The purpose of this investigation was to examine statistical data analysis techniques utilized in the Journal of Sport Management (JSM).JSM was selected because it is the official research publication of the North American Society for Sport Management (NASSM). It is the primary publication outlet for research of undergraduate and graduate degree programs in higher education throughout North America. All articles appearing in JSM from January 1987 to October 2002 were examined in this investigation. However, the type of articles selected for analyses were primary quantitative data base articles (i.e., those which used some type of numerical data analysis technique). The number of articles reviewed was 263. Of this amount 145 (55.1%) utilized quantitative data base techniques; 13 (5.0%) qualitative data base techniques; 5 (1.9%) utilized both quantitative and qualitative techniques; and 100 (38.0%) were coded as primarily scholarly review essays. For each of the quantitative articles, a coding process was utili zed that involved the following steps: (a) reading the article, (b) deciding whether a technique was considered primary in answering the study's research purpose(s), research question(s) and/or research hypothesis(es), and (c) categorizing each technique by level of statistical procedure (basic, intermediate, or advanced) as recommended by Goodwin & Goodwin (1985). Of 150 articles coded, a total of 365 statistical techniques were coded as primary and classified by level of difficulty. Of the three levels, nearly two thirds (61.6%) were coded as basic; nearly one fourth (23.3%) were coded as intermediate; and less than a fifth (15.1%) were coded as advanced techniques. Twenty-three statistical techniques were identified and coded. Descriptive techniques (35.9%) accounted for more than two thirds of the frequencies coded as basic techniques, followed by the t statisitics (7.4%), Pearson's product-moment correlation and chi-square, (7.8%) and (6.8%) respectively. The single most frequent intermediate techniques to be coded were factoral analyses of v ariance (7.1%) and multiple regression (6.0%). Factor analysis (5.8%) was the single most frequently technique employed as advanced and primary for answering the research questions and hypotheses. This investigation was an attempt to assist in developing a more reliable and valid body of knowledge for enabling the research consumers of JSM to better explain, predict, and understand sport industry management phenomena and concepts via research methods. There remains considerable room for improvement in the selection of statistical data analysis techniques of instruments utilized in the study of sport industry management education and research.
