RACISM IN SPORTS – HOW ATHLETES’ DESCRIPTIONS OF SELF MIGHT ALIGN WITH THOSE OF MASS MEDIA

This research proposal was submitted by mail on May 4, 2010 as the final paper for SOC 885: Methods of Sociological Inquiry taught by Dr. Tom Conner at Michigan State University.  This research proposal is intended to be used for the dissertation of John Girdwood in partial fulfillment to obtain a PhD in Sociology at MSU.

Assignment

“Select a published research article that uses one method to investigate a particular substantive problem” (Conner, 2009)

Article

Abstract

Race relations is an ongoing American social problem in need of constant study within the discipline of sociology (Bash, 1979).  There are a variety of ways to analyze race relations, from a global perspective down to an individual level.  To gain a better understanding of race relations in America, I will examine the issue of stereotyping by race in sports.  I will use one study that conducts content analysis on sports commentary in American mass media (Denham et al., 2002) to lay a foundation for further proposed research.  In the proposal, I will convey two other methods to attack the same problem.  Those methods are survey and interview.  By combining previous research (of the perception of the collegiate athlete in the mass media) with proposed research (to discover the perception of self by the collegiate athlete), I will be able to determine relationships of perception of race between self and other.  The findings will be directly relevant to race in American sports and generally applicable to further the study of race relations in America.

I will produce findings of whether “racism” and “stereotyping by race” exist in NCAA men’s basketball at the time of the study.  This objective aligns with Denham, who conducted “a comparative analysis of announcer commentary… [and] provided an appropriate heuristic for the study of race descriptors.”  The hypothesis presented in the current research proposal is that the athletes will convey descriptions of self that are similar to those descriptions professed by sports commentators in the Denham study.  One result of the current research proposal is a reworked heuristic that is appropriate for the study of race descriptors (especially in regard to athletes’ descriptions of self).

Background of the Problem

Due to the worldwide reach of sports, and the many groups and individuals that observe and participate in athletics, there is no need to discuss the matter of the existence of racism in sports.  However, gaining a better understand of the social norms that govern a specific league or national sports association is a viable route to uncovering how racist attitudes are expressed in the sporting realm.

RACISM IN SPORTS – HOW ATHLETES’ DESCRIPTIONS OF SELF MIGHT ALIGN WITH THOSE OF MASS MEDIA

Literature Review

Denham et al. (2002) address the issue of stereotyping by race in sports.  The authors suggested these standardized conceptions: “(a) white athletes are frequently praised for their perceived ‘intellect’ and ‘leadership capacity,’ while (b) black athletes are often praised for being ‘naturally talented’” (Denham, Billings, & Halone, 2002).  Many others have displayed research with similar findings, explaining the aforementioned points in a juxtaposed fashion, professing that black athletes are not praised for leadership and/or that whites are not seen to be athletic (Davis & Harris, 1998; Edwards, 1969; Hoose, 1989; Jackson, 1987, 1989).  Many of these stereotypes were developed in Wenner’s “Mediasport,” (1998) the ultimate reader of sports media sociology.  Denham focused on the aforementioned stereotypes within current (2002) sports commentary.

The assumptions of the sports commentators under study were that “black athletes are expected to succeed athletically; conversely, white athletes are expected to have an innate ability to overcome seemingly insurmountable odds to accomplish their athletic stature” (Denham, Billings, & Halone, 2002).  Those possessing the expectations were sports commentators.

Sports broadcasting is a branch of the mass media.  Denham discussed the portrayal of racial stereotypes in sports through the mass media, who hold the power of influence in regard to telling listeners and viewers what they should believe.  Prior research has detailed such power of influence held by mass media outlets (Girdwood, 2009).  Hove (2008) stated that “paradigmatic instances of influence [existing in mass media] include persuading, giving advice, and supplying people with reasons for their own convictions.”  A unidirectional sports broadcaster’s verbal influence differs from a discourse because it entails an asymmetric relation between speaker and listener.

In mass-mediated communication, using sports broadcasting as an example, influence rarely occurs through dialogue between speaker and listener. The asymmetry of this “mediated quasi-interaction” (Thompson, 1995) relieves both speaker and listener from the justificatory obligations (Hove, 2008) required during certain acts of communication. Hove conveys the example of scientific reputation facilitating laypeople’s willingness to accept researchers’ cognitive validity claims.  When the broadcaster is assumed to be an “expert” then listeners are generally inclined to take them at their word.

Although Hove focuses predominantly on mass media influence, the same type of discourse occurs on an interpersonal level when a survey is conducted.  As the viewpoints expressed by the “expert” hold merit when heard by the layperson, dialogue must host some element of pursuasion in the direction of communication from interviewer to athlete when initial biographical surveys are conducted.

Part I: Methodology of Published Research Article

Denham outlined three detailed areas of advancement that resulted from the study.  First, Denham focused on analyzing stereotypes existing at the highest level of college basketball.  A prior study by Eastman & Billings (2001) concentrated on racial stereotypes during regular season games.  Second, Denham extended the classification technique of Eastman & Billings (2001).  A 15-category scheme was first outlined by Eastman & Billings as a method for analyzing sportscaster dialogue.  The methodology of that 15-category scheme will be discussed later in this article.  Finally, Denham attempted to discover whether or not racial stereotypes of previous generations could be prevalently found as expressed by the commentators analyzed in the 2000 study.

Denham used content analysis as the primary methodology.  The unit of analysis was a line of broadcast commentary.  The research involved “content analysis of 1,118 descriptors embedded in commentator discourse.”  A descriptor “defines the syntax and semantics of a feature” (Li & Kuo, 2003) and, for purposes of this study, is a verbal element found within a sports broadcast.   The results of the content analysis led Denham to find that, “while black athletes continue to be praised for their athleticism and physicality, they also are receiving a greater number of comments about their intelligence and ability to lead.”

The coding procedure involved four individuals (two white males and two white females).  Two of the four individuals had been or was currently a student athlete.  The other two coders had never been and were not currently a student athlete, yet were familiar with the culture of collegiate athletics.

The method Denham uses to code for race is intriguing.  The four coders chose what race they thought the player appeared to be, white or black, and the code was determined regardless of actual racial background.  This is very appropriate because the study is based on perception, of which visual perception is included.  The perception of reality (James & Wilshire, 1984) is a valid framework to base a study on mass-mediated descriptions of events.  Denham concedes that further research is necessary to build more precise definitions of each athlete’s race but also that stereotypes are based on a broad construction of what constitutes a person’s race.

[Footnote: "Birrell (1989) has argued also that studies also need to move past examining black males almost exclusively and devote some focus to Native-Americans, Latino/a-Americans and Asian-Americans.  As noted in the present article, however, more than 99 percent of the athletes in the respective Final Fours could be identified as white or black" (Denham, Billings, & Halone, 2002).]

Denham proposed the following reasons for the chosen method of coding race: “(a) commentators were more likely commenting on the apparent race of an athlete rather than their genealogical biography, (b) all four coders were able to code all athletes into these two broad categories with 100% agreement by watching videotape of the telecasts, (c) no commentary within any of the telecasts specifically mentioned any other racial backgrounds, and (d) the choice is consistent with previous work on athlete race (see Eastman & Billings, 2001)” (Denham, Billings, & Halone, 2002).

Overall, Denham sought to advance the knowledge of stereotyping by race in sports through analyzing contextual suggestions by commentators during sports broadcasts.  The 2002 study addressed the social problem of racism within college basketball.  The social norms that govern sports broadcasting were analyzed to show exemplified racist attitudes that exist within one realm of American sports.

“Highest Level” Performers

College basketball is unique in that it hosts one of the most expansive collections of upper echelon teams to decide its champion.  Denham was able to analyze the prior trends discovered by Eastman & Billings by extending the research to include post-season play.  “This study addresses whether these same trends [racial characterizations] hold true at the highest level of college basketball” (Denham, Billings, & Halone, 2002).  Denham is correct in stating that the sixty-four teams participating in the annual NCAA Men’s and Women’s Final Four Basketball Tournaments are considered the consensus highest-caliber performing group.

The authors claim to make “several important advancements with respect to the knowledge base on stereotyping by race in sports” (Denham, Billings, & Halone, 2002).  However, the study was limited to an examination of “the broadcast commentary surrounding white and black athletes at the 2000 NCAA Men’s and Women’s Final Four College Basketball Tournaments” (Denham, Billings, & Halone 2002).  Although the authors claimed to make specific advancements regarding the knowledge base on racial stereotyping in sports, it is clear that they only contributed a better understanding of one side of the microphone: sports announcers.  More research is needed as to how the athletes themselves interpret, express, and understand stereotypes.

Taxonomy

One essential product of a content analysis study is the categorization language.  Taxonomic analysis is a useful method of classifying linguistic descriptors because taxonomic classification can “illustrate the internal organization of a domain and the relationship among the subcategories of the domain” (Polit & Beck, 2008).  In the Denham study, the ultimate domain is sports in general and the subcategory under analysis is NCAA Men’s and Women’s Basketball, particularly the participants of the 2000 tournaments which represent the “highest level” of college athletes at that time.

Later in this essay, I will show how classification techniques can occur in a much broader fashion, especially as a framework for ethnographic study.  However, when something as specific as collegiate men’s and women’s basketball is analyzed it is necessary to make the categories as clear and distinct as is appropriate. Therefore, ethnography was excluded as a methodology for this research proposal.

Denham implements the taxonomy first employed by Eastman and Billings (2001).  Denham sought to discover the usefulness of the 15-category scheme and its applicability to analyzing sportscaster dialogue.  The 15 categories under analysis by Denham are:

  1. physicality = athleticism (“good athlete,” “springs off the floor”)
  2. intelligence = mental skill (“thinks on her feet”)
  3. hard work = effort (“one of the hardest workers”)
  4. determination = motivation (“X got the best of her in the last game, so she is pushing here”)
  5. speed (“fast,” “quick on his feet”)
  6. physical power (“strong man”)
  7. mental power (“toughness off the bench”)
  8. positive consonance (“all of a sudden, she’s firing on all cylinders”)
  9. negative consonance (“not in his rhythm”)
  10. leadership (“senior leader”)
  11. versatility (“not a scorer”)
  12. team orientation (“giving everything for the team”)
  13. personality (“patient”)
  14. looks = appearance (“he’s much taller than X,” “she’s changed her hair style”)
  15. background (challenges, hardships, advantages; “her father coached her…”)
  16. other

It is clear that some of the categories can be grouped and it appears the list is in no particular order.  Later in this essay, I will group and reorder the list to align into more distinct and related categories.  The authors make no mention of the order of the list.

Previous Generations

The weakest of the three intended advancements of the study, whether or not racial stereotypes of previous generations could be found, was quite simply glanced over by Denham in the conclusion of the article.  Stated succinctly, “the stereotypes scholars have identified in previous studies did appear in this analysis” (Denham, Billings, & Halone 2002).  However, there was very little mention or proof of the specific stereotypes held be previous generations.  The authors only eluded to the position that the current racial stereotypes are similar to those from the past.

Denham only includes one category of classification in the 15-category scheme related to previous generations: background.  Furthermore, “background” is quite vague and includes many of the elements of SES (socio-economic status).  It seems appropriate to break down that category into more specific classifications such as family background, economic background, educational background, etc.  That may shed more light on other similar classification categories like intelligence (educational background), athletic background (participation in track & field as a sprinter might coincide with a basketball player being labeled “quick on his feet”), etc.

Content Analysis

Content analysis was an appropriate method to use for this study.  The unit of analysis (a line of broadcast commentary), descriptors (the syntax and semantics of a feature), and coding (using the Eastman & Billings 15-category scheme) were all appropriate.  Previous research has also incorporated content analysis into similar studies (Billings, Halone, & Denham, 2002; Billings & Eastman, 2001, 2003).

Content analytic methods (Kaid & Wadsworth, 1989; Emmert & Barker, 1989) have been employed in previous studies to analyze broadcast commentary in a variety of ways, e.g. race, sex, and gender (Haigh & Heresco, 2010; Holz Ivory, Gibson, & Ivory, 2009; Signorielli, 2009).  “Because the Men’s and Women’s Final Fours were telecast on major networks (CBS and ESPN, respectively) and occurred on the same extended weekend (March 31–April 3, 2000), conducting a comparative analysis of announcer commentary within both Final Fours provided an appropriate heuristic for the study of race descriptors within both men’s and women’s college basketball” (Denham, Billings, & Halone, 2002).  Utilizing content analysis to study mass-mediated communication is a commonly used methodology and there is strength in the foundation of research related to such a study.

A critique of the use of content analysis, as it applies to the research problem at hand, includes coverage of both the positive and negative aspects of the methodology.  I will now display the positive and negative aspects of content analysis, the inherent strengths and weaknesses of the method, as they apply to this research problem.

Strengths of Content Analysis Use by Denham

The first strength to using content analysis to approach the problem of racism in sports by examining the 2000 NCAA Men’s and Women’s Final Four Basketball Tournaments is that content (commentary) is available.  In other words, certain methods are automatically excluded from employment during the tournament.  Surveys, interviews, and experimentation are all examples of methods that would be nearly, if not completely, impossible to conduct during the tournament.  At this point in the season, the athletes are completely unavailable to researchers in a direct personal manner.  Therefore, content analysis is a workable method to pursue.

Second, it has long been theorized that race is a social fact (perhaps constructed and/or perceived embodiment) rather than a genetic fact (Fanon, 1986; Gilroy, 2000; Gooding-Williams, 2009; Gordon & Gordon, 1995; Uteng & Cresswell, 2008).  Furthermore, race may be an external social fact.  Therefore, it is appropriate and effective to utilize an examination of the other, or “audience” (Goffman, 1981), to discover elements of racial thought.

The final strength I will include regarding the use of content analysis is that it consists of a thorough investigation involving a large quantity of data, precisely 1,118 descriptors.  Had the researchers attempted another method, e.g. interviews, they would ultimately have collected less data to analyze.  It is not necessarily an automatic benefit to collect as much data as possible, but it certainly contributed to the strength of the research performed by Denham.

Weaknesses of Content Analysis Use by Denham

The first weakness is confessed by Denham that the coding group was “not a bona fide delphi panel.”  I assume Denham means the group did not utilize the Delphi Method defined by Jonassen, Tessmer, & Hannum (1999) to be “a structured group interview technique for seeking consensus among a group about ideas, goals, or other issues.”  It would not make sense that the panel would be such a group, so it is likely that Denham simply used a poor illustration to describe the weaknesses of the group which include homogeneity (all coders were white) and the fact that all four coders had some familiarity with college athletics culture (either were current or former athletes or were “familiar with the culture of collegiate athletics”).  It would have been beneficial to the research had at least one coder been oblivious to the culture of college athletics so that an alternate view could be inserted.

A second failure to use the method most effectively was the focus solely on the tournament rather than various parts of the season.  Denham admitted that, during the tournament, “announcers, and/or their producers, often choose a select few players (e.g. Mateen Cleaves) on which to focus, as evidence by the number of background descriptors, for any number of reasons (e.g. exceptional talent, overcoming adversity, superb leadership qualities)” (Denham, Billings, & Halone, 2002).  This fact is immensely important to the outcome of the research.

Although the study was presented as a content analysis of strictly the tournament, no discussion was given to what impact that had on the results.  In other words, Denham did not predict nor deny that these results were indicative of the rest of the season.  If the results are intended to be a contribution to overall research on stereotypes in sports, then there should have been at least mention of this.  Denham claims in the problem statement that this study “makes several important advancements with respect to the knowledge base on stereotyping by race in sports” however those advancements are severely limited in scope due to the choice of limiting the study to strictly the tournament games.

Part II: Introduction to Proposal for Additional Necessary Research

To expand on the research of Denham, and to further answer the research objective of making important advancements with respect to the knowledge base on stereotyping by race in sports, it is necessary to conduct further content analysis with a broader scope.  To accomplish this, I propose to use the same method (content analysis) but in an improved way and using two other methods as triangulation methods.  The additional methods are (i) survey and (ii) interview.  I will provide a brief introduction to triangulation and then elaborate on the use of survey and interview methods to advance the study of stereotype by race in sports.

Triangulation

Triangulation was “first used metaphorically in the social sciences to characterize the use of multiple methods to measure a single construct (Campbell 1956; Campbell and Fiske 1959; Garner 1954; Garner, Hake and Eriksen 1956)” (Walford & Massey, 1998).  In this proposal, I am constructing an analysis of stereotyping by race in sports.  The multiple methods include content analysis, survey, and interview.  Walford & Massey go on to define triangulation as “a practice also referred to as multiple operationism, convergent operationism, operational delineation, and convergent validation (Campbell and Fiske 1959).”  For purposes of this proposal, the most suitable alternate definition presented is “convergent validation” in that the three methods are expected to come together to support one another and give proof of the hypothesis (stated later in this essay).

Knafl and Breitmayer (1989) see convergence and completeness as the two principal aims of triangulation (Walford & Massey, 1998).  I expect the findings of each method used in the proposed research to converge in support of the hypothesis.  Additionally, the proposed study will be stronger than the previous research because the findings will be more complete.  Completeness will derive from overcoming the aforementioned weaknesses of the Denham study through the augmentation of data and analysis gained by the additional methods.

Research Proposal

The primary substantive problem I will address is stereotyping by race in sports.  The domain I will focus on is collegiate athletics.  Initially, the research will be limited to NCAA men’s basketball in order to gain a complete understanding of one gender, one sport, and one specific cohort of players.  Because of the global coverage of sports and the many groups and individuals that observe and participate in athletics, I will not discuss the general existence of racism in sports.  However, gaining a better understand of the social norms that govern NCAA men’s basketball is a valid route to uncovering how racist attitudes are expressed within a particular group.

This article is not meant as a theoretical or progressive discourse on racism.  This article is analytical.  This study is strictly an analysis of stereotyping by race in sports.  Therefore, it is necessary to define the term “racism” as it is used in this proposal.  In this essay, “racism” is defined as “stereotyping by race.”  I will not posit an opinion or findings that will contribute to an expanded definition of “racism” nor is “racist” language (i.e. “race descriptors”) interpreted as either morally good or bad for purposes of this article.  Simply put, I will present “racism” and “stereotyping by race” synonymously.

In sum, I will produce findings of whether “racism” and “stereotyping by race” exist in NCAA men’s basketball at the time of the study.  This objective aligns with Denham, who conducted “a comparative analysis of announcer commentary… [and] provided an appropriate heuristic for the study of race descriptors.”  The hypothesis presented in the current research proposal is that the athletes will convey descriptions of themselves that are similar to those descriptions professed by sports commentators in the Denham study.  One result of the current research proposal is a reworked heuristic that is appropriate for the study of race descriptors (especially in regard to athletes’ descriptions of self).

I will address stereotyping by race in sports through content analysis, survey, and interview methods.  First, I will describe the rationale for the changes I propose to the original method (content analysis).  Second, I will convey a detailed rationale for the selection of the other two methods (survey and interview).

Content Analysis

Denham implements the taxonomy first employed by Eastman and Billings (2001) to uncover the usefulness of their 15-category scheme as a feasible method for analyzing sportcaster dialogue.  Those categories are: physicality, intelligence, hard work, determination, speed, physical power, mental power, positive consonance, negative consonance, leadership, versatility, team orientation, personality, looks, background, and other.

There are several of the categories that can be appropriately blended together.  I will use the following synthesized categories of description:

  1. physical index = height and weight
  2. background = hometown and previous school
  3. academics = major and GPA or other mentions of academics
  4. family = any mention of family
  5. skills = shooting, ball handling, defense, moves, etc.

Physical index is a more controlled measure of #14 “looks = appearance.”  “Physical power” #6 has been excluded.  Skills are similar to #1 “physicality = athleticism” and includes #5 “speed.”  I have omitted #11 “versatility” because it was a description of lacking athletic skill (“not a scorer”).  Background is slightly more controlled here than in the Eastman and Billings category (“challenges, hardships, advantages”) and I have added the family category, originally included as #15 “background” by Eastman and Billings.  Academics is similar to #2 “intelligence = mental skill.”  “Mental power” #7 has been excluded.  I have omitted #10 “leadership” because I have gathered data on the academic standing of each athlete (i.e. class year) however it is unnecessary to discuss the “senior” label in relation or addition to being a “senior leader.”  The following categories are unnecessary: #3 hard work, #4 determination, #8 positive and #9 negative consonance, #12 team orientation, #13 personality, and #16 other.  Although the omitted categories hold some relevance to the Denham study of sportscaster commentary, these categories were deemed irrelevant to this study.

Limitations to this study prevented a broadened collection of data from years other than the 2009-2010 season.  In order to better pinpoint whether racial biases existed in previous generations, as Denham sought to find, further research is needed to analyze similar presentations of content from prior years.  I have described here the improvements made to the method of content analysis as it applies to the research problem at hand.  I will address changes to acquiring the actual content later in the essay.

Survey

Perhaps the primary consideration when performing a survey is efficiency.  Web-based surveys are often currently considered the most efficient means of conducting surveys.  “One question that arises when discussing the usefulness of web-based surveys is whether they gain the same response rates compared to other modes of collecting survey data.  A common perception exists that, in general, web survey response rates are considerably lower.  However, such unsystematic anecdotal evidence could be misleading and does not provide any useful quantitative estimate” (Manfreda, Bosnjak, Berzelak, Haas, & Vehovar, 2008).  The response rate is an important factor as is response time, another element of efficiency.

In a study done by Cobanoglu, Warde, & Moreo (2001), mail, fax and web-based surveys in a university setting were compared to determine response speed, response rate, and costs.  “The survey was distributed to 300 hospitality professors randomly chosen from [an] online directory. It was found that the fastest method was fax, with an average of 4.0 days to respond, followed by web surveys with 5.97 days. The slowest method, as expected, was mail surveys, with 16.46 days to respond.”  The Cobanoglu study is relative to the proposed research in this essay because of the similarity of a university setting and the proximity in year conducted.  Due to the cost of paper, ink, and analog phone dialing when faxing, the 1.97 day latency in receiving a web survey response is trivial.

Sparrow (2006) expressed the primary drawback of online polling to be relatively low internet penetration and the fact that the online route relies on the willingness of participants.  This weakness is easily overcome by the proposed research because (i) the survey in question is more like a questionnaire than a poll and (ii) the participants are not only willing but are somewhat forced into submitting responses.  The discussion is not whether the participants will contribute responses, the argument is whether or not a forced or quasi-forced participation inserts influence to the responses given.  This worry is minimal when compared to incentives that are normally given during surveys and interviews.  I see no difference between the somewhat forced participation by a college athlete in a study and any typical incentive exchanged for responses to a mail, telephone, or other survey.  Sparrow adds one more important point, that any method must seek “to ensure [online] respondents carefully consider the answers they give, and design questions and answer codes that do not inadvertently lead [online] respondents to certain answers.”  This sentiment is true for both web-based and other survey methods.

Online surveys are now a cost-free option.  The quality of web-based survey responses is comparable to data collected through phone and mail surveys (Coderre, St-Laurent, & Mathieu, 2004).  “A growing number of researchers regard the web as a speedy, cheap and effective alternative to traditional data collection methods.  Not only can web surveys deliver large samples within a short period of time, but also they can do so without the costs of interviewers, training, postage, data entry, and a myriad of other associated expenses.  Furthermore, industry experts argue that web-based surveys can offer higher quality data due to elimination of interviewer error and built-in checks that prohibit respondent errors (McCullough 1998; Dillman 2000).  Researchers will no doubt continue to gravitate toward web-based surveys because they are fast, cheap, and can produce large samples.  However, caution should be exercised before assuming that results obtained from web-based surveys produce data equivalent to telephone surveys” (Roster, Rogers, Albaum, & Klein, 2004).  It was thoughtful of Roster to warn of the possibility of dissimilar results, however that should be quite obvious.

If web-based surveys produce different data than telephone surveys, then it is necessary to determine which data is more accurate.  Roster finds that, in addition to the “substantial cost advantage to collecting data using web-based surveys (cost for the web survey was 53% lower than for the telephone survey), the findings also lend support to the notion that web surveys may be equally, if not more, accurate than telephone surveys in predicting behaviors.”  Continuing, Roster explains how web-based surveys can provide benefits in addition to interviews.

Interviewer effect is a commonly agreed upon element of the interview method.  Bailey (2007) posits that race and ethnicity, sex, social status, age, clothing and grooming of the interviewer (and interviewee) can affect responses during some interviews.  Although Bailey describes typically visual elements occurring within a face-to-face interview, Roster addresses interviewer effect within telephone surveys.  “The removal of interviewer apprehension also appears to have increased item omissions to demographic questions and led to more neutral or negative attitudinal evaluations in the web survey as opposed to the telephone survey.  Respondents to the web survey also seemed to adopt a more streamlined cognitive response style… This is an interesting finding that warrants further research, especially in light of the fact that this simpler factor structure proved to be just as predictive of overall behaviours and attitudes as the more complex structure elicited by responses to the telephone survey” (Roster, Rogers, Albaum, & Klein, 2004).  As a result of using multiple methods in this research proposal, one outcome of triangulation might include an analysis of different responses given during different methodological applications.  For example, the research might show trends of difference in responses to a survey versus responses conveyed during a one-on-one interview.

Interview

I will advance the knowledge base on stereotyping by race in sports with data obtained through interview.  First, I will describe a few different approaches to interview methodology, including the strengths and weakness of each.  “At one end of the spectrum lies the structured interview, commonly used in large-scale surveys, where question wording and ordering are closely specified, and response formats predetermined.  The advantages of this approach for quantitative studies are well-known: researchers can feel confident that the same ‘stimulus’ has been presented to all study participants, interviewer effects are minimized, and, provided the questions are well-worded, good reliability should be relatively easy to achieve” (Maughan, 2004).  Although the structured interview has those strengths, its weakness lies in its similarity to a survey.  In other words, the structured interview might inevitably be an interviewer reading aloud the same questions included on an online survey.  In this case, there is little difference from the online survey.

One method used to seek respondent answers that are not pre-programmed is the unstructured interview.  “Here the prime emphasis is on understanding the meaning of events or experiences to the respondent, rather than assuming that they are known in advance; to achieve this, questioning is open-ended and flexible, and the interview will characteristically unfold in quite different ways in different contexts” (Maughan, 2004).  Although this method reaches outside of the automatic response tendency, it might reach too far and get too far outside the scope of intention for the researcher.  For example, if I am seeking data regarding familial influence on male collegiate athletes and the interview develops into a discussion about non-athletic familial influence then I am simply wasting time during the interview.

The greatest incentive to using interview as a methodology for this proposal is that I will obtain the type of data that I am seeking.  Denham recorded what the mass media stereotypes were by conducting content analysis on sports broadcaster commentary.  I will uncover how the athlete perceives himself and his identity.  Furthermore, I will discover how the athlete wants others to perceive him and his identity.  There is no better route to find this than inserting an interviewer as the primary data receptor and questioner.  Because I place so much emphasis on the aspect of presenting the questions, it is of utmost importance to delve further into a discourse on interviewer effect.

Interviewer Effect

The primary element of interviewer effect is influence.  As previously discussed, Bailey (2007) believed that race and ethnicity, sex, social status, age, clothing and grooming of the interviewer (and interviewee) can affect responses during some interviews.  Each of those things listed that make up the interviewer will have influence on the athlete and his answers.

As opposed to an online survey that occurs individually, perhaps when the athlete is alone in his own home, the interview takes place in a group setting.  Even when only involving interviewer and interviewee, this is considered a small group and influence is inescapable.  When Berger & Conner published ”Performance Expectations and Behavior in Small Groups” (1969), their analysis applied to “a group of at least two persons who have a task to accomplish together” (Berger & Conner, 1969).  Their methodology can also be applied to a group interview session, even when the group contains only two people (interviewer/interviewee), if the group is charged with the task of compiling a complete biographical sketch of a player or players to be conveyed to the media.  This is how the methodology of Berger & Conner can be constructed to fit within this framework:

Ethnography

The method that was considered for this proposal, but ultimately excluded from use, was ethnography.  Initially, it appeared that ethnography would contribute to the research by providing descriptions of both the history of the athlete and the familial contributions throughout the life of the athlete.  “An ethnography is a descriptive account of social life and culture in a particular social system based on detailed observations of what people actually do” (Johnson, 2000).  However, the primary research objective of this proposal is not what athletes “do” but rather who they “are.”

Ethnography is “a research method most closely associated with anthropological studies of tribal societies, but it is also used by sociologists, especially in relation to groups, organizations, and communities that are part of larger and more complex societies such as hospitals, ethnic neighborhoods, urban gangs, or religious cults.  Ethnography plays an important part in ethnology, a branch of anthropology that studies how cultures develop historically and compare with other cultures.  See also ‘Participant Observation’” (Johnson, 2000).  When considering whether to use ethnography as a method for this proposal, I determined that collegiate athletics is a complex society in the way that a gang or certain professional group (like a hospital) is.  Additionally, the research is meant to study how this collegiate athletic culture has developed historically.  Further research would be necessary to study how the collegiate athletic culture compares with other cultures.  The limitations of this research are constrained to studying only the collegiate athletic culture.

As a methodology, ethnography is valuable to explaining phenomena within the lives of the individuals under study.  “Ethnography is the most basic form of social research.  It bears a close resemblance to the routine ways in which people make sense of the world in everyday life.  Some commentators regard this as its basic strength; others see it as a fundamental weakness” (Hammersley & Atkinson, 1995).  Although a strong method overall, ethnography is not a perfect fit for this proposal because I am not seeking to specifically analyze the lives of athletes.  I am performing research on the categories that athletes use to describe themselves: physically, their background, academics, family, and skill set.

There is one final important note to make on ethnography as it relates to this research proposal.  Within “studies that use multiple methods, many misleading and invalid claims are made in the name of triangulation.  This has profound implications for ethnography, since one of its defining characteristics is that it uses multiple methods” (Walford & Massey, 1998).  The charge of this research is to maintain validity and avoid misleading claims in the name of triangulation.  For this and previously listed reasons, ethnography will not be used for this research proposal.  However, it is relevant to point out that ethnography could be employed ideally as a stand-alone study to compare and contrast the findings of this multiple methods research proposal.

Part III: Methodology of Proposed Research

Content Analysis

I will use content analysis first to discover the social norms that involve NCAA participants.  I will compare the results of the content analysis to the Denham research to uncover how racist attitudes are expressed in collegiate sports.  The results will prove one of two things: (i) the perceptions of athletes and their respective academic institutions mirror those of sports commentators; or (ii) the perceptions of athletes and the academic institutions differ from sports commentators.  The specific perceptions in this study are those regarding the findings of Denham: “(a) white athletes are frequently praised for their perceived ‘intellect’ and ‘leadership capacity,’ while (b) black athletes are often praised for being ‘naturally talented’” (Denham, Billings, & Halone, 2002).  The proposed study will address other research questions in addition, such as the differences between white and black athletes in the following categories:

Two categories in particular, academics and skill set, are most aligned with the research of Denham.  By studying physical traits, I will be able to determine if the perceptions of differences in physicality between groups (white and black athletes) is warranted.  Analyzing differences in background and familial relationships will help further research in those respective areas.  Let me be clear, the purpose of this research is not to determine which group has more “intellect” or “leadership capacity” nor will the data find which individuals are “naturally talented.”  This research will uncover trends in descriptive portrayals of athletes on collegiate web pages.

I will analyze the content on the official school web pages for each individual athlete who participated in the 2010 NCAA Men’s Basketball Championship Tournament (see Appendix A).  The categories of initial content division are: school, jersey number, full name as presented on web site, year of experience (e.g. Freshman), position, height, weight, hometown/high school/last college, and description.  I will separate the name into first name, last name, and surname (e.g. Jr. or III).  Hyphenated last names will remain together as one last name.  For year of experience, I will list the year (Fr, So, Jr, Sr) and create a separate column for red-shirt.  The data includes mention of transfer and junior college experience.  Although those qualifiers are not necessary for this research problem, they could be useful to a future study.  I will divide prior location into two columns: hometown and high school/last school attended.  I am not certain yet if I will be able to distinguish between high school and last school attended.  I will create a column for academic major but I expect there to be very few listed.  Some schools include it as a distinguished element on the web page while other schools bury it in the player description.  The player description is where the majority of the “loose” content will originate.  It is the area of most variety.

I have reviewed the content to analyze its applicability to this proposal (Girdwood, 2010).  I have counted some initial key terms that match directly with those in the Denham study (Appendix C).  There were 1,614 relevant terms discovered.  Of course, some terms will be omitted once the research is performed.  For example, a player may have made an “appearance” in a high school tournament.  This mention of an “appearance” is in no way referring to the physical appearance in the Denham study (i.e. looks/appearance: “he’s much taller than X,” “she’s changed her hair style”).  Also, the 1,614 figure includes many counts of familial terms which is outside the scope of the Denham study and eludes to the further possibilities of this current research proposal.

[Footnote: As an initial first step of research, I have assembled the content under analysis.  Due to the immense length (1,008 pages), I have posted it online for viewing rather than include it as a printed item of the appendix.]

Survey

I will use an online survey to discover how individual athletes perceive themselves.  I will compare the results of the survey to the content analysis described above and to the Denham research to uncover how racist attitudes are expressed in collegiate sports.  The results will prove one of two things: (i) the perceptions of athletes themselves mirror those of sports commentators; or (ii) the perceptions of athletes themselves differ from sports commentators.  The specific perceptions in this study are those regarding the findings of Denham: “(a) white athletes are frequently praised for their perceived ‘intellect’ and ‘leadership capacity,’ while (b) black athletes are often praised for being ‘naturally talented’” (Denham, Billings, & Halone, 2002).  The proposed study will address other research questions in addition, such as the differences between white and black athletes in the following categories:

  1. physical index = height and weight
  2. background = hometown and previous school
  3. academics = major and GPA or other mentions of academics
  4. family = any mention of family
  5. skills = shooting, ball handling, defense, moves, etc.

Two categories in particular, academics and skill set, are most aligned with the research of Denham.  By studying physical traits, I will be able to determine if the perceptions of differences in physicality between groups (white and black athletes) is warranted.  Analyzing differences in background and familial relationships will help further research in those respective areas.  Again, the purpose of this research is not to determine which group has more “intellect” or “leadership capacity” nor will the data find which individuals are “naturally talented.”  This research will uncover trends in descriptive portrayals of athletes as they perceive their own selves.

I will distribute an online survey (Appendix B) to each individual athlete who participated in the 2010 NCAA Men’s Basketball Championship Tournament (see Appendix A) by contacting their respective academic institutions.  Due to the timing of the survey, it will measure the cohort one year subsequent to the individuals under study by content analysis.  As the study continues, content analysis will be performed on this same cohort.  As subsequent year data is accumulated, separate cohorts can be analyzed against one another for similarities and differences over time.

The survey method used in this proposal is the most closely aligned portion to match the research of Denham.  Sections of the survey mirror the descriptions of the commentators in the Denham study.  Since the proposal is meant to go beyond the findings of Denham, additional categories and descriptions have been added.

Interview

This interview will examine the following categories:

  1. physical index = height and weight
  2. background = hometown and previous school
  3. academics = major and GPA or other mentions of academics
  4. family = any mention of family
  5. skills = shooting, ball handling, defense, moves, etc.

However, the interview will be broken up into separate grouped categories through the use of interview “stations.”  Every effort will be made to prevent  the effect that race and ethnicity, sex, social status, age, clothing and grooming of the interviewer (and interviewee) might have on the  responses during the interviews.  The interview stations will be clearly marked as (1) Physical Attributes; (2) Historical Data; and (3) Current Data.  The following categorical focuses will align with each of the stations:

  1. Physical measurements (category #1: physical index)
    1. Height
    2. Weight
  2. Gathering historical data such as previous school and hometown (category #2: background)
    1. Includes family history (category #4: family)
    2. Includes statistical performance measures (category #5: skills)
  3. Assembling current data
    1. Includes educational status, e.g. academic major (category #3: academics)
    2. Includes current family situation (category #4: family)
    3. Includes current statistical performance measures (category #5: skills)

The preceding data is relatively static and straightforward.  In other words, if a player is currently living with his birth mother, it is highly unlikely that he would say otherwise.  Similarly, if a player has a registered collegiate academic major (of which he is aware) then it is highly unlikely he would lie and say otherwise.  Therefore, I will use finite time periods to limit the content conveyed and collected as interview responses.  The reason is that the interview can be viewed as a charge of “collective completion of the task [by interviewer/interviewee] in a finite time period” (Berger & Conner, 1969).  This is an extension of the study of influence and behavior in small groups.  The main component of difference between individual online survey versus interview is that the interview occurs within a group setting (where two or more are gathered).  As a result, I expect the answers given during interview to be a different perception of self conveyed by the athlete than the description given during the online survey.  Further clarification on interview procedure is laid out in Appendix D.

Conclusion

In this research proposal, I have described three methods to analyze perceptions of race in sports.  Those methods include: content analysis, interview, and survey.  I initially presented one study that conducts content analysis on sports commentary in American mass media (Denham et al., 2002) to lay a foundation for further proposed research.

Race relations is an ongoing American social problem in need of constant study within the discipline of sociology (Bash, 1979).  There are a variety of ways to analyze race relations, from a global perspective down to an individual level.  To gain a better understanding of race relations in America, I will examine the issue of stereotyping by race in sports.  By combining previous research (of the perception of the collegiate athlete in the mass media) with proposed research (to discover the perception of self by the collegiate athlete), I will be able to determine relationships of perception of race between self and other.  The findings will be directly relevant to race in American sports and generally applicable to further the study of race relations in America.

Appendix A

List of official school web pages

These web pages contained a roster of participants for each of the teams involved in the 2010 NCAA Men’s Basketball Championship Tournament.  Each respective web page roster had direct links to every individual player.  The content under analysis came from the individual player pages.

  1. Midwest
    1. Kansas:  http://www.kuathletics.com/sports/m-baskbl/mtt/kan-m-baskbl-mtt.html
    2. Ohio St: http://www.ohiostatebuckeyes.com/SportSelect.dbml?DB_OEM_ID=17300&SPID=10421&SPSID=87812
    3. Georgetown: http://www.guhoyas.com/sports/m-baskbl/mtt/gu-m-baskbl-mtt.html
    4. Maryland: http://www.umterps.com/sports/m-baskbl/mtt/md-m-baskbl-mtt.html
    5. Michigan St: http://www.msuspartans.com/sports/m-baskbl/mtt/msu-m-baskbl-mtt.html
    6. Tennessee: http://www.utsports.com/sports/m-baskbl/mtt/tenn-m-baskbl-mtt.html
    7. Oklahoma St: http://www.okstate.com/sports/m-baskbl/mtt/okst-m-baskbl-mtt.html
    8. UNLV: http://www.unlvrebels.com/sports/m-baskbl/mtt/unlv-m-baskbl-mtt.html
    9. UNI: http://www.unipanthers.com/sports/m-baskbl/mtt/niwa-m-baskbl-mtt.html
    10. Georgia Tech: http://ramblinwreck.cstv.com/sports/m-baskbl/mtt/geot-m-baskbl-mtt.html
    11. San Diego St: http://goaztecs.cstv.com/sports/m-baskbl/mtt/sdsu-m-baskbl-mtt.html
    12. New Mexico St: http://www.nmstatesports.com/SportSelect.dbml?DB_OEM_ID=1900&SPID=585&SPSID=9579
    13. Houston: http://www.uhcougars.com/sports/m-baskbl/mtt/hou-m-baskbl-mtt.html
    14. Ohio: http://www.ohiobobcats.com/sports/m-baskbl/mtt/ohio-m-baskbl-mtt.html
    15. UCSB: http://ucsbgauchos.cstv.com/sports/m-baskbl/mtt/ucsb-m-baskbl-mtt.html
    16. Lehigh: http://www.lehighsports.com/sports/mbball/rosters/index.asp
  2. West
    1. Syracuse: http://www.suathletics.com/roster.aspx?path=mbasket
    2. Kansas St: http://www.kstatesports.com/SportSelect.dbml?DB_OEM_ID=400&KEY=&SPID=213&SPSID=3087
      1. updated 9/06/2010: http://www.kstatesports.com/sports/m-baskbl/mtt/ksu-m-baskbl-mtt.html
    3. Pittsburgh: http://www.pittsburghpanthers.com/sports/m-baskbl/mtt/pitt-m-baskbl-mtt.html
    4. Vanderbilt: http://vucommodores.cstv.com/sports/m-baskbl/mtt/vand-m-baskbl-mtt.html
    5. Butler: http://www.butlersports.com/sports/m-baskbl/2009-10/roster
    6. Xavier: http://www.goxavier.com/sports/m-baskbl/mtt/xavi-m-baskbl-mtt.html
    7. BYU: http://www.byucougars.com/Roster.jsp?SP=111
    8. Gonzaga: http://www.gozags.com/sports/m-baskbl/mtt/gonz-m-baskbl-mtt.html
    9. Florida St: http://www.seminoles.com/sports/m-baskbl/mtt/fsu-m-baskbl-mtt.html
    10. Florida: http://gatorzone.com/basketball/men/bios.php
    11. Minnesota: http://www.gophersports.com/SportSelect.dbml?DB_OEM_ID=8400&SPID=3302&SPSID=38664
    12. UTEP: http://utepathletics.cstv.com/sports/m-baskbl/mtt/utep-m-baskbl-mtt.html
    13. Murray St: http://www.goracers.com/SportSelect.dbml?DB_OEM_ID=6700&KEY=&SPID=2583&SPSID=32205
      1. updated 9/06/2010: http://www.goracers.com/roster.aspx?path=mbball
    14. Oakland: http://www.ougrizzlies.com/sports/m-baskbl/mtt/oakl-m-baskbl-mtt.html
    15. North Texas: http://www.meangreensports.com/SportSelect.dbml?spid=564&spsid=9109&db_oem_id=1800
    16. Vermont: http://www.uvm.edu/~sportspr/mens_basketball/?Page=roster.php
  3. East
    1. Kentucky: http://www.ukathletics.com/sports/m-baskbl/mtt/kty-m-baskbl-mtt.html
    2. West Virginia: http://www.msnsportsnet.com/page.cfm?sport=mbball&show=roster
    3. New Mexico: http://www.golobos.com/sports/m-baskbl/mtt/nm-m-baskbl-mtt.html
    4. Wisconsin: http://www.uwbadgers.com/sports/m-baskbl/mtt/wis-m-baskbl-mtt.html
    5. Temple: http://www.owlsports.com/roster.aspx?path=mbball
    6. Marquette: http://www.gomarquette.com/sports/m-baskbl/mtt/marq-m-baskbl-mtt.html
    7. Clemson: http://clemsontigers.cstv.com/sports/m-baskbl/mtt/clem-m-baskbl-mtt.html
    8. Texas: http://www.texassports.com/sports/m-baskbl/mtt/tex-m-baskbl-mtt.html
    9. Wake Forest: http://wakeforestsports.cstv.com/sports/m-baskbl/mtt/wake-m-baskbl-mtt.html
    10. Missouri: http://www.mutigers.com/sports/m-baskbl/mtt/miss-m-baskbl-mtt.html
    11. Washington: http://www.gohuskies.com/sports/m-baskbl/mtt/wash-m-baskbl-mtt.html
    12. Cornell: http://cornellbigred.com/roster.aspx?path=mbball&
    13. Wofford: http://woffordterriers.com/roster.aspx?path=mbball&roster=89&
    14. Montana: http://www.montanagrizzlies.com/pages/roster.aspx?r=26&m=17
    15. Morgan St: http://www.morganstatebears.com/roster.aspx?path=mbball&
    16. East Tenn. St: http://www.etsubucs.com/sports/mbball/rosters/
  4. South
    1. Duke: http://www.goduke.com/SportSelect.dbml?DB_OEM_ID=4200&SPID=1845&SPSID=22727
    2. Villanova: http://www.villanova.com/sports/m-baskbl/mtt/nova-m-baskbl-mtt.html
    3. Baylor: http://www.baylorbears.com/sports/m-baskbl/mtt/bay-m-baskbl-mtt.html
    4. Purdue: http://www.purduesports.com/sports/m-baskbl/mtt/pur-m-baskbl-mtt.html
    5. Texas A&M: http://www.aggieathletics.com/sports/m-baskbl/mtt/tam-m-baskbl-mtt.html
    6. Notre Dame: http://www.und.com/sports/m-baskbl/mtt/nd-m-baskbl-mtt.html
    7. Richmond: http://www.richmondspiders.com/sports/m-baskbl/mtt/rich-m-baskbl-mtt.html
    8. California: http://www.calbears.com/sports/m-baskbl/mtt/cal-m-baskbl-mtt.html
    9. Louisville: http://www.uoflsports.com/sports/m-baskbl/mtt/lou-m-baskbl-mtt.html
    10. St. Mary’s (CA): http://www.smcgaels.com/SportSelect.dbml?DB_OEM_ID=21400&SPID=12536&SPSID=101618
    11. Old Dominion: http://www.odusports.com/sports/m-baskbl/mtt/oldd-m-baskbl-mtt.html
    12. Utah St: http://www.utahstateaggies.com/sports/m-baskbl/mtt/ust-m-baskbl-mtt.html
    13. Siena: http://www.sienasaints.com/sports/m-baskbl/mtt/sien-m-baskbl-mtt.html
    14. Sam Houston St: http://www.gobearkats.com/SportSelect.dbml?DB_OEM_ID=19900&KEY=&SPID=11358&SPSID=93024
    15. Robert Morris: http://www.rmucolonials.com/SportSelect.dbml?DB_OEM_ID=13900&KEY=&SPID=6516&SPSID=59502
    16. Ark.-Pine Bluff: http://www.uapblionsroar.com/roster.aspx?path=mbball&

Appendix B

This survey will examine the following categories:

  1. physical index = height and weight
  2. background = hometown and previous school
  3. academics = major and GPA or other mentions of academics
  4. family = any mention of family
  5. skills = shooting, ball handling, defense, moves, etc.

The text below includes the survey questions.  The text [in brackets] indicates the value of the unit of analysis (e.g. text or numeric) and will not be expressed in the survey.  The text before each question [in brackets] indicates the category to which the question applies.  Survey begins now:

Online survey

This survey will take approximately 20 minutes.  After you are asked for your race, height, and weight, there will be four remaining sections.  Please plan your time accordingly to ensure that each of the four sections receives equal effort put forth when answering the questions.  It is best to plan about five minutes per section.  Thank you for taking the time to participate in this survey.

[category #1] Please enter your height like this: 6-5 [numeric]

[category #1] Please enter your weight like this: 123 [numeric]

[category wild-card] Please choose your race: Caucasian, African-American, or Other [multiple choice]

[category #2] Please enter your hometown like this: Anytown, AB [text]

[category #2] Would you say that your hometown is the last place you lived? [yes/no]

[category #2] Please enter your last school attended.  You can enter like any of these: Washington High School or Jefferson Community College [text]

[category #2] Would you say that your last school attended was in the United States? [yes/no]

[category #3] Please list your college major now.  It is okay to generalize.  These are examples: Sociology, Business, Undecided [text]

[category #3] Please list your Grade Point Average.  It is okay to estimate like this: 2.67 [numeric]

[category #3] Please give an estimate of your GPA currently.  What grade would you say that you average currently? [A/B/C/D/E/F]

[category #3] Please give an estimate of your GPA at your previous school.  What grade would you say that you averaged at your most recent previous school? [A/B/C/D/E/F]

You are now half-way through the survey.

[category #4] Was your family involved in your sports life before you played for your current school? [yes/no]

[category #4] Is your family involved in your sports life now at your current school? [yes/no]

[category #4] Please list any family members that have played sports and the highest level they reached.  You can list as many or as few as you want.  Here are some examples: Dad, high school football or Cousin, pro basketball or Grandma, Olympic swimmer [text]

[category #4] Even if your family was never involved in your sports life, please take a moment to type a couple sentences about your family. [text]

[category #5] We would like to know how you describe yourself as a player.  Please take a moment to type a couple sentences describing you as an athlete. [text]

[category #5] We would like to know how you describe yourself as a teammate.  Please take a moment to type a couple sentences describing your style of play. [text]

[category #5] To end the survey, we are going to ask you a series of questions about how you play the game.  Please mark either “Yes” this describes you or “No” this does not describe you as a player.  These questions are meant to be answered quickly so please don’t spend too much time thinking about them.  Just answer whatever pops in your head first.  And, please be honest.  Remember, we’re not pro scouts; we’re nerdy researchers!

[These questions follow the 15 category taxonomy first employed by Eastman and Billings.  I have indicated which category.]

[#1] I am a physical player

[#1] I am athletic and consider myself a good athlete

[#1] I can spring off the floor

[#2] I am an intelligent player

[#2] I think well on my feet

[#3] I am a hard working player

[#4] I am determined to push hard when I play and stay motivated

[#5] I am fast and bring speed to the game

[#5] I am quick on my feet

[#6] I consider myself a strong man

[#7] I have a mental toughness when playing

[#8] Sometimes I get in the zone

[#9] Sometimes I feel out of rhythm

[#10] I bring leadership to my team

[#11] I am a versatile player

[#12] I am team oriented

[#12] My own performance is more important than winning

[#13] I can be patient during the game

[#14] I care about how I look when I play

[#15] When I grew up, life was tough

[#15] Sports come easy to me because I was raised with sports

Appendix C

Table 1

Appendix D

This interview will examine the following categories:

  1. physical index = height and weight
  2. background = hometown and previous school
  3. academics = major and GPA or other mentions of academics
  4. family = any mention of family
  5. skills = shooting, ball handling, defense, moves, etc.

The text below includes the collection of interview questions.  It is not necessary nor is it a goal of this research to obtain an answer to every question.  One of the goals of this research and interview is to see which direction the athlete takes the interview.  Therefore, the questions are generally vague and open ended.  The text [in brackets] indicates the value of the unit of analysis (e.g. text or number) and will not be spoken during the interview.  The text before each question [in brackets] indicates the category to which the question applies.  Interview begins now:

This interview will be broken up into three separate stations: (1) Physical Attributes; (2) Historical Data; and (3) Current Data.  The interview will last 20 minutes in total, 15 minutes for questioning and 100 seconds between each station.  Please be aware of the time.  You will participate in a brief 5 minute interview at each of the three stations so make your answers count!  There are five minutes at each station to answer five questions.  Don’t hold back and please provide clear and honest answers.  Please go to the first station listed on the card you were given and begin:

  1. Physical measurements (category #1: physical index)
    1. What is your height and weight?
    2. [Interviewer will make note of what interviewer believes the athlete's race is, then ask the next question.]   Are you Caucasian, African-American, or Other?  You can only choose one.
    3. Are you a physical player?
    4. Are you athletic?
    5. What’s your best physical trait?  [Interviewer is allowed to make suggestions, such as: "Do you jump well or are you strong or quick or fast or...?"]
  2. Gathering historical data such as previous school and hometown (category #2: background)
    1. Would you say that you had a rough childhood?   (category #4: family)
    2. Who was your most influential family member growing up?
    3. Did any of your family members play sports with you when you were a kid?
    4. Were you really athletic as a kid?  (category #5: skills)
    5. Was your game different as a kid and how so?  [Interviewer is allowed to make suggestions, such as: "Are you stronger now or did you have a late growth spurt or are you more team-oriented now or...?"]
  3. Assembling current data
    1. What is your current academic major in college? (category #3: academics)
    2. Are you better at school work now or was school easier when you were growing up?
    3. Who is the closest family member you stay in touch with? (category #4: family)
    4. What are some of the attributes in your current athletic skill set? (category #5: skills)
    5. Do you have goals for the future, either professionally or athletically?

Bibliography

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