Solved – How to compare the results of a survey from two time points and how to compare two groups within a time point

survey

Looking for some advice on running simple statistics on some survey data I have.

Part 1. I have two surveys, one from 2009 and one from 2013, with the exact same question but different respondents. How might I compare this question between years to see if they are significantly different or not? The survey was conducted anonymously from a pool of about 4000 people. Several hundred responses were collected for each year (~500 for 2009 & 700 for 2013).

Part 2. What is the best way to test for significance among groups of people based on their characteristics within one survey? T-Test?

I unfortunately only have access to Excel to do my stats. Any help would be appreciated!

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A few Suggestions:

1) Run Cluster iterations on both sets of data and compare the mean centers. 2) If there are too many questions and sets of questions belong to the same category, run a PCA to reduce the number of factors. 3) If your questions and responses are scalar - "rate on a scale of 1 to 5" then an attitudinal segmentation of people can be done.

It would be helpful if you can post the purpose of your survey, what you are hoping to achieve with it and a few more details about the nature of the data.

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Solved – Statistical tests to do data quality checks

The keywords to search for are "interviewer falsification". AAPOR/SRMS guidelines are a good starting place, and RTI system is useful, too. As far as I know the evidence, the interviewers may be able to get the first moments OK, but they have a more difficult time with higher order moments, so falsified data may be detected via unusual variances and correlations/crosstabs.

Combining Survey Data – How to Combine Data from 5 Surveys Spanning 10 Years

I think if each dataset is already weighted to your satisfaction, then you have a couple of different options. Which one is the right one may vary based on your objectives and the particulars of your existing data collection and weighting.

(#1) Union all of the datasets, along with their pre-calculated weights, and that's it.

This would be the right choice if each dataset was weighted towards a proper total count and didn't over-state the importance of any individual record relative to another dataset. If one dataset was weighted to reflect Total US Population, and another dataset was weighted in place to its own total count of respondents, then this would not be the right choice.

(#2) Calculate a weight for each dataset to multiply by each record's existing weight

This would be the right choice if each of your datasets are of equal importance regardless of their size. Example below...

(#3) Union all of the raw data and re-calculate the weights on the new, entire dataset

This would be the right choice if the reasons for non-response are similar across your different surveys - it results in the simplest data for you to work with, and it's the least likely to produce extreme weights.

Example for #2: each dataset is weighted to equal importance, with this "dataset weight" being multiplied by whatever weight has already been calculated within the dataset.

> Survey 1: 100 people   weight:  2
> Survey 2: 200 people   weight:  1
> Survey 3: 300 people   weight:  2/3
> Survey 4: 150 people   weight:  4/3
> Survey 5: 250 people   weight:  4/5

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Solved – Statistical tests to do data quality checks

Combining Survey Data – How to Combine Data from 5 Surveys Spanning 10 Years

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