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

### NBICT LAB will cover the section

Advanced Statistical Data Analysis Using SPSS 25/26

in the session Jan - Mar, 22 (Batch-6)

##### Instructor: Sadhan Verma, CEO & Data Scientist at NBICT LAB

### 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?

- Chi-square test of independence (R x C)
- Chi-square test of homogeneity (2 x C)
- Chi-square test of homogeneity (R x 2)
- Loglinear analysis
- Relative risk (2 x 2)
- Odds ratio (2 x 2)
- Fisher’s exact test (2 x 2 Independence)
- Standard Multiple Linear Regression with Assumption Testing
- Hierarchical Multiple Regression
- Binary Logistic Regression
- Ordinal Logistic Regression
- 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
- Reliability Test – Cronbach Alpha
- Principal Components Analysis (PCA)
- Exploratory Factor Analysis (EFA)
- Writing SPSS output tables in APA style

#### Previous Learners' Feedback

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

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

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

- Downloading the necessary resources
- What is IBM SPSS Statistics?
- Importance of learning statistical data analysis using SPSS
- Downloading IBM SPSS Statistics 25/26
- Uninstalling the older version of IBM SPSS Statistics
- Installing IBM SPSS Statistics 25/26
- Introducing a cloud drive for uploading classwork & homework
- Solving learners problems
- Saving the classwork on Cloud Drive
- Collecting the classwork & homework links
- Assigning the handout of the day
- Recording the attendance of the learners

*Day – 2: Dataset Preparation Part-I*

*Day – 2: Dataset Preparation Part-I*

- Downloading the necessary practice files
- Accessing the sample data files owned by IBM SPSS
- Dataset preparation in SPSS: Step by step (Multiple choice question, Multiple answers for a single question, Likert scale data, etc.)
- Creating a new data file in SPSS
- Clarification of Data Editor window and Output Viewer window
- Clarification of Data View and Variable View
- Creating a new variable
- Variable naming rules in SPSS
- Setting up variable properties: Type, Width, Decimals, Label, Values, Columns, Align, Measure, Missing, Role
- How to Use the Missing Column on the SPSS Variable View Tab
- Levels of measurement: Nominal, Ordinal, Interval, Ratio
- Entering data
- Saving the data file

- Collecting the Day-1 handout & Assigning the Day-2 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 3: Dataset Preparation Part-II*

*Day – 3: Dataset Preparation Part-II*

- Downloading the necessary practice files
- Dataset preparation in SPSS: Step by step
- Creating and defining a variable for an open-ended nominal measured response
- Creating and defining a variable for a multiple-choice nominal measured response
- Creating and defining a variable for an open-ended scale measured response
- Creating and defining a variable for a multiple-choice ordinal measured response
- Creating and defining variables the multiple answers for a single question
- Creating and defining variables for Likert scale response

- Collecting the Day-2 handout & Assigning the Day-3 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

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

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

- Downloading the necessary practice files
- Collecting data using an online questionnaire: Google Form
- Downloading an Excel dataset
- Making the Excel file suitable for merging with the SPSS dataset
- Making the SPSS dataset suitable for the Excel data
- Adding more data to an existing SPSS dataset
- Importing an Excel dataset to SPSS
- Collecting the Day-3 handout & Assigning the Day-4 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 5: Data Cleaning*

*Day – 5: Data Cleaning*

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

*Day – 6: Data Manipulation Part-I*

*Day – 6: Data Manipulation Part-I*

- Downloading the necessary practice files
- Computing a new variable as ID number
- Splitting data files by categorical variables
- Selecting cases with multiple conditions
- Computing another variable by data transformation
- Collecting the Day-5 handout & Assigning the Day-6 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 7: Data Manipulation Part-II*

*Day – 7: Data Manipulation Part-II*

- Downloading the necessary practice files
- Recoding a continuous variable to an ordinal variable
- Visual binning
- Recode into different variable

- Merging data files
- Exporting Data
- Collecting the Day-6 handout & Assigning the Day-7 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 8: Descriptive Statistics*

*Day – 8: Descriptive Statistics*

- Downloading the necessary practice files
- Measures of dispersion: Standard Deviation, Variance, Standard Error of the Mean
- Frequency analysis: Understanding a Frequency Table
- Crosstabs analysis
- Multiple response sets
- Collecting the Day-7 handout & Assigning the Day-8 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 9: ***Testing for Normality**

*Day – 9:*

**Testing for Normality**- Downloading the necessary practice files
- Procedure for one independent variable
- Calculating the Z scores for skewness and kurtosis (2.58)
- Shapiro-Wilk Test
- Inspection of the Histograms
- Inspection of the Normal Q-Q Plots

- Procedure for two independent variable
- Splitting the dataset
- Calculating the Z scores for skewness and kurtosis (2.58)
- Shapiro-Wilk Test
- Inspection of the Histograms
- Inspection of the Normal Q-Q Plots
- Unsplit the dataset

- Collecting the Day-8 handout & Assigning the Day-9 handout
- Solving learners problems
- Checking the classwork/homework of the learners
- Taking the attendance of the learners

*Day – 10: Pearson’s Correlation*

*Day – 10: Pearson’s Correlation*

- Downloading the necessary practice files
- What is Pearson’s correlation?
- Basic requirements of Pearson’s correlation
- The null and alternative hypothesis
- The example used in the guide
- Assumptions testing
- Testing for a linear relationship
- Testing for outliers
- Testing for normality

- The procedure of Pearson’s Product-Moment correlation analysis
- Interpreting results
- Coefficient of determination
- Reporting the result
- References & Bibliography
- Uploading classwork on Cloud Drive
- Assigning the Day-10 handout
- Solving learners problems

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

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

- Downloading the necessary practice files
- What is Pearson’s Partial Correlation?
- Basic requirements of Pearson’s partial correlation
- The example used in the guide
- The null and alternative hypothesis
- Assumptions testing
- Testing for linearity
- Generating a scatterplot matrix
- Generating partial regression plots

- Testing for normality and univariate outliers
- Testing for multivariate outliers (https://www.medcalc.org/manual/chi-square-table.php)

- Testing for linearity
- The procedure of Pearson’s Partial Correlation Analysis
- Interpreting results
- Reporting the result
- References & Bibliography
- Uploading classwork on Cloud Drive
- Assigning the Day-11 handout
- Solving learners problems

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

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

- Downloading the necessary practice files
- Spearman’s correlation
- What is Spearman’s correlation?
- Basic requirements of the Spearman’s correlation
- The null and alternative hypothesis
- The example used in the guide
- What is a monotonic relationship?
- Scatterplot procedure to determine if a monotonic relationship exists
- Running the main procedure
- Interpreting the results
- Reporting the result
- References & Bibliography

- Kendall’s tau-b (
*τ*_{b}) correlation- What is Kendall’s tau-b (
*τ*_{b}) correlation coefficient? - Examples for understanding Kendall’s tau-b (
*τ*_{b}) - Basic requirements of Kendall’s tau-b
- Understanding Kendall’s tau-b
- Null and alternative hypotheses
- Running the main procedure
- Interpreting the results
- Reporting the result
- References

- What is Kendall’s tau-b (
- Uploading classwork on Cloud Drive
- Assigning the Day-12 handout
- Solving learners problems

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

*Day-1: One Sample T-Test*

- What is one sample T-test?
- Background & requirements
- Assumptions
- Procedure
- Interpreting results
- Reporting
- References

*Day – 2: Independent Samples T-Test*

*Day – 2: Independent Samples T-Test*

- Downloading the necessary practice files
- What is the independent samples t-test?
- The null and alternative hypothesis
- Basic requirements of the independent samples t-test
- Determining if your data has outliers
- Determining if your data is normally distributed
- Running the main procedure
- Interpreting the result
- Reporting the result

*Day – 3: Paired Samples T-Test *

- What is the paired samples t-test?
- Basic requirements of the paired-samples t-test
- The null and alternative hypothesis
- Determining if your data has outliers
- Determining if your data is normally distributed
- Running the main procedure
- Interpreting the result
- Reporting the result
- Uploading classwork on Cloud Drive
- Assigning the Day-15 handout
- Solving learners problems

**Day – 4: Mann-Whitney U Test**

**Day – 4: Mann-Whitney U Test**

- Downloading the necessary practice files
- What is the Mann-Whitney U test?
- Examples for Mann-Whitney U test
- Basic requirements of the Mann-Whitney U test
- Determining the procedures of the Mann-Whitney U test
- New procedure for the Mann-Whitney U test
- Legacy procedure for the Mann-Whitney U test
- Generating medians
- Recalling the assumptions
- Legacy procedure to generate a population pyramid
- Similarly shaped distributions (when using the legacy procedure)
- Similarly shaped distributions (when using the new procedure)
- Determining shapes similarities
- Interpreting results
- Comparison of medians (when you have used the new procedure)
- Comparison of medians (when you have used the legacy procedure)

- Reporting
- Reporting using medians
- Reporting using mean ranks

- References & Bibliography
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-16 handout
- Taking attendance of the learners

*Day – 5: Wilcoxon Signed-Rank Test*

*Day – 5: Wilcoxon Signed-Rank Test*

- Downloading the necessary practice files
- What is the Wilcoxon Signed-Rank Test?
- Basic requirements of the Wilcoxon Signed-Rank Test
- The null and alternative hypothesis
- New procedure for the Wilcoxon signed-rank test
- Legacy procedure for the Wilcoxon signed-rank test
- Generating median statistics
- The distributional assumption (new procedure)
- The distributional assumption (old procedure)
- Interpreting the results
- Reporting the results
- References & Bibliography
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-17 handout
- Taking attendance of the learners

## Section Title: Chi-sqaure Tests

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

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

- What is the Chi-square test for association?
- Basic requirements of a chi-square test for association
- Downloading the necessary practice file
- Understanding the dataset
- Weighting cases procedure
- Running the main procedure
- Interpreting the result
- Reporting the result
- Uploading classwork on Cloud Drive
- Assigning the Day-13 handout
- Solving learners problems

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

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

- Downloading the necessary practice file
- What is the chi-square goodness-of-fit test?
- Basic requirements of the chi-square goodness-of-fit test
- Running the main procedure
- Interpreting the result
- Uploading classwork on Cloud Drive
- Assigning the Day-14 handout
- 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]**

- Downloading the necessary practice files
- What is One-way ANOVA?
- Basic requirements of the One-way ANOVA
- The null and alternative hypothesis
- The example used in the guide
- Running the Explore… procedure
- Determining if your data has outliers
- Determining if your data is normally distributed

- The one-way procedure without a post hoc test
- Basic interpretation of the results
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-18 handout
- Taking attendance of the learners

**Day – 2: One-way ANOVA [Part-II]**

- Downloading the necessary practice files
- The one-way procedure with a post hoc test
- GLM procedure for an effect size
- Generating the effect size called partial eta squared (η
^{2}) for a one-way ANOVA

- Generating the effect size called partial eta squared (η
- Interpreting results
- Interpreting the descriptive statistics
- Assumption of homogeneity of variance
- Results when homogeneity of variance is met
- Tukey post hoc test

- Graphing the output
- Denoting Significant Differences in Tables
- Reporting the result
- References & Bibliography
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-19 handout
- Taking attendance of the learners

**Day – 3: Kruskal-Wallis H Test**

- Downloading the necessary practice files
- What is the Kruskal-Wallis H test?
- Examples of Kruskal-Wallis H test
- Basic requirements of the Kruskal-Wallis H test
- Running the Kruskal-Wallis H test procedure
- Understanding mean rank
- Interpretation of the results
- Interpretation of the results after Post Hoc test
- Reporting the result
- References & Bibliography
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-20 handout
- Taking attendance of the learners

**Day – 4: Two-way ANOVA (Part-I)**

- What is two-way ANOVA?
- Situations of using two-way ANOVA
- Basic requirements of the two-way ANOVA
- Downloading the necessary practice files
- Understanding a two-way ANOVA
- What is the interaction effect?
- Understanding the example dataset

- Running the main procedure
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-21 handout
- Taking attendance of the learners

**Day – 5: Two-way ANOVA (Part-II)**

- Downloading the necessary practice files
- Procedure to detect outliers and assess normality
- Running the General Linear Model
- Splitting your file into each cell of the design
- Running the Explore… procedure to detect outliers and assess normality
- Unsplitting your file
- Determining if you have outliers
- Determining if your data is normally distributed

- Running the main procedure
- Determining if you have homogeneity of variances
- Interpretation of the results
- Determining whether an interaction effect exists
- Carrying out simple main effects
- Interpreting simple main effects
- Interpreting main effects

- Reporting the result
- References & Bibliography
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-22 handout
- 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**

- Downloading the necessary practice files
- What is Simple Linear Regression?
- What is the equation of Simple Linear Regression?
- Requirements of Simple Linear Regression
- Properties of the variables
- How to run a Scatter plot
- Understanding residuals in regression
- Independence of observations: no correlations between residuals (Durbin-Watson test)
- Outliers detection (Dealing with outliers: Casewise diagnostics)
- Homoscedasticity
- Checking for normality of residuals

- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-23 handout
- Taking attendance of the learners

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

- Downloading the necessary practice files
- The three main objectives of a simple linear regression
- Determining how well the model fits
- Percentage (or proportion) of variance explained
- Statistical significance of the model

- Interpreting the coefficients
- How to use the regression equation to make predictions
- Reporting the result in APA style
- Solving learners problems
- Uploading classwork on Cloud Drive
- Assigning the Day-24 handout
- Taking attendance of the learners

**Day – 3:** **Standard Multiple Linear Regression with Assumption Testing**

- Downloading the necessary practice files
- What is Standard Multiple Linear Regression?
- Requirements of Multiple Linear Regression
- Independence of observations
- Testing for linearity
- Testing for homoscedasticity
- Checking for multicollinearity
- Checking for unusual points
- Casewise diagnosis
- Studentized deleted residuals
- Checking for leverage points
- Checking for normality

- How to get the final regression equation
- Determining how well the model fits
- Statistical significance of the model
- Interpreting the coefficients
- The final equation of the regression model

- Reporting the result in APA style

**Day – 4:****Hierarchical Multiple Regression**- What is Hierarchical Multiple Regression?
- Downloading the necessary practice files
- Understanding the example data set
- Assumptions and requirements
- Procedure
- Interpretation
- Model comparisons
- Model coefficients

- Reporting the result in APA style

**Day – 5:****Binary Logistic Regression**- What is Binary/Binomial Logistic Regression?
- Downloading the necessary practice files
- Understanding the example data set
- Assumptions and requirements
- Properties of the variables
- Independence of observations
- Case number requirements
- Testing for linearity with the procedure to create natural log transformations
- Box-Tidwell (1962) procedure to test for linearity
- Interpreting the linearity assumptions

- Multicollinearity trap
- Outliers, leverage, or influential points

- Procedures of running the main analysis
- Result interpretation
- Data coding
- Baseline analysis
- Model fit: Statistical significance of the model
- How much variation in the dependent variable can be explained by the model
- Category prediction: The classification table (Sensitivity & Specificity)
- Variables in the equation

- Reporting/Writing the result

**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