SPSS Statistics 29 IBM SPSS Statistics 29

 

Includes the following:

• IBM SPSS Base
• IBM SPSS Advanced Statistics
• IBM SPSS Regression
• IBM SPSS Bootstrapping
• IBM SPSS Data Collection

• No limitation on the number of variables or cases
• Be sure you have all the add-ons needed for your course or dissertation!  The Standard version does not include all add-ons available and you may not purchase them separately or at a later time.  Consider the Grad Pack Premium. 

What’s new in SPSS V29?

• Several new extensions
• New modeling tools
• Workbook mode enhancements
• Search enhancements

• PLEASE NOTE: Will not run on the following:
  • • Windows Vista
    • Windows XP
    • Chromebooks
    • IPads
    • Android tablets
    • Smartphones

IBM SPSS Base Overview, Features and Benefits

Descriptive Statistics

Tests to Predict Numerical Outcomes and Identify Groups:

IBM SPSS Statistics Base contains procedures for the projects you are working on now and any new ones to come. You can be confident that you’ll always have the analytic tools you need to get the job done quickly and effectively.

• Factor Analysis – Used to identify the underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering:
  • Seven methods of factor extraction
  • Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations
  • Three methods of computing factor scores. Also, scores can be saved as variables for further analysis
• K-means Cluster Analysis – Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to specify the number of clusters. Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only.
• Hierarchical Cluster Analysis – Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left. Analyze raw variables or choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure. Statistics are displayed at each stage to help you select the best solution.
• TwoStep Cluster Analysis – Group observations into clusters based on nearness criterion, with either categorical or continuous level data; specify the number of clusters or let the number be chosen automatically.Discriminant – Offers a choice of variable selection methods, statistics at each step and in a final summary; output is displayed at each step and/or in final form.Linear Regression – Choose from six methods: backwards elimination, forced entry, forced removal, forward entry, forward stepwise selection and R2 change/test of significance; produces numerous descriptive and equation statistics.Ordinal regression—PLUM – Choose from seven options to control the iterative algorithm used for estimation, to specify numerical tolerance for checking singularity, and to customize output; five link functions can be used to specify the model.Nearest Neighbor analysis – Use for prediction (with a specified outcome) or for classification (with no outcome specified); specify the distance metric used to measure the similarity of cases; and control whether missing values or categorical variables are treated as valid values.

  IBM Advanced Statistics – More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis