SPSS CODE NOT APPLICALBE AS MISSING SOFTWARE

Here there are two main alternatives, (1) one could store the responses as a numeric vector, thereby losing the information about the labelled values, or (2) one could store the responses as a factor, thereby losing the information contained in the codes.

While this coding frame for responses to survey questionnaires is far from uncommon in the social sciences, it is not straightforward to retain this information in R objects.

In data sets that contain the results of such coding are essentially numeric data вЂ“ with some additional information about the вЂњvalue labelsвЂќ (the labels attached to the numeric values) and about the вЂњmissing valuesвЂќ (those numeric values that indicate responses that one usually does not want to include into statistical analysis). 1 A survey agency that actually conducts the interviews with a sample of voters may, according to common practice, use the following codes to collect the responses to the question about the vote intention: Suppose the response categories offered to the respondents are вЂњConservativeвЂќ, вЂњLabourвЂќ, вЂњLiberal DemocratвЂќ, вЂњOther partyвЂќ. As an illustrative example, suppose in a pre-election survey, respondents are asked about which party they are going to vote for in their constituency in the framework of a first-past-the-post electoral system. It is one of the aim of the вЂњmemiscвЂќ package to provide a bridge between social science data sets of variables that contain coded responses to questionnaire items, with their typical structures involving labelled numeric response codes and numeric codes declared as вЂњmissing valuesвЂќ.

SPSS CODE NOT APPLICALBE AS MISSING SOFTWARE

As a consequence, many social scientists exercise their everyday data management tasks with commercial software packages such as SPSS or Stata, but there may be social scientists who either cannot afford such commercial software or prefer to use, out of principle, open-source software for all steps of data management and analysis. The main reason for this is that the way that questionnaire item responses as they are usually coded in machine-readable survey data sets do not directly and easily translate into RвЂ™s data types for numeric and categorical data, that is, numerical vectors and factors. Nevertheless there is still room for improvement in terms of the support for data management tasks that arise in the social sciences, especially when it comes to handling data that come from social surveys and opinion surveys. R is a great tool to do data analysis and for data management tasks that arise in the context of big data analytics.

Spss code not applicalbe as missing