Download chapter 6: data analysis and interpretation and more Summaries Literature in PDF only on Docsity!
CHAPTER 6: DATA ANALYSIS AND INTERPRETATION
6.1. INTRODUCTION
Chapter Five described and explained in detail the process, rationale and purpose of the mixed methods research design, (cf. par. 5.7, p. 321, p. Fig. 16, p. 318; 17, p. 326; 18, p. 327). The mixed methods research design were applied in this research study to acquire an experiential overview of the extent of school sport management in a group of identified South African schools in accordance with their diverse needs. As was clearly outlined in Chapter Two, a combination of qualitative and quantitative research methodologies was employed for the purpose of more comprehensive responses to provide for unexpected developments and to clarify idiosyncratic circumstances. Furthermore, a theoretical framework based on an extensive literature study in Chapters Two, Three and Four assured the reliability (cf. par. 5.8.3, p. 329; 5.9.4, p. 342) and validity (cf. par. 5.8.3, p. 328; 5.9.5, p. 346) of the measuring instruments. Grounded in the conceptualisation of the rather sophisticated research process that was made possible by the illustration of a mixed research model (cf. par. 5.7.4.3, p. 326; Fig. 18, p. 327), the description of the research design and methods in Chapter Five represented the rationale for decisions and procedures pertaining to data collection and the deconstruction process.
In this chapter, the captured data from the qualitative and quantitative research is presented, analysed, described and interpreted in a systematic manner as the next step of the research process. The documentation and analysis process aimed to present data in an intelligible and interpretable form in order to identify trends and relations in accordance with the research aims (cf. par. 1.3.2, p. 12). In turn, the identified trends and relations in accordance with the research aims, would enable the researcher to develop a sport management programme for educator training in accordance with the diverse needs of South African schools.
The research results were firstly presented as an analysis of the qualitative data obtained from the individual semi-structured interviews (cf. par. 5.8.4.2, p. 332). The analysis of the qualitative data was followed by an analysis of the quantitative data that was recorded by the questionnaire (cf. par. 5.9.3, p. 339). Furthermore, it is important to remain mindful of the fact that the data from the qualitative and quantitative sections are connected, in that the results of qualitative data contributed to the development of the quantitative questionnaire for school sport managers^90 and related role players, concerning the relevant needs and competencies in accordance with the diverse needs of
(^90) Cf. p. par. 5.4, p. 316; 5.7.4, p. 324; 5.9, p. 333; 5.9.3, p. 339
schools (cf. Chap. 7). The comprehensive, connected data concludes with findings and recommendations (cf. Chap. 8). The focus now turns to the analysis and interpretation of the data for this study.
6.2 ANALYSIS AND INTERPRETATION OF DATA
Marshall and Rossman(1999:150) describe data analysis as the process of bringing order, structure and meaning to the mass of collected data. It is described as messy, ambiguous and time-consuming, but also as a creative and fascinating process. Broadly speaking - while it does not proceed in linear fashion -it is the activity of making sense of, interpreting and theorizing data that signifies a search for general statements among categories of data (Schwandt, 2007:6). There fore one could infer that data analysis requires some sort or form of logic applied to research. In this regard, Best and Khan (2006:354) clearly posit that the analysis and interpretation of data represent the application of deductive and inductive logic to the research. Verma and Mallick (1999:29) and Morrison (2012:22,24) on the other hand, state that the interpretive approach (cf. par. 5.2.1.3, p. 307), which involves deduction from the data obtained, relies more on what it feels like to be a participant in the action under study, which is part of the qualitative research. Very often the researchers rely on their experience of particular settings to be able to read the information provided by the subjects involved in the study. While this thesis employed a mixed method of data collection, namely a combination of qualitative (cf. par. 5.4.1, p. 316; 5.8.4, p. 330) and quantitative methods (cf. par. 5.4.1, p. 316; 5.9.3, p. 339), it focused on the adoption of a pragmatic position and also used a phenomenological approach in conducting this research.
Antonius (2003:2) succinctly states that the word data points to information that is collected in a systematic way and organised and recorded to enable the reader to interpret the information correctly. As such, data are not collected haphazardly, but in response to some questions that the researcher wishes to answer. Schostak and Schostak (2008:10) capture the essences of capturing data well when they further add, that data are not given as a fixed, but are open to reconfiguration and thus alternative ways of seeing, finding answers to questions one wishes to answer. Implicated in the preceding views of Antonius (2003:2) and Schostak and Schostak (2008:10) are the two methods used to analyse data, namely qualitative and quantitative.
Veal (2006:196); Schurink et al. (2011:397); Sesay (2011:95); Atkins and Wallace (2012:245) and Tuckman and Harper (2012:387) state that a qualitative study involves an inseparable relationship between data collection and data analysis in order to build a coherent interpretation of data. An
The results of qualitative data analysis guide subsequent data collection, and analysis is thus a less-distinct final stage of the research process than quantitative analysis, where data analysis does not begin until all data have been collected and condensed into numbers; Qualitative researchers create new concepts and theory by blending together empirical and abstract concepts, while quantitative researchers manipulate numbers in order to test a hypothesis with variable constructs; and Qualitative data analysis is in the form of words, which are relatively imprecise, diffuse and context based, but quantitative researchers use the language of statistical relationships in analysis.
Apart from Kreuger and Neuman, Robson (2011:408) also offers an equally important view on analysis and interpretation of data, when he posits that the process and products of analysis provide the bases for interpretation and analysis. It is therefore not an empty ritual, carried out for form‟s sake, between doing the study, and interpreting it, nor is it a bolt-on feature, which can be safely ignored until the data are collected. Robson (2011:468) further aptly points out that the central requirement in qualitative analysis is clear thinking on the part of the analyst.
In closing, it can be said that the researcher should keep in mind the sequential list provided by Miles and Huberman (1994:9) of what they describe as „ a fairly classic set of analytic moves‟ :
Giving codes to the initial set of materials obtained from observation, interviews and documentary analysis; Adding comments and reflections (commonly referred to as memos); Going through the materials trying to identify similar phrases, patterns, themes, relationships, sequences and differences between sub-groups; Taking identified patterns and themes out of the field to help focus the next wave of data collection; Gradually elaborating a small set of generalisations that cover the consistency one discerned in the data; and Linking the generalisations to a formalized body of knowledge in the form of constructs (theories.
From the preceding discussion of data analysis and interpretation, the views, ideas and suggestions expressed by different researchers and authors have been identified as important for
use in this study. In the next few paragraphs, the researcher will explore the analysis and interpretation of qualitative data collected for this thesis.
6.2.1 Analysis of qualitative data
Qualitative data analysis can be described as the process of making sense from research participants‟ views and opinions of situations, corresponding patterns, themes, categories and regular similarities (Cohen et al. , 2007:461). Nieuwenhuis (2007:99-100) captures the essence of data analysis well, when he provides the following definition of qualitative data analysis that serves as a good working definition: “..qualitative data analysis tends to be an ongoing and iterative process, implying that data collection, processing, analysis and reporting are intertwined, and not necessarily a successive process”. In short, as Gibbs (2007:vol. 6: 1) so aptly points out, qualitative data analysis is a process of transformation of collected qualitative data, done by means of analytic procedures, into a clear, understandable, insightful, trustworthy and even original analysis.
Marshall and Rossman (1999:150) state that qualitative data analysis is a search for general statements about relationships among categories of data. In contrast with quantitative methods, (cf. par. 5.9, p. 333; 6.1, p. 354; 6.3, p. 431) that examine cause and effect, Muijs (2011:9) posits that qualitative methods are more suited to looking at the meaning of particular events or circumstances. Creswell (2013:44) refer to meaning as the intention of the original author and further state that data analysis is both inductive and deductive and establishes patterns or themes. Patton (2002:432) posits that qualitative analysis transforms data into findings. This involves reducing the volume of raw information, sifting significance from trivia, identifying significant patterns and constructing a framework for communicating the essence of what the data reveal. Henning et al. (2004:127) summarise data analysis as a continuous, developing and repeating process during which transcribed data of interviews are investigated. Leedy and Ormrod (2010:135) further state that qualitative researchers construct interpretive narrative from their data and try to capture the complexity of the phenomenon under study. Qualitative researchers thus use a more personal, literary style, and they often include the participant‟s own language. Robson (2011:468) concurs with the views of Leedy and Ormrod (2010:135) and further reiterates that qualitative analysis remains much closer to codified common sense than to the complexities of statistical analysis of quantitative data (cf. par. 5.9.6, p. 350). Without reservation, in summing up, one could say that qualitative data analysis is based on assumptions, and the use of interpretive (theoretical) frameworks (cf. par. 5.2.1.3, p. 307) to ensure a final written report or presentation that includes the voices of participants, the reflexity of the researcher, a complex description and
Elaborating a set of generalisations, which suggest that certain relationships hold firm in the setting being examined, and affirming that these cover all the known eventualities in the data set. Formalizing these theoretical constructs and making inferences from them to other cases in place and time.
As we have seen so far from our discussion of qualitative data analysis, there are always variations in the number and description of steps for the same process by different authors. To the preceding body of knowledge, outlined by different authors, one can add the views of Watling and James (2012:385-395). According to these authors, the process of qualitative data analysis consists of six stages (steps), namely:
Defining and identifying data. From the outset, it is crucial to obtain a clear understanding of the meaning of data, and fundamentally, even more importantly, the data required in accordance with the research question and aims. Collecting and storing data. When collecting data, most researchers start to form opinions and judgement, which result in theories being developed, in the mind of the researcher, and as such one has to consider not only ways to collect data, but also to store data to make them accessible for analysis. So the interviews for instance can be recorded by means of a digital recorder, transcribed and stored (loaded) on a computer programme such as Atlas.tiTM^ Version 6 (Atlas.tiTM). Data reduction and sampling. During the data collection process (cf. par. 5.8.4, p. 330), reaching a point of saturation implies that all data were reduced, filtered and sampled through the process of analysis. It is therefore critical for the researcher when analysing data to determine what one already knows to be important or relevant, in accordance with the intended purpose of the investigation. Stated differently, the researcher needs to establish, on the one hand, which data are not relevant, and on the other hand, which data encapsulate the essence and evidence one wishes to focus on for a more detailed analysis. Hence, from the preceding can be inferred that it is important to establish incidences and similarities in the respective interviews. In addition, one should establish whether the expected reactions (responses) were obtained and if there are still deficiencies regarding certain questions. Structuring and coding data. Structuring and coding of data underpin the key research outcomes and can be used to shape the data to test, refine or confirm established theory, apply theory to new circumstances or use it to generate a new theory or model, or even in the case of
this study, develop a new measurement instrument, such as a questionnaire (cf. par. 5.9.3, p. 339). During coding, the corpus of data has to be divided into segments and these segments are assigned codes which relate to analytic themes being developed (Fielding, 2002:163) and applied consistently over the period of analysis and over a range of data. Basic coding, carried out as a first step in the analysis of data, is both useful in itself and acts as a preparation of the data for more advanced analysis at higher levels of abstraction (Punch, 2011:175). It can therefore be deduced that structuring and coding signifies an analytical process of elaboration of data, as for instance obtained from semi-structured interviews in related themes, on the hands of codes and structures to form (establish) an understandable framework and associations derived from the language of participants. The process of coding for this study will be considered in a later paragraph (cf. par. 6.2.2.2, p. 370). Theory building and testing. An important purpose of research is to generate new knowledge (Watling & James, 2012:392). To this end, it might be helpful to take into consideration the set of tactics for generating meaning from qualitative data, described by Miles and Huberman (1994:245-246), commented on in an ensuing paragraph. More specifically in relation to theory building and testing as part of the process of data analysis, it can be said that based upon the created framework, relevant diversions (distractions) can be made and insight in the research question under investigation can be obtained. In building and testing theory, it is important to view the reactions of respondents and whether they correspond or not, and also to ensure that a point of saturation of data is reached. Reporting and writing up research. In brief, the reporting and writing up of research entails to put words on paper, in the form of a report, constructing an argument based on the findings of what you have done, what you have seen and heard, participants you interviewed and the information that comes forth from the process of data analysis. Ultimately, the conclusions drawn from the information should contribute to the body of knowledge and represent new meaning and insight in the research question.
Creswell (2013:182-188), contrary to the view of Watling and James (2012:385-395), believes that the process of qualitative data analysis and interpretation can best be represented by a spiral image – a data analysis spiral, in which the researcher moves in analytic circles rather than using a fixed linear approach. One enters with data made up of text or images (e.g. photographs and videotapes), and exits with an account or a narrative. In between, the researcher touches on several facets of analysis, circling around and upwards towards completion of the process. Although the preceding belief of Creswell may be true, he also offers a valuable research tip when
The preceding paragraphs set out to provide the theoretical framework and context for the data analysis of this particular study. In brief, as I have shown, qualitative data are in the form of text, and the act of analysis involves the examining, of all elements of the data sets, to clarify concepts and constructs as well as the deconstruction of the textual data into manageable categories, patterns, themes and relationships according to the research aims (cf. par. 1.3.2, p. 12). The researcher has at his/her disposal different methods for the analysis of qualitative data, and various steps, procedures and processes are described by authors, experts and academics for qualitative data analysis. In addition to the preceding outline of the data analysis (cf. par. 6.2.1, p. 358), for this study the focus should now turn more specifically to the process of qualitative analysis.
6.2.1.1 Process of data analysis
Partington (2003:113) states that there is little standardisation with no absolutes where a specific type of qualitative data relates to a specific type of analysis. Neuman (2011:518) further opines that no single qualitative data analysis approach is widely accepted, while Schurink et al. (2011:403) posit that there are always variations in the number and description of steps for the same process of data analysis by different authors. From the preceding views, it can thus be inferred that each qualitative data analysis to some extent will be a uniquely designed event. With the preceding in mind, the qualitative data analysis of this research (responses from the semi- structured interviews) was done according to a qualitative content analysis process that integrated Creswell‟s (2013:182-188) analytic spiral with the process as described by Marshall and Rossman (1999:152-159) and Watling and James (2012:385-395), comments as offered by Gibbs (2007:vol.6: 1) and Creswell (2009:184-185) and recommendations by Henning et al. (2004:104- 109); Roberts et al. (2006:43); Davies (2007:181-184); Gall et al. (2007:257); McMillan and Schumacher (2010:322-323;366-377); Greeff (2011:359) and Schurink et al .(2011:403-404). The qualitative content analysis involved the following procedures:
Recording of data was done by audio recording on a digital voice recorder, while audio recording on another tape recorder served as backup of electronic failure and faults; and to ensure that all voices could be heard. Taking notes served as further backup and provided the context to the interviews. Verbatim transcription of the responses from the interview commenced as soon as possible; and was done by an expert to ensure a speedy completion. To ensure that the researcher became acquainted with the data for the purpose of analysis and interpretation, the original interview of the completed verbatim transcription was listened to again. Transcription notation
symbols, comments and the taking of field notes as suggested by Henning et al. (2004:76-77) were used to capture non-transcribable text to gain as much of the complete picture as possible. In an endeavour to ensure the reliability and validity of the data (cf. par. 5.8.3, p. 329), the verbatim transcribed interviews were presented to the respondents to verify and sign off. The entire transcribed text and field notes were thoroughly read at first to obtain an overall and comprehensive impression of the content and context before the abstraction process of coding began where units of meaning are identified or labelled. Codes are names or labels assigned to specific units or segments of related meaning identified within the field notes and transcripts (Henning et al. , 2004:104; Neuman, 2011:510). The transcribed text was arranged in meaningful themes and categories with the assistance of Atlas.tiTM^ Version 6 according to codes. As progress was made with the analysis, further sub themes and sub categories were included to identify meaning connections, relationships and trends. The coding process for the field notes and transcripts consisted of three steps described by Thiétart (2007:139) and Neuman (2011:510-514), namely: open coding, axial coding and selective coding. o Open coding involved the identification and naming of segments of meaning from the field notes and transcripts in relation to the research topic. The focus of open coding was on wording, phrasing, context, consistency, frequency, extensiveness and specificity of comments. Consequently, the segments of meaning from the field notes and transcripts were clearly marked (highlighted) and labelled in a descriptive manner. o Axial coding was done by reviewing and examining the initial codes that were identified during the previous procedure outlined above. Categories and patterns were identified during this step and organised in terms of causality, context and coherence. o Selective coding as the third and final coding procedure involved selective scanning of all codes that were identified for comparison, contrast and linkage to the research topic (question) as well as for a central theme or “key linkage” that might occur. The codes were eventually evaluated for relevance to the research aims (cf. par. 1.3.2, p. 12). Related codes were then listed in categories according to the research aims (cf. par. 1.3.2, p.
- and theoretical framework from the literature study (cf. par. 4.9, p. 296, Fig. 15, p. 289). From our discussion thus far, it follows that the analytic process was further informed by inquisitive questions to identify thematic relationships from the various categories, according to both the inductive and deductive reasoning process. Questions included amongst others (Henning et al. , 2004:106):
Figure 19: The qualitative content analysis process
The outlined process enabled a systematic and logic step-by-step approach for the analysis of the qualitative data and allowed the researcher to go beyond mere descriptive, comparative and explanatory ends to discover the rationale and motivation for responses (Thiétart, 2007:361). It is important to bear in mind that the qualitative process was approached like a spiral process and not as a fixed linear action. The content analysis outlined in the preceding paragraphs, and illustrated in Fig. 19 (cf. p. 366), often overlaps and implies that the various steps of analysis are regarded as mere procedural guidelines and not as rigid steps like that of a recipe (Schurink et al. , 2011:403). In other words, although the content analysis process should be systematic and can be a logic step-by-step approach, it nevertheless should be adaptable to the specific needs and requirements of the researchers.
The identified themes were further used as a basis for reasoning, argumentation, deliberation, contemplation and the formulation of syntheses and conclusions to develop, in combination with the quantitative data, a sport management programme for educator training in accordance with the diverse needs of South African schools (cf. par. 1.3.2, p. 12; Chap. 7).
To summarise, in relation to the current study, the qualitative data included the recorded responses from the semi-structured interviews (cf. par. 5.8.4.2, p. 332). The qualitative data analysis involved the deconstruction of the qualitative data into manageable categories, patterns, themes and relationships in accordance with the research aims (Mouton, 2001:108; Neuman, 2011:509-510). In accordance with the research aims (cf. par. 1.3.2, p. 12), the aim of the qualitative analysis of this research was to examine the various elements of the captured data to clarify concepts and to identify patterns, themes and relationships. By means of organising, reduction and the description of the data, the researcher attempted to interpret, make sense of, give meaning to school sport management and build theory to support the extensive literature review (cf. Chapter 2-4) in an endeavour to develop a measuring instrument, namely a questionnaire (cf. par. 5.7.4.2, p. 325) to determine the needs and competencies required by educators to manage school sport effectively in accordance with the diverse needs of South African schools (cf. par. 1.3. 2, p. 12, research aim 4). The methodology for the analysis of the qualitative data was based on the content analysis procedures (cf. par. 6.2.1, p. 358), and was presented in two sections according to the nature of the capturing data. At first, the focus turns to providing a discussion on the analysis gleaned from the semi-structured interview, which is followed by a discussion of the interpretation of the interviews and the analysis and interpretation of the quantitative part of the empirical investigation, namely the questionnaires.
6.2.2 Interview analysis
Interviewing as data-gathering method (cf. par.5.8.4.1, p. 331) was included to obtain additional data, clarify vague statements, permit further exploration of research topics, expand on the qualitative findings and yield a more in-depth experiential account of the extent of school sport management and the views of school sport managers on the competencies and needs of school sport managers for educator training according to the research aims (cf. par. 1.3.2, p. 12).
The recording of the interview data took place by means of note-taking and audio recording (cf. par. 6.2.1.1, p. 363). Verbatim transcripts of the interviews were compiled for analysis and interpretation. To ensure reliability and validity of data, the transcribed interviews were presented to respondents to verify and confirm the contents of the interviews (cf. Annexure O, on CD). However, only two respondents signed and returned the transcribed text, while all six respondents gave consent to the recording of the interview by means of a tape recorder and a backup recorder (cf. Annexure G, on CD). In addition, the notes taken during these interviews served as another backup during the recording procedure.
all quotations in a specific document that relate to a specific code. This process would involve significantly more time, were it done manually. Considering that not all hunches regarding code formations will be correct, computer-aided content analysis saves countless hours of fruitless work by providing data manipulation at the click of a mouse button. Atlas.tiTM^ allows codes to be dynamically linked to quotations and documents in a way that facilitates the quick and easy navigation of complex sets of data.
Processes inherent in Atlas.tiTM^ mean it fits well with qualitative content analysis as it allows formerly manual procedures to be accomplished more quickly, and establishes a definable audit trail. Sections of text is selected with the mouse and then either associated with an existing code (s), or used to define new ones. Also, using the program was a constant reminder of the systematic and procedural complexities necessary in order to produce a thematic content analysis of the quality required in the academic context. Although the interpretive phase still involved many hours of thought and interpretation, Atlas.tiTM^ facilitated this process by keeping track not only of all the data in isolation, but also in reference to the overall context. The checking and rechecking of assumptions and conclusions is also greatly facilitated by the use of computer aided analysis such as Atlas.tiTM.
In sum, concluding the discussion of computer assisted thematic content analysis, more specifically in relation to the current study; use was made of a software program, namely Atlas.tiTM^ Version 6 for the analysis and interpretation of the semi-structured interviews. Here originates the building of theory, by making use of codes assigned to the most important aspects also applicable (relevant) to this research report. Moreover, the said Atlas.tiTM^ programme appears to be the exception on the rule on all other software programmes, because it offers the user the opportunity to make all possible connections between the different codes of the qualitative data base (Kelle, 2004:483). Equally important is the quick recall of data like an idea obtained from the networks created, which makes this program more than useful and valuable (Creswell, 2008:249). In this regard, given the extent of the study and the purpose of the qualitative part of the report, the researcher made full use of the feature of Atlas.tiTM^ to compile (provide) networks from the most important quotes connected with certain codes. As a result then of the connection (merger) of data obtained from the interviews, the researcher was able to develop a theoretical framework and gain insight for the development of a questionnaire, which will be presented in another part of this report (cf. par. 5.9, p. 333; par. 6.3, p. 431).
6.2.2.2 Content analysis process
The analysis of the interview data was done according to the content analysis process as described in par. 5.8 (p. 328) and 6.2.1.1 (p. 363) At first, all notes and transcripts were read to gain an overview of the body and context of the gathered data. The reading process was followed by a three-step coding process that consisted of open, axial and elective coding procedures (cf. par. 6.2.1.1, p, 363), making use of the Atlas.tiTM^ Version 6 computer program. The open coding step led to the initial identification and marking of descriptive names for specific units or segments of meaning in relation to the research aims. These identified units of meaning to a large extent bore relation to and showed consistency with the questions that were asked during the interviews (cf. Annexure B, on CD). All these labelled units of meaning, as preliminary qualitative indicators, were again evaluated during the axial and selective coding steps for coherence and relevance to compile a final list of codes (cf. par. 6.2.1.1, p. 363; Fig. 20, p. 374). Each category was systematically labelled in accordance with the relevance of the data and theoretical framework from the literature study (cf. par. 4.8, p. 283; Fig. 15, p. 289). In Figure 20 (cf. p. 374) a comprehensive framework that was compiled by means of Atlas.tiTM^ Version 6, and which contains all codes of interest to the research report, is presented. The framework in particular outlines and illustrates the main components (categories) of the data analysis done for the qualitative part of this study.
The identified categories were: Core competencies Functional competencies Specialist competencies Resources Training Governance Specialists Compulsory Physical Education Funds Infrastructure Support
The related units of meaning from the notes and transcripts were systematically assigned to the final categories. After a brief introduction of each of the main components, a discussion followed
obtained a doctorate. The work experience varied from 01 to 09 years (2); 10 to 19 years (1) and more than 30 years (3). Some of the positive experiences of the interviewees were included in the analysis of the data (cf. par. 6.2.1.1, p. 363; 6.2.2, p. 367). Although the interviewees set aside time for the interview appointment, some interruptions did occur for them to attend to urgent school and academic matters. All interviews were however completed in full.
The presentation and discussion of the semi-structured interview data follow in the next sections.
6.2.3 Presentation and discussion of the semi structured interview data
The purpose of the semi-structured interview, as stated previously (cf. par. 1.3, p. 12; 6.2.1.1, p.
- was to identify related themes during the interpretation process that would contribute towards the development of an appropriate instrument for the quantitative phase of this research (cf. par. 5.7.4.1, p. 325; 5.7.4.2, p. 325; 6.2.1, p. 358).^91 From Fig. 20 (cf. p.374) it can be readily observed, that two themes in particular emanated from the analysis of the semi-structured interview, namely competencies and needs. This is also reflected in the research problem, namely to develop a sport management programme for educator training in accordance with the diverse needs of South African schools (cf. par. 1.3.2, p. 12).
Sport management competencies , as was already indicated elsewhere (cf. par. 1.2.1.5, p. 5) refers to the knowledge, skills, values and individual characteristics in a specific context (school sport) subjacent to the management action of school sport applied to a defined standard of performance of a particular task (Bellis, 1997:8; Hollander, 2000:20; Gerber, 2009:12; Soucie, 2013:2). For purposes of this study, sport management competencies were earlier identified and described as core, functional and specialist (par. 3.6.9, p. 195; 4.8, p. 283; 4.9, p. 296; Table 18, p. 265).
Needs, the second theme that emanated from the semi structured interview, relate to those things that are necessary to stay alive (Bright et al. , 2011:167). For the purpose of this study, needs would thus focus on those things (tangible and intangible) necessary to manage school sport effectively and efficiently. Having said that, one is also reminded that the school setting in the South African context is unique, and different schools from different socio-economic status, background (history), community (historically advantaged and historically disadvantaged), type of school (public or independent, secondary or primary), classification (combined, ex Model C,
(^91) Cf. also, par. 1.3.2, p. 12
technical, rural, agricultural and township), number of learners participating in sport, composition of the school (boys or girls only and combined), cultures, ethnical origin, number of educators available to offer sport– all of these impact on the competencies and needs required by the school sport manager. The development of a sport management programme for educator training should thus be a deliberate effort to ensure that the needs, demands, requirements and competencies of educators are met (Kerr, 2003:2; O‟Shea & Watson, 2007:53-54; Ko et al. , 2011:302; Veri & Walsh, 2012:50-51). In short, programme development is therefore a close (intimate) intertwinement between the needs of theory and practice and implies a good relationship between schools and HEIs which offer relevant programmes for training. This twofold approach of school (community or practice) and HEIs can be seen as the focus of this research. Schools, in relation to the current study, should identify clear goals, objectives, timeframes, training requirements and competencies required for the management of school sport, while the interests and responsibilities of HEIs to meet the needs, demands and requirements to schools to offer well qualified, trained and competent educators are dealt with in an organised, systematic manner in accordance with a planned structure and long term plan and goal. In Figure 21 (cf. p. 375), the competencies identified from the responses of the interviewees are depicted.
Responses assigned to this category represented the respondents‟ viewpoints and perceptions about the competencies required to manage school sport. As was stated elsewhere, in coherence with the literature overview, (cf. par; 3.6.9, p. 195; Fig. 12, p. 199; par 5.5, p. 319), some of the responses implicitly indicated that the competencies required to manage school sport should include core, functional and specialist competencies. Core competencies are those capabilities that are critical to a business achieving competitive advantage (Riley, 2012:2). Business dictionary (2013) takes a similar standpoint on their interpretation of core competencies, but add that in general, core competencies are the unique ability that a company acquires from its founders, or develops, and as such these core competencies cannot be easily imitated or replicated. Theoretically, a core competency should allow a company to expand into new end markets as well as provide a significant benefit to customers according to an unknown author in Investopedia (2013:10). In the context of the current study, the core competencies required to manage school sport should be seen as the central or innermost unique knowledge, skills, values and attitudes that are essential to manage school sport. Elsewhere (cf. par. 3.6.9, p. 195; 4.8, p. 283; 4.6.3.4, p. 257; Table 18, p. 265) reference was made that core competencies for the management of school sport can be divided into fundamental (foundational/essential) and general (basic day-to-day) management competencies.
