Often analysis of complex data could be a huge challenge. Inefficient analysis could lead to biased or improper conclusions. This is where SPSS is extremely handy, as it can perform efficient analysis of complex data, with wide range of available statistical tools. SPSS is a flagship statistical analysis tool from IBM. More information about SPSS could be found on the link given below.
The term SPSS stands for “Statistical Package for the Social Sciences”. It is a software package that was initially developed by SPSS Inc. SPSS was introduced initially in the year of 1968, and later in the year of 2009, was acquired by IBM. Hence, from 2009 onwards, it is referred to as IBM SPSS statistics.
SPSS software is primarily used for statistical analysis. It is also used for batched as well as interactive analysis. Initially, the software was developed for its application in the social sciences. However, currently, it is also used in the field of health sciences & marketing. IBM SPSS software is largely used for analysis of research as well as business problems. It has got a range of statistical tools, that make complex data analysis and complex problem resolution extremely easy. The data could be presented in a customized way, that could be easily understood by the stakeholders. SPSS enables easy management as well as easy reporting of data.
Before the SPSS software is installed, one needs to check the system requirements. The system requirements for the installation are given below:
This is the minimum hardware requirement for the installation of SPSS software. SPSS is a resource hungry software, and installation should be done only if the minimum system requirement is met.
As part of the first step, one needs to purchase the software and download. The software could be purchased from IBM directly, or from third party retailer like Studica. Once the software purchase has been made from an appropriate seller, one will receive an email with the download links for the software. The email will also have the authorization code. The authorization code will be required for the activation of the software, post installation.
The download screen will look like the following.
Now, go to the download folder and right click on the software that has been downloaded. Run as administrator.
The installer for the IBM SPSS software will be triggered, and the initial installation screen will be displayed. One needs to click on “Next” to move to the next screen.
Accept the license agreement, and click next.
In the next screen, it will prompt to install the python essentials. Click on yes, and the next.
In the next the screen, the license agreement for Python installation will be displayed. Click on yes, and proceed with the installation. Set the default folder for the installation and click on install.
The installation will begin. Once the installation is done click on finish.
The software is still not activated, hence it should not be launched at this point. In the next phase, the software needs to be activated. Launch the “IBM SPSS Statistics 24 License Authorization Wizard”. You can simply type the name of the application in the search box or if you are using Windows 10, this can be pulled up by giving a voice command to Cortana. Now the licensing window will be launched. If you are a single user, select the first option, else the second option.
In the next screen, there will be prompt for the activation code. Now paste the 20 character activation code from your emails, as shown in the screen below.
IBM SPSS Statistics software has been successfully installed as well as activated now, and it is ready for first use.
SPSS is used for analyzing as well as editing different forms of data. The data, in this case, could be from any source such as analytical data (Google Analytic), customer database or even research data. The new user interface is meant to create a user experience, through a simple, but a powerful interface. What you can expect from the new user interface?
In the next few sections, you will come to know how to navigate around SPSS and use some of the important features of the IBM SPSS Statistics software.
With the help of IBM SPSS Custom Tables, the SPSS statistics data could be summarized at ease. The summarized statistical data could be presented as a presentation or in a tabular format. Custom tables enable analytical capabilities, that helps the user to learn from their own data. With the help of advanced features, one can build a table that can be easily understood as well as interpreted by those who are even not familiar with the SPSS analysis. With custom tables, the survey results could be presented using any of the following:
With custom tables, one can customize the layout and format of the table. They can exclude any particular field, while can also manage the missing values. They can change the labels and formats before presenting it. The tables can be previewed and modified until the desired form of output is generated. Further, the output of the table can be controlled, and multiway information could be represented in a two-way table, with the help of available formats. Last but not least, with Custom Tables, one can automate the table building. The automation can be prototyped on an existing complex table structure, while similar kind of tables can be built automatically once the new dataset is available.
IBM SPSS Advanced Statistics helps to make the analysis more accurate and conclusion more effective with a set of sophisticated analytical techniques. This includes the following:
One can gain deeper insights from the available data, and these insights could be effectively used for solving real-world problems. SPSS advanced statistics is used popularly in different fields, such as medical research, market research, and manufacturing.
IBM SPSS Decision Tree module primarily helps to identify the groups, discover the relationships between the groups, and predict the future events. This module comprises the heavily visualized classifications along with the decision trees, that ultimately helps the users to develop as well as present categorical results. More importantly, it helps to develop an analysis, that can be easily catered to audiences who don’t have a technical background. With the help of the decision trees, classification modules can be created for any of the following purposes:
One can also create models for interaction identification and the category merging. Decision Trees module could be also used for discretization of continuous variables. With the decision tree module, CHAID (Chi-square Automatic Interaction Detector) algorithm could be used for data analysis. CHAID is quite popularly used for discovering the relationship between one single categorical response variable and other categorical predictor variables. With the help of the CHAID algorithm, patterns are the datasets filled categorical variables, could be identified within the dataset. Apart from CHAID, C&RT, Exhaustive CHAID, and QUEST algorithms are also available in the Decision Tree module, and they can be effectively used for identifying relationships.
Data analysis forms the core functionality of the IBM SPSS Statistics. One of the important tasks is data creation in SPSS tool. In order to create data, a new spreadsheet needs to be opened. This could be easily done from File > New > Data. The columns are the variables, while the rows are the individual cases, which will make the dataset. One can define a variable at the column heading, and then enter the data for the subjects. For example, for a group of 7 individuals, the subject, height, and weight can be entered as shown below. Here, there are three variables, subject, height, and weight.
The variables need to be determined beforehand, and columns need to be labeled accordingly. In case more variables are needed to be added, in that case, the columns can be simply added. Similarly, in the case of more records, more rows need to be added.
Data typing in SPSS is really easy, and convenient. If the variables are not defined, click on the variable view tab located at the right-hand bottom in order to define the variable. For a variable, the variable name along with the variable type, width and decimal field to be specified. The type will determine the type of variable like numeric or alphabets. Width will define the field width.
In the screen below, a numeric variable named “School_Class” has been defined, with a field width of 8. The label field is optional, and to be used in case you want to display the variable with a different name.
Now go back to the Data View, the variable school_class appears. Now values can be entered for each of the records or cases, for the defined variable.
Crosstab or Crosstabulation is used for describing the relationship between the two categorical variables. This kind of table is also known as a two-way table or contingency table. In the case of crosstabulation, one of the categories determine the table rows, while the other determines table columns. Hence, the number of times a particular combination of the categories occurred in the table is denoted by the cells of the table.
In order to perform Crosstab analysis, one needs to ensure that two categorical variables are available, and there are at least two groups for each of the variable. A limitation of Crosstab analysis is, it could be used only when there is a limited number of categories are there. In order to build a Crosstab analysis, the table dimension needs to be determined which will be R X C (Row X Column). A good example will be the following table.
The following table is for gender crosstabulation. The dimension of the table is 4 x 2. In the above table Class Rank is the row variable which has 4 categories, while Gender is the column variable with two categories. The crosstab could be built from Analyze > Descriptives Statistics > Crosstabs, and then the rows & columns need to be selected for creating table dimensions. This is shown below.
The Chi-Square test is done to determine if there is a relationship between two variables or not. In order to do a chi-square test, the data should pass two assumptions, which are mentioned as follows:
In order to do a Chi-Square test, first, a Crosstab needs to be built in a similar way that has been discussed in the last section. Now on the Crosstabs screen, select the statistics option.
The given options, select Chi-Square.
Click on continue and then click on Cells button. Once you are on the Cells button, select the following as shown below.
Now click on continue, and follow the wizard, the chi-square analysis output will be ready.
The SPSS Statistics version 25 supports Bayesian Statistics. The following analysis and tests could be done.
In the new SPSS, with custom tables, you can create your desired outputs from the given dataset. Once you click on “Create”, the custom table is created. You can add the analysis on to your output tab. This has been shown below.
Now export the custom table, and create your own chart for presentation using the Chart Builder in SPSS. In order to launch the utility, click on visualize from the menu bar and click on chart builder. You will get the following screen.
Select the type of chart you want, and start building your own chart.
With the help of IBM SPSS statistics, statistical analysis has become more convenient and user-friendly. It doesn’t matter if the user is an experienced statistician or a beginner, as IBM SPSS has a host of tools which makes even complex data analysis easy. Some of the key features are:
IBM SPSS Statistics are available in two options. So, for customers, there are two deployment options. These two options are:
One can select a module package based on their marketing. For example, for marketing research “Forecasting and Decision Trees” will be the most appropriate package, similarly for trend analysis “Subscription Base” will be the package.
The table given below provides a snapshot of comparison amongst all version IBM SPSS Statistics.
IBM SPSS Statistics has been one of the most consistent tools for statistical analysis. It has been one of the most reliable tools for complex data analysis. The best part is, the tool can do the reporting in such a way that the presented analysis can be easily understood even by someone who is not technically or statistically sound.
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