Intellectus Statistics Review

INTELLECTUS STATISTICS – TUTORIAL

In this video we’ll see how to conduct and interpret an exploratory factor analysis using this university program:

INTELLECTUS STATISTICS – FEATURES

• Interpreted output: both in the output viewer and in the downloaded document, the program interprets the statistical assumptions and the results of the analysis and generates tables and figures that correspond to the assumptions and the output. This innovation is essential for reducing statistical anxiety and presenting the student with all the information necessary to move from loading data to statistical understanding
• The projects tabs allow you to create and save projects: projects save all data, managed data and previously conducted analyzes so that you can log back in and pick up where you left off. It also means that collaboration can happen from colleagues
• Logical user interface: the software guides students through a logical step-by-step process that is sequential and easy. Uploading data, to manage the data, view the data, conduct analysis and review the interpreted output
• Measurement reminder level: measurement level is critical for conducting analyzes. The tool reminds students how to define measurement levels and provides examples on the level validation page
• Data analysis plan templates: over 40 templates to guide you in drafting your data analysis plan. Simply replace the blue text with your variable names
• Power analysis: calculator for power analysis, it provides the sample size needed for small, medium, and large effect sizes. Complete with review and references
• Preselect the measurement level of variables for analyzes: the tool allows you to select only the appropriate variable for a particular statistical test
• Non-parametric equivalent test is performed automatically: when the parametric assumptions of a test are violated, the non-parametric equivalent test is conducted and presented automatically
• Automated dummy code: when conducting some analyzes, ordinal and nominal level variables must be dummy coded and a reference variable must be selected. For example, when a regression analysis is selected, the nominal and ordinal variables are automatically coded in a fictitious way
• Centered interaction terms: for moderation analyzes, the tool mitigates multicollinearity by automatically centering the terms before creating the interaction term.