing program. In addition, there are a built-in modem and telecom­munications software that allow communication with other microcomputers either directly through the RS232 port or at a distance through telephone lines. The Model 102 can be easily in­terfaced with disk drives, cassette tape recorders, printers, bar code readers, and other peripheral devices.

Factors that influenced our selection of the Model 102 over other available portable micro­computers included the popularity and wide­spread software base, ready availability, com­paratively low price, small size (12 x 8 x 1.75 in.), low weight (about 4 lbs), battery-powered operation, full-size keyboard and embedded key pad, large LCD screen, BASIC language, real-time clock and calendar, built-in modem and software, and ability to easily interface with a variety of equally portable peripheral devices.

The Software

BOSCO (the Behavioral Observational System—COmputerized) is a series of BASIC programs, SETUP, BOSCO, and RELY, that allow the Model 102 to be used, respectively, for: (a) the creation of an observation system, (b) col­lection and analysis of continuous observational data, and (c) calculation of interobserver agree­ment. Each program takes up between 6 and 12K of the memory of the Model 102 (total of 32K conventionally expanded). Once loaded from disk or tape, running the programs simply involves moving the cursor over the desired pro­gram listing on the main menu and pressing the ENTER key. The programs remain in memory until erased or until battery power is lost. The computer’s built-in nickel-cadmium battery preserves the memory even when the computer is shut off, and it will retain its charge for several weeks.

Using SETUP to Create an Observation Code

As with the other two programs, SETUP is loaded into the Model 102’s RAM from either disk or cassette tape. Figure 1 shows the main menu of SETUP. Through the “Define Behaviors” option, SETUP enables code numbers and brief labels to be assigned to as many as several hundred mutually exclusive behavioral categories. One simply types in the behavioral category label in response to the on-screen prompts. SETUP also allows system parameters to be set—bell on/off, dump file on/off, printer codes, and maximum number of observations/sessions. The coding system can be easily modified through this program also. Once a coding system is defined, it is saves as a TEXT (.DO) file in the computer’s RAM, using the “File Operations” option; SETUP then can be erased from the computer’s memory to create additional space for subsequent observation files.

Figure 1: BOSCO SETUP Program Menus (PDF)

Conducting an Observation with BOSCO

Once loaded into RAM, BOSCO is initiated by moving the cursor over the name BOSCO and pressing enter. On-screen prompts then direct the observer through several observation menu options. The range of options is shown in Figure 2, which presents the main BOSCO menu. The HEADER option allows the observer to enter descriptive information about the par­ticular observation session—subject’s and observer’s names, date, time of the observation, etc. Actual observation is begun by selecting the ENTER DATA option from the menu. Observa­tions are then entered by keystrokes, using the embedded key pad to enter the code number cor­responding to the behavior observed. The code of the event becomes associated with the clock time of the observation. The event is assumed to continue (thereby accumulating clock time) un­til the code for the next event is entered or until a STOP code, created by the investigator under SETUP~ is entered. As currently structured BOSCO allows the recording of several hundred (299) behavioral events and their associated times within a single observation~ file. During observation the screen displays several important pieces of information in addition to the last code entered; an example of this screen and its infor­mation are presented in Figure 3. When the observation is completed (or the maximum number of events has been entered), it is necessary to save the data as a TEXT file (.DO) to the computer’s RAM storage area. Again, this is done by selecting the appropriate choice from the main BOSCO menu. Data files can later be loaded back into the program from this menu for purposes of examination, modification, or analysis. A maximum of 18 files can be stored in RAM before it is necessary to save them to a disk or tape. Of course, the number of files is also restricted by currently available RAM.

 Figure 2: Main BOSCO Menu

Figure 3: BOSCO Screen Display During Observation

Analyzing data with BOSCO and RELY

Data can be analyzed and presented on the LCD screen or a paper copy can be generated with the connection of a printer. As shown in Figure 4, there are three data analysis programs provided in BOSCO. Raw data analysis provides a print­out of each code entry by (a) code number, (b) ordinal position in the data series, (c) time of onset, and (d) number of seconds each entry was “on.″ Frequency analysis shows for each code (a) its total frequency, (b) total time (or duration) in seconds, (c) mean time in seconds, and (d) total percentage of the observation time. Finally, Log 1 sequential analysis shows the frequency and percentage of occasions that each recorded code was (a) preceded and (b) followed by all other codes.

A separate program, RELY, examines and calculates interobserver agreement between the files of two independent observers. This program must be loaded separately from BOSCO, and both observation files for comparison must be loaded into the same computer. As with BOSCO and SETUP, RELY is initiated by first placing the cursor over RELY on the main menu and press­ing enter. A series of on-screen prompts then asks that the names of the two observation files be entered and, next, that the user specify the window of time over which each entry will be compared. This agreement window is adjustable by the user from one second to several hours. Calculation of observer agreement is done using the point-by-point agreement formula (Kazdin, 1982). A print-out of each code entered by the primary observer is obtained, indicating agree­ment or disagreement with the second observer for that entry; an overall summary percentage of agreement for that session is also calculated and displayed or printed.

An option of the BOSCO program allows the separate recording of all keystrokes in a “dump” file. Each time the Enter key is pressed, the time is recorded. In this way, it is possible to accurately record and time events that would not be possible to record within the framework of the defined coding system.

Figure 4: BOSCO Data Analysis Subprogram

Applications of BOSCO and the Model 101/102 Computer

Throughout its evolution BOSCO has been ap­plied in a number of research studies at Peabody College. Next, we will briefly describe several of these studies to illustrate BOSCO’s actual and potential applications.

One focus of our research group has been the social development and peer interactions of handicapped children. Social delays may consist of a lack of initiations, inappropriate or ineffec­tive initiations, lack of responsiveness to another’s initiations, skill deficits in maintaining interactions once they have begun, or some com­bination of these problems. Thus, we needed an observation system that would allow us to iden­tify such things as who initiated an interaction (the handicapped subject or a peer), the type of initiation used, whether or not initiations received a response, and how often they led to some sus­tained interaction between the children.

Although paper-and-pencil data collection methods had been used in prior studies (Fox, Shores, Lindeman, & Strain, 1986; Hendrickson, Strain, Tremblay, & Shores, 1982; Tremblay, Strain, Hendrickson, & Shores, 1981), there were several problems facing the continued use of these techniques. For example, paper-and-pencil methods required observers to periodically shift their attention from the subject to the paper and pencil in order to record behavioral events. This, combined with the time it took to actually write down coding symbols, resulted in potential gaps in the observed behavioral flow and in loss of data for analysis. Also, paper-and-pencil pro­cedures seem less efficient in actual recording of continuous sequential observation. Without con­current use of other recording devices (e.g., stop watches), there was no way to directly and effi­ciently record both the frequency and the dura­tion of social behaviors and interactions, Finally, paper-and-pencil measurement procedures seem less efficient in the resultant data analyses. The tabulation of daily frequencies of behaviors, their duration, and the proportion of time certain ini­tiations resulted in social responses and inter­actions were integral to our research questions; yet such analyses were very labor-intensive and time-consuming when done by hand. Use of BOSCO and the Model 102 obviated these difficulties.

In a recent study (Savelle & Fox, 1988) BOSCO was used to record and analyze the ef­fects of different types of initiations on autistic children’s social responsiveness. Nonhandicapped age peers were taught to increase both reciprocal (e.g., sharing and verbal invitations to play) and nonreciprocal (statements, vocal attention-getting behaviors) initiations toward two autistic subjects. Changes in social respon­siveness were evaluated in three ways: (a) whether or not the autistic subjects made an in­itial positive response to either type of initiation, (b) if and for how long the children continued to interact following an initiation and response, and (c) whether or not the autistic subjects them­selves began to initiate interactions. The obser­vation component of BOSCO permitted us to more accurately record each of these parameters of interaction and their sequential relationships, while its data analysis component increased the speed with which we could make data-based decisions in this single-subject design study. Subsequently, we put BOSCO to similar use in a descriptive analysis of the differences and similarities of mentally retarded and non-handicapped children’s interactions with their nor­mally developing siblings (Savelle, Fox, & Phillips, 1987).

In a third and somewhat different study (Ragland & Fox, 1988) we were concerned with experimentally analyzing the relationship be­tween particular teacher behaviors and an autistic child’s self-injurious behavior (SIB)-head slap­ping/hitting. The student’s SIB had been persis­tent and increasing for some time, yet it was ir­regular enough that teaching staff had been unable to precisely determine what environmen­tal events reliably preceded the SIB. Informal observation and interviews with the classroom teacher were used to develop a multicategory observation system of teacher and student behaviors and a series of experimental probe con­ditions (e.g., social attention, task presentation, no demand-free play), some of which were hypothesized to be functionally related to self-injury. Observations using BOSCO were con­ducted during these probe conditions and dur­ing a naturally occurring school activity that had sometimes been associated with the student’s SIB. Use of the sequential recording and analysis capabilities of BOSCO in conjunction with the probe conditions allowed us to pinpoint a par­ticular task, shoe tying, and a series of teacher behaviors (presentation of the shoes, verbal in­structions for and physical prompting of shoe ty­ing) that reliably elicited SIB. In addition, the observational data indicated a strong relationship between the events identified in the probe con­ditions as eliciting SIB and those associated with SIB in the natural environment. This analysis in turn led to development of a set of intervention tactics designed to reduce SIB for this student.

Future Applications and Summary 

In each of the applications described here, BOSCO and the Model 102 represent distinct advantages over traditional data collection methods such as paper and pencil. These advan­tages include increased flexibility in the creation and modification of an observation system. preservation of the sequence of subject-environment interactions, simultaneous and ef­ficient recording of both frequency and duration of events, and more rapid and extensive analyses (e.g., conditional probabilities) of the resultant observations.

There are, of course, drawbacks to this and other similar computer-based observation systems. We have had our share of equipment failures— weakened or dead batteries forcing cancellation of observations, misaligned disk drive heads or bad disks causing loss of data. Human factors also enter into the “disadvan­tages” of the system. For example, there is initially more start-up time in observer training in order to familiarize observers with operation of the computer itself and, in some cases, to desensitize those observers who are computer-phobic. Even when both computer and observational training have been completed, there have still been observer errors resulting in loss of data, e.g., in­advertent erasure of a file before it has been saved to the computer’s memory or to disk. However, for the most part, these problems have counter­parts in the more traditional observational pro­cedures. With the proper management their likelihood of occurrence and impact can be reduced.

When compared to other electronic data col­lection instruments, the programmability of the Model 102 computer gives it a flexibility not available in “dedicated” devices such as the Datamyte and OS-3. Because it uses a relatively small portion of the Model 102’s memory, it will be possible to enhance BOSCO or other obser­vation programs to include additional recording, data analytic, and print-out capabilities. Other future plans for BOSCO are several. They include the development of a bar-code reading program to eliminate the time and distraction of keystroke entry of observation codes. Also in the planning stages is a program for both on-screen and print­out graphing of certain behavior parameters, e.g., session frequency and duration of behavior codes. The BOSCO user can thus be increas­ingly freed from dependency upon desktop and mainframe computers for most typical data analyses. Even if the Model 102 is used as a stand-alone data collection and analysis tool, the internal telecommunications package of the com­puter will allow data transfer to other computers running other programs and further expand its uses. It would be instructive, for instance, to transfer BOSCO files to an Apple II + or IIE com­puter for analysis with a decision-making pro­gram such as AIMSTAR (Hasselbring & Hamlett, 1984). With such modifications, BOSCO and the Model 102 can emerge not only as a research tool but as an efficient and effective package for practitioners such as teacher trainers, consulting teachers, and school psychologists, perhaps increasing the ease and accuracy of data-based educational assessment and programming. In the future, perhaps a system of programs that generate compatible files will allow “generaliza­tion across machines” and across programs.” Such generality, unfortunately, is often lacking today.

One of the early technological advances in the science of human behavior was the develop­ment and application of direct observational measurement procedures by Florence Goode­nough and other child development researchers some 60 years ago. With the exception of cer­tain unfortunate and unscientific regressions, these methods have become a staple in the analysis of children’s behavioral development. However, as we have noted, there are both methodological and practical limitations to these sampling procedures as originally conceived and, frequently, still practiced. Direct observational assessment continues to be both underused and incorrectly used by professionals (e.g., psychol­ogists, consultants, teachers) in field settings. We would argue that BOSCO and other similar hardware-software packages are a step, if not several steps, in the direction of remedying many problems in the assessment and analysis of behavior-environment interactions. Through the continued refinement, adaptation, and field-testing of these packages we can reach a point at which sophisticated behavioral observation can be as user-friendly and informative to teachers, parents, and program staff as it is now becoming for researchers.

 References

Bakeman, R. & Gottman, J.M. (1986). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.

Fox, J.J., Shores, R,E., Lindeman, D., & Strain, P. (1986). The effects of response dependent fading procedures in developing and maintain­ing social initiations of withdrawn preschool children. Journal of Abnormal Child Psychology, 14, 387-396.

Hasselbring, T.S., & Hamlett, C. (1984). Planning and managing instruction: Computer-based decision making. Teaching Exceptional Children, 16, 248-252.

Hendrickson, J.M., Strain, P., Tremblay, A., & Shores, R.E. (1981). Functional effects of peer social ini­tiations on the interactions of behaviorally hand­icapped children. Behavior Modification, 6, 323~353.

Johnston, J.M., & Pennypacker, H.S. (1960). Strategies and tactics of human behavioral research. Hillsdale, NJ: Lawrence Erlbaum.

Kazdin, A.E. (1982). Single-case research designs: Methods for clinical and applied settings (pp. 53-56). New York: Oxford University Press.

MacLean, W., Tapp, J., & Johnson, W. (1985). Alter­nate methods and software for calculating interobserver agreement for continuous observa­tion data. Journal of Psychopathology and Behavioral Assessment, 7, 65-73.

Martin, P., & Bateson, P. (1986). Measuring behavior: An introductory guide. Cambridge: Cambridge University Press.

Ragland, E,U., & Fox, J.J. (1988, November). Analogue probes as valid predictors of aberrant behavior in the natural environment. A paper presented at the 12th Annual TECBD National Con­ference on Severe Behavior Disorders of Children and Youth, Tempe, AZ.

Sackett, G.P. (1978). Measurement in observational research. In G.P. Sackett (Ed), Observing behavior. Volume 2: Data collection and analysis methods. Baltimore: University Park Press.

Savelle, S., & Fox, J.J. (1988). Differential effects of training In two classes of social initiations on the positive responses and extended interactions of preschool-age autistic children and their nonhandicapped peers. Monograph in Behavioral Disorders: Severe Behavior Disorders of Children and Youth, 11, 75-86.

Saville, S., Fox, J., & Phillips, M. (1987, March). The role of parents and siblings in handicapped children’s social development: Initial findings and future perspective. Paper presented at the Gatlinburg Conference on Research and Theory in Mental Retardation and Developmental Disabilities, Gatlinburg, TN.

Semmel, M.S., & Frick, T. (1980). CARTLO: Computer-Assisted Research into Teaching-Learning Outcomes. Bloomington: Indiana University, School of Education, Center for In­novation in Teaching the Handicapped.

Tremblay, A., Strain, R., Hendrickson, J.M., & Shores, R.E. (1981). Social interactions of normally developing preschool children: Using normative data for subject and target behavior selection. Behavior Modification, 5, 237.253.

 Preparation of this manuscript was supported in part by Grant #7-pO1-155051 from the National Institute of Child Health and Human Development and by Grant #GCO&302929 from the U.S. Department of Education, Office of Special Educa­tion and Rehabilitative Services. The authors wish to thank Wilma Davis for her assistance in typing this manuscript.