Data and Sample Clause Samples

Data and Sample. We conducted a three-year longitudinal multi-method field study of political-organizing organizations located in the United States that used e- mail listserves extensively for their communications. These organizations included, for instance, an international women’s organization, a university peace organization, an environmental organization, and a community improvement organization. We collected e-mail data from these organizations over a period of 36 months by becoming a member of the general list serves for the organizations, conducting interviews with members, taking field notes at meetings, and collecting hard copies of handouts from organizational meetings and other ideological materials, similar to the methods employed by ▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇▇ (1993). Given the complexity of influence attempts, as well as conflict, the use of qualitative research methods in conjunction with traditional quantitative methods is an especially useful way to study the relationships between influence, conflict, and performance (c.f. ▇▇▇▇▇ & ▇▇▇▇▇▇, 2004; Jehn, 1997; ▇▇▇, 1999). The quantitative data in our study stem primarily from text analyses and coding of our e-mail sample. Our total e-mail sample consisted of 7,617 e-mails, sent from a total of 631 members representing 10 different organizations. Fifty-four percent of the members were female, and the average member age was 33. All e-mails in our sample were sent to the entire organization via the group listserve, and the vast majority of organizational members participated on the e-mail listserve. For example, in an interview, one member stated: “E-mail is, like, very useful. I would say it is much more useful than, like, hanging flyers but does that make sense? It is just a mass mobilization.” No significant differences were found across the organizations on our variables of interest. On average, members communicated with each other over the e-mail listserve five times per week (and around actions every day), attended meetings once a month, and political action events (e.g., protests) two or three times in the year. We did have two organizations in which members only interacted online, which allowed us to compare conflict engagement between members who interacted only online and who interacted both online and face- to-face. In a multivariate analysis of the variables in our study, we did not find any significant differences to exist between members in organizations who only interacted online and members in organiza...
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Data and Sample. As literature suggests (Bakkli, 2015; Xu and Zhang, 2015; Du et al, 2004), this study will use the Chinese Health and Nutrition Survey, an appropriate dataset to study food inequality in China. The dataset is called Chinese Health and Nutrition Survey (CHNS), an ongoing project launched by the Carolina Population Center at the University of North Carolina in collaboration with the National Institute for Nutrition and Health at the Chinese Center for Disease Control and Prevention (CCDC). Conducted by a team of international researchers with background in public health, social science, demography, the Chinese Health and Nutrition Survey is designated to capture the effects of the health, nutrition, and family planning politics and how social economic transformation of Chinese society is affecting the health and nutritional status of its population (CHNS, 2010). The survey is conducted in multi-years, multi-stage and in random cluster process to draw a sample of about 7200 households with over 30,000 individuals in 15 provinces and municipal cities that “vary substantially in geography, economic development, public resources and health indicators” (CHNS, 2010). This survey is ideal for this study not only because its content but also because of its comprehensive coverage of Chinese cities across the social stratification spectrum. As discussed in previous paragraph, the dataset includes data collection from multi-years, multi-rounds, and multi-level of analysis. Years of data collection include 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011, and data has been collected at levels such as community, household, individual, job, food item, etc. Among all levels of data, only household level (individual level) data and selected community level data are published by the Carolina Population Center and available online. According to the Carolina Population Center, the community level data involves personal information that could be possibly identifiable by any third parties; therefore the community level data is in restricted usage and for application only. Though it would be idea to analyze data both from the household level and the community level, the amount of time and efforts required by acquiring the community level data does not allow this ideal situation to happen. Also, the community level of data contains several geographical indicators, which requires professional software other than the STATA, unnecessarily raising the level of complexity of thi...
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Data and Sample. ‌ This paper uses the 2011 wave of the China Household Finance Survey (CHFS) to investigate the impact of homeownership and other household characteristics on the HPF status. The CHFS was launched in 2011 and is collected in every two years by the Southwest University of Finance and Economics in Chengdu, China. The 2011 wave is one-year cross-sectional survey data containing approximately 8,400 households and 29,500 individuals. The survey interviews a large sample of the Chinese population about demographics, assets and debts, occupation, income, expenditure, insurance and social welfare plans, including the HPF program. Sampling weights are provided so that it is a nationally representative survey, and inferences made from the data are inferences of the population. Although the survey emphasizes urban households and interviews more urban households than rural households, this would not be an issue as the HPF is only available to the urban workers. In sum, this survey is suitable for the purpose of this paper. The original dataset contains 8438 households. Several households are dropped off from the sample. First, following Tang and ▇▇▇▇▇▇▇ (2017), only households with at least one urban Hukou family members are considered. This is because the House Provident Fund is only available to workers with the urban Hukou. This excludes 2185 households in the sample, 25.90% of the original sample. Second, following Tang and ▇▇▇▇▇▇▇ (2017) again, six households whose size of the house is less than 10 square meters are removed. Third, the paper only focuses on households whose heads are in the typical working age or have recently retired. In particular, 115 households with heads’ ages less than age 25 and 241 households with heads’ ages over 75 are excluded, i.e. about 4% of the original dataset. Households whose heads’ ages are less than 25 are not considered, as this group contains outliers that might contaminate the analysis. For example, a large proportion households below age 25 might not have entered the labor force due to education, thus they are not eligible for the HPF benefits. Although some of them have already worked before, it is hard to find this information from the survey. Moreover, the homeownership rate is extremely high for this group of households. A large portion of homeowners under age 25 receive transfers of homeownership or financial supports for home purchases from their parents, and including them in the analysis might distort the results. ...
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