Behavior and Sequential Analyses: Principles and Practice


Tom Sharpe & John Koperwas

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  • Chapters
  • Front Matter
  • Back Matter
  • Subject Index
  • Part I: Behavior Analysis: A History and Introduction

    Part II: Constructing Observational Systems

    Part III: Recording Tactics, Design Types, And Data Analyses

    Part IV: Application Illustrations and a Window to the Future

  • Copyright

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    First and foremost I would like to thank my wonderful family, who have always supported my professional efforts in academe and who have always been the main source of joy in my life. I dedicate this text to them. I would also like to dedicate this text to my professional family of graduate students and faculty colleagues who have contributed over the years to my tinkering with and packaging of the methods contained in this text, particularly those who have either been so gracious as to have allowed examples and illustrations of their efforts to be contained in this book, or who have endured my interminable cajoling interactions over a variety of thesis, dissertation, and research projects.


    I would like to dedicate my contribution to this text to my wife, Natalie, whose patience and continuous support offered me the opportunity to spend countless hours working on software development and design and some of the related information contained in this book.


    Foreword by Tom Sharpe

    Few truly original ideas come to pass in contemporary academe. Most of what passes for invention is merely existing ideas repackaged in appealing ways. But it is the repackaging which often moves science forward in important and insightful ways.

    — Delprato (1999, personal communication)

    Applied behavior analysis methods have provided a foundation for my professional efforts from the time I entered the education profession as a teacher and a coach. Before being introduced to the academic discipline in graduate school, I intuitively held the view that the most important variables to the improvement (or, in contrast, deterioration) of most educational situations were the daily practices that teachers and students, or coaches and athletes, were engaged in and how their verbal and nonverbal behaviors affected one another's actions. By chance, and based on my decision to return to graduate school at West Virginia University to supposedly enhance my professional marketability, I fell into what was considered one of the more prominent graduate programs in education and behavior analysis. I was directed to take most of my coursework in the psychology and educational psychology departments that included at that time a veritable hotbed of behavior analytic scholars. In agreement with the methodological proclivities of my physical education faculty mentor (Hawkins, 1992), I became completely at home with defining educational and social science phenomena behaviorally, with the construction of observational category systems for conducting data collection and analysis efforts, and with the methods of data presentation promoted by traditional behavioral methods. What began to plague me, however, were the challenges of describing and analyzing in comprehensive and inclusive ways what I saw as a highly complex and multi-event configuration of behavior interactions in educational settings. As I observed and analyzed many highly interactive settings, I found myself missing out on the documentation of potentially important behavioral events when using the traditionally accepted paper-and-pencil plus stopwatch recording methods coupled with partial interval or momentary time sampling recording techniques. In addition, I found the traditional method of observing only a few behaviors in isolation and using only a single measure of those behaviors extremely limiting to an accurate characterization of the educational settings I was interested in studying.

    If I analyze my past experiences with the proper level of scrutiny and reflection, I can look back on a series of seemingly chaotic and randomly ordered professional activities and find a few events that can be seen as definitive in my movement toward what has become my present situation—that is, enthusiastically attempting to organize and package the data collection and analysis methods contained in this book. During the struggle and questioning stage of my dissertation activities, I was cajoled by a variety of faculty to go to the Association for Behavior Analysis (ABA) National Convention for the usual reasons of gaining some professional insight, interacting with other like-minded faculty and graduate students, and seeing what there was to see on the research and development horizon in my areas of supposed interest. At the conference, I happened into a meeting for the Interbehaviorists in ABA special interest group. Completely naïve as to the conceptual, epistemological, and methodological interests and related political stature of the group, I sat in on a discussion of the work of J. R. Kantor by Ed Morris, Paul Mountjoy, Roger Ray, Dennis Delprato, and a variety of other Kantorian scholars. I have gone over this initial experience in my mind as I have written this text, for while I did not realize it at the time, that discussion provided a rich encapsulation of just the challenges I was beginning to articulate and attempt to navigate as a neophyte methodologist, and what I heard that day sent me on the professional road I have been traveling ever since.

    These gentlemen were conceptualizing an evolutionary pathway for behavior analysis to meet the description and analysis challenges of multiple organisms engaging in multiple behaviors in the context of multiple ecological event changes. Although most of the terminology and much of the methodological points made at that time simply washed over me, the idea of adding to the primarily linear Stimulus®Response®Consequence (S®R®C) model of B. F. Skinner to look at applied situations as a more complex system of events that oftentimes occur concurrently and affect one another in various degree turned a lightbulb on inside me. While gentlemen such as Ed Morris and Paul Mountjoy waxed long about the philosophical issues inherent to a more systems- and temporally oriented approach to studying behavior in complex and highly interactive settings, what intrigued me most were the contributions to the discussion by Roger Ray. Ray provided a technologically supported methodological position for inclusively describing and documenting behavior occurrences in all their complexity, including both more traditional measures of the characteristics of individual behavioral events (e.g., number, duration, rate, percentage, etc.) and time-based measures designed to explicitly characterize the interactions among those events (e.g., use of multilevel structural and functional categories, kinematic analyses, conditional probability relationships among multi-event occurrences, etc.).

    To this day, I come back to the enthusiasm Roger displayed for his work and the explanation and direction Roger provided me with at the end of that meeting. He explained just what he was working toward methodologically. He provided me with a host of materials to read designed to enhance my understanding. Through intermittent interactions, he has helped me to understand complex interactive behavior “in its complexity,” as my original mentor articulated (Hawkins, 1992, p. 1) and to express that understanding in my work. And from then to now I have been involved in just that, articulating ways in which behavioral data may be more inclusively and completely documented, described, and analyzed in applied settings in which multiple individuals exhibit multiple behaviors concurrently and in which the interactions among these behaviors have multiple potential functional relationships. What has equally stimulated my activities in this area over the years, and added in substantial ways to the stimulation of the culminating efforts that this book represents, has been my ongoing relationship with John Koperwas. John may be considered one of those few genuinely inquisitive and inventive minds in the contemporary field of software development who has provided a wealth of insight into what is methodologically possible through ever-advancing software programming capabilities. It is in this latter regard that his contributions to this text, and to the software applications referenced in the back of this text that are designed to support thoroughgoing implementation of the methodologies we recommend, have been invaluable.

    We have tried to remain true to the important foundations that those coming before us have provided in terms of a rigorous and thoroughgoing scientific method for the study of behavior in applied settings. Some of the most important influences on these text materials are those of Alan Kazdin (Kazdin, 1982), for his clear and concise representation of introductory behavior analytic and single case principles, and John Gottman (Bakeman & Gottman, 1986), who stipulated in a text referred to me by Roger Ray, and whose stipulation I find even more true today in the mainstream of education and social sciences, that

    A small percentage of current research employs observational measurement of any sort. This is true despite the recent increased availability of new technologies such as electronic notepads and videotape recording. It may always be the case because it is more costly to observe than to use other methods such as questionnaires. (p. xiv).

    What continues to surprise me is the limited impact of and support for behavior analysis activity in the education, social, and psychological sciences. In addition, many important methodology texts are not updated nor do they go into a new edition; nor do experienced behavior analysts tend to provide either new text materials or packaged data collection and analysis tools for the research and evaluation community that continues to be involved in this type of work.

    In relation to the opening quotation by Dennis Delprato, this book is our effort to compile and package in large part the work of others who have either come before us professionally, or who have made important individual methodological contributions to the applied analysis of behavior as an important research methodology. Although not all information related to behavior analysis is included here, and the finer points of some of the information contained in this text may be debated, this text does include all the information that we feel is important to students and faculty with an interest in behavior analysis methods. This text is also an outgrowth of discussions with many colleagues who have wished for such a text as they put together a series of isolated papers and reprints year after year when teaching courses in applied behavior analysis methods. Finally, this is a text designed to be compatible with the software tools described in the back of this book, which have been designed to collect, analyze, and visually represent data and to perform the many reliability, procedural fidelity, and other methodological functions that this book recommends.


    To accomplish the purposes of this text, four general sections are provided. The first, Behavior Analysis: A History and Introduction, provides an important summary of the historical evolution of applied behavior analysis and related single-subject research methods in the context of how the method may be compatible with others and helpful to the knowledge generation process in the education, social, and psychological sciences. The methods under the applied behavior analysis umbrella are summarized and a compatibility approach is postulated with respect to the many different methodological perspectives that currently exist in the mainstream scientific literature.

    Part II of this text, Constructing Observational Systems, provides a detailed procedural primer for constructing a coding or category system for particular research or assessment purposes, including the many assumptions and limitations that should be taken into consideration when conducting behavior analysis research. Reliability and treatment fidelity issues and procedures are discussed in detail, with close attention paid to the steps of criterion standard development, staff training, interobserver reliability, and treatment implementation accuracy. A series of category system illustrations are also provided and taken from a variety of education, social science, and psychology disciplines to provide the reader with hands-on familiarity with how behavior analysis efforts in these respective areas have been implemented with success.

    Part III of this text, Recording Tactics, Design Types, and Data Analyses, presents a variety of generally accepted techniques in the areas of collecting, analyzing, and visually representing data. Application procedure detail and potential advantages are provided regarding recording in real time, overcoming validity challenges through more sophisticated research design types, and issues related to graph preparation and the use of statistical analysis support for behavior data.

    The last part of this text, Application Illustrations and a Window to the Future, provides recommendations as to how applied behavior analysis methods may be used to enhance a variety of research and development, professional or clinical assessment, and instructional applications across a variety of education, social science, and psychological activities. Included are detailed illustrations of field-based professional evaluation activities, research and development opportunities designed to uncover information not previously available to other research methodologies, and laboratory simulation activities heretofore unavailable through other methods and without the aid of computer technology-supported behavior analysis.

    Exercises and Presentation Structure

    In order to achieve the objective of helping students and faculty understand and apply the principles set forth in this text, we have ordered the materials in the 10 chapters according to the logical steps one would undertake in actually conducting a research project. Each chapter includes a set of terms and definitions and a study guide to help the readers summarize and apply their understanding of the main points. These materials lead readers through the step-by-step procedures for designing and implementing behavioral research projects. For those readers interested in greater detail on a topic contained in a particular chapter, references are cited in the chapters and a list of references is provided at the end of the text. We also encourage readers who are already quite familiar with behavior analytic research practice and sophisticated behavior analysts to consult the reference materials provided for a more detailed treatment of the material herein. In addition, as this book was designed to be used as an introductory to intermediate text on behavior analysis research principles, readers will find information in select publications that provides important theoretical and applied complements to the materials here.

    Core Feature

    A core feature of this text is the importance of a return to the quantitative counting of behavior and event occurrences in a single-subject orientation as an accepted methodological practice. The creation of this text stems from our personal methodological interest and the current lack of a readily accessible text to articulate many points of information at an introductory to intermediate level. In addition, this book is intended as a response to mainstream researchers in the education, social, and psychological sciences who are outspoken in their recommendations to simply do away with behavior analysis as a legitimate method of inquiry due to its emphasis on mechanistic causal assumptions or its inability to more completely and accurately describe and analyze multiple occurrences of interactive behavior in inclusive and meaningful ways. In our view, these researchers have incorrectly perceived behavior analysis, its nature, and what it can and cannot do, to the point of requiring response. This text is also a response to cognitive methodologists who espouse internal explanatory mechanisms to account for behavioral complexity and contextual dependency and who eschew other methodological procedures outside of mainstream cognitive research as somehow inferior. Finally, it is a response to those scientists who have abandoned traditional views of scientific practice for a more existential approach in which investigation revolves around responses to questionnaires that investigate the subjective meanings ascribed to certain events as described by the participants operating within those events.

    Through the many professional influences of my early faculty mentors when I was in graduate school, and of the experienced and savvy faculty colleagues I have met in behavior research circles along my professional travels, as well as my own work, I have, with my coauthor, created this text. Through this text, I hope we have packaged in appealing ways a means for doing what Hawkins (1992) has described as not “abandoning behavior analysis but [succeeding in] taking it to another level” (p. 1). Therefore, this text provides what we hope is a compatible summary of traditional and contemporary applied behavior analysis methods that does justice to the principles and practice of applied behavior analysis and serves as an effective introduction to the many systems and sequential methodologies that have begun to frequent the contemporary behavior analysis literature.

  • Appendix A: Sequential Analysis Formulae

    As this text has illustrated, the types of data recording and data analysis methods advocated by direct quantitative observation of multiple behaviors and events, and their multiple characteristics as they interact with one another in time, are activities that are not only facilitated but in many cases made feasible by the use of computer-based recording and analysis tools. This is particularly the case with the sequential analysis of behavior-event data. This appendix provides a summary of terms often used when describing a mathematical equation construction and related sequential analysis process, as well as an introduction to the type of equations recommended for use in such an analysis. These equations are based on the important sequential analysis methodological work of Bakeman and Gottman (1986, 1997) and thus we only summarize them here. For a detailed discussion of the theoretical constructs on which mathematical modeling of sequential data are founded, and for a representative set of illustrations of the variety of equations that should be implemented in particular analysis activities, consult Bakeman and Gottman's (1986, 1997) foundational Observing Interaction: An Introduction to Sequential Analysis texts or Gottman and Roy's (1990) more advanced textbook materials on the subject. For a user-friendly and sophisticated computer-based sequential analysis tool, we recommend that the reader request a demonstration copy of the BEST software tools advertised in the flyer that accompanies this text and also available through Scolari/Sage Publications at or (805) 499–1325 for dedicated customer service.

    The reader may find it useful if we first define some terms that are specific to how we characterize a data record in relation to how we characterize events when analyzing how they tend to follow one another in time. Quantitative behavior-event data generated and analyzed by using the methods recommended throughout this text are defined as a set of observable events that have quantifiable start and stop times of occurrence. In other words, each behavior or event contained within a particular data record is typically represented by an alphanumeric type number1 and time stamp2 indicator for start and stop time parameters for particular behavior or event occurrences. Therefore, while separate behaviors or events may have the same type numbers, their time stamps are always different.3 A data record that is collected with a view toward analyzing behavior and event occurrences sequentially must necessarily include an ascending order of start time stamps for all recorded behaviors and events that when taken together form a chronological sequence.

    Any two behaviors or events that immediately follow one another in start time chronological sequence are considered to be linked. It is also important to note here that although two behaviors or events may be linked, the second behavior or event may begin prior to the first event's ending time stamp, allowing for an overlapping behavior-event occurrence record. The first of two linked behaviors or events is termed the predecessor and the second the successor. Linked behaviors and events are termed consecutive if no third data event's start time occurs chronologically between the start times of the linked events. Therefore, any behavior or event may be linked with many successors or predecessors but is consecutive with only one predecessor and one successor. Behaviors and events that are not immediately linked are termed proximate to any other event that follows each behavior or event and has a time stamp that isn't beyond a predefined lag time. Again, any behavior or event is immediately linked to at most one other event but may be proximately linked to many within a specified lag time preceding or following the start time of that particular behavior or event.

    The consecutive type numbers of a sequence of linked behaviors and events form a chain or sequence. As only type numbers and not time stamps are considered in the description of a particular chain or sequence, a chain may occur any number of times within a data set. The number of occurrences of a chain is termed the frequency of that chain. A set or list of different chains with some common behaviors and events forms a chain pattern. Probabilities of a particular chain occurring are defined as relative, or conditional, to a chain pattern within which it may be contained. In other words, a probability number is based on the ratio of the frequency of a particular chain of interest to the combined frequency of all of the chains within a given pattern.4

    When analyzing chain patterns, frequencies of chains of exactly two behaviors or events may also be searched to represent a matrix of succeeding and preceding events for all of the behaviors and events contained within a particular data set. If this procedure is implemented, the row index of the alphanumeric indicator within a matrix is the type number of the predecessor in the corresponding chain and the column index is the type number of the successor. The respective probability (and statistical) indices are then relative, or conditional, to the chain pattern consisting of all two-event chains.

    The sequential analysis applications described in this text, and contained in the software program described at the end of this text, are based on the specific mathematical equations in Bakeman and Gottman (1986) and on the theoretical summary information contained in portions of Gottman and Roy (1990). As this type of analysis is fairly complex in theoretical and methodological structure, we provide only a simplistic overview in this appendix of what we feel to be the most important points in relation to mathematical modeling of the sequential character of a behavior-event data set. We hope that the summary contained here will prove helpful to those incorporating this type of analysis into their direct observational activities, and that the source materials that we have referenced will provide an additional link to more complete mathematical discussion.

    To begin, complex mathematical modeling of just how each behavior in an observation system interacts with others in sequence comprises a complete sequential analysis. In a sense, a sequential analysis focuses first on the characteristics of particular behaviors within a data set but, most important, on the characteristics of the interactions or transactions among those behaviors as they present themselves over time. In the complete mathematical equations used to represent these behavior-event interactions, such representations are not limited to the effects of other immediately preceding and succeeding behaviors and events but, instead, may be analyzed as more complex patterns of interactive activity—patterns that a computer-based sequential analysis is well capable of uncovering and that many research literatures have begun hypothesizing as potential characteristics of optimally effective human interaction.

    As a mathematical model, sequential analysis focuses on the problem of identifying and quantifying immediate and more distant interactional relationships of particular behaviors and events in sequence. It provides a means for determining in a situation-specific manner the probable effects one behavior may have on another based on their repeatedly close appearances together in time. When implementing this type of analytic model, a first step is to compute the unconditional probability of occurrence of each of the behaviors and events in a particular data set by dividing the frequency of occurrence of a particular behavior or event by the total number of occurrences of all other behaviors and events in that data set. Next, the conditional probability of each possible behavior and event (including itself) is calculated as a function of the successive lags (or steps) of each event from each possible event with which it could have possibly occurred before. This is akin to counting the number of times each behavior or event follows each of the other events occurring within a data file. Included in this count are the number of times a behavior or event immediately follows another (termed lag-1), the number of times an event occurs one event away (termed lag-2), and so forth up to the largest sequential step of interest. The lag probabilities are computed by dividing the frequency of occurrence of each event at lag-n by the number of times the interactive event under analysis occurred.

    Sequential chains of interest in a sequentially ordered behavior-event data record are defined in terms of suffixes and prefixes (i.e., succeeding and preceding events). The suffix of a chain is defined as the last behavioral event appearing in it, and the prefix of the chain is the subchain obtained by omitting the suffix. Referring to our education example in Chapter 3, the behaviors of “instruction-engagement” may be a prefix and the behaviors of “feedback” a suffix in the behavior sequence instruction-engagement-feedback. A statistical Z-score transformation is then computed to determine the meaningfulness (or significance; meaningfulness is a term coined by Bakeman & Gottman, 1986) of a particular chain within a larger sequential data record.

    The meaningfulness of a particular behavior-event chain in a particular sequential data record is calculated by analyzing the conditional probabilities of all prefixes and suffixes permitted by the universe of chains in the data set. In other words, a particular behavior chain of interest is determined meaningful as a function of the larger sequential structure of a particular data file and as a function of of the number of total event occurrences within that data record.

    For those interested in an overview of mathematical modeling with respect to a sequential analysis of behavior-event chain prefixes and suffixes, the following summary may be helpful. Again, the suffix of a chain is defined as the last event number appearing in it, and the prefix of an event chain is the subchain obtained by omitting this suffix. For instance, events designated by 9–6 characterize a prefix and event 5 the suffix of the chain 9–6–5. The Z-score and related meaningfulness of this chain are calculated with respect to all prefixes and suffixes permitted by the universe of chains in the larger data file from which 9–6–5 originates. Consider, for example, a matrix “M” having rows indexed by the distinct prefixes permitted by the behaviors and events in a data record, and columns by the distinct suffixes contained in that same data record. Let M(i, j) be the frequency of the chain having prefix i and suffix j.


    The probability of an event chain having prefix i and suffix j is then calculated using


    The Z-score of the chain having prefix i and suffix j is calculated using

    and its corresponding meaningfulness is computed as

    If a behavior or event chain consists of a single event, it has no prefix and the formulas stated become undefined. When this is the case, f is the frequency of the given event, x is the number of the one-event chains that occur in the data file, n is the combined frequency of these chains, and

    The probability of the given chain then is then:

    and its corresponding Z-score is:

    Its meaningfulness is then:

    Analyzing behavior and event occurrences in interactive settings as a function of their relationships among one another in time provides for a wealth of additional information with respect to the study of interaction in the education, social, and psychological sciences. For example, preceding or succeeding matrices of dual chains may be built giving frequency, conditional probability, and statistical significance data cell by cell. Rates of responding across multiple stimulus events may thus be discerned. Using computer-based data analysis tools, the level of complexity of a sequential analysis is only limited by investigative interest and the original alphanumeric coding scheme used to collect the data file. When data collection is synchronized to a videotape record, multiple data collection files may be merged and arranged temporally in constructing a very complex overlapping event record, furthering the fine interactive discrimination capabilities such as that offered by the BEST software advertised in the flyer accompanying this text. Given that a sequential analysis application is based on the start times of behaviors and events that are recorded by a particular data collection program platform, and given the time sensitivity of the data collection mechanism that is made available by computer-based recording methods, sequential analyses of rapid and multiple occurrences of multiple overlapping events can be readily undertaken within a host of complex interactive situations.


    1. When using amenable computer tools such as BEST software, the type number is typically an integer between 1 and 36. Each type number indicates a particular behavior or event. On a computer keyboard that is used for data collection, the keys 1 to 9 represent themselves, 0 represents the number 10, A represents the number 11, B represents 12, and so forth, with Z representing 36. As most source code programming only recognizes numerical notations, while you may record letters with the computer keyboard, the actual data set and related sequential analysis representations use numbers to represent the letters used for recording.

    2. Each time stamp is typically represented by a positive integer measuring the time of the event from a particular onset or start time to a particular termination or end time in a specified time unit (e.g., seconds, etc.). Time measurement in a particular data record begins at time zero with the start-up of the data recording apparatus and continues in ticks (i.e., time units) in which in most computer-based programs approximately 51.2 ticks equals 1 second, and a time conversion application must also be included in the software program application.

    3. Although in principle the recording of two or more behaviors or events may be simultaneous with regard to start times, in practice each start time stamp is necessarily recorded as distinct, due to a computer-based data recorder's inability to register more than one event during the exact same time tick.

    4. This is typically termed a conditional probability (see Bakeman & Gottman, 1986, 1997, and Gottman & Roy, 1990, for a detailed discussion of this issue). To ascertain the unconditional probability of a given chain, a wider chain search that encompasses all possible patterns of a particular behavior-event length is typically implemented.

    Appendix B: Behavior Evaluation Strategy and Taxonomy (BEST) Software: Data Collection and Analysis Application

    Over the past decade, advances in computer technology development have facilitated the design and implementation of a variety of software-based applications for behavioral research. The Implications of Computer Technology section in Chapter 3 makes clear that computer hardware and software advances have provided a variety of appealing tools for the collection and analysis of real-time observational data. According to Kahng and Iwata (1998), using computer-based software tools is appealing due to their ability to significantly enhance the reliability and accuracy of recording in relation to the more traditional paper-and-pencil and stopwatch recording methods. Additional activities, such as data graphing, training staff according to a data collection criterion, and more sophisticated statistical and mathematical modeling of behavioral data, are also made feasible through computer-based tool use.

    As we have established throughout many of the chapters in this book, as computer-based tools continue to develop the capacity for more inclusive and varied alternatives, direct observation activities continues to improve and/or become available. It remains, however, as Kahng and Iwata stipulated 5 years ago, that computer-based tools for direct observation are not widely known to the professional communities that would benefit most from using them, and that they are difficult to access due to limited marketing and information sharing among professionals. This appendix, therefore, summarizes one commercially marketed computer-based data collection and analysis tool, Behavior Evaluation Strategy and Taxonomy (BEST) software, which was developed by the authors of this textbook. Although there are other, similar tools on the market and in the literature, the BEST tool summarized here (and advertised in the flyer included with this text) provides a representative example of one of the few tools that is marketed commercially and made available in a packaged and user-friendly format to professional and scientific communities. In addition, the BEST tool described in this appendix is representative of the majority of systems in that it is based on compatibility with IBM Windows operating platforms, and it includes a variety of features (e.g., interrater reliability, data file merging, data graphing, and sequential analysis applications) that may not be provided by other computer-based tools.

    Best Software Capability Summary

    The BEST software platforms are divided into two separate and distinct data collection and data analysis applications. Both are completely compatible with Windows 95, 98, 200, NT, and XP, and they operate identically in terms of menu structure, with similar user-friendly features. Data collection applications facilitate the construction of observation systems by defining alphanumeric keys on a computer keyboard. Up to 36 different behaviors and events may be recorded during a session, and each key may also be notated numerically and narratively for additional behavior and event subcategorization. A variety of recording methods are made available within the application and multiple occurrences of simultaneous or overlapping events may be recorded using this application. By generating a time-based data record with quantitatively measured start and stop times of each recorded event, response frequency, duration, intervals (variable duration), average duration and standard deviations, rate, latency, inter-response time, percentage of observational time; and time-based measures (such as first, last, span, longest, shortest, etc.) may be extracted using the data analysis program. Due to the time-based nature of the data file generated, a sophisticated sequential analysis application is also made readily available. A numerical and text notation feature also allows the recording of notes for unique or atypical event occurrences in the time-based sequence in which they occurred. In addition, pause and other data management features are available that permit the interruption and restarting of observational sessions as the need arises, and entry errors made while recording may be immediately edited.

    The data analysis program provides a variety of user-friendly options including the calculating of response frequency (total number and rate), duration, latency, interresponse time, percentage of observation time and related subintervals, percentage of trials, and conditional probabilities of sequentially based behavior and event relationships. The analysis application also allows the compartmentalizing of subgroups of behaviors and events to analyze as a logically grouped data file. Options also include the calculation of mean and median data, variability in relation to range and frequency distributions, and statistical significance data in relation to sequential analyses. Reliability programs with simple frequency, point-by-point, and Cohen's kappa options are also included to facilitate staff training and interrater reliability check procedures. Graphic analyses include tables, pie charts, temporal records, sequential analysis tables, and traditional time-series graphs. Statistical applications such as mean, standard deviation, and line of best fit are included to complement the standard graphic applications. All of BEST software's graphing applications are exportable to most commercial graphics programs, such as Windows Paint, Powerpoint, and Delta Graph.

    The BEST programs require an IBM-compatible desktop or laptop computer with a minimum 386 processor running a Windows operating platform. The applications have minimal RAM and hard disk requirements. Data collection applications for handheld PCs are available, as are digital video synchronization applications for the data collection platform and remote data collection apparatus for those who desire direct hookup to laboratory applications (e.g., bar press, lights, temperature switches, pellet containers, etc.) that obviate the constant presence of a human data collector. Fully functional demonstration copies are available on request (see the Scolari/Sage Publications flyer that accompanies this text or contact the developers at Included in the CD-ROM contained in the software package are example observation systems, example data files, a complete and illustrated users guide in PDF format, and a PDF format summary version of the materials contained this textbook. A complete software tutorial in movie and sound format is also included to provide an initial overview of the software programs’ many capabilities and applications. The latter materials require an Adobe Acrobat reader and a QuickTime movie player (but for those without these applications an Internet connect is provided on the CD-ROM to locate and download the appropriate free software for viewing these materials).

    Appealing advantages of BEST software include

    • behavioral, quantitative, and qualitative data collection with a push of a button
    • a wide range of sophisticated analyses, including descriptive and predictive statistics, qualitative memo-noting, and a variety of graphic and sequential analysis representations
    • complete compatibility with a wide variety of statistical and graphics packages
    • user customizability to specific data collection and analysis needs
    • immediate data-based feedback capability in field settings or as an ongoing evaluation tool
    • built-in reliability application for staff training and interobserver comparisons
    • data file merging and sorting functions for compiling purposes

    Its general features include

    • allowing the you, the user, to create your own category system to meet your specific observational needs
    • storing multiple observation systems storage for particular applications
    • recording the start and stop times of multiple events as they naturally occur, providing a variety of descriptive statistics
    • recording narrative fieldnotes in concert with behavioral and quantitative data
    • recording information live or synchronized with videotape at almost any location
    • qualitatively, quantitatively, and sequentially representing and analyzing observational data
    • providing staff training and ensure reliability of data collection
    • interfacing with other software programs for multitasking and remote site use
    • being compatible with a range of hardware, including Windows CE hand-held computers for data collection in the field

    The specific data collection capabilities of BEST software incude

    • recording and categorizing data using complex multiple event observation systems
    • using numerical and narrative notations to further delineate event types
    • recording multiple events simultaneously as they actually occur in time
    • taking advantage of a user-friendly screen representation when collecting data
    • facilitating the data collection process with multiple means of recording, including press and hold keys, toggle keys for turning on and off, and remote key access
    • pause feature for entering and exiting the same data collection episode at time of exit
    • editing data collection efforts on the fly and viewing data records as they are collected
    • automatic recording of response frequency, rates, percentage of total experimental time, shortest and longest event occurrences, event occurrence spans, duration, intervals, time samples, latency, inter-response time, and discrete trials
    • multitasking when collecting data by assigning keys to perform additional functions, such as starting another software application
    • taking advantage of MicroSoft CE hand-held compatibility when collecting data in particular field settings

    Its specific data analysis applications include

    • identifying frequency, total and mean duration, standard deviations, rate, and experimental time percentages of each category system event in tabular and graphic formats
    • identifying time-based information for each category system event, including first and last event occurrence, time-spans between events, longest and shortest event occurrences, and related means and standard deviations
    • searching for keywords and memos in narrative notations and represent narrative data within and across data files
    • processing and comparing multiple data files across one another, and across multiple recorded events and measurement types
    • conducting a variety of reliability analysis functions among data files including simple frequency, point-by-point, and Cohen's kappa
    • conducting sequential analyses of the time-based connections among events documented in terms of frequency, conditional probability, and statistical significance (Z-score transformations)
    • merging and time-sorting multiple data files enabling comprehensive observational description from videotape
    • performing event subgrouping routines to allow a separate analysis of subgroups of events within all program applications
    • representing data with a host of sophisticated graphing applications for individual data files and for multiple data files across event and measurement type
    • graphically analyzing mean, standard deviation, and regression across multiple data files and multiple events and measures
    • printing, saving, and clipboard/pasting data representations into other statistical analysis and graphics editing software packages


    Aeschleman, S. R. (1991). Single subject designs: Some misconceptions. Rehabilitation Psychology, 36, 43–49.
    Allport, G. W. (1961). Pattern and growth in personality. New York: Holt, Rinehart & Winston.
    Altmann, S. A. (1965). Sociobiology of rhesus monkeys: II. Stochastics of social communication. Journal of Theoretical Biology, 8, 490–522.
    Astley, C. A., Smith, O. A., Ray, R. D., Golanov, E. V., Chesney, M. A., Chalyan, V. G., Taylor, D. J., & Bowden, D. M. (1991). Integrating behavior and cardiovascular responses: The code. American Journal of Physiology, 261, 172–181.
    Bailey, J. S., & Burch, M. R. (2002). Research methods in applied behavior analysis. Thousand Oaks, CA: Sage.
    Bakeman, R. (1978). Untangling streams of behavior: Sequential analyses of observation data. In G. P.Sackett (Ed.), Observing behavior: Data collection and analysis methods (Vol. 2, pp. 63–78). Baltimore, MD: University Park Press.
    Bakeman, R., & Gottman, J. M. (1986). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.
    Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis (
    2nd ed.
    ). New York: Cambridge University Press.
    Barlow, D. H. (1980). Behavior therapy: The next decade. Behavior Therapy, 11, 315–328.
    Barlow, D. H., & Hersen, M. (1984). Single case experimental designs: Strategies for studying behavior change (
    2nd ed.
    ). Elmsford, NY: Pergamon.
    Bakker, G., & Clark, L. (1988). Explanation: An introduction to the philosophy of science. Mountain View, CA: Mayfield.
    Barone, T. (2001). Science, art, and the predispositions of educational researchers. Educational Researcher, 30(7), 24–28.
    Bergin, A. E., & Strupp, H. H. (1972). Changing frontiers in the science of psychotherapy. Chicago, IL: Aldine-Atherton.
    Berliner, D. C. (1986). In pursuit of the expert pedagogue. Educational Researcher, 15(7), 5–13.
    Berliner, D. C. (1992). Some perspectives on field systems research for the study of teaching expertise [Monograph]. Journal of Teaching in Physical Education, 12, 96–103.
    Bijou, S. W., Umbreit, J., Ghezzi, P. M., & Chao, C. (1986). Manual of instruction for identifying and analyzing referential linguistic interactions. Psychological Record, 36, 491–518.
    Binder, C. (1994). Measurably superior instructional methods: Do we need sales and marketing? In R.Gardner, D. M.Sainato, J. O.Cooper, T. E.Heron, W. L.Heward, J.Eshleman, & T. A.Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 21–31). Pacific Grove, CA: Brooks/Cole.
    Bliss, J., Monk, M., & Ogborn, J. (1983). Qualitative data analysis for educational research. London: Croom Helm.
    Bohme, G., Van Den Daele, W., & Krohn, W. (1978). The “scientification” of technology. In W.Krohn, E. T.Layton, & P.Weingart (Eds.), The dynamics of science and technology (pp 219–250). Dordrecht, The Netherlands: D. Reidel.
    Bronfenbrenner, U. (1979). Contexts of child rearing: Problems and prospects. American Psychologist, 34, 84–89.
    Brown, J. F., & Hendy, S. (2001). A step towards ending the isolation of behavior analysis: A common language with evolutionary science. The Behavior Analyst, 24, 163–171.
    Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science. New Haven, CT: Yale University Press.
    Buchler, J. (Ed.). (1955). Philosophical writings of Peirce. New York: Dover.
    Bushell, D., Jr., & Baer, D. M. (1994). Measurably superior instruction means close, continual contact with the relevant outcome data. Revolutionary! In R.Gardner, D. M.Sainato, J. O.Cooper, T. E.Heron, W. L.Heward, J.Eshleman, & T. A.Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 3–10). Pacific Grove, CA: Brooks/Cole.
    Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.
    Carnine, D. W., & Fink, W. T. (1978). Increasing the rate of presentation and use of signals in elementary classroom teachers. Journal of Applied Behavior Analysis, 11, 35–46.
    Carroll, L. (1946). Alice's adventures in wonderland. New York: Random House.
    Chance, P. (1998). First course in applied behavior analysis. Pacific Grove, CA: Brooks/Cole.
    Chatfield, C., & Lemon, R. E. (1970). Analyzing sequences of behavioural events. Journal of Theoretical Biology, 29, 427–445.
    Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
    Cohen, J. (1965). Some statistical issues in psychological research. In B. B.Wolman (Ed.), Handbook of clinical psychology. New York: McGraw-Hill.
    Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.
    Cooper, J. O., Heron, T. E., & Heward, W. L. (1987). Applied behavior analysis. Toronto: Merrill.
    Cooper, M. L., Thomson, C. L., & Baer, D. M. (1970). The experimental modification of teacher attending behavior. Journal of Applied Behavior Analysis, 3, 153–157.
    Cossairt, A., Hall, R. V., & Hopkins, B. L. (1973). The effects of experimenter's instructions, feedback, and praise on teacher praise and student attending behavior. Journal of Applied Behavior Analysis, 6, 89–100.
    Croll, P. (1986). Systematic classroom observation. Philadelphia: Falmer.
    Crosbie, J. (1993). Interrupted time-series analysis with brief single-subject data. Journal of Consulting and Clinical Psychology, 61, 966–974.
    Darst, P. W., Zakrajsek, D. B., & Mancini, V. H. (Eds.). (1989). Analyzing physical education and sport instruction. Champaign, IL: Human Kinetics.
    Davison, M., & McCarthy, D. (1988). The matching law: A research review. Hillsdale, NJ: Lawrence Erlbaum.
    Dawe, H. A. (1984). Teaching: Social science or performing art?Harvard Educational Review, 54, 111–114.
    Day, W. F. (1983). On the difference between radical and methodological behaviorism. Behaviorism, 11, 89–102.
    Delprato, D. J. (1992). Behavior field systems analysis: History and scientific relatives [Monograph]. Journal of Teaching in Physical Education, 12, 3–8.
    Delprato, D. J. (1999, May). Informal communication. Discussion conducted during a poster session display at the International Meeting of Applied Behavior Analysis, Chicago.
    DeProspero, A., & Cohen, S. (1979). Inconsistent visual analysis of intrasubject data. Journal of Applied Behavior Analysis, 12, 573–579.
    Doyle, W. (1990). Themes in teacher education research. In W. R.Houston, M.Haberman, & J.Sikula (Eds.), Handbook of research on teacher education (pp. 3–24). New York: Macmillan.
    Dunkin, M. J., & Biddle, B. J. (1974). The study of teaching. New York: Holt, Rinehart & Winston.
    Dwyer, D. (1996). We're in this together. Educational Leadership, 54(3), 24–26.
    Einstein, A., & Infeld, L. (1938). The evolution of physics. New York: Simon & Schuster.
    Eisner, E. W. (1983). The art and craft of teaching. Educational Leadership, 40, 4–13.
    Ekman, P. W., & Friesen, W. (1978). Manual for the facial action coding system. Palo Alto, CA: Consulting Psychologist.
    Eliot, T. S. (1971). The complete poems and plays. New York: Harcourt, Brace & World.
    Ellul, J. (1964). The technological society. New York: Knopf.
    Erickson, F. (1982). The analysis of audiovisual records as a primary data source. In A.Grimshaw (Ed.), Sound-image records in social interaction research [Special Issue]. Journal of Sociological Methods and Research, 11(12), 213–232.
    Espinosa, J. M. (1992). Probability and radical behaviorism. The Behavior Analyst, 15, 51–60.
    Faraone, S. V. (1983). The behavior as language analogy: A critical examination and application of conversational interaction. Behaviorism, 11, 27–43.
    Finn, C. E. (1988). What ails education research. Educational Researcher, 17(1), 5–8.
    Firestone, W. A. (1987). Meaning in method: The rhetoric of quantitative and qualitative research. Educational Researcher, 16(7), 16–21.
    Flanders, N. A. (1970). Analyzing teacher behavior. Reading, MA: Addison-Wesley.
    Friman, P. C., Wilson, K. G., & Hayes, S. C. (1998). Behavior analysis of private events is possible, progressive, and nondualistic: A response to Lamal. Journal of Applied Behavior Analysis, 31, 707–708.
    Fuchs, L. S., & Fuchs, D. (1986). Effects of systematic formative evaluation: A meta-analysis. Exceptional Children, 53, 199–208.
    Gardner, R., Sainato, D. M., Cooper, J. O., Heron, T. E., Heward, W. L., Eshleman, J., & Grossi, T. A. (Eds.). (1994). Behavior analysis in education: Focus on measurably superior instruction. Pacific Grove, CA: Brooks/Cole.
    Garrison, J. W. (1986). Some principles of a postpositivistic philosophy of science. Educational Researcher, 15(9), 12–18.
    Glaser, B., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.
    Good, T. L. (1979). Teacher effectiveness in the elementary school. Journal of Teacher Education, 30, 52–64.
    Gottman, J. M. (1979a). Detecting cyclicity in social interactions. Psychological Bulletin, 86, 338–348.
    Gottman, J. M. (1979b). Marital interaction: Experimental investigations. New York: Academic Press.
    Gottman, J. M., & Roy, A. K. (1990). Sequential analysis: A guide for behavioral researchers. New York: Cambridge University Press.
    Greenwood, C. R., Carta, J. J., Arreaga-Mayer, C., & Rager, A. (1991). The behavior analyst consulting model: Identifying and validating naturally effective instructional methods. Journal of Behavioral Education, 1, 165–191.
    Greenwood, C. R., Carta, J. J., & Atwater, J. (1991). Ecobehavioral analysis in the classroom: Review and implications. Journal of Behavioral Education, 1, 59–77.
    Greenwood, C. R., Delquadri, J. C., Stanley, S. O., Terry, B., & Hall, R. V. (1985). Assessment of eco-behavioral interaction in school settings. Behavioral Assessment, 7, 331–347.
    Greer, R. D. (1985). Handbook for professional change agents at the Margaret Chapman School. Hawthorne, NY: The Margaret Chapman School.
    Gresham, F. M., Gansle, K. A., & Noell, G. H. (1993). Treatment integrity in applied behavior analysis with children. Journal of Applied Behavior Analysis, 26, 257–264.
    Hake, D. F., & Olvera, D. (1978). Cooperation, competition, and related social phenomena. In A. C.Catania & T. A.Brigham (Eds.), Handbook of applied behavior analysis: Social and instructional processes (pp. 208–245). New York: Irvington.
    Hall, R. V., Panyon, M., Rabon, D., & Broden, M. (1968). Instructing beginning teachers in reinforcement procedures which improve classroom control. Journal of Applied Behavior Analysis, 1, 315–322.
    Hartmann, D. P., Gottman, J. M., Jones, R. R., Gardner, W., Kazdin, A. E., & Vaught, R. (1980). Interrupted time-series analysis and its application to behavioral data. Journal of Applied Behavior Analysis, 13, 543–559.
    Hawkins, A. (1992) Preface: A personal introduction [Monograph]. Journal of Teaching in Physical Education, 12, 1–2.
    Hawkins, A., & Sharpe, T. L. (Eds.). (1992). Field systems analysis: An alternative for the study of teaching expertise [Monograph]. Journal of Teaching in Physical Education, 12, 1–131.
    Hawkins, A., Sharpe, T. L., & Ray, R. (1994). Toward instructional process measurability: An interbehavioral field systems perspective. In R.Gardner, D. M.Sainato, J. O.Cooper, T. E.Heron, W. L.Heward, J.Eshleman, & T. A.Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 241–255). Pacific Grove, CA: Brooks/Cole.
    Hawkins, A., Wiegand, R. L., & Landin, D. K. (1985). Cataloguing the collective wisdom of teacher educators. Journal of Teaching in Physical Education, 4, 241–255.
    Hawkins, R. P. (1982). Developing a behavior code. In D. P.Hartmann (Ed.), Using observers to study behavior: New directions for methodology of social and behavior science (pp. 21–35). San Francisco: Jossey-Bass.
    Hawkins, R. P., & Dobes, R. W. (1977). Behavioral definitions in applied behavior analysis: Explicit or implicit. In B. C.Etzel, J. M.LeBlanc, & D. M.Baer (Eds.), New developments in behavioral research: Theory, methods, and applications. In honor of Sidney W. Bijou. Hillsdale, NJ: Lawrence Erlbaum.
    Hendel, C. W. (1963). Studies in the philosophy of David Hume. Indianapolis, IN: Bobbs-Merrill.
    Henry, J. (1886). The improvement of the mechanical arts. In Scientific writing of Joseph Henry (Vol. 1, pp. 306–324). Washington, DC: Smithsonian Institution.
    Henton, W. W., & Iverson, I. H. (1978). Classical conditioning and operant conditioning: A response pattern analysis. New York: Springer.
    Heward, W. L., & Cooper, J. O. (1992). Radical behaviorism: A productive and needed philosophy for education. Journal of Behavioral Education, 24, 345–365.
    The Holmes Group. (1990). Tomorrow's schools: Principles for the design of professional development schools. East Lansing, MI: Author.
    Howe, K. R. (1988). Against the quantitative-qualitative incompatibility thesis or dogmas die hard. Educational Researcher, 17(8), 10–16.
    Hrycaiko, D., & Martin, G. L. (1996). Applied research studies with single-subject designs: Why so few?Journal of Applied Sport Psychology, 8, 183–199.
    Huitema, B. E. (1986). Statistical analysis and single-subject designs: Some misunderstandings. In A.Poling & R.Fuqua (Eds.), Research methods in applied behavior analysis: Issues and advances (pp. 209–232). New York: Plenum.
    Hunt, P., Alwell, M., Farron-Davis, F., & Goetz, L. (1996). Creating socially supportive environments for fully included students who experience multiple disabilities. Journal of the Association for Persons With Severe Handicaps, 21, 53–71.
    Ingham, P., & Greer, R. D. (1992). Changes in student and teacher responses in observed and generalized settings as a function of supervisor observations. Journal of Applied Behavior Analysis, 25, 153–164.
    Issues in Interobserver Reliability. (1977). Journal of Applied Behavior Analysis, 10(2).
    Jacob, E. (1982). Combining ethnographic and quantitative approaches: Suggestions and examples from a study on Puerto Rico. In P.Gilmore & A.Glatthorn (Eds.), Children in and out of school: Ethnography and education (pp. 124–147). Washington, DC: Center for Applied Linguistics.
    Jacob, E. (1988). Clarifying qualitative research: A focus on traditions. Educational Researcher, 17(1), 16–24.
    Jacobson, N. S., & Anderson, E. A. (1982). Interpersonal skill and depression in college students: An analysis of the timing of self-disclosures. Behavior Therapy, 13, 271–282.
    Johnson, H., Blackhurst, A. E., Maley, K., Bomba, C., Cox-Cruey, T., & Dell, A. (1995). Development of a computer-based system for the unobtrusive collection of direct observational data. Journal of Special Education Technology, 12, 291–300.
    Johnson, L. M., & Morris, E. K. (1987). When speaking of probability in behavior analysis. Behaviorism, 15, 107–129.
    Johnson, S. M., & Bolstad, O. D. (1973). Methodological issues in naturalistic observation: Some problems and solutions for field research. In L. A.Hamerlynck, L. C.Handy, & E. J.Mash (Eds.), Behavior change: Methodology, concepts, and practice (pp. 7–67). Champaign, IL: Research Press.
    Johnston, J. M., & Pennypacker, H. S. (1980). Strategies and tactics of human behavioral research. Hillsdale, NJ: Lawrence Erlbaum.
    Jones, R. R., Vaught, R. S., & Weinrott, M. (1977). Time series analysis in operant research. Journal of Applied Behavior Analysis, 10, 151–166.
    Kahng, S. W., & Iwata, B. A. (1998). Computerized systems for collecting real-time observational data. Journal of Applied Behavior Analysis, 31, 253–261.
    Kamps, D. M., Leonard, B. R., Dugan, E. P., Boland, B., & Greenwood, C. R. (1991). The use of ecobehavioral assessment to identify naturally occurring effective procedures in classrooms serving students with autism and other developmental disabilities. Journal of Behavioral Education, 1, 367–397.
    Kantor, J. R. (1922). Can the psychophysical experiment reconcile introspectionists and objectivists. American Journal of Psychology, 32, 481–510.
    Kantor, J. R. (1953). The logic of modern science. Chicago: Principia.
    Kantor, J. R. (1959). Interbehavioral psychology. Granville, OH: Principia.
    Kantor, J. R. (1969). The scientific evolution of psychology (Vol. 2). Chicago: Principia.
    Kantor, J. R. (1970). An analysis of the experimental analysis of behavior (TEAB). Journal of the Experimental Analysis of Behavior, 13, 101–105.
    Kantor, J. R. (1977). Psychological linguistics. Chicago: Principia.
    Kantor, J. R. (1979). Psychology: Science or nonscience?The Psychological Record, 29, 155–163.
    Kauffman, J. M. (1996). Research to practice issues. Behavioral Disorders, 22, 55–60.
    Kazdin, A. E. (1982). Single case research designs. New York: Oxford University Press.
    Keppel, G. (1982). Design and analysis: A researcher's handbook (
    2nd ed.
    ). Englewood Cliffs, NJ: Prentice Hall.
    Kerlinger, F. N. (1986). Foundations of behavioral research (
    3rd ed.
    ). New York: Holt, Rinehart & Winston.
    Landin, D. K., Hawkins, A. H., & Wiegand, R. L. (1986). Validating the collective wisdom of teacher educators. Journal of Teaching in Physical Education, 5, 252–271.
    Landrum, T. J. (1997). Why data don't matter. Journal of Behavioral Education, 7, 123–129.
    Lawson, H. A. (1985). Knowledge for work in the physical education profession. Sociology of Sport Journal, 2, 9–24.
    Lawson, H. A. (1990). Sport pedagogy research: From information gathering to useful knowledge. Journal of Teaching in Physical Education, 10, 1–20.
    LeCompte, M. D., & Preissle, J. (1993). Ethnography and qualitative design in educational research (
    2nd ed.
    ). San Diego, CA: Academic Press.
    Levin, J. R., & O'Donnell, A. (1999). What to do about educational research's credibility gaps?Issues in Education, 5, 177–229.
    Lichtenstein, P. E. (1983). The interbehavioral approach to psychological theory. In N. W.Smith, P. T.Mountjoy, & D. H.Ruben (Eds.), Reassessment in psychology: The interbehavioral alternative (pp. 3–20). Washington, DC: University Press of America.
    Light, J., Collier, B., & Parnes, P. (1985a). Communicative interaction between young nonspeaking physically disabled children and their primary caregivers: I. Discourse patterns. Augmentative and Alternative Communication, 1, 74–83.
    Light, J., Collier, B., & Parnes, P. (1985b). Communicative interaction between young nonspeaking physically disabled children and their primary caregivers: II. Communicative functions. Augmentative and Alternative Communication, 1, 98–107.
    Light, J., Collier, B., & Parnes, P. (1985c). Communicative interaction between young nonspeaking physically disabled children and their primary caregivers: III. Modes of communication. Augmentative and Alternative Communication, 1, 125–133.
    Lindsley, O. R. (1981, December). Current issues facing standard celeration charts. Paper presented at the Winter Precision Teaching Conference, Orlando, FL.
    Lloyd, J. W. (1992). How do we know?Journal of Behavioral Education, 2, 333–335.
    Locke, L. F. (1989). Qualitative research as a form of scientific inquiry in sport and physical education. Research Quarterly for Exercise and Sport, 60, 1–20.
    Locke, L. F. (1992). Field systems research: Sport pedagogy perspectives [Monograph]. Journal of Teaching in Physical Education, 12, 85–89.
    Lutz, F., & Ramsey, M. (1974). The use of anthropological field methods in education. Educational Researcher, 3(10), 5–9.
    Magoon, A. J. (1977). Constructivist approaches in educational research. Review of Educational Research, 47, 651–693.
    Mahoney, M. J. (1974). Cognition and behavior modification. Cambridge, MA: Ballinger.
    Malott, R. W., & Whaley, D. L. (1983). Psychology. Holmes Beach, FL: Learning Publications.
    Martens, B. K., & Witt, J. C. (1988a). Ecological behavioral analysis. In M.Hersen, R. M.Eisler, & P. M.Miller (Eds.), Progress in behavior modification (Vol. 27, pp. 115–140). Beverly Hills, CA: Sage.
    Martens, B. K., & Witt, J. C. (1988b). Expanding the scope of behavioral consultation: A systems approach to classroom change. Professional School Psychology, 3, 271–281.
    Marx, M. H., & Hillex, W. A. (1963). Systems and theories in psychology. New York: McGraw-Hill.
    Mayer, R. E. (2000). What is the place of science in educational research?Educational Researcher, 29(6), 38–39.
    Mayer, R. E. (2001). Resisting the assault on science: The case for evidence-based reasoning in educational research. Educational Researcher, 30(7), 29–30.
    McDowell, C., & Keenan, M. (2001). Developing fluency and endurance in a child diagnosed with attention deficit hyperactivity disorder. Journal of Applied Behavior Analysis, 34, 345–348.
    McSweeney, F. K., Farmer, V. A., Dougan, J. D., & Whipple, J. E. (1986). The generalized matching law as a description of multiple-schedule responding. Journal of the Experimental Analysis of Behavior, 45, 83–101.
    Metzler, M. (1989). A review of research on time in sport pedagogy. Journal of Teaching in Physical Education, 8, 87–103.
    Michael, J. (1991). Historical antecedents of behavior analysis. The Applied Behavior Analysis Newsletter, 14(2), 7–12.
    Miles, M. B., & Huberman, A. M. (1984). Qualitative data analysis: Asourcebook of new methods. Newbury Park, CA: Sage.
    Miller, S. P., Harris, C., & Watanabe, A. (1991). Professional coaching: A method for increasing effective and decreasing ineffective teacher behaviours. Teacher Education and Special Education, 14, 183–191.
    Mjrberg, A. A. (1972). Ethology of the bicolor damselfish, Eupomaclatsus partitus (Pisces Pomacentridae): A comparative analysis of laboratory and field behaviour. Animal Behavior Monographs, 5.
    Morris, E. K. (1984). Public information, dissemination, and behavior analysis. The Behavior Analyst, 8, 95–110.
    Morris, E. K. (Ed.). (1989). The Interbehaviorist, 17(1), 2.
    Morris, E. K. (1991). Deconstructing “technological to a fault.”Journal of Applied Behavior Analysis, 24, 411–416.
    Morris, E. K. (1992). The aim, progress, and evolution of behavior analysis. The Behavior Analyst, 15, 3–29.
    Morris, E. K., Baer, D. M., Favell, J. E., Glenn, S. S., Hineline, P. N., Malott, M. E., & Michael, J. (2001). Some reflections on 25 years of the Association for Behavior Analysis: Past, present, and future. The Behavior Analyst, 24, 125–146.
    Morris, E. K., Higgins, S. T., & Bickel, W. K. (1983). Contributions of J. R. Kantor to contemporary behaviorism. In N. W.Smith, P. T.Mountjoy, & D. H.Ruben (Eds.), Reassessment in psychology: The interbehavioral alternative (pp. 51–89). Washington, DC: University Press of America.
    Mosteller, F. (1981). Innovation and evaluation. Science, 211(4485), 881–886.
    Moxley, R. A. (1989). Some historical relationships between science and technology with implications for behavior analysis. The Behavior Analyst, 12(1), 45–57.
    Myerson, J., & Hale, S. (1988). Choice in transition: A comparison of melioration and the kinetic model. Journal of the Experimental Analysis of Behavior, 49, 291–302.
    Neale, J. M., & Liebert, R. M. (1973). Science and behavior: An introduction to methods of research. Englewood Cliffs, NJ: Prentice Hall.
    Newman, B. (1992). The reluctant alliance: Behaviorism and humanism. Buffalo, NY: Prometheus.
    Nietzsche, F. W. (1978). Thus spake Zarathustra (W.Kaufmann, Trans.). New York: Penguin. (Original work published 1892)
    Odom, S. L., & Haring, T. G. (1994). Contextualism and applied behavior analysis: Implications for early childhood education for children with disabilities. In R.Gardner, D. M.Sainato, J. O.Cooper, T. E.Heron, W. L.Heward, J.Eshleman, & T. A.Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 87–99). Pacific Grove, CA: Brooks/Cole.
    Okyere, B. A., Heron, T. E., & Goddard, Y. (1997). Effects of self-correction on the acquisition, maintenance, and generalization of the written spelling of elementary school children. Journal of Behavioral Education, 7, 51–69.
    O'Reilly, M. F., & Renzaglia, A. (1994). A systematic approach to curriculum selection and supervision strategies: A preservice practicum supervision model. Teacher Education and Special Education, 17, 170–180.
    Page, T. J., Iwata, B. A., & Reid, D. H. (1982). Pyramidal training: A large scale application with institutional staff. Journal of Applied Behavior Analysis, 15, 355–352.
    Parker, M., & Sharpe, T. L. (1995). Peer tutoring—An effective coaching tool. Journal of Physical Education, Recreation and Dance, 66(8), 50–55.
    Parsonson, B. S., & Baer, D. M. (1978). The analysis and presentation of graphic data. In T. R.Kratochwill (Ed.), Single-subject research: Strategies for evaluating change (pp. 101–165). New York: Academic Press.
    Parsonson, B. S., & Baer, D. M. (1986). The graphic analysis of data. In A.Poling & R. W.Fuqua (Eds.), Research methods in applied behavior analysis (pp. 157–186). New York: Plenum.
    Penman, R. (1980). Communication processes and relationships. London: Academic Press.
    Peterson, L., Homer, A., & Wonderlich, S. (1982). The integrity of independent variables in behavior analysis. Journal of Applied Behavior Analysis, 15, 477–492.
    Pett, M. A., Vaughan-Cole, B., Egger, M., & Dorsey, P. (1988). Wrestling meaning from interactional data: An empirically-based strategy for deriving multiple molar constructs in parent-child interaction. Behavioral Assessment, 10, 299–318.
    Pronko, N. H. (1980). Psychology from the standpoint of an interbehaviorist. Monterey, CA: Brooks/Cole.
    Ray, R. D. (1983). Interbehavioral systems, temporal settings and organismic health. In N. W.Smith, P. T.Mountjoy, & D. H.Ruben (Eds.), Reassessment in psychology: The interbehavioral alternative (pp. 361–380). Washington, DC: University Press of America.
    Ray, R. D. (1992). Interbehavioral methodology: Lessons from simulation [Monograph]. Journal of Teaching in Physical Education, 12, 105–114.
    Ray, R. D., & Delprato, D. J. (1989). Behavioral systems analysis: Methodological strategies and tactics. Behavioral Science, 34, 81–127.
    Ray, R. D., Upson, J. D., & Henderson, B. J. (1977). A systems approach to behavior: III. Organismic pace and complexity in time-space fields. Psychological Record, 27, 649–682.
    Rechsly, D. J., & Wilson, M. S. (1996). Assessment in school psychology training and practice. School Psychology Review, 25, 9–23.
    Richardson, V. (1990). Significant and worthwhile change in teaching practice. Educational Researcher, 19(7), 10–18.
    Rist, R. (1977). On the relations among educational research paradigms: From disdain to detente. Anthropology and Education Quarterly, 8, 42–49.
    Rodger, R. S., & Rosebrugh, R. D. (1979). Computing a grammar for sequences of behavioral acts. Animal Behavior, 27, 737–749.
    Rosenshine, B. V., & Furst, N. (1973). The use of direct observation to study teaching. In R. M. W.Travers (Ed.), Second handbook of research on teaching (pp. 122–183). Chicago: Rand McNally.
    Ruben, D. H., & Delprato, D. J. (Eds.). (1987). New ideas in therapy. Westport, CT: Greenwood.
    Rubin, L. J. (1985). Artistry in teaching. New York: Random House.
    Russell, B. (1929). Mysticism and logic. New York: W. W. Norton.
    Russell, B. (1948). Human knowledge: Its scope and limits. New York: Simon and Schuster.
    Sackett, G. P. (1979). The lag sequential analysis of contingency and cyclicity in behavioral interaction research. In J. D.Osofsky (Ed.), Handbook of infant development (pp. 623–649). New York: John Wiley.
    Sackett, G. P. (1980). Lag sequential analysis as a data reduction technique in social interaction research. In D. B.Sawin, R. C.Hawkins II, L. O.Walker, & J. H.Penticuff (Eds.), Exceptional infant (Vol. 4, pp. 300–340). New York: Brunner/Mazel.
    Sage, G. H. (1989). A commentary on qualitative research as a form of scientific inquiry in sport and physical education. Research Quarterly for Exercise and Sport, 60(1), 25–29.
    Scheflen, A. E. (1982). Comments on the significance of interaction rhythms. In M.Davis (Ed.), Interaction rhythms (pp. 13–22). New York: Human Sciences Press.
    Schmidt, R. A. (1988). Motor control and learning: A behavioral emphasis (
    2nd ed.
    ). Champaign, IL: Human Kinetics.
    Schutz, R. W. (1989). Qualitative research: Comments and controversies. Research Quarterly for Exercise and Sport, 60(1), 30–35.
    Sharpe, T. L. (1997a). An introduction to sequential behavior analysis and what it offers physical education teacher education researchers. Journal of Teaching in Physical Education, 16, 368–375.
    Sharpe, T. L. (1997b). Using technology in preservice teacher supervision. The Physical Educator, 54, 11–19.
    Sharpe, T. L. (2001). Research paradigm and technology in physical education: Recommendations from a behavioral technologist. In J.Yoo (Ed.), Emergent trends in sport-based research and training (pp. 1–27). Seoul, Korea: The Research Institute for Sport Science, Chung-Ang University Press.
    Sharpe, T. L., Brown, M., & Crider, K. (1995). The effects of a sportsmanship curriculum intervention on generalized positive social behavior of urban elementary school students [Special Section]. Journal of Applied Behavior Analysis, 28, 401–416.
    Sharpe, T. L., Brown, M., & Foulk, L. (1999). Description and effects of positive social instruction using a recreational team sport environment. Proven Practice: Prevention and Remediation Solutions for Schools, 1, 68–72.
    Sharpe, T. L., Crider, K., Vyhlidal, T., & Brown, M. (1996). Description and effects of prosocial instruction in an elementary physical education setting. Education and Treatment of Children, 19, 435–457.
    Sharpe, T. L., Harper, W., & Brown, S. (1998). In response: Further reflections on technology, science, and culture. Quest, 50, 332–343.
    Sharpe, T. L., & Hawkins, A. (1992a). Field systems analysis: Prioritizing patterns in time and context among observable variables. Quest, 44, 15–34.
    Sharpe, T. L., & Hawkins, A. (1992b). The implications of field systems for teacher education[Monograph]. Journal of Teaching in Physical Education, 12, 76–84.
    Sharpe, T. L., & Hawkins, A. (1992c). Expert and novice elementary specialists: A comparative analysis [Monograph]. Journal of Teaching in Physical Education, 12, 55–75.
    Sharpe, T. L., & Hawkins, A. (1998). Technology and the information age: A cautionary tale for higher education. Quest, 50, 19–32.
    Sharpe, T. L., Hawkins, A., & Lounsbery, M. (1998). Using technology to study and evaluate human interaction: Practice and implications of a sequential behavior approach. Quest, 50, 389–401.
    Sharpe, T. L., Hawkins, A., & Ray, R. (1995). Interbehavioral field systems assessment: Examining its utility in preservice teacher education. Journal of Behavioral Education, 5, 259–280.
    Sharpe, T. L., & Koperwas, J. (2000). Software assist for education and social science settings: Behavior evaluation strategies and taxonomies (BEST) and accompanying qualitative applications. Thousand Oaks, CA: Sage-Scolari.
    Sharpe, T. L., & Lounsbery, M. (1998). The effects of a sequential behavior analysis protocol on the teaching practices of undergraduate trainees. School Psychology Quarterly, 12, 327–343.
    Sharpe, T. L., Lounsbery, M., & Bahls, V. (1997). Description and effects of sequential behavior practice in teacher education. Research Quarterly for Exercise and Sport, 68, 222–232.
    Sharpe, T. L., Lounsbery, M., Golden, C., & Deibler, C. (1999). Analysis of one ongoing district-wide collaborative approach to teacher education. Journal of Teaching in Physical Education, 19, 79–96.
    Sharpe, T. L., Lounsbery, M., & Templin, T. (1997). “Cooperation, collegiality, and collaboration”: Reinforcing the PETE scholar-practitioner model. Quest, 49, 214–228.
    Sharpe, T. L., Spies, R., Newman, R., & Spickelmier-Vallin, D. (1996). Assessing and improving the accuracy of inservice teachers’ perceptions of daily practice. Journal of Teaching in Physical Education, 15, 297–318.
    Shavelson, R. J., & Berliner, D. C. (1988). Erosion of the education research infrastructure: A reply to Finn. Educational Researcher, 17(1), 9–14.
    Shriver, M. D., & Kramer, J. J. (1997). Application of the generalized matching law for description of student behavior in the classroom. Journal of Behavioral Education, 7, 131–149.
    Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 51, 1–22.
    Siedentop, D. (1992). New folks in the neighborhood: A sport pedagogy perspective [Monograph]. Journal of Teaching in Physical Education, 12, 90–95.
    Siedentop, D., & Eldar, E. (1989). Expertise, experience, and effectiveness. Journal of Teaching in Physical Education, 8, 254–260.
    Silverman, S. (1996). How and why we do research. In S. J.Silverman and C. D.Ennis (Eds.), Student learning in physical education: Applying research to enhance instruction (pp. 35–51). Champaign, IL: Human Kinetics.
    Silverman, S., & Solmon, M. (1998). The unit of analysis in field research: Issues and approaches to design and data analysis. Journal of Teaching in Physical Education, 17, 270–284.
    Skinner, B. F. (1938) The behavior of organisms. New York: Appleton-Century-Crofts.
    Skinner, B. F. (1944). A review of Hull's Principles of behavior. The American Journal of Psychology, 57, 276–281.
    Skinner, B. F. (1945). The operational analysis of psychological terms. Psychological Review, 52, 270–277.
    Skinner, B. F. (1948). Walden Two. New York: Macmillan.
    Skinner, B. F. (1953). Science and human behavior. New York: Macmillan.
    Skinner, B. F. (1956). A case history in scientific methods. American Psychologist, 11, 221–233.
    Skinner, B. F. (1957). The experimental analysis of behavior. American Scientist, 45, 343–371.
    Skinner, B. F. (1968). The technology of teaching. New York: Appleton-Century-Crofts.
    Skinner, B. F. (1983). Notebooks. Englewood Cliffs, NJ: Prentice Hall.
    Skinner, B. F. (1984). Selection by consequences. The Behavioral and Brain Sciences, 7, 477–481.
    Skinner, B. F. (1989). Recent issues in the analysis of behavior. Toronto: Merrill.
    Smith, J. K. (1983). Quantitative versus qualitative research: An attempt to clarify the issue. Educational Researcher, 12(3), 6–13.
    Smith, M. C., & Lytle, S. L. (1990). Research on teaching and teacher research: Issues that divide. Educational Researcher, 19(2), 2–11.
    Smith, M. L. (1987). Publishing qualitative research. American Educational Research Journal, 24, 173–183.
    Sprague, J. R., & Horner, R. H. (1992). Covariation within functional response classes: Implications for treatment of severe problem behavior. Journal of Applied Behavior Analysis, 25, 735–745.
    Sprague, J. R., & Horner, R. H. (1994). Covariation within functional response classes: Implications for treatment of severe problem behavior. In T.Thompson & D. B.Gray (Eds.), Destructive behavior in developmental disabilities: Diagnosis and treatment. Sage focus editions, vol. 170 (pp. 213–242). Thousand Oaks, CA: Sage.
    Stallings, J., Needels, M., & Sparks, G. M. (1987). Observation for the improvement of classroom learning. In D.Berliner & B.Rosenshine (Eds.), Talks to teachers (pp. 129–158). New York: Random House.
    Stokes, T. F., & Baer, D. M. (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 19, 349–367.
    Sulzer-Azaroff, B. (1986). Behavior analysis and education: Crowning achievements and crying needs. Division 25 Recorder, 21, 55–65.
    Sulzer-Azaroff, B., & Mayer, G. R. (1991). Behavior analysis for lasting change. New York: Harcourt Brace Jovanovich College Publishers.
    Suppes, P. (1970). A probabilistic theory of causality. Amsterdam: North Holland Press.
    Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups. New York: John Wiley.
    Thomas, J. R., & Nelson, J. K. (1996). Research methods in physical activity (
    3rd ed.
    ). Champaign, IL: Human Kinetics.
    Thoreau, H. D. (1962). Walden and other writings. New York: Bantam.
    Titus, H. H., Smith, M. S., & Nolan, R. T. (1986). Living issues in philosophy (
    8th ed.
    ). Belmont, CA: Wadsworth.
    Touchette, P. E., MacDonald, R. F., & Langer, S. N. (1985). A scatter plot for identifying stimulus control of problem behavior. Journal of Applied Behavior Analysis, 18, 343–351.
    Toulmin, S. E. (1961). Foresight and understanding: An inquiry into the aims of science. Bloomington: Indiana University Press.
    Unks, G. (1986). Product oriented teaching: A reappraisal. Education and Urban Society, 18, 242–254.
    Utley, B. L., Zigmond, N., & Strain, P. S. (1987). How various forms of data affect teacher analysis of student performance. Exceptional Children, 53, 411–422.
    van der Mars, H. (1989). Systematic observation: An introduction. In P. W.Darst, D. B.Zakrajsek, & V. H.Mancini (Eds.), Analyzing physical education and sport instruction (pp. 3–17). Champaign, IL: Human Kinetics.
    von Mises, R. (1964). Mathematical theory of probability and statistics. New York: Academic Press.
    Wahler, R. G., & Hann, D. H. (1987). An interbehavioral approach to clinical child psychology: Toward understanding troubled families. In D. H.Ruben & D. J.Delprato (Eds.), New ideas in therapy (pp. 53–78). Westport, CT: Greenwood.
    Wampold, B. E. (1986). State of the art in sequential analysis: Comment on Lichtenberg and Heck. Journal of Counseling Psychology, 33, 182–185.
    Wampold, B. E. (1992). The intensive examination of social interactions. In T. R.Kratochwill & J. R.Levin (Eds.), Single-case research design and analysis: New directions for psychology and education (pp. 93–131). Hillsdale, NJ: Lawrence Erlbaum.
    Warger, C. L., & Aldinger, L. E. (1984). Improving teacher supervision: The preservice consultation model. Teacher Education and Special Education, 7, 155–163.
    Watkins, M. W., & Pacheco, M. (2000). Interobserver agreement in behavioral research: Importance and calculation. Journal of Behavioral Education, 10, 205–212.
    Watson, J. B. (1970). Behaviorism. New York: W. W. Norton.
    White, O. R. (1971). The “split middle” or “quickie” method of trend estimation. Eugene: University of Oregon Press.
    White, O. R. (1972). A manual for the calculation and use of the median slope—A technique of progress estimation and prediction in the single case. Eugene: University of Oregon Press.
    Willems, E., & Raush, H. (Eds.). (1980). Naturalistic viewpoints on psychological research. New York: Holt, Rinehart & Winspton.
    Wilson, R. A. (1986). Cosmic trigger: The final secret of the illuminati. Phoenix, AZ: Falcon.
    Wilson, S. (1977). The use of ethnographic techniques in educational research. Review of Educational Research, 47, 245–265.
    Witt, J. C., Noell, G. H., Lafleur, L. H., & Mortenson, B. P. (1997). Teacher use of interventions in general education settings: Measurement and analysis of the independent variable. Journal of Applied Behavior Analysis, 30, 693–696.

    Author Index

    About the Authors

    Tom Sharpe is an Associate Professor and Program Coordinator in the Department of Educational Leadership in the College of Education at the University of Nevada–Las Vegas. He draws from a wealth of varied professional experiences and activities in public and private school, coaching, and university teaching settings and from a long education and social science research career in a variety of graduate programs at different universities. Trained by many of the leading applied and experimental behavior analysts in the profession at West Virginia University, Tom has pursued academic work largely in the education and social science application of observational methodologies and in related computer-based tool development. He has authored over 100 refereed articles and book chapters and is a regular contributor to the principles and practice of applied behavior analysis through conference and workshop presentations and a variety of consulting activities.

    John Koperwas has been a practicing software developer for the past 20 years. After developing a variety of direct observation software and hardware systems, he went into a research and development collaboration with Tom Sharpe through Educational Consulting, Inc. John currently serves clients worldwide ranging from teacher education programs to medical rehabilitation clinics to public school districts and special education and activity-based outreach centers—all interested in the continuing development and use of the direct observation computer tools and related information offered through Educational Consulting. You may visit the company website at for more information on the authors’ background, experience, and current software development efforts.

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