SPSS is an IBM creation and stands for the Statistical Package for the Social Sciences. It is an effective yet simple software for analysing data inputs.
SPSS is an IBM creation and stands for the Statistical Package for the Social Sciences. It is an effective yet simple software for analysing data inputs. This data can come from just about any source you can imagine: dissertation research, a customer resource management database, in-house marketing research, or even the server log data of a website. SPSS is also able to open numerous file formats, usually those related to structured data such as Microsoft Excel, SQL databases, or even plain text files.
After opening SPSS and importing (or inputting) your data, the software will display this data in a spreadsheet-like fashion, similar to the below screenshot. Your data will be opened in the Data Editor window, and it will be displayed in one of two ways: Data view and Metadata (or Dictionary) view. The below screenshot is the normal ‘Data’ view, which allows you to see your displayed data:
Meanwhile, the Data Editor window of SPSS has a second viewable sheet. The first sheet is the one we just discussed (data view). The second sheet, shown below, shows the metadata for your particular data. ‘Metadata’ is just a fancy word that means ‘data about data’ and shows information about the meaning of your data, its variables, values, etc. This metadata view is generally known by programmers as a codebook, but in SPSS it’s called the dictionary. The screenshot shows you this ‘dictionary’ view of your data which, again, contains the metadata about your data:
For those users not yet oriented with SPSS, the look and feel of SPSS and its Data Editor window is very similar to a Microsoft Excel workbook that contains two separate but related sheets.
So far, you’ve been shown how SPSS actually displays your data. But how can you actually go about analysing this data? One simple way to do this is through the comprehensive menu options available in SPSS. To illustrate, suppose your data contains a variable showing the income of your survey respondents after 2010. You can compute the average income for this variable by simply clicking the SPSS Descriptive Statistics dialog window (refer to the below screenshot).
After you select the Descriptive Statistics dialog window, the output of your descriptive stats results will display in a separate window from the traditional Data Editor window. This new window is called the SPSS Output Viewer Window. Compared to the Data Editor window, this second window has a completely different format and structure. The following is a screenshot of this new Output Viewer Window:
Just to make sure you know, when you care an output window in SPSS, nothing in your data sheets will change in any way. Remember, the data editor and output view in SPSS are wholly and completely separated. For those users new to SPSS, you can compare the look and feel of the Output Viewer window in SPSS to slides in Microsoft PowerPoint that may contain items such as tables, charts, and blocks of texts.
The output data in SPSS, such as tables and charts, can easily be copied and pasted over into other applications. For example, many users use word processing applications like Microsoft Word, OpenOffice, or even Google Docs to create reports. Tables from SPSS are usually copied over into other applications in rich text format (.rtf). This means that these tables will keep their styling, format, and structure (e.g. fonts, borders, etc.). The below screenshot is an example of a table copied over into OpenOffice using the rich text format:
This output table by the way was created using the Descriptive Statistics option from the SPSS menu. SPSS does give users another option for running this particular command (to create the table) or even any other command you want. So, besides the Data Editor window we mentioned, and besides the Output Viewer window, there is a third window you can open called the Syntax Editor window. In this window, you can type certain commands that you’d like to run. For example:
will do the same thing as clicking the Descriptive Statistics dialog window and running the command from there.
It is not necessary to manually type the commands into the Syntax Editor window. You can actually paste the commands into the window by clicking through the options available in the SPSS menu bar. If so, then why exactly use the Syntax Editor window? There are several reasons to use it as opposed to the menu. The commands typed into the syntax can be saved, edited, quickly retrieved, rerun, and shared between other members of your project. This allows the effortless streamline of the project workflow and easy collaboration among users of your research.
For beginning SPSS users, the look and feel of the SPSS Syntax Editor window can be compared to Notepad or Command Prompt (basically a single window containing simple plain text).
Since you now have a basic grasp of how SPSS works, along with its main windows and views, it is useful to give you a brief overview of what SPSS can actually do. For most typical projects, SPSS is ideal to:
The following section contains a brief description of each of these features:
It must be kept in mind that SPSS has its own file format. However, SPSS can be used in conjunction with other file formats such as Microsoft Excel spreadsheets, simple text files (ASCII, UTF-8, etc.), database formats (such as those used by Microsoft SQL Server, MySQL, and Oracle), Stata, and more.
In the real word of research, the raw data you gather as a doctoral student for your dissertation will most likely need some editing before you can properly analyse them (whether in NVIVO or other statistical analysis programs besides SPSS). An example of this may include creating averages, means, sums, or even restructuring data. However, you can conduct all these tasks (and more comprehensive ones) seamlessly and effortlessly with SPSS. For doing this, you can use many functions in SPSS like date, string, numeric functions, and more.
All your basic charts and tables can be created with little effort in SPSS. However, one drawback inherent in SPSS is that the generated charts and tables are usually ugly and contain a rather clumsy structure and layout. But to solve this problem, you can actually develop and apply certain SPSS chart templates. But to do this will require some experience and expertise in SPSS.
With SPSS, you have a multitude of statistical tests like ANOVA, chi-square tests, t-tests, and more. You also have multivariate analysis features for regression analysis, cluster analysis, factor analysis, and more.
You can save your data in a variety of formats in SPSS. These formats include Microsoft Excel file formats, plain text, SAS, State, and more.
Keep in mind that the file formats you can save for your output are even more numerous. You can copy-paste images of charts in .png and other image formats. In addition, for tables, you can use rich text format as previously mentioned. You can also batch export a number of common formats that include, among others, .doc, .docx, .html, .htm, and .pdf.
If you’re stuck on a project and need guidance and help for SPSS analysis, or if you simply have a question in SPSS and need an expert’s helping hand, feel free to contact us by simply putting your question or concern. We’ve been in the dissertation consulting field for over the decade and know the SPSS program inside out. Therefore, don’t hesitate to contact us, no matter how big or small your concern!
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