It gives you additional numeric precision, and you’re also able to include several attributes if you need to break things down further: Much as I’m a huge fan of matrix coding queries, if you’re taking the results across to SPSS, I’d recommend the new crosstab query over a matrix coding query. The table below shows you an example of a cross tab query – this is from the sample project, and looks at the child nodes underneath ‘Attitude’, split by gender: The results of both matrix coding queries and the new crosstab query provide a numeric breakdown of your codes against specific attributes (or codes against cases if that is of more interest), and both can be exported to SPSS. Once imported, my demographics appear as attributes in my classification sheet, and I can run queries on specific subgroups as required. For example, I can specify what unique identifier I would like to use from my SPSS data, and can also ask for value labels to be imported rather than numbers (value labels in SPSS are typically used on categorical variables, you can label the numbers 1 and 2 as ‘male’ and ‘female’ for example). I can now import these directly into my NVivo project, and have a reasonable amount of control as to how it’s imported. On a mixed methods project, I’ll often have my demographics stored in SPSS rather than something like Excel. In order to run these types of queries, NVivo needs to know the demographics (or other characteristics) of your participants – that’s where classification sheets come in. They’re incredibly useful for running queries in NVivo – for example, you might want to look at what males said about a particular topic versus females. If you’re new to the idea of classifications, they’re a way of recording demographic or descriptive information within your project – details such as the gender of your research participants. In terms of how I’ll be using NVivo 12 with my SPSS data, one of the key things for me will be the import of classification sheets. Neither of these were a massive issue, but I’m not a fan of double-handling files (I like to keep things simple – research is hard enough without dealing with multiple versions of files) and I also think time is precious, so anything that saves me time is a good thing! Why am I so excited? Mixed methods data analysis was always possible in NVivo, but it was necessary to use Excel as an intermediate step – SPSS files had to be saved in Excel before importing as a classification sheet, and the results of queries had to be exported to Excel before being taken over to SPSS for further analysis. In the Assign to Classification drop down menu, select your classification (e.g., People).NVivo and SPSS both feature heavily in the toolkit I use as a researcher, so I was pretty excited when I heard that NVivo 12 would enable me to work with SPSS data for my mixed methods research. In the Select Location dialog box, set the location to store your case nodes (e.g., the Cases folder) (If you create case nodes from the ribbon (Create > Case) or by right clicking into List View, you will need to create each case individually and manually code the file to the case). Creating cases from the files in this way codes the file to the case. Right Click, then select Create as, Create as Cases. Select all the data files that represent your study participants. In this step, the classification is linked to the case of each participant so that all attributes in the classification are available for the participant.Ĭlassifications must be linked to the case node that represents the participant, not to the data file (e.g. Step 3: Create cases for each participant, code each participant’s data to the case, link the cases to the classification
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