SPSS Corporate Training

SPSS Corporate Training Course Outline

SPSS for BEGINNER Training Course Content

1. Overview

1.1 Introduction to SPSS

1.2 Overview of SPSS for Windows

1.3 Navigating

1.4. Getting your data in

1.5. Importing Data from a Text File

1.6. Saving your work

1.7. Exporting

1.7. Opening data using Syntax

2. Entering Data in SPSS

2.1. The Data Editor

2.2. The Syntax Editor

2.3 The Output Viewer

3. Creating and Modifying Data in SPSS

3.1. Creating and Defining Variables

3.2. Inserting and Deleting Cases and Variables

3.3. Computing New Variables

3.4. Using Condition during computing variable

3.5. Recoding Variables

3.5. Sorting Cases

3.6. Selecting Cases

3.7. Listing Cases

4. Using SPSS Syntax

4.1. What is Syntax?

4.2. Basic Syntax Rules

4.3. Using Syntax

4.4. Journal Files

4.5. Comparing Drop-Down Menus versus Syntax

4.6. When should I use syntax?

4.7. Learning Syntax

5. Descriptive Statistics

5.1. Mean, Sum, Standard Deviation, Variance

5.2. Minimum Value, Maximum Value, and Range

5.3. Skewness

5.4. Kurtosis

6. Describing Data

6.1. Frequency distributions

6.2. Parametric vs. Non-parametric statistics

7. Graphical Representation

7.1. Bar Diagram

7.2. Line Diagram

7.3. Scatter Plot

7.4. Leaf and Stem Diagram

7.5. Box Plot

SPSS for INTERMEDIATE Training Course Content

1. Custom Table

1.1. Table Builder Interface

1.2. Stacking Variables

1.3. Nesting Variables

1.4. Layers

1.5. Stacking Categorical Variables

1.6. Stacking with Crosstabulation

1.7. Nesting Categorical Variables

1.8. Swapping Rows and Columns

2. Correlation

2.1. Pearson Correlation

2.2. Common Uses

2.3. Data Requirements

2.4. Hypotheses

2.5. Test Statistic

2.6. Data Set-Up

2.7. Run a Bivariate Pearson Correlation

2.8. Example: Understanding the linear association between weight and height

3. Regression

3.1. Example: Predicting Job Performance from IQ

3.2. Scatterplot Performance with IQ

3.3. Pearson Correlation Performance with IQ

9.4. Linear Relation – General Formula

3.5. Prediction Formula for Performance

3.6. B Coefficient – Regression Slope

3.7. Regression Intercept (“Constant”)

3.8. Regression Residuals

3.9 Error Variance

3.10. R-Square – Predictive Accuracy

3.11. Inferential Statistics

3.12. R-Square Adjusted

3.13. Standard Errors and Statistical Significance

4. Chi-Square Test

4.1. Chi-Square Test of Independence

4.2. Application of Chi Square distribution

4.3. Data Requirements

4.4. Hypotheses

4.5. Test Statistic

4.6. Data Set-Up

4.7. Run a Chi-Square Test of Independence

4.8. Example: Chi-square Test for 3×2 Table

4.9. Example: Chi-square Test for 2×2 Table

5. One Sample t Test

5.1. One Sample t Test

5.2. Common Uses

5.3. Data Requirements

5.4. Hypotheses

5.5. Test Statistic

5.6. Data Set-Up

5.7. Run a One Sample t Test

5.8. Example

6. Paired Samples t Test

6.1. Paired Samples t Test

6.2. Common Uses

6.3. Data Requirements

6.4. Hypotheses

6.5. Test Statistic

6.6. Data Set-Up

6.7. Run a Paired Samples t Test

6.8. Example

7. Independent Samples t Test

7.1. Independent Samples t Test

7.2. Common Uses

7.3. Data Requirements

7.4. Hypotheses

7.5. Levene’s Test for Equality of Variances

7.6. Test Statistic

7.7. Data Set-Up

7.8. Run an Independent Samples t Test

7.9. Example: Independent samples T test when variances are not equal

8. One-Way ANOVA

8.1. One-Way ANOVA

8.2. Common Uses

8.3. Data Requirements

8.4. Hypotheses

8.5. Test Statistic

8.6. Data Set-Up

8.7. Run a One-Way ANOVA

8.8. Example

Final Student Project