Stephen Lacy is a professor in the Michigan State University School of Journalism. He has written or co-written more than 95 refereed journal articles, more than 60 refereed conference papers, twelve book chapters and four books, including a content analysis text, Analyzing Media Messages. He has co-edited two other books and written numerous other articles. He is former co-editor of the Journal of Media Economics. Since receiving his Ph.D. in journalism from the University of Texas at Austin in 1986, he has conducted multiple large content analysis projects involving a wide range of media, including citizen journalism websites, television, newspapers, magazines and radio. His content analysis projects have been funded by the Project for Excellence in Journalism, the Knight Foundation, the Pew Foundation, and the National Science Foundation. Professor Lacy served as director of the Michigan State University School of Journalism from 1998 to 2003 and is a former president of Association for Education in Journalism and Mass Communication. In addition, he edited a group of weekly suburban newspapers and worked as a reporter at a suburban daily, all near Dallas, Texas. He received his undergraduate degree in economics (1971) from the University of Illinois at Urbana.
Roles & Responsibilities
School of Journalism Director of Graduate Studies
Awards, Honors and Recognitions
Deakin University Thinker-in-Residence, Australia, 2013. Michigan State University Honors Professor, Honors College, 2009-2012. Paul J. Deutschmann Award for Excellence in Research – Association for Education in Journalism and Mass Communication, 2010. Distinguished Faculty Award, Michigan State University, 2000. Teacher-Scholar Award, Michigan State University, 1990. Baskett Mosse Award for Faculty Development – Association for Education in Journalism and Mass Communication and the Accrediting Council on Education in Journalism and Mass Communication, 1989.
JRN 816 – Applied Research Methods in Journalism JRN 818 – Media Markets and Managers: Innovative to Traditional Models CAS 992 – Content Analysis