Data and Methods Sample Clauses

Data and Methods. To better understand couple dynamics, the DHS men’s questionnaire asks husbands about their reproductive preferences and attitudes toward family planning. For husbands in a polygynous marriage, the questions are asked for each of their wives/partners. This analysis uses DHS matched couples’ data from 14 sub-Saharan countries: Benin, Burkina Faso, Ghana, and Mali from Western Africa; Chad from central Africa; and Ethiopia, Kenya, Malawi, Mozambique, Namibia, Rwanda, Uganda, Zambia, and Zimbabwe from eastern and southern Africa. All surveys in this analysis were conducted between 1999 and 2004. The data for women are based on women age 15-49, while the data for men are based on men age 15-59 (with the exception of Kenya, Malawi, Uganda, and Zimbabwe, where the interviewed men are age 15- 54; and Benin, where the interviewed men are age 15-64). The men’s questionnaire is similar in structure to the women’s questionnaire but shorter. To the extent possible, the questions and response categories in the two questionnaires are worded identically to be comparable across countries. The section on fertility preferences includes a question on fertility intentions and ideal number of children. For fertility intentions, women and men were asked, “Would you like to have (a/another) child or would you prefer not to have any (more) children?” For ideal number of children (ideal family size), women and men were asked one of two questions, depending on whether or not they had children. Those who did not have children were asked, “If you could choose exactly the number of children to have in your lifetime, how many would that be?” Respondents who had at least one living child were asked, “If you could go back to the time you did not have children and could choose exactly the number of children to have in your lifetime, how many would that be?” In this study, a woman is defined as infecund if she had no births and no pregnancies in the past five years but has had a birth or pregnancy at some time, and has been married for the past five years but did not use contraception during that period.

Data and Methods. This analysis uses matched couples’ data from DHS surveys in 10 sub-Saharan African countries to investigate spousal agreement (or disagreement) on a range of family planning issues. The analysis uses data from Benin, Burkina Faso, Chad, and Mali in West and Central Africa, and from Malawi, Namibia, Rwanda, Uganda, Zambia, and Zimbabwe in Eastern and Southern Africa. The surveys were conducted between 1999 and 2004. The data for women are based on women age 15-49, while the data for men are based on men aged 15-59 (with the exception of Malawi and Benin, where men are age 15-54 and 15-64, respectively). The men’s questionnaire is similar in structure to the women’s questionnaire but shorter. To the extent possible, questions and response categories in both questionnaires are worded identically to be comparable across countries. In this analysis, infecundity is measured by childbearing experience of the woman, that is, a woman is defined as infecund if she has had no births and no pregnancies in the past five years but has had a birth or pregnancy at some time, and has been married for the past five years but did not use a contraceptive method during that period. Wealth status of the household is measured using the wealth index. The wealth index is constructed from household asset data using principal components analysis (▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇, 2004). Based on the first factor loading, the wealth index score divides the population into five quintiles. In this paper, “poor” refers to the bottom two quintiles, “middle” refers to the middle quintile, and “rich” refers to the top two quintiles.

Data and Methods. The data obtained in this study were obtained by a survey among PhD candidates at Leiden University, a large and broad research university in the Netherlands. In this section, we first describe which variables were included. Second, we expand on the survey methodology and description of respondents.

Data and Methods. In this study we estimate associations between CDC’s Social Vulnerability Index and daily mortality counts in census tracts that were affected by the April 2011 tornado outbreak under the following hypothesis: the SVI modifies the association between tornadoes and mortality in the April 2011 tornado outbreak. The tornado event affected census tracts in five states: Arkansas, Mississippi, Alabama, Tennessee, and Georgia. Of the five states, all but Tennessee elected to participate in the study. Datasets of mortality counts by either census tract or ZIP code (depending on the format of individual state surveillance systems) were assembled for the other four states. These datasets contain the census tract-specific (or ZIP code-specific) overall mortalities (as opposed to direct event-specific fatalities) on the date of the tornado event as well as the mortalities on the same day of the week from the prior week (baseline mortality). For all mortality data provided by ZIP code (Arkansas and Alabama), HUD-USPS crosswalk files were used to convert mortality from ZIP codes to census tracts. HUD-USPS crosswalk files were created by the U.S. Department of Housing and Urban Development (HUD) to provide a data allocation method between disparate geographic units that is based on residential addresses rather than area or population (DHUD, 2015). This conversion produces non-integer mortality estimations at baseline and on the date of the event for each census tract. The estimations were rounded to the nearest whole integer values to be used as inputs in a Poisson regression analysis. All spatial analysis and geographic data manipulation were completed in ArcGIS 10.2.2. Further data cleaning and statistical analysis were performed using SAS 9.4. Tornado tracks for the April 25-28 storm were obtained from the National Weather Service (NWS) (NOAA, 2016). The study area was defined as any census tract in the four participating states that was intersected by an NWS –designated tornado track. Overall 368 census tracts were included in the study. Table 2.1 summarizes collected mortality data by tornado magnitude.

Data and Methods. Below, we provide a summary of the survey methodology and measured variables. A more elaborate description of the survey questionnaire, methodology and variables is given in a working paper (▇▇▇▇▇▇▇, ▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇, ▇▇▇ ▇▇▇▇▇▇▇, & ▇▇▇ ▇▇▇ ▇▇▇▇▇▇▇, 2015). The survey sample consisted of 2,193 PhD graduates who obtained a PhD from Utrecht University (a broad research university), Delft University of Technology (engineering and technology), Wageningen University (an agricultural university), or Erasmus University Rotterdam (focused on medicine and social sciences, especially economics and management) between April 2008 and March 2009 or from Leiden University (a broad research university) between January 2008 and May 2012. An invitation to the survey (which was open from 23 October 2013 until 21 January 2014) was sent through email or LinkedIn, in which the prospective respondents were informed on the purpose and content of the survey in the invitation, and strict confidentiality guaranteed, only aggregate results (impossible to trace back to individuals) to be published. Furthermore, a test of the survey showed the survey took 20 minutes to complete on average, which was also written in invitation letter, so the respondents would know which response burden to expect. In the online survey itself, the instructions made explicit it was possible to quit the survey. Up to three reminders were sent if respondents had not completed the survey. In total, 1,133 started the survey (52%), and 960 progressed to the final question (44%). Survey data were anonymized before analysis and the key to the respondents’ names and unique survey analysis ID stored in a secured folder. Non-response analysis showed that the respondents were representative of the survey set regarding gender, age, year of PhD, and city of PhD (Waaijer et al., 2015). However, Dutch nationals seemed to be overrepresented in the survey compared to the country of birth of the entire sample. In this study, we used variables on type of job, perception of career prospects, research performance and personal characteristics. Three sectors of employment were distinguished: academic R&D (dubbed academia in the paper for brevity), non-academic R&D (dubbed non-academic research) and non-R&D (dubbed outside research). The classification of respondents into these categories was based on two variables: involvement in R&D and type of employer. We follow the Organisation for Economic Co-operation and Development’s (O...

Data and Methods. 4.1 Data This study used 2010 data from the China Family Panel Studies (CFPS) (2010) to examine the relevant factors that influence Chinese household decision of gift-exchange. The Institute of Social Science Survey (ISSS) of Peking University launched this annual longitudinal survey in 2010 to collect individual-, family-, and community-level data in contemporary China. There have been multiple waves of surveys conducted, among which 2010, 2011, and 2012 are released. This paper used the 2010 survey results, which cover 25 out of 34 provincial-level administrative divisions that include 23 provinces, four Municipalities, five Autonomous Regions, and two Special Administrative Regions (for additional details see Appendix Table 1). Except for Shanghai and Gansu provinces, of which the share of community numbers is relatively higher than their population share in the entire Chinese population, the weights in other provinces are fairly reasonable compared to the Chinese population distribution. Applying the sample weights in the dataset can solve this issue. As for the age-structure, the median and mean of individual age are both around 45 years old; the average percentage of people over 60 years old is about 18%. This nationally representative survey involves 57,155 individuals that come from 14,960 households across China. 33.52% of the households come from urban residential communities (▇▇ ▇▇▇ ▇▇▇), while 66.48% of them come from villages (Cun ▇▇▇ ▇▇▇). The minority ethnicity consists of approximately 10% of the sample. As for occupations, 28.45% of the adults are employed. To analyze the potential impact of occupation on gift-exchange decisions, individuals that were marked as household representations were also categorized into ten industries (for additional details see Appendix Table2). There are several reasons for which only the 2010 dataset was used. First of all, compared to the datasets in other years, it contains a wealth of information about household level financial decision-making, particularly the part that is relevant to the present study on gift-exchange. Also, it provides enough demographic and other relevant information to be controlled in regression analyses. Moreover, the present study does not focus on the over-time changes of behaviors; therefore using one of the panel datasets will not only suffice the purpose of the study, but also avoid the issues that exist potentially in panel datasets. 4.2 Estimation sample and variables Since th...

Data and Methods. With an untested institution involving a complex web of actors and a potentially tangled causal network, qualitative within-case analysis can yield very meaningful results (▇▇▇▇▇▇▇ 2006: 251). This is achieved by collecting a wide array of relevant data for each point of analysis, including bylaws, policy changes, news articles, first-hand accounts, secondary sources, participation data, government papers and announcements, and Internet statistics. These sources are used to piece together ICANN’s entire history from multiple perspectives. Consequently, the predictions of each different hypothesis can be meaningfully compared to the historical record at each point of analysis. The units of analysis are turning points in ICANN’s evolution: its formation, its involvement with the United Nations and other organizations during the World Summit for the Information Society, and its shift from U.S. oversight to an international multistakeholder model of governance. The independent variables for this study vary for each hypothesis. For Hypothesis 1, the independent variables are the power and preferences of states. For Hypothesis 2, the independent variables are the power and preferences of corporations. For Hypothesis 3, the independent variables are the power and preferences of individuals and groups in civil society. For Hypothesis 4, the independent variable is past institutional rules. The dependent variable for all hypotheses is institutional form. Because this is not a quantitative analysis, these hypotheses are not proven with statistical significance. The in- depth, theory-driven historical evaluation of each hypothesis does, however, provide strong indicators in support or refutation of each perspective, and is a solid foundation for future quantitative work. Here, I describe the different sources of evidence and how they are used in my analysis. Much of my initial work was guided by anecdotal evidence derived from observation of and conversations with ICANN participants. On July 8, 2015 I observed and wrote a record of the hearing titled “Internet Governance Progress After 53” in which ▇▇▇▇ ▇▇▇▇▇▇▇, ICANN’s CEO at the time, and NTIA Administrator ▇▇▇▇▇▇▇▇ ▇▇▇▇▇▇▇▇▇▇ testified before the House Committee on Energy and Commerce Subcommittee on Communications and Technology about ICANN’s progress transitioning IANA authority away from U.S. oversight. I was struck by the intensity of the politics surrounding such a seemingly technical and remote institution. ...

Data and Methods. Data and sample

Data and Methods. For the county level analysis, the unit of analysis is each county, and the independent variable is a binary variable that codes for whether the majority of members of each county’s elections board is white. The dependent variable is the rejection rate (in percent) of minorities in each county compared to their percentage of the voting age 10 The only county I encountered that does not have an explicitly partisan balanced board is ▇▇▇▇▇▇▇▇ county, which awards 4 seats to the party that received the most votes in the past general election, and 3 to the party that received the second most votes. population and registered electorate in each county. For this model, unlike the individual level model, I used data for 2014 and 2015 only. The reason for using only 2014 and 2015 is twofold. First, they are the only two complete years in the rejected data set (it contains July-December 2013 and January-July 2016). Second, 2014 accounts for 66% of all the rejections, most likely because it was an election year. Being a non-election year, 2015 serves as a good comparison to see whether registration or rejection practices change during an election year. Further, using a cross-sectional analysis simplifies this model, as the Elections Boards were constantly changing throughout the 3-year span. I collected the names of each county’s elections board members for 2013-2016, and later only used those from 2014-15. Those members from 2014 were compared only with rejections, Voting Age populations and registered voter counts from 2014—the same, respectively, for 2015. To collect the names of elections board members, I first sent an e-mail to the Secretary of State’s office asking if they had a current list of the Elections Boards for the counties. I followed this request up in-person with an elections assistant on the phone, but was told that they “would look for it,” and never heard back. I then e-mailed every county elections director and chief registrar using the contacts provided on the Secretary of State’s office website. In that e-mail I expressed that I was a student and resident of Georgia and requested the names of the board members from 2013-2016, as well as information on the appointment or elections process through which people end up on the board. From that initial e-mail I heard back from about 70 counties. It is worth noting that the information I was requesting is simply the names of current or recently serving public officials. I was not asking for any informa...