编码簿是您项目的支柱。 它列出了您用于对数据进行分类的编码（主题/概念），并包含编码说明。 这些描述使您或您的团队更容易采用一致的编码方法。
Codebook Exchange Format（.qdc）是在定性数据分析（QDA）工具之间共享编码簿的新标准。
仅NVivo 12 PLUS
如果选择英语以外的用户界面语言，则应确保计算机上已安装该语言 - 有关详细信息，请参阅Microsoft Windows联机帮助。
A growing number of researchers combine qualitative and quantitative techniques to deliver robust research results.
We have added new features to support this mixed methods approach to data analysis.交叉表查询
The new Crosstab query gives you a fast way to check how coding is distributed across the cases, or different types of cases, in your project.
For example, with just a few clicks you could check:
You can double-click the cells in the Crosstab to read the underlying qualitative responses.
You can also export the numerical data for further statistical analysis in specialized tools like SPSS.
While you can get similar results using a Matrix coding query, the Crosstab query
Refer to Crosstab query for more information and step-by-step instructions.导出到SPSS格式
You can now export numerical data from NVivo into an SPSS data file (.sav).
For example, you could export a classification sheet or the results of a Crosstab query to an SPSS data file.
There is no need to edit the file before importing it into SPSS. You can open the file in SPSS and begin your statistical analysis right away.
Refer to Export query results for more information.学习曲线更短，可用性更好
Based on user feedback, we have simplified terminology and streamlined navigation to make NVivo easier to learn and quicker to use.
NVivo terminology has been simplified to better reflect the research process:
You will notice the following changes in Navigation View:
Navigation View reflects the new terminology and provides faster access to your data.
To keep your most frequently used items within easy reach, you can now pin them to Quick Access at the top of Navigation View:
Cases, classifications and attributes are more accessible and easier to work with:
Memos, Framework Matrices, Annotations and See Also links are centralized under Notes:
Queries, sets and search folders are organized under Search
The ribbon has been re-designed to make commonly used features easier to find.
Commands on the main tabs have been reorganized and many ribbon tabs are hidden until you need to work with them.
For example, the Document ribbon tab is displayed only when you are working in a document:
It includes options for coding, annotating, visualizing or querying the document you are working with. Everything you need is in one place.
This reduction in clutter makes it easier to find the tools you need, when you need them.
If you are familiar with NVivo 11 menus, you can download and use the NVivo 12 ribbon mapping spreadsheet to locate a menu command on the ribbon.
The Find Bar at the top of Detail View has been simplified to help you find items more easily. Results are displayed as you type.
You can now use auto coding to create cases for multiple speakers in an interview transcript:
You can enter the name of each speaker in a transcript and NVivo:
This is especially useful when you are working with focus groups and want to create a case for each participant.
Refer to Automatic coding in documents for step-by-step instructions.更多的基于模式的编码控制
This feature is available in Plus edition. Learn more
Pattern based auto coding is a way of automatically coding content based on previous coding activity. NVivo uses terms from existing nodes to check for similarities in the data being coded.
This process has been updated to provide greater transparency and give you more control over the results.
You can now see which terms are being used to assess coding similarity and you can include or exclude them as needed:
Refer to Automatic coding using existing coding patterns for more information about this technique.
NVivo 12.0 (Windows) — 20 March 2018
NVivo 12 (Windows) is available in two editions—Pro and Plus. Some of the features described in this topic may not be available in all editions of the software.
NVivo 12 MAC版更新说明
NVivo 12 Mac is now multilingual
Autocode transcripts by speaker
Introducing concept maps
More on-screen visibility
Code more context within datasets
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