Discover the Sample Proforma for R&D that Streamlines Your Documentation Process

Sample proforma for R&D

now that we understand survey weights let's learn about some common design structures and how they're specified using the popular survey package suppose we want to estimate the average amount of time Pennsylvania residents spend on social media this means our target population is the residents of Pennsylvania and our study variable is the weekly hours spent on social media suppose we have a list of every Pennsylvania resident and we randomly pick 200 people to survey these 200 people are denoted by the pink dots this method is called simple random sampling everyone had an equal chance of being selected for our serving once we've collected the data and imported it into our we now need to tell our about our sampling design to do so we will use the survey design function in Thomas Lumley's wonderful survey package for simple random sampling we need to specify the data frame the column that stores the survey weights the column that contains the population size which statisticians call FPC short for finite population correction and the column that stores staging information since there's only one sampling stage in simple random sampling we set ID equal to tilde 1 the till does in front of weights and N imply that these the names of columns in the data set P a sample to recap the survey design function tells our what sampling design generated the data and creates an object that contains the data an important design information notice that if we randomly select 200 Pennsylvania n--'s that many of these residents are concentrated around Pennsylvania's two largest cities Philadelphia and Pittsburgh since these places have a higher concentration of people if we want to estimate the social media usage by county we can't since some counties such as Warren and McKean have no observations to fix this issue we can group our population into counties and then take a simple random sample in each county this is called stratified simple random sampling and the groups are called strata this sampling design works well to get a more diverse sample and as useful in computing estimates for subgroups to specify this sampling design we need to map the column in the data set that contains the groupings to the strata argument and survey design for our example the groups are the counties the entries of FPC should now reflect the population size of the Associated County now this sampling design can be expensive to implement imagine you plan to interview each sampled person at their home in that case this design will cost a lot of gas money and travel time to cut down on costs another commonly used design is a cluster sampling design the population are grouped into what are called clusters but now instead of taking a sample within each cluster a simple random sample of clusters shown in black are drawn within each sample cluster a simple random sample of people is selected for in-person surveys it is very common to cluster by area as it cuts down on travel time to specify a cluster sampling design we need to change ID and FPC to reflect the sampling stages for our example we first sampled County and then we sampled people within County so we set ID to County plus person ID for FPC we provide the column width number of counties denoted here by n1 plus the calm with the number of people denoted here by n2 let's practise specifying sampling designs