Spss value labels
My experience was that the base data frame in R does not easily lend itself to work easily with these labels. In my talk at the EARL conference last year, I also discussed a specific type of trade-off agreement question where any interpretation of the data is particularly sensitive to the value labels:.Respondents with a different classification within the survey (e.g. “full-time employees” vs “retirees”) may also have answered a statement that is worded slightly differently but their responses are reflected using a single variable in the data: for instance, employees may be asked about their satisfaction with their current employer in the survey, and retirees asked about their previous employer.What is your gender?) and value labels (e.g. 1 = Male, 2 = Female, 3 = Other, …), which is true in the case of categorical variables.Įven for ordinal Likert scale variables such as “ On a scale of 1 to 10, how much do you agree with…”, the meaning of the value is highly dependent on the nuanced wording of the agree-disagree statement. Survey data generally cannot be analysed independently of the variable labels (e.g. One of the big reasons for this “pain” was due to survey labels. Funnily enough, when I first started out to use R a couple of years ago, I didn’t think R was at all intuitive or easy to work with survey data. Since a significant proportion of my typical analysis projects involves survey data, I’m always on the look out for new and better ways to improve my R analysis workflows for surveys.