File Formats Supported by Q

The best file formats for use in Q are those that contain a lot of metadata. These files greatly reduce the amount of time taken to analyze data in Q as well as decreasing the chance of mistakes being made in analysis.

Data

Good data file formats

The following data file formats are typically the best for use in Q.

• SPSS Dimensions (Data Collection Model) data files, which are created by the more modern SPSS data collection packages. These files have file extension (*.mdd). Note that Q expects an .mdd file and a .ddf file. Some .mdd files will come with a different type of data file (e.g. .sav, .csv, .sss). In these cases you should use the other data file directly. Other .mdd files have no extra data file and instead refer to a database. You will need to connect to these databases directly from Q. MDD and Triple S files have the best metadata, and will be best at setting up your questions correctly, and MDD files store data efficiently.
• Triple-S data files. This is a standard and open data file format for survey data. Q supports the Triple-S version 2.0 , which can describe both fixed-format and CSV data files. Triple-S data will come in two files. One file contains the metadata and will have a file extension of .xml or .sss and a second file containing the raw data, either as a fixed format (typically .dat file or .csv file). Please note: not all .xml files are Triple-S files (the easiest way to check is to try and import into Q). Where you have hierarchical data files, these are set up in Q by importing each as a separate Triple-S data file and then using Q's facilities for Multiple Data Files to link them together (i.e., Q does not use the Triple-S control files for hierarchical data). Additionally, the Triple-S file format is not efficient in terms of how much space it uses, so for huge projects it can be beneficial to use another format. Triple S and MDD files have the best metadata, and will be best at setting up your questions correctly.
• SPSS data files (.sav). This is the most widely available of the good formats. There are lots of different ways that these files can be created and some are much better than others; see SPSS Data File Specifications. SPSS .sav files load faster than the other two, and thus are good for large numbers of cases.
• SPSS Syntax files. This is the worst of the “good” file types but is the easiest to create if the software being used to export the data cannot export in one of the previous three file formats. Q only supports a subset of SPSS syntax (see Creating SPSS Syntax Files for Use in Q).

Sub-optimal data file formats

These file formats do not contain metadata (i.e., there is nothing in them to indicate if a 1 means, say, Male) and, as a consequence, their use will greatly increase the amount of setup required within Q.

• CSV files (.csv). See Excel and CSV Data File Specifications for recommendations on how to set up CSV files and Using Excel and CSV Files in Q for an overview.
• Excel spreadsheets (.xls and .xlsx). These work identically to CSV files; see the extra documentation referred to above. Note that Q will only read the data from the first sheet in your Excel workbook, and that the spreadsheet should have no blank rows or columns before the data, and should contain at most one row of headings. (Although Q version 4.9+ can ignore blank rows or columns.)
• SQL databases, including Microsoft SQL Server, MySQL and Oracle.
• Access and Excel file databases

See also

• Setting Up Your Data in Q for an overview of how to set up data in Q
• Data File Setup for Tracking Studies
• Setup for Advertising Trackers
• Data File Setup for Studies with Multiple Versions of the Questionnaire
• Data File Setup for Questions with Rotations or Randomization
• MaxDiff Specifications contains instructions for the specifications of MaxDiff (maximum difference experiments).
• Experiments Specifications contains specifications for the set up of conjoint, discrete choice and other types of choice experiments.
• Manipulating data files
• Basic Workflow For Checking and Cleaning a Project
• Constructing Variables to Make Analysis Easy
• Setting Statistical Assumptions When Setting Up Projects
• Weighting (Sample balancing)
• Advanced Data Tidying
• Exporting, Copying and Printing
• Updating Projects with New or Revised Data
• Converting Other Files Types into SPSS or CSV Data Files)

Further reading: Market Research Analysis Software SPSS Alternatives Data Analysis Software