Statistical Analysis Using IBM SPSS Statistics (V25) SPVC - 0K51AG

Beschreibung

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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Introduction to statistical analysis
Identify the steps in the research process
Principles of statistical analysis

Examine individual variables
Identify measurement levels
Chart individual variables
Summarize individual variables
Examine the normal distribution
Examine standardized scores

Test hypotheses about individual variables
Identify population parameters and sample statistics
Examine the distribution of the sample mean
Determine the sample size
Test a hypothesis on the population mean
Construct a confidence interval for the population mean
Tests on a single variable: One-Sample T Test, Paired-Samples T Test, and Binomial Test

Test the relationship between categorical variables
Chart the relationship between two categorical variables
Describe the relationship: Compare percentages in Crosstabs
Test the relationship: The Chi-Square test in Crosstabs
Assumptions of the Chi-Square test
Pairwise compare column proportions
Measure the strength of the association

Test on the difference between two group means
Compare the Independent-Samples T Test to the Paired-Samples T Test
Chart the relationship between the group variable and scale variable
Describe the relationship: Compare group means
Test on the difference between two group means: Independent-Samples T Test
Assumptions of the Independent-Samples T Test

Test on differences between more than two group means
Describe the relationship: Compare group means
Test the hypothesis of equal group means: One-Way ANOVA
Assumptions of One-Way ANOVA
Identify differences between group means: Post-hoc tests

Test the relationship between scale variables
Chart the relationship between two scale variables
Describe the relationship: Correlation
Test on the correlation
Assumptions for testing on the correlation
Treatment of missing values

Predict a scale variable: Regression
What is linear regression?
Explain unstandardized and standardized coefficients
Assess the fit of the model: R Square
Examine residuals
Include 0-1 independent variables
Include categorical independent variables

Introduction to Bayesian statistics
Bayesian statistics versus classical test theory
Explain the Bayesian approach
Evaluate a null hypothesis: Bayes Factor
Bayesian procedures in IBM SPSS Statistics

Overview of multivariate procedures
Overview of supervised models
Overview of models to create natural groupings

Ziel

Introduction to statistical analysis
Examine individual variables
Test hypotheses about individual variables
Test the relationship between categorical variables
Test on the difference between two group means
Test on differences between more than two group means
Test the relationship between scale variables
Predict a scale variable: Regression
Introduction to Bayesian statistics
Overview of multivariate procedures

Anforderungen an die Teilnehmer

Familiarity with basic concepts in statistics, such as measurement levels, mean, and standard deviation.
Familiarity with the windows in IBM SPSS Statistics either by experience with IBM SPSS Statistics (version 18 or later) or completion of the IBM SPSS Statistics Essentials (V25) course.

Teilnehmer

Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base.
Anyone who wants to refresh their knowledge and statistical experience.