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Plugged In: Predicting Podcast Audiences and their Political Participation Monica Chadha Alex Avila Homero Gil de Zuniga School of Journalism University.

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Presentation on theme: "Plugged In: Predicting Podcast Audiences and their Political Participation Monica Chadha Alex Avila Homero Gil de Zuniga School of Journalism University."— Presentation transcript:

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 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 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 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 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 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 20.5 21.4 38.1 31.0 9.5

7 T2- Demographic Regression Bs.e.WaldExp (B) Demographics Gender (Female)-.424*.2094.131.65 Race (White)-.794***.23611.350.45 Age-.046.0302.289.96 Education.124#.0723.0161.13 Income.058*.0274.6991.06 Nagelkerke’s R Square.072*** Cell entries are B coefficients (unstandardized), standard error, Wald χ 2 and odds ratio. N=958 * p <.05, ** p <.01, *** p <.001

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 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 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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