SPSS Masterclass

About the course

What you'll learn

Analyse any type of numerical data using SPSS with confidence
Independently plan your research study from scratch.
Understand the research design and results presented in high quality journal articles
Do data analysis accurately and present the results in standard format.

Course content

Sections: 29

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Activities: 0

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Resources: 394

1. Introduction Know Your Instrutor

1. Downloading and Installing IBM SPSS Statistics 24 on Windows
2. Downloading SPSS Grad Pack Student Version

3. Dataset & Resources

1. Quantitative Method Articles
2. Philosophy of Research
3. Research Papers with Fascinating Ideas

1. Statistics Definition and Types
2. Parametric vs Non-Parametric Statistics Assumptions
3. Statistics Basics

1. Conceptualizing Variables IV, DV, Control, Moderators & Mediating Variables
2. Variable Type Numeric Defining Names, Width, Decimal & Labels for variables
3. Variable Type Comma & Dot
4. Variable Type Scientific Notation
5. Variable Type Date and Time Stamps
6. Variable Type Dollar
7. Variable Type Custom Currency
9. Variable Type Restricted Numeric
10. Defining Values & Labels
11. Defining Missing Values Discrete, Range & System-Missing Values
12. Setting Columns & Alignment
13. Defining Measures Scales of Measurement

1. Types of Data Files in SPSS Statistics
2. Opening an Excel data file in SPSS
3. Opening a Comma Separated or CSV file type in SPSS

2. COMPUTE VARIABLE function What it is and What it can do for us
3. Calculating Total using COMPUTE function
4. Exercise Try COMPUTE using IF
5. Exercise Solution COMPUTE using IF
6. RECODE FUNCTION Why to Recode Variable
7. Why We have Two RECODE Functions
8. How to do RECODE INTO DIFFERENT VARIABLE in SPSS
9. COMPUTING Total After RECODE
10. Recode into Same Variable

1. Setting Data for Descriptive Analysis
2. Types of Descriptive Statistics
3. Understanding Three Different Descriptive Tabs in SPSS
4. Calculating Frequencies
5. Descriptives Analysis Using Crosstab
6. Measures of Central Tendency Mean, Median, Mode - Concept and Uses
7. Calculating and Interpreting Mean, Median & Mode
8. Confirming Mode with Frequencies
9. Explore Option Calculating Grouped Descriptives
10. Explore Option Interpreting Groupwise Mean and 95% Confidence Interval of Mean
11. 5% Trimmed Mean Concept, Use & Interpretation
12. Explore Median, Standard Deviation, Variance, Minimum, Maximum, & Range
13. Quartiles and Inter-Quartile Range using Explore Option.
14. Skewness and Kurtosis Fundamentals Explained
15. Calculating & Interpreting Significance Level of Skewness
16. Kurtosis Calculation, Interpretation and Understanding Significance Level
17. Standard Error of Mean Concept, Calculation & Interpretation

1. Independent sample t-test Defining input options
2. Independent sample t-test Interpreting descriptive output (Mean, SD, SE)
3. Independent Sample t-test Interpreting Levene's test, t, p, SE & 95% CI
4. APA Style write-up for Independent Sample t-test

1. When to use Paired Sample t-test
2. Calculating Paired Sample t-test in SPSS
3. Interpreting Paired Sample t-test Output
4. APA Style write-up for Paired Sample t-test

1. When to Use One-Way ANOVA
2. Calculating One-Way ANOVA in SPSS
3. Interpreting ANOVA output Descriptive Statistics
4. Interpreting Output ANOVA Summary Table
5. Doing Post-hoc analysis in ANOVA Homogeneity of Variance Test & Post-hoc
6. Trend Analysis & Means Plot in ANOVA
7. Contrast Analysis in ANOVA

1. What is regression
2. When to Use Linear Regression Vs. Multiple Regression
3. Defining SPSS Input Options for Linear Regression
4. Interpreting Linear Regression Output Variables & Model Summary
5. Interpreting Linear Regression Output Constant, B, Beta, SE & t

1. What is Multiple Regression
2. Assumptions of Multiple Regression Linearity & Testing Linearity in SPSS
3. Assumptions 2 Independence of ErrorsLack of Autocorrelations & Testing in SPSS
4. Assumptions 3 Homoscedasticity of Errors & Testing it in SPSS
5. Assumptions 4 Multivariate Normality & Testing it in SPSS
6. Assumptions 5 Multicollinearity & Testing it in SPSS
7. Choosing a Method of Multiple Regression Enter Method
8. Choosing a Method of Multiple Regression Stepwise and Forward Selection Method
9. Choosing a Method of Multiple Regression Backward Elimination Method
10. Running Stepwise and Forward Selection Method of Regression in SPSS
11. Choosing a Method of Multiple Regression Remove Method

1. What is Hierarchical Regression Analysis and when to use it
2. Setting Data and Defining Model in Hierarchical Regression
3. Refining Model and Detecting Multicollinearity through Correlation Matrix
4. Taming Bad Data Using beta, R squared and p values to further refine model
5. Interpreting the Output of Hierarchical Regression.

1. What is Factor Analysis
2. Understanding Latent Variables and Indicators in FA
3. Sample Researches Using FA in Social Science & Engineering
4. Historical Origin of FA & Its Application in Test Construction
5. Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
6. Setting Data for Factor Analysis
7. Understanding Selection Variable
8. Univariate Descriptives & Initial Solutions Descriptive
9. Correlation Matrix Coefficients, Significance, Determinant, KMO & Bartlett's
10. Understanding Inverse, Reproduced, Anti-Image
11. Extraction Method Principle Component Analysis
12. Extraction Method Principle Axis Factoring
13. Extraction Method Maximum Likelihood Estimation
14. Choosing Correlation vs. Covariance Matrix for Factor Analysis
15. Interpreting Correlation Matrix & Unrotated Factor Solution
16. Determining number of factors Scree Plot vs. Kaiser's eigen value criteria
17. Factor Rotation What it is and why its done
18. Rotation Methods Varimax, Quartimax, Equamax, Direct Oblimin, Promax
19. Calculating Factor Scores Regression, Bartlett, Anderson-Rubin
20. Factor Score Coefficient Matrix
21. Missing Value Analysis Listwise, Pairwise, Replace with Mean
22. Sort by Size & Suppressing Smaller Coefficients
23. Project in Factor Analysis Part 1 Identifying Dimensions of Personality
24. Project in Factor Analysis Part 2 Identifying Dimensions of Personality
25. Project in Factor Analysis Part 3 Identifying Dimensions of Personality
26. Project in Factor Analysis Part 4 Factor Naming
27. Project in Factor Analysis Part 5 Reliability Analysis of Factors
28. Project in Factor Analysis Part 6 Presenting Results in APA Style

1. Chi Square Test Introduction and When to Use Chi-Square Test
2. Assumptions of Chi-square Test
3. Formula for Calculation of Chi-Square Test
4. Setting Data for Calculation of Chi-Square using Crosstabs Option
5. Testing Assumptions of Chi-Square test Using Crosstabs Option
6. Interpreting Output of Chi-Square Test and APA Style Reporting
7. One-way Chi Square When to use and how its different from two-way Chi square
8. Setting Data for One-way Chi Square Test
9. Weigh Cases, Calculation, Interpretation & APA Write-up for One-Way Chi Square
10. Practice Data set for One-Way Chi square
Sample data

1. Introduction to Reliability Analysis
2. What is Reliability
3. Reflective vs. Formative Models of Scale
4. Should We Report Cronbach's Alpha or Composite Reliability
5. Type of Reliability Test-Retest Reliability
6. Type of Reliability Parallel Form
7. Type of Reliability Internal Consistency Reliability
8. Understanding Cronbach's Alpha
9. Assumptions of Cronbach's Alpha
10. Formula of Cronbach's Alpha
11. Range of Cronbach's Alpha
12. Calculating Reliability Understanding Scale if an Item is Deleted Option
13. Interpreting Case Processing Summary & Alpha Coefficient
14. Improving Reliability of a Scale Diagnosing Missing Values
15. Improving Reliability Diagnosing Scale Mean and Variances
16. Improving Reliability Diagnosing Item-Total Correlations
17. Improving Reliability Removing Ambiguous and Redundant Items
18. Item Discrimination Index

1. Graphs & Data Visualization in SPSS An Introduction
2. Which Graph is Suitable for Me Rules for Creating Graphs Part 1
3. Which Graph is Suitable for Me Rules for Creating Graphs Part 2
4. Creating a Bar Diagram in SPSS
5. How to Change Background Color of Bar Diagram in SPSS
6. How to Change Color & Patterns of Bars in Bar Diagram
7. How to Adjust Width of Bars in Bar Diagram
8. How to Rename X & Y Axes of Bar Diagram in SPSS
9. Understanding Error Bars over Bar Diagram What, Why & How
10. How to Create Bar Diagrams with Error Bars in SPSS
11. How to Use Multipliers (Standard Error & Standard Deviation)
12. Creating Clustered Bar Diagrams in SPSS
13. Pie Charts Understanding and Setting Dataset
14. Pie Charts Vs. Bar Diagram When to Use Pie Vs. Bar Diagram
15. How to create Pie Chart in SPSS
16. Pie Chart How to Change the Color of Pie
17. Pie Chart How to Merge Slices

1. 1.What is Logistic Regression
2.Understanding the Logistic Regression Model.
3.Understanding and Logistic Regression Model Shape, Logit and Probabilities
4.Understanding the Equation of Logistic Regression
5.Requirements for Logistic Regression Analysis.
6.Assumptions of Logistic Regression
7.Concept of Odd Ratios (in Brief)
8.Setting Data and Understanding the Data File
9.How to Code the Binary Dependent Variable in Logistic Regression
10.Understanding Block Option and Interaction Option
11.Selecting Method and Coding Categorical Variable as Dummy Variable
12.Understanding Save Option Predicted Probabilities & Group Membership
13.Understanding Save Option Influence - Cook's Distance & DFBeta Options
14.Understanding Save Residuals – Standardized.
15.Understanding Classification Plots Option
16.Understanding Hosmer-Lemeshow Goodness of Fit Test Option
17.Understanding Case-wise Listing of Residuals.
18. Understanding Correlation of Estimates Option
19. Understanding Iteration History Option
20. Understanding CI for Exp(B) Option
21. Including Constant in Model
22. Understanding Classification Cutoff .5 & Bootstrapping
23. Output Understanding Case Processing Summary & Dummy Variable Coding
24. Output Understanding Block 0 vs Other Blocks & Iteration History
25. Output Understanding -2 Log Likelihood & R squares (Cox n Snell, Negelkerke)
26. Output Understanding Classification Table (Sensitivity & Specificity)
27. Output Variables in Equation - Baseline Model Interpretation
28. Output Hosmer-Lemeshow & Contingency Table for Baseline Model
29. Output Interpretation of Hosmer-Lemeshow Test for Default Model.
30. Output Interpreting Variables in Equation for Default Model
31. Output Interpreting Wald's Test for Default Model
32. Odd Ratios (in Depth) Part 1 - Fundamentals, Derivation & Calculation
33. Odd Ratios (in Depth) Part 2 - Calculating Odds of Lung Cancer w Smoking
34. Interpreting Odd Ratios in Variables in Equation Table
35. Interpreting Correlation Table and Understanding Multi-collinearity
36. Classification Plot Interpretation & Application
37. Interpreting Case-wise Listing of Residuals Output
38. Interpreting Predicted Probabilities and Group Membership
39. Interpreting Cook's Distance and DFBeta
40. Interpreting Omnibus Test Output
41. Explaining Pseudo R Squares - 2Log Likelihood, Cox & Snell and Negelkerke
42. Writing Final equation of Logistic Regression Manually
43. APA Style Presentation of Table and Results

1. Introduction to Mediation and Moderation Analysis
3. Understanding Moderation analysis and its Regression Model - I
4. Understanding Moderation analysis and its Regression Model - II.
5. Statistical Equation of Moderation
6. Understanding Mediation Direct, Indirect and Total Effects
7. Understanding Difference Between Moderation & Mediation
8. Downloading & Installing Process Macro
9. Examples of moderation Story of Infosys and Uber
10. Whats is Mediation Understanding a Mediation Model
11. Whats is Full n Partial Mediation
11.1 10 Whats is full n partial mediation
12. Understanding Direct Indirect & Total Effects
13. What is Sobel Test
14. Partially Standardized vs Completely Standardized Indirect Effects
15. Understanding Ratios of Indirect effect Indirect to Total vs Indirect to Direct
16. What is Proportion of Variance Explained by Indirect Effect
17. Moderation analysis Dataset & Hypothesis Development
18. Understanding Model Numbers
19. Moderation Variables, Bootstrapping, Covariates, Proposed Moderator W,Z, V, Q
20. Moderation Options Mean Center for Products
21. ModerationOptions Heteroscedasticity Consistent SE, OLSML CI, Data Plotting
22. Moderation Conditioning Johnson-Neyman
23. Moderation Multi-categorical
24. Dealing with Long Names
25. Explanation of Output of Moderation Analysis
26. Plotting Moderation effect in SPSS and Excel
27. APA Style Presentation of Moderation Effect, Chart and Table
28. Conceptual Model of Mediation Does Glucose Mediates the Influence of Diabetes
29. Checking Suitability of Data for Mediation Analysis
30. Mediation M-Variables, Model Number, Bootstrap Sample, and Covariates
31. MediationOptions OLSML Confidence Interval & Effect Size
32. MediationOption Sobel Test
33. MediationOptions Total Effect Model, Compare Indirect Effect, Print Model Cov
34. Mediation Conditioning, Multi-categorical, and Long Names
35. Mediation Output Understanding Covariance Matrix Output
36. Explaining Mediation Output-Part 1
37. Explaining Mediation Output-Part 2
38. Explaining Mediation Output-Part 3
39. Mediation Output Partially and Fully Standardized Indirect Effects
40. Mediation Output Ratio of Indirect to Total Effect & Indirect to Direct Effect
41. Mediation Output R-squared Mediation Effect Size
42. Mediation Output Normal Theory Test for Indirect Effect
43. Mediation Output Kappa Squared and Why It is Suppressed
44. Calculating Preacher and Kelly's Kappa Squared Manually
45. APA Style Presentation of the Results of Mediation Analysis

2. Introduction to (General Linear Models) GLM
3. What are General Linear Models (GLM)
4. What are Generalized Linear Models (GLIM)
5. What are Exponential Distributions
6. Examples and Applications of Generalized Linear Models (GLIM)
7. General Linear Models (GLM) vs Generalized Linear Models(GLIM)

2. What is Repeated Measure Design (Example 1 Depression Study)
3. What is Repeated Measure Design (Example 2 Performance under Noise Study)
4. What is Repeated Measure Design (Example 3 Control Group Study)
5. Should I do Repeated Measure ANOVA or Paired Sample t-test
6. Assumptions of Repeated Measure ANOVA
7. Explaining Multivariate Tests
8. Understanding Pillai's Trace & Wilk's Lambda
9. Understanding Hotelling's Trace
10. Understanding Roy's Largest Root
11. What is Sphericity Understanding Sphericity through an Example
12. Understanding Mauchly's Test of Sphericity
13. Understanding the Dataset
14. Formulating Research Question and Hypothesis based on Data
15. Understanding Within-subject Factor Naming
16. Understanding Measurement Name Option
17. Understanding Between Subject Factor and Covariate Options
18. Understanding Preliminary Output
19. Model Full Factorial, BuildCustom Terms & Main and Interaction Effects
20. Explaining TYPE I, Type II, Type III, and Type IV Sum of Squares
21. Contrast Simple, Polynomial, Repeated, Deviation, Difference, Helmert
22. Defining Plots Exploring All Options
23. Introduction to Post-hoc Tests Two Families of Tests
24. When to Use Tukey's and Scheffe's Tests
25. Explaining Bonferroni correction
26. Explaining LSD Test
27. Tukey,s HSD, Tukey's WSD and SNK Test
28. Waller-Duncan, Dunnett’s T, Scheffe, Sidak, Duncan, and Hochberg Gabriel’s Test
29. Games Howell, Tamhane's T2 and T3 Tests Non-parametric Post-hoc Tests

1. Introduction to Correlation
2. What is Correlation
3. Types of Correlations Positive and Negative Correlations
4. Understanding Correlation coefficient and its Range
5. Which Correlation Coefficient to Use and When
6. Introduction to Pearson's Correlation Origin, Use & Why its so Popular
7. Why it is Called Product Moment Correlation Coefficient
8. Assumptions of Pearson's Product Moment Correlation
9. Calculation of r Deviation Score formula
10. Calculation of r Z-Score Formula
11. Calculation of r Raw Score Formula
12. Calculation of r Co-variance Formula
13. Manual Calculation of r using Raw Score Method
14. Importance of Correlation Coefficient
15. Spurious Correlations Correlation does not signify causation
16. Pearson Correlation as a Coefficient of Variability (R-squared)
17. Calculation of r in SPSS Checking Assumptions
18. Calculation of r in SPSS Understanding Pearson, Two tailed, and Bootstrapping
19. Interpretation of Output of r
20. Bootstrapping the Correlation Coefficient (r)
21. Writing Output of r in APA style
22. Fixing the Bootstrap Bug in SPSS 25
23. Introduction to Biserial and Point Biserial Correlations
24. When to Use Biserial and When to Use Point Biserial Corrleation
25. Calculation and Interpretation of Biserial Correlation in SPSS
26. APA Style Reporting of Biserial Correlation Output
27. Exercise Calculating a Point Biserial Correlation between Gender and Salary
28. How to Calculate Point Biserial Corrleation in SPSS
29. How to Interpret Point Biserial Corrleation in SPSS
30. How to Report Point Biserial Correlation Output in APA style
31. Introduction to Spearman's Rank Order Correlation Coefficient (Rho)
32. When to Use Rank Order Correlation Coefficient Four Examples
33. Who gave Rho and How it is Denoted
34. Assumptions of Spearman's Rank Order Correlation Coefficient
35. Understanding the formula Rho and Ranking Method
36. How to deal with Tied Ranks while Calculating Rho
37. Should I Rank My Variables First then calculate Rho
38. Calculating and Interpreting Rho in SPSS
39. Rho is r on Ranked Data Proof
40. APA style Reporting of Spearman's Rank Order Correlation Coefficient
Data

1. Introduction What is Difference between Association and Correlation
2. Understanding Concordant and Discordant Pairs
3. Understanding Pairs by Column and Rows Calculation
4. Introduction to Kendall's Tau
5. When to Use Kendall's Tau instead of Spearman's Rho
6. Assumptions of Kendall's Tau
7. Range and Interpretation of Kendall's Tau
8. Types of Kendall's Tau Coefficients Tau a, Tau b, Tau c & Kendall's W
9. Kendall's Tau a Concept, When to use and Formula
10. Kendall's Tau b and Tau c Introduction and When to Use Them
11. Kendall's Tau b Formula
12. Kendall's Tau c Formula and When to Use
13. Kendall's Tau a, Tau b, Tau c, and Kendall's W A Comparison of Usage
14. Kendall's Tau b in SPSS Checking Tied Ranks
15. Kendall's Tau b APA Style Reporting
16. Kendall's Tau c Assumption Checking
17. Kendall's Tau c Calculation and Interpretation in SPSS
18. Kendall's Tau c APA Style Reporting
19. Kendall's W Introduction and When to Use
20. Kendall's W Understanding the Formula
21. Kendall's W in SPSS (using Non-Parametric Auto-Dialogue Box)
22. Kendall's W in SPSS (using Non-Parametric Legacy Dialogue Box)
23. Kendall's W APA Style Output Reporting
Data

1. Fixing the Bootstrap Bug in SPSS 25.

Documents
3. Assignment 1 Explanation of Que 1, 2 and 3
4. Assignment 1 Explanation of Que 4, 5, 6, 7 and 8
5. Assignment 1 Explanation of Que 9 and 10

1. Introduction What is ANCOVA and When to Use It
2. What is meaning of covariate Does it mean control
3. Ways of ANCOVA and Requirements for Doing ANCOVA
4. Understanding Assumptions and Dataset
5. Study Design and Dataset
6. Understanding Options DV, Fixed Factors and Random Factors
7. Understanding Covariates and WLS weight and why Post-hoc Button Gets Inactive
9. Importance of Controlling Covariate
8. Testing Assumptions
10. Explaining Options Model Sum of Squares and Contrast
11. Explaining Options Plots
12. Explaining Options Estimated Marginal (EM) Means
13. Explaining Options Compare Main Effects (LSD, Bonferroni, Sidak) & SAVE
14. Options Descriptives, Effect Size Parameter Estimates Homogeneity Test Residual
15. Explaining Output Part 1
16. Explaining Output Part 2
17. APA Style Presentation of ANCOVA Results

1. Why Social Scientists Should Learn Programming
2. Programmability Options in SPSS Python, R and Visual Basic
3. Installing and Running Python
4. Accessing Python from SPSS
5. Understanding Extension Bundle Option
6. Three Rules of Writing Python Programme
7. Your First Python Programme in SPSS Hello World
8. Unit Basic Concepts - Understanding Variables and Operators
9. Data Types in Python
10. What is Data
11. What are data Types
12. What are Data Structures
13. Data Types vs. Data Structures in Python
14. Primitive Data Types in Python
15. Non-Primitive Data Types Lists, Stack, Queue, Map
16. Non-Primitive Data Types Tupple, Set, Frozen Set, Dictionary
17. Arithmetical Options in Python Using Python as a Calculator
18. Unit- Print Function Impressing Guide -Printing a Name Million Times in Seconds.
19. Learning to Print a Paragraph in Python
20. Printing a Text Pattern in Python
21. Clearing Screen How to Define Clear Screen Function in Pycharm
22. Unit Functions in Python - List of Built in Functions
23. Input Function
24. Min and Max Functions

Enrolment options

SPSS Masterclass: Learn SPSS From Scratch to Advanced

Course modified date: 9 Feb 2024

What you'll learn

Analyse any type of numerical data using SPSS with confidence
Independently plan your research study from scratch.
Understand the research design and results presented in high quality journal articles
Do data analysis accurately and present the results in standard format.

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SPSS Masterclass: Learn SPSS From Scratch to Advanced

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SPSS Masterclass: Learn SPSS From Scratch to Advanced

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