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The Development of the Network Examination for Student Socialization (NEXSS) Observational Instrument

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Title: The Development of the Network Examination for Student Socialization (NEXSS) Observational Instrument
Language: English
Authors: Rhoades, Jesse Lee, Hastmann, Tanis Joy
Source: Measurement in Physical Education and Exercise Science. 2014 18(1):53-71.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 19
Publication Date: 2014
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Physical Education, Measures (Individuals), Social Networks, Reliability, Validity, Systems Analysis, Theories, Models, Socialization, Observation, Video Technology, Student Surveys, Sociometric Techniques, Correlation
Geographic Terms: North Dakota
DOI: 10.1080/1091367X.2013.841701
ISSN: 1091-367X
Abstract: The complexity of learning has plagued the educational establishment for decades. Recently, ideas of complexity theory and complex adaptive systems have made headway in how we think of institutions of learning. This study developed and tested an instrument for the modeling of underlying social structures, as an element of complexity, within the physical education learning environment. Currently, there are no instruments capable of producing valid and reliable models of underlying student social networks in physical education classes. This study sought to develop and test one of the first instruments for the explicit purpose of this modeling. The Network Examination for Student Socialization was developed as a product of this effort. Testing demonstrated the Network Examination for Student Socialization is both a reliable and valid instrument for modeling underlying student social structures within physical education classes. It is hoped that in years to come, the Network Examination for Student Socialization can play a key role in the study of complexity within physical education.
Abstractor: As Provided
Number of References: 34
Entry Date: 2014
Accession Number: EJ1029742
Database: ERIC
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  Value: <anid>AN0093009079;7mm01jan.14;2019Mar26.12:55;v2.2.500</anid> <title id="AN0093009079-1">The Development of the Network Examination for Student Socialization (NEXSS) Observational Instrument. </title> <p>The complexity of learning has plagued the educational establishment for decades. Recently, ideas of complexity theory and complex adaptive systems have made headway in how we think of institutions of learning. This study developed and tested an instrument for the modeling of underlying social structures, as an element of complexity, within the physical education learning environment. Currently, there are no instruments capable of producing valid and reliable models of underlying student social networks in physical education classes. This study sought to develop and test one of the first instruments for the explicit purpose of this modeling. The Network Examination for Student Socialization was developed as a product of this effort. Testing demonstrated the Network Examination for Student Socialization is both a reliable and valid instrument for modeling underlying student social structures within physical education classes. It is hoped that in years to come, the Network Examination for Student Socialization can play a key role in the study of complexity within physical education.</p> <p>Keywords: validity; network modeling; complexity theory; reliability</p> <hd id="AN0093009079-2">INTRODUCTION</hd> <p>Research on instruction and learning is perpetually in a state of flux. Traditionally, this area of science has been based on linear, Newtonian cause and effect reductionist principles. This perspective, however, has been challenged in recent years by the concepts of chaos, complexity, and non-linear dynamics ([<reflink idref="bib10" id="ref1">10</reflink>]; [<reflink idref="bib22" id="ref2">22</reflink>]). Self-organization and emergence have sparingly been applied to education, and even less in physical education, however, many researchers believe that complexity thinking and its description of learning have come of age ([<reflink idref="bib19" id="ref3">19</reflink>]; [<reflink idref="bib28" id="ref4">28</reflink>]).</p> <p>Complexity Theory describes the world as a place of order and chaos, order on the verge of chaos and even orderly disorder ([<reflink idref="bib25" id="ref5">25</reflink>]). The order and chaos of our world is captured in the concept of interacting systems. There are three basic types of systems within the natural world, first is the simple system, which tends to have few elements and operates in a relatively easy to predict cause and effect fashion. Second are complicated systems which have a large number of elements, however, their behaviors are still predictable. The third type of system is the complex system, these systems tend to have a vast number of elements and elude precise prediction ([<reflink idref="bib9" id="ref6">9</reflink>]; [<reflink idref="bib22" id="ref7">22</reflink>]; [<reflink idref="bib25" id="ref8">25</reflink>]; [<reflink idref="bib23" id="ref9">23</reflink>]; [<reflink idref="bib32" id="ref10">32</reflink>]).</p> <p>The principle difference between complex and simple/complicated systems is that complex systems are described as open, while simple/complicated systems are closed. Closed systems do not allow for the transfer of energy or agents with their environments. Complex or open systems, on the other hand, allow energy and system agents to flow between the complex system and its surrounding environment ([<reflink idref="bib22" id="ref11">22</reflink>]). Complexity theory explains that elements within a complex system tend to self-organize, and through this self-organization systems produce emergent behaviors that co-evolve with their environment ([<reflink idref="bib9" id="ref12">9</reflink>]; [<reflink idref="bib22" id="ref13">22</reflink>]; [<reflink idref="bib23" id="ref14">23</reflink>]; [<reflink idref="bib25" id="ref15">25</reflink>]; [<reflink idref="bib32" id="ref16">32</reflink>]). Emergent system behaviors are mediated by feedback from the environment. Feedback affects complex systems in two fashions. First, the emergent behavior can elicit amplifying feedback which will allow the emergent behavior to strengthen, and grab hold as a dominant system behavior. Second, emergent behaviors can be confronted with dampening feedback that atrophy and subdue those behaviors ([<reflink idref="bib25" id="ref17">25</reflink>]). In this respect, dampening feedback will "wash out" inefficient or non-productive system behaviors, while amplifying feedback will reinforce more efficient behaviors. Thus, a complex system will adapt to its environment and produce behaviors that will aid in its success ([<reflink idref="bib25" id="ref18">25</reflink>]).</p> <hd id="AN0093009079-3">Complex Systems in Physical Education</hd> <p>Many education researchers espouse this view of non-linear system dynamics and its applications in education. However, direct instruction, which is a dominant instructional technique in physical education, embraces closed system linearity. This form of instruction imposes a top-down order that tends to prescribe learning and generally stifles emergent knowledge ([<reflink idref="bib12" id="ref19">12</reflink>]). While direct instruction has been found to be an effective means of learning in physical education ([<reflink idref="bib33" id="ref20">33</reflink>]), it has been questioned whether there are alternative instructional methods by which transformative learning can be elicited ([<reflink idref="bib12" id="ref21">12</reflink>]; [<reflink idref="bib18" id="ref22">18</reflink>]). Transformative learning can best be described as knowledge that arises from the co-evolution of the learner and his/her environment in a non-linear manner ([<reflink idref="bib25" id="ref23">25</reflink>]).</p> <p>While transformative learning is an interesting dimension of complexity in physical education, the emergence of social structures within physical education could also hold promise. One of the first steps toward examining the complexity of social structure is the development of a valid and reliable social structure observational instrument within the physical education learning environment. An extensive search of current physical education observational instruments rendered no acceptable instrument which has the expressed intention of examining social structures within the physical education learning environment ([<reflink idref="bib8" id="ref24">8</reflink>]). An instrument which could observe networked interactions among students in physical education and allow for detailed examination of those interactions could be a useful tool in future research. Additionally, this type of instrument could make a real contribution in the understanding of underlying social networks in sports teams, afterschool programs or even intramural sports.</p> <p>It was the lack of acceptable observational instruments for the complexity of social structures in physical education that led to the purpose of this study, which was the development and validation of the Network Examination for Student Socialization (NEXSS). The NEXSS was designed to track, score, and analyze student social interactions within physical education.</p> <hd id="AN0093009079-4">Self-organization and Emergence of Social Structure</hd> <p>The principles of self-organization and emergence have become recognized as common threads throughout the natural world ([<reflink idref="bib9" id="ref25">9</reflink>]; [<reflink idref="bib23" id="ref26">23</reflink>]). Further, complexity theory supports the notion of learning environments as complex systems ([<reflink idref="bib5" id="ref27">5</reflink>]); as such, learning environments should exhibit self-organization and the emergence of social structures. Students' social self-organization would be exhibited by student grouping based on no a priori design ([<reflink idref="bib2" id="ref28">2</reflink>]). Additionally, even within top-down imposed order, for instance teacher-imposed student groupings, self-organized social structure should emerge within the constraints of the imposed order ([<reflink idref="bib20" id="ref29">20</reflink>]; [<reflink idref="bib23" id="ref30">23</reflink>]). Social structure, especially within physical education, where learning is expressed within the psychomotor domain, and observed by other students, could provide a wealth of data regarding information flows within these learning environments.</p> <p>Further, an instrument, much like the NEXSS, which provides an accurate modeling of social structure within physical education, could allow researchers to more closely examine the evolution of social structure within these learning environments. This ability would allow researchers to study a key notion of complexity thinking; evolution, and adaptation. This could provide valuable insight into physical education pedagogy.</p> <hd id="AN0093009079-5">METHODS AND RESULTS BY PHASE</hd> <p>The current study employed five phases in an effort to develop the NEXSS instrument and examine its reliability and validity. Phases for this study were: Phase I: Instrument Development; Phase II: Data Collection; Phase III: Data Reduction; Phase IV: Reliability Testing; and Phase V: Validity Testing.</p> <hd id="AN0093009079-6">Phase I: Instrument Development</hd> <p>The NEXSS was developed with the goal of accurately modeling self-organized student social structures within the physical education learning environment. The most basic form of human interaction can be described as a dyad ([<reflink idref="bib7" id="ref31">7</reflink>]). A dyad is defined as two individuals connected by a single interaction. Additionally, a more complicated form of social interaction is a triadic relationship; this is represented by three individuals with three connections ([<reflink idref="bib7" id="ref32">7</reflink>]). Further, a combination of dyads and triads among a large number of individuals can be used to generate large scale sociometric diagrams or models (see Figure 1). Through the utilization of computer technology, sociometric software is able to construct large network models through the identification of dyadic and triadic relationships. Additionally, sociometric software allows for the calculation of network measures. Specifically, sociometric programs are able to calculate betweenness centrality. This form of centrality is a measure of agent/student placement within a network. Essentially, this measures the number of shortest pathways that pass through any given agent/student within a network ([<reflink idref="bib14" id="ref33">14</reflink>];[<reflink idref="bib16" id="ref34">16</reflink>]; [<reflink idref="bib21" id="ref35">21</reflink>]; [<reflink idref="bib34" id="ref36">34</reflink>]). NodeXL was selected as the software platform which would be utilized with NEXSS as it is widely available, free to download, user friendly, and allows for a simple output within a Microsoft excel document. NodeXL specifically, allows for the fast calculation of betweenness centrality, by use of Brand's algorithm ([<reflink idref="bib16" id="ref37">16</reflink>]). Brand's algorithm has been found to be a fast and accurate method of calculating betweenness centrality for agents within a social network ([<reflink idref="bib4" id="ref38">4</reflink>]). Figure 1 is an example created by the authors based on theoretical data that illustrates the application of betweenness centrality for modeling social networks. In this model, larger elements represent a higher degree of betweenness while smaller elements represent a lower degree. It should be noted that NodeXL allows for a numeric representation of these betweenness values which can be exported for statistical analysis.</p> <p>Graph: FIGURE 1 Theoretical example of network models († indicates corresponding dyad streams).</p> <hd id="AN0093009079-7">Expert consultations</hd> <p>The authors solicited opinions during the NEXSS development from three experts within the field of physical education. These experts represented the fields of pedagogy, sports psychology, and sports sociology. Additionally, two of these experts had teaching experience, and all three are researchers within the field of physical education. They specifically provided insights on interaction constructs and scoring criteria. Additionally, these experts were consulted regarding training and data reduction procedures. During consultations, these experts provided several points of feedback, and as a result, the NEXSS scoring constructs were modified. Specifically, these experts aided in creating scoring criteria that were fast and efficient. Finally, these experts were consulted on the reliability and validity testing that was conducted for this study.</p> <hd id="AN0093009079-8">Developmental pilot observations</hd> <p>Development of the NEXSS, as well as reliability and validity testing required the use of two participant pools. The first pool was used for the development of the NEXSS observational constructs and to test filming techniques. The second participant pool was used to conduct an extended field trial of the NEXSS, from which data were used for reliability and validity testing. This section will describe products from the first participant pool.</p> <hd id="AN0093009079-9">Filming techniques</hd> <p>The NEXSS attempts to capture every contact that takes place between students during an observed physical education class. In order to achieve this, a camera setup must be established that allows each portion of the gymnasium to be observed by at least one camera. Observations of the first participant pool demonstrated that camera placement is of paramount concern for observing student interactions. In the current study, it was found that a five camera setup was optimal. This setup was achieved by placing four cameras in the four corners of the gymnasium. Each camera was assigned a quadrant; these quadrants represented one quarter of the gym floor which was directly in front of the cameras view. Each camera was only responsible for recording interactions that took place in its own assigned quadrant. Additionally, because the NEXSS is designed to be a "hands off" instrument, in that it does not require invasive instrumentation, no audio was recorded, in that it would require the outfitting of each participant with a microphone.</p> <p>Additionally, it was determined that a fifth camera would be needed in order to capture teacher initiated mass groupings. In the current study, these mass groupings would overwhelm any one camera, making careful observation extremely difficult. It was observed that these mass groupings became self-organized and social interactions rapidly took place. For these reasons it became apparent that a special camera placement would be necessary to capture these mass grouping events. The fifth camera placement was determined by the location the teacher grouped students in mass, and changed as needed, based on group movements.</p> <p>Overall, the most significant finding regarding camera placement was that with the complexities of the learning environment, there cannot be a prescribed placement setup for the NEXSS. The overriding principle that should govern the setup is that; each interaction that takes place during an observed lesson should be recorded by at least one camera. In the current study, this was achieved with five cameras, other studies may require fewer cameras, yet others may require more. This is the nature of complex systems, and as such, must be the nature of the camera setup for the NEXSS instrument.</p> <hd id="AN0093009079-10">Observational constructs</hd> <p>Two primary observational constructs were identified for the NEXSS. In any situation there are two basic forms of activities occurring, overt and covert actions. It could be theorized that covert interactions are taking place; however, these may not be observable without more invasive instrumentation. Overt interactions, those that are visible, can be captured with very little instrumentation. It was for this reason that the authors in consultation with experts within the field of physical education decided to capture only overt interactions. To this end, the NEXSS needed to be able to detect the occurrence of student overt interactions. Second, the NEXSS needed to be able to score those interactions.</p> <hd id="AN0093009079-11">Overt interactions</hd> <p>An overt interaction, for the NEXSS, was described as two individuals who were mutually engaged with one another in the learning environment. Overt interactions were determined by two criteria. First, there needed to be a voluntary initiation action, which was defined as a simple head turn, observed verbalization, passing of equipment, or even physical contact. The second criterion was reciprocation, which was defined as any reciprocal action that a student took in what appeared to be a response to an initiation action. A reciprocal action could be an observed verbalization, receiving equipment or reciprocal physical contact. For either of these criteria to be met, these actions had to be observed during video recorded lessons.</p> <p>It was found, throughout our data, that some interactions could not be clearly viewed. Primarily, this occurred when the view of the participant was obstructed by other students, and subsequently was not viewable by any other camera. On the rare occasion that this occurred, the interaction would not be recorded.</p> <hd id="AN0093009079-12">Scoring criteria</hd> <p>Once the above criteria had been met and an overt interaction was determined to have taken place, the interaction would subsequently be scored. The ability to score an interaction would allow for dyadic relationships to be ranked. The observations of the first participant pool aided researchers in the development of scoring constructs and criteria, allowing for the accurate description of student interactions. Further, the authors wished to set precedent with the NEXSS instrument in the defining of scoring criteria for social interactions, as there are no existing instruments with the explicit goal of modeling social interactions within physical education. Additionally, through consultation with experts in physical education, these scoring criteria were determined to be three interaction characteristics which could easily be identified and coded through video observation. After the determination of possible scoring criteria, literature was sought for each of the chosen criteria; this literature basis for the scoring criteria is presented in each criteria subsection below. Finally, expert consultations allowed for a refining of these criteria. The final developed scoring criteria were: distance of interaction, duration of interaction, and eye contact during interaction.</p> <hd id="AN0093009079-13">Distance</hd> <p>Distance is a key element in human interaction. One of the first insights into distance as a key construct within human interactions was done by [<reflink idref="bib17" id="ref39">17</reflink>], in which he described the personal reaction bubble (PRB). Through his PRB, he coined the term "proxemics" and described how the closer the distance of a human interaction takes place, the more intimate that contact is. Since [<reflink idref="bib17" id="ref40">17</reflink>] work, many authors have expanded on his ideas of proximity during interaction ([<reflink idref="bib24" id="ref41">24</reflink>]; [<reflink idref="bib26" id="ref42">26</reflink>]). The NEXSS scores the distance of interactions in three increments: near (under two feet), medium (between two and ten feet), and far (beyond ten feet). Due to the lack of frame of reference markers for distance throughout a standardized gymnasium, the researchers employed a rather rudimentary method of assessing distance within film data. This method utilized the students themselves as basic frames of reference for distance. Observers compared the distance of participants to proportional representative body parts. In the current study where participants were of adult age, a measure of two feet was determined to be approximately the length from heel to knee or a shin length. Further, ten feet was determined to be two lengths of the participant from feet to head. Admittedly, this measurement criterion is rudimentary and inexact; however, it provided a measure by which observers could quickly judge distance, and it reduced prospective error. Further, in future studies it will be necessary to establish reference points within the learning environment that can be used to estimate relative distances. In the current study, anatomical distances were used because of the ease of estimating anatomical distances within an adult population, however, this may not be the case in future applications. It is imperative that any researcher using this instrument place a reference object for distance in the frames of view. The easiest reference apparatus would be a grid system placed on the floor. This grid system would allow for an easy way of quickly estimating distances between students.</p> <hd id="AN0093009079-14">Duration</hd> <p>Duration has been postulated as a possible influence on the strength of interpersonal relationships ([<reflink idref="bib3" id="ref43">3</reflink>]). The duration, or time spent in an overt interaction is a key component of complex learning environments, where students self-organize for varying amounts of time. Additionally, through our expert consultations, it was determined that duration should be included in the instrument. The NEXSS scored duration in three increments: short (under two seconds), medium (between two and ten seconds) and long (over ten seconds). Typically, the observer had two options on the measurement of time of overt interactions. First, observers were able to use a stopwatch, however, this was found to be cumbersome as it required the observer to take his/her eyes off of the video. The second, and what was found to be optimal, was simply using the displayed time on the video, this allowed the observer to keep his/her eyes on the video and keep accurate time.</p> <hd id="AN0093009079-15">Eye contact</hd> <p>It has been described by several authors that eye contact is an indication of close personal interactions ([<reflink idref="bib1" id="ref44">1</reflink>]; [<reflink idref="bib30" id="ref45">30</reflink>]). The NEXSS scores eye contact based on two binary criteria. First, if it was visible that the initiator and the recipient were facing each other during their overt interaction, they would be determined to have made eye contact. If, however, the participants were not facing each other during the interaction or the film did not allow a clear enough view to determine whether the participants were facing each other during the interaction, by default the interaction would be coded as having no eye contact.</p> <hd id="AN0093009079-16">Coding procedure</hd> <p>During any given physical education lesson, hundreds of student interactions may occur, many of which happen simultaneously and would make live coding for the NEXSS quite impossible. It is for this reason that the NEXSS was designed to be used only with video recorded data.</p> <p>NEXSS coding of video recorded lessons can be performed through two methods. First, is the use of the paper coding document, illustrated in Figure 2. Second, is the direct data entry of NEXSS data into a computer spreadsheet. During field testing, both of these methods were observed to have pros and cons. The paper form allowed for a greater ability of the observer to concentrate on the video recorded lesson without the distraction of typing. It was, however, also observed that data entry errors sometimes occurred when NEXSS paper coding sheets were manually entered. The primary reason for these errors was the amount of data that the individual NEXSS sheets contain. The NEXSS sheet requires tightly formatted numeric data, which can become confusing during data entry. It was found that the direct computer entry method reduced these errors in that the coder did not have to transcribe the data sheets subsequent to the coding session. Additionally, the direct entry method allowed for a quick accounting of data and the ability of the coder to quickly move between participants, where the paper coding tended to be cumbersome and inhibit the coding process. For these reasons, it was determined that novice coders should use the paper coding sheets, with repeated meticulous checks of data entry, until such a time as they are comfortable enough with the NEXSS that they can use the direct coding method.</p> <p>Graph: FIGURE 2 NEXSS score card.</p> <hd id="AN0093009079-17">Training procedure</hd> <p>During the development of the NEXSS, a training protocol was developed in an effort to better assist future researchers in the application of this instrument. This training protocol was modified from a similar method used by [<reflink idref="bib31" id="ref46">31</reflink>] in their effort to assure quality in the usage of the academic learn time in physical education (ALT-PE). A similar training protocol was more recently used by [<reflink idref="bib11" id="ref47">11</reflink>] in their utilization of the ALT-PE instrument. The NEXSS training protocol targeted two constructs: first, the criteria for identifying an overt interaction and second, criteria for accurately determining duration, distance and eye contact of overt interactions. The training procedure was conducted in the following steps: (a) discussed overt interaction criteria; (b) practiced coding overt interactions; (c) discussed disagreements; (d) practiced identifying overt interactions; (e) calculated inter-observer agreement for observed overt interactions; (f) checked inter-observer agreement. If not at least 80% on inter-observer agreement, repeated steps four, five, and six until the 80% level was reached; (g) discussed scoring criteria for distance, duration and eye contact; (h) practiced scoring overt interactions; (i) discussed disagreements; (j) practiced scoring overt interactions; (k) tested inter-observer agreement for scoring the overt interactions; and (l) checked inter-observer agreement. If not 80%, repeated steps j, k, and l until the 80% level was reached.</p> <hd id="AN0093009079-18">Reduction procedure</hd> <p>Once NEXSS coding was completed, it was necessary to complete a three step data cleaning process to assure data integrity. During step I, frequency tests were conducted for participant IDs, results of these frequency tests were compared against the master participant ID list. This step allowed for the correction of participant ID coding errors that may have occurred during data entry. Step II required the pairing of dyads. Each interaction had both an initiator and a responder, because these interactions are coded for each of the participants, each participant should have a coding record for individual interactions. During step II, each participant's data was checked to make sure all data were accounted for. This pairing of dyads allowed for the detection of missing data. When missing pairs were found, the original film data was examined to determine the appropriate coding for each of the participants in the dyadic pairing. Finally, during step III, once all dyad pairs were confirmed, scores were compared for individual interactions. In NEXSS data, each side of a dyad should have the same coding for an interaction. When inaccuracies were found, the original film data were re-examined in order to apply the accurate coding for each interaction.</p> <p>Once data were coded and cleaned, a reduction process was applied. The NEXSS provided a large and repetitive amount of interactions and scoring of those interactions. Scores for individual interactions were based on distance, duration, and eye contact. For instance, an interaction that was under two feet, lasted for over ten seconds, and had eye contact was considered a stronger dyadic relationship when compared to an interaction that took place over ten feet, with no eye contact, and under two seconds. Additionally, frequency of interaction needed to be taken into account; consequently, all interactions were summed by score. This allowed the ranking of dyadic connections based on score and frequency of interactions. This reduction process allowed for the selection of the strongest dyadic relationships with any given student. After cleaning and ranking of dyadic connections, a pristine dyad stream remained from the reduction process. This dyad stream then was entered into NodeXL.</p> <hd id="AN0093009079-19">Phase II: Data Collection</hd> <p>After the initial development of the NEXSS, it was subjected to an extended field trial. This extended field trial had two major objectives; first, to test construct reliability, and second, to examine the validity of the instrument. During this extended field trial, a basic instruction course in basketball, at the University of North Dakota was filmed during a series of 14 class sessions. Specifically, the classes were filmed during a daily 15-min skill development activity. This section of class was uniquely appropriate for this study in that the students were allowed to self-organize. This portion of the class had no top-down control other than the instruction to work on basketball skills. This lack of top-down control is essential in the promotion of self-organization within a complex system, like a classroom setting. This allowed for an examination of self-organized groupings and the development of an underlying social structure within the class, which the NEXSS has been expressly designed to model. As a consequence, classes that did not meet these criteria were eliminated from the film data. After the elimination of the classes not meeting our inclusion criteria, a total of seven days were used in the data analysis. Consequently, the author's recommend that the most appropriate application of the NEXSS is during skill development activities with a minimum of top-down control, generally focusing on instructor feedback.</p> <hd id="AN0093009079-20">Phase III: Data Reduction</hd> <p>Data were coded, using the NEXSS, by two research groups. These coding groups had two distinct functions in the current study. First, selected portions of film data were coded by a panel of three graduate students. The purpose of this panel was exclusively to perform reliability testing on the overt interaction scoring constructs. This group had little to no experience in observing physical activity-based learning. This allowed the authors to demonstrate that with the prescribed training protocol most individuals should be able to provide reliable NEXSS scoring. The second group consisted of a single graduate re<emph>s</emph>earch assistant, who had extensive experience in observation of instructional environments, and the first author, who is an expert in pedagogical kinesiology with extensive experience in field observations in physical education. The purpose of this group was to test the reliability of the overt interaction identification, and to code the entire film database for later validity testing.</p> <hd id="AN0093009079-21">Phase IV: Reliability Testing</hd> <p>Reliability testing was used to examine the ability of the NEXSS to provide reliable scoring for duration, distance and eye contact during overt interactions. Additionally, the ability of NEXSS observations to accurately identify overt interactions was subjected to reliability testing.</p> <hd id="AN0093009079-22">Scoring construct reliability</hd> <p>In an effort to support the reliability of the NEXSS, a panel of three graduate students were trained on the NEXSS, and coded selected segments of the extended field trial. This panel would concentrate on reliability testing for the scoring constructs, which is the ability of an observer to accurately score the duration, distance and eye contact of an overt interaction. This panel was shown clips that had been determined to contain overt interactions. The panel was shown these clips from the initiation to the termination of each interaction. The panel was asked to code each of the interactions for distance, duration and eye contact using the NEXSS. During the training process, disagreements in coding were discussed and reviewed. Due to the objective nature of the NEXSS, the main areas of disagreement were codes that were close to border positions. For example, if an interaction took place at a distance of two feet, a disagreement was generally based on the threshold between coding states (less than two feet or between two and ten feet). Generally, during the discussion of disagreements, the interaction was reviewed and discussed until an agreement was reached. Importantly, the agreement would not be isolated to the individual interaction but would attempt to set precedence between panel members in an effort to build reliability between the panel members. It was found that this group was able to achieve an 80% agreement for all of the scoring constructs within five hours of their instruction on the NEXSS. The results for this panel are illustrated in Table 1. It should be noted that of the inter-observer agreements listed in Table 1, two were 79%, which fell slightly below the 80% and was an oversight by the researchers. Ideally, all inter-observer agreements would be 80% or above. The panel had an overall percentage of agreement of 82% for duration, 86% for distance, and 89% for eye contact. The authors determined that though the individual observer agreements fell slightly below the 80% criteria, that the instrument should proceed to validity testing.</p> <p>TABLE 1 Inter-Observer Agreements for Overt Interactions</p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>Observer 1</td><td>Observer 2</td><td>Observer 3</td></tr></thead><tbody><tr><td>Distance</td><td /><td /><td /></tr><tr><td> Observer 1</td><td /><td /><td /></tr><tr><td> Observer 2</td><td>84%<sup>(16/19)</sup></td><td /><td /></tr><tr><td> Observer 3</td><td>79%<sup>(15/19)</sup></td><td>95%<sup>(18/19)</sup></td><td /></tr><tr><td>Duration</td><td /><td /><td /></tr><tr><td> Observer 1</td><td /><td /><td /></tr><tr><td> Observer 2</td><td>79%<sup>(15/19)</sup></td><td /><td /></tr><tr><td> Observer 3</td><td>84%<sup>(16/19)</sup></td><td>84%<sup>(16/19)</sup></td><td /></tr><tr><td>Eye Contact</td><td /><td /><td /></tr><tr><td> Observer 1</td><td /><td /><td /></tr><tr><td> Observer 2</td><td>95%<sup>(18/19)</sup></td><td /><td /></tr><tr><td> Observer 3</td><td>89%<sup>(17/19)</sup></td><td>84%<sup>(16/19)</sup></td><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0093009079-23">Overt interaction constructs reliability</hd> <p>After interaction scoring construct reliability was established by the first panel of graduate students, it was determined that the entire filming database would be coded using the NEXSS. This phase was used to establish overt interaction criteria reliability. Similar to the first panel, a second group of observers consisting of a graduate research assistant and the first author were trained on the NEXSS using the same above-described 12-step training protocol. This process took approximately five hours and ended when the 80% inter-observer reliability criterion was achieved. Inter-observer reliability was calculated between the first author and graduate student for the overt interaction construct, and the overt interaction scoring constructs. Overall overt interaction criteria rendered an 82%<sups>(19/23)</sups> inter-observer agreement. Scoring construct inter-observer agreements were: distance = 94%<sups>(17/18)</sups>, duration = 89%<sups>(16/18)</sups>, and eye contact = 89%<sups>(16/18)</sups>. After inter-observer reliability had been established between the first author and the graduate research assistant, they proceeded to code the entire filmed database. The first author coded a majority of data; 88.6%<sups>(1426/1608)</sups>, however, the graduate research assistant coded approximately 11.3%<sups>(182/1608)</sups> of all film data. A random check was performed at the halfway point of coding and attempted to correct any reliability issues that may have developed during the active coding stage. This random check showed an 81%<sups>(139/175)</sups> inter-observer agreement for the identification of interactions. Additionally, inter-observer agreements were found for the following: distance = 84%<sups>(117/139)</sups>, duration = 78%<sups>(108/139)</sups>, and eye contact = 88%<sups>(123/139)</sups>. It should be noted that duration was slightly less than the 80% cutoff. However, this was found during the random check which was performed to help correct any reliability issues that developed during the coding process. Overall, these repeated reliability measures supported the construct reliability of the NEXSS.</p> <hd id="AN0093009079-24">Phase V: Validity Testing</hd> <p>In order to establish criterion validity, the NEXSS output was compared against a social structure measure. Criterion measures, however, of social structure in the classroom setting are very difficult to obtain. It should be noted that the NEXSS has a large amount of face validity, in that observers were able to reliably observe and score student social interactions from video recordings. In spite of this face validity, the authors felt it was important to attempt to establish a measure of criterion validity of the NEXSS. In this effort to test criterion validity, the authors employed a simple social interaction survey.</p> <hd id="AN0093009079-25">Survey</hd> <p>Students completed a brief sociometric survey during four consecutive class periods. The survey requested that participants identify five students who they interacted with on a regular basis during class. The survey was limited to five responses; however, participants were instructed that if they could not name five students that they should indicate as many as they were able. This survey is illustrated in Figure 3. Survey data produced a list of dyadic connections between students. Once this compiled dyad list was assembled, it was entered into NodeXL. These data allowed NodeXL to create an overall self-reported sociometric network model. Further, NodeXL was then used for the calculation of betweenness centrality for each of the participants. The overall self-reported sociometric model for participants is illustrated in Figure 4. Additionally, Figure 4 illustrates the network model with the betweenness centrality values for participants applied and illustrated. Essentially, this model based on self-reported survey data functioned as a criterion comparison with observed NEXSS data.</p> <p>Graph: FIGURE 3 Interaction survey.</p> <p>Graph: FIGURE 4 Overall sociometric model for basic instruction basketball class.</p> <hd id="AN0093009079-26">NEXSS versus survey</hd> <p>Essentially, the purpose of the NEXSS was to accurately describe and model social interactions that took place during the course of a physical education class. It should be noted that there are various forms of social structure; both strong and weak connections are replicated by the NEXSS. Strong connections might be a close friend with whom a student interacts constantly during a class. Weak connections may be a person with whom a student interacts very few times. The NEXSS attempts to replicate the social structure of the physical education environment in such a way as to be able to discern between strong and weak connections. It does this through two mechanisms: first through a record of cumulative interactions and second through the scoring of interactions. Observed interactions are compiled into a summed score which allows the weighing of dyadic relationships between students. In the current study, only the top five strongest dyadic relationships for each participant were used to model the learning environment. The reason for this was that the survey to which the NEXSS would be compared asked for the top five social connections between students. This allowed for the comparison of sociometric measurement between the survey data and NEXSS observation data.</p> <p>Subsequently, sociometric network models were created for each day of NEXSS observation. Each day's sociometric model had the brand algorithm for betweenness centrality applied to it. This process allowed for the comparison of network agent/student placement between observed and indicated interactions. Pearson product correlations for observed class periods are displayed in Table 2. These coefficient values ranged between <emph>r</emph> =.08 and <emph>r</emph> =.62. While some of these correlation coefficients were encouraging, some days failed to exhibit appropriate correlation with survey data. In an effort to more extensively compare observations with survey data, observational days were paired and compiled into groups of two, three, and four day groupings. These compilations showed that when paired, <emph>r</emph>-values ranged between <emph>r</emph> =.27 and <emph>r</emph> =.73, when observational days were tripled, compiled <emph>r</emph>-values ranged between <emph>r</emph> =.56 and <emph>r</emph> =.72. Again, these <emph>r</emph>-values are illustrated in Table 2. It is important to note that when compiling days of observation, it is not simply a matter of adding to the overall sociometric model, this addition can dramatically affect the overall calculation of betweenness centrality. It should be noted that when days are compiled and correlation values increase, it indicates that a significant structural change has occurred in the sociometric model, which is more similar to the self-reported interactions of the participants in this study. These results indicate that to get an accurate representation of underlying social structures, the instrument should be used during three consecutive days of instruction. Overall, these data support the notion that the NEXSS is a valid measure of social structure.</p> <p>TABLE 2 Correlational Coefficients for Betweenness Centrality Values Between Survey and Observed Data</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Observation</td><td>R</td></tr></thead><tbody><tr><td>Day 1</td><td>.38</td></tr><tr><td>Day 2</td><td>.26</td></tr><tr><td>Day 6</td><td>.16</td></tr><tr><td>Day 7</td><td>.50<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 8</td><td>.62<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 9</td><td>.57<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 14</td><td>.08</td></tr><tr><td>Day 1–Day 2</td><td>.56<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 6–Day 7</td><td>.63<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 7–Day 8</td><td>.73<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 8–Day 9</td><td>.67<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 9–Day 14</td><td>.27</td></tr><tr><td>Day 6–Day 8</td><td>.66<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 7–Day 9</td><td>.72<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 8–Day 14</td><td>.56<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 6–Day 14</td><td>.60<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td>Day 1–Day 14</td><td>.56<xref ref-type="fn" rid="TFN2001" /></td></tr><tr><td><ext-link id="TFN2001" />*<italic>p</italic> <.01</td></tr></tbody></table> </ephtml> </p> <hd id="AN0093009079-27">DISCUSSION</hd> <p>The current study was one of the first to develop and validate an observational instrument for the modeling of social structure in physical education classes. Overall, analyses of the data gathered in this study suggest that the NEXSS is a valid and reliable form of social structure assessment. The current study employed five phases to develop validity and reliability: (a) instrument development, (b) data collection, (c) data reduction, (d) reliability testing, and (e) validity testing.</p> <p>Extensive care was taken to establish the content domain of the instrument in phase I. Researchers reviewed the literature, identified the two observational constructs (occurrence of student interaction), identified the three scoring criteria for overt interactions (distance, duration, and eye contact), conducted pilot observations, developed coding, training, and data reduction procedures, and consulted experts on the NEXSS. Specifically, data analysis showed that through the prescribed 12-step training protocol, reliable results for the NEXSS can be achieved with approximately five hours of training.</p> <p>After developing the content domain, data were collected to allow researchers to establish construct validity and reliability for the instrument. This was followed by reducing the data from the filmed observations of the dyadic relationships between students. In phase IV, reliability testing was carried out by scoring the constructs and overt interactions. Construct reliability testing resulted in an 80% agreement for all constructs following a five hour training period. Results from the reliability testing showed high inter-observer reliability. Thus, it is important to note that future studies using the NEXSS should be confident that following the prescribed training protocol, coders should be able to establish an 80% inter-observer reliability across their observations with approximately five hours of training.</p> <p>Lastly, researchers established criterion validity by comparing the NEXSS against a social interaction survey completed by students. Comparisons between the social interaction survey and observed NEXSS indicated low correlation between observed and reported interactions. However, when three consecutive days of data were compiled, comparisons revealed a greater degree of correlation between observed and reported interactions. Specifically, three consecutive compiled days of instruction showed correlation coefficients approaching, at or above <emph>r</emph> =.70. The authors speculate that student absences for individual days may have contributed to compilation data having a greater degree of correlation between observed and reported interactions. This finding indicates that the most appropriate application of the NEXSS, for modeling underlying social structures, is with the filming of three consecutive days of physical education instruction.</p> <hd id="AN0093009079-28">Strengths and Limitations</hd> <p>This was a novel study with several strengths. This study is unique in that it focused on the social structure in physical education classes to better understand the best learning environment to promote emergent learning. Second, this is one of the first attempts at the creation of an instrument for the modeling of physical education learning environments through complexity theory. Eventually, the NEXSS may be paired with data regarding emergent motor competencies in an effort to understand more closely how student networks aid in the diffusion of learned behaviors. Third, researchers spent approximately nine months, beginning in December of 2011 and concluding in August of 2012, in an effort to thoroughly and rigorously develop and test the NEXSS. Finally, multiple validity assessments were carried out, including face validity, content validity, construct validity and criterion validity, as well as reliability testing.</p> <p>Along with the strengths of the current study, the following limitations should be noted. First, students self-reported who they interacted with on a regular basis during class. Second, several students were absent for the observations. The average class attendance for the observed basic instruction course was 72% (25.92/36) participants. Further, several of the participants were habitually absent. In spite of these absences, however, there was still a high correlation between observed and indicated interactions. Third, all student interactions were from a small geographic area in North Dakota, and both male and female students were included. There may be differences in the key elements for a learning environment that promote emergent learning based on gender, race/ethnicity or age differences of the students. Fourth, the first author coded the majority of the data simply out of necessity in that the graduate research assistant was not able to spend the enormous amount of time necessary to code more of the data. Finally, this study examined the validity of the NEXSS in a collegiate learning environment with an instructor that provided a great deal of time for learning through skill practice and practical application. Physical activity based learning, however, takes place in a variety of learning environments in which a much more top-down approach is taken, such as team sports. However, it should be noted that even within a top-down controlled system, it is possible to have bottom-up emergence within the top-down constraints ([<reflink idref="bib20" id="ref48">20</reflink>]; [<reflink idref="bib23" id="ref49">23</reflink>]).</p> <hd id="AN0093009079-29">NEXSS Applications and Implications</hd> <p>The NEXSS instrument is designed to provide a method for the reliable and valid modeling of social structure within physical education. The main NEXSS application is in studying social structure changes over time. This application is significant in that it provides a clear method by which investigators can study the evolution of student social structure. This idea of evolution and adaptation is an essential notion within complexity theory ([<reflink idref="bib18" id="ref50">18</reflink>]) and as such, the NEXSS has the potential to expand our understanding of complex social structures within physical education. Future investigations could use the NEXSS to model social structure in an effort to track possible knowledge diffusion throughout a learning environment. These studies could identify key individuals within an underlying social structure that either amplify or dampen a possible process of knowledge diffusion. This idea of knowledge diffusion is supported by the notion of spontaneous social synchronization; an observed phenomena in which members within social groupings synchronize their learned motor behaviors ([<reflink idref="bib27" id="ref51">27</reflink>]). This notion of knowledge diffusion may be essential in the development of innovative curricular designs based on information diffusion patterns within physical activity based learning environments. Finally, the NEXSS should be applicable to other learning environments. For instance, sports teams, intramurals, after-school programs or any number of recreational activities, could use the NEXSS to examine the underlying social structure of their participants.</p> <hd id="AN0093009079-30">Applications and implication for the classroom</hd> <p>Survey systems for social network analysis have been used in a number of classroom investigations ([<reflink idref="bib13" id="ref52">13</reflink>]; [<reflink idref="bib15" id="ref53">15</reflink>]; [<reflink idref="bib29" id="ref54">29</reflink>]), however, this process relies heavily on self-reporting ([<reflink idref="bib6" id="ref55">6</reflink>]). It is theorized by the authors that the NEXSS could be used as a possible alternative within the classroom environment. Strengths of the NEXSS are that it relies on direct observation and requires no interruption of the educational environment. Conceptually, every class meeting could be recorded and coded with the NEXSS instrument, without any interruption to the learning environment. It is for this reason that the authors believe the NEXSS is an improvement over the more traditional survey methods; however, reliability and validity testing would need to be conducted in these environments to confirm this notion.</p> <hd id="AN0093009079-31">Applications and implications for field research</hd> <p>Finally, it is important to note that though this study supports the NEXSS as both valid and reliable, the NEXSS is not a practitioner's instrument. This instrument is meant for research, as it is far too time consuming for a teacher or school administrator to consider its usage in the clinical setting. The NEXSS is best suited for researchers who are examining the physical education learning environment as a complex system. Specifically, the researcher's envision the NEXSS being paired with other student knowledge evaluations, which could show promise in modeling informational flows within learning environments. With this said, the NEXSS may show a great deal of promise in the future as it is one of the only instruments designed to model these phenomena.</p> <hd id="AN0093009079-32">ACKNOWLEDGEMENTS</hd> <p>The authors would like to acknowledge the College Research Council in the College of Education and Human Development at the University of North Dakota for their generous financial contributions to this project.</p> <ref id="AN0093009079-33"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref44" type="bt">1</bibl> <bibtext> Argyle, M. and Dean, J.1965. Eye-contact, distance and affiliation. American Sociological Association, 28: 289–304.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref28" type="bt">2</bibl> <bibtext> Arrow, H., McGrath, J. E. and Berdahl, J. L.2000. 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Social networks analysis: Methods and applications, Cambridge, UK: Cambridge University Press.</bibtext> </blist> </ref> <aug> <p>By JesseLee Rhoades and TanisJoy Hastmann</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib22" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib19" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib28" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib25" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib23" firstref="ref9"></nolink> <nolink nlid="nl7" bibid="bib32" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib12" firstref="ref19"></nolink> <nolink nlid="nl9" bibid="bib33" firstref="ref20"></nolink> <nolink nlid="nl10" bibid="bib18" firstref="ref22"></nolink> <nolink nlid="nl11" bibid="bib20" firstref="ref29"></nolink> <nolink nlid="nl12" bibid="bib14" firstref="ref33"></nolink> <nolink nlid="nl13" bibid="bib16" firstref="ref34"></nolink> <nolink nlid="nl14" bibid="bib21" firstref="ref35"></nolink> <nolink nlid="nl15" bibid="bib34" firstref="ref36"></nolink> <nolink nlid="nl16" bibid="bib17" firstref="ref39"></nolink> <nolink nlid="nl17" bibid="bib24" firstref="ref41"></nolink> <nolink nlid="nl18" bibid="bib26" firstref="ref42"></nolink> <nolink nlid="nl19" bibid="bib30" firstref="ref45"></nolink> <nolink nlid="nl20" bibid="bib31" firstref="ref46"></nolink> <nolink nlid="nl21" bibid="bib11" firstref="ref47"></nolink> <nolink nlid="nl22" bibid="bib27" firstref="ref51"></nolink> <nolink nlid="nl23" bibid="bib13" firstref="ref52"></nolink> <nolink nlid="nl24" bibid="bib15" firstref="ref53"></nolink> <nolink nlid="nl25" bibid="bib29" firstref="ref54"></nolink>
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– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Physical+Education%22">Physical Education</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Networks%22">Social Networks</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Validity%22">Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+Analysis%22">Systems Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Theories%22">Theories</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Socialization%22">Socialization</searchLink><br /><searchLink fieldCode="DE" term="%22Observation%22">Observation</searchLink><br /><searchLink fieldCode="DE" term="%22Video+Technology%22">Video Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Surveys%22">Student Surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Sociometric+Techniques%22">Sociometric Techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22North+Dakota%22">North Dakota</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/1091367X.2013.841701
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1091-367X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The complexity of learning has plagued the educational establishment for decades. Recently, ideas of complexity theory and complex adaptive systems have made headway in how we think of institutions of learning. This study developed and tested an instrument for the modeling of underlying social structures, as an element of complexity, within the physical education learning environment. Currently, there are no instruments capable of producing valid and reliable models of underlying student social networks in physical education classes. This study sought to develop and test one of the first instruments for the explicit purpose of this modeling. The Network Examination for Student Socialization was developed as a product of this effort. Testing demonstrated the Network Examination for Student Socialization is both a reliable and valid instrument for modeling underlying student social structures within physical education classes. It is hoped that in years to come, the Network Examination for Student Socialization can play a key role in the study of complexity within physical education.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Ref
  Label: Number of References
  Group: RefInfo
  Data: 34
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2014
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1029742
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1029742
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/1091367X.2013.841701
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 53
    Subjects:
      – SubjectFull: Physical Education
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Social Networks
        Type: general
      – SubjectFull: Reliability
        Type: general
      – SubjectFull: Validity
        Type: general
      – SubjectFull: Systems Analysis
        Type: general
      – SubjectFull: Theories
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Socialization
        Type: general
      – SubjectFull: Observation
        Type: general
      – SubjectFull: Video Technology
        Type: general
      – SubjectFull: Student Surveys
        Type: general
      – SubjectFull: Sociometric Techniques
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: North Dakota
        Type: general
    Titles:
      – TitleFull: The Development of the Network Examination for Student Socialization (NEXSS) Observational Instrument
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Rhoades, Jesse Lee
      – PersonEntity:
          Name:
            NameFull: Hastmann, Tanis Joy
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2014
          Identifiers:
            – Type: issn-print
              Value: 1091-367X
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Measurement in Physical Education and Exercise Science
              Type: main
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