International Statistical Review / Revue Internationale de Statistique, Vol. Duncan and Graham Kalton Issues of Design and Analysis of Surveys across Time. In this regard, readers might find Greg J. Princeton University Press for many examples of time trends. Social Trends in American Life: Findings from the General Social Survey Since 1972. There are few surveys that have been replicated over a period of more than 45 years.See Marsden, PV I(ed) 2012. Thus YEAR has been seen as a major variable of interest and not merely as a stratification variable.
#Gss 2014 codebook software
As software for analysis of complex survey designs became widely available and as the survey became used for much more than teaching purposes investigators began to push for more sample design information and NORC has responded.Ī major rationale for the GSS has been the investigation of time trends, for example in attitudes toward abortion, capital punishment, gun control and many other variables. Over time, the design has become increasingly complex to the point where each biennial survey now contains a panel and a repeated cross sectional component. Of course technically competent people understood that there was a design effect but for the purpose of undergraduate education it was ignored in part because software to handle the complex survey design was not available until much later in the game.
#Gss 2014 codebook code
"self weighting." The code book and documentation were also kept relatively simple. It was intended to be a basic data set for undergraduate education and as a basis for "social indicators" research and was deliberately kept simple, e.g. The first couple of years used quote sampling. The GSS dates back to 1972 at which time things were, relative to to what we do now, much simpler.
Bulletin of the International Statistical Institute, Contributed Papers 2, 103-104. Weights for combining surveys across time or space. Note also this interesting article about weighting for multi-year analysis.Ĭhu, Adam, J Michael Brick, and Graham Kalton. However, the fact that the stratification changed in many years suggests that the super-stratum approach may be best. I haven't found any guidance about what to do for GSS. Or, rather, I've created "superstrata" that grouped year and year-specific strata. Multiple year surveys: When multi-year surveys draw independent sample in each year, I've always treated the years as strata. Appendix A of the codebook shows many changes over the years, including changes to the sampling frame and target population. but I see nothing about this in the Codebook for the combined data 1972-2014 (( ). Apparently GSS has switched to a rotating panel design ( ). I have a suspicion it won't matter much either way, but I wonder if there is any consensus or controversy over whether or not to do this. On the one hand, the advice to treat year as a stratum variable sounds reasonable on the other hand I don't remember seeing similar advice anywhere else.
Instead, I include dummies for Year in my models. My current analysis uses svyset without year as a stratum variable. I haven't seen this approach recommended before, and am not sure if this is the best route to take.
This code is similar to the UCLA code you sent me, with the addition of year (see ).
He writes that "it is reasonable to treat Year as the stratum variable because the surveys from each year are independent, and Year is a fixed variable." His code to set up pooled GSS data is: svyset sampcode, strata(year). Predicted values from these two scenarios differ substantially, suggesting that decreasing confidence in institutions and increasing unemployment scarring may explain about half of the observed decline in US social trust.Do you have an opinion on treating Year as a stratum variable with pooled data? I ask because Donald Treiman recommends this in his book Quantitative Data Analysis. We then use 1973–2018 GSS data to predict trust based on observed values for unemployment, confidence in institutions, and satisfaction with income, versus an alternative counterfactual scenario in which the values of those three predictors are held constant at their mean levels in the early 1970s. Findings from fixed-effects linear regression models suggest that all but social ties matter. We use three-wave panel data from the General Social Survey (2006–2014) to study the effects of four possible individual-level sources of changes in social trust: job loss, social ties, income, and confidence in political institutions. Surprisingly few studies analyze whether individual-level explanations can account for this decrease. The US has experienced a substantial decline in social trust in recent decades.
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