Sample Analysis Of A Population Survey For The Public: Fill & Download for Free

I’m going to answer this by taking a completely different tack:Survey methodology.First a smattering of background on myself. I’m a professional spatial analyst who has training in creation, application, analysis, and utilization of properly created surveys.So when I read the methodology section I’m a bit confused how this can be taken as reliable. The methodology is found here (among other places): https://object.cato.org/sites/cato.org/files/survey-reports/pdf/the-state-of-free-speech-and-tolerance.pdfSpecifically when discussing the process of collection we only get this:The Cato Institute 2017 Free Speech and Tolerance Survey was conducted by the Cato Institute in collaboration with YouGov. YouGov collected responses August 15 to 23, 2017, from 2,547 Americans 18 years of age and older who were matched down to a sample of 2,300 to produce the final dataset.andData on the moral acceptability of punching a Nazi come from a Cato Institute/YouGov survey conducted August 21 to 22, 2017, of 1,141 respondents, with a margin of error of +/- 4.5 percentage points, which adjusts for the impact of weighting.For more details we are told this:YouGov conducted the surveys online with its proprietary Web-enabled survey software, using a method called Active Sampling. Restrictions are put in place to ensure that only the people selected and contacted by YouGov are allowed to participate.And that’s that.So there is no information on sampling area, sampling method, contact method, randomizing process (if any), selection, and anything else relevant for how a survey is run. Now to be clear not all of those issues show up in survey methodologies but most do and by judging the survey’s methodology you can judge the quality of the survey.As an example of a survey that shows this informational detail see this one: Attitudes and Beliefs About Domestic Violence: Results of a Public Opinion SurveyData for the current study were gathered in January through March of 2000 through telephone interviewing of a random sample of 1,200 residents in six communities in New York (please see preceding article for details on sampling, response rate, and interview protocol). The communities were selected based on two dimensions: the degree of urbanization and the character of the local criminal justice system’s DV policy and practices. Overall, the sample reflected the demographic characters of these communities: About one half of respondents were female, one half were married, 80% were White, and about one third were college graduates.Questions on direct and secondary experience with partner violence were included in the survey. About one third of respondents reported that a partner had acted violently toward them (35% of female respondents, 26% of male), and almost one in five respondents acknowledged having used violence toward a partner. Almost all who reported using violence toward a partner also claimed to have been the recipients of violence. The survey also included eight items about secondhand experience with violence (knowing a victim, knowing a perpetrator, witnessing or overhearing a DV incident, knowing someone who had received counseling because of violence as either a victim or a perpetrator, knowing of a situation when police were called to an incident, knowing someone who received an order of protection, knowing someone who had used a shelter or victim services). Secondhand familiarity with violence was commonplace: Almost two thirds of respondents acknowledged knowing a victim, one half acknowledged knowing a perpetrator, and almost one in four knew of someone who had sought victim services. To capture variation in the level of experience with violence, we created a simple additive scale comprising these eight dichotomous items (see details in preceding article).And for comparison here’s a PEW survey methodology: MethodologyThe American Trends Panel (ATP), created by Pew Research Center, is a nationally representative panel of randomly selected U.S. adults recruited from landline and cellphone random-digit-dial (RDD) surveys. Panelists participate via monthly self-administered web surveys. Panelists who do not have internet access are provided with a tablet and wireless internet connection. The panel is being managed by Abt Associates.Data in this report are based on 4,573 respondents who participated in both the Aug. 8-21, 2017, and the Sept. 14-28, 2017, waves of the panel. The margin of sampling error for the full sample of 4,573 respondents is plus or minus 2.4 percentage points.Members of the ATP were recruited from several large, national landline and cellphone RDD surveys conducted in English and Spanish. At the end of each survey, respondents were invited to join the panel. The first group of panelists was recruited from the 2014 Political Polarization and Typology Survey, conducted Jan. 23 to March 16, 2014. Of the 10,013 adults interviewed, 9,809 were invited to take part in the panel and a total of 5,338 agreed to participate.1The second group of panelists was recruited from the 2015 Pew Research Center Survey on Government, conducted Aug. 27 to Oct. 4, 2015. Of the 6,004 adults interviewed, all were invited to join the panel, and 2,976 agreed to participate.2The third group of panelists was recruited from a survey conducted April 25 to June 4, 2017. Of the 5,012 adults interviewed in the survey or pretest, 3,905 were invited to take part in the panel and a total of 1,628 agreed to participate.3Notice in both of these we have a immediate breakdown of who is surveyed, how they were selected, a description of how the survey was conducted, how survey results were refined in process, and what exactly was going on. Conversely YouGov and Cato offer none of that level of detail meaning we have to take their word the survey was run properly and effectively.So with that in mind we really cannot take anything meaningful from this report since we don’t know how it was run in specific, we don’t know how people were selected in specific, we don’t know what screening happened, and we don’t know what is actually going on in the survey since those are not on the YouGov site. Instead we get this:The respondents in each survey were matched to a sampling frame on gender, age, race, education, party identification, ideology, and political interest. The frame was constructed by stratified sampling from the full 2013 American Community Survey (ACS) sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November supplement of the Current Population Survey (CPS), as well as the National Exit Poll. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life Survey. The matched cases were weighted to the sampling frame using propensity scores. The matched cases and the frame were combined and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, gender, race/ethnicity, years of education, non-identification with a major political party, census region, and ideology. The propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. The weights were then post-stratified to match the election outcome of the National Exit Poll, as well as the full stratification of four-category age, four-category race, gender, and four-category education.The State of Free Speech and Tolerance in AmericaWhich is more meta-textual than detail though it does give some detail but does not link us to it.So in the end anything this says should be disregarded at most or taken with many grains of salt at best because we cannot say with certainty that it is a reliable set of core data to build results from. So this study proves nothing of worth though it will be interpreted as very valuable by those who want it to have value.

Econometrics, statistics, and machine learning answer different sorts of questions.ML excels at finding patterns in data and using these patterns for classification and prediction. I discovered this myself a couple years ago, through an analysis of the economics literature that required the research team to classify articles into economics fields (like labor and macro) and research styles (like theory and econometrics). The project was motivated by frustration with complaints lodged against academic economics in the wake of the Great Recession (perhaps you’ve seen the movie version: Inside Job). I thought: “What’s with all the whining? “Economics has never been better!“ Economics is increasingly an empirical field, driven by data and experimentation, with less pure theorizing. And our data analysis relies increasingly on research tools like randomized trials and regression discontinuity methods, that make it especially convincing.My colleagues and I wanted evidence that might support of this optimistic view of economic scholarship. We came up with the idea of examining citations from disciplines outside economics (Here’s a non-technical summary of our “Deep Impact” project: Economics gets out more often). As part of this effort, we needed to classify cited publications into fields and styles of economic analysis. Of course, I know a labor paper when I read one, and although I’m not sure how to define “empirical work,” I think I can recognize that too! Classification is easy: read the papers. But Econlit, which indexes economics scholarship, has almost 150,000 papers (American Economic Association). Not all make a fascinating read. So, we taught the machine to read and classify papers. This seems to work well: machine classification agrees with human classification about 85% of the time. (Two humans agree with each other about 85% of the time as well.)The discipline of statistics was born out of a desire to work with data efficiently, primarily by drawing relatively small samples from larger populations of interest instead of collecting data on everyone. For example, the Census Bureau collects the data used to compute the US unemployment rate every month. Rather than asking ever American whether and how much they’re working, the Bureau asks a sample of around 60,000 households as part of the Current Population Survey (CPS). Statisticians are concerned with properly designing and analyzing such samples. They’re especially concerned with ensuring the represents the population as a whole and with quantifying the uncertainty that arises from statistical sampling (each CPS sample that we might draw generates a different unemployment rate; this phenomenon is called “sampling variance”). Statisticians can say how sensitive the rate is to the particular sample chosen. Statistics quantifies this uncertainty by reporting probabilities rather than claiming to discover the absolute truth behind an underlying statistic.Econometricians shares machine learners’ interest in classification and prediction, as well as statisticians’ concern with sample representativeness and sampling variance. We’re distinguished, however, by a longstanding focus on causal effects, especially the consequences of economic decisions and social policy.Will a college degree actually makeyou richer? Students, their families, and policy-makers want to know whether college tuition is a good investment. It’s a fact that college grads earn a lot more than high school grads, on average, a conclusion that ML and statistical analyses support. But the fact that college graduates earn a lot more than high school graduates is just the beginning of a causal inquiry. College may be advantageous. But perhaps it’s really the already-advantaged who are most likely to go to college. Econometricians refer to this sort of mix-up as the problem of “selection bias”. Econometrics offers powerful tools that, wielded with judgement and skill, can overcome the problem of selection bias. We can tell, for example, whether financial aid for college pays off in the form of higher earnings and taxes, or merely subsidizes schooling for those who are anyway likely to succeed. Big data and fancy prediction algorithms play but a supporting role in this effort.

Recent international poll cited by CBS:Agree/Disagree? "The climate change we are currently seeing is largely the result of human activity."In order of agreement:China 93%Argentina 84%Italy 84%Spain 82%Turkey 80%France 80%India 80%Brazil 79%Belgium 78%S. Korea 77%S. Africa 76%Total 76%Sweden 74%Germany 72%Canada 71%Japan 70%Poland 68%Russia 67%Australia 64%GB 64%US 54%Global Trends Survey | EnvironmentEDIT ADD: A Pew survey of the gap between public opinion and that of scientists, published January 2015, found that while 87% of scientists in the American Association for the Advancement of Science agreed that "Climate change is mostly due to human activity," only 50% of the American public agreed--a humongous 37 point spread.Public and Scientists’ Views on Science and SocietyAs to why:"A new study conducted by Dr. Robert Brulle, a professor of sociology and environmental science in Drexel University’s College of Arts and Sciences, along with Jason Carmichael of McGill University and J. Craig Jenkins of Ohio State University, set out to identify the informational, cultural and political processes that influence public concern about climate change."The study, which was published today in Climatic Change, one of the top 10 climate science journals in the world, reveals that the driving factor that most influences public opinion on climate change is the mobilizing efforts of advocacy groups and elites.“'Public opinion regarding climate change is likely to remain divided as long at the political elites send out conflicting messages on this issue,' said Brulle."The study conducted an empirical analysis of the factors affecting U.S. public concern about the threat of climate change between January 2002 and December 2010. The five factors that were examined were extreme weather events, public access to accurate scientific information, media coverage, elite cues and movement/countermovement advocacy."While media coverage exerts an important influence, the study revealed that this coverage is itself largely a function of elite cues and economic factors. Weather extremes have no effect on aggregate public opinion, and promulgation of scientific information to the public on climate change has a minimal effect."The implication would seem to be that information-based science advocacy has had only a minor effect on public concern, while political mobilization by elites and advocacy groups is critical in influencing climate change concern."- See more at: Page on drexel.eduAnd in America the corporate elites stand firmly against acknowledging global warming:"To uncover how the [climate change] countermovement was built and maintained, Brulle developed a listing of 118 important climate denial organizations in the U.S...The final sample for analysis consisted of 140 foundations making 5,299 grants totaling $558 million to 91 organizations from 2003 to 2010."- See more at: Page on drexel.edu[EDIT ADD]Most Americans who deny climate change are Republican, and here's one reason why--it's what their leaders tell them to believe:90% of the Republican leadership in both House & Senate deny climate change.17 out of 22 Republican members of the House Committee on Science, Space & Technology, or 77%, are climate deniers.22 out of 30 Republican members of the House Energy and Commerce Committee, or 73%, deny the reality of climate change.100% of Senate Environment and Public Works Committee Republicans have said climate change is not happening or that humans do not cause it.--from a 2013 Center for American Progress survey