## Hands-on Virtual Workshop on Statistical Data Analysis Using SPSS 25/26

### Section Title: Advanced Statistical Data Analysis Using SPSS 25/26

Training Days: Saturday, Monday, Wednesday; Training Time: 09:00 pm to 11:00 pm;

Possible Date for the inauguration class: Feb 12, 2022;

Available Seats: 50 out of 60; Total Number of Classes: 30 (Minimum)

#### What Will You Learn?

1. Chi-square test of independence (R x C)
2. Chi-square test of homogeneity (2 x C)
3. Chi-square test of homogeneity (R x 2)
4. Loglinear analysis
5. Relative risk (2 x 2)
6. Odds ratio (2 x 2)
7. Fisher’s exact test (2 x 2 Independence)
8. Standard Multiple Linear Regression with Assumption Testing
9. Hierarchical Multiple Regression
10. Binary Logistic Regression
11. Ordinal Logistic Regression
12. One-way Repeated Measures ANOVA
13. Two-way Repeated Measures ANOVA
14. Three-way ANOVA
15. Friedman test
16. One-way MANOVA
17. Two-way MANOVA
18. One-way ANCOVA
19. Two-way ANCOVA
20. Three-way ANOVA
21. Reliability Test – Cronbach Alpha
22. Principal Components Analysis (PCA)
23. Exploratory Factor Analysis (EFA)
24. Writing SPSS output tables in APA style

## Section Title: Dataset Preparation, Data Manipulation, Descriptive & Correlation Analysis

#### Day – 1: Introduction and Making the Environment Ready

2. What is IBM SPSS Statistics?
3. Importance of learning statistical data analysis using SPSS
5. Uninstalling the older version of IBM SPSS Statistics
6. Installing IBM SPSS Statistics 25/26
7. Introducing a cloud drive for uploading classwork & homework
8. Solving learners problems
9. Saving the classwork on Cloud Drive
10. Collecting the classwork & homework links
11. Assigning the handout of the day
12. Recording the attendance of the learners

#### Day – 2: Dataset Preparation Part-I

2. Accessing the sample data files owned by IBM SPSS
3. Dataset preparation in SPSS: Step by step (Multiple choice question, Multiple answers for a single question, Likert scale data, etc.)
1. Creating a new data file in SPSS
2. Clarification of Data Editor window and Output Viewer window
3. Clarification of Data View and Variable View
4. Creating a new variable
5. Variable naming rules in SPSS
6. Setting up variable properties: Type, Width, Decimals, Label, Values, Columns, Align, Measure, Missing, Role
7. How to Use the Missing Column on the SPSS Variable View Tab
8. Levels of measurement: Nominal, Ordinal, Interval, Ratio
9. Entering data
10. Saving the data file
4. Collecting the Day-1 handout & Assigning the Day-2 handout
5. Solving learners problems
6. Checking the classwork/homework of the learners
7. Taking the attendance of the learners

#### Day – 3: Dataset Preparation Part-II

2. Dataset preparation in SPSS: Step by step
1. Creating and defining a variable for an open-ended nominal measured response
2. Creating and defining a variable for a multiple-choice nominal measured response
3. Creating and defining a variable for an open-ended scale measured response
4. Creating and defining a variable for a multiple-choice ordinal measured response
5. Creating and defining variables the multiple answers for a single question
6. Creating and defining variables for Likert scale response
3. Collecting the Day-2 handout & Assigning the Day-3 handout
4. Solving learners problems
5. Checking the classwork/homework of the learners
6. Taking the attendance of the learners

#### Day – 4: Importing Dataset and Adding More Data

2. Collecting data using an online questionnaire: Google Form
4. Making the Excel file suitable for merging with the SPSS dataset
5. Making the SPSS dataset suitable for the Excel data
6. Adding more data to an existing SPSS dataset
7. Importing an Excel dataset to SPSS
8. Collecting the Day-3 handout & Assigning the Day-4 handout
9. Solving learners problems
10. Checking the classwork/homework of the learners
11. Taking the attendance of the learners

#### Day – 5: Data Cleaning

2. Understanding the practice files
3. What is data cleaning & why is it necessary?
4. How to clean the coding errors
5. How to detect and clean outliers
6. How to take care of missing values
7. Understanding reverse coding
8. Collecting the Day-4 handout & Assigning the Day-5 handout
9. Solving learners problems
10. Checking the classwork/homework of the learners
11. Taking the attendance of the learners

#### Day – 6: Data Manipulation Part-I

2. Computing a new variable as ID number
3. Splitting data files by categorical variables
4. Selecting cases with multiple conditions
5. Computing another variable by data transformation
6. Collecting the Day-5 handout & Assigning the Day-6 handout
7. Solving learners problems
8. Checking the classwork/homework of the learners
9. Taking the attendance of the learners

#### Day – 7: Data Manipulation Part-II

2. Recoding a continuous variable to an ordinal variable
1. Visual binning
2. Recode into different variable
3. Merging data files
4. Exporting Data
5. Collecting the Day-6 handout & Assigning the Day-7 handout
6. Solving learners problems
7. Checking the classwork/homework of the learners
8. Taking the attendance of the learners

#### Day – 8: Descriptive Statistics

2. Measures of dispersion: Standard Deviation, Variance, Standard Error of the Mean
3. Frequency analysis: Understanding a Frequency Table
4. Crosstabs analysis
5. Multiple response sets
6. Collecting the Day-7 handout & Assigning the Day-8 handout
7. Solving learners problems
8. Checking the classwork/homework of the learners
9. Taking the attendance of the learners

#### Day – 9: Testing for Normality

2. Procedure for one independent variable
1. Calculating the Z scores for skewness and kurtosis (2.58)
2. Shapiro-Wilk Test
3. Inspection of the Histograms
4. Inspection of the Normal Q-Q Plots
3. Procedure for two independent variable
1. Splitting the dataset
2. Calculating the Z scores for skewness and kurtosis (2.58)
3. Shapiro-Wilk Test
4. Inspection of the Histograms
5. Inspection of the Normal Q-Q Plots
6. Unsplit the dataset
4. Collecting the Day-8 handout & Assigning the Day-9 handout
5. Solving learners problems
6. Checking the classwork/homework of the learners
7. Taking the attendance of the learners

#### Day – 10: Pearson’s Correlation

2. What is Pearson’s correlation?
3. Basic requirements of Pearson’s correlation
4. The null and alternative hypothesis
5. The example used in the guide
6. Assumptions testing
1. Testing for a linear relationship
2. Testing for outliers
3. Testing for normality
7. The procedure of Pearson’s Product-Moment correlation analysis
8. Interpreting results
9. Coefficient of determination
10. Reporting the result
11. References & Bibliography
13. Assigning the Day-10 handout
14. Solving learners problems

#### Day – 11: Pearson’s Partial Correlation

2. What is Pearson’s Partial Correlation?
3. Basic requirements of Pearson’s partial correlation
4. The example used in the guide
5. The null and alternative hypothesis
6. Assumptions testing
1. Testing for linearity
1. Generating a scatterplot matrix
2. Generating partial regression plots
2. Testing for normality and univariate outliers
3. Testing for multivariate outliers (https://www.medcalc.org/manual/chi-square-table.php)
7. The procedure of Pearson’s Partial Correlation Analysis
8. Interpreting results
9. Reporting the result
10. References & Bibliography
12. Assigning the Day-11 handout
13. Solving learners problems

#### Day – 12: Spearman’s & Kendall’s Tau-B Correlation

2. Spearman’s correlation
1. What is Spearman’s correlation?
2. Basic requirements of the Spearman’s correlation
3. The null and alternative hypothesis
4. The example used in the guide
5. What is a monotonic relationship?
6. Scatterplot procedure to determine if a monotonic relationship exists
7. Running the main procedure
8. Interpreting the results
9. Reporting the result
10. References & Bibliography
3. Kendall’s tau-b (τb) correlation
1. What is Kendall’s tau-b (τb) correlation coefficient?
2. Examples for understanding Kendall’s tau-b (τb)
3. Basic requirements of Kendall’s tau-b
4. Understanding Kendall’s tau-b
5. Null and alternative hypotheses
6. Running the main procedure
7. Interpreting the results
8. Reporting the result
9. References
5. Assigning the Day-12 handout
6. Solving learners problems

## Section Title: T-Tests & Non-parametric T-Tests

#### Day-1: One Sample T-Test

1. What is one sample T-test?
2. Background & requirements
3. Assumptions
4. Procedure
5. Interpreting results
6. Reporting
7. References

#### Day – 2: Independent Samples T-Test

2. What is the independent samples t-test?
3. The null and alternative hypothesis
4. Basic requirements of the independent samples t-test
5. Determining if your data has outliers
6. Determining if your data is normally distributed
7. Running the main procedure
8. Interpreting the result
9. Reporting the result

#### Day – 3: Paired Samples T-Test

1. What is the paired samples t-test?
2. Basic requirements of the paired-samples t-test
3. The null and alternative hypothesis
4. Determining if your data has outliers
5. Determining if your data is normally distributed
6. Running the main procedure
7. Interpreting the result
8. Reporting the result
10. Assigning the Day-15 handout
11. Solving learners problems

#### Day – 4: Mann-Whitney U Test

2. What is the Mann-Whitney U test?
3. Examples for Mann-Whitney U test
4. Basic requirements of the Mann-Whitney U test
5. Determining the procedures of the Mann-Whitney U test
6. New procedure for the Mann-Whitney U test
7. Legacy procedure for the Mann-Whitney U test
8. Generating medians
9. Recalling the assumptions
10. Legacy procedure to generate a population pyramid
11. Similarly shaped distributions (when using the legacy procedure)
12. Similarly shaped distributions (when using the new procedure)
13. Determining shapes similarities
14. Interpreting results
1. Comparison of medians (when you have used the new procedure)
2. Comparison of medians (when you have used the legacy procedure)
15. Reporting
1. Reporting using medians
2. Reporting using mean ranks
16. References & Bibliography
17. Solving learners problems
19. Assigning the Day-16 handout
20. Taking attendance of the learners

#### Day – 5: Wilcoxon Signed-Rank Test

2. What is the Wilcoxon Signed-Rank Test?
3. Basic requirements of the Wilcoxon Signed-Rank Test
4. The null and alternative hypothesis
5. New procedure for the Wilcoxon signed-rank test
6. Legacy procedure for the Wilcoxon signed-rank test
7. Generating median statistics
8. The distributional assumption (new procedure)
9. The distributional assumption (old procedure)
10. Interpreting the results
11. Reporting the results
12. References & Bibliography
13. Solving learners problems
15. Assigning the Day-17 handout
16. Taking attendance of the learners

## Section Title: Chi-sqaure Tests

#### Day – 1: Chi-square Test for Association (2 x 2)

1. What is the Chi-square test for association?
2. Basic requirements of a chi-square test for association
4. Understanding the dataset
5. Weighting cases procedure
6. Running the main procedure
7. Interpreting the result
8. Reporting the result
10. Assigning the Day-13 handout
11. Solving learners problems

#### Day – 2: Chi-Square Goodness-of-Fit Test

2. What is the chi-square goodness-of-fit test?
3. Basic requirements of the chi-square goodness-of-fit test
4. Running the main procedure
5. Interpreting the result
7. Assigning the Day-14 handout
8. Solving learners problems
• Day – 3: Chi-square test of independence (R x C)
• Day – 4: Chi-square test of homogeneity (2 x C)
• Day – 5: Chi-square test of homogeneity (R x 2)

## Section Title: Association

• Loglinear analysis
• Relative risk (2 x 2)
• Odds ratio (2 x 2)
• Fisher’s exact test (2 x 2 Independence)

## Section Title: Analysis of Variance

Day – 1: One-way ANOVA [Part-I]

2. What is One-way ANOVA?
3. Basic requirements of the One-way ANOVA
4. The null and alternative hypothesis
5. The example used in the guide
6. Running the Explore… procedure
1. Determining if your data has outliers
2. Determining if your data is normally distributed
7. The one-way procedure without a post hoc test
8. Basic interpretation of the results
9. Solving learners problems
11. Assigning the Day-18 handout
12. Taking attendance of the learners

Day – 2: One-way ANOVA [Part-II]

2. The one-way procedure with a post hoc test
3. GLM procedure for an effect size
1. Generating the effect size called partial eta squared (η2) for a one-way ANOVA
4. Interpreting results
1. Interpreting the descriptive statistics
2. Assumption of homogeneity of variance
3. Results when homogeneity of variance is met
4. Tukey post hoc test
5. Graphing the output
6. Denoting Significant Differences in Tables
7. Reporting the result
8. References & Bibliography
9. Solving learners problems
11. Assigning the Day-19 handout
12. Taking attendance of the learners

Day – 3: Kruskal-Wallis H Test

2. What is the Kruskal-Wallis H test?
3. Examples of Kruskal-Wallis H test
4. Basic requirements of the Kruskal-Wallis H test
5. Running the Kruskal-Wallis H test procedure
6. Understanding mean rank
7. Interpretation of the results
8. Interpretation of the results after Post Hoc test
9. Reporting the result
10. References & Bibliography
11. Solving learners problems
13. Assigning the Day-20 handout
14. Taking attendance of the learners

Day – 4: Two-way ANOVA (Part-I)

1. What is two-way ANOVA?
2. Situations of using two-way ANOVA
3. Basic requirements of the two-way ANOVA
5. Understanding a two-way ANOVA
1. What is the interaction effect?
2. Understanding the example dataset
6. Running the main procedure
7. Solving learners problems
9. Assigning the Day-21 handout
10. Taking attendance of the learners

Day – 5: Two-way ANOVA (Part-II)

2. Procedure to detect outliers and assess normality
1. Running the General Linear Model
2. Splitting your file into each cell of the design
3. Running the Explore… procedure to detect outliers and assess normality
4. Unsplitting your file
5. Determining if you have outliers
6. Determining if your data is normally distributed
3. Running the main procedure
4. Determining if you have homogeneity of variances
5. Interpretation of the results
1. Determining whether an interaction effect exists
2. Carrying out simple main effects
3. Interpreting simple main effects
4. Interpreting main effects
6. Reporting the result
7. References & Bibliography
8. Solving learners problems
10. Assigning the Day-22 handout
11. Taking attendance of the learners
• One-way Repeated Measures ANOVA
• Two-way Repeated Measures ANOVA
• Three-way ANOVA
• Friedman test
• One-way MANOVA
• Two-way MANOVA
• One-way ANCOVA
• Two-way ANCOVA
• Three-way ANOVA

## Section Title: Regression

Day – 1:  Simple Linear Regression: Overview, Requirements, & Procedure

2. What is Simple Linear Regression?
3. What is the equation of Simple Linear Regression?
4. Requirements of Simple Linear Regression
1. Properties of the variables
2. How to run a Scatter plot
3. Understanding residuals in regression
4. Independence of observations: no correlations between residuals (Durbin-Watson test)
5. Outliers detection (Dealing with outliers: Casewise diagnostics)
6. Homoscedasticity
7. Checking for normality of residuals
5. Solving learners problems
7. Assigning the Day-23 handout
8. Taking attendance of the learners

Day – 2:  Simple Linear Regression: Result Interpretation & Reporting

2. The three main objectives of a simple linear regression
3. Determining how well the model fits
1. Percentage (or proportion) of variance explained
2. Statistical significance of the model
4. Interpreting the coefficients
5. How to use the regression equation to make predictions
6. Reporting the result in APA style
7. Solving learners problems
9. Assigning the Day-24 handout
10. Taking attendance of the learners

Day – 3: Standard Multiple Linear Regression with Assumption Testing

2. What is Standard Multiple Linear Regression?
3. Requirements of Multiple Linear Regression
1. Independence of observations
2. Testing for linearity
3. Testing for homoscedasticity
4. Checking for multicollinearity
5. Checking for unusual points
1. Casewise diagnosis
2. Studentized deleted residuals
3. Checking for leverage points
4. Checking for normality
4. How to get the final regression equation
1. Determining how well the model fits
2. Statistical significance of the model
3. Interpreting the coefficients
4. The final equation of the regression model
5. Reporting the result in APA style
1. Day – 4: Hierarchical Multiple Regression
1. What is Hierarchical Multiple Regression?
3. Understanding the example data set
4. Assumptions and requirements
5. Procedure
6. Interpretation
1. Model comparisons
2. Model coefficients
7. Reporting the result in APA style
2. Day – 5: Binary Logistic Regression
1. What is Binary/Binomial Logistic Regression?
3. Understanding the example data set
4. Assumptions and requirements
1. Properties of the variables
2. Independence of observations
3. Case number requirements
4. Testing for linearity with the procedure to create natural log transformations
1. Box-Tidwell (1962) procedure to test for linearity
2. Interpreting the linearity assumptions
5. Multicollinearity trap
6. Outliers, leverage, or influential points
5. Procedures of running the main analysis
6. Result interpretation
1. Data coding
2. Baseline analysis
3. Model fit: Statistical significance of the model
4. How much variation in the dependent variable can be explained by the model
5. Category prediction: The classification table (Sensitivity & Specificity)
6. Variables in the equation
7. Reporting/Writing the result
3. Day – 6: Ordinal Logistic Regression

## Section Title: Dimention Reduction

• Reliability Test – Cronbach Alpha
• Principal Components Analysis (PCA)
• Exploratory Factor Analysis (EFA)
• Writing SPSS output tables in APA style