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Chadha

ISOJ 2010

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Chadha

  1. 1. Plugged In: Predicting Podcast Audiences and their Political Participation Monica Chadha Alex Avila Homero Gil de Zuniga School of Journalism University of Texas – Austin
  2. 2. Introduction and Rationale Researchers know little about who podcast audiences are. The technology is being adopted rapidly. Little is known about the secondary effects of this technology- namely political participation.
  3. 3. Podcast- operationalized “A digital audio or video file that is episodic; downloadable; program-driven, mainly with a host and/or theme; and conveniently accessible, usually via an automated feed -- such as Really Simple Syndication (RSS) feed -- with computer software.”
  4. 4. RQ and Hypotheses RQ: Who is downloading podcasts? What is a typical demographic snapshot of a podcast listener and do demographic variables predict podcast use? H1: Podcast use for news will predict political participation online. H2: Podcast use for news will predict political participation offline.
  5. 5. Methodology Sample data provided by the Media Research Lab at the University of Texas at Austin. Information was collected via a web-based survey between Dec 15, ‘08 and Jan 5, ‘09. Sample was matched with the important demographic variables of the U.S. National Census, specifically gender and age. 1,482 valid cases; response rate was 17.3 percent.  Final sample = 958 participants
  6. 6. Findings: T1- Demographics Users N=115 Podcast type Politics N=39 Sports N=17 Entertainment N=59 News N=40 Education N=39 Other N=42 Gender Female Male 57.4 42.6 56.4 43.6 64.7 35.3 55.9 44.1 65.0 35.0 56.4 43.6 64.3 35.7 Race White Non-White 72.2 27.8 66.7 33.3 82.4 17.6 72.9 27.1 70.0 30.0 69.2 30.8 73.8 26.2 Income Bel 39,999 40–69,999 70-109,999 110,000 up 20.8 25.1 36.4 17.4 25.7 20.5 36.0 18.0 29.4 17.7 23.5 29.4 17.0 23.8 40.8 18.7 25.0 20.0 40.0 15.0 23.1 28.3 28.3 20.5 21.4 38.1 31.0 9.5
  7. 7. T2- Demographic Regression B s.e. Wald Exp (B) Demographics Gender (Female) -.424* .209 4.131 .65 Race (White) -.794*** .236 11.350 .45 Age -.046 .030 2.289 .96 Education .124# .072 3.016 1.13 Income .058* .027 4.699 1.06 Nagelkerke’s R Square .072***  Cell entries are B coefficients (unstandardized), standard error, Wald χ2  and odds ratio. N=958  * p < .05, ** p < .01, *** p < .001
  8. 8. T3 – Online/Offline Part. Online Political Participation Offline Political Participation Demographics Age Education Gender (Female) Income Ethnicity R Square .031 .139*** .027 - .088** .027 2.9%*** .064* .277*** .050# .069* .059# 12.3%*** Media use & Partisanship Media Use Partisanship R Square change .297*** .121*** 11.9%*** .189*** .066* 4.6%*** Podcast Use Podcast Use R Square change .164*** 2.6%*** .105** 1.1%** TOTAL R SQUARE 17.4%*** 18%*** N=958. Cell entries are standardized Beta coefficients.     # p < .10, * p < .05, ** p < .01, *** p < .001
  9. 9. Conclusions Males tend to use podcasts more than females. Also, higher income bracket = increased likelihood of using podcasts. Minorities seem to use this technology to a larger degree than White individuals. Entertainment seems to be the most popular genre for podcast users. Established an empirical relationship between podcast use and political participation, online and offline.
  10. 10. Conclusions Males tend to use podcasts more than females. Also, higher income bracket = increased likelihood of using podcasts. Minorities seem to use this technology to a larger degree than White individuals. Entertainment seems to be the most popular genre for podcast users. Established an empirical relationship between podcast use and political participation, online and offline.

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