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SPSS
Business
Business Analytics & Intelligence
SPSS Masterclass: Learn SPSS From Scratch to Advanced
The Only Course You Will Ever Need to Learn IBM SPSS Statistics, Research, & Data Analysis Scratch to Advanced Level
4.4
(6792)
34998 enrolled on this course
Last updated 11/2021
Scholarsight Learning
Description
Data is the new frontier of 21st century. According to a Harvard Business Report (2012) data science is going to be the hottest job of 21st century and data analysts have a very bright career ahead. This course aims to equip learners with ability of independently carrying out in-depth data analysis with professional confidence and accuracy. It will specifically help those looking to derive business insights, understand consumer behaviour, develop objective plans for new ventures, brand study, or write a scholarly articles in high impact journals and develop high quality thesis/project work.
A good knowledge of quantitative data analysis is a sine qua none for progress in academic and corporate world. Keeping this in mind this course has been designed in such way that students, researchers, teachers and corporate professionals who want to equip themselves with sound skills of data analysis and wish to progress with this skill can learn it in in-depth and interesting manner using IBM SPSS Statistics.
Lesson Outcomes
On completion of this course you will develop an ability to independently analyze and treat data, plan and carry out new research work based on your research interest. The course encompasses most of the major type of research techniques employed in academic and professional research in most comprehensive, in-depth and stepwise manner.
Pedagogy
The focus of current training program will be to help participants learn statistical skills through exploring SPSS and its different options. The focus will be to develop practical skills of analyzing data, developing an independent capacity to accurately decide what statistical tests will be appropriate with a particular kind of research objective.
The program will also cover how to write the obtained output from SPSS in APA format.
Pre-requisite
A love for data analysis and statistics, research aptitude and motivation to do great research work.
Who this course is for:
PhD students and researchers looking to master SPSS skills and publish in high impact journals
Professionals looking for a career in analytics in corporate sector
Faculty members looking to master SPSS and advance their data analysis skills
What you'll learn
Analyse any type of numerical data using SPSS with confidence
Independently plan your research study and Data Analysis from scratch.
Understand the research design and results presented in high quality journal articles
Do data analysis accurately and present the results in APA format.
Data Entry and Data Cleaning in SPSS
Data Organization Using SPSS
Data Transformation Using SPSS
Sample as well as Population Level Descriptive Analysis Using SPSS
Analysis of Group Differences Using t-Test and ANOVA
Linear and Multiple Regression Analysis in SPSS
Hierarchical and Advanced Regression Analysis in SPSS
Logistic Regression
Exploratory Factor Analysis (EFA)
Chi-Square and Measures of Association
Reliability Analysis and Scale Validation Using SPSS
Graphical Representation and Advanced Data Visualization Using SPSS
Moderation and Mediation Analysis Using PROCESS Macro in SPSS
General Linear Modelling Vs. Generalized Linear Modelling in SPSS
Repeated Measure ANOVA
Correlational Analysis in SPSS
Analysis of Associations in SPSS
Analysis of Covariance (ANCOVA)
Multivariate Analysis of Variance (MANOVA)
SPSS Programming Using Python
Ratio Statistics in SPSS
TURF Analysis in SPSS
Survival Analysis
Meta Analysis
Requirements
- You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
- No previous design experience is needed.
- No previous Adobe XD skills are needed.
Course Content
27 sections • 95 lectures
Expand All Sections
1.1-Practice dataset, PPT and Resources
1.2-How to get answer to your queries fast?
2.1-Conceptualizing Variables: IV, DV, Control, Moderators & Mediating Variables
2.2-Variable Type Numeric: Defining Names, Width, Decimal & Labels for variables
2.3-Variable Type: Comma & Dot
2.4-Variable Type: Scientific Notation
2.5-Variable Type: Date and Time Stamps
2.6-Variable Type: Dollar
2.7-Variable Type: Custom Currency
2.8-Variable Type: String
2.9-Variable Type: Restricted Numeric
2.10-Defining Values & Labels
2.11-Defining Missing Values: Discrete, Range & System-Missing Values
2.12-Setting Columns & Alignment
2.13-Defining Measures: Scales of Measurement
3.1-Types of Data Files in SPSS Statistics
3.2-Opening an Excel data file in SPSS
3.3-Opening a Comma Separated or CSV file type in SPSS
4.1-Dataset and Resources: RECODE Function
4.2-COMPUTE VARIABLE function: What it is and What it can do for us?
4.3-Calculating Total using COMPUTE function
4.4-Exercise: Try COMPUTE using IF
4.5-Exercise Solution: COMPUTE using IF
4.6-RECODE FUNCTION: Why to Recode Variable?
4.7-Why We have Two RECODE Functions?
4.8-How to do RECODE INTO DIFFERENT VARIABLE in SPSS?
4.9-COMPUTING Total After RECODE
4.10-Recode into Same Variable
5.1-Setting Data for Descriptive Analysis
5.2-Types of Descriptive Statistics
5.3-Understanding Three Different Descriptive Tabs in SPSS
5.4-Calculating Frequencies
5.5-Descriptives Analysis Using Crosstab
5.6-Measures of Central Tendency: Mean, Median, Mode - Concept and Uses
5.7-Calculating and Interpreting Mean, Median & Mode
5.8-Confirming Mode with Frequencies
5.9-Explore Option: Calculating Grouped Descriptives
5.10-Explore Option: Interpreting Groupwise Mean and 95% Confidence Interval of Mean
5.11-5% Trimmed Mean: Concept, Use & Interpretation
5.12-Explore: Median, Standard Deviation, Variance, Minimum, Maximum, & Range
5.13-Quartiles and Inter-Quartile Range using Explore Option
5.14-Skewness and Kurtosis: Fundamentals Explained
5.15-Calculating & Interpreting Significance Level of Skewness
5.16-Kurtosis: Calculation, Interpretation and Understanding Significance Level
5.17-Standard Error of Mean: Concept, Calculation & Interpretation
6.1-Descriptive Analysis: Population Descriptives
7.1-Independent sample t-test: Defining input options
7.2-Independent sample t-test: Interpreting descriptive output (Mean, SD, SE)
7.3-Independent Sample t-test: Interpreting Levene's test, t, p, SE & 95% CI
7.4-APA Style write-up for Independent Sample t-test
8.1-When to use Paired Sample t-test?
8.2-Calculating Paired Sample t-test in SPSS
8.3-Interpreting Paired Sample t-test Output
8.4-APA Style write-up for Paired Sample t-test
9.1-When to Use One-Way ANOVA?
9.2-Calculating One-Way ANOVA in SPSS
9.3-Interpreting ANOVA output: Descriptive Statistics
9.4-Interpreting Output: ANOVA Summary Table
9.5-Doing Post-hoc analysis in ANOVA: Homogeneity of Variance Test & Post-hoc
9.6-Trend Analysis & Means Plot in ANOVA
9.7-Contrast Analysis in ANOVA
10.1-What is regression?
10.2-When to Use Linear Regression Vs. Multiple Regression?
10.3-Defining SPSS Input Options for Linear Regression
10.4-Interpreting Linear Regression Output: Variables & Model Summary
10.5-Interpreting Linear Regression Output: Constant, B, Beta, SE & t
11.1-What is Multiple Regression?
11.2-Assumptions of Multiple Regression: Linearity & Testing Linearity in SPSS
11.3-Assumptions 2: Independence of Errors/Lack of Autocorrelations & Testing in SPSS
11.4-Assumptions 3: Homoscedasticity of Errors & Testing it in SPSS
11.5-Assumptions 4: Multivariate Normality & Testing it in SPSS
11.6-Assumptions 5: Multicollinearity & Testing it in SPSS
11.7-Choosing a Method of Multiple Regression: Enter Method
11.8-Choosing a Method of Multiple Regression: Stepwise and Forward Selection Method
11.9-Choosing a Method of Multiple Regression: Backward Elimination Method
11.10-Running Stepwise and Forward Selection Method of Regression in SPSS
11.11-Choosing a Method of Multiple Regression: Remove Method
12.1-What is Hierarchical Regression Analysis and when to use it?
12.2-Setting Data and Defining Model in Hierarchical Regression
12.3-Refining Model and Detecting Multicollinearity through Correlation Matrix
12.4-Taming Bad Data: Using beta, R squared and p values to further refine model
12.5-Interpreting the Output of Hierarchical Regression
13.1-Personality Dataset
13.2-What is Factor Analysis?
13.3-Understanding Latent Variables and Indicators in FA
13.4-Sample Researches Using FA in Social Science & Engineering
13.5-Historical Origin of FA & Its Application in Test Construction
13.6-Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
13.7-Setting Data for Factor Analysis
13.8-Understanding "Selection Variable"
13.9-Univariate Descriptives & Initial Solutions: Descriptive
13.10-Correlation Matrix: Coefficients, Significance, Determinant, KMO & Bartlett's
13.11-Understanding Inverse, Reproduced, Anti-Image
13.12-Extraction Method: Principle Component Analysis
13.13-Extraction Method: Principle Axis Factoring
13.14-Extraction Method: Maximum Likelihood Estimation
13.15-Choosing Correlation vs. Covariance Matrix for Factor Analysis
13.16-Interpreting Correlation Matrix & Unrotated Factor Solution
13.17-Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria
13.18-Factor Rotation: What it is and why its done?
13.19-Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax
13.20-Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin
13.21-Factor Score Coefficient Matrix
13.22-Missing Value Analysis: Listwise, Pairwise, Replace with Mean
13.23-Sort by Size & Suppressing Smaller Coefficients
13.24-Project in Factor Analysis Part 1: Identifying Dimensions of Personality
13.25-Project in Factor Analysis Part 2: Identifying Dimensions of Personality
13.26-Project in Factor Analysis Part 3: Identifying Dimensions of Personality
13.27-Project in Factor Analysis Part 4: Factor Naming
13.28-Project in Factor Analysis Part 5: Reliability Analysis of Factors
13.29-Project in Factor Analysis Part 6: Presenting Results in APA Style
14.1-Chi Square Test: Introduction and When to Use Chi-Square Test?
14.2-Assumptions of Chi-square Test
14.3-Formula for Calculation of Chi-Square Test
14.4-Setting Data for Calculation of Chi-Square using Crosstabs Option
14.5-Testing Assumptions of Chi-Square test Using Crosstabs Option
14.6-Interpreting Output of Chi-Square Test and APA Style Reporting
14.7-One-way Chi Square: When to use and how its different from two-way Chi square?
14.8-Setting Data for One-way Chi Square Test
14.9-Weigh Cases, Calculation, Interpretation & APA Write-up for One-Way Chi Square
14.10-Practice Data set for One-Way Chi square
15.1-Introduction to Reliability Analysis
15.2-What is Reliability?
15.3-Reflective vs. Formative Models of Scale
15.4-Should We Report Cronbach's Alpha or Composite Reliability?
15.5-Type of Reliability: Test-Retest Reliability
15.6-Type of Reliability: Parallel Form
15.7-Type of Reliability: Internal Consistency Reliability
15.8-Understanding Cronbach's Alpha
15.9-Assumptions of Cronbach's Alpha
15.10-Formula of Cronbach's Alpha
15.11-Range of Cronbach's Alpha
15.12-Calculating Reliability: Understanding Scale if an Item is Deleted Option
15.13-Interpreting Case Processing Summary & Alpha Coefficient
15.14-Improving Reliability of a Scale: Diagnosing Missing Values
15.15-Improving Reliability: Diagnosing Scale Mean and Variances
15.16-Improving Reliability: Diagnosing Item-Total Correlations
15.17-Improving Reliability: Removing Ambiguous and Redundant Items
15.18-Item Discrimination Index
16.1-1. What is Logistic Regression?
16.2-Logistic Regression (External Resource)
16.3-2. Understanding the Logistic Regression Model
16.4-3. Understanding and Logistic Regression Model: Shape, Logit and Probabilities
16.5-4. Understanding the Equation of Logistic Regression
16.6-5. Requirements for Logistic Regression Analysis
16.7-6. Assumptions of Logistic Regression
16.8-7. Concept of Odd Ratios (in Brief)
16.9-8. Setting Data and Understanding the Data File
16.10-9. How to Code the Binary Dependent Variable in Logistic Regression
16.11-10. Understanding Block Option and Interaction Option
16.12-11. Selecting "Method" and Coding Categorical Variable as "Dummy" Variable
16.13-12. Understanding Save Option: Predicted Probabilities & Group Membership
16.14-13. Understanding Save Option: Influence - Cook's Distance & DFBeta Options
16.15-14. Understanding Save: Residuals – Standardized
16.16-15. Understanding Classification Plots Option
16.17-16. Understanding Hosmer-Lemeshow Goodness of Fit Test Option
16.18-17. Understanding Case-wise Listing of Residuals
16.19-Understanding Correlation of Estimates Option
16.20-Understanding "Iteration History" Option
16.21-Understanding "CI for Exp(B)" Option
16.22-Including Constant in Model
16.23-Understanding "Classification Cutoff .5 & Bootstrapping"
16.24-Output: Understanding Case Processing Summary & Dummy Variable Coding
16.25-Output: Understanding Block 0 vs Other Blocks & Iteration History
16.26-Output: Understanding -2 Log Likelihood & R squares (Cox n Snell, Negelkerke)
16.27-Output: Understanding Classification Table (Sensitivity & Specificity)
16.28-Output: Variables in Equation - Baseline Model Interpretation
16.29-Output: Hosmer-Lemeshow & Contingency Table for Baseline Model
16.30-Output: Interpretation of Hosmer-Lemeshow Test for Default Model
16.31-Output: Interpreting Variables in Equation for Default Model
16.32-Output: Interpreting Wald's Test for Default Model
16.33-Odd Ratios (in Depth): Part 1 - Fundamentals, Derivation & Calculation
16.34-Odd Ratios (in Depth): Part 2 - Calculating Odds of Lung Cancer w/ Smoking
16.35-Interpreting Odd Ratios in Variables in Equation Table
16.36-Interpreting Correlation Table and Understanding Multi-collinearity
16.37-Classification Plot: Interpretation & Application
16.38-Interpreting Case-wise Listing of Residuals Output
16.39-Interpreting Predicted Probabilities and Group Membership
16.40-Interpreting Cook's Distance and DFBeta
16.41-Interpreting Omnibus Test Output
16.42-Explaining Pseudo R Squares: - 2Log Likelihood, Cox & Snell and Negelkerke
16.43-Writing Final equation of Logistic Regression Manually
16.44-APA Style Presentation of Table and Results
17.1-Introduction to Mediation and Moderation Analysis
17.2-Data, PPT & Resources
17.3-Understanding Moderation analysis and its Regression Model - I
17.4-Understanding Moderation analysis and its Regression Model - II
17.5-Statistical Equation of Moderation
17.6-Understanding Mediation: Direct, Indirect and Total Effects
17.7-Understanding Difference Between Moderation & Mediation
17.8-Downloading & Installing Process Macro
17.9-Examples of moderation: Story of Infosys and Uber
17.10-Whats is Mediation: Understanding a Mediation Model
17.11-Whats is Full n Partial Mediation?
17.12-Understanding Direct Indirect & Total Effects
17.13-What is Sobel Test?
17.14-Partially Standardized vs Completely Standardized Indirect Effects
17.15-Understanding Ratios of Indirect effect: Indirect to Total vs Indirect to Direct
17.16-What is Proportion of Variance Explained by Indirect Effect?
17.17-Moderation analysis: Dataset & Hypothesis Development
17.18-Understanding Model Numbers
17.19-Moderation: Variables, Bootstrapping, Covariates, Proposed Moderator W,Z, V, Q
17.20-Moderation> Options: Mean Center for Products
17.21-Moderation>Options: Heteroscedasticity Consistent SE, OLS/ML CI, Data Plotting
17.22-Moderation> Conditioning: Johnson-Neyman
17.23-Moderation: Multi-categorical
17.24-Dealing with Long Names
17.25-Explanation of Output of Moderation Analysis
17.26-Plotting Moderation effect in SPSS and Excel
17.27-APA Style Presentation of Moderation Effect, Chart and Table
17.28-Conceptual Model of Mediation: Does Glucose Mediates the Influence of Diabetes?
17.29-Checking Suitability of Data for Mediation Analysis
17.30-Mediation: M-Variables, Model Number, Bootstrap Sample, and Covariates
17.31-Mediation>Options: OLS/ML Confidence Interval & Effect Size
17.32-Mediation>Option: Sobel Test
17.33-Mediation>Options: Total Effect Model, Compare Indirect Effect, Print Model Cov
17.34-Mediation: Conditioning, Multi-categorical, and Long Names
17.35-Mediation> Output: Understanding Covariance Matrix Output
17.36-Explaining Mediation Output-Part 1
17.37-Explaining Mediation Output-Part 2
17.38-Explaining Mediation Output-Part 3
17.39-Mediation Output: Partially and Fully Standardized Indirect Effects
17.40-Mediation Output: Ratio of Indirect to Total Effect & Indirect to Direct Effect
17.41-Mediation Output: R-squared Mediation Effect Size
17.42-Mediation Output: Normal Theory Test for Indirect Effect
17.43-Mediation Output: Kappa Squared and Why It is Suppressed?
17.44-Calculating Preacher and Kelly's Kappa Squared Manually
17.45-APA Style Presentation of the Results of Mediation Analysis
17.46-Relevant Literature: Mediation and Moderation Analysis using PROCESS
18.1-Dataset and Resources: GLM
18.2-Introduction to (General Linear Models) GLM
18.3-What are General Linear Models (GLM)?
18.4-What are Generalized Linear Models (GLIM)?
18.5-What are Exponential Distributions?
18.6-Examples and Applications of Generalized Linear Models (GLIM)
18.7-General Linear Models (GLM) vs Generalized Linear Models(GLIM)
19.1-Dataset and Resources: One-Way Repeated Measure ANOVA
19.2-What is Repeated Measure Design (Example 1: Depression Study)
19.3-What is Repeated Measure Design (Example 2: Performance under Noise Study)
19.4-What is Repeated Measure Design (Example 3: Control Group Study)
19.5-Should I do Repeated Measure ANOVA or Paired Sample t-test?
19.6-Assumptions of Repeated Measure ANOVA
19.7-Explaining Multivariate Tests
19.8-Understanding Pillai's Trace & Wilk's Lambda
19.9-Understanding Hotelling's Trace
19.10-Understanding Roy's Largest Root
19.11-What is Sphericity: Understanding Sphericity through an Example
19.12-Understanding Mauchly's Test of Sphericity
19.13-Understanding the Dataset
19.14-Formulating Research Question and Hypothesis based on Data
19.15-Understanding "Within-subject Factor Naming"
19.16-Understanding "Measurement Name" Option
19.17-Understanding "Between Subject Factor and Covariate" Options
19.18-Understanding Preliminary Output
19.19-Model: Full Factorial, Build/Custom Terms & Main and Interaction Effects
19.20-Explaining TYPE I, Type II, Type III, and Type IV Sum of Squares
19.21-Contrast: Simple, Polynomial, Repeated, Deviation, Difference, Helmert
19.22-Defining Plots: Exploring All Options
19.23-Introduction to Post-hoc Tests: Two Families of Tests
19.24-When to Use Tukey's and Scheffe's Tests?
19.25-Explaining Bonferroni correction
19.26-Explaining LSD Test
19.27-Tukey,s HSD, Tukey's WSD and SNK Test
19.28-Waller-Duncan, Dunnett’s T, Scheffe, Sidak, Duncan, and Hochberg Gabriel’s Test
19.29-Games Howell, Tamhane's T2 and T3 Tests: Non-parametric Post-hoc Tests
20.1-Introduction to Correlation
20.2-What is Correlation?
20.3-Types of Correlations: Positive and Negative Correlations
20.4-Understanding Correlation coefficient and its Range
20.5-Which Correlation Coefficient to Use and When?
20.6-Introduction to Pearson's Correlation: Origin, Use & Why its so Popular?
20.7-Why it is Called Product Moment Correlation Coefficient?
20.8-Assumptions of Pearson's Product Moment Correlation
20.9-Calculation of r : Deviation Score formula
20.10-Calculation of r: Z-Score Formula
20.11-Calculation of r : Raw Score Formula
20.12-Calculation of r : Co-variance Formula
20.13-Manual Calculation of r using Raw Score Method
20.14-Importance of Correlation Coefficient
20.15-Spurious Correlations: Correlation does not signify causation
20.16-Pearson Correlation as a Coefficient of Variability (R-squared)
20.17-Calculation of r in SPSS: Checking Assumptions
20.18-Calculation of r in SPSS : Understanding Pearson, Two tailed, and Bootstrapping
20.19-Interpretation of Output of r
20.20-Bootstrapping the Correlation Coefficient (r)
20.21-Writing Output of r in APA style
20.22-Fixing the Bootstrap Bug in SPSS 25
20.23-Introduction to Biserial and Point Biserial Correlations
20.24-When to Use Biserial and When to Use Point Biserial Corrleation?
20.25-Calculation and Interpretation of Biserial Correlation in SPSS
20.26-APA Style Reporting of Biserial Correlation Output
20.27-Exercise: Calculating a Point Biserial Correlation between Gender and Salary
20.28-How to Calculate Point Biserial Corrleation in SPSS
20.29-How to Interpret Point Biserial Corrleation in SPSS
20.30-How to Report Point Biserial Correlation Output in APA style
20.31-Introduction to Spearman's Rank Order Correlation Coefficient (Rho)
20.32-When to Use Rank Order Correlation Coefficient: Four Examples
20.33-Who gave Rho and How it is Denoted?
20.34-Assumptions of Spearman's Rank Order Correlation Coefficient
20.35-Understanding the formula Rho and Ranking Method
20.36-How to deal with Tied Ranks while Calculating Rho?
20.37-Should I Rank My Variables First then calculate Rho?
20.38-Calculating and Interpreting Rho in SPSS
20.39-Rho is r on Ranked Data: Proof
20.40-APA style Reporting of Spearman's Rank Order Correlation Coefficient
21.1-Introduction: What is Difference between Association and Correlation
21.2-Understanding Concordant and Discordant Pairs
21.3-Understanding Pairs by Column and Rows Calculation
21.4-Introduction to Kendall's Tau
21.5-When to Use Kendall's Tau instead of Spearman's Rho
21.6-Assumptions of Kendall's Tau
21.7-Range and Interpretation of Kendall's Tau
21.8-Types of Kendall's Tau Coefficients: Tau a, Tau b, Tau c & Kendall's W
21.9-Kendall's Tau a : Concept, When to use and Formula
21.10-Kendall's Tau b and Tau c : Introduction and When to Use Them
21.11-Kendall's Tau b Formula
21.12-Kendall's Tau c : Formula and When to Use
21.13-Kendall's Tau a, Tau b, Tau c, and Kendall's W: A Comparison of Usage
21.14-Kendall's Tau b in SPSS: Checking Tied Ranks
21.15-Kendall's Tau b: APA Style Reporting
21.16-Kendall's Tau c : Assumption Checking
21.17-Kendall's Tau c : Calculation and Interpretation in SPSS
21.18-Kendall's Tau c : APA Style Reporting
21.19-Kendall's W : Introduction and When to Use
21.20-Kendall's W : Understanding the Formula
21.21-Kendall's W in SPSS (using Non-Parametric Auto-Dialogue Box)
21.22-Kendall's W in SPSS (using Non-Parametric Legacy Dialogue Box)
21.23-Kendall's W : APA Style Output Reporting
22.1-Fixing the Bootstrap Bug in SPSS 25
23.1-Introduction: What is ANCOVA and When to Use It?
23.2-What is meaning of covariate? Does it mean control?
23.3-Ways of ANCOVA and Requirements for Doing ANCOVA
23.4-Understanding Assumptions and Dataset
23.5-Study Design and Dataset
23.6-Understanding Options: DV, Fixed Factors and Random Factors
23.7-Understanding Covariates and WLS weight and why Post-hoc Button Gets Inactive
23.8-Testing Assumptions
23.9-Importance of Controlling Covariate
23.10-Explaining Options: Model Sum of Squares and Contrast
23.11-Explaining Options: Plots
23.12-Explaining Options: Estimated Marginal (EM) Means
23.13-Explaining Options: Compare Main Effects (LSD, Bonferroni, Sidak) & SAVE
23.14-Options: Descriptives, Effect Size Parameter Estimates Homogeneity Test Residual
23.15-Explaining Output: Part 1
23.16-Explaining Output: Part 2
23.17-APA Style Presentation of ANCOVA Results
24.1-What is MANOVA?
24.2-When to Use MANOVA?
24.3-Multivariate Test Decision Tree: When to Use ANOVA, MANOVA, ANCOVA, MANCOVA?
24.4-Assumptions of MANOVA
24.5-Research Questions and Study Design
24.6-Hypotheses Development
24.7-Understanding MANOVA Window in SPSS
24.8-Specifying Model: Full Factorial, Build Terms, and Custom Terms
24.9-Understanding Model Sum of Squares
24.10-What is Contrast?
24.11-Understanding Simple Contrast
24.12-Understanding Repeated Contrast
24.13-Understanding Polynomial Contrast
24.14-Understanding Deviation Contrast
24.15-Understanding Difference and Helmert Contrast
24.16-Understanding Estimated Marginal Means
24.17-Understanding SAVE Option
24.18-Understanding Descriptives, Effect Size, Observed Power, Noncent Parameter
24.19-Understanding Parameter Estimates
24.20-Understanding SSCP Matrix and Residual SSCP Matrix
24.21-Understanding Transformation Matrix and Homogeneity Test
24.22-Understanding Spread and Residual Plots
24.23-Understanding Lack of Fit Test
24.24-Understanding General Estimable Function
25.1-Why Social Scientists Should Learn Programming?
25.2-Programmability Options in SPSS: Python, R and Visual Basic
25.3-Installing and Running Python
25.4-Accessing Python from SPSS
25.5-Understanding Extension Bundle Option
25.6-Three Rules of Writing Python Programme
25.7-Your First Python Programme in SPSS: Hello World
25.8-Unit: Basic Concepts - Understanding Variables and Operators
25.9-Data Types in Python
25.10-What is Data?
25.11-What are data Types?
25.12-What are Data Structures?
25.13-Data Types vs. Data Structures in Python
25.14-Primitive Data Types in Python
25.15-Non-Primitive Data Types: Lists, Stack, Queue, Map
25.16-Non-Primitive Data Types: Tupple, Set, Frozen Set, Dictionary
25.17-Arithmetical Options in Python: Using Python as a Calculator
25.18-Unit- Print Function: Impressing Guide -Printing a Name Million Times in Seconds
25.19-Learning to Print a Paragraph in Python
25.20-Printing a Text Pattern in Python
25.21-Clearing Screen: How to Define Clear Screen Function in Pycharm
25.22-Unit: Functions in Python - List of Built in Functions
25.23-Input Function
25.24-Min and Max Functions
26.1-Introduction and Usage
26.2-What is Average Absolute Deviation (AAD)
26.3-What is Coefficient of Dispersion (COD)
26.4-What is Coefficient of Variation (COV)
26.5-What is Price Related Bias
26.6-What is Price Related Differential PRD
26.7-Understanding Confidence Interval of PRD and Concentration Index
26.8-Calculating Ratio Statistics in SPSS: Hypothesis Setting Data and Choosing Op
26.9-Interpretation of Output of Ratio Statistics
26.10-APA Style Write Up of Ratio Statistics
27.1-What is TURF Analysis and When to Use It?
27.2-Defining TURF Analysis
27.3-What TURF Calculates?
27.4-Who Discovered TURF Analysis?
27.5-Assumptions of TURF Analysis?
27.6-Applications of TURF Analysis
27.7-Manual Calculation of TURF
27.8-TURF Analysis in SPSS: Understanding Input Options
27.9-Interpreting SPSS Output of TURF Analysis: Part 1
27.10-Interpreting SPSS Output of TURF Analysis: Part 2
27.11-APA Style Write Up of TURF Analysis
28.1-Introduction to Advanced Data Visualization in SPSS
28.2-What is Meaning of a Distribution?
28.3-Understanding Statistical Distributions
28.4-Creating Frequency and Probability Distributions in SPSS
28.5-Understanding Fat Tail and Thin Tail Distributions
28.6-Difference between FAT tail and THIN Tail Distributions
28.7-PP Plots in SPSS
28.8-Interpreting PP Plots in reference to the mid line
28.9-What are QQ Plots
28.10-Creating and Interpreting QQ Plots in SPSS
28.11-How to Use QQ Plot to improve Normality of Your Variable
28.12-Relationship Maps in SPSS
29.1-Introduction to Survival Analysis
29.2-What is Survival Analysis?
29.3-History of Survival Analysis
29.4-Terminology: Time Event and Residual Life
29.5-Terminology: Understanding Hazard, Hazard Rate and Survival Rate
29.6-Terminology: Understanding Conditional Probability in Survival Analysis
29.7-Terminology: Understanding Censored and Uncensored Data
29.8-Terminology: Understanding Mean Time Between Failures vs. Mean Time To Failure
29.9-Purpose of Survival Analysis
29.10-Understanding Nature of Data Required to Run Survival Analysis
29.11-Types of Survival Analysis
29.12-When to Use Kaplan-Meier vs Cox Regression?
29.13-Research Examples of Kaplan-Meier and Cox Regression Methods
29.14-Setting Data for Survival Analysis
29.15-Setting Objective and Hypothesis of Your SA Research
29.16-Understanding Input Options of Kaplan Meier Method
29.17-Interpreting Case Processing Summary and Survival Tables
29.18-Interpreting Mean Survival Median and Percentiles
29.19-Interpreting Pairwise Comparison
29.20-Interpreting Survival Function Plot
29.21-Interpreting Survival Function with other Statistical Results
29.22-APA Style Report Writing of Kaplan Meier Survival Analysis
29.23-Project in Survival Analysis: Replicating a Real Study
29.24-References on Survival Analysis
30.1-Introduction to Meta Analysis
30.2-What is Meta Analysis and Why You Should Know About It?
30.3-Relationship Between Systematic Review and Meta Analysis
30.4-Locating Meta Analysis in the Family of Other Research Techniques
30.5-Is Meta-Analysis is Better than Hypothesis Testing?
30.6-Outcome of Meta Analysis: Effect Size, Forest Plot, and Publication Bias
30.7-Statistical Model of Meta-Analysis
30.8-What is Effect Size?
30.9-Understanding and Calculation of Cohen's d
30.10-Downloading and Installing Meta Analysis Package in JAMOVI
30.11-Types of Meta Analytic Techniques in Software Packages
30.12-Aggregate Data (AD) vs. Individual Participant Data (IPD) Meta-Analysis
30.13-Organizing Your Data for a Correlation Based Meta Analysis
30.14-Meta Analysis in Jamovi- Part 1 Data Import and Input Options
30.15-Understanding Model Estimators
30.16-Understanding Fixed Effect Estimator and Its Formula
30.17-Understanding Random Effect Model and When to Use Fixed Vs. Random Effect Model?
30.18-How to Troubleshoot 'need finite xlim values' error in Jamovi?
30.19-Understanding Maximum Likelihood and Restricted Maximum Likelihood Estimators
30.20-Understanding DerSimonian-Laird Model Estimator
30.21-Understanding and calculation of Hedges g estimator
31.1-Descriptive Analysis in SPSS
31.2-Answers to Assignment 1
31.3-Assignment 1: Explanation of Que 1, 2 and 3
31.4-Assignment 1: Explanation of Que 4, 5, 6, 7 and 8
31.5-Assignment 1: Explanation of Que 9 and 10
32.1-SPSS 28: What is New?
32.2-SPSS 26: What is new?
33.1-Downloading and Installing IBM SPSS Statistics 24 on Windows
33.2-Downloading SPSS Grad Pack: Student Version
34.1-Great References on Quantitative Methods
34.2-Philosophy of Research
34.3-Research Papers with Fascinating Ideas
35.1-Chapter 1 Introduction: Conceptual Foundations
35.2-Chapter 2 Importing Data in SPSS
35.3-Chapter 3 Data Entry in SPSS
35.4-Chapter 4 Data Manipulation in SPSS
35.5-Chapter 5 Descriptive Statistics in SPSS
35.6-Chapter 6 t-Test - Independent Sample and Correlated
35.7-Chapter 7 One Way ANOVA
35.8-Chapter 8 Linear Regression
35.9-Chapter 9 Multiple Regression
35.10-Chapter 10 Hierarchical Regression Analysis
35.11-Chapter 11 Exploratory Factor Analysis
36.1-Bonus Lecture
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