Statistical Analysis Using IBM SPSS Statistics (V26) (0G51BG) – Details

Detaillierter Kursinhalt

Introduction to statistical analysis • Identify the steps in the research process • Identify measurement levels Describing individual variables • Chart individual variables • Summarize individual variables • Identify the normal distribution • Identify standardized scores Testing hypotheses • Principles of statistical testing • One-sided versus two-sided testing • Type I, type II errors and power Testing hypotheses on individual variables • Identify population parameters and sample statistics • Examine the distribution of the sample mean • Test a hypothesis on the population mean • Construct confidence intervals • Tests on a single variable Testing on the relationship between categorical variables • Chart the relationship • Describe the relationship • Test the hypothesis of independence • Assumptions • Identify differences between the groups • Measure the strength of the association Testing on the difference between two group means • Chart the relationship • Describe the relationship • Test the hypothesis of two equal group means • Assumptions Testing on differences between more than two group means • Chart the relationship • Describe the relationship • Test the hypothesis of all group means being equal • Assumptions • Identify differences between the group means Testing on the relationship between scale variables • Chart the relationship • Describe the relationship • Test the hypothesis of independence • Assumptions • Treatment of missing values Predicting a scale variable: Regression • Explain linear regression • Identify unstandardized and standardized coefficients • Assess the fit • Examine residuals • Include 0-1 independent variables • Include categorical independent variables Introduction to Bayesian statistics • Bayesian statistics and classical test theory • The Bayesian approach • Evaluate a null hypothesis • Overview of Bayesian procedures in IBM SPSS Statistics Overview of multivariate procedures • Overview of supervised models • Overview of models to create natural groupings