Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385839106> ?p ?o ?g. }
- W4385839106 endingPage "20" @default.
- W4385839106 startingPage "1" @default.
- W4385839106 abstract "ABSTRACTA wealth of research examines the relationship between digital media consumption and political participation. Research typically defines participation broadly and focuses on Western contexts. We seek to add to the understanding of this relationship by focusing more directly on the relationship between digital media consumption and the propensity to vote among young people in a less democratic context. To do so, we examine a set of Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan) that have varying degrees of democratization. We test whether digital media consumption stimulates voting among respondents aged 18–30, and if this is contingent on how free and fair are the elections. Our results suggest that in the most democratic country, Kyrgyzstan, the relationship between digital media use and the propensity to vote is relatively flat while digital media use in less democratic countries, overall, is associated with a decrease in the propensity to vote.KEYWORDS: digital mediasocial mediavotingpolitical participationCentral Asia AcknowledgementsWe thank Tolganay Umberaliyeva and Mereilim Kalenova for sharing the data with us.Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 In October 2020, following election results the citizens of Kyrgyzstan viewed as fraudulent, protest erupted across the country, eventually resulting in the President of Kyrgyzstan being forced to step down from his position and casting a shadow on the future of the country with both China and Russia having urged for a return to order (Kim, Lee, and King Citation2020).2 We use the Varieties of Democracy (V-Dem) index of electoral democracy to classify each country.3 According to the United Nations Children’s Fund (UNICEF) (https://www.unicef.org/), young people under 30 years of age comprise 60% in Uzbekistan, 54% in Kazakhstan, 68% in Tajikistan and 49% in Kyrgyzstan.4 Sairambay (Citation2022) defines hypeization as ‘when new media assist to generate political participation because of hype and/or fashion to boost one’s popularity and/or to earn followers in social media’.5 The data were collected by Public Opinion (a research institute in Kazakhstan) and Shark (a research institute in Tajikistan).6 For a detailed description of the sampling procedure in Kazakhstan, see https://library.fes.de/pdf-files/bueros/kasachstan/13343.pdf. Unfortunately, the Friedrich Ebert Foundation – an initiator and a sponsor of this comparative youth study – did not prepare reports on the other countries in English. Nonetheless, they have reports for each country in Russian (see https://opinions.kz/ru/issledovaniya/molodezh-tsentralnoj-azii). There were very few missing values as is typical with face-to-face surveys. In fact, our propensity to vote measure (see below) was the only variable with any missing values in our models. There were only 4% of cases missing or the full sample (9% for Kazakhstan, 1% for Kyrgyzstan, 4% for Tajikistan and 1% for Uzbekistan). Even though the missing values are low, to prevent bias in our estimates assuming the data were missing at random (MAR), we decided to replace the missing values using multiple imputation. Through this process, we replicate 30 datasets where missing data are substituted with draws from the posterior distribution of the missing value conditional on observed values assuming a normal distribution (for a full description of the multiple imputation methodology, see Little and Rubin Citation2014). These observed values are based on all variables in our analyses. Instead of pooling our model results and using Rubin’s Rules to correct for any deflation in standard errors, we decided to circumvent this necessity by taking the average across each imputed value to replace the missing case. Because the number of replicate datasets is high, 30 replicate sets, across imputation error should approach a normal distribution, making the mean imputation value for any missing case an unbiased estimate.7 Kazakhstan (mean = 2.05, SD = 0.96), Kyrgyzstan (mean = 2.12, SD = 1.04), Tajikistan (mean = 2.27, SD = 1.04) and Uzbekistan (mean = 2.44, SD = 0.91).8 See https://www.v-dem.net/ for a complete description.9 See https://www.v-dem.net/ for information about regime cut-points.10 See https://databank.worldbank.org/ for individuals using the internet (% of population).11 The distribution of each of the ordinal variables by country is graphically presented in the Appendix in the supplemental data online. There is some country-level variation.12 Before concluding the ordered logistic was the best fit, we fit the model using ordinary least squares (OLS) and tested whether the assumptions were violated. We plotted the residuals, and it was clear that the relationship is not linear and the error is heteroskedastic (see the Appendix in the supplemental data online for the plot available at https://jasongainous.academia.edu/research#dataappendicesetc). This is confirmed with the Breusch–Pagan test where we reject the null hypothesis of homoskedasticity with a p-value close to 0.Additional informationFundingThis research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan [grant number BR18574218]." @default.
- W4385839106 created "2023-08-16" @default.
- W4385839106 creator A5052839605 @default.
- W4385839106 creator A5059910260 @default.
- W4385839106 creator A5061505538 @default.
- W4385839106 creator A5083995782 @default.
- W4385839106 creator A5089462849 @default.
- W4385839106 creator A5089598496 @default.
- W4385839106 date "2023-08-15" @default.
- W4385839106 modified "2023-10-06" @default.
- W4385839106 title "Digital media consumption and voting among Central Asian youth: why democratic context matters" @default.
- W4385839106 cites W1030509328 @default.
- W4385839106 cites W1507386223 @default.
- W4385839106 cites W1603856629 @default.
- W4385839106 cites W1691674015 @default.
- W4385839106 cites W1894718218 @default.
- W4385839106 cites W1912888988 @default.
- W4385839106 cites W1966690111 @default.
- W4385839106 cites W1991326951 @default.
- W4385839106 cites W1995381477 @default.
- W4385839106 cites W2015628539 @default.
- W4385839106 cites W2015746028 @default.
- W4385839106 cites W2029941278 @default.
- W4385839106 cites W2045137613 @default.
- W4385839106 cites W2068731469 @default.
- W4385839106 cites W2082762300 @default.
- W4385839106 cites W2090969900 @default.
- W4385839106 cites W2093274991 @default.
- W4385839106 cites W2100182663 @default.
- W4385839106 cites W2123263586 @default.
- W4385839106 cites W2126239920 @default.
- W4385839106 cites W2143833394 @default.
- W4385839106 cites W2146307317 @default.
- W4385839106 cites W2147942759 @default.
- W4385839106 cites W2148388782 @default.
- W4385839106 cites W2300547832 @default.
- W4385839106 cites W2322636197 @default.
- W4385839106 cites W2539314010 @default.
- W4385839106 cites W2579130806 @default.
- W4385839106 cites W2599996591 @default.
- W4385839106 cites W2613739267 @default.
- W4385839106 cites W2738937440 @default.
- W4385839106 cites W2759841235 @default.
- W4385839106 cites W2790133730 @default.
- W4385839106 cites W2807017482 @default.
- W4385839106 cites W2898083987 @default.
- W4385839106 cites W2921995091 @default.
- W4385839106 cites W2941053381 @default.
- W4385839106 cites W2970101985 @default.
- W4385839106 cites W3003900694 @default.
- W4385839106 cites W3020993032 @default.
- W4385839106 cites W3035240513 @default.
- W4385839106 cites W3093993741 @default.
- W4385839106 cites W3096345287 @default.
- W4385839106 cites W3208707499 @default.
- W4385839106 cites W4205325953 @default.
- W4385839106 cites W4223993120 @default.
- W4385839106 cites W4224027703 @default.
- W4385839106 cites W4224037080 @default.
- W4385839106 cites W2752468466 @default.
- W4385839106 cites W2766011678 @default.
- W4385839106 doi "https://doi.org/10.1080/02634937.2023.2237519" @default.
- W4385839106 hasPublicationYear "2023" @default.
- W4385839106 type Work @default.
- W4385839106 citedByCount "0" @default.
- W4385839106 crossrefType "journal-article" @default.
- W4385839106 hasAuthorship W4385839106A5052839605 @default.
- W4385839106 hasAuthorship W4385839106A5059910260 @default.
- W4385839106 hasAuthorship W4385839106A5061505538 @default.
- W4385839106 hasAuthorship W4385839106A5083995782 @default.
- W4385839106 hasAuthorship W4385839106A5089462849 @default.
- W4385839106 hasAuthorship W4385839106A5089598496 @default.
- W4385839106 hasConcept C138921699 @default.
- W4385839106 hasConcept C144024400 @default.
- W4385839106 hasConcept C162324750 @default.
- W4385839106 hasConcept C166957645 @default.
- W4385839106 hasConcept C17058734 @default.
- W4385839106 hasConcept C17744445 @default.
- W4385839106 hasConcept C199539241 @default.
- W4385839106 hasConcept C205649164 @default.
- W4385839106 hasConcept C2778660142 @default.
- W4385839106 hasConcept C2779343474 @default.
- W4385839106 hasConcept C30772137 @default.
- W4385839106 hasConcept C36289849 @default.
- W4385839106 hasConcept C47768531 @default.
- W4385839106 hasConcept C520049643 @default.
- W4385839106 hasConcept C555826173 @default.
- W4385839106 hasConcept C94625758 @default.
- W4385839106 hasConceptScore W4385839106C138921699 @default.
- W4385839106 hasConceptScore W4385839106C144024400 @default.
- W4385839106 hasConceptScore W4385839106C162324750 @default.
- W4385839106 hasConceptScore W4385839106C166957645 @default.
- W4385839106 hasConceptScore W4385839106C17058734 @default.
- W4385839106 hasConceptScore W4385839106C17744445 @default.
- W4385839106 hasConceptScore W4385839106C199539241 @default.
- W4385839106 hasConceptScore W4385839106C205649164 @default.
- W4385839106 hasConceptScore W4385839106C2778660142 @default.
- W4385839106 hasConceptScore W4385839106C2779343474 @default.