Hi Dr. B., I just want wanted to take a quick second to thank you for posting this video about Using SPSS for Complex Samples. I was stuck for over two days not knowing how to account for data weighting before I run my analyses. Although I received the dataset with some instructions I was still unsure how to proceed. Thanks again, Dr. B.
Dr Banerjee, This is awesome - I have struggled so much to find good examples using complex samples design in spss! Especially for NHANES! If you have time, a Cox regression example would be so so helpful!
The finite population correction (FPC) is used in the calculation of the standard error of the estimate. If the value of the FPC is close to 1, it will have little impact and can be safely ignored. The finite population correction factor = (1 - the sampling fraction) is close to 1 and has a negligible effect on the formula for the design based estimate of variance.
Thank you so much for this video! Question regarding the model tab--does this need to be used or can it stay on the "main effects" grayed out function. My dependent variable is 0/1 to CV disease and I have several factors (categorical covariates) and 2 (continuous) covariates. I am unsure how to use that "Model" function/if it needs to be used and what impact that has. Thank you!
Thank you for the question. I am not sure, I am quite understanding the question. Are you saying in your statistical package you are unable to find the Model function? Or is there something else that you are saying?
Dr. Banerjee, Thank you for the video. 1. For a dataset like the National survey on drug use and health (NSDUH) which provides the weight variable for each of the years dataset is released. If you were to combine dataset say 2015-2020 would one need to calculate the weights or just use the weight variable already in the combined dataset?
When creating estimates for single years of data, use ANALWT. When combining years of data to produce an annual average estimate over that time period, an adjusted weight variable will need to be created by averaging ANALWT over the number of years. e.g. An adjusted weight for combined 2015 to 2020 data (6 years) ADJWT3=ANALWT/6.
Thanks very much for this video! Do you happen to know if hierarchical linear modeling (aka multilevel or mixed models) can be performed using the complex samples module in SPSS 28?
Multi-level modeling is more complicated in SPSS especially with complex samples. However, there are various ways that this can be employed in R. The two statistical packages that are especially informative are BIFIEsurvey package and Wemix package in R. Sampling weights are used more frequently in NHANES whereas replicate weights are typically used in HINTS (Health Information National Trends Survey) and BRFSS (where post-stratification raking is used after applying design weights.
Good job on the video. I followed the steps to create a plan file to analyze NHANES combined (2015 - 2018). After successfully using the appropriate weight variable (MEC4YR), I tried to do just a frequency of the Race variable and to my surprise, it gave me a bunch of numbers (e.g., 34093150.64) instead of a number and percentages. Do you know what might be causing this?
Felix, thank you for this question. This value more than likely corresponds to the weighted sample size of 34,093,151 respondents. In other words, your response corresponds to 34 million individuals within the United States. This is the power of NHANES.
Hi Dr. B., I just want wanted to take a quick second to thank you for posting this video about Using SPSS for Complex Samples. I was stuck for over two days not knowing how to account for data weighting before I run my analyses. Although I received the dataset with some instructions I was still unsure how to proceed. Thanks again, Dr. B.
Thank you for this video, this was really, really helpful!
Glad it was helpful!
Dr Banerjee,
This is awesome - I have struggled so much to find good examples using complex samples design in spss! Especially for NHANES! If you have time, a Cox regression example would be so so helpful!
@hayleybillingsley8909 here is the video th-cam.com/video/d6i0ITeOHeQ/w-d-xo.htmlsi=IKY4ihI4TbkfjfgF
Why did you unselect the finite population correction?
The finite population correction (FPC) is used in the calculation of the standard error of the estimate. If the value of the FPC is close to 1, it will have little impact and can be safely ignored. The finite population correction factor = (1 - the sampling fraction) is close to 1 and has a negligible effect on the formula for the design based estimate of variance.
Thank you so much for this video! Question regarding the model tab--does this need to be used or can it stay on the "main effects" grayed out function. My dependent variable is 0/1 to CV disease and I have several factors (categorical covariates) and 2 (continuous) covariates. I am unsure how to use that "Model" function/if it needs to be used and what impact that has. Thank you!
Thank you for the question. I am not sure, I am quite understanding the question. Are you saying in your statistical package you are unable to find the Model function? Or is there something else that you are saying?
Dr. Banerjee,
Thank you for the video.
1. For a dataset like the National survey on drug use and health (NSDUH) which provides the weight variable for each of the years dataset is released. If you were to combine dataset say 2015-2020 would one need to calculate the weights or just use the weight variable already in the combined dataset?
When creating estimates for single years of data, use ANALWT. When combining years of
data to produce an annual average estimate over that time period, an adjusted weight variable
will need to be created by averaging ANALWT over the number of years. e.g. An adjusted
weight for combined 2015 to 2020 data (6 years) ADJWT3=ANALWT/6.
@@skbanergt3 Thank You for the response.
very helpful, thanks!!
Thanks very much for this video! Do you happen to know if hierarchical linear modeling (aka multilevel or mixed models) can be performed using the complex samples module in SPSS 28?
Multi-level modeling is more complicated in SPSS especially with complex samples. However, there are various ways that this can be employed in R. The two statistical packages that are especially informative are BIFIEsurvey package and Wemix package in R. Sampling weights are used more frequently in NHANES whereas replicate weights are typically used in HINTS (Health Information National Trends Survey) and BRFSS (where post-stratification raking is used after applying design weights.
@@skbanergt3 Thanks very much for your response!
Good job on the video. I followed the steps to create a plan file to analyze NHANES combined (2015 - 2018). After successfully using the appropriate weight variable (MEC4YR), I tried to do just a frequency of the Race variable and to my surprise, it gave me a bunch of numbers (e.g., 34093150.64) instead of a number and percentages. Do you know what might be causing this?
Felix, thank you for this question. This value more than likely corresponds to the weighted sample size of 34,093,151 respondents. In other words, your response corresponds to 34 million individuals within the United States. This is the power of NHANES.
@@skbanergt3 When you say the weighted sample size you mean the "estimate" column in the output of the frequency table?
Felix, had the same problem you need to enable table percentages in the statistics option.