# Statistical Analysis Using IBM SPSS Statistics (V26) - 0G51B

• 2000
• ETC-Seminar-ID: 0G51B
• Sprache: English

## Beschreibung

This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

Nach Abschluss dieses Seminars haben die Teilnehmer Wissen zu folgenden Themen:
• 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

### expand_more chevron_right Zielgruppe

Dieses Seminar richtet sich an: • 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.

### expand_more chevron_right Vorkenntnisse

Für dieses Seminar werden folgende Kenntnisse empfohlen:
• 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.

### expand_more chevron_right Detail-Inhalte

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

• 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

### Kontaktoption wählen

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• expand_more chevron_right event_available 06.02.-07.02.2023 06.02.2023 roomVirtual-Training (VILT)
• expand_more chevron_right Virtual Classroom 1.705,00
• Live Online Training im virtuellen Klassenraum
• Live Vortrag inkl. Interaktion mit dem/der Trainer*in
• Seminarunterlagen, Teamwork, Labs
• Keine hohen Hardware Anforderungen, dennoch Zugriff auf die gewohnte professionelle Übungsumgebung
• keine Anfahrt ins Seminarzentrum notwendig
• expand_more chevron_right event_available 17.07.-18.07.2023 17.07.2023 roomVirtual-Training (VILT)
• expand_more chevron_right Virtual Classroom 1.705,00
• Live Online Training im virtuellen Klassenraum
• Live Vortrag inkl. Interaktion mit dem/der Trainer*in
• Seminarunterlagen, Teamwork, Labs
• Keine hohen Hardware Anforderungen, dennoch Zugriff auf die gewohnte professionelle Übungsumgebung
• keine Anfahrt ins Seminarzentrum notwendig
• expand_more chevron_right event_available 10.10.-11.10.2023 10.10.2023 roomVirtual-Training (VILT)
• expand_more chevron_right Virtual Classroom 1.705,00
• Live Online Training im virtuellen Klassenraum
• Live Vortrag inkl. Interaktion mit dem/der Trainer*in
• Seminarunterlagen, Teamwork, Labs
• Keine hohen Hardware Anforderungen, dennoch Zugriff auf die gewohnte professionelle Übungsumgebung
• keine Anfahrt ins Seminarzentrum notwendig